Handbook of Neuroscience for the Behavioral Sciences, 2 Volume Set

Preface

in the neurosciences and behavioral sciences and informs about relevant theory, methods, and research in these two increasingly synergistic disciplines. The Handbook is designed to make neuroscience accessible to psychologists and other behavioral scientists with minimal background in biology or neuroscience, while at the same time offering information, constructs and approaches that will enhance the knowledge, teaching and research of active investigators at the intersection of psychology and neuroscience. In addition, the Handbook is designed to provide an accessible background in and to highlight currently active areas within the behavioral sciences for neuroscientists, who may have a minimal background in behavioral sciences. To accomplish the dual purposes of exposing behavioral scientists to neuroscience and neuroscientists to psychology, we have adopted a unique organization in this two volume Handbook. The first volume includes a Foundations section that features a set of chapters that provide brief introductions to major questions and approaches at distinct levels of analysis. These include topics such as the logical basis of integrative neuroscience, developmental processes, comparative approaches, biological rhythms, neuropharmacology, neuroendocrinology, neuroimmunology, neuroanatomy, neuropsychology, and functional neuroimaging. These chapters are overviews that provide the reader with a basic conceptual orientation to the types of approaches and measures available and how they can be applied and interpreted. These chapters provide the basic background to allow the reader to comprehend and evaluate the subsequent chapters of the handbook, as well as the broader neuroscience literature. The subsequent sections are comprised of groups of chapters organized around major psychological themes. In Volume 1, these include: Sensation and Perception, Attention and Cognition, and Learning and Memory. This organization carries over to Volume 2, with major sections being: Motivation and Emotion, Social Processes, Psychological Disorders and Health and Aging.

The notion that 100 billion neurons give rise to human behavior proved daunting up through the twentieth century because neuroscientists were limited by existing technologies to studying the properties of single neurons or small groups of neurons. Characterizing simple neural circuits has led to an understanding of a variety of sensory processes, such as the initial stages in vision, and relatively simple motor processes, such as the generation of locomotion patterns. However, unraveling the neural substrates of more complex behaviors, such as the ability of an animal to navigate in its environment, to pay attention to relevant events in its surroundings, to perceive and communicate mental states including the beliefs and desires of others and to form and maintain interpersonal and group relationships remains one of the major challenges for the neurosciences in the twenty-first century. In contrast to more elementary behaviors, these complex behavioral processes depend on interactions within elaborate networks extending across distinct brain structures. Elucidating the neural bases of complex behaviors, therefore, may require sophisticated approaches and methods that have only recently, or have yet to be, developed. These include the ability to record electrical brain activity with multi-electrode arrays in freely behaving animals or humans, neuroimaging methods that can noninvasively monitor brain activity, and an increasing cornucopia of technologies from molecular biology and genetics that allow investigators to analyze the cellular bases of behaviors. These approaches are not only revealing the underlying neurobiology of behavior, but are establishing the foundation for an understanding for the biological bases of a variety of physical and mental health problems. As a result of these developments, the neurosciences are reshaping the landscape of the behavioral sciences, and the behavioral sciences are of increasing importance to the neurosciences, especially for the rapidly expanding investigations into the highest level functions of the brain. The Handbook of Neurosciences for the Behavioral Sciences provides an introduction to graduate students and scholars xi

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xii

Preface

Throughout the Handbook, the goal has been to integrate information across disciplines and levels of organization/ analyses, from the cellular/molecular to systems to behavioral/social levels. The organization of the Handbook thus avoids artificial dichotomies such as lower level vs. higher level processes, or neuroscience vs. behavioral science sections. Coverage of motor systems, for example, is included in the section on Attention and Cognition and discussion of somatovisceral function is included in the context of Motivation and Emotion, providing the reader with the broadest and most interdisciplinary perspectives on a given topic. Cross referencing across chapters has been emphasized, to underscore the fact that a neuroscientific perspective may illuminate connections across areas of study in psychology, where none may have traditionally been recognized.

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The intended audience for the Handbook is broad, including graduate students, psychologists and other behavioral scientists who seek knowledge and understanding about neuroscience. It is a resource for active behavioral neuroscientists as well as those with minimal background in the biological sciences. It should also be of interest to neuroscientists who want an introduction to contemporary psychological issues, presented in a neuroscientific context. None of this would be possible without the tremendous efforts and high quality of the chapter authors. We thank them all for their contributions and we hope you will find this Handbook of value in understanding the behavioral neurosciences. GARY G. BERNTSON JOHN T. CACIOPPO

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Chapter 1

Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference JOHN T. CACIOPPO AND GARY G. BERNTSON

Charles Darwin (1873) concluded his seminal treatise on the expression of emotions by noting the adaptive value of the structures and behaviors that are observed in humans and the utility of comparative studies to discern the origin of these structures and behaviors. Darwin’s emphasis on the biological and functional underpinnings of behavior influenced William James (1890), who opined as follows:

is associated with the behavioral element, but it does not address whether the behavioral element is caused by the neural element (Cacioppo & Tassinary, 1990). The second approach is illustrated by the case of a patient who had suffered a neurosyphilitic lesion in the front part of his brain and was being attended by the physician Paul Pierre Broca. The patient was known as “ Tan” because this was the only word he was left able to speak, but in other regards his mental processes and behavior appeared relatively normal. In a postmortem autopsy, Broca determined that Tan’s lesion was in the posterior third of the inferior frontal gyrus. This region became known as Broca’s area and was surmised to be the speech center of the brain based on the changes in behavior associated with damage to this region. This case illustrates the methodological approach of comparing differences in or manipulating neural elements to investigate their effects on cognition, emotion, or behavior—that is, the study of psychological or behavioral processes () as a function of neural processes (). In Bayesian terms, this can be specified as P(/). Stating it in this way highlights a limitation of this approach: It provides evidence that a neural element is sufficient to influence a behavioral element, but it does not address whether the neural element is necessary. Thus, these two approaches provide unique nonredundant information (Sarter, Bernston, & Cacioppo, 1996). For much of the twentieth century, the notion that 100 billion neurons gave rise to the human mind and behavior proved daunting, especially when one tried to say anything specific about this feat. To make this problem tractable, neuroscientists initially studied simple circuits and behaviors. The notion, illustrated for instance by Sherrington’s (1906) work on the integrative action of the nervous system based on his studies of spinal cord reflexes, was that fundamental principles governing the operation of neural circuits and how such mechanisms relate to behavior could be understood as well in simpler systems,

A science of the mind must reduce such complex manifestations {of behavior} to their elements. A science of the brain must point out the functions of its elements. A science of the relations of the mind and brain must show how the elementary ingredients of the former correspond to the elementary functions of the latter. (p. 28)

Several celebrated clinical cases of the nineteenth century illustrate, at least at a gross level, two distinct approaches to investigation of these elements and relationships. An Italian named Bertino suffered a head injury that left his frontal lobes partially exposed (Raichle, 2000). Angelo Mosso (1881), an Italian physiologist, observed a sudden increase in the magnitude of pulsations over the frontal lobes with the ringing of local church bells and the chiming of a clock signaling the time for required prayer. Based on these observations, Mosso posited that changes in blood flow were associated with changes in cognition. To test this hypothesis, Mosso asked Bertino to multiply 8 by 12, a task that was accompanied by an increase in brain pulsation. These observations set the stage for contemporary functional brain mapping using hemodynamic measurements (Raichle, 2000), and illustrate the general approach of manipulating cognition, emotion, or behavior to determine their effects on neural functions—() as a function of psychological or behavioral processes (). In Bayesian terms, this can be specified as P(/). Stating it in this way highlights a limitation of this approach: It provides evidence that a neural element or mechanism 3

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Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference

assuming that the difference between simple and complex systems could be extrapolated from an established set of theories. The organization and function of simpler circuits can inform and be informed by the study of the behavior of the more complex circuits and organisms of which they are a part, but complex circuits cannot be explained on the basis of simpler ones alone. For instance, the phenotypic expression (e.g., behavior) of strains of mice with specific genes inactivated (i.e., knockout mice) has been known to depend on the genetic background (e.g., Gerlai, 1996); the effects of the social context, in contrast, were thought to be unimportant. Crabbe, Wahlsten, and Dudek (1999) demonstrated that the specific behavioral effects associated with a given knockout could vary dramatically across environmental contexts (e.g., experimenters, testing environments, laboratories). When these authors expanded the traditional approach to include multilevel integrative analyses of genetics, neural processes, and behavior, they observed new patterns of data that were not predictable based on what was known about the component elements. Stephen Jay Gould (1985) noted, “We often think, naively, that missing data are the primary impediments to intellectual progress—just find the right facts and all problems will dissipate. But barriers are often deeper and more abstract in thought” (p. 151). This observation is especially true in the investigation of complex systems, where patterns of data rather than single data points are important to grasp. Human behavior (including mental behavior) is the most complex system science has investigated. In this endeavor, as in all complex sciences, theory and empirical investigation must proceed together, each informing the other. As William James (1890) suggested, the need for theoretical models applies to psychological and behavioral elements (), biological and neural elements (), and the relations between the former and the latter. Whether manipulating or measuring neural mechanisms, or both, mapping these changes to behavior requires that tasks be well designed, which is to say the functional component processes must be well specified by some behavioral theory and body of empirical work. Theory and research in the neurosciences, however, are just as crucial to discerning patterns of integrative relations. Being inattentive to the properties, constraints, and operating possibilities of the neural mechanisms that underlie cognition, emotion, and behavior is inefficient, at best, because one misses the opportunity to build more plausible theories and to focus efforts more precisely on the constructs and interpretations that need to be considered. The resulting theories within and bridging across the behavioral sciences and the neurosciences are themselves only evolving approximations, but they provide an important means for advancing

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understanding of the complex neural and behavioral mechanisms at work. The evolution of these theories is promoted by the open theoretical dialogue between the neurosciences and behavioral sciences. This Handbook is designed to make theory and research in the neurosciences accessible to psychologists and other behavioral scientists while also highlighting for neuroscientists currently active areas within the behavioral sciences and discussing information, constructs, and approaches at the intersection of the behavioral sciences and the neurosciences. Complex states tend to be multiply determined, and human behavior is certainly no exception. Nineteenthcentury neurologist John Hughlings Jackson emphasized the hierarchical structure of the central nervous system and the re-representation of functions at multiple levels of this neuraxis (Jackson, 1884/1958). Subsequent neuroanatomical investigations have revealed this structure to be more heterarchical than hierarchical (see Berntson et al., this volume), but Jackson’s point remains that information is processed concurrently at multiple levels of the organization within the nervous system. Primitive protective responses to aversive stimuli, for instance, exist at the level of the spinal cord, as in stereotypic flexor withdrawal responses to nociceptive stimulation. These protective reactions are expanded and embellished at higher levels of the nervous system (Berntson, Boysen, & Cacioppo, 1993). The evolutionary development of higher neural systems, such as the limbic system, endowed organisms with an expanded behavioral repertoire including escape reactions, aggressive responses, and even the ability to anticipate and avoid aversive encounters. Humans were not the first bipedal creatures or the first to use tools, but humans, apparently uniquely, contemplate the history of the earth, the reach of the universe, the origin of the species, the genetic blueprint of life, and the physical basis of their own unique mental existence. These feats are the result of evolution endowing humans not only with primitive, lower level adaptive reactions but also with the unmatched information-processing capacities of the cerebrum. At progressively higher levels of neural organization, there is a general expansion in the range and relational complexity of representational operations and contextual controls and in the breadth and flexibility of discriminative and adaptive responses (Berntson et al., 1993). The adaptive flexibility of higher level neural systems comes at a cost, however, given the finite information-processing capacity of neural circuits. Greater flexibility means a less rigid relationship between inputs and outputs; a greater range of information that must be integrated; and a slower, more serial-like mode of processing. Consequently, the evolutionary layering of higher processing levels onto lower substrates has adaptive advantage in that lower

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Mapping across Levels of Organization 5

and more efficient processing levels may continue to be expressed. The processes expressed at lower and higher levels of the neuraxis can also interact in myriad ways, as illustrated by the diabetic who can overcome the withdrawal reflex when self-injecting insulin and the professional golfer who chokes when putting on the final green with a major championship at stake. Statistical models now exist that include stochastic error terms at various hierarchical levels of aggregation that are applicable to the data matrices that span biological and behavioral levels of organization. Our goal here is not to review these statistical models (cf. Weinstein, Vaupel, & Wachter, 2008) but to provide a more generic discussion of the conceptual issues that arise when mapping between the neural and behavioral domains. The mappings of elements across levels of organization may begin as associations but must move to mechanisms to explicate the neural basis of psychological and behavioral processes. This progression can be hindered by inattention to the logic underlying inferences about the functional import of neural structures or processes simply because one is dealing with observable biological events, as doing so can yield simple and restricted descriptions of empirical relationships or erroneous interpretations of these relationships. In the remainder of this chapter, therefore, we review logical issues involved in mapping constructs across levels of organization.

MAPPING ACROSS LEVELS OF ORGANIZATION One of the simplest methods of mapping across neural () and behavioral () levels of organization is the correlative approach. There are notable success stories to illustrate this approach. For instance, Suomi and colleagues found that peer-raised monkeys, compared to monkeys raised by their biological mothers, exhibit more aggression and less grooming behaviors, and typically remain at the bottom of the social hierarchy (Suomi, 1999). These monkeys are further characterized by lower cerebrospinal fluid concentrations of 5-hydroxyindoleacetic acid (5-HIAA, a serotonin metabolite) than their mother-reared counterparts. Impulsive rhesus monkeys in the wild are also characterized by high cerebrospinal fluid 5-HIAA concentration levels and the behavioral tripartite of aggressiveness, infrequent grooming, and low standing in the social hierarchy (cf. Suomi, 1999). These correlational studies have contributed to productive research on serotonin transporter gene (5-HTT) polymorphisms, environmental influences, and behavior in monkeys and humans. Of course, not every association identified in a correlative approach proves robust or informative. The associations

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uncovered in correlative research, especially atheoretical correlative investigations, run the risk of yielding false discoveries (i.e., nonreplicable associations), and this risk increases with the number of possible associations that are examined. False discovery rate techniques have been developed to help mitigate this problem, but these techniques do not eliminate the problem (cf. Munafo et al., 2003). The development and adoption of false discovery rate methods represents an advance in dealing with Type I error rates, however, because the cost of near-zero false discovery rates is a high false-negative rate. The ratio of the number of missed small discoveries to false discoveries can be substantially greater than 1 in studies of complex behavioral outcomes, and small associations can carry large theoretical ramifications. Therefore, the cost of missing important but small associations (Type II errors) can sometimes be greater than the cost of a Type I error. Independent replication, therefore, is of paramount importance. In addition, bioinformatics tools and multivariate techniques permit a reduction in the number of measures to more meaningful functional sets of measures. For instance, microarray studies can now be performed on hundreds of thousands of gene transcripts, but the upstream transcription control pathways are typically of greater interest than the individual gene expressions in these studies. Cole, Yan, Galic, Arevalo, and Zack (2005) introduced the Transcription Element Listening System (TELiS), which combines sequence-based analysis of gene regulatory regions with statistical prevalence analyses to identify transcription factor binding motifs that are overrepresented among the promoters of up- or downregulated genes. Cortisol can regulate a wide variety of physiological processes via nuclear hormone receptor–mediated control of gene transcription. Cortisol activation of the glucocorticoid receptor exerts broad anti-inflammatory effects by inhibiting pro-inflammatory signaling pathways. In longitudinal research on middle-aged and older adults, we found that perceptions of social isolation predict higher morning rises in cortisol the following day (Adam, Hawkley, Kudielko, & Cacioppo, 2006). Social isolation is also associated with increased risk of inflammation-mediated diseases. One possible explanation for inflammation-related disease in individuals with high cortisol levels involves impaired glucocorticoid receptor–mediated signal transduction that prevents the cellular genome from effectively “hearing” the anti-inflammatory signal sent by circulating glucocorticoids (Cole et al., 2007). Consistent with this hypothesis, a systematic examination of genome-wide transcriptional alterations in circulating leukocytes using TELiS showed increased expression of genes carrying pro-inflammatory elements and decreased expression of genes carrying anti-inflammatory glucocorticoid response

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Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference

elements in lonely relative to nonlonely middle-aged adults (Cole et al., 2007). Impaired transcription of glucocorticoid response genes and increased activity of pro-inflammatory transcription control pathways provide a functional genomic explanation for elevated risk of inflammatory disease in individuals who chronically perceive high levels of social isolation. In sum, a strength of the correlative approach is often in identifying associations that might be replicable and worthy of further study rather than in post hoc hypothesis testing. An important goal of scientific theory is to describe the causal interrelationships among factors, thereby explicating the mechanism responsible for an association. The correlative approach may generate elements (e.g., genes, neurophysiological circuits, cognitive processes) or contextual moderators that are candidates for this causal mechanism. The correlative approach may not indicate the nature of the specificity of the association across levels of organization. For convenience, consider the constructs or measures at each level of organization as elements within a domain or set, as William James (1890) suggested. The mapping between elements across such sets can take one of the following forms (see Figure 1.1): • A one-to-one relation, such that an element in one set or level of organization is associated with one and only one element in another set, and vice versa. An example of a one-to-one relation is neurons in V1 tuned to a given stimulus feature such as line orientation (see Hubel & Wiesel, 2005). • A one-to-many relation, meaning that an element of interest in one set is associated with multiple elements in another set. An example is visual perception, which proceeds along ventral and dorsal streams (see Chapter 11). • A many-to-one relation, meaning that two or more elements in one set are associated with one element in another set. (This differs from a one-to-many relation only when the order of the mapping across levels of organization—e.g., behavioral [e.g., cognitive] to biological—is specified.) An example is the finding that a particular movement or the observation of that particular movement activates the same neurons (termed mirror neurons) in area F5 of the monkey brain (see Chapter 16). • A many-to-many relation, meaning two or more elements in one set are associated with the same (or an overlapping) subset of elements in another set. Faces and objects may differ in terms of the region of maximal response in functional magnetic resonance imaging studies, but both faces and objects are associated with activation of multiple regions in the ventral temporal lobe (e.g., fusiform gyrus, parahippocampal gyrus; Haxby et al., 2001).

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• A null relation, meaning there is no association between the specified element in a neural set and those observed in a behavioral set. Even though all elements in  map into one or more elements in , not all elements in  map into elements in , and a particular element of interest in  may not map into the set of elements known or measured in . Association studies involving elements with a one-toone relation (absent confoundings and measurement error) produce high correlations, whereas association studies involving elements characterized by a null relation yield an essentially zero correlation. The strength of the association between elements across levels of organization can vary a great deal, however, for one-to-one, one-to-many, and many-to-one mappings, and a many-to-many mapping between two elements across levels of organization can produce correlation coefficients that are quite small, making them difficult to distinguish from a null relation unless the sample size is large or one or more elements are manipulated. Thus, the initial establishment of an association between elements across levels of organization through a correlative approach is typically not sufficient to determine the specificity of the mapping.

Psychological

Biological

One-to-One

One-to-Many

Many-to-One

Many-to-Many

Null

Figure 1.1 Possible relationships between elements in two adjacent levels of organization (domains). For illustrative purposes, these domains have been labeled Psychological and Biological.

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Toward Stronger Inferences in the Interpretation of Brain–Behavior Relationships 7

Why might it be important to go beyond thinking of associations to considering the nature of the relationship between elements at different levels of organization? First, it is important if one is to move efficiently from association to the specification of mechanisms. Second, the nature of the mappings between elements at different levels of organization determines the limits of interpretation one can draw about an association. Consider research in which a biological measure (e.g., activation of the anterior cingulate cortex as measured by fMRI) is shown to covary with a behavioral task (e.g., lying; Langleben et al., 2002). This established association may then be used to justify an interpretation of differences in the neural element (i.e., activation of the anterior cingulate) as evidence of differences in the behavioral element (i.e., lying). This form of inference can be problematic, however. Even if one knew that variations in lying were associated with corresponding variations in anterior cingulate activity, inferring lying based on anterior cingulate activity ignores the possibility that other antecedent conditions could also produce variations in anterior cingulate activity. That is, it ignores the specificity of the association or mapping to the construct about which one would like to draw the inference. Such errors, in turn, can slow theoretical development. It is tempting to suggest that these issues do not apply to genetics (or brain processes) because there is no doubt that they play a causal role in the production of complex behaviors. To say that genes are causal is not equivalent, however, to specifying which gene or set of genes is associated with and causal in a particular phenotypic expression or, for that matter, to specifying the mechanism by which associated genes might influence a particular phenotype. Gottesman and Gould (2003) suggested that the number of genes involved in a phenotype is directly related to both the complexity of the phenotype and the difficulty of genetic analysis (see also Butcher, Kennedy, & Plomin, 2006). Although difficult to discern, such causal linkages will be more easily resolved if attention is paid to the implications of the many-to-many mapping problem. The mapping between elements across levels of organization may become more complex (e.g., many-to-many) as the number of intervening levels of organization increases. The exception to this statement is when mappings among elements across adjacent levels of organization is one-to-one, but such mappings are atypical. Accordingly, the likelihood of complex and potentially obscure mappings increases as one fails to consider intervening levels of organization. Admittedly, it is not always obvious which of several levels of organization might be “adjacent,” except perhaps when level of organization refers to a temporal rather than

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spatial scope. This caveat that mapping across levels of organization may be fostered by the incremental mapping of elements between proximal levels nevertheless may have heuristic value. For instance, endophenotypes such as neurocognitive deficits have proven to be valuable explanatory constructs between genes and psychiatric diseases (e.g., Gottesman & Gould, 2003; Nuechterlein, Robbins, & Einat, 2005), and in theory the same situation should apply to any mapping that goes from neural elements to complex behaviors. For this reason, we focus here on the mappings between two adjacent levels of organization. The issues raised about the mappings between adjacent levels of organization can be extended to any number of adjacent levels of organization.

TOWARD STRONGER INFERENCES IN THE INTERPRETATION OF BRAIN–BEHAVIOR RELATIONSHIPS There is an intuitive appeal to the view that a proper understanding of the neural substrates of cognition and behavior may be couched in terms of the selective activation of regions of the brain during particular behavioral tasks. Although progress has been made in this regard, the manner in which many inferences are drawn about the behavioral significance of localized brain activity is more complex than is sometimes assumed. A major goal of neuroscientific studies of behavior can be expressed as   f(). That is, ultimately one wishes to specify the biological mechanisms responsible for various behavioral (including mental) phenomena. Contrasting tasks that are thought to differ in only one or more cognitive or behavioral operations () between the associated neural events () are sometimes interpreted as showing that neural structure (or process)  is associated with behavioral operation . These data are also sometimes treated as revealing much the same information that would have been obtained had neural structure (or process)  been stimulated or ablated and a consequent change in behavioral function  observed. This form of interpretation reflects the explicit assumption that there is a fundamental localizability of specific behavioral operations and the implicit assumption that there is an isomorphism between  and . Such observations may represent a starting point for a series of internally consistent propositions that lead to a general conclusion, but problems with the logic of this approach can lead this conclusion astray (Cacioppo & Tassinary, 1990). In particular, research has shown that when a behavioral element varies when a neural element is manipulated (or vice versa), this does not necessarily imply the existence of an isomorphism between these elements (Sarter, et al., 1996).

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Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference

For instance, causal hypotheses regarding a specific neural structure or process () underlying a cognitive or behavioral operation () are of the form   f(). They necessarily imply that  is always followed by  but do not necessarily imply that  is always preceded by . This is what it means for  to have multiple (parallel) determinants. Furthermore, brain events () may be of interest to the extent that they index a cognitive operation or state so that the inferences are of the form   f() rather than not   (not ). Thus, one important aim of neural measurements can be specified by the conditional probability of  given , or P(/)  1. The typical structure of investigations in which neural measurements are made, in contrast, can be written as the conditional probability of  given , or P(/)  x. For example, brain imaging techniques provide information about  as a function of , but the conditional probabilities P(/) and P(/) are not equivalent unless there is a 1:1 relationship between brain structure  and cognitive function . Most of the early discoveries of functional locationism that have had lasting impact involved the ablation or direct electrical stimulation of specific nuclei (i.e., P[/]), whereas the evidence for Gall’s localization theory was based primarily on relating cranial features to extreme behaviors (i.e., P[/]), which was interpreted to mean that individuals with these cranial features were destined toward these behaviors (i.e., P[/]). This latter interpretation does not follow from the form of the data on which it was based because P(/) does not equal, or even approximate, P(/) unless there is an isomorphism between  and . Careful attention to the structure of inference, therefore, may contribute to a more productive dialogue between neuroscientists and behavioral scientists. Approaches such as stimulation and ablation studies and brain imaging research provide complementary rather than redundant information about the relationship between brain structures (or events) and cognitive functions. This is because stimulation and ablation studies bear on the relationship P(/), whereas brain imaging studies provide information about P(/). Despite the formal parallelism between these expressions, there is a fundamental asymmetry in the heuristic power of studies aimed at the demonstration of P(/) versus P(/). The causal role of  in process  can be examined in a straightforward fashion by direct experimental manipulation of . The loss of cognitive function by inactivation of neural processes can serve to establish  as necessary for function . Moreover, addressing a more complex avenue of research, facilitation of cognitive functions by electrical or neurochemical brain activation can further establish that  is a sufficient condition for function .

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THE IMPORTANCE OF SPECIFYING CONTEXT: TAXONOMY OF MAPPINGS Tests, assays, or measured biological responses (i.e., a physiological event that exceeds some decision threshold) have two different but related sets of characteristics. Analytic sensitivity is the ability to consistently detect very low levels of the target analyte. Stated another way, sensitivity is the true-positive rate. Considering all true (T) and false (F) outcomes of the test, for both positive (P; analyte present) and negative (N; analyte absent) conditions, sensitivity  TP/(TP  FN). In contrast, analytic specificity refers to the ability of a test to selectively detect only the target analyte and not others; specificity  TN/(TN  FP). For instance, blood sugar levels will vary in a predictable fashion for several hours after one ingests a dosage of glucose. Deviations from the normative values in blood sugar level across time mark a possible problem in metabolism because the blood glucose tolerance test (a procedure for mapping the glucose–blood sugar association) is sensitive and specific as long as the appropriate testing procedures are followed (e.g., fasting prior to the test) to eliminate the other known influences on the observed blood sugar excursions over the course of the test. This illustrates how a mapping between elements in  and  can be simplified by paying attention to potential confounding and contextual factors, by which we mean specifically P(not /) and P(not /). Furthermore, the diagnostic value of any given  (i.e., a measured response, as defined by some decision criteria such as corrected p  .05) as a measure of  depends not only on the sensitivity and specificity of the measured response but on the base rates for true positives and for true negatives. The sensitivity, defined quantitatively above, represents the true detection probability, and the specificity represents 1 minus the false detection probability. The chance that the measured response correctly indexes the targeted state is called the positive predictive value (PPV) and equals the fraction of detections that are true hits, that is, PPV  TP/(TP  FP). In contrast, the chance that the absence of this measured response (termed a negative screen in medicine) is correct is called the negative predictive value (NPV), where NPV  TN/(TN  FN). The properties of sensitivity and specificity and the PPV and NPV, of course, depend in part on the elements involved in the mapping. For instance, the adrenocortical hormone cortisol is released by the adrenal cortex under conditions of stress and hence is considered a stress hormone and is often used as a marker of stress. Assays with high sensitivity and specificity for cortisol are available and can be used to measure this hormone in plasma, urine, or saliva; these assays may provide an accurate measure

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The Importance of Specifying Context: Taxonomy of Mappings

The scientist is usually looking for invariance whether he knows it or not. Whenever he discovers a functional relation between two variables his next question follows naturally: under what conditions does it hold? In other words, under what transformation is the relation invariant? The quest for invariant relations is essentially the aspiration toward generality, and in psychology, as in physics, the principles that have wide application are those we prize. (p. 20)

Is the mapping between two elements across levels of organization universally generalizable, or is it moderated by other factors? If it is generalizable without qualification, then the association requires no attention to characteristics of the context or sample population; that is, the mapping has external validity. If external validity is absent, then the reason for this becomes a theoretically interesting question regarding P(not-/) or P(not-/). Invariant associations were once assumed, but statistical methods are now sufficiently developed to test for potential moderators (e.g., Baron & Kenny, 1986), and increasing attention is being paid to the operation of moderator variables. A taxonomy of associations between elements across levels of organization is summarized in Figure 1.2. The initial step is often to establish that variations in an element in

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Context-Bound

Context-Free

Maker

Invariant

Outcome

Concomitant

Specificity

One-to-One

Generality

Many-to-One Many-to-Many

of adrenocortical activity. This represents a proximal mapping, as the adrenal cortex is the primary source of cortisol, although other factors (e.g., clearance) can also affect the measure. When cortisol is used as a marker of stress, however, the sensitivity, specificity, PPV, and NPV may all be quite different. In this case the mapping is more distal, as there are several mediating links between stress and cortisol secretion. Although potent stressors generally yield a cortisol response, the sensitivity of a cortisol assay for stress is considerably lower, as minor stresses may not trigger a measurable cortisol response. That is, there may be many more false negatives in the equation sensitivity  TP/(TP  FN). Moreover, the selectivity of a cortisol assay for stress is also considerably lower, as other variables may also impact cortisol release. Cortisol levels vary across the day and with activity, among other variables, so there may be more false positives in the equation specificity  TN/(TN  FP). The PPV and NPV depend on the threshold used to define a response, but it should be obvious that these values, too, will be lowered by poor sensitivity and specificity. Consequently, the utility of a cortisol assay for stress may be limited to more significant stressors, and to enhance specificity, extraneous variables that can impact cortisol must be taken into account statistically. Another dimension is the generality of the mapping. In his influential Handbook of Experimental Psychology, S. S. Stevens (1951) advised the following:

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Figure 1.2 Taxonomy of mappings among elements between adjacent levels of organization.

one domain are associated with variations in an element in another, thereby establishing an association. An outcome is defined as a mapping in which multiple elements at one level of organization (e.g., biological) are related to an element at another level of organization (e.g., behavioral), and this many-to-one mapping may change across contexts. Initial association studies typically do not address issues of specificity or generality, and the treatment of such associations as invariants is premature. An invariant relationship refers to a universal isomorphic (one-to-one) mapping between elements across levels or organization (see Figure 1.2). Invariant mappings permit the inference of an element at one level of organization based on the measurement of its isomorphic element at another. A marker is defined as a one-to-one, nonuniversal (e.g., context-dependent) relationship between elements across levels of organization (see Figure 1.2). Many medical diagnostic tests that have sensitivity and specificity only if explicit procedures are followed to eliminate other influences are examples of markers. Inferences based on markers are similar to those for invariants as long as all other elements involved in the mapping are either experimentally or statistically controlled. Finally, a concomitant refers to a many-to-one but universal association between elements across levels of organization and is similar to outcomes, except that the latter is not universal. Outcome and concomitant mappings enable strong inferences to be drawn about theoretical constructs based only on hypothetico-deductive logic (Platt, 1964). Specifically, when two theoretical models differ in predictions regarding one or more outcomes or concomitants, then the logic of the experimental design allows theoretical

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10

Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference

inferences to be drawn about elements at one level of organization based on the measured elements in another. When a new effect or association is found not to generalize to specific contexts or individuals, concerns are typically expressed about the methodological differences between the studies. Such a finding raises several important questions, including whether the original association is replicable; and, if replicable, whether the diminution in effect size attributable to measurement issues (e.g., reliability, construct validity) or to the operation of a moderator variable. The latter is an important theoretical question. It is for this reason that careful attention to the psychometric properties of all measures, regardless of their level of organization, to ensure their reliability and validity (including construct validity) is especially important in the design and analysis of neuroscientific studies of behavioral processes.

SUMMARY Given the complexity of neural and behavioral processes, attention to the form of scientific reasoning can be especially important. Many behavioral processes are multiply determined. To the extent that this is the case, investigators who assume rather than establish an invariant relationship between elements in the behavioral and neural domains are at risk for predictably faulty interpretations. That is, the sensitivity and specificity of the mapping of neural elements into behavioral levels of organization may be context dependent, and paying attention to these issues improves the quality of inductive inferences. Interdisciplinary research that crosses neural and behavioral levels of organization raises issues about how might one productively think about concepts, hypotheses, theories, theoretical conflicts, and theoretical tests across levels of organization. Abstract constructs such as those developed by behavioral scientists provide a means of understanding highly complex activity without needing to specify each individual action of the simplest components, thereby providing an efficient means of describing the behavior of a complex system (e.g., working memory). Chemists who work with the periodic table on a daily basis nevertheless use recipes rather than the periodic table to cook, not because food preparation cannot be reduced to chemical expressions but because it is not cognitively efficient to do so. Reductionism, in fact, is one of several approaches for bettering science based on the value of data derived from distinct levels of organization to constrain and inspire the interpretation of data derived from other levels of organization. In reductionism, the whole is as important to study as are the parts, for only in examining the interplay across levels of organization can the underlying principles and mechanisms be ascertained.

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Our goal in this chapter has been to outline a simple model to aid in thinking about elements from different levels of organization and the inferences drawn from observations of these elements. Contemporary work has demonstrated that theory and methods in the neurosciences can constrain and inspire behavioral hypotheses, foster experimental tests of otherwise indistinguishable theoretical explanations, and increase the comprehensiveness and relevance of behavioral theories. Several principles further suggest that comprehensive theories of behavior will be advanced by the joint consideration of multilevel integrative analyses that span neural and behavioral processes. One that we have discussed is the principle of multiple determinism, which specifies that a target event at one level of organization, but especially at molar or abstract (e.g., cognitive or behavioral) levels of organization, can have multiple antecedents within or across levels of organization. A corollary to this principle, the corollary of proximity, is that the mapping between elements across levels of organization becomes more complex (e.g., many-to-many) as the number of intervening levels of organization increases. An important implication of this corollary is that the likelihood of complex and potentially obscure mappings increases as one skips levels of organization. The principle of nonadditive determinism specifies that properties of the whole are not always readily predictable from the properties of the parts. For instance, the behavior of nonhuman primates following the administration of amphetamine or placebo can appear similar unless each primate’s position in the social hierarchy is considered. When this behavioral factor is taken into account, amphetamine is found to increase dominant behavior in primates high in the social hierarchy and to increase submissive behavior in primates low in the social hierarchy. Thus, the effects of physiological changes on behavior can appear unreliable until the analysis is extended across levels of organization. A strictly physiological (or behavioral) analysis, regardless of the sophistication of the measurement technology, may not reveal the orderly relationship that exists. The emergence of an orderly pattern of data when spanning levels of organization is one of the unique opportunities of neuroscientific investigations of behavior. Finally, the principle of reciprocal determinism specifies that there can be mutual influences between microscopic (e.g., biological) and macroscopic (e.g., social) factors in determining behavior. For example, not only has the level of testosterone in nonhuman male primates been shown to promote sexual behavior, but the availability of receptive females influences the level of testosterone in nonhuman primates. These principles illustrate that the mechanisms underlying mind and behavior may not be fully explained

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References 11

by a biological or a behavioral approach alone but rather may require a multilevel integrative analysis. The contributions to this volume are designed with this in mind.

Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001, September 28). Distributed and overlapping representations of face and objects in ventral temporal cortex. Science, 293, 2425–2430. Hubel, D. H., & Wiesel, T. N. (2005). Brain and visual perception: The story of a 25-year collaboration. Oxford, England: Oxford University Press.

REFERENCES Adam, E. K., Hawkley, L. C., Kudielka, B. M., & Cacioppo, J. T. (2006). Day-to-day dynamics of experience-cortisol associations in a population-based sample of older adults. Proceedings of the National Academy of Sciences, 103, 17058–17063. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. Berntson, G. G., Boysen, S. T., & Cacioppo, J. T. (1993). Neurobehavioral organization and the cardinal principle of evaluative bivalence. Annals of the New York Academy of Sciences, 702, 75–102. Butcher, L. M., Kennedy, J. K. J., & Plomin, R. (2006). Generalist genes and cognitive neuroscience. Current Opinion in Neurobiology, 16, 1–7. Cacioppo, J. T., & Tassinary, L. G. (1990). Inferring psychological significance from physiological signals. American Psychologist, 45, 16–28. Cole, S. W., Hawkley, L. C., Arevalo, J. M., Sung, C. Y., Rose, R. M., & Cacioppo, J. T. (2007). Social regulation of gene expression in human leukocytes. Genome Biology, 8(9), R189.

James, W. (1890). The principles of psychology (Vol. 1). New York: Holt. Langleben, D. D., Schroeder, L., Maldjian, J. A., Gur, R. C., McDonald, S., Ragland, J. D., et al. (2002). Brain activity during simulated deception: An event-related functional magnetic resonance study. Neuroimage, 15, 727–732. Mosso, A. (1881). Ueber den Kreislauf des Blutes im menschlichen Gehirn [About the circulation of the blood in the human brain]. Leipzig, Germany: Veit. Munafo, M. R., Clark, T. G., Moore, L. R., Payne, E., Walton, R., & Fint, J. (2003). Genetic polymorphisms and personality in healthy adults: A systematic review and meta-analysis. Molecular Psychiatry, 8, 471–484. Nuechterlein, K. H., Robbins, T. W., & Einat, H. (2005). Distinguishing separable domains of cognition in human and animal studies: What separations are optimal for targeting interventions? Schizophrenia Bulletin, 31, 870–874. Platt, J. R. (1964, October 16). Strong inference. Science, 146, 347–353.

Cole, S. W., Yan, W., Galic, Z., Arevalo, J., & Zack, J. A. (2005). Expressionbased monitoring of transcription factor activity: The TELiS database. Bioinformatics, 21, 803–810.

Raichle, M. E. (2000). A brief history of human functional brain mapping. In A. W. Toga & J. C. Mazziotta (Eds.), Brain mapping: The systems (pp. 33–77). San Diego, CA: Academic Press.

Crabbe, J. C., Wahlsten, D., & Dudek, B. C. (1999, June 4). Genetics of mouse behavior: Interactions with laboratory environment. Science, 284, 1670–1672.

Sarter, M., Berntson, G. G., & Cacioppo, J. T. (1996). Brain imaging and cognitive neuroscience: Towards strong inference in attributing function to structure. American Psychologist, 51, 13–21.

Darwin, C. (1873). The expression of the emotions in man and animals. New York: Appleton.

Sherrington, C. S. (1906). The integrative action of the nervous system. New York: Scribner ’s.

Gerlai, R. (1996). Gene-targeting studies of mammalian behavior: Is it the mutation or the background genotype? Trends in Neurosciences, 19, 177–181.

Stevens, S. S. (1951). Handbook of experimental psychology. New York: Wiley.

Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry: Etymology and strategic intentions. American Journal of Psychiatry, 160, 636–645. Gould, S. J. (1985). The flamingo’s smile: Reflections in natural history. New York: Norton.

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Jackson, J. H. (1958). Evolution and dissolution of the nervous system (Croonian Lectures). In J. Taylor (Ed.), Selected writings of John Hughlings Jackson. New York: Basic Books. (Original work published 1884.) Vol 2, pp. 3–92.

Suomi, S. (1999). Attachment in rhesus monkey. In J. Cassidy & P. Shaver (Eds.), Handbook of attachment: Theory, research, and clinical applications (pp. 181–197). New York: Guilford Press. Weinstein, M., Vaupel, J. W., & Wachter, K. W. (2008). Biosocial surveys. Washington, DC: The National Academies Press.

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Chapter 2

Developmental Neuroscience MYRON A. HOFER

revealed how changes in the expression patterns of genes provide a common basis for both developmental and evolutionary changes (S. Carroll, 2005). Developmental biology (as embryology) had long been isolated from evolutionary biology, and most scientists thought that differences between species were accounted for by differences between those species’ genes. But with the advent of rapid gene sequencing in the past few years, it is now appreciated that differences between species lie in how a common set of highly conserved genes is regulated during development to produce the extraordinary differences in animal body plans and behaviors ranging from those of flies and worms to humans. Evolution is now viewed as taking place through changes in the processes and course of development, and development, in turn, is now seen as a major source of novelty and of variation between individuals upon which selection can act in the course of evolution. Rapid progress in the molecular genetic mechanisms of early development in the past few years has revealed an unexpected plasticity in the regulation of gene expression that enables a relatively few conserved cellular processes to be linked together in a variety of potentially adaptive patterns in response to genetic mutation or to environmental change. This “facilitated” variation (Kirschner & Gerhart, 2005) is capable of generating the useful novelties that random mutation and selection by themselves have seemed far less capable of producing in the course of evolution. New findings have also shown that the plasticity of behavior development in response to environmental interactions and genetic change (mutation, recombination) is capable of generating novel forms of adaptive variation upon which selection can act. By exposing previously hidden (genomically silenced) genes to selection and by genetic “accommodation” through selection acting at multiple genetic sites over generations, novelties that were at first environmentally induced can gradually become independent of their initiating environments (West-Eberhard, 2003). This focus on changes in gene regulation as a central mechanism of development has led to a wave of new

The development of an adult organism from a single cell is one of the most familiar processes of nature. It has been studied by scientists for more than two centuries without the emergence of a generally accepted explanatory theory. Ironically, evolution, the other great historical process in biology, has been far more difficult to study, yet Darwin’s simple but powerful theory has guided scientists for 150 years. One reflection of the lack of an agreed-on set of developmental principles has been the persistence, in scientific as well as lay circles, of the seemingly endless nature versus nurture debate. Another is the gap that exists between the language, methods, and concepts used by scientists studying development at the psychological, behavioral, and the cellular/molecular levels. Less discussed than these issues, but more fundamental, has been the uncertainty about how evolution and development are related. The topic was opened with Ernest Haeckel’s (1892) resilient nineteenth-century formulation that “ontogeny recapitulates phylogeny” (pp. 422–544) that was finally laid to rest in 1977 with the publication of Ontogeny and Phylogeny by Stephen Jay Gould. In the past few decades, developmental psychobiology and, more recently, developmental neuroscience have made considerable progress in advancing an interdisciplinary approach to the study of development. New methods for the study of early behavior in animal model systems and in human fetuses have begun to build connections between events and concepts at the cellular/molecular, physiological, behavioral, and psychological levels. For it is in early development that one can best see how each of these levels of biological function emerge in sequence and come to work together.

EVOLUTION AND DEVELOPMENT In the past few years a new field, evolutionary developmental biology, or evo-devo, has emerged as new methods of functional genetic analysis and manipulation have 12

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Evolution and Development 13

discoveries in the area of “epigenetic” processes involving the remodeling of chromatin, the protein matrix surrounding, supporting, and controlling the access of molecular regulatory signals to the DNA strands within chromosomes (Baylin & Schuebel, 2007). These molecular rearrangements, which take place through simple chemical processes such as methylation and acetylation, provide, for the first time, a direct cellular/molecular link between the outside environment and the genes of the organism during development, a concrete and specifiable locus for the much-sought-after gene–environment interaction. Developmental Selection: The Evolution of Development The emphasis in evo-devo is on the proposed role of development in evolution, but what implications does this new evolutionary role have for the concept of development? What competitive advantages did the evolution of multicellular development convey? What selective pressures have shaped development during its own evolution, and what can these tell scientists about the adaptive functions that they should expect modern-day developmental processes to carry out? The question of how and when development evolved is essentially the question of the origins of multicellularity. It leads to one of the last remaining mysteries in the fossil record of evolution, the Cambrian explosion (Gould, 1989; Knoll, 2003). In this abrupt change in the number and variety of marine fossils, examples of all major animal groups or phyla that exist today (e.g., vertebrates, mollusks,

Cell division

Sexual recombination

arthropods, worms) appeared abruptly approximately 500 million years ago. For the more than 3 billion years before that, only fossils of single-celled organisms have been found. Modern-day representatives of these ancient organisms, such as algae and amoebae, seem little changed in form since that time, but they have been found to possess all the characteristics of developing cells in multicellular animals (see Figure 2.1) such as rapid multiplication, migration, adhesion, the capacity for sexual as well as asexual reproduction, and the capacity for differentiation (e.g., from an amoeba to a flagellate form). These adaptive cellular/molecular mechanisms evolved in response to “signal” molecules in their environments that accompanied or produced changing conditions. What is missing in these protozoa is the organization of these cellular/molecular processes into a linear series of events involving many such cells so as to produce structures that grow in size, shape, and complexity of form and function (see Figure 2.2). This is called development, and it must have emerged very rapidly (in geologic time), as it was crucial for the appearance of multicellular life. The prerequisite for this emergence seems to have been a gradual increase in the complexity of the genome of unicellular animals. Evidence for this lies in the progressive appearance of unicellular lineages whose living members showed larger and larger numbers of introns (the noncoding segments of DNA that facilitate “shuffling” of gene positions along the DNA strand), a multiplication of promoters of gene activation, and the appearance of novel regulatory elements leading to a greater degree of interaction between genes. These genomic changes allowed for

Adhesion Migration

Sporulation

Apoptosis

Differentiation

Molecular signals

Environmental signals

Figure 2.1 Protozoan precursors of developmental processes. Note: Unicellular organisms evolved nearly all of the cellular/molecular mechanisms needed for multicellular development over the 3 billion years of life prior to the Cambrian explosion of diverse multicellular organisms

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500 million years ago. These processes were selected for their capacity to enhance the adaptive capacity of these single-cell organisms to changes in the surrounding environment.

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Developmental Neuroscience

Note: The rapid evolution of metazoan (multicellular) organisms took place through a reorganization of the various individual cellular/molecular mechanisms of unicellular organisms (represented on the left; see also Figure 2.1) into the integrated, linear processes of development, a process driven by the selective advantage of larger and more complex multicellular

organisms capable of creating and exploiting novel ecological niches. During evolution, cells continued to be regulated by signal molecules in the intercellular environment of the developing metazoan organism, as they had been in unicellular organisms. A return to single cells marks the beginning of the next multicellular generation of metazoans, depicted on the far right.

the rapid emergence of a novel system of genetic control for all multicellular animals in which only a limited set of genes within each cell is expressed at any one time while the vast majority are silenced. Genes in the fertilized egg, or zygote, express signal proteins that diffuse into the intercellular space and then into neighboring cells, where they act as transcription factors regulating the genetic control of growth and function as well as the expression of a further wave of different intercellular signal proteins. The timing, amount, and structure of these waves of signal proteins create a virtual cascade of intercellular communication, and with it a complex pattern of growth and function over time. These developmental cascades specify and organize the construction of a multicellular organism and its complex array of functions, including behavior. Signal molecules in the intercellular environment continue to act to regulate the timing and nature of cellular changes, as they had in unicellular organisms. The development of all multicellular organisms is strikingly similar in its early stages. An egg, fertilized by one other smaller cell, divides repeatedly, forming a ball of similarly appearing cells, the zygote. Gradually, the zygote becomes a blastocyst, with two layers of cells: the endoderm inside and the ectoderm outside. These two sheets of cells become bent and folded, developing a radial symmetry. The development of the most primitive multicellular animals, such as sponges, hydra, and jellyfish, stops here. Behaviorally, these simple creatures show primarily local cellular “irritability” and generalized “flowing” body movements coordinated by nerve nets. But in the metazoan animals of the Cambrian period, an invagination of the hollow blastocyst occurs and a third layer of

cells, the mesoderm, forms between the two other layers. These layers bend to form shapes with symmetry in three planes, and they expand to form groups and sheets of cells that interact with groups of different cells deriving from the other layers. As these cells change in form and location, they differentiate into radically different cell types in different regions to become organs with multiple different functions. One of those organs, the nervous system, becomes the basis for the evolving complexity of animal behavior that so interests neuroscientists. There are good reasons to think that the selective advantage responsible for the evolution of this developmental plan for multicellular life was that it made possible the construction of novel, much larger, and more complex animals. These animals not only were able to use the previously dominant unicellular species as food but were capable of finding and even creating new ecological niches: territories, food sources, and ways of life that bacteria and protozoa were incapable of inhabiting, defending, or utilizing. Bonner (1993) defined development as the growth phase of the life cycle and demonstrated that its duration (and complexity) is highly correlated with the size (and complexity) of any given life form, ranging from bacteria to whales, each enabled to exploit larger and more complex niches. This view of development as construction is widely accepted and has too often led to an assumption that all developmental mechanisms are organized to carry out a single function: the building of a successful adult. The rapid evolution of development, however, was shaped not only by the selective advantages provided by the size and functional complexity of the adults that were constructed. Other developmental processes and functions

Figure 2.2 The evolution of development.

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Evolution and Development 15

had to be selected in order to adapt immature forms to the changing environments they inhabited as embryos, larvae, fetuses, newborns, and weanlings on their path to adulthood. The unique characteristics of larval forms in amphibians, and of intrauterine/placental structures and early nursing interactions in mammals, are examples of the “ontogenetic adaptations” that play such a prominent role in the development of animals that one can study today (Oppenheim, 1984). Furthermore, as the processes mediating the developmental functions of construction and adaptation evolved, they were also being shaped and modified by selection for their “evolvability” (R. Carroll, 2002). That is, processes that created potentially useful variations in the course of development and others that promoted the replication of a successful variant developmental path in the next generation were differentially selected. In the same way that genetic mechanisms for variation (recombination and mutation) and for heritability (the copying of DNA during cell division) were shaped by selection in the evolution of single cells, so a set of developmental mechanisms for “facilitating” variation and for transmitting successful variant paths to the next generation was shaped by selection in the evolution of development in multicellular organisms. Thus, the developmental processes that scientists study today have been organized and shaped in their evolution by the four components of what I have called developmental selection: construction, adaptation, variation, and inheritance (Bateson, Hofer, Oppenheim, & Wiedenmayer, 2007; Hofer, 2005). As with many other features of organisms, developmental processes have been co-selected; that is, processes selected for their contribution to the construction and ontogenetic adaptation of young animals have also been shaped by selection for their heritability and their capacity for variation (see Figure 2.3). For example, the embedding of embryos and fetuses in the internal environments of their mothers, and the sustained close physical interactions of newborns and infants with their parents (Rosenblum & Moltz, 1983), gain a new significance and interpretation if one thinks of them in terms of developmental selection. These features of development can now be viewed as inherited, transgenerational environments that act as a protective scaffolding and matrix for the construction of a descendant in the next generation and as a source of potentially useful variation in the future. In this way, long and complex early developmental pathways can be organized in a linear fashion, regulated by interactions with the previous generation so as to be re-created, with further variations, in the next generation. The usefulness of the concept of developmental selection, it seems to me, is that it defines a set of functions that developmental processes are organized to carry out.

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Figure 2.3 Developmental selection. Note. During evolution, developmental processes are co-selected not only for (a) construction of an adult, but also for their capacity to (b) adapt the immature organism to its age-specific environments, (c) produce potentially useful variations in the course and/or outcome of development, and (d) facilitate the inheritance of successful variants in the next generation.

Knowing the ultimate (i.e., evolutionary) functions of developmental processes should be useful for forming new hypotheses about how a given process works. For example, in the field of sociobiology, the evolutionary principles of kin selection led to the discovery of new processes at work within the parent–offspring relationship, such as parent– offspring conflict. In the case of development, asking how specific events and processes contribute to age-specific adaptation, variation, and inheritance of a selected developmental path in the next generation should increase understanding of development considerably beyond the current approach of asking questions that are limited to the function of constructing an adult. Examples are given in the sections on attachment and the regulation of development. Levels of Organization Developmental neuroscience involves a journey in time across levels of organization from the molecular genetics of an embryonic cell to the mental experiences of a conscious mind (see Figure 2.4). The gap that exists between biology and psychology is slowly narrowing as new biological methods have become increasingly able to approach the brain systems underlying more and more complex cognitive and emotional processes. In the study of early behavior development, the conceptual models of psychologists and the working models of biologists converge, as they are being applied to the same behavioral phenomena. Observations and insights from these different levels of organization are beginning to contribute to one another ’s understanding, as I illustrate with regard to the area of early attachment in the next section. Evolutionary principles offer a conceptual common ground that can be shared by both neuroscience and psychology,

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Developmental Neuroscience

Size Complexity

Time/Age Figure 2.4 Levels of organization in developmental neuroscience. Note: During development, new properties emerge at each level that are not reducible to lower-level events. A full understanding that extends across levels requires repeated efforts at “translation” as well as extensive knowledge of events at each level. Early development affords significant simplification of this process at all levels.

providing answers to questions about how the human mind and brain have come into being and why they have their present forms. The historical nature of both development and evolution bridges the gap that exists between the reductionist emphasis of the molecular/cellular neurosciences and the holistic emphasis on inner experience and meaning that is the central focus of some branches of psychology. Early human development traverses a series of levels of scale and organization (as illustrated in Figure 2.4) from the molecular and intercellular interactions of the embryo, to the integrated systems and behavior of the fetus, to the emerging cognitive and affective capacities of the child, and finally to the inner experience that even language cannot fully describe. The biological, behavioral, and psychological processes at work at those levels of organization seem very different. But the new properties that emerge at higher levels arise from the combined operation of simpler component processes taking place at the lower level. Understanding those transitions, and the emergence of new properties at higher levels, is one of the central questions for research in early animal and human development as well as for attempts to integrate neuroscience and psychology in general. In early development, behavior provides a crucial link between the levels of brain systems and of psychological constructs, as is illustrated in the following sections.

THE EARLY DEVELOPMENT OF ATTACHMENT In this section I outline how a strategy of attempting to understand the component processes underlying the psychological constructs created in studying early human

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attachment can provide new and potentially useful ways of thinking in a more general way about development as it occurs at multiple levels of organization. The idea of “early attachment” exists in a number of forms in people’s ways of speaking and thinking. In its most general sense, the phrase refers to a set of behaviors observed in infants and the feelings and thought processes (conscious and/ or unconscious) that we suppose infants have with their mothers or caretakers, based on our own experiences and memories and the psychological concepts we have formed for ourselves or learned from others (see also Volume 2, Chapters 36–42 and Chapters 47–49). Within this range of use of the word attachment, several different schools of thought have coalesced within psychology. Common to all, however, are three themes: (1) some sort of emotional tie or bond that is inferred to develop between the infant and his or her caretaker that keeps the infant physically close, (2) a series of responses to separation that constitute the infant’s emotional response to interruption or rupture of that bond, and (3) the existence of different patterns or qualities of the interaction between infants and mothers that persist over time and lead to longterm effects of this experience on the social and emotional functioning of offspring throughout life, even extending to a repetition of specific patterns of mothering by daughters in the next generation. These observations and the psychological concepts of attachment have been extremely useful in human developmental psychology, but they leave a number of observations unexplained and questions unanswered. Furthermore, when my colleagues and I found close similarities in the behavior and separation responses of a far less evolved mammal, the laboratory rat pup, this suggested that we were missing a deeper layer of biological processes underlying the psychological concepts of attachment theory. The unanswered questions left open by developmental attachment theory are posed in the sections that follow. The answers that came from our laboratory research illustrate how evolutionary developmental theory and the concept of developmental selection help to better explain the nature and functions of early attachment at the psychological as well as the biological levels. Deconstructing the First Attachment Bond Infants of mammalian species that are born in an immature state, such as the human and the laboratory rat, face a daunting task. They must find a way to identify, remember, and prefer their own mother, and they must use these capacities to reorganize their simple motor repertoires, long adapted to the uterine environment, so as to be able to approach, remain close to, and orient themselves to their mothers.

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The Early Development of Attachment

Until recently it was assumed that these bonding processes were well beyond the capacities of newborn mammals (except in precocial species such as the sheep) and that the closeness of the relationship depended almost entirely on maternal behavior until well into the nursing period (Bowlby, 1982; Kraemer, 1992; Volume 2, Chapter 48). It was supposed that an attachment bond builds up slowly in the weeks or days after birth through repeated mother– infant interactions, starting with stereotyped reflexes in the newborn. But the past decade has produced a number of studies revealing evidence of earlier and earlier learning, extending even into the prenatal period, as is described here. In addition, coordinated motor acts have been demonstrated experimentally in fetuses in response to specific stimuli that will not be encountered until after birth. Thus, the solutions for the infant’s tasks appear to be found much earlier than previously thought and appear to take place through novel developmental processes that had not been imagined until recently. These developmental processes clearly function as age-specific adaptations to the unique environments of early development, playing only a supportive role in growth of size and complexity, for many of these behaviors (e.g., nursing) have been shown not to be precursors of later behaviors. Prenatal Origins The first strong evidence for fetal learning came from studies on early voice recognition in humans, in which it was found that babies recognize and prefer their own mother ’s voice, even when tested within hours after birth (De Casper & Fifer, 1980). Bill Fifer continued these studies in our department using an ingenious device through which newborns can choose between two tape-recorded voices by sucking at different rates on a pacifier rigged to control an audiotape player (Fifer & Moon, 1995). He has found that newborn infants, in the first hours after birth, prefer human voices to silence, female voices to male, their native language to another language, and their own mother to another mother reading the same Dr. Seuss story. In order to obtain more direct evidence for the prenatal origins of these preferences (rather than very rapid postnatal learning), Fifer filtered the high-frequency components from the tapes to make the mother ’s voice resemble recordings of maternal voice by hydrophone placed within the amniotic space of pregnant women. This altered recording, in which the words were virtually unrecognizable to adults, was preferred to the standard mother ’s voice by newborns in the first hours after birth, a preference that tended to wane in the second and third postnatal days. Furthermore, there is evidence that newborns prefer familiar rhythmic phrase sequences to which they have been repeatedly exposed prenatally when pregnant mothers daily read out loud a specific text in a quiet place (De Casper & Spence, 1988).

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In a striking interspecies similarity, rat pups were shown to discriminate and prefer their own dams’ amniotic fluid to that of another dam when offered a choice in a head-turning task (Hepper, 1987). Newborn pups were also shown to require amniotic fluid on a teat in order to find and attach to it for their first nursing attempt (Blass, 1990). Robinson and Smotherman (1995) directly tested the hypothesis that pups begin to learn about their mothers’ scent in utero. They were able to demonstrate one trial taste aversion learning and classical conditioning in late-term rat fetuses using intraoral cannula infusions and perioral stimulation. Taste aversions learned in utero were expressed in the free-feeding responses of weanling rats nearly 3 weeks later. The authors went on to determine that aversive responses to vibrissa stimulation were attenuated or blocked by intraoral milk infusion, a prenatal “comfort” effect they found to be mediated by a central kappa-opioid receptor system. These forms of fetal learning, involving maternal voice in humans and amniotic fluid in rodents, appear to play an adaptive role in preparing the infant for its first extrauterine encounter with its mother. They are thus the earliest origins as yet found for attachment to the mother. The spontaneous motor acts needed for an attachment system also appear to be developing prior to birth. Rat fetuses engage in a number of spontaneous behaviors in utero, including curls, stretches, and trunk and limb movements. These acts were observed to increase markedly in frequency with progressive removal of intrauterine space constraints as pups were observed first through the uterine wall, then through the thin amniotic sac, and finally unrestrained in a warm saline bath (Smotherman & Robinson, 1986). When newborn pups are observed prior to their first nursing bout they resemble exteriorized fetuses, until the mother lowers her ventrum over them. Their behavior then changes rapidly over the first few nursing bouts into a complex repertoire as described in the next section. How Newborns Approach and Orient to Their Mothers When newborn pups in their first extrauterine experience are stimulated gently by soft surfaces from above, as when the mother hovers over them, they show a surprisingly vigorous repertoire of behaviors (Polan & Hofer, 1999). These include the spontaneous curling and stretching seen prenatally but also locomotor movement toward the suspended surface, directed wriggling, audible vocalizations, and, most strikingly, turning upside down toward the surface above them. Evidently these behaviors propel the pup into close contact with the ventrum, maintaining it in proximity and keeping it oriented toward the surface. They thus appear to be very early attachment behaviors. In a series of experiments my colleague and I found that these are not

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stereotyped reflex acts but organized responses that are graded according to the number of maternal-like stimulus modalities present on the surface presented experimentally (e.g., texture, warmth, odor; Polan & Hofer, 1999). Furthermore, they were enhanced by periods of prior maternal deprivation, suggesting the rapid development of a motivational component. We found that by 2 days of age, pups discriminate their own mother ’s odor and prefer it to equally familiar nest odors (Polan & Hofer, 1998). Hepper (1987) showed that by the first postnatal week, pups discriminate and prefer their own mother, father, and siblings to other lactating females, males, or agemates. Recent work in humans inspired by these findings in lower animals has shown that human newborns, too, are capable of slowly locomoting across the bare surface of their mother ’s abdomen and locating the breast scented with amniotic fluid in preference to the untreated breast (Varendi, Porter, & Winberg, 1996). Although newborns are attracted to natural breast odors even before the first nursing bout (Makin & Porter, 1989), amniotic fluid can override this effect. Apparently, human newborns are not as helpless as previously thought but possess approach and orienting behaviors that anticipate the recognized onset of specific maternal comfort responses at 6 to 8 months. These events and processes can best be understood in terms of the four primary functions of development (construction, adaptation, inheritance, and variation). More and more complex behavior patterns are constructed, beginning with simple local movements and progressing through organized graded responses to specific stimuli. Then with a unique rapid learning process, behavior becomes organized into an adaptive repertoire of nursing-related behaviors. These developmental steps function in part as transient adaptations first to the confines of the uterus and then to the requirements of staying close, finding a nipple, and initiating sucking. One can also see how the inherited developmental environment of the uterus and the maternal nest supply a matrix and a template for the formation and organization of species-specific mother–infant interaction patterns. Thus, the developmental environments and events of the uterine cavity and maternal ventrum function to ensure that the same developmental path will be repeated in the next generation (i.e., inherited). As differences occur in the kinds and intensities of maternal stimulation of pups or in other aspects of the interaction, variation from one generation to the next will also take place. It is in the process of fulfilling these functions of development (adaptation, inheritance, and variation) that the organization and construction of more complex repertoires of behavior takes place. Looking for novel development processes that fulfill the functions of variation and inheritance should lead to a deeper understanding of how development works.

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A Novel Postnatal Learning System Bowlby (1969) was uncertain about exactly how a behavioral attachment system or bond developed in slowmaturing mammals and hypothesized that some learning mechanism must exist that is similar to the phenomenon of “imprinting” in birds, made famous by Konrad Lorenz (1996). Scientists are now beginning to gain an understanding of how such a specific proximity–maintenance system develops in animals and humans at the levels of basic learning mechanisms and the brain systems mediating them. Regina Sullivan and Steven Brake in our lab discovered that within 2 to 3 days of birth, neonatal rat pups were capable of learning to discriminate, prefer, approach, and maintain proximity to an odor that had been associated with forms of stimulation that naturally occurred within the early mother– infant interaction (e.g., milk or stroking; Sullivan, Hofer, & Brake, 1986). Random presentations of the two stimuli had no such effect, a control procedure that identified the change in behavior as being due to associative conditioning and not some nonspecific effect of repeated stimulation. Because the learning required only two or three paired presentations and because the preference was retained for many days, it seemed to qualify as the long-sought “imprinting-like process” that is likely central to attachment in slow-developing mammals. Indeed, a human analogue of this process was found by Sullivan, who showed that when human newborns were presented with a novel odor and were then rubbed repeatedly along their torsos to simulate maternal care, the next day they became activated and turned their head preferentially toward that odor (Sullivan et al., 1991). This suggests that rapid learning of orientation to olfactory cues is an evolutionarily conserved process in mammalian newborns. Early attachment-related odors appear to retain value into adulthood, although the role of the odor in modifying behavior appears to change with development. Work done independently in the labs of Celia Moore (Moore, Jordan, & Wong, 1996) and Elliot Blass (Fillion & Blass, 1986) has demonstrated that adult male rats showed evidence of enhanced sexual performance when exposed to females scented with the artificial odors with which their mothers had been scented during the male rats’ infancy. Aversive Learning of the Attachment Bond Clinical observations have taught that not only does attachment occur to supportive caretakers, but children can endure considerable pain and even injury while becoming strongly attached to an abusive caretaker. Although it may initially appear to be counterproductive from an evolutionary perspective to form and maintain an attachment to an abusive caretaker, it may be better for a slow-developing mammalian infant to have a bad caretaker than none at all.

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The Early Development of Attachment

This aspect of human attachment is also represented in the infant rat. We found that during the first postnatal week, a surprisingly broad spectrum of stimuli can function as reinforcers to produce an odor preference in rat pups (Sullivan, Brake, Hofer, & Williams, 1986; Sullivan, Hofer, et al., 1986). These stimuli range from apparently rewarding ones such as milk and access to the mother (Alberts & May, 1984; Brake, Sager, Sullivan, & Hofer, 1982; Pedersen, Williams, & Blass, 1982; Wilson & Sullivan, 1994) to apparently aversive ones such as moderate shock and tailpinch (Camp & Rudy, 1988; Sullivan, Hofer, et al., 1986), stimuli that elicit immediate escape responses from the pups. It should be noted that threshold to shock (Stehouwer & Campbell, 1978) and the pup’s behavioral response (Emerich, Scalzo, Enters, Spear, & Spear, 1985) do not change between the ages of 9 and 11 days. As pups mature and reach an age when leaving the nest becomes more likely, olfactory learning comes to more closely resemble learning in adults. Specifically, odor aversions are easily learned by 2-week-olds, and acquisition of odor preferences is limited to odors paired with stimuli of positive value (Sullivan & Wilson, 1995). Thus, the learning that underlies early attachment develops through a transiently adaptive “paradoxical” phase during which positive associations take place in response to a very broad range of contingent events (including painful stimulation) while pups are confined to the nest. It becomes more selective at a time in development when pups begin leaving the nest and encountering novel odors not associated with the mother. Brain Substrates for Two Kinds of Early Attachment Learning Early rapid aversive learning has been traced to focal odor-specific areas in the olfactory bulb by Sullivan and Donald Wilson. Certain cell types alter their firing rates in response to a specific odor as a result of learning experience (Sullivan, Wilson, Wong, Correa, & Leon, 1990; Wilson & Sullivan, 1994). This altered firing rate is the result of activation of norepinephrine pathways leading from the locus coeruleus. Indeed, behavioral learning can be driven by electrical stimulation of the locus or norepinephrine injection in the olfactory bulb in association with the novel odor, without the association of any maternal stimuli with the novel odor. The period during which aversive learning of position associations takes place ends about 10 days postnatal, but this “sensitive period” can be extended by a few repeated brief associations of odor with shock each day until weaning begins about a week later. The period from 12 to 15 days of age is an interesting one because during this time, if the mother is present during the association of odor and aversive stimulation, preference

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learning takes place, but in the mother ’s absence, the odor is subsequently avoided. Recent studies of neural and hormonal substrates has helped to explain this unusual developmental pattern (Moriceau, Wilson, Levine, & Sullivan, 2006). As illustrated in Figure 2.5, the primary brain substrate activated by the later developing avoidance learning in 12- to 15-day-old pups with the mother absent was the amygdala, not the locus coeruleus and olfactory bulb. However, in the mother ’s presence, there was no amygdala activation. Instead, positive association learning took place, mediated by the locus coeruleus. Corticosterone levels were previously known to be reduced by maternal presence at this age. Moriceau and Sullivan administered increased corticosterone to the pups learning in the mother ’s presence and less corticosterone to those learning in the mother ’s absence. The results showed that corticosterone levels mediated the maternal “switch” between the formation of positive and negative association learning and did so by switching the neural substrates mediating the response between the locus coeruleus/olfactory bulb and the amygdala. It is relatively easy to see how these seemingly complex paths of development in early systems for learning are likely to have evolved. Only outside the nest, and in the absence of the mother, is it crucial to learn an avoidance response to all painful stimuli. In interactions with the mother, learning to avoid her as a result of painful or uncomfortable associated conditions would likely lead to far more dangerous circumstances. The specialized environmental contingencies of early life have selected, during evolution, a sequence of the development of learning that would be hard to explain except within the concept of developmental selection. The next steps in this research will be to learn which genetic, experiential, and developmental processes were involved in the creation of these developmental paths during evolution and how they

Early

Corticosterone levels low

Late

Corticosterone levels high

Figure 2.5 Summary of transition between two learning systems and their neural substrates in the formation of early attachment in rat pups. Note. Early ⫽ birth to 10 days of age. Late ⫽ 11 to 15 days. Differences in properties and known mechanisms of the two forms of learning can be shifted in time across the 10-day transition point according to corticosterone levels and/or presence or absence of mother during learning.

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became integrated into the developmental sequences and mechanisms this work has revealed. Parallel Processes in Human Maternal Attachment Learning Successful mother–infant interactions require the reciprocal responding of both individuals in the mother–infant dyad. Human mothers rapidly learn about their baby’s characteristics and can identify their baby’s cry, odor, and facial features within hours of the baby’s birth (Eidelman & Kaitz, 1992; Kaitz, Lapidot, & Bronner, 1992; Porter, Cernoch, & McLaughlin, 1983). An animal model for this rapid learning by mothers has received considerable attention (Brennen & Kaverne, 1997; Fleming, O’Day, & Kraemer, 1999). Indeed, there are interesting parallels between the early attachment behavior of infants and the attachment behavior of the newly parturient mother. In rats and sheep, a temporally restricted period of postpartum olfactory learning in the mother involving norepinephrine facilitates the mother ’s learning about her young (Levy, Gervais, Kindermann, Orgeur, & Piketty, 1990; Moffat, Suh, & Fleming, 1993). It is possible that mammalian mothers and their young use similar neural circuitry to form their reciprocal attachments, both abusive and normal.

EARLY SEPARATION AS LOSS OF REGULATION In Bowlby (1969, 1982) and Harlow’s (1958) work, as well as in the clinical observations of Anna Freud and Dorothy Burlingham (1943) a generation before, it was maternal separation that revealed the existence of an “animal tie” between mother and infant and a deeper layer of processes beneath the apparently simple interactions of mother and infant. Bowlby (1969, 1982) viewed these processes as primarily psychological. The behavioral and physiological responses of the infant to separation, in their conception, were a consequence of the rupture of a psychological bond that was formed as part of an integrated psychophysiological organization that Bowlby called the attachment system. More recent research, however, has revealed a network of simple behavioral and biological processes that underlie this and other psychological concepts used to understand early human social relationships (see Volume 2, Chapter 47). The Separation Cry One of the best known responses to maternal separation is the infant’s separation cry, a behavior that occurs in a wide variety of species (Lester & Boukydis, 1985; Newman,

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1998). In the rat, this call is in the ultrasonic range (40 kHz) and appears in the first or second postnatal day. Pharmacological studies in a number of labs (reviewed in Hofer, 1996) have shown that the ultrasonic vocalization (USV) response to isolation is attenuated or blocked in a dose-dependent manner by clinically effective anxiolytics that act at benzodiazepine and serotonin receptors. Conversely, USV rates are increased by compounds known to be anxiogenic in humans, such as benzodiazepine receptor inverse agonists (beta-carboline, FG 1742) and GABA-A receptor ligands such as pentylenetetrazol (Brunelli & Hofer, 2001; Miczek, Tornatsky, & Vivian, 1991). Within serotonin and opioid systems, receptor subtypes known to have opposing effects on experimental anxiety in adult rats and humans also have opposing effects on infant USV calling rates (see Figure 2.6). Neuroanatomical studies in infant rats have shown that stimulation of the periaquaductal grey area produces USV calls, and chemical lesions of this area prevent calling (Goodwin & Barr, 1998). The more distal motor pathway is through nucleus ambiguus and both laryngeal branches of the vagus nerve. The engagement of higher centers known to be involved in cats and primates suggests a neural substrate for isolation calls involving primarily the hypothalamus, amygdala, thalamus, and hippocampus, and cingulate cortex, brain areas known to be involved in adult human and animal anxiety responses (Newman, 1998). This evidence strongly suggests that separation produces an early affective state resembling anxiety in rat pups, one that is expressed by the rate of infant calling. This calling behavior, and its inferred underlying affective state, develops as a communication system between mother and pup. The evolution of such a response is clarified by the finding that infant rat USV is a powerful stimulus for the lactating rat, capable of causing her to interrupt an ongoing nursing bout, initiate searching outside the nest, and direct her search toward the source of the calls (Smotherman, Bell, Hershberger, & Coover, 1978). The mother ’s retrieval response to the pup’s vocal signals then results in renewed contact between pup and mother. This contact, in turn, quiets the pup (as represented along the bottom right of Figure 2.6). This entire behavioral system fades out as infants cease to vocalize when isolated during the weaning period, showing that it represents a developmental ontogenetic adaptation. In the psychological concept of attachment, vocal separation and comfort responses are conceptualized as emotional expressions of interruption and reestablishment of a social bond. Such a formulation would predict that because pups recognize their own mothers by the mothers’ scents (as previously described), pups made acutely anosmic would fail to show a comfort response. But anosmic pups

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Early Separation as Loss of Regulation

Figure 2.6 The regulatory control system for infant ultrasonic vocalization. Note. BZi ⫽ benzodiazepine inverse agonist; CRH ⫽ corticotropinreleasing hormone; DA ⫽ dopamine; 5-HT ⫽ 5-hydroxytryptamine (serotonin); GABA ⫽ gamma-amino butyric acid; NA ⫽ noradrenaline; NMDA ⫽ N-methyl-D-aspartic acid; OT ⫽ oxytocin; VP ⫽ vasopressin. Moving counterclockwise from the far right in the diagram, interactions of a rat pup with its mother (proximity, warmth, etc.) act over multiple

show comfort responses that are virtually unaffected by the loss of their capacity to recognize their mothers in this way (Hofer & Shair, 1991). Instead Harry Shair, Susan Brunelli, and I have found multiple regulators of infant USV within the contact between mother and pup: warmth, tactile stimuli, and milk as well as her scent (Hofer, 1996). Provision of stimulation in these modalities separately (e.g., artificial fur lacking warmth or scent) and then progressively combining modalities elicited graded responses. The full “comfort” quieting response was elicited only when all modalities were presented together, and maximum calling rates occurred when all were withdrawn at once. In essence, we found parallel regulatory systems involving different sensory modalities (see Figure 2.6). These functioned in a cumulative or additive way, with the rate of infant calling reflecting the sum total of effective regulatory stimuli present at any given point in time. These processes at the behavioral/biological level underlie the psychological concept of separation anxiety. They do not supplant it. They operate at a different level of organization; the psychological concepts can be thought of as emerging from the lower-level component processes. Searching for What Was Lost Experiments in our laboratory have shown that infant rats also have more complex and lasting responses to maternal

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pathways (olfactory, thermal, etc.) to activate infant sensory systems (center top) and then brain neurotransmitter receptor systems (in box on the left). Those receptors in the (⫹) column, when activated, increase calling rate (ultrasonic vocalization), and those in the (–) column suppress calling. Maternal separation (lower center) results in a rapid burst of calling that gradually subsides. Continuing counterclockwise from the center of the diagram, ultrasonic vocalization by the pup stimulates maternal retrieval, licking, and so on, reinstating contact and maternal interactions (far right), thus closing the circle.

separation, similar to primates in a number of different physiological and behavioral systems. A number of years ago colleagues and I found slower developing changes following maternal separation, similar to those of Bowlby’s (1969, pp. 27–28) “despair” phase (see Figure 2.7A). This was not an integrated psychophysiological response as Bowlby had supposed but the result of a novel mechanism (see Figure 2.7B). As separation continued beyond the initial vocal response, each of the individual systems of the infant rat responded to the loss of one or another of the components of the infant’s previous interaction with its mother. Providing one of these components to a separated pup (e.g., maternal warmth) maintained the level of brain biogenic amine function underlying the pup’s general activity level for up to 3 days (Stone, Bonnet, & Hofer, 1976) but had no effect on other systems. For example, the pup’s cardiac rate continued to fall to 60% of its normal level over 24 hours regardless of whether supplemental heat was provided (Hofer, 1971). We found that the heart rate, normally maintained by sympathetic autonomic tone, was regulated by provision of milk acting on receptors in the lining of the pup’s stomach (Hofer & Weiner, 1975). With loss of the maternal milk supply, sympathetic tone fell and cardiac rate was reduced by 40% in 12 to 18 hours. By studying a number of additional systems—such as those controlling sleep–wake states, activity level, sucking pattern, and blood pressure (Brake et al., 1982; Hofer, 1975,

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Developmental Neuroscience

(A)

(B)

Growth Hormone

Activity Heart Rate REM Sleep

Figure 2.7 Two schematic representations of the dynamics of early separation. Note. A: as conceptualized in the framework of attachment theory by John Bowlby and B: as found to result from loss of regulatory interactions within the mother–infant relationship.

1976; Shear, Brunelli, & Hofer, 1983)—we found different components of the mother–infant interaction such as olfaction, taste, touch, warmth, and texture that normally either upregulated or downregulated each of these functions (or, in the case of sleep states, regulated rhythmic patterning). Thus, we concluded that in maternal separation, all of these regulatory components of the mother–infant interaction are withdrawn at once (see Figure 2.7). This widespread loss creates a pattern of increases or decreases in level of function of the infant’s systems, depending on whether the particular system had been up- or downregulated prior to separation by specific components of the previous mother– infant interaction. We called these components hidden regulators because they were not evident when one simply observed the ongoing mother–infant relationship. These studies revealed a novel mechanism for the infant response to separation, an event that previously could be

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conceptualized only at the psychological level, as an emotional response. In younger infants this was conceived to be the result of rupture of the bond between mother and infant, and in older children the stress resulting from perception of the loss of the mother ’s emotional, physical, and nutritional support. At the biological level of organization we found that separation resulted in the withdrawal or loss of a number of different physical interactions between mother and infant that normally regulated infant physiology and behavior on an ongoing basis. Thus, we found that infant and mother compose a partial fusion of two individual homeostatic units into a single common homeostatic organization. When separated, each returns to its individual set points at lower or higher levels of function. The regulation of maternal milk letdown by infant sucking is a wellknown example of a hidden regulatory system present in all adult female mammals. When the suckling interaction

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Early Separation as Loss of Regulation

is interrupted, the mother ’s milk production and periodic letdown become greatly reduced. The observations of psychologists on the evident changes in mental state of infants during separation and the psychological constructs used to explain them remain valid and useful. However, the processes at the biological level described here can enlarge psychological understanding and support newer psychological concepts of emotional and perceptual regulation. It is not that rat pups respond to loss of regulatory processes, whereas humans respond to emotions of love, sadness, anger, and grief. Human infants, as they mature, can respond at the level of complex affective responses and symbols as well as at the level of regulatory interactions. And rat pups respond at the level of an affective state expressed through separation calling as described previously. Even adult humans continue to respond in important ways at the sensorimotor– physiologic level in their social interactions (Hofer, 1984; Stern & McClintock, 1998). Examples include the role of social interactions in entraining sleep–wake and menstrual rhythms, the disorganizing effects of sensory deprivation, and the remarkable effects of social support on the course of medical illness. This extended homeostatic system of mother and infant represents an aspect of mammalian early development similar to the intertwined nature of the fetus and mother, essentially an extension of the symbiosis recognized in the intrauterine period of development. By applying the concept of developmental selection, it is evident that these evolved adaptations function to provide a living environment that is inherited from one generation to the next to serve as a guiding matrix, enabling a specific early developmental course to be repeated (see Figure 2.8). Variations in maternal behavior are capable of producing variations in infant physiology and behavior through the regulatory processes described. These variations can be immediately adaptive. For example, the major thermoregulatory, cardiovascular, and behavioral responses of pups separated for a period of hours slow metabolism, shunt blood away from the digestive tract to the heart and brain, and profoundly reduce spontaneous behavior, inducing a hibernation-like state that promotes survival until the mother returns. Furthermore, variations in infant development that are repeated over generations (e.g., because of increased maternal foraging demands during climate change creating long absences) become targets for selection and can become increasingly efficient and finally genetically fixed, producing an alternative infant phenotype. In this way one can better understand why these widespread and complex regulatory process evolved to become a feature of mammalian early development, serving the developmental functions of inheritance and variation as well as construction and adaptation.

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Hidden Regulators of Early Development Other investigators, using this approach, have since discovered other maternal regulatory systems of the same sort. For example, Saul Schanberg, Cynthia Kuhn, and colleagues found that separation of the dam from rat pups produced a rapid (30-min) fall in the pup’s growth hormone levels, and vigorous tactile stroking of maternally separated pups (mimicking maternal licking) prevented the fall in growth hormone (Kuhn & Schanberg, 1991). Brain substrates for this effect were then investigated, and it now appears that growth hormone levels are normally maintained by maternal licking, acting through serotonin (5HT) 2A and 2C receptor modulation of the balance between growthhormone-releasing factor and somatostatin that together act on the anterior pituitary release of growth hormone (Katz, Nathan, Kuhn, & Schanberg, 1996). The withdrawal of maternal licking by separation allows growth-hormonereleasing factor to fall and somatostatin to rise, resulting in a precipitous fall in growth hormone and cessation of growth processes generally for several days. However, a parallel process in prematurely born human infants showed longer-term regulation. There are several biological similarities between this maternal deprivation effect in rats and the growth retardation that occurs in some variants of human reactive attachment disorders of infancy. Applying this new knowledge about the regulation of growth hormone to lowbirth-weight, prematurely born babies, Tiffany Field and coworkers (1986) joined the Schanberg group. They used a combination of stroking and limb movement, administered 3 times a day for 15 min each time and continuing throughout the babies’ 2 weeks of hospitalization. This intervention increased weight gain, head circumference, and behavior development test scores in relation to a randomly chosen control group, with earlier discharge from the intensive care unit and other enhanced maturational effects discernible 6 months later. Clearly, early regulators are also effective in humans, and over time periods as long as weeks to months. As experimenters began to realize that infants’ separation responses revealed a network of individual regulatory processes within the mother–infant interaction, an important implication of this finding emerged: These ongoing regulatory interactions could, over the long term, act to shape the development of an infant’s brain and behavior throughout the preweaning period when mother and infant remained in close proximity. And when maternal behavior changed in response to changes in her environment, this could change the course of her offspring’s development. We could now think of the mother–infant interaction as a long-term regulator of development, with variations in

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Developmental Neuroscience

the intensity and patterning of mother–infant interactions gradually shaping the development of behavior and physiology. These processes go far beyond the adaptive evolutionary role of attachment as a protection against predators proposed by Bowlby (1969). They also go beyond the role described in the previous section of a flexible adaptive response of the infant to the environmental conditions that caused maternal separation. Here, development is regulated along a different path for a substantial period of time. With the mother absent for even longer time periods, perhaps never to return, and without another effective source of regular stimulation and caretaking, an overall slowing of growth occurs to the point that a smaller adolescent and adult with lower nutritional needs will develop. Subsequent research has shown long-term effects on offspring even from briefer and less-extreme mother–infant experiences.

LONG-TERM MATERNAL REGULATION OF DEVELOPMENT In clinical work and in attachment theory, the psychological construct of an enduring mental representation is generally used to denote an internal working model that is formed early in the infant’s developing mind, through his or her particular interaction with parents or consistent caretakers. This conceptual model helps to organize and explain how the nature of peoples’ later relationships and responses to stress seem to be shaped by their experiences as infants and children and even transmitted to their offspring in the next generation through particular patterns of mothering behavior expressive of the mother ’s mental representation of how mothers behave toward their infants. In research with a simple mammalian working model system, we found evidence of similar long-lasting and transgenerational effects that can be attributed to the processes of the maternal regulatory interactions described previously. For example, Sigurd Ackerman, Herbert Weiner, and I found that permanent maternal separation of juvenile rat pups early in the weaning period (early weaning) produced a greatly increased vulnerability to stress-produced gastric ulcer in adolescence and early adulthood (Skolnick, Ackerman, Hofer, & Weiner, 1980). To our great surprise, we found that this effect persisted in the next generation in the normally reared offspring of mothers that had been separated early as infants. We also found that, as adult mothers, early-weaned females spent less time and interacted less with their infants. However, a cross-fostering study revealed that this was not the result of the reduced maternal behaviors of the early weaned mothers. For if the pups of normally reared mothers were substituted for the

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early-weaned mother ’s own at birth, these cross-fostered pups were not vulnerable as adolescents. Instead, the pups born to early-weaned mothers but reared by normally reared mothers inherited the vulnerability. This suggested an unknown intrauterine or germ cell line transmission mechanism. In a different study on the development of hypertension in a strain of rat that had been selected over generations for high blood pressure (SHR), Michael Myers, Susan Brunelli, and I found that naturally occurring variation in 3 out of the 12 maternal behaviors we observed in the hypertensive and control strains (WKY) of inbred (genetically homogenous) rats did indeed appear to regulate the development of the physiological trait in the next generation: the levels of blood pressure in their offspring. Because the animals in each strain were genetically identical, and because the levels of maternal behaviors were significantly correlated with the magnitude of effect on adult offspring blood pressure both within and between strains, it seemed very likely that it was the variation in maternal behaviors acting on the pups that produced these long-term effects (Myers, Brunelli, Squire, Shindeldecker, & Hofer, 1989). The findings of these two studies led us to realize that both the hidden postnatal maternal regulators that we had discovered in our separation studies and a different class of early-weaning-induced prenatal or germ line influences could have long-term regulatory effects on later development, even into the next generation. In their implications for human development, they appeared to represent a level of biological developmental processes that underlie and coexist with the psychological processes inferred to mediate the lasting effects clinically observed between early relationships and their later mental representations in patients (Hofer, 1980, 2005). However, the biology of these effects was most unusual and difficult to explain in terms of the familiar processes studied in developmental psychobiology at the time. In addition, the evolutionary basis for such long-term developmental effects was unclear. Now, with a new understanding of the role of development in evolution, one can begin to make some sense of them (see Figure 2.8). The concept of developmental selection states that developmental processes have been selected, in part, to function as creators of potentially useful variation and to provide developmental mechanisms of inheritance necessary for selection of the most successful of these variations. The regulatory processes hidden within early mother–infant interactions have adaptive value for the infant within the unique environment of young mammals, but they also can function to regulate the early course of development. Because development consists of series of cascades of gene regulation patterns, an early diversion is capable of setting the pattern of downstream

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Long-Term Maternal Regulation of Development

Figure 2.8 Overlapping life cycles provide a template for inheritance and a matrix for creating variation throughout early development in humans and other mammals. Note. In a single generation, the origin of germ cells (lower left) takes place during the early embryonic period of the mother while she is in the grandmother ’s womb. A number of years later, these germ cells unite at

regulation onto different paths. This can extend the early effects of variations in maternal behavior on infants into adolescence and even adulthood. Moreover, because some of the neural substrates for adult behavior, such as maternal behavior, are present even in infancy, maternal regulation that affects early gene expression in these neural precursors can likewise be modified, with the result that when the systems mature, their patterns of function will be different. In the case of maternal behavior, this inherited environment can result in transmission (inheritance) of an early experience effect from one generation to the next. As evolution continues, if an environmental or social change persists over several generations and can be transmitted across generations by maternally driven developmental processes, then this developmental variation will gradually become genetically modified by repeated selection. Variants in the structure of genes (alleles) responsible for expression of the transcription factors that activate the cascades of gene expression patterns will be selected to the extent that survival and reproductive success is enhanced in these variant individuals. In this way, new and even more adaptive developmental paths will be gradually created over generations because of this “genetic accommodation”

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conception, and development continues within the mother ’s womb and throughout a long period of postnatal proximity and interactions with the mother, grandparents, and other caretakers. Development has been shown to be affected by such extended influences as the age of the father prior to conception and the proximity of grandparents.

of developmentally created novelties. Often an environmental or maternal behavior variable will be maintained by selection as a “trigger” for one or another developmental path, creating alternative phenotypes to be expressed by a singe genotype, greatly enhancing the range of adaptability of the evolving species. The plausibility of this developmental–evolutionary scenario is supported by the widespread existence of alternative developmental phenotypes (reviewed in WestEberhard, 2003) in organisms ranging from phage viruses to humans. In our experiments described above, we revealed the existence of such alternative developmental paths. In the early weaning experiments, we found that the susceptibility to gastric erosions was greatly increased in adolescent and young adults, but older adults were actually less vulnerable than normally reared animals. The whole life trajectory of the trait had been shifted by the social/ environmental trigger of early weaning. Physiologically, the response to early weaning may have been adaptive. Instead of sleeping throughout most of the 24-hr immobilization stress used to induce gastric ulcer as the normally weaned animals did, the early-weaned juveniles remained awake. This altered response could prevent them from

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being surprised by predators in the absence of protection by their mother. The physiological price paid was the risk of gastric ulceration if immobilization was prolonged. Early weaning seems to have also produced long-term changes that altered the uterine environment mothers provided to their young (possibly a hormonal or epigenetic change) that induced the alternative developmental phenotype in their fetuses, through a different mechanism, as a “predictive” response pre-adapting offspring for the expected environment in the next generation. Here, as in the section on maternal regulation of early development, the mother is revealed as a potential transducer of environmental change effects on future generations. Changes in the environment have long been demonstrated to affect maternal behavior, and it is highly adaptive if their young can begin to adapt to changed conditions early in their development (enabling them to become effectively preadapted as adults). Such preadaptations have been studied for several decades by ecologists and evolutionary biologists and are referred to simply as maternal effects (Mousseau & Fox, 1998). A well-known example in insects is that the location in which the female lays her eggs differs according to the season. Females lay their eggs in warmer locations in response to changes in the light in the fall. This reduces the time required before hatching and thus increases survival of the young before the onset of winter. Epigenetic Mechanisms of Long-Term Maternal Regulation as Preadaptation The work of Michael Meaney and his colleagues over the past decade has greatly increased understanding of the biological processes at work in these lasting effects of early relationships, as described in Cameron et al. (2005). These researchers discovered that normal variation among mothers in a colony in the same maternal behaviors implicated in our earlier studies (Myers et al, 1989) of offspring blood pressure development (the level of maternal licking and grooming of pups) systematically modified (regulated) the development of a number of different traits in adult offspring (i.e., adrenocortical stress response, behavioral fear response, measures of learning, and sexual and maternal behavior in adult offspring). Furthermore, these different phenotypes can be produced in offspring as a result of adverse environmental events occurring in the parental generation, such as repeated immobilization of pregnant females. This stress was shown to affect maternal behavior, resulting in physiological and behavioral changes in offspring that preadapted them to more challenging environments (Champagne & Meaney, 2006). The cellular/molecular mechanisms mediating these complex transgenerational effects of variations in maternal

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care have been analyzed in detail by the Meaney group (Weaver et al., 2007). These studies have revealed a novel layer of long-term epigenetic modification of gene expression in offspring caused by maternal care differences acting on the molecular configuration of the chromatin matrix surrounding certain specific genes in the offspring, permanently altering their expression levels and causing longterm effects. Revealing the Structure of Alternative Developmental Pathways In all of these studies just described, alternative developmental pathways appear to be potentially available within the genetic potential of a given strain of rat. One or another of these can be expressed, depending on certain specific social/environmental eliciting or triggering experiences. It occurred to Susan Brunelli and me that such alternative developmental paths or trajectories might also be revealed by repeated selection for high levels of a particular trait in infancy. If the trait were part of a genetically organized and extended developmental pathway, the later stages of that pathway, and any associated traits, should be revealed in adults of the selected line after a number of generations of repeated selection based on the level of the infantile trait. The trait we chose was the infant separation call described previously (see “The Separation Cry”), and we picked a differential selection procedure for the laboratory that was based on certain evolutionary considerations. It is known that sensory and perceptual adaptations to infant USV have evolved in female rodents, for example, an auditory frequency response threshold tuned to the exact frequency of infant USV (45 kHz) that enables the mother ’s sensitive and specific search, retrieval, and caregiving responses (Ehret, 1992). But the infant’s isolation call can also be used by predators to locate the infant. Not surprisingly, predator odors dramatically suppress USV in isolated pups, a specific fear response (Takahashi, 1992). The evolution of the infant separation cry has involved what is known as an evolutionary trade-off, a ratio of risk to benefit that is thought to have shaped many behaviors. In this case, the theory predicts that in environments with many predators, infants that show less separation-induced vocalization will gradually increase in the population. However, when nest disruption and scattering of pups occurs frequently (e.g., through flooding) and fewer predators exist, high rates of isolation calling would be advantageous. To explore some of these hypothetical evolutionary processes and the role of development in them, we have been conducting an experimental model of evolution in the laboratory. We selectively bred adult rats that had shown relatively high or relatively low rates of USV responses to

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27

separation as 10-day-old infants (Brunelli & Hofer, 2007). We found that in as few as five generations, two distinct lines emerged that differed widely on this infantile trait. Cross-fostering showed no evidence that being reared from birth by a mother from the other line changed the level of isolation calling (Brunelli, Vinocur, Soo-Hoo, & Hofer, 1997). Clearly, the selected trait has a strong genetic basis. But because there have been no systematic studies of selective breeding for an infant behavior trait (that we have been able to discover), we did not know how repeated selection might affect the developmental course of separation calling rates in descendents. Furthermore, we wondered whether other traits related to vocalization or to an underlying early anxiety state might also be affected through their genetic, physiological, or behavioral links to the systems controlling the infants’ vocal response to separation. We found that infants’ calling during isolation was elevated in high-line pups over a randomly bred control line starting as early as the response develops at 3 days of age, whereas low-line pups had already decreased their calling rates below controls at this early age (Hofer, Brunelli, & Shair, 2001). The greatest difference between high and low lines was at the age of repeated selection, 10 days postnatal. Response differences were much less evident at 14 days, and the three lines converged as the response ceased to occur in all weanling pups at 18 to 20 days postnatal. Thus, selection resulted in high-line pups showing a marked increase in the already high rates of newborn pups and maintaining this level up to and including the age of selection. However, the low-line pups showed the opposite, a more rapid decline than normal from 3 to 10 days of age. In short, selection at 10 days of age appeared to be acting on the whole developmental trajectory of the vocal response to separation, shifting it in time: either delaying

or hastening the normal gradual decline in isolation calling with age. In more recent studies, as summarized in Figure 2.9, Brunelli found a number of other behaviors and physiological responses at different ages in the high and low lines that had been altered by selection at 10 days of age (Brunelli & Hofer, 2007). Those differences appeared to form two coherent groups of traits. In high-line juveniles tested in isolation at 18 days (when isolation calling no longer occurs), both defecation/urination and (sympathetically mediated) heart rate acceleration were greater than in controls. As adolescents, rough-and-tumble play behavior and the short high-frequency vocalizations that accompany these interactions were reduced in the high-line compared to randomly bred controls in the first few play bouts. Highline adults were significantly slower to emerge into an open test arena and avoided the center region more completely than the low lines. In addition, high-line adults showed a much more passive response to the Porsolt swim test, a pattern associated with depression-like states in rodents. The low-line rats, as they developed into juveniles, showed the greatest heart rate acceleration of the three lines during isolation testing and a much-delayed return to baseline due to major vagal withdrawal. Low-line adolescents were deficient on all play behaviors on all days of testing and emitted the fewest play calls. As adults, the lowline animals were quicker to emerge into the open area, explored its center more than the highs, and were more active in the swim test. When confronted with an unfamiliar male, 70% of low-line males engaged in aggressive behavior compared to 30% of randomly bred controls. These groups of traits suggest a characterization of the high line as anxious and passive, whereas the lows were exploratory, active, and aggressive. Apparently, selection

Figure 2.9 Summary of developmental effects created in two lines of rats by repeated selection, over more than 25 generations, for an infant anxiety trait: either high or low levels of isolationinduced ultrasonic vocalization (USV).

are widely used as representing close equivalents in animals to these two human affective states. Plus signs (⫹) denote increased levels of behaviors, and minus signs (–) denote decreased levels.

Note.“Depression-like” and “anxiety-like” refer to behavioral responses observed in tests that have been validated pharmacologically and that

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for high and low rates of separation calling in infancy, as would occur during evolution under different ecological conditions, selects for a lifelong developmental path involving associated traits. Two alternative phenotypes, or temperaments (as illustrated in Figure 2.9; see also Volume 1, Chapter 15), were created by repeated selection acting at a single time point early in development, suggesting that a genetic potential exists for more than one organized developmental path. A pup could be set on one or another path when alleles for associated traits that enhanced one or another level of the infantile separation calling trait are gradually accumulated by repeated selection. These results resemble the studies from the Meaney group (reviewed in Cameron et al., 2005) in which early environmental change and different maternal behavior patterns had long-term effects on specific patterns of associated traits in adult offspring. Thus, the developmental structures for two or more behavioral phenotypes with adaptive potential are present and can be realized either through selection over 15 or 20 generations, or in a more rapid but shorter-lived transgenerational response to certain types of stress from the parental to the offspring generations.

SUMMARY Scientists’ understanding of development has reached a new phase with the rapid advances in molecular genetics that for the first time allow them to see in detail how development takes place through changes in gene expression over time. These advances have led to an integration of evolutionary and developmental biologies (evo-devo) based in large part on the insight that novel forms are generated in evolution as much through variation in the regulation of genes during development as by changes in gene frequency in populations through natural selection. It is generally agreed that there is still no general theory of development comparable to evolutionary theory (Bateson et al., 2007), but scientists do have a new understanding of the relationship between those two great historical processes of biology. Multicellular development has its origin in the Cambrian explosion of major animal groups (phyla) appearing suddenly in the fossil record 500 million years ago. During and after this major transition in evolution, developmental processes were selected that promoted the construction of larger and more complex organisms capable of inhabiting and exploiting new ecological niches. But these construction processes were also shaped by selection for their capacity to create ontogenetic adaptations that enabled survival of early developmental forms in their own unique and transient environments. Furthermore, developmental

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processes that enabled novel and potentially useful variations in developmental paths that enabled the re-creation of successful paths in the next generation were also selected for their capacity to facilitate the evolution of successful multicellular forms. Identifying these evolutionary selection pressures for construction, ontogenetic adaptation, variation, and inheritance (developmental selection) provides an understanding of the functions being carried out by developmental processes, similar to the understanding gained through identifying the evolutionary functions of stress responses or of social behaviors. In this chapter, I have used the principles of developmental selection in discussing what has been learned recently about the development of early attachment: its prenatal origins, approach and contact maintenance systems, learning of specific maternal discrimination and preference, novel mechanisms for separation responses, and hidden regulators of early development. Finally, I have described the evolution and development of long-term effects of early attachment and the resulting role of mother–infant interaction in the shaping of alternative pathways of development and in the formation of temperament. Another theme of this chapter has been the concept of levels of organization, a way of thinking that is extremely useful in trying to understand how events and processes that are studied by neuroscientists in cells and in brain circuits are related to the observed behavior and inner experiences studied by psychologists. I have tried to illustrate how psychological constructs such as the bond, emotional responses to separation, mental representations and long-term developmental effects deriving from parent–infant interactions can be better understood by learning more about the component neurobiological processes that underlie and have given rise to the psychological constructs and theory. Both the evolutionary developmental approach and the research revealing the component developmental processes of attachment are in the early stages of their own development; there is a great deal still to learn about them. Perhaps the most important gap in this understanding of development is in the area of the self-organizing processes that hold together and guide the seemingly endless series of cascades and networks of gene regulation that underlie development at its deepest level. This “construction” component of development not only embodies as-yet unknown principles of self-organization but is clearly supported, directed, and organized also from the “outside,” by the environment inherited from the previous generation, in ways scientists are only beginning to understand. This environmental matrix begins with the early life of germ cells developing within the parents when they are still embryos, and with the signal proteins and transcription

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References 29

factors formed in the maternal egg prior to fertilization. It continues through the long-term maternal effects exerted by the symbiotic relationship of the embryonic, fetal, and infant stages of development all the way to the “given” properties of the outside environment within which the young must grow up. In addition to these general areas in which scientists know far too little, specific new biological processes are being discovered that experts never knew existed, such as the controls over gene regulation maintained within the genome itself (e.g., the intricate regulation of DNA transcription and translation by the protein “skeleton” of chromatin). Lastly, many mechanisms for variation have evolved within all of these developmental processes as a result of their having enhanced the opportunities for selection. These processes for facilitating variation are just beginning to be studied. The perspective I see is that there is no end of exciting opportunities for research, that the approaches for investigating developmental neuroscience suggested in this chapter are only a beginning, and that these approaches will soon be modified in interesting ways, perhaps by some of the readers of this chapter.

REFERENCES Alberts, J. R., & May, B. (1984). Nonnutritive, thermotactile induction of filial huddling in rat pups. Developmental Psychobiology, 17, 161–181. Bateson, P., Hofer, M., Oppenheim, R., & Wiedenmayer, C. (2007). Developing a framework for development: A discussion. Developmental Psychobiology, 49, 77–86. Baylin, S. B., & Schuebel, K. E. (2007, August 2). Genomic biology: The epigenomic era opens. Nature, 448, 548–549. Blass, E. M. (1990). Suckling: Determinants, changes, mechanisms, and lasting impressions. Developmental Psychology, 26(4), 520–533. Bonner, J. T. (1993). Life cycles: Reflections of an evolutionary biologist. Princeton, NJ: Princeton University Press. Bowlby, J. (1969). Attachment: Attachment and loss (Vol. 1). New York: Basic Books. Bowlby, J. (1982). Attachment and loss: Retrospect and prospect. American Journal of Orthopsychiatry, 52, 664–678. Brake, S. C., Sager, D. J., Sullivan, R., & Hofer, M. (1982). The role of intraoral and gastrointestinal cues in the control of sucking and milk consumption in rat pups. Developmental Psychobiology, 15, 529–541. Brennen, P. A., & Kaverne, E. B. (1997). Neural mechanisms of mammalian olfactory learning. Progress in Neurobiology, 51, 457–451. Brunelli, S. A., & Hofer, M. A. (2001). Selective breeding for an infantile phenotype (isolation calling): A window on developmental processes. In E. Blass (Ed.), Handbook of behavioral neurobiology (pp. 433–482). New York: Plenum Press. Brunelli, S. A., & Hofer, M. A. (2007). Selective breeding for infant rat separation-induced ultrasonic vocalizations: Developmental precursors of passive and active coping styles. Behavioural Brain Research, 182, 193–207.

c02.indd Sec5:29

Brunelli, S. A., Vinocur, D. D., Soo-Hoo, D., & Hofer, M. A. (1997). Five generations of selective breeding for ultrasonic vocalization (USV) responses in N:NIH strain rats. Developmental Psychobiology, 31, 255–265. Cameron, N., Parent, C., Champagne, F., Fish, E., Kuroda, K., & Meaney, M. (2005). The programming of individual differences in defensive responses and reproductive strategies in the rat through variations in maternal care. Neuroscience and Biobehavioral Review, 29, 843–865. Camp, L. L., & Rudy, J. W. (1988). Changes in the categorization of appetitive and aversive events during postnatal development of the rat. Developmental Psychobiology, 21, 25–42. Carroll, R. (2002). Evolution of the capacity to evolve. Journal of Evolutionary Biology, 15, 911–921. Carroll, S. (2005). Endless forms most beautiful: The new science of evo-devo. New York: Norton. Champagne, F. A., & Meaney, M. J. (2006). Stress during gestation alters postpartum maternal care and the development of the offspring in a rodent model. Bio Psychiatry, 59, 1227–1235 De Casper, A. J., & Fifer, W. P. (1980, June 6). Of human bonding: Newborns prefer their mothers’ voices. Science, 208, 1174–1176. De Casper, A. J., & Spence, M. J. (1988). Prenatal maternal speech influences a newborn’s perception of speech sounds. Infant Behavior and Development, 9, 133–150. Ehret, G. (1992). Preadaptations in the auditory system for mammals for phoneme perception. In M. E. H. Schouten (Ed.), The auditory processing of speech: From sounds to words (pp. 99–112). Berlin, Germany: Mouton de Gruyter. Eidelman, A. I., & Kaitz, M. (1992). Olfactory recognition: A genetic or learned capacity? Journal of Developmental and Behavioral Pediatrics, 13(2), 126–127. Emerich, D. F., Scalzo, F. M., Enters, E. K., Spear, N. E., & Spear, L. P. (1985). Effects of 6-hydroxydopamine-induced catecholamine depletion on shock-precipitated wall climbing of infant rat pups. Developmental Psychobiology, 18, 215–227. Field, T. M., Schanberg, S. M., Scafidi, F., Bauer, C. R., Vega-Lahr, N., Garcia, R., et al. (1986). Tactile/kinesthetic stimulation effects on preterm neonates. Pediatrics, 77, 654–658. Fifer, W. P., & Moon, C. M. (1995). The effects of fetal experience with sound. In J. P. Lecanuet, W. P. Fifer, N. Krasnegor, & W. P. Smotherman (Eds.), Fetal development: A psychobiological perspective (pp. 351–368). Hillside, NJ: Erlbaum. Fillion, T. J., & Blass, E. M. (1986, February 14). Infantile experience with suckling odors determines adult sexual behavior in male rats. Science, 231, 729–731. Fleming, A. S., O’Day, D. H., & Kraemer, G. W. (1999). Neurobiology of mother-infant interactions: Experience and central nervous system plasticity across development and generations. Neuroscience and Biobehavioral Review, 23, 673–685. Freud, A., & Burlingham, D. (1943). War and children. New York: Medical War Books. Goodwin, G. A., & Barr, G. A. (1998). Behavioral and heart rate effects of infusing kainic acid into the dorsal midbrain during early development in the rat. Developmental Brain Research, 107(1), 11–20. Gould, S. J. (1989). A wonderful life. New York: Norton. Haeckel, E. (1892). The history of creation (Lankester, Trans.). In Natürliche Schöpfungsgeschichte (8th ed., Vol. 2, pp. 422–544). London: Kegan Paul, Trench, Trubner. Harlow, H. F. (1958). The Nature of Love. American Psychology, 12, 673–685. Hepper, P. G. (1987). The amniotic fluid: An important priming role in kin recognition. Animal Behaviour, 35, 1343–1346.

8/18/09 4:57:26 PM

30

Developmental Neuroscience

Hofer, M. A. (1971, June 4). Cardiac rate regulated by nutritional factor in young rats. Science, 172, 1039–1041. Hofer, M. A. (1975). Studies on how early maternal separation produces behavioral change in young rats. Psychosomatic Medicine, 37, 245–264. Hofer, M. A. (1976). The organization of sleep and wakefulness after maternal separation in young rats. Developmental Psychobiology, 9, 189–205. Hofer, M. A. (1980). Effects of reserpine and amphetamine on the development of hyperactivity in maternally deprived rat pups. Psychosomatic Medicine, 42, 513–520. Hofer, M. A. (1984). Relationships as regulators: A psychobiologic perspective on bereavement. Psychosomatic Medicine, 46, 183–197. Hofer, M. A. (1996). Multiple regulators of ultrasonic vocalization in the infant rat. Psychoneuroendocrinology, 21(2), 203–217. Hofer, M. A. (2005). The psychobiology of early attachment. Clinical Neuroscience Research, 4, 291–300.

Moriceau, S., Wilson, D. A., Levine, S., & Sullivan, R. M. (2006). Dual circuitry for odor-shock conditioning during infancy: Corticosterone switches between fear and attraction via amygdala. Journal of Neuroscience, 26, 6737–6748. Mousseau, T. A., & Fox, C. W. (1998). Maternal effects as adaptations. Oxford, England: Oxford University Press. Myers, M. M., Brunelli, S. A., Squire, J. M., Shindeldecker, R. D., & Hofer, M. A. (1989). Maternal behavior of SHR rats and its relationship to offspring blood pressures. Developmental Psychobiology, 22, 29–53. Newman, J. D. (Ed.). (1998). The physiological control of mammalian vocalization. New York: Plenum Press. Oppenheim, R. W. (1984). Ontogenetic adaptations in neural development: Towards a more “ecological” developmental psychobiology. In H. F. R. Prechtl (Ed.), Continuity of neural functions from prenatal to postnatal life (pp. 16–30). Philadelphia: Lippincott.

Hofer, M. A., Brunelli, S. A., & Shair, H. N. (2001). Developmental effects of selective breeding for an infantile trait: The rat pups ultrasonic isolation call (USV). Psychobiology, 39, 1–16.

Pedersen, P. E., Williams, C. L., & Blass, E. M. (1982). Activation and odor conditioning of suckling behavior in 3-day-old albino rats. Journal of Experimental Psychology: Animal Behavior Processes, 8, 329–341.

Hofer, M. A., & Shair, H. N. (1991). Trigeminal and olfactory pathways mediating isolation distress and companion comfort responses in rat pups. Behavioral Neuroscience, 105, 699–706.

Polan, H. J., & Hofer, M. A. (1998). Olfactory preference for mother over home nest shavings by newborn rats. Developmental Psychobiology, 33, 5–20.

Hofer, M. A., & Weiner, H. (1975). Physiological mechanisms for cardiac control by nutritional intake after early maternal separation in the young rat. Psychosomatic Medicine, 37, 8–24.

Polan, H. J., & Hofer, M. A. (1999). Maternally directed orienting behaviors of newborn rats. Developmental Psychobiology, 34, 269–279.

Kaitz, M., Lapidot, P., & Bronner, R. (1992). Parturient women can recognize their infants by touch. Developmental Psychobiology, 1, 35–39. Katz, L. M., Nathan, L., Kuhn, C. M., & Schanberg, S. M. (1996). Inhibition of GH in maternal separation may be mediated through altered serotonergic activity at 5-HT2A and 5-HT2C receptors. Psychoneuroendocrinology, 21(2), 219–235. Kirschner, M. W., & Gerhart, J. C. (2005). The plausibility of life: Resolving Darwin’s dilemma. New Haven, CT: Yale University Press. Knoll, A. H. (2003). Life on a young planet: The first three billion years of evolution on earth. Princeton, NJ: Princeton University Press.

Porter, R. H., Cernoch, J. M., & McLaughlin, F. J. (1983). Maternal recognition of neonates through olfactory cues. Physiology and Behavior, 30, 151–154. Robinson, S. R., & Smotherman, W. P. (1995). Habituation and classical conditioning in the rat fetus: Opioid involvements. In J.-P. Lecanuet, N. A. Krasnegor, W. P. Fifer, & W. P. Smotherman (Eds.), Fetal development: A psychobiological perspective (pp. 295–314). Hillside, NJ: Erlbaum. Rosenblum, L. A., & Moltz, H. (1983). Symbiosis in parent-offspring interactions. New York: Plenum Press.

Kraemer, G. W. (1992). A psychobiological theory of attachment. Behavioral and Brain Sciences, 15, 493–511.

Shear, M. K., Brunelli, S. R., & Hofer, M. A. (1983). The effects of maternal deprivation and of refeeding on the blood pressure of infant rats. Psychosomatic Medicine, 45, 3–9.

Kuhn, C. M., & Schanberg, S. M. (1991). Stimulation in infancy and brain development. In B. J. Carroll & J. E. Barrett (Eds.), Psychopathology and the brain (pp. 97–111). New York: Raven Press.

Skolnick, N. J., Ackerman, S. H., Hofer, M. A., & Weiner, H. (1980, June 6). Vertical transmission of acquired ulcer susceptibility in the rat. Science, 208, 1161–1163.

Lester, B. M., & Boukydis, C. F. (Eds.). (1985). Infant crying: Theoretical and research perspectives. New York: Plenum Press.

Smotherman, W. P., Bell, R. W., Hershberger, W. A., & Coover, G. D. (1978). Orientation to rat pup cues: Effects of maternal experiential history. Animal Behaviour, 26, 265–273.

Levy, F., Gervais, R., Kindermann, U., Orgeur, P., & Piketty, V. (1990). Importance of beta-noradrenergic receptors in the olfactory bulb of sheep for recognition of lambs. Behavioral Neuroscience, 104, 464–469. Lorenz, K. (1996). On aggression. New York: Harcourt, Brace & World. Makin, J. W., & Porter, R. H. (1989). Attractiveness of lactating females’ breast odors to neonates. Child Development, 60, 803–810.

Smotherman, W. P., & Robinson, S. R. (1986). Environmental determinants of behaviour in the rat fetus. Animal Behaviour, 34, 1859–1873. Stehouwer, D. J., & Campbell, B. A. (1978). Habituation of the forelimbwithdrawal response in neonatal rats. Journal of Experimental Psychology: Animal Behavior Processes, 4, 104–119. Stern, K., & McClintock, M. K. (1998, March 12). Regulation of ovulation by human pheromones. Nature, 392, 177–179.

Miczek, K. A., Tornatsky, W., & Vivian, J. (1991). Ethology and neuropharmacology: Rodent ultrasounds. In B. Oliver, J. Mos & J. L. Slanger (Eds.), Animal Models in Psychopharmacology (pp. 409–427). Basel, Switzerland: Birkhäuser.

Stone, E. A., Bonnet, K. A., & Hofer, M. A. (1976). Survival and development of maternally deprived rats: Role of body temperature. Psychosomatic Medicine, 38, 242–249.

Moffat, S. D., Suh, E. J., & Fleming, A. S. (1993). Noradrenergic involvement in the consolidation of maternal experience in postpartum rats. Physiology and Behavior, 53, 805–811.

Sullivan, R. M., Brake, S. C., Hofer, M. A., & Williams, C. L. (1986). Huddling and independent feeding of neonatal rats can be facilitated by a conditioned change in behavioral state. Developmental Psychobiology, 19, 625–635.

Moore, C. L., Jordan, L., & Wong, L. (1996). Early olfactory experience, novelty, and choice of sexual partner by male rats. Physiology and Behavior, 60, 1361–1367.

c02.indd Sec5:30

Sullivan, R. M., Hofer, M. A., & Brake, S. C. (1986). Olfactory-guided orientation in neonatal rats is enhanced by a conditioned change in behavioral state. Developmental Psychobiology, 19, 615–623.

8/18/09 4:57:27 PM

References 31

c02.indd Sec5:31

Sullivan, R. M., Taborsky-Barba, S., Mendoza, R., Itano, A., Leon, M., Cotman, C. W., et al. (1991). Olfactory classical conditioning in neonates. Pediatrics, 87, 511–518.

Varendi, H., Porter, R. H., & Winberg, J. (1996). Attractiveness of amniotic fluid odor: Evidence of prenatal olfactory learning? Acta Paediatrica, 85, 1223–1227.

Sullivan, R. M., & Wilson, D. A. (1995). Dissociation of behavioral and neural correlates of early associative learning. Developmental Psychobiology, 28, 213–219.

Weaver, I. C. G., D’Alessio, A. C., Brown, S. E., Hellstrom, I. C., Dymov, S., Sharma, S., Szyf, M., & Meaney, M. J. (2007). The transcription factor nerve growth factor-inducible protein A, mediates epigenetic programming: Altering epigenetic marks by immediate-early genes. Journal of Neuroscience, 27, 1756–1768.

Sullivan, R. M., Wilson, D. A., Wong, R., Correa, A., & Leon, M. (1990). Modified behavioral and olfactory bulb responses to maternal odors in preweanling rats. Brain Research. Developmental Brain Research, 53, 243–247.

West-Eberhard, M. J. (2003). Developmental plasticity and evolution. Oxford, England: Oxford University Press.

Takahashi, L. K. (1992). Ontogeny of behavioral inhibition induced by unfamiliar adult male conspecifics in preweanling rats. Physiology and Behavior, 52, 493–498.

Wilson, D. A., & Sullivan, R. M. (1994). Neurobiology of associative learning in the neonate: Early olfactory learning. Behavioral and Neural Biology, 61, 1–18.

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Chapter 3

Comparative Cognition and Neuroscience CHARLES T. SNOWDON AND KATHERINE A. CRONIN

all, insects, fish, birds, and mammals. Presumably, in environmental conditions where color vision would benefit the reproductive success of individuals, individuals possessing mutations leading to color vision would be more likely to survive and would leave more surviving offspring with the color vision mutation. Many cognitive and behavioral phenomena appear as examples of convergent evolution: families as the basic reproductive unit in many birds, marmosets, tamarins, titi monkeys, and humans; enhanced spatial memory in seed-caching birds and rodents; vocal learning in birds, marine mammals, and humans, and so forth. In this chapter, we first discuss the methods and cautions of comparative studies and then focus on a selection of cognitive phenomena for which there are good comparative data and at least some information on the neural bases. We include social processes among the phenomena that we

Developmental processes are typically viewed from the perspective of an individual’s history, but these processes can also be viewed from the broader historical context of evolution by natural selection. The study of the evolution of cognition and its neural correlates involves the use of a comparative method: the study of multiple species that differ in one factor that may influence the selective pressures on the trait of interest. Factors investigated may include phylogeny (e.g., birds vs. mammals, or apes vs. monkeys), social structure or social organization (e.g., multimale, multifemale groups vs. families), mating system (e.g., monogamy vs. polygamy), group size (e.g., pair with offspring vs. many individuals), foraging behavior (e.g., generalist vs. specialist, or clumped vs. distributed food resources), or ranging behavior (e.g., territorial vs. migratory). Thus, someone interested in the evolutionary origins of language might compare the abilities of humans with those of other great apes. Someone interested in spatial memory might compare two closely related species, one that stores food in many locations and recovers the food later and another that does not store food. The typical view of evolutionary processes is that they diverge. As new species form and become reproductively isolated, they diverge from one another. From this perspective, those species that have shared ancestry and are similar on most variables but differ on some critical one (e.g., monogamous vs. polygamous mating systems in some closely related rodent species) are the comparisons of greatest interest. However, an additional view of evolution is one of convergence (see Figure 3.1). Two quite distantly related species may face similar ecological problems and may independently reach similar solutions. One example is color vision, which has independently appeared in some, but not

Trait variation B

C

D

E

Time

A

Figure 3.1 Convergent and divergent evolution. Letters represent species on this hypothetical phylogenetic tree. The less the horizontal distance between species, the more similar the species are on the trait of interest. Species D and species E are more closely related to each other than are species B and species C. However, species D and species E are less similar on the trait of interest and are an example of divergent evolution, whereas species B and species C are more similar on the trait of interest and are an example of convergent evolution.

We thank Bridget Pieper and Carla Boe-Nesbit for critical comments on the manuscript and Andrew Fox for critical feedback on neural mechanisms of cooperation. Our research was supported by U.S. Public Health Service Grant MH035215 and a Hilldale Professorship from the University of Wisconsin, Madison. KAC was supported by a National Science Foundation Graduate Fellowship. 32

Handbook of Neuroscience for the Behavioral Science, edited by Gary G. Berntson and John T. Cacioppo. Copyright # 2009 John Wiley & Sons, Inc. c03.indd 32

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Methods and Cautions 33

call cognition. Thus, we review cooperation and prosocial behavior, social learning, spatial memory, and pair bonding. An obvious omission is comparative work on bird song learning and its neural mechanisms, which is covered in Chapter 45.

METHODS AND CAUTIONS Choosing Species for Comparison The selection of appropriate species for comparison depends on the nature of the question being addressed. However, often species are chosen on the basis of phylogenetic similarity alone. For example, chimpanzees (Pan troglodytes) and bonobos (Pan paniscus) are the closest relatives of humans, but these apes diverged from humans more than 4.5 million years ago. Behaviorally, chimpanzees differ significantly from bonobos by being more competitive and aggressive, and both species differ from humans in significant ways. Nonetheless, because both species have relatively large brains that are similar in form to those of humans, these species may be good models for comparisons with humans involving some cognitive abilities. However, they may not be good models for comparisons with some human social and emotional processes because the social environments of these species differ greatly from our own. Comparative models might also be chosen from species that are quite different but share some interesting commonality. For example, human fathers often appear to display competence in caring for and bonding with their infants, something rarely, if ever, observed in other great apes. However, fathers in several New World primate species and in some rodents and many birds exhibit spontaneous care of infants and form long-lasting bonds, making these species potentially better candidates than great apes and Old World primates for studying paternal behavior (Snowdon & Ziegler, 2007). As we show here, species with extensive coordination of infant care between different group members are more likely to exhibit social learning (Coussi-Korbel & Fragaszy, 1995) and cooperative behavior (Cronin, Kurian, & Snowdon, 2005). Ecological factors affecting foraging behavior can also lead to differences in rapidity of social learning (Lefebvre & Palameta, 1988). As we also show, social factors or mating systems can lead closely related species, such as monogamous versus polygynous mice and voles, to have very different behavior, hormones, and neural organizations than we would predict from phylogeny alone. Important comparisons may also be made within species. Call and Tomasello (1996) and Thompson, Oden, and Boysen (1997) have shown differences in performance on social learning and second-order analogical reasoning tasks between so-called enculturated chimpanzees that have had extensive

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human interactions during development compared with chimpanzees that have been reared naturally by their own species. It is not clear what aspects of enculturation are important for high-level cognitive function, but the ability to interact comfortably with the humans who are testing the animals may lead these chimps to exhibit better performance than those with less of a history of interaction with humans. Differences within a single species can also inform researchers of developmental influences on cognition. Seasonal differences may also produce behavioral and neural variation within a species or even an individual. Male birds sing under the influence of testosterone stimulated by increasing day length. Furthermore, changes in the size and neural complexity of brain areas track seasonal differences, with consequent changes in rates of singing or song structure and complexity (Alger & Riters, 2006; Nottebohm, 1981; G. T. Smith, Brenowitz, Beecher, & Wingfield, 1997; see Chapter 4 for more details). No Scala Naturae Hodos and Campbell (1969) argued against the concept of scala naturae, a popular misconception that behavioral changes follow a natural phylogenetic scale from earlier evolved species exhibiting simpler behavior to more complex and more recently evolved species necessarily exhibiting more complex behavior. You still can find psychology texts talking about invertebrates having less complex behavior than fish, which in turn are simpler than birds, which are simpler than mammals. The reality of evolution is much messier than a simple linear hierarchy implies. Honeybees have a complex communication system to indicate the location of nectar (Von Frisch, 1950). Salmon remember the odors of their home streams over several years of ocean living and can return to their natal streams to spawn (Hasler, 1966). Clark’s nutcrackers (Nucifraga columbiana) of the U.S. Southwest can store up to 30,000 seeds each fall and can remember the location of enough of these seeds to survive the winter (Balda & Kamil, 1992). Cotton-top tamarins (Saguinus oedipus; Cronin & Snowdon, 2008; Hauser, Chen, Chen, & Chuang, 2003) and common marmosets (Callithrix jacchus; Burkart, Fehr, Efferson, & van Schaik, 2007) spontaneously display cooperative and reciprocal behaviors that a human parent would admire in his or her children, yet chimpanzees display little evidence of similar positive social behavior (Jensen, Hare, Call, & Tomasello, 2006; Silk et al., 2005; Vonk et al., 2008; see following discussion). Vonk and Povinelli (2006) argued that evolution creates no presumption about the phylogenetic distribution of psychological systems among closely related species, and yet, even among presumably sophisticated primatologists who might be expected to have a more subtle appreciation of the

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processes of evolution, we often encounter the idea that primates are somehow more special than other mammals and that apes have cognitive skills surpassing all others. This leads to the frequent dismissal of nonprimate species with complex social structures such as hyenas, wolves, and other social carnivores as well as species more difficult to study, such as marine mammals. In these species one can find evidence of group-specific differences in communication and behavior that might be called culture if seen in apes, as well as coordination of complex social behavior, such as teaching in meerkats (Thornton & McAuliffe, 2006) and communal hunting in hyenas (Drea & Frank, 2003). There is often evidence of clear relationships between aspects of body size, brain size, and cognition. The patterns of allometric relationships may often illuminate interesting exceptions from a strict phylogenetic progression. For example, Figure 3.2 plots body size and encephalization quotient (EQ) for several primate species grouped by phylogeny. There is little variation across New World monkeys, Old World monkeys, apes, and extinct hominoids in brain size as a proportion of body size. However, the two genera with the highest EQs are squirrel monkeys and capuchin monkeys, both New World primates, and one of the genera with the lowest EQ is the gorilla. Note also that modern humans have a significantly greater EQ than any other primate species. A modern reincarnation of the scala naturae is research that uses allometry of brain size to explain the evolution of language. Dunbar (reviewed in Dunbar, 2003) has shown that among terrestrial Old World primates there is a correlation between group size and neocortex size. This suggests that the cognitive demands of social complexity were driving forces in the evolution of brain size (the social

brain hypothesis). Dunbar ’s measure of social complexity was the number of grooming relationships that are possible within a group, as grooming is an essential component of maintaining social relationships in many primates. As group size increases linearly, the number of potential dyadic and higher order relationships increases exponentially. According to Dunbar, typical human groups average 150 in a social cohort, too large to allow individual relationships to be maintained by grooming. Thus, humans have evolved language, using gossip as a proxy for social grooming. In fact, there does appear to be a relationship between typical group size and neocortex volume among the species selected. But is the description of the relationship between group size, social complexity, and brain size adequate? Figure 3.3A shows New World primates that diverged from the ape lineage earlier than Old World monkeys. The monkeys with the largest brains and the largest groups (muriquis, Brachyteles spp.) almost never groom one another (Strier, 1997), whereas the primates with brains with less neocortical volume and the smallest group sizes (marmosets and tamarins) groom extensively. One field study of common marmosets reported adult pairs grooming each other more than 20% of the day (Lazaro-Perea, de Fatima Arruda, & Snowdon, 2004). Figure 3.3B shows that for both New World primates and apes there is a linear relationship between EQ and group size; so the basic relationship between group size and brain size still holds, but not for grooming time. These findings argue against a clear relationship between group size, grooming, social complexity, and the evolution of the neocortex. One lesson from this is that limiting the selection and range of species studied can potentially lead to different

8 New World primates

Encephalization Quotient

7

Old World primates Apes

6

Extinct hominoids 5

Modern humans

4 3 2 1 0 0.1

1

10 Body Weight

Figure 3.2 Encephalization quotient (EQ) plotted against log body weight for several genera of primates. Note the variation within each group of primates (Saimiri and Cebus as New World primates have higher EQ’s than all other

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100

1000

nonhuman primates. Gorillas, furthest to the right, have a small EQ relative to all great apes and most monkeys. Only modern humans have an EQ that is not in the range of other species. (Drawn from data in Aiello & Dean, 1990)

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Methods and Cautions 35 (A)

25

Time Grooming (%)

20

15

10

5

0 0

(B)

20

40

60 Group Size

80

60 Group Size

80

100

120

3.5

Encephalization Quotient

3 2.5 2 1.5 1 0.5 0 0

20

40

conclusions. Another lesson is that brain size is not necessarily synonymous with complexity. As a nonprimate corollary of this, G. T. Smith et al. (1997) found that although the sizes of brain areas related to song production changed with the seasons in song sparrows, some of the change was due to changes in average neuron size and neuron density rather than in the number of neurons. Complexity must be carefully defined, and we should not assume that differences in size must necessarily be related to differences in complexity. Anthropomorphism and Anthropocentrism Many of the problems with the scala naturae view among scientists otherwise quite sophisticated about natural selection and evolution may be due to both anthropomorphism (ascribing human traits to nonhuman animals) and anthropocentrism (seeing cognition in other species through the lens of human abilities). When species are more similar to humans, it may be difficult to avoid ascribing humanlike cognition to these species. Even when we try hard to

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100

120

Figure 3.3 The linear regression from New World primates is shown by a solid line, for Old World primates by a dotted line and for Apes by a dashed line. A: Grooming time as a function of group size in selected genera of New World primates, Old World primates and apes. Regression lines for Old World primates and apes have a positive slope, whereas New World primates show a negative relationship between grooming time and group size. (Data from Dunbar, 1991) B: Encephalization quotient as a function of group size in the same genera showing a positive relationship between brain size and group size for New World primates and apes and no clear trend for Old World primates. EQ data from Aiello and Dean (1990) and group size data from Dunbar (1991).

avoid being anthropomorphic, it is nearly impossible to avoid being anthropocentric. This has led to the idea that increasingly complex social life (as measured by mean group size) must be accompanied by increasingly complex cognitive and social skills (Barrett, Henzi, & Rendall, 2007). As mentioned, social group size may not be a good index of brain complexity, and many species far removed from humans have displayed complex cognition on one or more dimensions in the context of their environment. Furthermore, it may be a mistake to assume that all of human behavior is governed by complex cognitive processes. Many of us engage in automatic behaviors (routines in cooking, driving to work, semantically meaningless phrases used in greetings, etc.). We may be overrating our own cognitive ability simply because we are the species doing the rating. Cognitive complexity must be seen through the lens of the species being studied. None of this is to deny the very real abilities of humans but instead to urge scientists to be more modest when evaluating human abilities (and those of other great apes) compared with those of other species.

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Problem of Plasticity Comparing species on different aspects of cognition is one way to gain a deeper understanding of cognitive processes and cognitive evolution, yet animals, human and nonhuman, are notoriously plastic and are able to modify behavior. For example, ecological differences between captive and wild environments may lead to different behavior within the same species. Leavens, Hopkins, and Bard (2005) have shown that pointing behavior is observed readily in captive chimpanzees interacting with humans to obtain food, whereas pointing has never been observed in the wild. Unlike wild tamarins, captive-born cotton-top tamarins do not exhibit fear when exposed to snakes, a natural predator (Campbell & Snowdon, in press; Hayes & Snowdon, 1990). Captive-born tamarins responded with equal arousal to playbacks of calls of some natural predators and vegetarian howler monkeys, suggesting that initial responses to predator calls may be flexible and may be related to certain acoustic properties rather than innate predator recognition (Friant, Campbell, & Snowdon, 2008). Captive tamarins spontaneously produced alarm calls when fed a familiar food that had been adulterated with pepper, although they had never alarm called at that food previously (Snowdon & Boe, 2003). They also gave mobbing vocalizations toward a caretaker acting as though coming to catch a monkey, despite their lack of arousal toward natural predators (Campbell & Snowdon, 2007). These results on captive chimpanzees and tamarins suggest that captivity represents an ecological niche to which animals may adapt, leading to changes in behavioral contexts different from those in which their wild conspecifics would show similar behavior. Withinspecies comparisons of individuals living under different social or environmental conditions can identify the environmental (as opposed to phylogenetic and genetic) variables that can affect cognitive processing and behavioral expression. This contextual flexibility may paradoxically utilize simpler neural processes than those hypothesized for contextually inflexible cognitive processes. For example, with respect to communication between animals, Owren and Rendall (1997) have argued for flexibility in the contexts in which signals are used and how they are responded to by others. They offered a simple model of affective conditioning by which individuals learn the relationships between signals and affective state. As environments change, organisms can acquire new signal–meaning correspondences. Saffran, Aslin, and Newport (with human infants; 1996) and Hauser, Newport, and Aslin (with cotton-top tamarins; 2001) have demonstrated that human babies and monkeys can learn about statistical regularities in auditory input, a simple but rapid

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way in which organisms can acquire contingencies between sounds. Thus, an animal in a novel context (e.g., captivity) can quickly learn to associate alarm calls with caretakers or veterinarians rather than leopards and snakes, or can learn to eat foods different from those found in the wild and still give appropriate food calls. These basic processes of conditioning provide a general way to learn rapidly about associations of signals and events in the environment without reliance on a host of hypothetical modality and context-specific modules that have been shaped by evolution (Tooby & Cosmides, 1990). Successful adaptation is more likely to occur when organisms can respond rapidly to changes in environment than rely on hard-wired species-specific stimulus–response connections. Two striking examples of plasticity come from work on rodents. For the first, Marler and colleagues studied two species of mice (Table 3.1). The California mouse (Peromyscus californicus) is one of the few species known from field studies to be both socially and genetically monogamous (Ribble, 1991). Males defend their mates and play an important role in infant care, with infant survival being affected by fathers (Gubernick & Tefari, 2000). The white-footed mouse (P. leucopus) is a close relative but is polygamous. These mice do not form pair bonds, and individual males and females may mate with many others. Males provide little, if any, infant care and do not show territorial defense. Bester-Meredith and Marler (2001) successfully cross-fostered these mice and showed that California mice reared by white-footed mice exhibited reduced paternal care and reduced territorial defense behavior, becoming more similar to their foster species. Furthermore, the patterns of distribution of vasopressin (a neuropeptide hormone related to aggression) staining in the brains of cross-fostered California mice more closely approximated the distribution patterns seen in white-footed mice than those seen in their own species. Within California mice, sires that were retrieved less as young retrieved their pups less often when they became adults (Bester-Meredith & Marler, 2001). An experimental increase in the amount of retrievals experienced by California mice offspring led to decreased attack latencies in both males and females and greater vasopressin immunoreactivity in the dorsal bed nucleus of the stria terminalis (Frazier, Trainor, Cravens, Whitney, & Marler, 2006). Cross-fostering (CF) to another species can change not only behavior but also brain organization and brain function. The specific nature of the crossfostering experience that leads to behavioral and neural changes is fairly minimal. In the second example, the work of Meaney and collaborators has shown that variations in maternal licking and grooming in rats (Rattus norvegicus) are related to variations in stress reactivity, with greater amounts of licking

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Social Learning 37 TABLE 3.1 Effects of cross-fostering (CF) on behavior and arginine vasopressin (AVP) brain staining in mice. Species CrossFostered

Territorial Aggression

Neutral Arena Aggression

Paternal Care

AVPImmuno Staining

Whitefooted mouse

No effect

Control < CF

No effect

No effect

California mouse

Control ⬎ CF

No effect

Control ⬎ CF

Control ⬎ CF

Note. From “Paternal Behavior and Aggression: Endocrine Mechanisms and Non-Genomic Transmission of Behavior,” by C. A. Marler, J. K. Bester-Meredith, and B. C. Trainor, 2003, Advances in the Study of Behavior, 32, pp. 263–323. Adapted with permission. Elsevier Science 2003.

and grooming reducing stress reactivity of the pups when they are adults. Furthermore, when offspring become adults they show similar licking and grooming patterns as their mothers. Cross-fostering studies showed these effects to be nongenomic but purely a result of the early grooming and licking received (Francis, Diorio, Liu, & Meaney, 1999). High levels of maternal licking and grooming translated into differences in hippocampal glucocorticoid receptor messenger RNA expression (Liu et al., 1997) as well as increased synapse formation in the hippocampus, increased expression of N-methyl-D-aspartic acid receptors, increased cholinergic innervation of the hippocampus, and enhanced spatial learning and memory (Liu, Diorio, Day, Francis, & Meaney, 2000). Furthermore, high levels of early maternal care are associated with differences in responsiveness of oxytocin receptors to stimulation by estrogen (Champagne, Diorio, Sharma, & Meaney, 2001). That such small but significant differences in early rearing can have profound effects on behavior and brain function in several species should caution us about thinking about behavior as being determined solely by the direct effects of natural selection. Organisms are not static entities but respond flexibly and dynamically to their environments.

SOCIAL LEARNING Social learning (or socially mediated learning) has been studied in a wide range of species. Social learning occurs when the acquisition of behavior is influenced by the activities of other individuals, either directly or indirectly (Box, 1984). Much research has focused on the mechanisms that enable social learning, such as an observer ’s attention being drawn to a particular object as a result of another individual interacting with that object (stimulus enhancement;

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Spence, 1937), or the increased likelihood of performing a familiar motor action after seeing it performed by another individual (response facilitation; Byrne, 1994). More than seven mechanisms that enable social learning have been identified (Whiten & Ham, 1992). One mechanism, imitation, has received the most attention and has been categorized as the most challenging and unique form of social learning (Byrne, 1999). Some have argued that imitation requires theory of mind, or the ability to conceive of the intentions of others (Premack & Woodruff, 1978; see also Stone, this volume). Because theory of mind is widely thought to be an ability restricted to humans and potentially other great apes (Byrne, 1999), it has been argued that imitation is impossible for other taxa (Whiten & Ham, 1992). Miklosi (1999) reported that although some studies have provided data in support of imitative abilities in other species, others have quickly criticized and reinterpreted the results. The prevalence of imitative ability across taxa, and the cognitive mechanisms that underlie imitation, will likely be a hotly debated issue for some time, especially because some researchers with beliefs in scala naturae will cling to the uniqueness of imitation for humans and great apes. In order to evaluate the social learning capability or tendency of a group or species, researchers typically employ one of three methodologies. The first is simply exposing an individual trained to perform a skill to one or more naive individuals and then measuring whether the skill is expressed by the formerly naive individual(s). The second, transmission or diffusion chains, uses the same idea but measures whether the formerly naive individual subsequently transfers the acquired behavior to another naive individual (e.g., Galef & Allen, 1995). The third is the dual action task, in which there are two functionally equivalent methods available for solving a task. Individuals observe one or the other method and then are tested on whether their performance matches the method employed by the demonstrator (e.g., Humle & Snowdon, 2008). Social learning can permit behavioral calibration to the unpredictable properties of an environment with a degree of specificity not permitted by genetically coded information (Galef & Laland, 2005). However, it is a common misconception to assume that social learning is always more advantageous than individual learning, and indiscriminate copying of surrounding individuals is unlikely to be a stable strategy in all species (Boyd & Richerson, 1988; Laland, 2004). Theoretical models have demonstrated that social learning is more effective than individual learning when two conditions are met: (1) the cost of individually acquiring accurate information is high, and (2) knowledgeable individuals are present that have experienced the same environment (Boyd & Richerson, 1988). Therefore,

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we would expect that even closely related species might display different propensities to acquire information socially if they have evolved in different environments. Laland has expanded on these models to argue that natural selection should have favored heuristics dictating when individuals should acquire information socially, and from whom the information should be obtained. Decades ago, Klopfer (1959, 1961) proposed two hypotheses to account for interspecific variation in social learning. He reasoned that solitary species should demonstrate less social learning than gregarious species, and species with conservative foraging strategies should exhibit less social learning than species with opportunistic foraging styles. In fact, most attempts to predict or explain interspecific differences in social learning to date have been based on differences in either sociality or feeding ecology. Influence of Sociality on Social Learning Coussi-Korbel and Fragaszy (1995) presented a model relating social learning to social organization. They argued that behavioral coordination in time and/or space is common to all forms of social learning and that the extent to which behavioral coordination is expressed predicts social learning. An example of behavioral coordination in time (but not space) is a flock of birds feeding simultaneously while the nearest neighbors are at some distance. An example of behavioral coordination in space (but not time) is when an individual approaches a location where a conspecific was previously active but is no longer present. It is only behavioral coordination in both time and space that requires physical proximity between individuals. Differences in the amount of spatial proximity sought out and tolerated by conspecifics vary greatly across species and can often be related to larger social constructs such as dominance structures or mating systems. In despotic species, proximity between conspecifics, particularly near food or desirable items, will be infrequent. In more egalitarian species, however, proximity between conspecifics will be more common. Therefore, the social context in which an individual is immersed is likely to influence opportunities for, and subsequent expression of, social learning. However, it is not always the case that hierarchically organized species demonstrate less social learning. It appears that when the social hierarchy promotes close, constant monitoring of other individuals and their behavior, the probability for social learning may increase because of the increased attention paid to conspecifics. When the attention structure brought about by the hierarchy promotes social learning, it should do so in a heterogeneous fashion (Coussi-Korbel & Fragaszy, 1995). That is, not all

c03.indd Sec2:38

individuals will acquire the socially learned behavior, but rather those that are positioned relatively lower within the hierarchy will be more likely to attend to a demonstrator that is relatively more highly ranked and to subsequently express the modeled behavior. Some studies have made explicit attempts to relate social organization to social learning by performing interspecific comparisons of phylogenetically similar species. Cambefort (1981) compared the discovery and propagation of a feeding skill in chacma baboons (Papio ursinus) and vervet monkeys (Cercopithecus aethiops). The results were also compared with those of a previous study of mandrills (Mandrilli sphinx; Jouventin, Pasteur, & Cambefort, 1976). These three primate species belong to the same family, Cercopithecidae (Nowak, 1999), but exhibit different social organizations. The propagation of the socially learned feeding skill was closely related to the social structure or, more specifically, the attention structure of each species. Vervet monkeys exhibited the weakest hierarchy and group cohesion, and there was no social transmission of the novel foraging skill. In chacma baboons, which exhibited intermediate hierarchy strength, group cohesion, and attention to social partners, there was weak transmission of the novel foraging skill. Finally, in mandrills, which demonstrated the strongest hierarchy, strongest group cohesion, and most attention to social partners, there was fast social propagation of the novel foraging skill. Therefore, the pattern of transmission of the novel behavior mapped onto the hierarchical organization of the group. Another study compared the social transmission of flavor preferences in two hamster species—the golden hamster (Mesocricetus auratus), which is solitary, and the dwarf hamster (Phodopus campbelli), which is moderately social (Lupfer, Frieman, & Coonfield, 2003). When given a choice between a flavor chosen by a demonstrator and another flavor, dwarf hamsters preferred the demonstrator ’s flavor, whereas golden hamsters preferred the demonstrator ’s flavor only if the demonstrator was their mother. In the wild, adult dwarf hamsters interact with one another to share burrows and raise pups. In contrast, golden hamsters rarely interact with one another outside of the mother–pup relationship. Therefore, the degree of social learning expressed may have been predicted by the social organization of the species. A similar finding was reported when the more social pinyon jay (Gymnorhinus cyanocephalus) was compared to the less social Clark’s nutcracker. The individual and social learning abilities of the two species were compared, and the results indicated that pinyon jays learned faster socially than they did individually, whereas the nutcrackers’ performance was not enhanced in the social learning condition (Templeton, Kamil, & Balda, 1999).

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Social Learning 39

Whereas interspecies comparisons provide insights about the effect of the social organization of a species on social learning, intraspecies comparisons allow for investigation of the effects of social relationships within a species on social learning. Nicol and Pope (1994) found that the social transmission of a behavior (keypecking) in flocks of laying hens (Gallus gallus) was influenced by the hierarchy of the group and that social learning was greatest when the demonstrator was a dominant individual. A similar effect of social relationships was found in a study of cooperatively breeding common marmosets. Dyads were presented with a task requiring both individuals to act, whereby one manipulated an apparatus to bring food into the reach of the second individual, who could then retrieve the food. Dyads in which the more dominant individual assumed the role of retriever were more successful at solving the coproduction task. However, in the successful dyads, dominant individuals did not consume more rewards than subordinates, even though they had more direct access to the food rewards (Werdenich & Huber, 2002). Schwab, Bugnyar, Schloegl, and Kotrschal (2008) demonstrated that affiliative relationships among kin enhanced the performance of common ravens (Corvus corax) in a social learning task. Drea and Wallen (1999) reported intriguing results indicating that subordinate rhesus macaques (Macaca mulatta) performed less well on learning tasks when in the presence of dominant individuals, even though they had learned the information equally well. Drea and Wallen (1999) were not investigating social learning specifically, but their findings, combined with those of other studies on the effects of social relationships on performance, indicate that the identity of the demonstrator, as well as by-standers in the experimental setup, likely influences the degree of social learning expressed. Intraspecies comparisons can also elucidate developmental differences in social learning. Dependence on social learning may vary in predictable ways throughout the lifetime of an individual (Galef & Laland, 2005). As mentioned previously, there are likely fewer opportunities for social learning in a species that is rarely in proximity with conspecifics, but during periods of offspring dependence proximity will be more frequent and social learning opportunities may be quite regular. Black rats (Rattus rattus) learn socially to open pinecones to obtain seeds; however, it is only the pups, not adults, that are able to acquire the technique by observing experienced rats (Aisner & Terkel, 1992). Across many studies and species, it seems that juveniles are more likely than adults to incorporate new actions into their behavioral repertoire (e.g., Goodall, 1986; InoueNakamura & Matsuzawa, 1997; Kawai, 1965). Chimpanzees are generally adept at social learning both in the wild (reviewed in Matsuzawa, 2001) and in

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captivity (reviewed in Whiten, Horner, & Litchfield, 2004). Chimpanzee societies exhibit a strict dominance hierarchy, with the most tolerant and longest lasting relationships existing between mother and offspring. An intraspecific comparison of tool use acquisition by young chimpanzees in Gombe National Park, Tanzania, indicated a striking sex difference in the social acquisition of the skill. Females began using tools for termite fishing at a younger age than males, although there was no difference in the behavior of the mother (the model) toward males and females. Young females spent more time watching their mothers use the tools, whereas males spent more time playing at the termite mound. Therefore, the attention paid by the juveniles was an influential factor in determining the onset of the socially learned skill (Lonsdorf, Eberly, & Pusey, 2004). Again, the direction of attention and the close social relationship appears crucial for predicting the occurrence of social learning. Influence of Feeding Ecology on Social Learning Predictions about the prevalence of social learning across species have also been made on the basis of feeding ecology. Specifically, species with opportunistic or generalist lifestyles, in which individuals are exposed to more environmental variation, should be more likely to demonstrate social learning than species that are conservative or specialists (Johnston, 1982; Klopfer, 1961). This hypothesis rests on the reasoning that social learning allows an individual to modify its behavior to the current environment more efficiently or quickly than would be possible by either individual learning or genetically determined behavior. In this sense, social learning is an adaptive specialization shaped by natural selection. The influence of feeding ecology on social learning has been explored experimentally. Klopfer (1961) demonstrated that greenfinches (Chloris chloris) learned a food discrimination task less well in pairs than individually, as opposed to great tits (Parus major), which did not suffer a learning decrement in pairs. He speculated that a failure to learn socially about novel foods would only fail to be maladaptive in species that display conservative feeding habits, such as the great tit. Dolman, Templeton, and Lefebvre (1996) compared two populations of Barbados Zenaida doves (Zenaida aurita) with different foraging styles. One population consistently exhibits conspecific aggression while foraging, whereas the other forages in flocks without aggression. The aggressively foraging species performed better on a social learning task that employed a heterospecific demonstrator, whereas the nonaggressive species performed better with a conspecific demonstrator. In this study, the type of feeding environment appeared to predict

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the pattern of social learning expressed, as the population that typically interferes with conspecifics to compete for food was unable to acquire a socially learned skill from a conspecific. The prevalence of social learning throughout the animal kingdom seems to be best explained by a combination of social, developmental, and ecological factors. Phylogenetic predictions alone appear to do a relatively poor job of explaining interspecific variation in social learning, especially if one considers that comparisons across populations of a single species often lead to as much variation as between-species comparisons. Social Learning and the Brain It is unlikely that the neuronal bases that underlie social learning differ dramatically from mechanisms known to underlie other forms of learning (for a review of the neuronal basis of learning, see Chapter 26). However, an additional pattern of neuronal activation may be unique to social learning and may not be involved in asocial learning such as classical or operant conditioning or trial-and-error learning. Mirror neurons have been identified in macaques (Macaca ssp.) in the ventral premotor and rostral inferior parietal cortex (see Figure 8.11 in Chapter 8 for the approximate location). The defining characteristic of mirror neurons is that they fire both when the animal performs an object-oriented action with its hand or mouth, and when the animal observes another individual (human or conspecific) performing the same motor action (di Pellegrino, Fadiga, Fogassi, Gallese, & Rizzolatti, 1992; Gallese, Fadiga, Fogassi, & Rizzolatti, 1996; Rizzolatti, Fadiga, Gallese, & Fogassi, 1996). A leading hypothesis regarding the function of the mirror neuron system posits that mirror neurons are the basis of action understanding (Rizzolatti, Fogassi, & Gallese, 2001). Indeed, accumulating evidence suggests that the understanding of the meaning of actions determines the discharge of mirror neurons, rather than observations of the actions themselves. Umiltà et al. (2001) demonstrated that the majority of mirror neurons of rhesus monkeys respond during the observation of partially hidden actions, when full visual information about the action is not necessary to recognize the goal. The mirror neuron system also responds to the sound of a goal-related action, even when the monkey cannot see the action (Keysers et al., 2003; Kohler et al., 2002). These observations provide support for the interpretation that the mirror neuron system recognizes actions performed by other individuals and matches these actions on neurons that code the same action, providing the observer with the motoric perspective of the actor. It is through this interpretation that the mirror neuron

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system has garnered the attention of those interested in social learning. A growing amount of information available from EEG, transcranial magnetic stimulation, functional magnetic resonance imaging (fMRI), and positron emission tomography (PET) studies indirectly supports the presence of a mirror neuron system in humans (reviewed in Rizzolatti & Craighero, 2004). However, data suggest that there are some key differences between the mirror neuron systems of macaques and humans. For example, the mirror neuron system of humans, but not rhesus monkeys, responds to intransitive, meaningless movements. Perhaps this difference in sensitivity of the mirror neuron system sheds some light on the different imitative tendencies of humans and monkeys: Human children will “over imitate” (Horner & Whiten, 2005), copying movements that are irrelevant to accomplishing the task. (For a more detailed review of the mirror neuron system, see Chapter 16.) It is tempting to speculate that the mirror neuron system underlies social learning, given that the system allows for matching of the observation of motor actions with their execution. However, more data are needed on the involvement of motor neurons during the observation and acquisition of socially learned skills to understand whether mirror neurons are central to social learning processes. Furthermore, given that there are fundamental differences between macaques and humans in the properties of the mirror neuron system, data from additional species, ideally species that vary in their expression of social learning, will also be necessary to elucidate the role of the mirror neuron system in social learning.

SPATIAL MEMORY Spatial memory can be very important to help animals navigate through their environments to find mates or food. Because closely related species differ in the degree to which they rely on spatial memory, the comparative approach has been extremely powerful for understanding the neural mechanisms that underlie these behavioral differences. Foraging and food-caching behavior vary between species in both corvids (crows, ravens, jays) as well as parids (chickadees, tits, titmice). Some species must store food each fall and remember the locations of storage sites in order to find food over the winter. The Clark’s nutcracker from Arizona has been estimated to store up to 30,000 pine nuts each fall and appears to remember their locations even when the ground cover is transformed by snow (Balda & Kamil, 1992). Because other species within the same family do not need to store food over the winter, some have predicted that the food-caching species will show greater

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Spatial Memory 41

Brain Mechanisms and Spatial Memory The hippocampus is known as a primary region involved in both spatial memory and in transforming short-term to longterm memories. Several studies have shown differences in hippocampal volume, or the structure of neurons within the hippocampus, as a function of ecological differences. Birds from 3 seed-caching families had greater hippocampal volume relative to both body weight and brain volume than birds from 10 families that do not cache seeds (see Figure 3.4; Sherry, Jacobs, & Gaulin, 1992). Hampton and Shettleworth (1996) found large hippocampal volume and better spatial nonmatching-to-sample performance in food-storing black-capped chickadees (Poecile atricapillus) compared with nonfood-storing dark-eyed juncos

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100 Hippocampal Complex Volume (mm3)

spatial learning ability and greater spatial memory than closely related species that do not store food. Several experimental studies support these predictions. Bednekoff, Balda, Kamil, and Hile (1997) showed that Clark’s nutcracker and pinyon jays (Gymnorhinus cyanocephalus) had better memory for seed cache location up to 60 days than noncaching Mexican jays (Aphelecoma ultramarina) and western scrub jays (A. coerulescens), although all species showed good recall up to 250 days after learning. Bond, Kamil, and Balda (2007) found better serial reversal learning performance in highly social pinyon jays compared with Clark’s nutcracker and Western scrub jays. Western scrub jays, in contrast, are able to learn to avoid caches where stored food has spoiled with passage of time, suggesting that scrub jays have a form of declarative memory that involves not only space but also time (Clayton, Yu, & Dickinson, 2001). Therefore, each species of corvid has particular abilities that serve functions appropriate for that species. In rodents, Barkley and Jacobs (2007) found that Merriam’s kangaroo rat (Dipodomys merriami), which hoards food, was more accurate in remembering locations for food than the nonhoarding Great Basin kangaroo rat (D. microps). Monogamous versus polygynous species differ in spatial memory requirements because males of monogamous species have relatively small territories and home ranges relative to those of males from polygynous species that must travel over a wider range to locate multiple females. Gaulin and Fitzgerald (1989) hypothesized that polygynous male meadow voles (Microtus pennsylvanicus) would learn spatial mazes more rapidly than females of the same species, whereas there would be no sex differences between males and females of monogamous pine and prairie voles (M. pinetorium and M. ochrogaster). Using a series of Hebb-Williams spatial mazes, Gaulin and Fitzgerald found a sex difference in polygynous voles with males learning faster, but, as predicted, there was no sex difference in maze acquisition in monogamous voles.

Nonstoring Food storing

10

1 1

10

100

1000

Body Weight

Figure 3.4 Hippocampus volume plotted against mean body weight for 13 families of birds. Open symbols indicate families with some species that store food. Both axes are plotted logarithmically. (Adapted from Sherry, Jacobs, & Gaulin, 1992)

(Junco heyemalis). Within these species, there is also variation in caching behavior, spatial memory, and hippocampal volume. Pravosudov and Clayton (2002) compared chickadees from Alaska (where food resources are scarce) with those from Colorado and found that Alaskan birds cached more food, recovered food more efficiently, were more accurate on spatial (but not on nonspatial) learning tasks and had greater hippocampal volume with more neurons. Migratory subspecies of juncos (which presumably have greater need for spatial memory) performed better than nonmigrating juncos in a spatial memory task and had more densely packed neurons in the hippocampus. Polygynous male meadow voles, which must travel over a greater distance to find multiple females than monogamous males or females of either type, also had larger hippocampal volume than females and than either sex of monogamous pine voles (see Figure 3.5; Jacobs, Gaulin, Sherry, & Hoffman, 1990). As noted previously, polygamous males also were faster to solve multiple spatial maze problems than were polygamous females, whereas monogamous males and females were equal in solving spatial mazes (Gaulin & Fitzgerald, 1989). However, increased hippocampal volume is not universally found in species that do more seed caching. Pravosudov and de Kort (2006) found that noncaching Western scrub jays had similar hippocampal volumes to food-caching European jackdaws (Corvus monedula), and Brodin (2005) found that willow tits (Parus montanus) from Europe had twice the hippocampal volume of blackcapped chickadees but did not differ in food hoarding behavior. We cautioned earlier about accepting size alone as a measure of complexity. Precise measures of neuronal

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42

Comparative Cognition and Neuroscience 700 Male Female

Home Range Size (m2)

600 500 400 300 200 100 0 Meadow Vole

Pine Vole

0.05 Male Female

Hippocampal Volume Relative to Brain Volume

0.049 0.048 0.047 0.046 0.045 0.044 0.043 0.042 0.041 Meadow Vole

Pine Vole

Figure 3.5 Home range size and relative volume of hippocampus in polygamous meadow voles and monogamous pine voles. Male meadow voles have significantly greater home range sizes and hippocampus volume than female meadow voles and monogamous voles of either sex. (Adapted from Jacobs, Gaulin, Sherry, & Hoffman 1990)

number, density, and dendritic fields may be more accurate measures of brain differences across species. Much more than spatial memory may be involved in food caching, such as remembering about food quality and time of storage (Clayton, 1998). This suggests that not only the hippocampus but many other brain areas may be involved in cognitive processes relating to foraging and food caching. COOPERATION, RECIPROCITY, AND DONATION Cooperative behavior emerges numerous times throughout the animal kingdom and, as is the case for social learning, its presence does not fit with a simple phylogenetic or brain size explanation. Cooperative interactions between conspecifics can be divided into two categories based on the distribution of costs and benefits to the actors. In one form of cooperation, cooperation for mutual benefit, the act is beneficial to all individuals involved (West, Griffin, & Gardner, 2007). The second form of cooperation occurs when one individual incurs a cost, albeit potentially temporary, while providing a benefit to a second individual. From an evolutionary

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perspective, this costly form of cooperation is most often accounted for by kin selection (W. D. Hamilton, 1964) or reciprocal altruism (reciprocity; Trivers, 1971). The most commonly observed form of mutually beneficial cooperation is cooperative hunting (Packer & Ruttan, 1988). Data from African wild dogs (Lycaon pictus) and spotted hyenas (Crocuta crocuta) demonstrate that hunting success, prey mass, and the probability of multiple kills increased with the number of adults taking part in the hunt (Creel & Creel, 1995; Drea & Frank, 2003). Mutually beneficial cooperative hunting also occurs among African lions (Panthera leo; Schaller, 1972; Scheel & Packer, 1991). In the open plains of Etosha National Park in Namibia, lionesses have acquired preferential hunting roles within their pride, and hunts were more likely to be successful when the group size was large and huntresses occupied their preferred roles (Stander, 1992). Elaborate displays of cooperative hunting have also been observed in some wild populations of chimpanzees (Pan troglodytes; Boesch & Boesch, 1989; Gilby, Eberly, & Wrangham, 2008). Cooperative hunting is not limited to megavertebrates, however. A South American spider (Anelosimus eximius) weighing approximately 1 mg works in groups of a few to several thousand individuals to construct large basket-shaped webs that cover several cubic meters, allowing the spiders to cooperatively capture and subdue prey up to 30 times their own body size (Rypstra & Tirey, 1991). Mutually beneficial cooperation occurs outside of the hunting context as well. For example, many species exhibit mobbing, which is an antipredator behavior characterized by multiple individuals simultaneously attacking or harassing a predator. This behavior has been observed in mammals, fish, birds, and insects (Bartecki & Heymann, 1987; Curio, 1978; Dominey, 1983; Hennessy & Owings, 1978; Hoogland & Sherman, 1976; Shields, 1984). Other examples of mutually beneficial cooperative behavior include alliance formation, in which two or more individuals combine efforts against a third (reviewed in Dugatkin, 2002); mutual grooming (Dugatkin, 1997); and even cooperative mate acquisition (DuVal, 2007). Cooperative interactions that are not mutually beneficial are more difficult to explain within evolutionary theory. If one actor incurs a cost and a second actor acquires a benefit, the question arises as to why the first actor takes part in the costly act. If the individuals are related, then kin selection is the mechanism most commonly referenced to account for this behavior. It is assumed that costs incurred by the actor are offset by the indirect fitness benefits obtained through increasing the survival of relatives with shared genes (W. D. Hamilton, 1963). Well-documented examples of kin selection include cooperative breeding or helping at the nest, whereby reproductively mature individuals remain in

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Cooperation, Reciprocity, and Donation 43

their natal group to assist in the rearing of younger siblings (behavior in birds reviewed in Brown, 1987; behavior in mammals reviewed in Solomon & French, 1997). If the individuals are unrelated, then reciprocal altruism (Trivers, 1971) is often credited as the mechanism for maintaining the apparently costly behavior. Under reciprocal altruism, one individual incurs a cost and provides a benefit to a second individual at present, and at a later time the benefit is repaid by the second individual. In his seminal paper, Trivers listed the following factors that should increase the likelihood of reciprocal altruism occurring in a species: (a) long life span, (b) low dispersal rate, (c) high degree of mutual dependence, (d) extensive parental care, (e) lack of strong dominance hierarchies, and (f) tendency to aid in combat. Examples of reciprocal altruism in the wild include reciprocal sharing of blood meals by vampire bats (Desmodus rotundus; Wilkinson, 1984), predator inspection by sticklebacks (Gasterosteus aculeatus; Milinski, 1987), and reciprocal grooming in impalas (Aepyceros melampus; Hart & Hart, 1992). However, many have argued that empirical evidence for reciprocity is lacking and that, although the idea of reciprocity is theoretically appealing, its occurrence is extremely rare outside of humans (Hammerstein, 2003; Stevens & Hauser, 2004). Schuster and colleagues (Schuster, 2002; Schuster & Perelberg, 2004) have argued that coordinating behavior with a conspecific, often one involved in cooperative behavior, may be intrinsically rewarding. They posited from experiments on rats that economic analyses of the costs and benefits of cooperative interactions would not provide enough information to an outside observer to determine whether the cooperative interaction was beneficial to an actor because intrinsic rewards also play a role, as they do in other social interactions. This hypothesis remains to be tested on additional species and in varying contexts, but it provides a novel way of thinking about when cooperative behavior may emerge across taxa when economic reasoning falls short of explaining observed cooperative behavior. Investigations of cooperation historically focused on the selective pressures that could lead to its emergence (Alexander, 1974; Axelrod & Hamilton, 1981; Brown, 1983; I. M. Hamilton, 1963; W. D. Hamilton, 1964; Mayr, 1961; Trivers, 1971). However, interest in the proximate mechanisms of cooperation has grown (Brosnan & de Waal, 2002), and this may explain why cooperative behavior emerges in some taxa but not others. Recent studies have included investigations into the actors’ understanding of the partner ’s role in the cooperative act, the effects of various spatial and temporal reward distributions on cooperative performance, the impact of the relationship between actors on their ability to cooperate, the social system of the

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species presented with an opportunity to cooperate, and the cognitive skills required to cooperate. Understanding of the partner ’s role in a cooperative act varies across species. Boesch and Boesch (1989) were the first to call attention to and distinguish between cooperation in which the actors take into account their partner ’s behavior and cooperation in which individuals are mutually attracted to the same resource and act independently of one another. The authors conceptually organized the hunting behavior of wild chimpanzees into four categories of increasing complexity based on the degree to which actors integrated their own behavior with that of their partner. This categorization scheme has been subsequently used to understand partner interactions in many cooperative contexts (Chalmeau, Lardeux, Brandibas, & Gallo, 1997; Chalmeau, Visalberghi, & Gallo, 1997; Cronin et al., 2005; Mendres & de Waal, 2000). Great apes (including humans) appear to understand the role their partner plays in a cooperative act and adjust their behaviors accordingly (Brownell, Ramani, & Zerwas, 2006; Chalmeau & Gallo, 1996a, 1996b; Chalmeau, Lardeux, et al., 1997). Variables used to evaluate understanding of the partner typically include measures of attempts to solve the apparatus in the absence of the partner and glances exchanged between actors. Investigations of whether monkeys understand the role of their partner have produced mixed results both within and across species. Some studies with tufted capuchin monkeys demonstrated that capuchins did not take into account the role of their partner when confronted with a cooperative task. The subjects solved the task but did so by chance alone, as determined by the high number of uncoordinated attempts by each actor (Chalmeau, Visalberghi et al., 1997; Visalberghi, 1997; Visalberghi, Pellegrini Quarantotti, & Tranchida, 2000). Others have argued that capuchin monkeys can solve a cooperative task and take into account their partner ’s role when the task design is intuitive, that is, when the subjects are able to see how the apparatus works and presumably understand the effects their actions have on the apparatus (Mendres & de Waal, 2000). We investigated the extent to which pair-bonded cotton-top tamarins understood the role of their partner in a cooperative problem-solving task and found that tamarins adjusted their behavior based on the presence or absence of their partner (Cronin et al., 2005), providing evidence that cotton-top tamarins are capable of understanding their partner ’s role in a cooperative task. Whether an individual will attend to a partner ’s behavior will likely vary with the amount of behavioral coordination and attentiveness to social cues typically expressed by that species. Intra- and interspecific variation in cooperation may be affected by the relationship between actors, as is the case

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for social learning. As argued by van Schaik and Kappeler (2006), individuals bonded over an extended length of time likely do not evaluate the immediate costs and benefits of their behavior but rather evaluate the long-term benefits and costs exchanged throughout the relationship. Dominance asymmetries may also affect cooperative success, either in the form of coercion by dominants to solve the task or avoidance of the task by subordinates (Chalmeau, 1994; Chalmeau & Gallo, 1996b; Chalmeau, Lardeux, et al., 1997; Tebbich, Taborsky, & Winkler, 1996). Melis, Hare, and Tomasello (2006) have found that tolerance of cofeeding in chimpanzee dyads is predictive of their success on a cooperative task. In addition, the social characteristics of a species (e.g., their characteristic degree of tolerance for nearby conspecifics, behavioral coordination, and mutual dependence) may influence the likelihood of successful cooperation (Snowdon & Cronin, 2007). Trivers (1971) noted that in species with strong dominance hierarchies, the likelihood of reciprocal altruism is reduced. Because of high levels of intragroup competition in Guinea baboons (Papio papio), Japanese macaques (Macaca fuscata), and rhesus macaques (M. mulatta), these species did not coordinate efforts to move heavy food-baited stones (Burton, 1977; Fady, 1972; Petit, Desportes, & Thierry, 1992), whereas Tonkean macaques (M. tonkeana), which are characterized by less strict dominance hierarchies and greater social tolerance, were more often successful at coordinating their actions to displace the baited stone (Petit et al., 1992). Consistent with the idea that social context can predict performance on cooperative tasks are the observations that chimpanzees, characterized by a strict dominance hierarchy and low social tolerance, performed better on competitive tasks than cooperative tasks (Hare & Tomasello, 2004) and that the socially tolerant bonobos outperformed chimpanzees on cooperative tasks (Hare, Melis, Woods, Hastings, & Wrangham, 2007). Attempts to investigate the evolutionary origins of the human tendency to act in the best interest of others (i.e., to act prosocially) have led to interesting results. Initial investigations into the evolutionary origins of prosociality indicated that humans’ closest living relatives, chimpanzees, overwhelmingly refused to donate food to a social partner, even when the potential donor could do so with very little effort and could not obtain the food for itself (Jensen et al., 2006; Silk et al., 2005; Vonk et al., 2008). One interpretation that followed from these findings was that the human tendency to act prosocially must have emerged recently in our evolutionary history because the trait was not present in the common ancestor of chimpanzees and humans. However, as we cautioned earlier in this chapter, convergent as well as divergent evolution should be considered. Some evidence has emerged to indicate that prosocial tendencies may be evident elsewhere in the primate order,

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more specifically in a species that exhibits a social system similar to that of humans: the cooperatively breeding common marmoset. Burkart and colleagues (2007) have shown that common marmosets donated food to conspecifics in a task nearly identical to that used with chimpanzees. The authors interpreted their findings as evidence that a cooperatively breeding social system, one shared by humans and cooperatively breeding marmosets and tamarins, is key to the emergence of prosocial tendencies. Additional research is needed to determine whether the social systems of the species are the most important factor in these findings, as marmosets and chimpanzees differ in many other ways as well. However, these initial results highlight the importance of considering social factors in addition to phylogeny. Discussions of cognitive requirements for cooperation are most often made in relation to the ability to understand the role of the partner, as discussed previously, or the ability to engage in reciprocal altruism. Some have argued that the paucity of empirical evidence for reciprocal altruism is due to its steep cognitive demands (Hammerstein, 2003; Stevens & Hauser, 2004). Cognitive skills hypothesized to be necessary for reciprocal altruism include numerical quantification, time estimation, delay of gratification, detection and punishment of cheaters, analysis and recall of reputation, and inhibitory control (Stevens & Hauser, 2004). To date, these cognitive requirements have been discussed only on theoretical grounds and have not been examined empirically. Others assert that reciprocal altruism does not require complex cognition. Two forms of reciprocal altruism that do not require advanced cognition have been put forth. The first is generalized reciprocity, in which individuals decide whether to cooperate based on prior experiences, irrespective of the identity of the current partner (Pfeiffer, Rutte, Killingback, Taborsky, & Bonhoeffer, 2005). Generalized reciprocity does not require analysis and recall of individual reputations. This concept has been well exemplified in recent studies with Norway rats (Rattus norvegicus; Rutte & Taborsky, 2007, 2008). The second form of reciprocal altruism is symmetry-based or attitudinal reciprocity. Symmetry-based reciprocity occurs when closely bonded individuals help one another without stipulating returns. Because of the symmetrical and long-lasting characteristics of the relationship, the benefits generally balance out over a lifetime without the need for any purposeful “scorekeeping” (de Waal & Luttrell, 1988). We have found that unrelated, pair-bonded cottontop tamarins not only cooperate for mutual rewards but also cooperate when rewards are reciprocally distributed between actors and when rewards are repeatedly received by a single individual in the dyad. The tamarins were sensitive to the reward scenarios, cooperating most consistently for mutual rewards and least consistently when the rewards repeatedly went to the same individual, but cooperation

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Cooperation, Reciprocity, and Donation 45

persisted nonetheless (Cronin et al., 2005; Cronin & Snowdon, 2008). The tamarins’ cooperative performance in this study may have been due to attitudinal reciprocity as described by de Waal and Luttrell (1988); that is, the tamarins were cooperative with their long-term mates without keeping track of the exact costs and benefits incurred. Further studies of cooperation in tamarin dyads that differ in their social relationships are needed to further test this interpretation. As with the other phenomena discussed here, cooperative behavior emerges throughout the animal kingdom with little regard for phylogeny. Investigations into the social organizations of species and the social relationships within species have proved the most fruitful for explaining and predicting species and individual differences. Unfortunately, little is known about the neural processes that underlie cooperative interactions in nonhuman animals. However, scientists can gain some insight and begin to make predictions about neural involvement from recent studies performed on the most cooperative species: humans. Cooperation and the Brain Studies emerging from the nascent field of neuroeconomics have begun to elucidate some of the regions of the brain involved in human cooperative behavior (see also McCabe, this volume). These studies have primarily used fMRI data collected while humans engage in cooperative games. However, a single cooperative interaction necessarily includes multiple sequential stages, including the stage in which an individual decides whether to cooperate and the stage in which he or she experiences the outcome of the

cooperative interaction. Each stage is likely to recruit different brain regions, and fMRI investigations have aimed to isolate different stages of cooperative interactions. A recent fMRI study utilized the prisoner ’s dilemma to evaluate neural activation during the initial stage of cooperation, when participants decided whether to cooperate with a confederate. The prisoner ’s dilemma is a widely used game in which two individuals are given the simultaneous choice either to cooperate with the other individual or to defect. The payoff to the individual depends on his or her own choice and the choice of the partner, which is revealed once both individuals have made their choices. Regardless of the other player ’s choice, the choice to defect yields a higher payoff than the choice to cooperate. But if both players defect, both do worse than if both had cooperated (see Figure 3.6A). Rilling et al. (2007) contrasted activations when the participant decided to cooperate with those when the participant decided to defect. Results indicated that the choice to defect was associated with stronger activation in the rostral anterior cingulate cortex (rACC) and the dorsolateral prefrontal cortex (DLPFC). The activation of the rACC and the DLPFC is interesting because the DLPFC is often associated with executive control and goal maintenance and the rACC with the processing of aversive experiences and the detection of cognitive conflict. This pattern of activation seems to indicate that defection is not a simple, favorable choice by participants. The choice to cooperate was correlated with activation in the orbitofrontal cortex (OFC; see Figure 8.12 in Chapter 8). The OFC has been implicated in the processing of rewarding and punishing factors and in emotional decision making

Cooperate Defect

Player A

Player B Cooperate

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R⫽3

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Figure 3.6 A. Prisoner’s dilemma matrix. Payoff awarded to Player A is the top value within each cell; payoff awarded to Player B is the bottom value within each cell. Different payoff values can be used, however this rule must be followed: T > R > P > S. B. Trust game diagram. Each node represents a point at which a player must make a decision. At the first node, Player 1 either

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P1: $0 P2: $405 P2 decision P1 decision

P1: $180 P2: $225 P1: $45 P2: $45

(B)

decides to split the benefits equally, or to allow Player 2 a move. If Player 1 decides to allow Player 2 a decision, the pot increases and both the potential gain and loss increase for Player 1. Player 2 then decides between an option that allows both players rewards but provides him/herself with a greater reward, or an option that provides him/herself with all the rewards. Different payoff values can be used. (Diagram modified from McCabe et al., 2001).

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(Bechara, Damasio, & Damasio, 2000; Ichihara-Takeda & Funahashi, 2006). Furthermore, Rilling et al. (2007) found a negative correlation between OFC and DLPFC activation that they interpreted as interplay between emotional and cognitive control leading up to the execution of cooperation or defection. Additional insight into the brain regions recruited during the decision to cooperate with another individual comes from fMRI investigations of humans engaged in a trust game that required individuals to decide whether to invest money in a second individual who could increase or decrease the payoff to the first individual (see Figure 3.6B). The investigation revealed that those participants who consistently attempted cooperation exhibited more specific prefrontal cortex (PFC) activation when playing the game with a human as compared to a computer employing a fixed, known probabilistic strategy (McCabe, Houser, Ryan, Smith, & Trouard, 2001). However, differential PFC activation between social and nonsocial versions of the game was not found for individuals who rarely cooperated. Using another version of the trust game, King-Casas et al. (2005) reported that the magnitude of neuronal activity in the dorsal striatum correlated with the intention to trust the other player, and the peak of the response shifted earlier in the decision process as player reputations developed in this reciprocal game. Rilling et al. (2002) found that different patterns of neural activation determined by whether the playing partner was identified as a computer or a human. Greater activation was observed in the anteroventral striatum, the rACC, and the OFC during social cooperation than during cooperation with a computer. The OFC and ventral striatum are recruited while one is anticipating an economic reward (e.g., Padoa-Schioppa & Assad, 2006; Roesch & Olson, 2004; Schultz, Dayan, & Montague, 1997), however Rilling et al. (2002) controlled for monetary gain in the social versus nonsocial contrast. Some investigations have specifically examined the differences in neural activation following a potentially cooperative interaction. Using the prisoner ’s dilemma and fMRI, Rilling, Sanfey, Aronson, Nystrom, and Cohen (2004) found that participants displayed increased neural activity in the ventral striatum in response to reciprocated interactions and decreased neural activity in the ventromedial PFC in response to unreciprocated interactions. The authors interpreted these results to indicate that the mesolimbic dopamine system processes errors in predictions about whether a social partner will act reciprocally. In fact, many of the regions recruited during cooperative interactions are the same regions classically involved in processing reward. The neural circuitry often involved in reward includes the caudate and particularly the ventral striatum, amygdala, and the medial and orbital PFCs

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(reviewed in Cardinal, Parkinson, Hall, & Everitt, 2002; see also Volume 2, Chapter 40). It is interesting that even when the effects of tangible rewards are controlled, social cooperative interactions continue to engage some of the reward circuitry, suggesting that cooperating with another individual may be intrinsically rewarding. Similarly, Harbaugh, Mayr, and Burghart (2007) found that voluntary monetary donations also increase the neural activity of reward regions. It seems that data obtained from neuroscientific investigations as well as behavioral observations (i.e., Schuster, 2002; see above) converge to support the hypothesis that acting cooperatively with conspecifics is rewarding, and because these regions are rich in dopamine, it is likely that dopamine is involved in these cooperative interactions. The neuropeptide oxytocin may also be involved in cooperative social interactions. Oxytocin is a nonapeptide produced in the supraoptic and paraventricular nuclei of the hypothalamus. Oxytocin is released into the periphery from magnocellular neurons of the supraoptic nuclei, which project to the posterior pituitary. In addition, oxytocin is released centrally from parvocellular neurons of the paraventricular nuclei (Uvnas-Moberg, 1998). Oxytocin has been identified repeatedly in the coordination of positive social interactions in a wide range of species (reviewed in Uvnas-Moberg, 1998). In addition to having a well-known role in the onset of uterine contractions, milk letdown, and mother–infant bond formation (Gimpl & Fahrenholz, 2001; Levy, Kendrick, Goode, Guevara-Guzman, & Keverne, 1995; Nelson & Panksepp, 1998), oxytocin facilitates the formation and maintenance of important relationships outside the mother–infant context. Oxytocin is integral to the formation of pair bonds in monogamous voles (Carter, 1998; Carter, DeVries, & Getz, 1995; Young, 1999; see Box 3.1 and below). Oxytocin correlates with the expression of trust in humans (Zak, Kurzban, & Matzner, 2005) and increases the amount of generosity (donation) in humans (Zak, Stanton, & Ahmadi, 2007). Furthermore, treatment with oxytocin increases one’s willingness to accept risk in interpersonal interactions (Kosfeld, Heinrichs, Zak, Fischbacher, & Fehr, 2005). Because of the involvement of oxytocin in positive social interactions, some have begun to speculate that oxytocin may play a role in prosocial behaviors such as cooperation, reciprocity, and donation (i.e., Rutte & Taborsky, 2007). The term cooperation is used in different ways by economists, psychologists, and ethologists, and, as we have shown, investigations of cooperative behavior have spanned studies of individuals acting altruistically, engaging in reciprocity, taking risks for kin, providing benefits to mates, and engaging in economic games. The common link in all of these forms of cooperation is that they provide a benefit of some sort for another individual, and the most

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Pair Bonds

BOX 3.1 OXYTOCIN AND VASOPRESSIN Oxytocin (OT) and arginine vasopressin (AVP, also known as antidiuretic hormone) are closely related in structure but have different physiological roles. Both are made of nine amino acids (peptides) folded into a ring, and the two hormones differ in only two amino acids. These hormones are released into the blood from the posterior pituitary gland at the base of the brain, but they are also released from neural tissue in the hypothalamus and are active in several areas of the brain. Until 20 years ago, the best known role for these hormones was in the regulation of peripheral physiological processes. OT is involved in uterine contractions at birth (and indeed a synthetic version of OT is frequently used to induce labor) and in the milk letdown reflex in nursing. AVP is involved in the contraction and relaxation of smooth muscle (e.g., blood vessels) and regulates transport of water and sodium across cell walls, especially in the kidney. In recent years, other behavioral functions of these hormones have become widely known. OT is involved in the formation of pair bonds in monogamous female rodents (Carter, 1998), and AVP is involved in pair bond formation in monogamous male rodents. AVP is also involved in the aggressive behavior shown by territorial monogamous male rodents (Marler et al., 2003). AVP also plays a role in paternal behavior in monogamous rodents (Marler et al., 2003), and OT plays an important role in regulating female sexual behavior and maternal behavior. Both hormones are anxiolytic (i.e., they reduce anxiety), and release of OT during deep massage, stroking, and sexual intercourse has been hypothesized to play an important role in social bonding in humans. Indeed Gonzaga, Turner, Keltner, Campos, and Altemus (2006) found that OT levels increased during displays of romantic love, but not sexual arousal, in college women, suggesting a role of OT in human pair bonding and social reinforcement. information known so far about the brain has come from humans engaged in neuroeconomic games. However, these patterns of neuronal activation provide just a starting point for investigations of other forms of cooperative behavior. Much more information is needed to determine whether species that typically cooperate with one another, such as cooperatively hunting hyenas, have regional specializations or patterns of neuronal activation that differ from those of species that do not typically cooperate with one another. If a comparative neuroscience approach were to

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be applied to cooperative behavior, one might speculate that in species in which it is beneficial to act cooperatively with conspecifics, cooperative behavior will have become associated with the brain’s reward system throughout the evolutionary history. But hypotheses of this sort remain to be tested.

PAIR BONDS In many mammalian species, close affiliative relationships between males and females outside of mating are rare. The closest relationships are found in species with biparental or cooperative care such as prairie voles, titi monkeys, tamarins, and marmosets. In mammals, females incur the costs of gestation and lactation, and males can never be certain of paternity of infants. Under such conditions, it has been assumed that males will be more successful attempting to fertilize as many females as possible rather than investing in parental care. However, in species with biparental or cooperative care, fathers often play a critical role in infant survival, and thus the reproductive success of both parents depends on joint infant care. Biparental, socially monogamous species are expected to have closer, more affiliative social relationships than polygamous species, and it is, in fact, under these conditions that close relationships between mates are found. The formation of the relationship has been well studied. Williams, Catania, and Carter (1992) found that prairie vole females cohabiting with a male for 24 hours with or without mating developed a strong bond. Cohabitation with mating for less than 24 hours, but not cohabitation alone, also led to pair formation. Savage, Ziegler, and Snowdon (1988) found that newly paired cotton-top tamarins spent more time in contact, in grooming, and engaged in sexual activity than did established pairs, with males initiating affiliation more often than females. Widowski, Porter, Ziegler, and Snowdon (1992) found that exposure to a novel cottontop tamarin male with no direct physical contact led to ovulation in reproductively suppressed female cotton-top tamarins. Schaffner, Shepherd, Santos, and French (1995) also found that male black tufted-ear marmosets (Callithrix kuhli) initiated more proximity behavior in the first 40 days after pairing and that sexual behavior decreased over time. Silva and Sousa (1997) studied variation in pair formation in sexually naive common marmosets and found that those that became pregnant within the first 10 weeks had more affiliative behavior, especially grooming, and greater coordination of behavior than those pairs that did not conceive immediately. This is the only study on the reproductive consequences of within-species variation in pair formation.

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Determination that a pair bond exists requires some experimental documentation of attachment: that an animal is distressed upon separation from its mate and displays greater affiliation than baseline upon reunion, that an individual will behave aggressively toward intruders, or that an individual will preferentially seek contact with its mate when given a choice between conspecifics. Mendoza and Mason (1986b) found greater disturbance, more aggression to intruders, and higher cortisol levels in the monogamous titi monkey (Callicebus moloch) compared to the polygynous squirrel monkey (Saimiri sciureus). When separated and offered a choice between mate and infant, titi monkeys chose their mates preferentially. Infants that were separated from both parents chose fathers over mothers (Mendoza & Mason, 1986a). Subsequently, when paired adult titi monkeys were separated for 30 min or 5 days and tested with either their mate or an opposite-sex stranger, male and female titi monkeys showed equal affiliation upon reunion with the mate. In contrast, they showed high levels of arousal when placed with an opposite-sex stranger. These results suggest strong and lasting relationships (FernandezDuque, Mason, & Mendoza, 1997). Cotton-top tamarins also exhibit distress when separated from mates for short time periods, with increased rates of long calling (which is used for within-group cohesion and by lost animals) during the period of separation and increased affiliative and sexual behavior when reunited after separation (Snowdon & Ziegler, 2007). Males are more vocal and appear more disturbed by separation than females. They also display levels of aggression toward intruders of both sexes, with males displaying equal amounts whether the mate is present or absent and females displaying higher levels only in the presence of the mate. Males also groom females significantly more often than females groom males (Snowdon & Ziegler, 2007). All of these results suggest that males are more responsible for maintaining relationships than females. Partners in pair-bonded species can also buffer against the effects of stress. T. E. Smith and French (1997) tested male and female tufted-ear marmosets in novel environments and found that levels of cortisol were lower when pairs were together in a novel environment than when they were alone. Rukstalis and French (2005) played back calls of the mate to isolated marmosets and found that the calls alone reduced cortisol levels as well as stress-related behavior. In summary, lasting adult heterosexual affiliative relationships are found in a few species that are socially monogamous or cooperatively breed. Several studies have looked at behavioral changes during pair formation and have evaluated attachment through studies involving challenges by unfamiliar animals of same and opposite sex and studies involving separation and reunion. The individual

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recognition of a specific individual as a mate and the differential response to the mate than to other potential mates are also key components of a pair bond. What are the neural and hormonal mechanisms that maintain these relationships? Role of Oxytocin and Vasopressin Oxytocin is a neuropeptide that has been implicated in affiliative relationships. In the past 15 years, extensive research, primarily on monogamous voles, has indicated the importance of oxytocin in formation of affiliative relationships, especially in females (for reviews, see the following: mother–infant relationships, Nelson & Panksepp, 1998; and heterosexual adult relationships, Carter, 1998; Carter et al., 1995; Insel, 2003). Oxytocin, but not the closely related nonapeptide vasopressin, appears critical for female prairie voles in relationship formation (Insel & Hulihan, 1995). However, vasopressin levels increase in male prairie voles after pair formation, suggesting a sex difference in which hormones are involved in a pair bond for these rodents (Winslow, Hastings, Carter, Harbaugh, & Insel, 1993). In non-pair-bonding species of voles, oxytocin is not effective for inducing a pair bond and has a different distribution of receptors in the brain (reviewed by Young, 1999). Nonetheless, oxytocin infused chronically into male rats (a polygamous species) led to increased social interactions (Witt, Winslow, & Insel, 1992). Pedersen and Boccia (2002) found that oxytocin was critical for initiation and maintenance of sexual behavior in female rats. Oxytocin injected into the medial preoptic area of male rats also facilitated social recognition, whereas vasopressin did not (Popik & van Ree, 1991). However, Dantzer, Koob, Bluthé, and Le Moal (1988) reported that vasopressin in the septal region facilitated social memory in rats. Oxytocin knockout mice fail to recognize familiar conspecifics, and oxytocin administered to the medial amygdala prior to initial exposure facilitated social recognition (Bielsky & Young, 2004; Ferguson, Aldag, Insel, & Young, 2001). Rosenblum et al. (2002) compared highly affiliative bonnet macaques (Macaca radiata) with socially distant pigtail macaques (M. nemestrina) and found that the more affiliative bonnet macaques had higher cerebrospinal fluid oxytocin levels and lower corticotrophin-releasing hormone levels than the pigtail macaques. Winslow, Noble, Lyons, Sterk, and Insel (2003) reported that mother-reared male rhesus macaques had elevated cerebrospinal fluid oxytocin levels compared with nursery-reared macaques, and there was a significant correlation between oxytocin levels and affiliative behavior. In contrast, vasopressin levels did not differ with rearing condition but correlated positively with fearful behavior in rhesus macaques.

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Pair Bonds

Interest in oxytocin in humans has increased recently because of the role of oxytocin in both mother–infant attachment and adult affiliative behavior. Wismer Fries, Ziegler, Kurian, Jacoris, and Pollak (2005) found that children had elevated oxytocin levels when performing a task that included contact by the mother but not contact by an unfamiliar female. In contrast, children adopted from Eastern European orphanages several years previously showed low oxytocin responses to both adopted mothers and an unfamiliar female, indicating that early experience affects the oxytocin system. Oxytocin administered intranasally increases trust among humans (Kosfeld et al., 2005; Zak et al., 2005). Infusions of oxytocin into people with autism and with Asperger syndrome reduced repetitive behavior (Hollander et al., 2003) and increased retention of social cognition (Hollander et al., 2006). These studies support the role of oxytocin in producing calm behavior and facilitating social memories in humans. Turner, Altemus, Enos, Cooper, and McGuinness (1999) found that women asked to recount a negative experience of loss or abandonment showed decreased levels of serum oxytocin correlated with the degree of negative emotion expressed. Using data from Turner et al. (1999), Gonzaga et al. (2006) reported that women engaging in affiliation signals with a romantic partner had a significant positive correlation between the amount of positive signaling and serum oxytocin. Grewen, Girdler, Amico, and Light (2005) reported higher plasma oxytocin in both men and women in relationships with strong partner support. Thus, changes in oxytocin appear to track affiliative and positive emotional states in both sexes and across many species. In humans, peripheral measures of oxytocin levels appear to correlate with differences in affiliation, and peripheral infusions of oxytocin can increase affiliative behavior and social memory. Most comparative research on pair bonds has involved evaluating species differences; however, within-species individual differences in relationship quality and hormonal correlates are found in nonhuman primates as well. We have observed a fivefold variation in the amount of affiliative behavior between pairs of cotton-top tamarins and have also observed a fivefold variation in urinary oxytocin levels in both sexes that correlates with the amount of affiliative behavior expressed. Variation in sexual behavior explains most of the variance in male oxytocin levels, whereas variation in contact and grooming explains most of the variance in female oxytocin levels (Snowdon et al., in preparation). In summary, there is clear evidence for the importance of oxytocin for pair bonding in females of monogamous vole species and for the importance of vasopressin in male voles. Oxytocin can supersede the effects of mating

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in pair bond formation. Oxytocin affects sexual behavior in female rodents and macaques and also appears to be important for social recognition in male rodents. In rodents, tamarins, and human primates, peripheral measures of oxytocin appear to be correlated with affiliation, attachment, trust, and positive emotions, and peripheral administration of oxytocin can alter affiliative behavior. Brain Mechanisms Comparative work between monogamous and polygynous voles has shown that monogamous female voles but not polygamous voles have increased oxytocin receptor density in the nucleus accumbens and caudate putamen and greater vasopressin receptor density in the ventral pallidum (Table 3.2; Young & Wang, 2004). Little is known about oxytocin distribution in monogamous, pair-bonded primate species, but Wang, Moody, Newman, and Insel (1997) found that there were no sex differences in the distribution of immunoreactive oxytocin neurons and fibers in the common marmoset. Oxytocin immunoreactive neurons were found in the paraventricular and supraoptic nuclei of the hypothalamus, the bed nucleus of the stria teminalis, and the medial amygdala. Vasopressin cells were found in the paraventricular, supraoptic, and suprachiasmatic nuclei and in the lateral area of the hypothalamus. The only sex difference was that males had a greater density of vasopressin reactive cells in the bed nucleus of the stria terminalis than females. In a biparental species, in which both parents are essential for infant care and both sexes contribute to the formation and maintenance of a pair bond, it is reasonable to find no sex differences in distribution of oxytocin-reactive neurons in the brain. In further work on common marmosets, Wang, Toloczko, et al. (1997) found vasopressin receptor binding in the nucleus accumbens, diagonal band, lateral septum, bed nucleus of the stria terminalis, amygdala, and anterodorsal and ventromedial hypothalamus, in addition to areas with immunoreactivity. Marmosets differed from voles in important ways: No vasopressin-producing cells were found in the amygdala. There was no plexus of immunoreactive fibers in the lateral septum. There was much greater visualization of vasopressin immunoreactive cells in the bed nucleus of the stria terminalis. Taken together, the results on marmosets suggest relatively little sexual dimorphism, an extensive overlap of oxytocin and vasopressin immunoreactive cells, and a different distribution than that found in rodents. So far, pair formation and maintenance has been treated as a unitary, species-specific trait, and yet there may be considerable individual variation within species, as we have noted. Recently, Hammock and Young (2005) described variation in social engagement and affiliative

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licking and grooming suggests the possibility for an interaction between early experience and gene expression.

TABLE 3.2 Oxytocin and arginine vasopressin receptor distribution as a function of mating system in voles and monogamous common marmosets. Brain Area

Monogamous vs. Polygamous Volea,b

Monogamous Primatec

SUMMARY

Oxytocin Nucleus accumbens

Monogamous

NA

Prelimbic cortex

Monogamous

NA

Bed nucleus of stria terminalis

Monogamous

NA

Midline thalamus

Monogamous

NA

Ventral reunions

Monogamous

NA

Lateral amygdala

Monogamous

NA

Central amygdala

Both

NA

Lateral septum

Polygamous

NA

Ventromedial hypothalamus

Polygamous

NA

Diagonal band

Monogamous

Yes

Laterodorsal thalamus

Monogamous

No

Central amygdala

Monogamous

Yes

Basolateral amygdala

Monogamous

Yes

Bed nucleus stria terminalis

Monogamous

Yes

Nucleus accumbens

Both

Yes

Accessory olfactory bulb

Both

No

Superior colliculus

Both

No

Lateral septum

Polygamous

Yes

Periventricular hypothalamus

NA

Yes

Ventromedial hypothalamus

NA

Yes

Suprachiasmatic hypothalamus NA

Yes

Vasopressin

a Oxytocin data from “Oxytocin Receptor Distribution Reflects Social Organization in Monogamous and Polygamous Voles,” by T. R. Insel and L. E. Shapiro, 1992, Proceedings of the National Academy of Sciences, USA, 89, pp. 5981–5985. b Vasopressin data from Insel, Wang & Ferris (1994). c Marmoset vasopressin data from “Vasopressin in the Forebrain of Common Marmosets (Callithrix jacchus): Studies with in situ Hybridization, Immunocytochemistry and Receptor Autoradiography,” by Z. Wang, D. Toloczko, L. J. Young, K. Moody, J. D. Newman & T. R. Insel, 1997, Brain Research, 768, pp. 147–156. Note. NA ⫽ not reported or known.

behavior in monogamous male prairie voles and correlated this variation with a polymorphism in the promoter region of the vasopressin 1a receptor. Thus, variation in the promotor region of the receptor genes, and consequently the expression of vasopressin 1a receptors, may account for behavioral variation within a species. Similar studies remain to be done with respect to oxytocin receptors. The variation in receptor sensitivity to estrogen described by Champagne et al. (2001) as a function of early maternal

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We have demonstrated that both the proximate and ultimate mechanisms contributing to inter- and intraspecific variation across a wide range of cognitive phenomena can be best understood through a comparative lens that integrates not only phylogeny and brain size but also social and ecological factors. In our discussions of social learning, cooperation, spatial memory, and pair bonding, we have emphasized strong theoretical predictions about socioecological influences on the expression of these behaviors within and across taxa, and, when data have been available, we have highlighted underlying neuronal and hormonal differences that may relate to the observed variation. One major implication of this comparative perspective is that no single species will likely serve as a suitable model for understanding the myriad of interesting human cognitive abilities. For example, there is strong momentum to use various species of macaques as model species for humans in neuroscience, but we should keep in mind the potential implications of the differing social and ecological pressures that macaque and human lineages have faced since the divergence of our ancestry nearly 40 million years ago. Awareness of the cognitive domains in which macaques and humans would be expected from a socioecological perspective to differ may sharpen interpretations of findings from macaque studies and generate interesting hypotheses about interspecific variation if we were to look beyond humans and macaques. To better understand the selective pressures that have contributed to the wide range of expression of cognitive abilities across taxa, we must embrace data collected on primates and nonprimates alike and rely on multifaceted hypotheses that incorporate social and ecological pressures as well as phylogeny and brain size. Interdisciplinary collaboration is integral to accomplishing this feat but should yield a more comprehensive and generalizable understanding of both human and nonhuman cognition.

REFERENCES Aiello, L., & Dean, C. (1990). An introduction to human evolutionary anatomy. London: Academic Press. Aisner, R., & Terkel, J. (1992). Ontogeny of pine cone opening behaviour in the black rat Rattus rattus. Animal Behaviour, 44, 327–336. Alexander, R. D. (1974). The evolution of social behavior. Annual Review of Ecology and Systematics, 5, 325–383.

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References 51 Alger, S. J., & Riters, L. V. (2006). Lesions to the medial preoptic nucleus differentially affect singing and nest box-directed behaviors within and outside of the breeding season in European starlings (Sturnus vulgaris). Behavioral Neuroscience, 120, 1326–1336. Axelrod, R., & Hamilton, W. D. (1981, March 27). The evolution of cooperation. Science, 211, 1390–1396.

Call, J., & Tomasello, M. (1996). The effects of humans on the cognitive development of apes. In A. E. Russon, K. S. Bard, & S. T. Parker (Eds.), Reaching into thought (pp. 371–403). Cambridge, England: Cambridge University Press.

Barkley, C. L., & Jacobs, L. F. (2007). Sex and species differences in spatial memory in food-storing kangaroo rats. Animal Behaviour, 73, 321–329.

Cambefort, J. P. (1981). A comparative study of culturally transmitted patterns of feeding habits in the chacma baboon (Papio ursinus) and the vervet monkey (Cercopithecus aethiops). Folia Primatologica, 36, 243–263.

Barrett, L., Henzi, P., & Rendall, D. (2007). Social brains, simple minds: Does social complexity really require cognitive complexity? Philosophical Transactions of the Royal Society. Series B., 362, 561–575.

Campbell, M. W., & Snowdon, C. T. (2007). Vocal response of captivereared Saguinus oedipus during mobbing. International Journal of Primatology, 28, 257–270.

Bartecki, U., & Heymann, E. W. (1987). Field observation of snakemobbing in a group of saddleback tamarins Saguinus fuscicollis nigrifrons. Folia Primatologica, 48, 199–202.

Campbell, M. W., & Snowdon, C. T. (in press). Can captive-reared cottontop tamarins. Saguinus oedipus, learn to mob a predator? (in press). International Journal of Primatology.

Bechara, A., Damasio, H., & Damasio, A. R. (2000). Emotion, decision making and the orbitofrontal cortex. Cerebral Cortex, 10, 295–307.

Cardinal, R. N., Parkinson, J. A., Hall, J., & Everitt, B. J. (2002). Emotion and motivation: The role of the amygdala, ventral striatum, and prefrontal cortex. Neuroscience and Biobehavioral Reviews, 26, 321–352.

Balda, R. P., & Kamil, A. C. (1992). Long-term spatial memory in Clark’s nutcracker, Nucifraga columbiana. Animal Behaviour, 44, 761–769.

Bednekoff, P. A., Balda, R. P., Kamil, A. C., & Hile, A. G. (1997). Longterm spatial memory in four seed-caching corvid species. Animal Behaviour, 53, 335–341. Bester-Meredith, J. K., & Marler, C. A. (2001). Vasopressin and aggression in cross-fostered California mice (Peromyscus californicus) and white-footed mice (Peromyscus leucopus). Hormones and Behavior, 40, 51–64. Bielsky, I. F., & Young, L. J. (2004). Oxytocin, vasopressin and social recognition in mammals. Peptides, 25, 1565–1574. Boesch, C., & Boesch, H. (1989). Hunting behavior of wild chimpanzees in the Tai national park. American Journal of Physical Anthropology, 78, 547–573. Bond, A. B., Kamil, A. C., & Balda, R. P. (2007). Serial reversal learning and the evolution of behavioral flexibility in three species of North American corvids (Gymnorhinus cyanocephalus, Nucifraga columbiana, Aphelocoma californica). Journal of Comparative Psychology, 121, 372–379. Box, H. O. (1984). Primate behavior and socioecology. London: Chapman & Hall. Boyd, R., & Richerson, P. J. (1988). An evolutionary model of social learning: The effects of spatial and temporal variation. In T. R. Zentall & B. G. Galef (Eds.), Social learning: Psychological and biological perspectives (pp. 29–48). Hillsdale, NJ: Erlbaum. Brodin, A. (2005). Hippocampal volume does not correlate with foodhoarding rates in the black-capped chickadee (Poecile atricapillus) and willow tit (Parus montanus). Auk, 122, 819–828.

c03.indd Sec6:51

Byrne, R. W. (1999). Imitation without intentionality: Using string parsing to copy the organization of behaviour. Animal Cognition, 2, 63–72.

Carter, C. S. (1998). Neuroendocrine perspectives on social attachment and love. Psychoneuroendocrinology, 23, 779–818. Carter, C. S., DeVries, A. C., & Getz, L. L. (1995). Physiological substrates of mammalian monogamy: The prairie vole model. Neuroscience and Biobehavioral Reviews, 19, 303–314. Chalmeau, R. (1994). Do chimpanzees cooperate in a learning task? Primates, 35, 385–392. Chalmeau, R., & Gallo, A. (1996a). Cooperation in primates: Critical analysis of behavioural criteria. Behavioral Processes, 35, 101–111. Chalmeau, R., & Gallo, A. (1996b). What chimpanzees (Pan troglodytes) learn in a cooperative task. Primates, 37, 39–47. Chalmeau, R., Lardeux, K., Brandibas, P., & Gallo, A. (1997). Cooperative problem solving by orangutans (Pongo pygmaeus). International Journal of Primatology, 18, 23–32. Chalmeau, R., Visalberghi, E., & Gallo, A. (1997). Capuchin monkeys, Cebus apella, fail to understand a cooperative task. Animal Behaviour, 54, 1215–1225. Champagne, F., Diorio, J., Sharma, S., & Meaney, M. J. (2001). Naturally occurring variations in maternal behavior in the rat are associated with differences in estrogen-inducible central oxytocin receptors. Proceedings of the National Academy of Sciences, USA, 98, 12736–12741. Clayton, N. S. (1998). Memory and the hippocampus in food-storing birds: A comparative approach. Neuropharmacology, 37, 441–452.

Brosnan, S. F., & de Waal, F. B. M. (2002). A proximate perspective on reciprocal altruism. Human Nature, 13, 129–152.

Clayton, N. S., Yu, K. S., & Dickinson, A. (2001). Scrub jays (Aphelocoma coerulescens) form integrated memories of the multiple features of caching episodes. Journal of Experimental Psychology: Animal Behavior Processes, 27, 17–29.

Brown, J. L. (1983). Cooperation: A biologist’s dilemma. Advances in the Study of Behavior, 13, 1–37.

Coussi-Korbel, S., & Fragaszy, D. M. (1995). On the relation between social dynamics and social learning. Animal Behaviour, 50, 1441–1453.

Brown, J. L. (1987). Helping and communal breeding in birds: Ecology and evolution. Princeton, NJ: Princeton University Press.

Creel, S., & Creel, N. M. (1995). Communal hunting and pack size in African wild dogs, Lycaon pictus. Animal Behaviour, 50, 1325–1339.

Brownell, C. A., Ramani, G. B., & Zerwas, S. (2006). Becoming a social partner with peers: Cooperation and social understanding in one- and two-year-olds. Child Development, 77, 803–821.

Cronin, K. A., Kurian, A. V., & Snowdon, C. T. (2005). Cooperative problem solving in a cooperatively breeding primate (Saguinus oedipus). Animal Behaviour, 69, 133–142.

Burkart, J. M., Fehr, E., Efferson, C., & van Schaik, C. P. (2007). Otherregarding preferences in a non-human primate: Common marmosets provision food altruistically. Proceedings of the National Academy of Sciences, USA, 104, 19762–19766.

Cronin, K. A., & Snowdon, C. T. (2008). The effects of unequal reward distributions on cooperative performance by cottontop tamarins, Saguinus oedipus. Animal Behaviour, 75, 245–257.

Burton, J. J. (1977). Absence of spontaneous cooperative behavior in a troop of Macaca fuscata confronted with baited stones. Primates, 18, 359–366.

Curio, E. (1978). The adaptive significance of avian mobbing: I. Teleonomic hypotheses and predictions. Zeitschrift fur Tierpsychologie, 48, 175–183.

Byrne, R. W. (1994). The evolution of intelligence. In P. J. B. Slater & T. R. Halliday (Eds.), Behavior and evolution (pp. 223–265). Cambridge, England: Cambridge University Press.

Dantzer, R., Koob, G. F., Bluthé, R.-M., & Le Moal, M. (1988). Septal vasopressin modulates social memory in rats. Brain Research, 457, 143–147.

8/17/09 1:58:35 PM

52

Comparative Cognition and Neuroscience

de Waal, F. B. M., & Luttrell, L. M. (1988). Mechanisms of social reciprocity in three primate species: Symmetrical relationship characteristics or cognition? Ethology and Sociobiology, 9, 101–118.

Gonzaga, G. C., Turner, R. A., Keltner, D., Campos, B., & Altemus, M. (2006). Romantic love and sexual desire in close relationships. Emotion, 6, 163–179.

di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., & Rizzolatti, G. (1992). Understanding motor events: A neurophysiological study. Experimental Brain Research, 91, 176–180.

Goodall, J. (1986). The chimpanzees of Gombe: Patterns of behavior. Cambridge, MA: Belknap Press.

Dolman, C. S., Templeton, J., & Lefebvre, L. (1996). Mode of foraging competition is related to tutor preference, Zenaida aurita. Journal of Comparative Psychology, 110, 45–54. Dominey, W. J. (1983). Mobbing in colonially nesting fishes, especially the bluegill, Lepomis macrochirus. Copeia, 1086–1088. Drea, C. M., & Frank, L. G. (2003). The social complexity of spotted hyenas. In F. B. M. de Waal & P. L. Tyack (Eds.), Animal social complexity: Intelligence, culture, and individualized societies (pp. 121–148). Cambridge, MA: Harvard University Press. Drea, C. M., & Wallen, K. (1999). Low-status monkeys “play dumb” when learning in mixed social groups. Proceedings of the National Academy of Sciences, USA, 96, 12965–12969. Dugatkin, L. A. (1997). Cooperation among animals: An evolutionary perspective. Oxford, England: Oxford University Press. Dugatkin, L. A. (2002). Animal cooperation among unrelated individuals. Naturwissenschaften, 89, 533–541. Dunbar, R. I. M. (1991). Functional significance of grooming in primates. Folia Primatologica, 57, 121–131. Dunbar, R. I. M. (2003). The social brain: Mind, language, and society in evolutionary perspective. Annual Review of Anthropology, 32, 163–181. DuVal, E. H. (2007). Social organization and variation in cooperative alliances among male lance-tailed manakins. Animal Behaviour, 73, 391–401. Fady, J. C. (1972). Absence of instrumental-type cooperation in feral. Papio papio. Behaviour, 43, 157–164. Ferguson, J. N., Aldag, J. M., Insel, T. R., & Young, L. J. (2001). Oxytocin in the medial amygdala is essential for social recognition in the mouse. Journal of Neuroscience, 21, 8278–8285. Fernandez-Duque, E., Mason, W. A., & Mendoza, S. P. (1997). Effects of duration of separation on responses to mates and strangers in the monogamous titi monkey (Callicebus moloch). American Journal of Primatology, 43, 225–237. Francis, D., Diorio, J., Liu, D., & Meaney, M. J. (1999, November 5). Nongenomic transmission across generations of maternal behavior and stress responses in the rat. Science, 286, 1155–1158. Frazier, C. R. M., Trainor, B. C., Cravens, C. J., Whitney, T. K., & Marler, C. A. (2006). Paternal behavior influences development of aggression and vasopressin expression in male California mouse offspring. Hormones and Behaviour, 50, 699–707. Friant, S. C., Campbell, M. A., & Snowdon, C. T. (2008). Captive-born cotton-top tamarins (Saguinus oedipus) respond similarly to vocalizations of predators and non-predators. American Journal of Primatology, 70, 707–710. Galef, B. G., Jr., & Allen, C. (1995). A new model system for studying behavioural traditions in animals. Animal Behaviour, 50, 705–715. Galef, B. G., Jr., & Laland, K. N. (2005). Social learning in animals: Empirical studies and theoretical models. BioScience, 55, 489–499. Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119, 593–609. Gaulin, S. J. C., & Fitzgerald, R. W. (1989). Sexual selection for spatial learning ability. Animal Behaviour, 37, 322–331.

Grewen, K. M., Girdler, S. S., Amico, J., & Light, K. C. (2005). Effects of partner support on resting oxytocin, cortisol, norepinephrine, and blood pressure before and after warm partner contact. Psychosomatic Medicine, 67, 531–538. Gubernick, D. J., & Tefari, T. (2000). Adaptive significance of male parental care in a monogamous mammal. Proceedings of the Royal Society of London. Series B, 267, 147–150. Hamilton, W. D. (1963). The evolution of altruistic behavior. American Naturalist, 97, 354–356. Hamilton, W. D. (1964). The genetical evolution of social behavior. Journal of Theoretical Biology, 7, 1–52. Hammerstein, P. (2003). Why is reciprocity so rare in social animals? A protestant appeal. In P. Hammerstein (Ed.), The genetic and cultural evolution of cooperation (pp. 83–93). Cambridge, MA: MIT Press. Hammock, E. A. D., & Young, L. J. (2005, June 10). Microsatellite instability generates diversity in brain and sociobehavioral traits. Science, 308, 1630–1634. Hampton, R. R., & Shettleworth, S. J. (1996). Hippocampus and memory in food-storing and in a nonstoring bird species. Behavioral Neuroscience, 110, 946–964. Harbaugh, W. T., Mayr, U., & Burghart, D. R. (2007, June 15). Neural responses to taxation and voluntary giving reveal motives for charitable donations. Science, 316, 1622–1625. Hare, B., Melis, A. P., Woods, V., Hastings, S., & Wrangham, R. (2007). Tolerance allows bonobos to outperform chimpanzees on a cooperative task. Current Biology, 17, 619–623. Hare, B., & Tomasello, M. (2004). Chimpanzees are more skilful in competitive than in cooperative cognitive tasks. Animal Behaviour, 68, 571–581. Hart, B. L., & Hart, L. A. (1992). Reciprocal allogrooming in impala, Aepyceros melampus. Animal Behaviour, 44, 1073–1083. Hasler, A. D. (1966). Underwater guideposts: The homing of salmon. Madison: University of Wisconsin Press. Hauser, M. D., Chen, K. M., Chen, F., & Chuang, E. (2003). Give unto others: Genetically unrelated cotton-top tamarin monkeys preferentially give food to those who altruistically give food back. Proceedings of the Royal Society of London. Series B, 270, 2363–2370. Hauser, M. D., Newport, E. L., & Aslin, R. N. (2001). Statistical learning of the speech stream in a non-human primate: Statistical learning in cotton-top tamarins. Cognition, 78, B53–B64. Hayes, S. L., & Snowdon, C. T. (1990). Predator recognition in cotton-top tamarins (Saguinus oedipus). American Journal of Primatology, 20, 283–291. Hennessy, D. F., & Owings, D. H. (1978). Snake species discrimination and the role of olfactory cues in the snake-directed behavior of the California ground squirrel. Behaviour, 65, 115–124. Hodos, W., & Campbell, C. B. G. (1969). Scala naturae: Why there is no theory in comparative psychology. Psychological Review, 76, 337–350. Hollander, E., Bartz, J., Chaplin, W., Phillips, A., Sumner, J., Soorya, L., et al. (2006). Oxytocin increases retention of social cognition. Biological Psychiatry, 61, 498–503.

Gilby, I. C., Eberly, L. E., & Wrangham, W. R. (2008). Economic profitability of social predation among wild chimpanzees: Individual variation promotes cooperation. Animal Behaviour, 75, 351–360.

Hollander, E., Novatny, S., Hanratty, N., Yaffe, R., deCaria, C., Aronowitz, B. R., et al. (2003). Oxytocin infusion reduces repetitive behaviors in adults with autism and Asperger ’s disorders. Neuropsychopharmacology, 28, 193–198.

Gimpl, G., & Fahrenholz, F. (2001). The oxytocin receptor system: Structure, function, and regulation. Physiological Reviews, 81, 629–683.

Hoogland, J. L., & Sherman, P. W. (1976). Advantages and disadvantages of bank swallow coloniality. Ecological Monographs, 46, 33–58.

c03.indd Sec6:52

8/17/09 1:58:36 PM

References 53 Horner, V., & Whiten, A. (2005). Causal knowledge and imitation/emulation switching in chimpanzees (Pan troglodytes) and children (Homo sapiens). Animal Cognition, 8, 164–181.

pigeons. In T. R. Zentall & B. G. Galef, Jr. (Eds.), Social learning: Psychological and biological perspectives (pp. 141–164). Hillsdale, NJ: Erlbaum.

Humle, T., & Snowdon, C. T. (2008). Socially biased learning in the acquisition of a complex foraging task in juvenile cottontop tamarins, Saguinus oedipus. Animal Behaviour, 75, 267–277.

Levy, F., Kendrick, K. M., Goode, J. A., Guevara-Guzman, R., & Keverne, E. B. (1995). Oxytocin and vasopressin release in the olfactory bulb of parturient ewes: Changes with maternal experience and effects on acetylcholine, gamma-aminobutyric acid, glutamate and noradrenaline release. Brain Research, 669, 197–206.

Ichihara-Takeda, S., & Funahashi, S. (2006). Reward-period activity in primate dorsolateral prefrontal and orbitofrontal neurons is affected by reward schedules. Journal of Cognitive Neuroscience, 18, 212–226. Inoue-Nakamura, N., & Matsuzawa, T. (1997). Development of stone tool use by wild chimpanzees (Pan troglodytes). Journal of Comparative Psychology, 111, 159–173. Insel, T. R. (2003). Is social attachment an addictive disorder? Physiology and Behavior, 79, 351–357. Insel, T. R., & Hulihan, T. J. (1995). A gender-specific mechanism for pair bonding: Oxytocin and partner preference formation in monogamous voles. Behavioral Neuroscience, 109, 782–789.

Liu, D., Diorio, J., Tannenbaum, B., Caldji, C., Francis, D., Freedman, A., et al. (1997, September 12). Maternal care hippocampal glucocorticoid receptors and hypothalamic-pituitary-adrenal responses to stress. Science, 277, 1659–1662. Lonsdorf, A. V., Eberly, L. E., & Pusey, A. E. (2004, April 15). Sex differences in learning in chimpanzees. Nature, 428, 715–716.

Insel, T. R., & Shapiro, L. E. (1992). Oxytocin receptor distribution reflects social organization in monogamous and polygamous voles. Proceedings of the National Academy of Sciences, USA, 89, 5981–5985.

Lupfer, G., Frieman, J., & Coonfield, D. (2003). Social transmission of flavor preferences in two species of hamsters (Mesocricetus auratus and Phodopus campbelli). Journal of Comparative Psychology, 117, 449–455.

Insel, T. R., Wang, Z.-X., & Ferris, C. F. (1994). Patterns of brain vasopressin receptor distribution associated with social organization in microtine rodents. Journal of Neuroscience, 14, 5381–5392.

Marler, C. A., Bester-Meredith, J. K., & Trainor, B. C. (2003). Paternal behavior and aggression: Endocrine mechanisms and non-genomic transmission of behavior. Advances in the Study of Behavior, 32, 263–323.

Jacobs, L. F., Gaulin, S. J. C., Sherry, D. G., & Hoffman, G. E. (1990). Evolution of spatial cognition: Sex specific patterns of spatial behavior predict hippocampal size. Proceedings of the National Academy of Sciences, USA, 87, 6349–6352.

Matsuzawa, T. (Ed.). (2001). Primate origins of human cognition and behavior. Tokyo: Springer-Verlag.

Jensen, K., Hare, B., Call, J., & Tomasello, M. (2006). What’s in it for me? Self-regard precludes altruism and spite in chimpanzees. Proceedings of the Royal Society of London. Series B, 273, 1013–1021. Johnston, T. D. (1982). The selective costs and benefits of learning: An evolutionary analysis. Advances in the Study of Behavior, 12, 65–106. Jouventin, P., Pasteur, G., & Cambefort, J. P. (1976). Observational learning of baboons and avoidance of mimics: Exploratory tests. Evolution, 31, 214–218.

Mayr, E. (1961, November 10). Cause and effect in biology. Science, 134, 1501–1506. McCabe, K., Houser, D., Ryan, L., Smith, V., & Trouard, T. (2001). A functional imaging study of cooperation in two-person reciprocal exchange. Proceedings of the National Academy of Sciences, USA, 98, 11832–11835. Melis, A. P., Hare, B., & Tomasello, M. (2006). Engineering cooperation in chimpanzees: Tolerance constraints on cooperation. Animal Behaviour, 72, 275–286.

Kawai, M. (1965). Newly acquired pre-cultural behavior of the natural troop of Japanese monkeys on Koshima Islet. Primates, 6, 1–30.

Mendoza, S. P., & Mason, W. A. (1986a). Contrasting response to intruders and involuntary separation by monogamous and polygamous New World monkeys. Physiology and Behavior, 38, 795–801.

Keysers, C., Kohler, E., Umiltà, M. A., Nanetti, L., Fogassi, L., & Gallese, V. (2003). Audiovisual mirror neurons and action recognition. Experimental Brain Research, 153, 628–636.

Mendoza, S. P., & Mason, W. A. (1986b). Parental division of labor and differentiation of attachments in a monogamous primate (Callicebus moloch). Animal Behaviour, 34, 1336–1347.

King-Casas, B., Tomlin, D., Anen, C., Camerer, C. F., Quartz, S. R., & Montague, P. R. (2005, April 1). Getting to know you: Reputation and trust in a two-person economic exchange. Science, 308, 78–83.

Mendres, K. A., & de Waal, F. B. M. (2000). Capuchins do cooperate: The advantage of an intuitive task. Animal Behaviour, 60, 523–529.

Klopfer, P. H. (1959). Social interactions in discrimination learning with special reference to feeding behavior in birds. Behaviour, 14, 282–299. Klopfer, P. H. (1961). Observational learning in birds: The establishment of behavioral modes. Behaviour, 17, 71–80. Kohler, E., Keysers, C., Umiltà, M.A., Fogassi, L., Gallese, V., & Rizzolatti, G. (2002, August 2). Hearing sounds, understanding actions: Action representation in mirror neurons. Science, 297, 846. Kosfeld, M., Heinrichs, M., Zak, P. J., Fischbacher, U., & Fehr, E. (2005, June 2). Oxytocin increases trust in humans. Nature, 435, 673–676. Laland, K. N. (2004). Social learning strategies. Learning and Behavior, 32, 4–14. Lazaro-Perea, C., de Fatima Arruda, M., & Snowdon, C. T. (2004). Grooming as a reward? Social function of grooming between females in cooperatively breeding marmosets. Animal Behaviour, 67, 627–636.

c03.indd Sec6:53

Liu, D., Diorio, J., Day, J. C., Francis, D. D., & Meaney, M. J. (2000). Maternal care hippocampal synaptogenesis and cognitive development in rats. Nature Neuroscience, 3, 799–806.

Miklosi, A. (1999). The ethological analysis of imitation. Biological Reviews, 74, 347–374. Milinski, M. (1987, January 29). Tit for tat in sticklebacks and the evolution of cooperation. Nature, 325, 433–435. Nelson, E. E., & Panksepp, J. (1998). Brain substrates of infant-mother attachment: Contributions of opioids, oxytocin, and norepinephrine. Neuroscience and Biobehavioral Reviews, 22, 437–452. Nicol, C. J., & Pope, S. J. (1994). Social learning in small flocks of laying hens. Animal Behaviour, 47, 1289–1296. Nottebohm, F. (1981, December 18). A brain for all seasons: Cyclical anatomic changes in song control nuclei of the canary brain. Science, 214, 1368–1370. Nowak, R. M. (1999). Walker ’s primates of the world. Baltimore: Johns Hopkins University Press.

Leavens, D. A., Hopkins, W. D., & Bard, K. A. (2005). Understanding the point of chimpanzee pointing: Epigenesis and ecological validity. Current Directions in Psychological Science, 14, 185–189.

Owren, M. J., & Rendall, D. (1997). An affect-conditioning model of nonhuman primate vocal signaling. In M. D. Beecher, D. H. Owings, & N. S. Thompson (Eds.), Perspectives in ethology (Vol. 12, pp. 399– 346). New York: Plenum Press.

Lefebvre, L., & Palameta, B. (1988). Mechanisms, ecology, and population diffusion of socially learned, food-finding behavior in feral

Packer, C., & Ruttan, L. (1988). The evolution of cooperative hunting. American Naturalist, 132, 159–198.

8/17/09 1:58:36 PM

54

Comparative Cognition and Neuroscience

Padoa-Schioppa, C., & Assad, J. A. (2006, May 11). Neurons in the orbitofrontal cortex encode economic value. Nature, 441, 223–226. Pedersen, C. A., & Boccia, M. L. (2002). Oxytocin maintains as well as initiates female sexual behavior: Effects of a highly selective oxytocin antagonist. Hormones and Behavior, 41, 170–177. Petit, O., Desportes, C., & Thierry, B. (1992). Differential probability of “coproduction” in two species of macaque (Macaca tonkeana, M. mulatta). Ethology, 90, 107–120. Pfeiffer, T., Rutte, C., Killingback, T., Taborsky, M., & Bonhoeffer, S. (2005). Evolution of cooperation by generalized reciprocity. Proceedings of the Royal Society of London. Series B, 272, 1115–1120. Popik, P., & van Ree, J. M. (1991). Oxytocin but not vasopressin facilitates social recognition following injection into the medial preoptic area of the rat brain. European Neuropsychopharmacology, 1, 555–560. Pravosudov, V. V., & Clayton, N. S. (2002). A test of the adaptive specialization hypothesis: Population differences in caching memory and the hippocampus in black-capped chickadee (Poecile atricapilla). Behavioral Neuroscience, 116, 515–522. Pravosudov, V. V., & de Kort, S. R. (2006). Is the Western scrub-jay (Aphelocoma californica) really an underdog among food-caching corvids when it comes to hippocampal volume and food caching propensity? Brain, Behavior and Evolution, 67, 1–9. Premack, D. G., & Woodruff, G. (1978). Does the chimpanzee have a theory of mind? Behavioral and Brain Sciences, 1, 515–526. Ribble, D. O. (1991). The monogamous mating system of Peromyscus californicus as revealed by DNA fingerprinting. Behavioral Ecology and Sociobiology, 29, 161–166. Rilling, J. K., Glenn, A. L., Jairam, M. R., Pagnoni, G., Goldsmith, D. R., Elfenbein, H. A., et al. (2007). Neural correlates of social cooperation and non-cooperation as a function of psychopathy. Biological Psychiatry, 61, 1260–1271. Rilling, J. K., Gutman, D. A., Zeh, T. R., Pagnoni, G., Berns, G. S., & Kilts, C. D. (2002). A neural basis for social cooperation. Neuron, 35, 395–405. Rilling, J. K., Sanfey, A. G., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2004). Opposing bold responses to reciprocated and unreciprocated altruism in putative reward pathways. NeuroReport, 15, 2539–2543. Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169–192. Rizzolatti, G., Fadiga, L., Gallese, V., & Fogassi, L. (1996). Premotor cortex and the recognition of motor actions. Cognitive Brain Research, 3, 131–141. Rizzolatti, G., Fogassi, L., & Gallese, V. (2001). Neurophysiological mechanisms underlying the understanding of imitation and action. Nature Reviews Neuroscience, 2, 661–670. Roesch, M. R., & Olson, C. R. (2004, April 9). Neuronal activity related to reward value and motivation in primate frontal cortex. Science, 304, 307–310. Rosenblum, L. A., Smith, E. L. P., Altemus, M., Scharf, B. A., Owens, M. J., Nemeroff, C. B., et al. (2002). Differing concentrations of corticotrophin releasing factor and oxytocin in the cerebrospinal fluid of bonnet and pigtail macaques. Psychoneuroendocrinology, 27, 651–660. Rukstalis, M., & French, J. A. (2005). Vocal buffering of the stress response: Exposure to conspecific vocalizations moderates urinary cortisol excretion in isolated marmosets. Hormones and Behavior, 47, 1–7. Rutte, C., & Taborsky, M. (2007). Generalized reciprocity in rats. Public Library of Science, 5, 1421–1425. Rutte, C., & Taborsky, M. (2008). The influence of social experience on cooperative behaviour of rats (Rattus norvegicus): Direct vs. generalised reciprocity. Behavioral Ecology and Sociobiology, 62, 499–505. Rypstra, A. L., & Tirey, R. S. (1991). Prey size, prey perishability and group foraging in a social spider. Oecologia, 86, 25–30.

c03.indd Sec6:54

Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996, December 13). Statistical leaning in 8-month-old infants. Science, 274, 1926–1928. Savage, A., Ziegler, T. E., & Snowdon, C. T. (1988). Sociosexual development, pair bond formation and mechanisms of fertility suppression in female cotton-top tamarins (Saguinus oedipus oedipus). American Journal of Primatology, 14, 345–359. Schaffner, C. M., Shepherd, R. E., Santos, C. V., & French, J. A. (1995). Development of heterosexual relationship in Wied’s black tufted-ear marmosets (Callithrix kuhli). American Journal of Primatology, 36, 185–200. Schaller, G. B. (1972). The Serengeti lion. Chicago: University of Chicago Press. Scheel, D., & Packer, C. (1991). Group hunting behaviour of lions: A search for cooperation. Animal Behaviour, 41, 697–709. Schultz, W., Dayan, P., & Montague, P. R. (1997, March 14). A neural substrate of prediction and reward. Science, 275, 1593–1599. Schuster, R. (2002). Cooperative coordination as a social behavior. Human Nature, 13, 47–83. Schuster, R., & Perelberg, A. (2004). Why cooperate? An economic perspective is not enough. Behavioural Processes, 66, 261–277. Schwab, C., Bugnyar, T., Schloegl, C., & Kotrschal, K. (2008). Enhanced social learning between siblings in common ravens, Corvus corax. Animal Behaviour, 75, 501–508. Sherry, D. F., Jacobs, L. F., & Gaulin, S. J. C. (1992). Spatial memory and adaptive specialization of the hippocampus. Trends in Neuroscience, 15, 298–303. Shields, W. M. (1984). Barn swallow mobbing: Self defence, collateral defence, group defence or paternal care? Animal Behaviour, 32, 132–148. Silk, J. B., Brosnan, S. F., Vonk, J., Henrich, J., Povinelli, D. J., Richardson, A. S., et al. (2005, October 27). Chimpanzees are indifferent to the welfare of unrelated group members. Nature, 437, 1357–1359. Silva, H. P. A., & Sousa, M. B. C. (1997). The pair-bond formation and its role in the stimulation of reproductive function in female common marmosets (Callithrix jacchus). International Journal of Primatology, 18, 387–400. Smith, G. T., Brenowitz, E. A., Beecher, M. D., & Wingfield, J. C. (1997). Seasonal changes in testosterone, neural attributes of song control nuclei, and song structure in wild songbird. Journal of Neuroscience, 17, 6001–6010. Smith, T. E., & French, J. A. (1997). Social and reproductive conditions modulate urinary cortisol excretion in black tufted-ear marmosets (Callithrix kuhli). American Journal of Primatology, 42, 253–267. Snowdon, C. T., & Boe, C. Y. (2003). Social communication about unpalatable foods in tamarins (Saguinus oedipus). Journal of Comparative Psychology, 117, 142–148. Snowdon, C. T., & Cronin, K. A. (2007). Cooperative breeders do cooperate. Behavioural Processes, 76, 138–141. Snowdon, C. T., & Ziegler, T. E. (2007). Growing up cooperatively. Journal of Developmental Processes, 2, 40–66. Solomon, N. G., & French, J. A. (1997). Cooperative breeding in mammals. Cambridge, England: Cambridge University Press. Spence, K. W. (1937). Experimental studies of learning and the higher mental processes in infra-human primates. Psychological Bulletin, 34, 806–850. Stander, P. E. (1992). Cooperative hunting in lions: The role of the individual. Behavioral Ecology and Sociobiology, 29, 445–454. Stevens, J. R., & Hauser, M. D. (2004). Why be nice? Psychological constraints on the evolution of cooperation. Trends in Cognitive Sciences, 8, 60–65. Strier, K. B. (1997). Subtle cues of social relationships in male muriqui monkeys (Brachyteles arachnoides). In W. G. Kinzey (Ed.), New world primates: Evolution ecology and behavior (pp. 109–118). New York: Aldine de Gruyter.

8/17/09 1:58:37 PM

References 55 Tebbich, S., Taborsky, M., & Winkler, H. (1996). Social manipulation causes cooperation in keas. Animal Behaviour, 52, 1–10. Templeton, J. J., Kamil, A. C., & Balda, R. P. (1999). Sociality and social learning in two species of corvids: The pinyon jay (Gymnorhinus cyanocephalus) and the Clark’s nutcracker (Nucifraga columbiana). Journal of Comparative Psychology, 113, 450–455. Thompson, R. K. R., Oden, D. L., & Boysen, S. T. (1997). Languagenaive chimpanzees (Pan troglodytes) judge relations between relations in a conceptual matching-to-sample task. Journal of Experimental Psychology: Animal Behavior Processes, 23, 31–43.

Wang, Z., Toloczko, D., Young, L. J., Moody, K., Newman, J. D., & Insel, T. R. (1997). Vasopressin in the forebrain of common marmosets (Callithrix jacchus): Studies with in situ hybridization, immunocytochemistry and receptor autoradiography. Brain Research, 768, 147–156. Werdenich, D., & Huber, L. (2002). Social factors determine cooperation in marmosets. Animal Behaviour, 64, 771–781.

Thornton, A., & McAuliffe, K. (2006, July 14). Teaching in wild meerkats. Science, 313, 227–229.

West, S. A., Griffin, A. S., & Gardner, A. (2007). Social semantics: Altruism, cooperation, mutualism, strong reciprocity and group selection. Journal of Evolutionary Biology, 20, 415–432.

Tooby, J., & Cosmides, L. (1990). The past explains the present: Emotional adaptations and the structure of ancient environments. Ethology and Sociobiology, 11, 375–424.

Whiten, A., & Ham, R. (1992). On the nature and evolution of imitation in the animal kingdom: Reappraisal of a century of research. Advances in the Study of Behavior, 21, 239–283.

Trivers, R. L. (1971). The evolution of reciprocal altruism. Quarterly Review of Biology, 46, 35–57.

Whiten, A., Horner, V., & Litchfield, C. A. (2004). How do apes ape? Learning and Behavior, 32, 36–52.

Turner, R. A., Altemus, M., Enos, T., Cooper, B., & McGuinness, T. (1999). Preliminary research on plasma oxytocin in normally-cycling women: Investigating emotion and interpersonal distress. Psychiatry, 62, 97–113.

Widowski, T. M., Porter, T. A., Ziegler, T. E., & Snowdon, C. T. (1992). The stimulatory effect of males on the initiation, but not the maintenance, of ovarian cycling in cotton-top tamarins (Saguinus oedipus). American Journal of Primatology, 26, 97–108.

Umiltà, M. A., Kohler, E., Gallese, V., Fogassi, L., Fadiga, L., Keysers, C., et al. (2001). I know what you are doing: A neurophysiological study. Neuron, 31, 155–165.

Wilkinson, G. S. (1984, March 8). Reciprocal food sharing in the vampire bat. Nature, 308, 181–184.

Uvnas-Moberg, K. (1998). Oxytocin may mediate the benefits of positive social interactions and emotions. Psychoneuroendocrinology, 23, 819–835. van Schaik, C. P., & Kappeler, P. M. (2006). Cooperation in primates and humans: Closing the gap. In P. M. Kappeler & C. P. van Schaik (Eds.), Cooperation in primates and humans: Mechanisms and evolution (pp. 3–21). Berlin, Germany: Springer-Verlag. Visalberghi, E. (1997). Success and understanding in cognitive tasks: A comparison between (Cebus apella) and (Pan troglodytes). International Journal of Primatology, 18, 811–830. Visalberghi, E., Pellegrini Quarantotti, B., & Tranchida, F. (2000). Solving a cooperation task without taking into account the partner ’s behavior: The case of capuchin monkeys (Cebus apella). Journal of Comparative Psychology, 114, 297–301. Von Frisch, K. (1950). Bees: Their vision, chemical senses and language. Ithaca, NY: Cornell University Press. Vonk, J., Brosnan, S. F., Silk, J. B., Henrich, J., Richardson, A. S., Lambeth, S. P., et al. (2008). Chimpanzees do not take advantage of very low cost opportunities to deliver food to unrelated group members. Animal Behaviour, 75, 1757–1770. Vonk, J., & Povinelli, D. J. (2006). Similarity and difference in the conceptual system of primates: The unobservability hypothesis. In E. Wasserman & T. Zentall (Eds.), Comparative cognition: Experimental explorations of animal intelligence (pp. 363–387). Oxford, England: Oxford University Press.

c03.indd Sec6:55

Wang, Z., Moody, K., Newman, J. D., & Insel, T. R. (1997). Vasopressin and oxytocin immunoreactive neurons and fibers in the forebrain of male and female common marmosets (Callithrix jacchus). Synapse, 27, 14–25.

Williams, J. R., Catania, K. C., & Carter, C. S. (1992). Development of partner preferences in female prairie voles (Microtus ochrogaster): The role of social and sexual experience. Hormones and Behavior, 26, 339–349. Winslow, J. T., Hastings, N., Carter, C. S., Harbaugh, C. R., & Insel, T. R. (1993, October 7). A role for central vasopressin in pairbonding in monogamous prairie voles. Nature, 365, 545–548. Winslow, J. T., Noble, P. L., Lyons, C. K., Sterk, S. M., & Insel, T. S. (2003). Rearing effects on cerebrospinal fluid oxytocin concentration and social buffering in rhesus monkeys. Neuropsychopharamacology, 28, 910–918. Wismer Fries, A. B., Ziegler, T. E., Kurian, J. R., Jacoris, S., & Pollak, S. D. (2005). Early experience in humans is associated with changes in neuropeptides critical for regulating social behavior. Proceedings of the National Academy of Sciences, USA, 102, 17237–17240. Witt, D. M., Winslow, J. T., & Insel, T. R. (1992). Enhanced social interactions in rats following chronic, centrally infused oxytocin. Pharmacology, Biochemistry, and Behavior, 43, 855–861. Young, L. J. (1999). Oxytocin and vasopressin receptors and speciestypical social behaviors. Hormones and Behavior, 36, 212–221. Young, L. J., & Wang, Z. (2004). The neurobiology of pair bonding. Nature Neuroscience, 7, 1048–1054. Zak, P. J., Kurzban, R., & Matzner, W. T. (2005). Oxytocin is associated with human trustworthiness. Hormones and Behaviour, 48, 522–527. Zak, P. J., Stanton, A. A., & Ahmadi, S. (2007). Oxytocin increases generosity in humans. Public Library of Science, 2(11), E1128.

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Chapter 4

Biological Rhythms LANCE J. KRIEGSFELD AND RANDY J. NELSON

“a rose is not necessarily and unqualifiedly a rose . . . it is a very different biochemical system at noon and at midnight.” —Colin Pittendrigh, 1965

When it comes to success, the expression “timing is everything” is an oft-touted mantra. Traditionally, however, timing was not considered important in the study of the mechanisms underlying behavior. Yet, behavioral constructs such as learning, memory, sensation, perception, attention, and motivation vary markedly according to the time of day or season of the year. Motivation can be defined generally as why individuals do what they do. From a neurobiological perspective, an animal eats because its hunger circuits are activated by specific neurochemicals. However, the response of circuits underlying motivated behaviors is quite different given time of day or year, and it is essential to consider timing when asking questions about the biological mechanisms underlying behavior. To add to this example, an animal eats at night in response to the impact of its internal biological clock on the responsiveness of hunger circuits to specific neurochemical signals. Despite the impact of timing systems on brain and behavior, their influence on dependent measures of interest is frequently ignored. For example, learning and memory performance are typically examined during the middle of the day in nocturnal (night active) rodents, a time when they are normally asleep. As a result, many reported deficits in performance can be attributed to time-of-day effects rather than (or in addition to) the intended manipulation. One goal of this chapter is to underscore the impact of biological rhythms on behavior and physiology to encourage a full appreciation of the significance and magnitude of such timed changes for investigators in the behavioral sciences.

FUNCTIONAL SIGNIFICANCE OF BIOLOGICAL RHYTHMS One of the most predictable features of life on earth is the regular pattern of environmental changes associated with the movement of the planet. Life evolved in a cyclic environment. Except for organisms living at the bottom of the oceans or deep within caves, day follows night, and the seasons change. These orderly and predictable changes in the environment have existed since life first began to evolve, although the timing of these events may have changed slightly over time. The rotation of the earth results in periodic exposure to the radiation of the sun, which causes predictable changes in light and ambient temperatures, as well as associated changes in the relative humidity of the air and in the oxygen levels of aqueous habitats. The biological clocks of animals and plants permit them to start or stop locomotor activities or activate photosynthetic machinery, respectively, in preparation for light. The action of the master biological clock in synchronizing individual bodily functions has been compared to the role of the conductor of an orchestra. As a result, internal processes serve to prepare the body for certain activities to occur later; for example, the elevated adrenal secretions coinciding with the morning onset of activity prepare an individual for increased activity levels and for breaking the nightly fast. All eukaryotic and most prokaryotic organisms tested to date, from unicellular organisms to humans, display circadian rhythms. Predictably, circadian rhythms have not evolved in organisms that live for less than 24 hours. Similarly, circannual rhythms have evolved only in animals that live for a year or more. Physiological systems reveal a wide range of rhythmic changes. For instance, neurotransmitter turnover, body temperature, and blood plasma levels of adrenalin, potassium, sodium, cortisol, androgens, and growth

We thank Zachary Weil for helpful comments on an earlier version of this chapter. The authors were supported during the preparation of this chapter by NIH grants HD050470 (LJK), MH57353, and NSF grant OIS 04-16897 (RJN). 56

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hormone all show pronounced circadian rhythms. Some of these changes may be on the order of 100% to 200% from baseline values. Peak daily cortisol concentrations, for example, which usually occur just prior to or immediately after awakening, coincide with the onset of activity in the morning. This programmed elevation of cortisol concentrations increases blood pressure and cardiac output prior to the active phase of the day. Increased cortisol concentrations are not driven by increased activity levels because the same circadian rhythm is observed in bedridden patients under constant conditions (Aschoff, 1965). In some instances, neurochemical receptors are produced prior to a circadian-programmed increase in ligand production, with both processes being coordinated by the circadian system. Body temperature peaks in mid-afternoon, when people are most active, but again, muscular activity is not solely responsible for this “heating” (Refinetti & Menaker, 1992). It may seem that the concept of biological clocks and programmed temporal changes in behavioral function is at odds with the concept of homeostasis in biological sciences and medicine. Homeostatic processes work to maintain physiological parameters within specific and often narrow ranges. Biologists and physicians frequently considered large fluctuations in physiology and behavior to be pathological; until recently, many resisted the idea of the programmed changes in physiology and behavior that we now understand to underlie homeostatic processes. As a result, we should not consider the appropriate parameters for a specific system to be fixed but, instead, variable relative to time of day or year. What orchestrates physiology and behavior so that organisms engage in appropriate behaviors at suitable times of day or during optimal seasons of the year? Some rhythms are the result of environmental factors that impose temporal control over behavior and physiology—these rhythms are termed driven rather than endogenous (from within). In contrast, most temporal changes in brain and behavior are controlled by internal biological clocks. This chapter focuses on these latter clocks in the control of behavior.

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Chronobiology has borrowed terms and concepts extensively from the field of engineering. For example, a rhythm is defined as a recurrent event that is characterized by its period, frequency, amplitude, and phase (Vitaterna, Takahashi, & Turek, 2001). Period is the length of time required to complete one cycle of the rhythm in question (e.g., the amount of time required to go from peak to peak or trough to trough). Frequency is computed as the number of completed cycles per unit of time (e.g., 6 cycles/day). Amplitude is the amount of change above and below the average value; that is, the distance of the peak from the average. The phase represents a point on the rhythm relative to some objective time point during the cycle. For example, under standard conditions, the phase of onset of the activity portion of a mouse’s activity-rest cycle corresponds closely with the onset of dark (Figure 4.1). Phase relations among various biological rhythms can also be described (e.g., the onset of the active phase of the daily sleep-wake cycle tightly corresponds with the peak of stress hormones secretion in both mice and humans). Many biological rhythms have been recognized for thousands of years. In the past, however, rhythms in migration, daily activity patterns, or hibernation were attributed to exogenous factors. Although exogenous factors may serve a permissive or synchronizing role for biological rhythms, endogenous timing mechanisms mediate many of the observed rhythms in brain and behavior. The best evidence to document whether a rhythm is driven by exogenous or endogenous signals is obtained via isolation studies (Aschoff, 1981; DeMairan, 1729; Thrun, Moenter, O’Callaghan, Woodfill, & Karsch, 1995; Vitaterna et al., 2001; Zucker & Boshes, 1982). Persistence of a biological rhythm in the absence of environmental cues provides compelling evidence that the rhythm under study is generated from within the individual and not driven by the environment. If a biological rhythm disappears under constant conditions, then it is reasonable to suggest that some cyclic cue in the environment drives the biological rhythm. This logic

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Figure 4.1 Components of biological rhythms. Rhythms can be analyzed in terms of amplitude, frequency, and period length. Note. The relationship from one rhythm to another is expressed in terms of phase relationships. Both cycles have a period of 24 hours (frequency  1 day).

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has now been applied in hundreds of studies that have firmly established the existence of biological rhythms driven by endogenous biological clocks. Incredibly, the period of the daily activity rhythm of one individual can be transferred to another individual by means of brain tissue transplants that contain the master biological clock (Lehman et al., 1987; Ralph, Foster, Davis, & Menaker, 1990). All animals (and all eukaryotic and some prokaryotic plants) studied to date have endogenous clocks that mediate biological rhythms (Roenneberg & Merrow, 2002). The periods of biological rhythms range from the 1-ms cycle of firing among some neurons to longer cycles such as the 90-minute cycle of REM sleep, the 4- or 5-day estrous cycles of rats, the annual cycle of hibernation, the 17-year cycle of cicada emergence, or the 100-year cycle of century plant flowering. How are these biological rhythms generated? Are they all mediated by biological clocks? Certainly, the periods of some biological rhythms, including most central nervous system and cardiovascular rhythms vary widely within the same individual depending on activity. The periods of other endogenous cycles, such as the wake-sleep cycle, are largely constant for the same individual, but there may be great inter-individual or species variation. Four types of biological rhythms can be identified, however, that are typically coupled with environmental factors, and the periods of these rhythms vary little under natural conditions. These relatively constant biological rhythms mimic the periods of the geophysical cycles of night and day (circadian), the tides (circatidal), the phases of the moon (circalunar), and the seasons of the year (circannual; Dunlap, Loros, & DeCoursey, 2004). These rhythms persist when animals are isolated from the respective environmental cues, but when isolated, these rhythms only approximate the periods of the environmental cues to which they are normally synchronized. Thus, the terms for many biological rhythms use the prefix circa (Halberg, 1959). Although isolation experiments are necessary to determine the extent of endogenous generation of any biological rhythm, it should be emphasized that the environment often exerts permissive effects on endogenously driven biological rhythms. The evidence for circadian or circannual rhythms is much stronger than for circatidal and circalunar rhythms, especially among vertebrates. Thus, this chapter focuses on circadian and circannual rhythms in behavioral neuroscience. Light serves as an important environmental time cue, or zeitgeber (from the German, meaning “time giver”), for most species (Morin & Allen, 2006; Roenneberg, Daan, & Merrow, 2003). Temperature is an important zeitgeber for some poikilothermic animals, and possibly a secondary zeitgeber for birds and mammals, but light is the primary zeitgeber among homeothermic animals (Foster,

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Hankins, & Peirson, 2007). As mentioned previously, the internal clock runs with a period that is approximately 24 hours in constant conditions, yet the functions controlled by this clock recur precisely every 24 hours when entrained (synchronized) to a 24-hour light-dark cycle. Because day length varies across the year in relatively small increments, internal clocks must exhibit plasticity. If a mouse is placed in constant dim light so that there is no daily zeitgeber, then the onset time of its locomotor activity begins to drift out of synchrony with local time; similarly, if a person is put into a windowless room for weeks, his wake-sleep cycle will drift out of phase with local conditions. Without the daily light-dark cycle providing a daily reset, the endogenous circadian clock of a hamster or human only approximates 24-hour cycles. Biological rhythms that are not synchronized with environmental cues are called free-running. Each individual displays its own free-running period (abbreviated as ), which remains relatively constant. The free-running periods of biological clocks are precise, but are not exactly 24 hours. The observation that different hamsters display an array of different free-running periods when housed in the same room suggests that they are not synchronized by each other ’s behavior, and that subtle geophysical cues are not providing temporal information. However, social factors can provide a zeitgeber for humans living in constant conditions (Mistlberger & Skene, 2004, 2005; Turek & Zee, 1999). Appropriately timed exercise or exogenous melatonin also influences human circadian timing (Mistlberger & Skene, 2005). As noted, individual animals display species-specific times of locomotor activity onset that are often linked to the timing of food intake, water consumption, and social activities (Mistlberger & Skene, 2004, 2005). If the zeitgeber is phase-shifted, then animals adapt their activity to the new regimen in a few days (length of time is dependent on the extent of change). The circadian rhythms of other physiological processes, such as adrenocortical hormone release, body temperature, and blood plasma volume, may require more or less time to synchronize to the new lighting regime (Davidson, Yamazaki, Arble, Menaker, & Block, 2006; Yamazaki et al., 2000). This process of adaptation to rapid phase shifts in environmental lighting results in what is commonly called “jet lag” when people travel through different time zones. Ultradian (shorter than circadian) and infradian (longer than circadian) rhythms are biological rhythms that do not correspond to any known geophysical cycles. Ultradian cycles are commonly observed; for example, the 90-minute cycle characteristic of REM sleep is a welldocumented ultradian rhythm (Schulz & Lavie, 1985). The pulsatile secretion of several hormones, including gonadotropin-releasing hormone (GnRH), luteinizing hormone

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Circadian Control of Brain and Behavior 59

(LH), testosterone, growth hormone, and corticosterone, represent ultradian rhythms (Schulz & Lavie, 1985). Ultradian rhythm variation in free estradiol and cortisol concentrations during menstrual cycles correlates with depressive symptoms (Bao et al., 2004) as well as sleep disturbances (Voss, 2004). Ultradian rhythms in locomotor activity, feeding, and metabolism have been reported in a number of high-metabolic mammalian species such as shrews and voles (reviewed in Liu, Li, & Wang, 2007), and may be related to ancient metabolic cell cycles (Lloyd, Lemar, Salgado, Gould, & Murray, 2003). Infradian rhythms are less frequently observed than ultradian rhythms; such biological rhythms are longer than a day, but shorter than a lunar month. Generally, infradian rhythms in testicular function are rare among vertebrates. Dormice (Glis glis) have been reported to display infradian rhythms of about 60 days in body mass and body fat content (Grimes, Melnyk, Martin, & Mrosovsky, 1981; Melnyk, Mrosovsky, & Martin, 1983a, 1983b). The most common types of infradian rhythms are those associated with ovarian cycles; that is, estrous or menstrual cycles. Hamsters and mice display 4-day estrous cycles; rats have estrous cycles of either 4 or 5 days. Estrous cycles are 16 days in guinea pigs and sheep. Estrous cycles persist in constant conditions (i.e., self-sustaining), but are endogenously generated rhythms that do not correspond to any known geophysical cue. Although human menstrual cycles approximate a lunar month, the length of the menstrual cycle seems to be only coincidentally similar to the length of the lunar month, rather than reflecting any adaptive link to the lunar cycle (Knobil & Hotchkiss, 1988). CIRCADIAN CONTROL OF BRAIN AND BEHAVIOR Evolution of the Circadian System From prokaryotic to eukaryotic organisms, the 24-hour solar cycle has had a pervasive impact on the evolution of life. As suggested previously, a variety of conditions require that organisms internally track daily time. For instance, all energetic requirements for normal health and functioning cannot be simultaneously fulfilled, and peaks in energetic processes must be partitioned throughout the day. Likewise, all behavioral requirements (e.g., foraging, mating, nest building) cannot be simultaneously performed. To allow organisms to anticipate daily environmental change and synchronize their behavior and physiology accordingly, individuals have evolved an endogenous circadian timekeeping mechanism. Not only does this system allow for the coordination of internal physiology, but it also allows animals to predict recurring 24-hour events, such

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as food availability and predator activity, and adjust their behavior appropriately. Perhaps the most direct evidence for the adaptive significance of circadian rhythms comes from studies of cyanobacteria—a group of prokaryotes. Analysis of relative fitness of different strains of cyanobacteria shows that strains with a circadian period similar to the light/dark cycle of the environment have greater reproductive success (Ouyang, Andersson, Kondo, Golden, & Johnson, 1998; Woelfle, Ouyang, Phanvijhitsiri, & Johnson, 2004). Strains whose clocks were disrupted were defeated by strains with a functional clock, but only when held in a light/dark cycle; any competitive advantage was lost in constant conditions. In multicellular organisms, this competitive advantage is also observed; Arabidopsis thaliana, as in cyanobacteria, grow faster and survive longer when housed in light cycles closest to their endogenous circadian period (Dodd et al., 2005). Circadian rhythms are strikingly ubiquitous, with most behavioral, biochemical, and physiological responses of organisms showing daily variation that persists in constant environmental conditions. As described next, some genes are part of the cellular clockwork mechanism, whereas others are controlled directly or indirectly by these core clock genes (i.e., clock-controlled genes). To further understand the evolution of the circadian system, one logical step is to determine the percent of the genome under circadian control across taxa. In Arabidopsis, about 6% of the estimated 8,000 genes studied are rhythmic (Harmer et al., 2000). In contrast, in the retina of Xenopus perhaps only a few critical proteins (0.2%) are directly under the control of the circadian clock (C. B. Green & Besharse, 1996). In Drosophila, an oligonucleotide-based high-density array to measure gene expression changes on a whole genome level revealed that approximately 7% to 9% of genes express a circadian pattern (Ceriani et al., 2002; Claridge-Chang et al., 2001; C. B. Green & Besharse, 1996; McDonald & Rosbash, 2001). Investigations of head versus body rhythms showed that the genes exhibiting circadian patterns have little overlap, suggesting specificity of function for core clock genes (CCGs) in distinct systems (Ceriani et al., 2002). In mice the same general conclusion has been reached by comparing gene expression patterns between liver and heart (Storch et al., 2002). While 8% to 10% of genes are rhythmically expressed in one of the tissues, there are few genes that show circadian regulation in both tissues. Even the genes that have a rhythm in both tissues are frequently out of phase, suggesting independent local function. Determination of similarities and differences across species in clock gene and CCG homologues, and the signals that set their phase, will be necessary to fully understand the evolution of the circadian system.

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Biological Rhythms

Adaptive Significance of the Circadian System Animals have evolved to synchronize their endogenous circadian rhythms with the environment in order to promote survival (e.g., DeCoursey & Krulas, 1998). Numerous behaviors are restricted to specific times of day in response to a variety of selection pressures. For example, diurnal species (i.e., species active during the day) confine behaviors such as feeding, locomotion, foraging, and reproduction to the light hours in order to avoid predation. Likewise, nocturnal (i.e., active at night) species such as owls are active at night to maximize the availability of nocturnal prey (e.g., small rodents). As described later, the circadian “clock” that synchronizes rhythms in behavior and physiology is localized to a discrete bilateral nucleus in the anterior hypothalamus. If this master brain clock is destroyed, animals lose their ability to restrict behaviors to a particular time of day, and this arrhythmicity may compromise survival (DeCoursey & Krulas, 1998; DeCoursey, Krulas, Mele, & Holley, 1997). Humans have evolved to maintain maximum performance during daytime and circadian variation in learning and memory, attention, reaction time, and perception are prominent (Babkoff, Caspy, Mikulincer, & Sing, 1991; Colquhoun, 1981). This is especially important for individuals working night shifts that require critical cognitive performance. Generally, human performance peaks with the daily afternoon peak in body temperature, although verbal reasoning appears to peak earlier in the circadian cycle (Colquhoun, 1981). When people were maintained in the laboratory for a month in either 24- or 24.6-hour days, about half of the individuals adapted with peak melatonin occurring during the normally scheduled sleep period (synchronized), whereas half displayed peak melatonin while awake (nonsynchronized). Nonsynchronized individuals reduced total sleep time, as well as sleep latency and rapid eye movement (REM) latency. Cognitive performance was impaired among the nonsynchronized individuals and enhanced among the synchronized subjects (Wright, Hull, Hughes, Ronda, & Czeisler, 2006). People are likely to be nonsynchronized when working during the night shifts, but resorting to a normal day-night schedule during the weekend, or when undergoing repeated jet travel across several time zones. The ability of people to estimate time intervals also varies on a circadian basis. For example, time perception evaluations at 0900, 1300, 1700, and 2100 hours revealed that short-term time perception was more influenced by circadian phase than by memory load or various psychophysiological factors (Kuriyama et al., 2003). Such human studies should be interpreted cautiously because either approximately onethird of the day is missing or circadian influences are masked by sleep deprivation. These latter difficulties can be overcome by distributing small meals and naps throughout the day and having subjects maintain a constant routine designated by

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the researchers (Brown & Czeisler, 1992; el-Hajj Fuleihan et al., 1997; Khalsa, Jewett, Duffy, & Czeisler, 2000). One study of visual selective attention used a different technique to dissociate the effects of circadian phase and time awake (Horowitz, Cade, Wolfe, & Czeisler, 2003). After 38 hours of no sleep, observers increased reaction times for spatial configuration and conjunction tasks. Observers traded accuracy for speed when sleepy, which could lead to decision errors. Indeed, extended shift durations lead to increased impairments in attentional errors, significant medical errors, and so-called adverse events in critical care units (Barger et al., 2006). In a web-based analysis of medical errors, interns working more than 5 extended-duration shifts per month reported more attentional failures during lectures, rounds, and clinical activities, including surgery; fatiguerelated preventable adverse events (including fatality) increased by 300% in these interns. Temporal variation in behavior is associated with temporal variation in underlying physiology and biochemistry. These underlying processes are modulated by endogenously driven circadian rhythms that are synchronized by environmental time cues. Clearly, a body clock that is not synchronized with the environment is not adaptive. Some rhythms, however, exhibit a peak in activity during one time of day while other rhythms are at a nadir at this time. For example, in humans, cortisol concentrations peak at dawn while melatonin and prolactin concentrations peak in the middle of the night. The relationship between daily fluctuations in physiology and the environmental light/dark cycle is different for nocturnal and diurnal species. Thus, although a myriad of rhythms have a period of about 24 hours, each of these rhythms has a unique phase with respect to one another and to the light/dark cycle (Figure 4.2). Stated differently, although bodily rhythms exhibit different phases

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Figure 4.2 Examples of circadian rhythms in humans. Note. Shaded areas represent times of sleep whereas the unshaded areas represent times of activity. All parameters exhibit unique peaks and troughs relative to each other. This phase relationship among individual rhythms is critical for optimal functioning and the maintenance of homeostasis.

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Circadian Control of Brain and Behavior 61

relative to some objective point in time, the phase relationship among rhythms remains stable, unless a perturbation occurs (e.g., jet lag). Consequently, circadian rhythms help to maintain homeostasis within the body. In addition, the phase of specific rhythms helps to prepare the body for necessary daily activities in advance of their actual occurrence. For example, cortisol rhythms rise in humans prior to waking in order to facilitate the onset of morning activity (e.g., Van Cauter & Refetoff, 1985; Weitzman et al., 1971). It is not difficult to imagine how physiological functioning would suffer without an internal circadian clock synchronized to the environment. Most of us have experienced general feelings of malaise and other maladies following a long flight across time zones. While we can recover within a few days from acute jet lag, millions of frequent flyers, shift workers, individuals with sleep disorders, and other individuals whose work day is not fixed are exposed chronically to such temporal disruptions. These individuals provide the opportunity to examine the effects of more chronic circadian disruptions. In fact, this loss of synchrony between the circadian clock in the brain and the environment leads to pronounced clinical pathologies. One recent study found that elderly mice subjected to temporal disruptions equivalent to a flight from Washington to Paris, once a week for 8 weeks, die sooner a result of their bodies being out of sync with local time (Davidson, Sellix, et al., 2006). Flight attendants frequently traveling across time zones exhibit cognitive deficits associated with reductions in temporal lobe structures (Cho, 2001; Cho, Ennaceur, Cole, & Suh, 2000). Numerous studies show that shift workers have a higher incidence of cancer (Conlon, Lightfoot, & Kreiger, 2007; Hansen, 2006; Kubo et al., 2006; O’Leary et al., 2006; Patel, 2006), diabetes (Karlsson, Alfredsson, Knutsson, Andersson, & Toren, 2005; Morikawa et al., 2005; Poole, Wright, & Nattrass, 1992; Robinson, Yateman, Protopapa, & Bush, 1990; Sanborn, Currie, & Bailey, 1982), ulcers (Costa, 1996; Koda et al., 2000; Kolmodin-Hedman & Swensson, 1975; Segawa et al., 1987), hypertension and cardiovascular disease (Alstadhaug, Salvesen, & Bekkelund, 2005; Costa, 1996; Hwang & Lee, 2005; Kivimaki et al., 2006; Wolk, Gami, Garcia-Touchard, & Somers, 2005), psychological disorders (Bildt & Michelsen, 2002; De Koninck, 1991; Leonard, Fanning, Attwood, & Buckley, 1998; Munakata et al., 2001; Skipper, Jung, & Coffey, 1990; Venuta, Barzaghi, Cavalieri, Gamberoni, & Guaraldi, 1999), and a host of other clinical issues. In fact, just changing the time of day during which cancer chemotherapy is administered can nearly double the chances of survival in patients suffering from cancers with an estimated 30% to 40% 5-year survival rate, including childhood leukemia and colorectal carcinomas (Hrushesky, 1990, 1993, 1995; Kanabrocki et al., 2006; Lee & Balick, 2006; Levi, 1987, 1994, 2001, 2002; Takimoto, 2006). These findings, although largely correlated, point to a critical role

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for internal circadian timing in maintaining normal brain functioning and peripheral physiology. The two fundamental functions of the circadian system, internal organization and entrainment to the environment, are fundamental for optimal regulation of physiology and behavior. For each aspect of physiology and behavior, the circadian system sits upstream of a regulatory system, modulating the timing and synchronization of events. The mechanisms controlling circadian function—coordination of bodily systems and synchronization with the environment— represents the focus of the following sections. The Brain Clock Whereas a “master” circadian clock has been localized to the suprachiasmatic nucleus (SCN) located in the anterior hypothalamus in mammals (Figure 4.3; Moore & Eichler, 1972; Stephan & Zucker, 1972), it is now more appropriate to conceptualize the circadian system as an assembly comprised not only of a master clock, but also a series of subordinate clocks whose phase and coordinated activity is set by the SCN. As described in the following sections, the SCN has direct access to environmental time via retinal projections to the clock. Because subordinate central and peripheral clocks do not have access to such time cues, it is necessary for the SCN to communicate such information throughout the CNS and periphery. In addition to the core clock genes (CCGs) responsible for SCN and subordinate clock function, CCGs represent important output and local coordination systems. The stability of this hierarchical arrangement is necessary for normal body functioning and disease prevention. Numerous lines of evidence indicate that the master mammalian circadian clock is located in the SCN. The first

Figure 4.3 The mammalian circadian clock is located in the suprachiasmatic nucleus (SCN) of the anterior hypothalamus. Note. The SCN is pictured in this schematic of a coronal section through a rodent brain. The SCN is situated at the base of the brain directly above the optic chiasm (OC) and directly surrounding the third ventricle (V3). The sagittal schematic in the upper right corner depicts the approximate rostral-caudal location depicted in the coronal section.

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Biological Rhythms

indication that the SCN is the master clock comes from studies in which lesions ablating the SCN abolish circadian rhythmicity in adrenal corticoid secretion and locomotor behavior (Moore & Eichler, 1972; Stephan & Zucker, 1972). SCN-lesioned animals continue to show the full range of normal behaviors, but their temporal organization is lost and never recovers, irrespective of how early in development the lesions are performed (Mosko & Moore, 1979). The initial conclusion that the SCN serves as the master clock in the brain has been confirmed in the subsequent 30 years by converging lines of research involving in vivo, ex vivo, and in vitro studies carried out in many different laboratories. For example, transplants of donor SCN tissue into the brains of arrhythmic, SCN-lesioned hosts restore circadian rhythmicity in behavior (Lehman et al., 1987; Ralph et al., 1990). Importantly, rhythms are restored with the period of the donor SCN, indicating that the transplanted tissue does not act by restoring host-brain function but that the “clock” is contained in the transplanted tissue. Further evidence that clock function is contained within the SCN comes from studies demonstrating that circadian rhythms in neural firing rate persist in isolated SCN tissue maintained in culture (D. J. Green & Gillette, 1982; Groos & Hendriks, 1982; Shibata, Oomura, Kita, & Hattori, 1982). These studies confirmed that input from extra-SCN brain sites is not necessary for circadian rhythms in this nucleus (D. J. Green & Gillette, 1982; Groos & Hendriks, 1982; Shibata et al., 1982). In hypothalamic slice preparations, the SCN is intrinsically capable of sustaining not only circadian rhythms in neuronal firing rate, but also rhythms in glucose utilization and vasopressin secretion (Gillette & Reppert, 1987; D. J. Green & Gillette, 1982; Newman & Hospod, 1986). Primary cultures and organotypic explants of the rat SCN are similarly characterized by the distinctive capacity to generate circadian rhythms in vasopressin and vasoactive intestinal polypeptide (VIP) release for multiple cycles (Earnest & Sladek, 1987; Shinohara, Honma, Katsuno, Abe, & Honma, 1994; Watanabe, Koibuchi, Ohtake, & Yamaoka, 1993). Vasopressin and VIP rhythms produced by the same SCN explant are independently phased suggesting that these circadian rhythms may be generated by neurons that comprise two separable populations of oscillators within the SCN (Shinohara et al., 1994). An excellent overview of these studies in historical perspective is available (Weaver, 1998). The Molecular Clock Within a cell, circadian rhythms are produced by an autoregulatory transcriptional/translational negative feedback loop that takes approximately 24 hours (Box 4.1). While the general mechanism for circadian oscillations at the

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cellular level is common among organisms, the components comprising the feedback loop differ. For the purpose of clarity, only the core mammalian feedback loop is described. Earlier work proposed a core feedback loop that begins when two proteins, CLOCK and BMAL1, bind to one another and drive the transcription of messenger RNA (mRNA) of the Period (Per) and Cryptochrome (Cry) genes by binding to the E-box (CACGTG) domain on these gene promoters. Three Period (Per1, Per2, and Per3) and two cryptochrome genes (Cry1 and Cry2) have been identified. The mRNA for these genes is translated into PER and CRY proteins in the cytoplasm of the cell over the course of the day. Throughout the day, these proteins build up within the cytoplasm, and when they reach high enough levels, they form hetero- and homo-dimers. These newly formed dimers then feed back to the nucleus where they bind to the CLOCK:BMAL1 protein complex to turn off their own transcription (Figure 4.4).

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Figure 4.4 A simplified model of the intracellular mechanisms responsible for mammalian circadian rhythm generation. Note. The process begins in the cell nucleus when CLOCK and BMAL1 proteins dimerize to drive the transcription of the Per (Per1, Per2, and Per3) and Cry (Cry1 and Cry2) genes. In turn, Per and Cry are translocated to the cytoplasm and translated into their respective proteins. Throughout the day, PER and CRY proteins rise within the cell cytoplasm. When levels of PER and CRY reach a threshold, they form heterodimers, feed back to the cell nucleus, and negatively regulate CLOCK:BMAL1 mediated transcription of their own genes. This feedback loop takes approximately 24 hours, thereby leading to an intracellular circadian rhythm. From Kriegsfeld and Silver, 2006. Reprinted with permission.

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BOX 4.1 TRANSCRIPTION, TRANSLATION, AND POSTTRANSLATIONAL EVENTS Gene transcription is the process by which the sequence of nucleotides in a single strand of DNA is transcribed into a single strand of complementary RNA. Transcription factors bind to the beginning of the DNA sequence where the gene to be transcribed is located. In order to synthesize RNA, the two tightly twisted strands of DNA must be unraveled by enzymes called helicases. A gene consists of a unique linear sequence of DNA. Among eukaryotic organisms, some of the nucleotide sequences within the gene are noncoding sequences, called introns, which alternate with coding sequences, called exons. There are special markers denoting the start and end points of each gene. A distinct sequence of nucleotides, called a promoter or facilitory region, marks the start of the gene. The binding of a transcription factor to the promoter allows special enzymes, called RNA polymerases, to attach to the promoter and begin the process of RNA synthesis. The sequence of RNA nucleotides, determined by the sequence of nucleotides along the DNA, eventually determines the sequences of amino acids in the protein gene product of the specific gene in question. After transcription, enzymes clip out the intron sequences; then other enzymes splice together the remaining segments (exons) to form messenger RNA (mRNA). The mRNA leaves the cell nucleus, travels to the rough endoplasmic reticulum (RER), and serves as the template for translation into a linear sequence of amino acids, which occurs on ribosomes. After translation is completed, further processing occurs. The penultimate product is typically packaged into vesicles before being transported to the Golgi apparatus. Additional posttranslational processing usually occurs within the rough endoplasmic reticulum, the Golgi apparatus, and in the vesicles to yield the final version of the peptide/protein. More recently, it has become clear that the cellular clockwork is more complex, with a number of integrated feedback loops whose regulators are often, themselves, controlled by elements of the core clock mechanism. Two other promoter elements have emerged as important for circadian rhythm generation, DBP/E4BP4 binding elements (D boxes) and REV-ERB/ROR binding elements (RREs; Ueda et al., 2005). REV-ERB, an orphan nuclear receptor, negatively regulates the activity of the CLOCKBMAL1 complex and is also acted on by PER and CRY. Transcription of REV-ERB is controlled by the same mechanism controlling Per and Cry transcription. Similarly, the transcription factor DPB is positively regulated by the

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CLOCK:BMAL1 complex (Ripperger & Schibler, 2006) and acts as an important output mechanism by driving rhythmic transcription of other output genes via a PAR basic leucine zipper (PAR bZIP; Lavery et al., 1999). Whereas most work to date has focused on transcriptional regulation as the key mechanism driving cellular rhythms, posttranscriptional and posttranslational events are also critical for circadian coordination (Baggs & Green, 2003; C. Kramer, Loros, Dunlap, & Crosthwaite, 2003; Reddy et al., 2006). In addition to transcriptional/translational control of cellular clock function, regulatory kinases also play a pronounced role in regulation of circadian period. Over a decade ago, a circadian mutation called tau was identified that resulted in a shortened circadian period in Syrian hamsters (Ralph & Menaker, 1988). It is now known that the tau locus is encoded by casein kinase I epsilon (CKI; Lowrey et al., 2000; H. Wang, Ko, Koletar, Ralph, & Yeomans, 2007). In normal rodents, CKI phosphorylates PER and “tags” it for degradation throughout the day. Eventually, PER acts to overwhelm CKI, and dimerizes with CRY to feed back to the cell nucleus. The mutant form of CKI is unable to phosphorylate PER, leading to a short circadian period in tau mutant mice due to premature nuclear entry of PER:CRY dimers (Lowrey et al., 2000; Vielhaber, Eide, Rivers, Gao, & Virshup, 2000). The pronounced effects of single circadian gene mutations have been dramatized in a study identifying the genetic basis for a sleep abnormality in humans known as familial advanced sleep-phase syndrome (FASPS). In affected individuals, sleep onset occurs very early, around 19:30 hr, sleep duration is normal, and wake-up time is advanced to about 4:30 hr. This sleep disorder was found to be the result of a single point mutation in the CKI binding region of the PER2 gene, causing hypophosphorylation by CKI in vitro (Toh et al., 2001). Thus, as in the tau mutant, abnormal phosphorylation of PER protein by CKI likely leads to premature negative feedback of PER:CRY heterodimers, thereby speeding the “gears” of the circadian clock. A role for Per3 in human delayed sleep phase syndrome has also been reported (Archer et al., 2003). Whereas extraordinary progress has been made in uncovering the mechanisms responsible for clock function at the molecular level, the complexity of this mechanism is still not fully understood. Determining the specific interactions among complementary feedback loops will allow a further understanding of cellular clock function. Because downstream effects of CCGs are crucial in translating cellular clock function to physiological outcomes, it will be necessary to broaden our understanding of the means by which core clock mechanisms convey relevant timing information at a systems level.

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Ubiquity of Circadian Clocks The genes regulating circadian rhythmicity and their protein products have been found in numerous sites, including extra-SCN brain loci and in the periphery (Abe et al., 2002; Balsalobre, Damiola, & Schibler, 1998; Kriegsfeld, Korets, & Silver, 2003; Yamazaki et al., 2000). These findings led to questions regarding the unique nature of the master oscillator in the SCN, the functional significance of extra-SCN oscillators, and mechanisms of coordination of these widely dispersed clocks. As described in the following sections, the SCN serves to coordinate cellular oscillators throughout the CNS and periphery. Without a master clock, all other timing systems cease to function as independent cellular oscillators in target systems lose their combined coordination. The loss of coherent rhythmicity in the SCN or peripheral tissue can be due to dampening of rhythms in individual cells or to loss of synchrony among a population of cells in the tissue. Use of Per1-luciferase reporter animals indicates that rhythms in peripheral tissues damp then disappear over time due to uncoupling (desynchronization) among oscillators that retain their individual rhythms (Nagoshi et al., 2004; D. Welsh, 2004). Presumably, peripheral clock cells normally get phase information from the SCN to synchronize individual oscillators to each other. In this view, the SCN sets the phase of peripheral circadian clocks daily, coordinating the activity of tissues and organs of the body relative to one another, thereby maintaining homeostasis. However, the phase of peripheral clocks is also significantly influenced by the daily food intake schedule (e.g., Stokkan, Yamazaki, Tei, Sakaki, & Menaker, 2001). In contrast to hamsters, which show strong circadian organization of nocturnal feeding, common voles (Microtus arvalis) feed throughout the day with an ultradian rhythm of 2 to 3 hours. In voles, the clock-gene mRNAs display high circadian amplitudes in the SCN, but display virtually no cyclicity in the liver (van der Veen et al., 2006). Entrainment of the Circadian System As suggested previously, in order to be adaptive for an organism, circadian rhythms must be synchronized to local environmental time. In addition to a direct visual pathway from retinal ganglion cells to the visual cortex, there is also a direct retinohypothalamic tract (RHT) projecting from the optic nerve to the SCN (Klein & Moore, 1979; Moore & Klein, 1974; Figure 4.5). This second visual pathway is necessary and sufficient to entrain (synchronize) the SCN to the environmental light/dark cycle. If the primary visual pathway is transected at the level of the optic tract beyond the optic chiasm (i.e., caudal to the SCN), then the animal

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Figure 4.5 The visual pathway mediating entrainment in mammals. Note. Light stimulates intrinsically photosensitive ganglion cells within the eyes that convey illumination information directly to the SCN of the hypothalamus. This pathway is separate from the classical visual system and uses melanopsin as the light-sensitive photopigment.

is visually blind, but the circadian system continues to respond to photic cues by synchronizing the animal to the light/dark cycle (Klein & Moore, 1979). These studies demonstrate the route whereby environmental photic information can reach the SCN. However, the specific retinal photoreceptor responsible for transmitting this signal was initially enigmatic, as entrainment was independent of traditional, image-forming photoreceptors; mice lacking both rod and cone photoreceptors (rd/rd) exhibit grossly normal entrainment even though they are visually blind (Foster et al., 1993; Freedman et al., 1999; Lucas, Freedman, Munoz, Garcia-Fernandez, & Foster, 1999; Van Gelder, 2001). These findings led to a search for a novel nonrod/noncone photoreceptor. Several years of rapid discovery beginning in this millennium identified a subset of light-responsive ganglion cells containing the photopigment melanopsin (Berson, Dunn, & Takao, 2002; Hannibal & Fahrenkrug, 2002). These ganglion cells project directly to the SCN and were initially thought to be the sole photoreceptors necessary for entrainment. However, melanopsin deficient mice exhibit only minor impairments in entrainment (Lucas et al., 2003; Panda et al., 2002; Ruby et al., 2002). This discrepancy was resolved by showing that entrainment is abolished in mice doubly mutant for both melanopsin and traditional rod/cone photoreceptors (Hattar et al., 2003; Panda et al., 2003). These findings suggest that traditional rod/cone photoreceptors project to

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specialized light-responsive ganglion cells that then transmit this integrated photic information directly to the SCN. Thus, these two types of receptors likely work together to entrain the circadian clock, and either receptor type can support entrainment in the absence of the other.

Circadian Output and the Control of Behavior Diffusible SCN Output Early work in which SCN grafts were shown to restore circadian patterns in activity-related behaviors suggested that circadian rhythmicity could be supported by diffusible output from the clock (Lehman et al., 1987; Ralph et al., 1990; Silver, Lehman, Gibson, Gladstone, & Bittman, 1990). This supposition was based on the fact that transplants restored circadian function independent of the establishment of neural SCN connections with the host brain. This possibility was demonstrated definitively by encapsulating donor SCN tissue in a membrane that prevented neural outgrowth while allowing the diffusion of signals between graft and host; behavioral rhythms were still restored under these conditions (Silver, LeSauter, Tresco, & Lehman, 1996). One candidate diffusible signal is prokineticin-2 (PK2; Cheng et al., 2002). This protein is expressed rhythmically in the SCN and its receptor is present in all major SCN targets (Cheng, Bittman, Hattar, & Zhou, 2005; Cheng et al., 2002). Likewise, PK2 administration during the night (when levels are low) inhibits wheel-running behavior. Whether this signal normally operates in a diffusible manner and/or is released synaptically requires further examination. A second candidate diffusible signal is transforming growth factor-alpha (TGF-alpha; A. Kramer et al., 2001). As with PK2, TGF-alpha is expressed rhythmically in the SCN and its administration inhibits wheel-running behavior. The receptor for TGF-alpha is also expressed in the subparventricular zone (SPVZ), the major target of the SCN. Again, the degree that TGF-alpha is released in a diffusible manner under normal conditions requires further study. Studies in which fiber output is eliminated from the SCN (allowing only for diffusible output), in conjunction with administration of PK2 and TGF-alpha antagonists, are necessary to begin to answer this question. Although it is intriguing to speculate on the role of these signals in communicating information from the SCN, the problem of unequivocally identifying an endogenous, physiologically relevant diffusible SCN signal is complex. The necessary and sufficient criteria to confirm the existences of a diffusible signal in a fluid volume have been summarized previously (Nicholson, 1999). First, evidence that the removal or replacement of the signaling substance results in a change in the response being controlled and an

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assay of the substance should indicate that it is present or increases, or both, in a well-defined temporal relationship to the response (and similarly declines when the response disappears). In addition, evidence must be obtained that a fluid compartment is the conduit for a diffusible or transported signal. The signal must have access to and enter the compartment where the fluid dynamics and turnover in the compartment should allow appropriate movement of the signal. Although PK2 and TGF-alpha meet some of these criteria, further research is necessary to clarify the role of these molecules in communicating circadian information. Neural Control of Neurosecretory Factors In contrast to behavioral rhythms (e.g., locomotion, drinking, gnawing), endocrine rhythms require neural projections from the SCN to endocrine targets; endocrine rhythms are abolished after knife cuts severing SCN fibers (Hakim, DeBernardo, & Silver, 1991; Nunez & Stephan, 1977) and are not restored in SCN-lesioned transplanted animals (Meyer-Bernstein et al., 1999; Nunez & Stephan, 1977; Silver et al., 1996), presumably due to inadequate neural innervation of the host brain by the graft. Further evidence for a neural SCN output signal regulating hormone secretion is seen in studies of female hamsters. When housed in constant light, the activity of a subset of hamsters “splits” into two separate activity bouts within a 24-hour interval. These split females display two daily preovulatory LH surges, each approximately half the concentration of a single surge in a nonsplit female (Swann & Turek, 1985). Under normal conditions, both halves of the bilaterally symmetrical SCN are active in synchrony. In ovariectomized, estrogen-implanted split hamsters examined during one of their activity bouts, however, activation of the SCN occurs on one side of the brain (monitored by expression of the early immediate gene FOS), but not on the other, suggesting that each half of the SCN can control an activity bout (de la Iglesia, Meyer, Carpino, & Schwartz, 2000). Remarkably, FOS activation in GnRH neurons was only seen on the side of the brain in which SCN FOS expression occurred (de la Iglesia et al., 2000; de la Iglesia, Meyer, & Schwartz, 2003). These findings suggest that the precise timing of the LH surge is derived from a neural signal originating in the SCN and communicated to ipsilateral GnRH neurons, as a diffusible output signal would reach both sides of the brain. Importantly, some hypothalamic sites are activated ipsilaterally, while others are activated either ipsilaterally or bilaterally in the split animal, again supporting the notion of multiple SCN output pathways (Yan, Foley, Bobula, Kriegsfeld, & Silver, 2005). Neural output from the SCN has been extensively investigated in rats and hamsters using tract-tracing techniques (Kalsbeek, Teclemariam-Mesbah, & Pevet, 1993;

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Kriegsfeld, Leak, Yackulic, LeSauter, & Silver, 2004; Leak & Moore, 2001; Morin, Goodless-Sanchez, Smale, & Moore, 1994; Stephan, Berkley, & Moss, 1981; Watts & Swanson, 1987). Importantly, many of these monosynaptic projections target brain regions containing neuroendocrine cells producing hypothalamic-releasing hormones. Direct projections have been traced from the SCN to the medial preoptic area (MPOA), supraoptic nucleus (SON), anteroventral periventricular nucleus (AVPV), the paraventricular nucleus (PVN), the dorsomedial nucleus of the hypothalamus (DMH), and the lateral septum and the arcuate (Arc). The SCN also projects to the pineal through a multisynaptic pathway (Klein, 1985; Klein et al., 1983). There is abundant evidence for direct neural SCN control of neuroendocrine cell populations (Buijs, Hermes, & Kalsbeek, 1998; Buijs, van Eden, Goncharuk, & Kalsbeek, 2003; Egli, Bertram, Sellix, & Freeman, 2004; Gerhold, Horvath, & Freeman, 2001; Horvath, Cela, & van der Beek, 1998; Kalsbeek & Buijs, 2002; Kalsbeek, Fliers, Franke, Wortel, & Buijs, 2000; Kalsbeek, van Heerikhuize, Wortel, & Buijs, 1996; Kriegsfeld, Silver, Gore, & Crews, 2002; Van der Beek, Horvath, Wiegant, Van den Hurk, & Buijs, 1997; Van der Beek, Wiegant, Van der Donk, Van den Hurk, & Buijs, 1993; Vrang, Larsen, & Mikkelsen, 1995). Because these cell populations can regulate neurochemicals that are secreted into the CSF (Reiter & Tan, 2002; Skinner & Caraty, 2002; Skinner & Malpaux, 1999; Tricoire, Moller, Chemineau, & Malpaux, 2003) or general circulation, SCN-derived signals can control widespread systems in the brain and body. Considered together, the findings summarized previously suggest several possibilities. For example, behavioral rhythms may be controlled by a diffusible signal(s), whereas endocrine rhythms may require neural output. Alternatively, behavioral and endocrine rhythms can both be supported by diffusible signals, but the threshold for supporting behavioral rhythms is lower. Finally, behavioral rhythms are controlled by both neural and diffusible signals, and either can maintain rhythmic function, while endocrine rhythms can only be supported via neural connections. Definitive identification of biologically significant endogenous diffusible signal(s) and the precise mode of SCN control is a current line of inquiry.

SEASONAL CHANGES IN BRAIN AND BEHAVIOR In common with daily fluctuations in energy availability, intake, and requirements, energy availability and requirements vary throughout the year, and individuals must parse their various activities across the year to maximize energy

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utilization and survival. Thus, all energetically expensive activities (e.g., mating, migrating, foraging, nest building, and thermoregulation) cannot be simultaneously performed. To allow anticipation of annual fluctuations in energy conditions and synchronization of their behavior and physiology accordingly, individuals have evolved mechanisms to determine time of year. In common with the circadian system, seasonal timekeeping mechanisms permit the coordination of internal physiology, and also allow animals to predict recurring seasonal events, such as food availability, general weather conditions, and predator activity, and adjust their behavior appropriately. Seasonal Changes in Reproductive Function Whereas many seasonal changes in behavior have been documented, breeding seasons represent the most salient seasonal change in behavior. In addition to mating behaviors, other associated behaviors such as food intake, aggression, and territorial defense show marked seasonal fluctuations. In general, small animals tend to breed during the long days of spring and summer, whereas large animals with relatively extended periods of gestation, breed during the short days of autumn. In both cases, offspring are produced in the spring when food availability is at a seasonal maximum. The specific timing of breeding represents a selective compromise among competing trade-offs such as the availability of food for gestation or lactation. Syrian hamsters (Mesocricetus auratus) have been studied as the exemplar of a long-day breeder. Individuals of this species reduce circulating concentrations of reproductive steroid hormones, LH, and follicle-stimulating hormone (FSH), after exposure to simulated winter day lengths or appropriate melatonin treatment (Bartness, Powers, Hastings, Bittman, & Goldman, 1993; Swann & Turek, 1988). In sheep, the most commonly studied short-day breeder, exposure to short days leads to increased LH secretion, manifested as an increase in the frequency of pulsatile LH secretion (Lehman et al., 1997). Such changes in pituitary gonadotropin secretion lead to changes in gonadal growth and gonadal steroid hormone secretion and the subsequent onset of mating behavior during the autumn. Circulating testosterone concentrations decrease rapidly in response to the short day via enhanced negative feedback effects on gonadotropin-releasing hormone (GnRH) secretion in male rodents (Turek, 1977). Short-day male rodents display lower circulating testosterone concentrations compared with long-day males. Because testosterone modulates many behaviors (in addition to reproduction), this hormone is another potential mediator of seasonal adjustments in the brain, which could act alone or in combination with melatonin or other hormones. Examples of testosterone affecting

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seasonal behaviors include the combined effect of testosterone and photoperiod on locomotor activity in male Syrian hamsters (Ellis & Turek, 1983) and the reinstatement by exogenous testosterone of female odor preference in castrated male meadow voles maintained in long or short days (Ferkin & Gorman, 1992). Testosterone replacement prevents elevated food intake in castrated male deer mice (Blank, Korytko, Freeman, & Ruf, 1994) and restores aggressive behaviors in Syrian hamsters maintained in short days (Jasnow, Huhman, Bartness, & Demas, 2000). Although no correlation has been reported between testosterone concentrations and spatial learning and memory among male meadow voles (Galea, Kavaliers, Ossenkopp, & Hampson, 1995), short days reduce testosterone, hippocampal volume, and spatial learning in deer mice (Perrot-Sinal, Kavaliers, & Ossenkopp, 1998). However, direct manipulation of testosterone in seasonally breeding rodents and the potential interaction between photoperiod and testosterone has rarely been investigated in learning and memory paradigms (Pyter, Trainor, & Nelson, 2006). Testosterone increases the volume of song-production brain regions in songbirds (Ball et al., 2004) and altered neurochemistry (Bittman, Tubbiola, Foltz, & Hegarty, 1999) and neuroanatomy (Gomez & Newman, 1991) in photoperiodic rodents. For example, testosterone increases the number of cells expressing the proopiomelanocortin gene in the hypothalamus (Bittman et al., 1999) and restores neuronal branching and morphology within the amygdala (Gomez & Newman, 1991) of castrated Syrian hamsters. Steroid hormones have important effects on learning and memory although there are species differences. Despite the presence of androgens and estrogens in both males and females, for the most part studies on males have focused on the role of androgens whereas studies on females have focused on the role of estrogens. In addition to manipulating photoperiod to induce seasonal changes in brain and behavior, studies have mimicked similar seasonal results by manipulating melatonin, the physiological signal into which ambient photoperiod is transduced (Bartness et al., 1993; Carter & Goldman,

Figure 4.6 Input pathway from the retina to the pineal gland in mammals.

Pineal

SCN

PVN

OC

Eye SCG

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1983; Nelson, Badura, & Goldman, 1990). The synthesis of melatonin occurs exclusively at night and is inhibited directly by light. The duration of melatonin release is proportional to the length of the dark phase (Illnerova, Hoffmann, & Vanecek, 1984); consequently, short-day animals experience longer durations of melatonin than do long-day animals. Infusion of appropriate duration profiles of physiological concentrations of melatonin to pinealectomized hamsters has shown the critical role of melatonin duration in signaling day length (Bartness et al., 1993). Peromyscus and other small rodents, whose gestations are relatively short (about 3 weeks), are long-day breeders and respond reproductively to long-day (short-duration) melatonin signals. Melatonin affects circadian rhythms of behavior (Golombek, Pevet, & Cardinali, 1996), but also provides seasonal information throughout the body. Melatonin receptors are distributed discretely throughout the rodent brain (Drew et al., 2001; Dubocovich, Rivera-Bermudez, Gerdin, & Masana, 2003; Weaver, Carlson, & Reppert, 1990). Importantly, melatonin receptors are present in the hippocampal area (entorhinal cortex) and other nonreproductive regions of rodent brains (Musshoff, Riewenherm, Berger, Fauteck, & Speckmann, 2002; Weaver et al., 1990), providing a neural substrate for direct effects of melatonin on learning and memory and other cognitive and motivated behaviors (described next). Manipulation of melatonin duration to mimic long or short days induces seasonal changes in behavior. Removal of the source of melatonin via pinealectomy impairs photoperiodic responses (Bartness & Goldman, 1989; Goldman, 2001). The neural mechanisms regulating circadian changes in melatonin secretion have been well characterized (Figure 4.6). Circadian rhythms in melatonin secretion in most mammals depend on neural efferents from the SCN to the region of the paraventricular nucleus of the hypothalamus (PVN). This projection continues through the medial forebrain bundle to the superior cervical ganglion (SCG) of the spinal cord. From the SCG, sympathetic neurons drive pineal melatonin secretion during the dark, while melatonin production and secretion are inhibited during the light

Note. Light information is transduced into a neural signal in the retina and transmitted via a direct retino-hypothalamic tract to the suprachiasmatic nucleus (SCN). From the SCN, fibers synapse in the paraventricular nucleus of the hypothalamus (PVN). From the PVN, fibers travel through the medial forebrain bundle to the superior cervical ganglion (SCG). Postganglionic fibers from the SCG then project to the pineal gland to modulate melatonin production/secretion. OC  Optic chiasm.

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portion of the LD cycle (Cassone, 1990; Ganguly, Coon, & Klein, 2002). This multisynaptic pathway has been confirmed using the transneuronal retrograde tracer, pseudorabies virus, injected into the pineal gland (Card, 2000; Larsen, Enquist, & Card, 1998). This technique confirmed the links in the pathway from the SCN to the pineal gland, and also suggested that two parallel circuits from the SCN (one from the dorsomedial and one ventrolateral) likely drive melatonin secretion. This day-night regulation of melatonin is regulated by the SCN (for a review, see Cassone, 1990), and, as with other hormonal systems mentioned previously, lesions of the SCN abolish circadian rhythms in melatonin production and secretion (Scott, Jansen, Kao, Kuehl, & Jackson, 1995; Tessonneaud, Locatelli, Caldani, & Viguier-Martinez, 1995). Although melatonin influences GnRH secretion, this hormone does not appear to act directly on GnRH neurons. Given the widespread distribution of the GnRH system, it has been difficult to determine the melatonin-sensitive systems that, in turn, act on GnRH neurons to regulate their activity. Because regression of the reproductive axis is, in part, due to melatonin-mediated increases in the negative feedback of sex steroids, those brain regions co-expressing melatonin receptors and androgen receptors may be critical in this response. The dorsomedial hypothalamus (DMH) of Syrian hamsters binds both melatonin and androgen with high affinity (Maywood, Bittman, & Hastings, 1996). Lesions of the DMH block short-day and melatonin-induced regression of the reproductive system (Lewis, Freeman, Dark, Wynne-Edwards, & Zucker, 2002; Maywood et al., 1996), suggesting that the DMH is a key target of melatonin in this species. In other species such as Siberian hamsters, melatonin feedback to the SCN is critical for melatonin-induced seasonal alterations in several physiological parameters. Lesions of the SCN block the effects of daily, long-duration melatonin infusions (i.e., short-day pattern) on body mass, fat pad distribution, and reproductive function (Bartness, Goldman, & Bittman, 1991; Bittman, Bartness, Goldman, & DeVries, 1991). This finding suggests that the circuit beginning with the SCN also requires the SCN as a target. In addition to acting on the DMH, melatonin binding is largely seen in the pars tuberalis of seasonally breeding mammals (Bittman & Weaver, 1990; Weaver et al., 1990). In hypothalamic-pituitary transected sheep, melatonin implants in region of the pars tuberalis reduce prolactin secretion in a manner similar to short-day lengths (Lincoln & Clarke, 1997). However, melatonin implants in this region do not affect gonadotropin secretion, suggesting that this region may be important for the regulation of the lactotropic, but not gonadotropic, photoperiodic effects (Lincoln & Clarke, 1997). Research on the means by which melatonin regulates other parameters is still in its infancy.

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It is unclear whether these melatonin-sensitive targets project directly on the GnRH system or indirectly via projections to other peptidergic systems. One candidate intermediary system is kisspeptin, a peptide recently shown to have marked stimulatory effects on the GnRH system. After chronic exposure to short days, Siberian hamsters with suppressed reproductive function exhibit marked reduction in kisspeptin cell labeling in the anteroventral periventricular nucleus, a neural target of the SCN (Greives et al., 2007; Mason et al., 2007). In common with other species, a subset of individual Siberian hamsters fails to respond to day-length information, however, and maintains their reproductive function. These so-called photoperiod nonresponsive individuals exhibit kisspeptin expression akin to that of long-day animals, suggesting that short photoperiods (and melatonin) are ignored by the brains of these animals. These results suggest an important role for kisspeptin in coordinating and relaying environmentally relevant information to the reproductive axis as well as a role for this peptide in regulating seasonal changes in reproductive function (Revel et al., 2006, 2007). A number of studies suggest a role for clock genes in the control of seasonality (Hofman, 2004; Johnston et al., 2003; Lincoln, Andersson, & Loudon, 2003). In Syrian and Siberian hamsters, photoperiod alters the duration of clock and clock-controlled gene expression, while the amplitude of gene expression is influenced by photoperiod in the pars tuberalis (Johnston et al., 2003; Messager, Hazlerigg, Mercer, & Morgan, 2000). In sheep, however, the relative timing of clock genes is altered by photoperiod in the pars tuberalis, providing a mechanism of temporal encoding and downstream control (Hazlerigg, Andersson, Johnston, & Lincoln, 2004; Lincoln et al., 2003; Lincoln, Johnston, Andersson, Wagner, & Hazlerigg, 2005; Lincoln, Messager, Andersson, & Hazlerigg, 2002). These correlational results are intriguing and suggest that phase and/or amplitude of clock and clock-controlled genes in SCN brain targets and endocrine glands may predict their responsiveness to upstream signals on a daily schedule. Seasonal Changes in the Avian Song System Associated with seasonal breeding are activities related to reproduction such as territorial defense, migration, or communication. For example, male canaries sing more frequently in spring than in winter, and they appear to lose components of their songs after each breeding season and incorporate new song components each spring (Brenowitz & Beecher, 2005). In spring, as day length increases, the testes grow and secrete androgens, song frequency increases, song repertoire enlarges, and the higher vocal center (HVC) and the robust nucleus of the archistriatum (RA), two brain

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nuclei necessary for song production, double in size. Under autumnal day lengths, testes regress in size and androgen production virtually stops, frequency of singing decreases, song repertoire shrinks, and the HVC and RA regress in size (Nottebohm, 2005). Treatment with testosterone in autumn mimics spring hormonal conditions and supports song production. The seasonal plasticity in behavior has been assumed to reflect the seasonal changes in brain morphology induced by hormonal adjustments (Tramontin & Brenowitz, 2000), although the precise mechanisms underlying seasonal plasticity in song complexity remains unspecified (Brenowitz, Lent, & Rubel, 2007). Estrogens, converted from testicular androgens or produced de novo in CNS neurons (Schlinger, Soma, & London, 2001), appear necessary to activate the neural mechanisms underlying the song system in birds. Androgens enter neurons containing aromatase, which converts them to estrogens. Aromatase is generally localized in neurons adjacent to other neurons containing estrogen receptors in the hypothalamus and preoptic area of songbird brains, as well as in limbic structures and in the structures constituting the neural circuit controlling bird song. The brain appears to be the primary source of estrogens, which activate masculine behaviors in many bird species (London, Monks, Wade, & Schlinger, 2006). Photoperiod is important in some birds to mediate these changes, including recruitment of new neurons (Nottebohm, 2005). Testosterone, or its metabolites including estrogens, appears to drive the seasonal changes in brain structure and behavior. In addition to seasonal changes in singing behavior, substantial seasonal changes occur in the morphology of several song nuclei. For example, the volume of the HVC and RA increases by 99% and 77%, respectively, among male canaries maintained under spring day lengths (12 hours light/day) relative to birds in autumnal conditions ( 12 hours light/day; Nottebohm, 1981). Similar results have been reported for more than 25 other bird species (reviewed in Ball, Riters, & Balthazart, 2002). These seasonal changes in the size of specific brain structures are probably mediated by testosterone or its metabolites (Ball et al., 2002; London et al., 2006). Testosterone appears to act via brain-derived neurotrophic factor (BDNF) to promote survival of new neurons in the brains of adult songbirds (Rasika, Alvarez-Buylla, & Nottebohm, 1999). Testosterone upregulates BDNF in the HVC of adult male and female canaries and infusion of antibody against BDNF blocks androgen-induced neurogenesis. Behavioral feedback is important because singing increases BDNF expression in the HVC in proportion to the number of songs produced (Li, Jarvis, Alvarez-Borda, Lim, & Nottebohm, 2000). BDNF seems to determine life expectancy of newly born cells in adult songbird brains.

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Vernal elevation in testosterone concentrations coincident with long days provokes BDNF production that protects new neurons within 10 days of cell birth (Alvarez-Borda, Haripal, & Nottebohm, 2004). Other avian models of seasonal brain plasticity include seasonal change in hippocampal volume of food-caching (Hoshooley & Sherry, 2007; Smulders, Sasson, & DeVoogd, 1995) and brood parasitic (Sherry, Forbes, Khurgel, & Ivy, 1993) birds. The hippocampus is involved in spatial learning and memory, and generally, species with a larger relative hippocampal volume display better spatial learning and memory (Sherry, Jacobs, & Gaulin, 1992). Hippocampal size is reduced during the winter in these bird species when spatial learning and memory performance associated with food storing (Barnea & Nottebohm, 1994, 1996) and nest parasitism (Sherry et al., 1993) is reduced. Comparable studies of seasonal brain plasticity in mammals have been relatively rare, despite the fact that seasonal breeding has been well-documented in nontropical mammals. Seasonal Changes in Mammalian Brain and Behavior Although the brain constitutes only about 2% to 3% of the total body mass in rodents, it consumes over 10% of total energy expenditure (Mink, Blumenschine, & Adams, 1981). Thus, minor reductions in brain mass could save significant energy (Jacobs, 1996). Seasonal changes in brain weight have been documented in rodents and shrews (Yaskin, 1984). Brain weights are higher in summer than winter (Yaskin, 1984). Winter brain mass is reduced in grey squirrels (Sciurus carolinensis; Lavenex et al., 2000b) and ferrets (Mustela putorius; Weiler, 1992). Also, brain mass (absolute and corrected for body mass) and specific brain regions (e.g., hippocampus) are reduced during winter in common shrews (Sorex araneus) and bank voles (Clethrionomys glareolus; Yaskin, 1984). A significant part of the seasonal change in brain weight might merely reflect differences in water content; however, the neocortex and the basal portion of the brains (i.e., the corpus striatum) of rodents and shrews show seasonal cytoarchitectural changes. Seasonal changes in hippocampal morphology have been reported in hibernating ground squirrels (Citellus undulatus; Popov, Bocharova, & Bragin, 1992) and meadow voles (Microtus pennsylvanicus; Galea & McEwen, 1999), although hippocampal volume did not vary with season in grey squirrels (Lavenex, Steele, & Jacobs, 2000). In European hamsters (Cricetus cricetus) and sheep, seasonal changes in the innervation of the brain have been reported (Buijs et al., 1986; Xiong, Karsch, & Lehman, 1997). Seasonal changes in neuroendocrine function (Wehr, 1998), hypothalamic peptide expression (Hofman & Swaab,

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1995), and serotonin function (Alstadhaug et al., 2005; Brewerton & George, 1990) have also been reported in humans. Seasonal changes in human behavioral pathology are also observed in anxiety and depression (Enns et al., 2006; Lewy, Lefler, Emens, & Bauer, 2006; Rosenthal et al., 1984; Sigmon et al., 2007), migraine headaches (Alstadhaug et al., 2005; Brewerton & George, 1990), as well as incidence, severity, and mortality of strokes (Carolei, Marini, De Matteis, Di Napoli, & Baldassarre, 1996; H. Wang, Sekine, Chen, & Kagamimori, 2002; Y. Wang et al., 2003). Thus, despite the relative lack of seasonal organization of reproductive function, it is apparent that humans retain responsiveness to photoperiod (reviewed in Bronson, 1995, 2004), and that photoperiod-mediated adjustments in rodent brain and behavior may be important to understand seasonal changes in human brain and behavior. Photoperiod also affects cell division in the dentate gyrus and subependymal zone of adult mammals (Huang, DeVries, & Bittman, 1998). Long-term exposure to short days in Syrian hamsters doubles the number of new neurons produced in these brain regions, as well as in the hypothalamus and cingulate-retrosplenial cortex (Huang et al., 1998). There are no appreciable photoperiodic differences in brain volume of either the granule cell layer of the hippocampus or the dentate. These results are in contrast to studies of Peromyscus which likely represents a species difference. No differences in cell proliferation are observed in brains of grey squirrels (Lavenex et al., 2000), although the use of low doses of BrdU may not pick up differences (Gould & Gross, 2002). Motoneurons controlling penile muscles, neuromuscular junctions, and somas are smaller in adult male Siberian hamsters and white-footed mice exposed to short days compared with animals exposed to long days (Forger & Breedlove, 1987). Adult male deer mice (Peromyscus maniculatus) maintained in short days have smaller brains and lower adjusted hippocampal volume relative to those maintained in long days (Perrot-Sinal et al., 1998). Thus, several examples of seasonal influences on several brain measures exist, but what is lacking is an understanding of how seasonal factors influence neural structures in mammals. It is likely that seasonal brain changes will be subtle in mammals compared to birds because birdsong generally displays an all-or-none seasonality; seasonal changes in learning and memory are consistent, albeit subtle, and such variation likely provides important grist for evolutionary processes. Seasonal Changes in Learning and Memory Deer mice and white-footed mice tested during the breeding season display better spatial learning performance compared to mice tested during the nonbreeding season (Galea,

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Kavaliers, Ossenkopp, Innes, & Hargreaves, 1994; Pyter, Reader, & Nelson, 2005). This may represent differences in territory size, and therefore spatial memory requirements (Jacobs, 1996). The relationship among home range size and spatial learning and memory has been established in several rodent species. For example, male Peromyscus maniculatus have larger home ranges than females during the breeding season that decline in winter (Bronson, 1985, 1988). This is paralleled by superior spatial learning and memory performance in male Peromyscus as compared to females in long, but not short, days (Galea, Kavaliers, & Ossenkopp, 1996; Galea et al., 1994). The effects of photoperiod on learning and memory are consistent with a life-history explanation of reproductive strategy. Both hippocampal size and spatial learning and memory performance are sexually dimorphic among polygynous rodents (Jacobs, 1996). Polygynous male rodents outperform females in spatial tasks and have larger hippocampi (Galea et al., 1994, 1996; Jacobs, 1996). In common with birds, spatial learning and memory performance is often positively correlated with hippocampal size in mammals (Jacobs, Gaulin, Sherry, & Hoffman, 1990; Sherry, Jacobs, & Gaulin, 1992). In food caching birds, one might predict that hippocampal volume increases during winter (Pravosudov & Clayton, 2002), and that caching behaviors and hippocampal volume should increase among individuals inhabiting particularly harsh conditions. Indeed, black-capped chickadees (Poecile atricapillus) from the most harsh conditions (i.e., exposed to lowest temperatures, shortest day lengths, and most snow cover) had the largest hippocampal volumes and most hippocampal neurons compared to birds from mild conditions (Roth & Pravosudov, 2008). Thus, environmental conditions can shape specific brain structures in precise ways to enhance survival and reproductive success. Seasonal Changes in Aggression It is well documented that androgens regulate aggressive behavior, especially the dramatic male-male interactions associated with mating territories. For instance, many male mammals and birds set up and defend territories before the onset of the breeding season (Goymann, Landys, & Wingfield, 2007; Wingfield, Jacobs, & Hillgarth, 1997). Females often choose males on the basis of resources available in these territories so evolutionary pressures are high on males to compete successfully. Aggressive behavior is costly, both in terms of energy and in terms of potential injury or death. Thus, individuals often exchange information about the likely outcome of the aggressive encounter without actual combat. The energetic costs and survival from wounds vary seasonally leading to fluctuations in

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the likelihood of aggressive encounters as the cost-benefit ratio changes. Individuals of several rodent species undergo a seasonal shift from highly territorial, asocial behavior during the breeding season to a social, highly interactive existence during winter. Such species typically undergo reproductive quiescence at the end of the breeding season in response to short days. The resulting decrease in androgen secretion may be necessary or permissive for the seasonal shift in sociality, but in wood rats (Neotoma fuscipes) nonsteroidal mechanisms mediate the seasonal change in social behavior (G. S. Caldwell, Glickman, & Smith, 1984). The seasonal change in social organization confers several advantages. During the breeding season, rodents control resources that promote their own survival and that of their offspring, and they often aggressively exclude nonkin from access to resources. During the winter, however, this strategy is abandoned in favor of group living that conserves energy and enhances survival in the face of low temperatures and reduced food availability. Many species of rodents conserve energy during the winter by forming aggregations of huddling animals (Madison, 1984). In these aggregations, different sexes and even different species commingle. Even in the absence of huddling behavior, animals may tolerate one another better in close quarters during the winter than during the breeding season. For example, male meadow voles (Microtus pennsylvanicus) are highly territorial in the spring and summer and occupy open meadows, whereas red-backed voles (Clethrionomys gapperii) breed in forest habitats. During the winter months, meadow voles migrate into the spruce forest habitats occupied by the redbacked voles, presumably to take advantage of the protective cover provided by the trees. In some cases, they share nests with individuals of other rodent species (Madison, 1984). Consistent for a role of androgens mediating both mating and aggression, male meadow voles trapped during the winter and tested in paired encounters in a neutral arena displayed less interspecific aggression than voles trapped in summer. The winter reduction in aggressiveness permits energy-saving group huddling. As the animals enter their breeding condition in the spring, they reestablish mutually exclusive territories. Some males within a population do not undergo reproductive regression, maintaining testicular function and producing sperm and androgens during simulated winter conditions (for review, see Prendergast, Kriegsfeld, & Nelson, 2001). The advantages of continuous breeding capability evidently incur substantial hidden costs because only a minority of each population adopts this strategy. One such cost may be that reproductively competent males, because of unusual aggressiveness during the winter, are unable to participate in communal huddling and thus incur greater energetic costs in overwintering.

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High behavioral and energetic costs associated with the maintenance of the reproductive system in winter may explain why nonregressive types do not normally predominate in temperate or boreal-zone populations of rodents (Prendergast et al., 2001). This observation is supported by a field study of winter nesting behavior of prairie voles (M. ochrogaster; McShea, 1990). Most voles in the population studied were reproductively inactive during the winter and formed groups of huddling individuals. Two males, however, remained in breeding condition and never huddled with other animals. In pairwise aggression tests, these two males were much more aggressive than reproductively quiescent individuals. In another study, reproductive status also influenced odor preferences of meadow voles maintained in simulated winter day lengths (Gorman, Ferkin, Nelson, & Zucker, 1993). Males that retained reproductive capability in winter day lengths preferred the odors of females that also failed to inhibit reproduction during short days. This preference may facilitate the sporadic occurrences of winter breeding frequently reported for this species (reviewed in Nelson, 1987). As noted, many if not most species reduce aggression outside of the breeding season. For some species, however, aggression must be maintained throughout the year independent of breeding season. For example, over-wintering migrant birds must compete with residents within local feeding flocks. In such cases, aggression would likely be modulated by mechanisms unrelated to reproduction. In hamsters, short days increase male resident-intruder aggression (P. sungorus; Demas, Polacek, Durazzo, & Jasnow, 2004; Wen, Hotchkiss, Demas, & Nelson, 2004; M. auratus; H. K. Caldwell & Albers, 2004; Garrett & Campbell, 1980; Jasnow et al., 2000). This effect is paradoxical because increased aggression occurs when testosterone concentrations are at a nadir. Despite the lack of plasma androgens, estrogens may still be important (see the following discussion). Adrenalectomy prevents increased aggression in short days in Siberian hamsters (Demas et al., 2004). This mechanism may be involved in winter aggression of birds. Studies of zebra finches (Taeniopygia guttata) show that the adrenal hormone dehydroepiandrosterone (DHEA) can be indirectly converted into estrogens within the brain (Soma, Alday, Hau, & Schlinger, 2004); however, DHEA does not appear to influence aggression in Siberian hamsters (Scotti, Belén, Jackson, & Demas, 2008). In common with hamsters, short day lengths increase aggression levels in beach mice (Peromyscus polionotus). Hormone manipulation studies revealed that the  estrogen receptor subtype (ER ) agonist PPT (propylpyrazole-triol) and the ER, agonist DPN (diarylpropionitrile) increased aggression in short-day P. polionotus and decreased aggression in “long-day” mice (Trainor, Lin, Finy, Rowland, &

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Nelson, 2007). These results suggested that photoperiod regulates processes that occur after estrogens bind to their appropriate receptors. Steroid hormones, including estrogens, can affect physiological and behavioral processes via slow (hours to days) genomic or fast (seconds to minutes) nongenomic pathways (Vasudevan & Pfaff, 2007). Estradiol injections of beach mice resulted in rapid ( 15 minutes) increases in aggression in short-day mice, but not longday mice (Trainor et al., 2007). This suggests that estradiol increases aggression via nongenomic actions in short days, but not in mice housed in long days. Moreover, gene chip analyses indicated that estrogen-dependent expression of genes containing estrogen response elements in their promoters was decreased in the bed nucleus of the stria terminalis (BNST) of short-day mice compared with that of long-day mice. These data suggest that the environment regulates the effects of steroid hormones on aggression in P. polionotus by determining the molecular pathways that are activated by steroid receptors (Nelson & Trainor, 2007). Another factor that plays an important role in regulating aggression is neuronal nitric oxide synthase (nNOS or NOS-1; 11). nNOS produces the neurotransmitter, nitric oxide (NO), as a by-product of the conversion of arginine into citrulline in the central and peripheral nervous systems (Nelson & Trainor, 2007). Nitric oxide produced from neurons appears to be involved in regulating some aggressive behaviors. For example, male mice with targeted disruption of the nNOS gene (nNOS–/–) display sustained aggressive behavior and persistent sexual behavior (Nelson & Chiavegatto, 2001; Nelson et al., 1995). Castration and testosterone replacement studies indicate that testosterone is necessary, but not sufficient, to provoke elevated aggressive behavior in nNOS–/– mice (Kriegsfeld, Dawson, Dawson, Nelson, & Snyder, 1997). Because gonadal steroid hormones influence neuronal nitric oxide synthase (nNOS), and this enzyme has been implicated in aggressive behavior, nNOS expression was hypothesized to be decreased in short-day male Siberian hamsters and negatively correlated with the display of territorial aggression. Again, all short-day housed hamsters were significantly more aggressive than long-day animals, regardless of whether they regressed (responsive) or maintained (nonresponsive) gonadal size or testosterone concentrations (Wen et al., 2004). Short-day animals, both reproductively responsive and nonresponsive morphs, also displayed significantly fewer nNOS-immunoreactive cells in brain areas associated with aggression including the anterior and basolateral amygdaloid areas and paraventricular nuclei as compared to long-day hamsters. Together, these results suggest that seasonal aggression in male Siberian hamsters is regulated by photoperiod, through mechanisms that are likely independent from gonadal steroid hormones.

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SUMMARY The predictable daily and seasonal changes in the environment have led to the evolution of clock mechanisms that permit anticipation of daily and seasonal events to maximize reproductive fitness and survival. Responses to natural light cycles result in adaptive temporal organization in humans and other animals. Since the invention and use of electrical lights, starting around the turn of the twentieth century, this temporal organization has been dramatically altered. Light at night has significant social, ecological, behavioral, and health consequences that are only now becoming apparent for both daily and seasonal rhythms. Increased understanding of circadian organization may lead to individually tailored medical treatments. Appreciation that there are individuals who are “morning larks” or “night owls” may lead to individualized learning programs that schedule cognitive demanding tasks at circadian-appropriate times-ofday. As we learn more about entrainment effects of light, improvements in lighting schedules or melatonin treatment for nightworkers should be made. Understanding the genes underlying the biological clock function has been a remarkable tour de force in molecular biology. As more precision in the mechanisms underlying biological clock function is revealed, we need to improve understanding of the output signals of the central clocks to the periphery. How are circadian rhythms in cell cycling, metabolism, or physiology entrained and maintained? What are the consequences of poor coupling among oscillators? Additional research is necessary to appreciate the health effects of faulty circadian output signals. Behavioral phenotype is the result of a gene and environment interactions. As we have become increasingly sophisticated about the mechanisms underlying gene expression, we need to simplify the environmental variables that affect gene expression to improve our studies. Studies of photoperiod on behavior are important because they allow precise environmental probing of the geneenvironment interactions. Future research will capitalize on the use of simple, yet precise, environmental manipulations to understand the behavioral effects of differential gene expression. Additional studies of the effects of global warming on organisms low on the food chain (e.g., plants and insects) are also warranted. If the timing of abundance of plants and insects shift, then this will affect breeding success of amphibian, reptiles, birds, and mammals that rely solely on photoperiod to time reproduction. Shifts in seasonal links among food, survival, and reproduction may already be affecting populations of amphibians and important pollinating insects, which could have enormous effects on our food availability and survival.

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Importantly, understanding biological clocks and their associated biological rhythms is critical for a full understanding of behavior. Behavior is not constantly expressed, but rather, displays a temporal organization across the day or year. In order to understand why a behavior occurs, an appreciation of the temporal organization of individuals is necessary. It is not appropriate to study learning and memory in nocturnal rats tested in the middle of our day. It is not appropriate to maintain animals on seasonally ambiguous photoperiods of 12 hours of light and 12 hours of dark per day. Not attending to biological rhythms will confound behavioral studies of humans and other animals.

REFERENCES Abe, M., Herzog, E. D., Yamazaki, S., Straume, M., Tei, H., Sakaki, Y., et al. (2002). Circadian rhythms in isolated brain regions. Journal of Neuroscience, 22, 350–356. Abizaid, A., Gao, Q., & Horvath, T. L. (2006). Thoughts for food: Brain mechanisms and peripheral energy balance. Neuron, 51, 691–702. Alstadhaug, K. B., Salvesen, R., & Bekkelund, S. I. (2005). Seasonal variation in migraine. Cephalalgia, 25, 811–816. Alvarez-Borda, B., Haripal, B., & Nottebohm, F. (2004). Timing of brainderived neurotrophic factor exposure affects life expectancy of new neurons. Proceedings of the National Academy of Sciences, USA, 101, 3957–3961. Anand, B. K., & Brobeck, J. R. (1951a). Hypothalamic control of food intake in rats and cats. Journal of Biological Medicine, 24, 123–140. Anand, B. K., & Brobeck, J. R. (1951b). Localization of a “feeding center ” in the hypothalamus of the rat. Proceedings of the Society of Experimental Biological Medicine, 77, 323–324. Aravich, P. F., & Sclafani, A. (1983). Paraventricular hypothalamic lesions and medial hypothalamic knife cuts produce similar hyperphagia syndromes. Behavioral Neuroscience, 97, 970–983. Archer, S. N., Robilliard, D. L., Skene, D. J., Smits, M., Williams, A., Arendt, J., et al. (2003). A length polymorphism in the circadian clock gene Per3 is linked to delayed sleep phase syndrome and extreme diurnal preference. Sleep, 26, 413–415. Aschoff, J. (1965). Circadian rhythms in man. Science, 148, 1427–1432. Aschoff, J. (1981). Annual rhythms in man. In J. Aschoff (Ed.), Handbook of behavioral neurobiology (Vol. 4, pp. 475–490). New York: Plenum Press. Babkoff, H., Caspy, T., Mikulincer, M., & Sing, H. C. (1991). Monotonic and rhythmic influences: A challenge for sleep deprivation research. Psychological Bulletin, 109, 411–428. Baggs, J. E., & Green, C. B. (2003). Nocturnin, a deadenylase in Xenopus laevis retina: A mechanism for posttranscriptional control of circadianrelated mRNA. Current Biology, 13, 189–198. Ball, G. F., Auger, C. J., Bernard, D. J., Charlier, T. D., Sartor, J. J., Riters, L. V., et al. (2004). Seasonal plasticity in the song control system: Multiple brain sites of steroid hormone action and the importance of variation in song behavior. Annals of the New York Academy of Sciences, 1016, 586–610. Ball, G. F., Riters, L. V., & Balthazart, J. (2002). Neuroendocrinology of song behavior and avian brain plasticity: Multiple sites of action of sex steroid hormones. Frontiers in Neuroendocrinology, 23, 137–178. Balsalobre, A., Damiola, F., & Schibler, U. (1998). A serum shock induces circadian gene expression in mammalian tissue culture cells. Cell, 93, 929–937.

c04.indd Sec4:73

Bao, A. M., Ji, Y. F., Van Someren, E. J., Hofman, M. A., Liu, R. Y., & Zhou, J. N. (2004). Diurnal rhythms of free estradiol and cortisol during the normal menstrual cycle in women with major depression. Hormones and Behavior, 45, 93–102. Barnea, A., & Nottebohm, F. (1994). Seasonal recruitment of hippocampal neurons in adult free-ranging black-capped chickadees. Proceedings of the National Academy of Sciences, USA, 91, 11217–11221. Barnea, A., & Nottebohm, F. (1996). Recruitment and replacement of hippocampal neurons in young and adult chickadees: An addition to the theory of hippocampal learning. Proceedings of the National Academy of Sciences, USA, 93, 714–718. Bartness, T. J., & Goldman, B. D. (1989). Mammalian pineal melatonin: A clock for all seasons. Experientia, 45, 939–945. Bartness, T. J., Goldman, B. D., & Bittman, E. L. (1991). SCN lesions block responses to systemic melatonin infusions in Siberian hamsters. American Journal of Physiology, 260(1, Pt. 2), R102–R112. Bartness, T. J., Powers, J. B., Hastings, M. H., Bittman, E. L., & Goldman, B. D. (1993). The timed infusion paradigm for melatonin delivery: What has it taught us about the melatonin signal, its reception, and the photoperiodic control of seasonal responses? Journal of Pineal Research, 15, 161–190. Bernardis, L. L., & Bellinger, L. L. (1998). The dorsomedial hypothalamic nucleus revisited: 1998 update. Proceedings of the Society of Experimental Biological Medicine, 218, 284–306. Berson, D. M., Dunn, F. A., & Takao, M. (2002). Phototransduction by retinal ganglion cells that set the circadian clock. Science, 295, 1070–1073. Bildt, C., & Michelsen, H. (2002). Gender differences in the effects from working conditions on mental health: A 4-year follow-up. International Archives of Occupational and Environmental Health, 75, 252–258. Bittman, E. L., Bartness, T. J., Goldman, B. D., & DeVries, G. J. (1991). Suprachiasmatic and paraventricular control of photoperiodism in Siberian hamsters. American Journal of Physiology, 260(1, Pt. 2), R90–R101. Bittman, E. L., Tubbiola, M. L., Foltz, G., & Hegarty, C. M. (1999). Effects of photoperiod and androgen on proopiomelanocortin gene expression in the arcuate nucleus of golden hamsters. Endocrinology, 140, 197–206. Bittman, E. L., & Weaver, D. R. (1990). The distribution of melatonin binding sites in neuroendocrine tissues of the ewe. Biology of Reproduction, 43, 986–993. Blank, J. L., Korytko, A. I., Freeman, D. A., & Ruf, T. P. (1994). Role of gonadal steroids and inhibitory photoperiod in regulating body weight and food intake in deer mice (Peromyscus maniculatus). Proceedings of the Society of Experimental Biological Medicine, 206, 396–403. Boulos, Z. Rosenwasser, A. M., & Terman, M. (1980). Feeding schedules and the circadian organization of behavior in the rat. Behavioural Brain Research, 1, 39–65. Brenowitz, E. A., & Beecher, M. D. (2005). Song learning in birds: Diversity and plasticity, opportunities and challenges. Trends in Neuroscience, 28, 127–132. Brenowitz, E. A., Lent, K., & Rubel, E. W. (2007). Auditory feedback and song production do not regulate seasonal growth of song control circuits in adult white-crowned sparrows. Journal of Neuroscience, 27, 6810–6814. Brewerton, T. D., & George, M. S. (1990). A study of the seasonal variation of migraine. Headache, 30, 511–513. Bronson, F. H. (1985). Mammalian reproduction: An ecological perspective. Biology of Reproduction, 32, 1–26. Bronson, F. H. (1988). Mammalian reproductive strategies: Genes, photoperiod and latitude. Reproduction and Nutritional Development, 28(2B), 335–347. Bronson, F. H. (1995). Seasonal variation in human reproduction: Environmental factors. Quarterly Review of Biology, 70, 141–164.

8/18/09 5:11:35 PM

74

Biological Rhythms

Bronson, F. H. (2004). Are humans seasonally photoperiodic? Journal of Biological Rhythms, 19, 180–192.

norepinephrine and clonidine. European Journal of Pharmacology, 232(2/3), 277–234.

Brown, E. N., & Czeisler, C. A. (1992). The statistical analysis of circadian phase and amplitude in constant-routine core-temperature data. Journal of Biological Rhythms, 7, 177–202.

Davidson, A. J., Sellix, M. T., Daniel, J., Yamazaki, S., Menaker, M., & Block, G. D. (2006). Chronic jet-lag increases mortality in aged mice. Current Biology, 16, R914–R916.

Buijs, R. M., Hermes, M. H., & Kalsbeek, A. (1998). The suprachiasmatic nucleus-paraventricular nucleus interactions: A bridge to the neuroendocrine and autonomic nervous system. Progress in Brain Research, 119, 365–382.

Davidson, A. J., Yamazaki, S., Arble, D. M., Menaker, M., & Block, G. D. (2006). Resetting of central and peripheral circadian oscillators in aged rats. Neurobiology of Aging. 29(3), 471 – 477.

Buijs, R. M., Pevet, P., Masson-Pevet, M., Pool, C. W., de Vries, G. J., Canguilhem, B., et al. (1986). Seasonal variation in vasopressin innervation in the brain of the European hamster (Cricetus cricetus). Brain Research, 371, 193–196. Buijs, R. M., van Eden, C. G., Goncharuk, V. D., & Kalsbeek, A. (2003). The biological clock tunes the organs of the body: Timing by hormones and the autonomic nervous system. Journal of Endocrinology, 177, 17–26. Caldwell, G. S., Glickman, S. E., & Smith, E. R. (1984). Seasonal aggression independent of seasonal testosterone in wood rats. Proceedings of the National Academy of Sciences, USA, 81, 5255–5257. Caldwell, H. K., & Albers, H. E. (2004). Effect of photoperiod on vasopressin-induced aggression in Syrian hamsters. Hormones and Behavior, 46, 444–449. Card, J. P. (2000). Pseudorabies virus and the functional architecture of the circadian timing system. Journal of Biological Rhythms, 15, 453–461. Carolei, A., Marini, C., De Matteis, G., Di Napoli, M., & Baldassarre, M. (1996). Seasonal incidence of stroke. Lancet, 347, 1702–1703. Carter, D. S., & Goldman, B. D. (1983). Antigonadal effects of timed melatonin infusion in pinealectomized male Djungarian hamsters (Phodopus sungorus sungorus): Duration is the critical parameter. Endocrinology, 113, 1261–1267. Cassone, V. M. (1990). Melatonin: Time in a bottle. Oxford Review of Reproductive Biology, 12, 319–367. Ceriani, M. F., Hogenesch, J. B., Yanovsky, M., Panda, S., Straume, M., & Kay, S. A. (2002). Genome-wide expression analysis in Drosophila reveals genes controlling circadian behavior. Journal of Neuroscience, 22, 9305–9319. Cheng, M. Y., Bittman, E. L., Hattar, S., & Zhou, Q. Y. (2005). Regulation of prokineticin 2 expression by light and the circadian clock. BMC Neuroscience, 6, 17. Cheng, M. Y., Bullock, C. M., Li, C., Lee, A. G., Bermak, J. C., Belluzzi, J., et al. (2002). Prokineticin 2 transmits the behavioural circadian rhythm of the suprachiasmatic nucleus. Nature, 417, 405–410. Cho, K. (2001). Chronic “jet lag” produces temporal lobe atrophy and spatial cognitive deficits. Nature Neuroscience, 4, 567–568. Cho, K., Ennaceur, A., Cole, J. C., & Suh, C. K. (2000). Chronic jet lag produces cognitive deficits. Journal of Neuroscience, 20, RC66. Claridge-Chang, A., Wijnen, H., Naef, F., Boothroyd, C., Rajewsky, N., & Young, M. W. (2001). Circadian regulation of gene expression systems in the Drosophila head. Neuron, 32, 657–671. Clifton, P. G., Rusk, I. N., & Cooper, S. J. (1991). Effects of dopamine D1 and dopamine D2 antagonists on the free feeding and drinking patterns of rats. Behavioral Neuroscience, 105, 272–281. Colquhoun, D. (1981). Rhythms in performance. In J. Aschoff (Ed.), Handbook of Behavioral Neurobiology, 4, 333-348. Conlon, M., Lightfoot, N., & Kreiger, N. (2007). Rotating shift work and risk of prostate cancer. Epidemiology, 18, 182–183. Costa, G. (1996). The impact of shift and night work on health. Applied Ergonomics, 27(1), 9–16. Currie, P. J. & Wilson, L. M. (1992). Yohimbine attenuates elonidineinduced feeding and macronutrient selection in genetically obese (ob/ ob) mice. Pharmacology, Biochemistry, and Behavior, 43, 1039–1046. Currie, P. J. & Wilson, L. M. (1993). Potentiation of dark onset feeding in obese mice (genotype ob/ob) following central injection of

c04.indd Sec4:74

De Koninck, J. (1991). [Biological rhythms associated with sleep and psychological adjustment]. Journal of Psychiatry and Neuroscience, 16, 115–122. DeCoursey, P. J., & Krulas, J. R. (1998). Behavior of SCN-lesioned chipmunks in natural habitat: A pilot study. Journal of Biological Rhythms, 13, 229–244. DeCoursey, P. J., Krulas, J. R., Mele, G., & Holley, D. C. (1997). Circadian performance of suprachiasmatic nuclei (SCN)-lesioned antelope ground squirrels in a desert enclosure. Physiology and Behavior, 62, 1099–1108. de la Iglesia, H. O., Meyer, J., Carpino, A., Jr., & Schwartz, W. J. (2000). Antiphase oscillation of the left and right suprachiasmatic nuclei. Science, 290, 799–801. de la Iglesia, H. O., Meyer, J., & Schwartz, W. J. (2003). Lateralization of circadian pacemaker output: Activation of left- and right-sided luteinizing hormone-releasing hormone neurons involves a neural rather than a humoral pathway. Journal of Neuroscience, 23, 7412–7414. Della Fera, M. A., & Baile, C. A. (1979). CCK-octapeptide injected in CSF causes satiety in sheep. Annals of Veterinary Research, 10(2/3), 234–236. DeMairan, J. J. (1729). Observation bontanique. [Botanical Observation]’ Academie de Royale Science, 35–36. Demas, G. E., Polacek, K. M., Durazzo, A., & Jasnow, A. M. (2004). Adrenal hormones mediate melatonin-induced increases in aggression in male Siberian hamsters (Phodopus sungorus). Hormones and Behavior, 46, 582–591. Dodd, A. N., Salathia, N., Hall, A., Kevei, E., Toth, R., Nagy, F., et al. (2005). Plant circadian clocks increase photosynthesis, growth, survival, and competitive advantage. Science, 309, 630–633. Drew, J. E., Barrett, P., Mercer, J. G., Moar, K. M., Canet, E., Delagrange, P., et al. (2001). Localization of the melatonin-related receptor in the rodent brain and peripheral tissues. Journal of Neuroendocrinology, 13, 453–458. Dube, M. G., Kalra, S. P., & Kalra, P. S. (1999). Food intake elicited by central administration of orexins/hypoeretins: Identification of hypothalamic sites of action, Brain Research, 842, 473–477. Dubocovich, M. L., Rivera-Bermudez, M. A., Gerdin, M. J., & Masana, M. I. (2003). Molecular pharmacology, regulation and function of mammalian melatonin receptors. Frontiers in Bioscience, 8, D1093–D1108. Dunlap, J. C., Loros, J. J., & DeCoursey, P. J. (2004). Chronobiology: Biological timekeeping. Sunderland, MA: Sinauer. Earnest, D. J., & Sladek, C. D. (1987). Circadian vasopressin release from perifused rat suprachiasmatic explants in vitro: Effects of acute stimulation. Brain Research, 422, 398–402. Egli, M., Bertram, R., Sellix, M. T., & Freeman, M. E. (2004). Rhythmic secretion of prolactin in rats: Action of oxytocin coordinated by vasoactive intestinal polypeptide of suprachiasmatic nucleus origin. Endocrinology, 145(7), 3386–3394. el-Hajj Fuleihan, G., Klerman, E. B., Brown, E. N., Choe, Y., Brown, E. M., & Czeisler, C. A. (1997). The parathyroid hormone circadian rhythm is truly endogenous: A general clinical research center study. Journal of Clinical Endocrinology and Metabolism, 82, 281–286. Ellis, G. B., & Turek, F. W. (1983). Testosterone and photoperiod interact to regulate locomotor activity in male hamsters. Hormones and Behavior, 17, 66–75. Enns, M. W., Cox, B. J., Levitt, A. J., Levitan, R. D., Morehouse, R., Michalak, E. E., et al. (2006). Personality and seasonal affective

8/18/09 5:11:35 PM

References 75 disorder: Results from the CAN-SAD study. Journal of Affective Disorders, 93(1/3), 35–42.

revisiting the Challenge Hypothesis. Hormones and Behavior, 51, 463–476.

Ferkin, M. H., & Gorman, M. R. (1992). Photoperiod and gonadal hormones influence odor preferences of the male meadow vole: Microtus pennsylvanicus. Physiological Behavior, 51, 1087–1091.

Grandison, L., & Guidotti, A. (1977). Stimulation of food intake by muscimol and beta endorphio. Neuropharmacology, 16, 533–536.

Forger, N. G., & Breedlove, S. M. (1987). Seasonal variation in mammalian striated muscle mass and motoneuron morphology. Journal of Neurobiology, 18, 155–165. Foster, R. G., Argamaso, S., Coleman, S., Colwell, C. S., Lederman, A., & Provencio, I. (1993). Photoreceptors regulating circadian behavior: A mouse model. Journal of Biological Rhythms, 8(Suppl.), S17–S23. Foster, R. G., Hankins, M. W., & Peirson, S. N. (2007). Light, photoreceptors, and circadian clocks. Methods Molecular Biology, 362, 3–28. Freedman, M. S., Lucas, R. J., Soni, B., von Schantz, M., Munoz, M., David-Gray, Z., et al. (1999). Regulation of mammalian circadian behavior by non-rod, non-cone, ocular photoreceptors. Science, 284, 502–504. Galea, L. A., Kavaliers, M., & Ossenkopp, K. P. (1996). Sexually dimorphic spatial learning in meadow voles Microtus pennsylvanicus and deer mice Peromyscus maniculatus. Journal of Experimental Biology, 199(Pt. 1), 195–200. Galea, L. A., Kavaliers, M., Ossenkopp, K. P., & Hampson, E. (1995). Gonadal hormone levels and spatial learning performance in the Morris water maze in male and female meadow voles, Microtus pennsylvanicus. Hormones and Behavior, 29, 106–125. Galea, L. A., Kavaliers, M., Ossenkopp, K. P., Innes, D., & Hargreaves, E. L. (1994). Sexually dimorphic spatial learning varies seasonally in two populations of deer mice. Brain Research, 635, 18–26. Galea, L. A., & McEwen, B. S. (1999). Sex and seasonal differences in the rate of cell proliferation in the dentate gyrus of adult wild meadow voles. Neuroscience, 89, 955–964. Ganguly, S., Coon, S. L., & Klein, D. C. (2002). Control of melatonin synthesis in the mammalian pineal gland: The critical role of serotonin acetylation. Cell and Tissue Research, 309, 127–137.

Green, D. J., & Gillette, R. (1982). Circadian rhythm of firing rate recorded from single cells in the rat suprachiasmatic brain slice. Brain Research, 245, 198–200. Green, J., Pollak, C. P., & Smith, G. P. (1987). The effect of desynchronization on meal patterns of humans living in time isolation. Physiological Behavior, 39, 203–209. Greives, T. J., Mason, A. O., Scotti, M. A., Levine, J., Ketterson, E. D., Kriegsfeld, L. J., et al. (2007). Environmental control of kisspeptin: Implications for seasonal reproduction. Endocrinology, 148, 1158–1166. Grimes, L. J., Melnyk, R. B., Martin, J. M., & Mrosovsky, N. (1981). Infradian cycles in glucose utilization and lipogenic enzyme activity in dormouse (Glis glis) adipocytes. General and Comparative Endocrinology, 45, 21–25. Groos, G., & Hendriks, J. (1982). Circadian rhythms in electrical discharge of rat suprachiasmatic neurones recorded in vitro. Neuroscience Letter, 34, 283–288. Hakim, H., DeBernardo, A. P., & Silver, R. (1991). Circadian locomotor rhythms, but not photoperiodic responses, survive surgical isolation of the SCN in hamsters. Journal of Biological Rhythms, 6, 97–113. Halberg, F. (1959). Physiological 24-hour periodicity in human beings and mice, the lighting regimen and daily routine. In R. B. Withrom (Ed.), Photoperiodism and related phenemena in plants and animals (pp. 803–878). Washington, DC: American Association for the Advancement of Science. Hannibal, J., & Fahrenkrug, J. (2002). Melanopsin: A novel photopigment involved in the photoentrainment of the brain’s biological clock? Annals of Medicine, 34, 401–407.

Gao, Q., & Horvath, T. I. (2007). Neurobiology of feeding and energy expenditure. Annual Review of Neuroscience, 30, 367–398.

Hansen, J. (2006). Risk of breast cancer after night- and shift work: Current evidence and ongoing studies in Denmark. Cancer Causes Control, 17, 531–537.

Garrett, J. W., & Campbell, C. S. (1980). Changes in social behavior of the male golden hamster accompanying photoperiodic changes in reproduction. Hormones and Behavior, 14, 303–318.

Harmer, S. L., Hogenesch, J. B., Straume, M., Chang, H. S., Han, B., Zhu, T., et al. (2000). Orchestrated transcription of key pathways in Arabidopsis by the circadian clock. Science, 290, 2110–2113.

Gerhold, L. M., Horvath, T. L., & Freeman, M. E. (2001). Vasoactive intestinal peptide fibers innervate neuroendocrine dopaminergic neurons. Brain Research, 919, 48–56.

Hattar, S., Lucas, R. J., Mrosovsky, N., Thompson, S., Douglas, R. H., Hankins, M. W., et al. (2003). Melanopsin and rod-cone photoreceptive systems account for all major accessory visual functions in mice. Nature, 424, 76–81.

Gillette, M. U., & Reppert, S. M. (1987). The hypothalamic suprachiasmatic nuclei: Circadian patterns of vasopressin secretion and neuronal activity in vitro. Brain Research Bulletin, 19, 135–139. Goldman, B. D. (2001). Mammalian photoperiodic system: Formal properties and neuroendocrine mechanisms of photoperiodic time measurement. Journal of Biological Rhythms, 16, 283–301. Golombek, D. A., Pevet, P., & Cardinali, D. P. (1996). Melatonin effects on behavior: Possible mediation by the central GABAergic system. Neuroscience Biobehavioral Review, 20, 403–412.

Hazlerigg, D. G., Andersson, H., Johnston, J. D., & Lincoln, G. A. (2004). Molecular characterization of the long-day response in the Soay sheep, a seasonal mammal. Current Biology, 14, 334–339. Hofman, M. A. (2004). The brain’s calendar: Neural mechanisms of seasonal timing. Biological Reviews of the Cambridge Philosophical Society, 79, 61–77. Hofman, M. A., & Swaab, D. F. (1995). Influence of aging on the seasonal rhythm of the vasopressin-expressing neurons in the human suprachiasmatic nucleus. Neurobiology and Aging, 16, 965–971.

Gomez, D. M., & Newman, S. W. (1991). Medial nucleus of the amygdala in the adult Syrian hamster: A quantitative Golgi analysis of gonadal hormonal regulation of neuronal morphology. Anatomical Record, 231, 498–509.

Horowitz, T. S., Cade, B. E., Wolfe, J. M., & Czeisler, C. A. (2003). Searching night and day: A dissociation of effects of circadian phase and time awake on visual selective attention and vigilance. Psychological Science, 14, 549–557.

Gorman, M. R., Ferkin, M. H., Nelson, R. J., & Zucker, I. (1993). Reproductive status influences odour preferences of the meadow vole (Microtus pennsylvanicus). Canadian Journal of Zoology, 71, 1748–1754.

Horvath, T. L., Cela, V., & Van der Beek, E. M. (1998). Gender-specific apposition between vasoactive intestinal peptide-containing axons and gonadotrophin-releasing hormone-producing neurons in the rat. Brain Research, 795, 277–281. Hoshooley, J. S., & Sherry, D. F. (2007). Greater hippocampal neuronal recruitment in food-storing than in non-food-storing birds. Developmental Neurobiology, 67, 406–414.

Gould, E., & Gross, C. G. (2002). Neurogenesis in adult mammals: Some progress and problems. Journal of Neuroscience, 22, 619–623. Goymann, W., Landys, M. M., & Wingfield, J. C. (2007). Distinguishing seasonal androgen responses from male-male androgen responsiveness-

c04.indd Sec4:75

Green, C. B., & Besharse, J. C. (1996). Identification of a novel vertebrate circadian clock-regulated gene encoding the protein nocturnin. Proceedings of the National Academy of Sciences, USA, 93, 14884–14888.

8/18/09 5:11:35 PM

76

Biological Rhythms

Hrushesky, W. J. (1990). Cancer chronotherapy: A drug delivery challenge. Progress in Clinical and Biological Research, 341A, 1–10. Hrushesky, W. J. (1993). Circadian cancer pharmacodynamics. Annali dell’Istituto Superiore di Sanita, 29, 705–710.

Kivimaki, M., Virtanen, M., Elovainio, M., Vaananen, A., KeltikangasJarvinen, L., & Vahtera, J. (2006). Prevalent cardiovascular disease, risk factors and selection out of shift work. Scandanavian Journal of Work and Environmental Health, 32, 204–208.

Hrushesky, W. J. (1995). Cancer chronotherapy: Is there a right time in the day to treat? Journal of Infusional Chemotherapy, 5, 38–43.

Klein, D. C. (1985). Photoneural regulation of the mammalian pineal gland. Ciba Found Symp, 117, 38–56.

Huang, L., DeVries, G. J., & Bittman, E. L. (1998). Photoperiod regulates neuronal bromodeoxyuridine labeling in the brain of a seasonally breeding mammal. Journal of Neurobiology, 36, 410–420.

Klein, D. C., & Moore, R. Y. (1979). Pineal N-acetyltransferase and hydroxyindole-O-methyltransferase: Control by the retinohypothalamic tract and the suprachiasmatic nucleus. Brain Research, 174, 245–262.

Hwang, S. Y., & Lee, J. H. (2005). Comparison of cardiovascular risk profile clusters among industrial workers. Taehan Kanho Hakhoe Chi, 35, 1500–1507. Illnerova, H., Hoffmann, K., & Vanecek, J. (1984). Adjustment of pineal melatonin and N-acetyltransferase rhythms to change from long to short photoperiod in the Djungarian hamster Phodopus sungorus. Neuroendocrinology, 38, 226–231.

Klein, D. C., Smoot, R., Weller, J. L., Higa, S., Markey, S. P., Creed, G. J., et al. (1983). Lesions of the paraventricular nucleus area of the hypothalamus disrupt the suprachiasmatic leads to spinal cord circuit in the melatonin rhythm generating system. Brain Research Bulletin, 10, 647–652.

Jacobs, L. F. (1996). The economy of winter: Phenotypic plasticity in behavior and brain structure. Biological Bulletin, 191, 92–100.

Knobil, E., & Hotchkiss, J. (1988). The menstrual cycle and its neuroendocrine control. In E. Knobil & J. D. Neill (Eds.), The physiology of reproduction (Vol. 2, pp. 1971–1994). New York: Raven Press.

Jacobs, L. F., Gaulin, S. J., Sherry, D. F., & Hoffman, G. E. (1990). Evolution of spatial cognition: Sex-specific patterns of spatial behavior predict hippocampal size. Proceedings of the National Academy of Sciences, USA, 87, 6349–6352.

Koda, S., Yasuda, N., Sugihara, Y., Ohara, H., Udo, H., Otani, T., et al. (2000). [Analyses of work-relatedness of health problems among truck drivers by questionnaire survey]. Sangyo Eiseigaku Zasshi, 42(1), 6–16.

Jasnow, A. M., Huhman, K. L., Bartness, T. J., & Demas, G. E. (2000). Short-day increases in aggression are inversely related to circulating testosterone concentrations in male Siberian hamsters (Phodopus sungorus). Hormones and Behavior, 38, 102–110.

Kolmodin-Hedman, B., & Swensson, A. (1975). Problems related to shift work. A field study of Swedish railroad workers with irregular work hours. Scandanavian Journal of Work and Environmental Health, 1, 254–262.

Johnston, J. D., Cagampang, F. R., Stirland, J. A., Carr, A. J., White, M. R., Davis, J. R., et al. (2003). Evidence for an endogenous per1- and ICERindependent seasonal timer in the hamster pituitary gland. FASEB Journal, 17, 810–815.

Kramer, A., Yang, F. C., Snodgrass, P., Li, X., Scammell, T. E., Davis, F. C., et al. (2001). Regulation of daily locomotor activity and sleep by hypothalamic EGF receptor signaling. Science, 294, 2511–2515.

Kalra, S. P., Dube, M. G., Pu, S., Xu, B., Horvath, T. L., & Kalra, P. S. (1999). Interacting appetite-regulating pathways in the hypothalamic regulation of body weight. Endocrine Reviews, 20, 68–100.

Kramer, C., Loros, J. J., Dunlap, J. C., & Crosthwaite, S. K. (2003). Role for antisense RNA in regulating circadian clock function in Neurospora crassa. Nature, 421, 948–952.

Kalsbeek, A., & Buijs, R. M. (2002). Output pathways of the mammalian suprachiasmatic nucleus: Coding circadian time by transmitter selection and specific targeting. Cell Tissue Research, 309, 109–118.

Kriegsfeld, L. J., Dawson, T. M., Dawson, V. L., Nelson, R. J., & Snyder, S. H. (1997). Aggressive behavior in male mice lacking the gene for neuronal nitric oxide synthase requires testosterone. Brain Research, 769, 66–70.

Kalsbeek, A., Fliers, E., Franke, A. N., Wortel, J., & Buijs, R. M. (2000). Functional connections between the suprachiasmatic nucleus and the thyroid gland as revealed by lesioning and viral tracing techniques in the rat. Endocrinology, 141, 3832–3841.

Kriegsfeld, L. J., Korets, R., & Silver, R. (2003). Expression of the circadian clock gene Period 1 in neuroendocrine cells: An investigation using mice with a Per1:GFP transgene. European Journal of Neuroscience, 17, 212–220.

Kalsbeek, A., Teclemariam-Mesbah, R., & Pevet, P. (1993). Efferent projections of the suprachiasmatic nucleus in the golden hamster (Mesocricetus auratus). Journal of Comparative Neurology, 332, 293–314.

Kriegsfeld, L. J., Leak, R. K., Yackulic, C. B., LeSauter, J., & Silver, R. (2004). Organization of suprachiasmatic nucleus projections in Syrian hamsters (Mesocricetus auratus): An anterograde and retrograde analysis. Journal of Comparative Neurology, 468, 361–379.

Kalsbeek, A., van Heerikhuize, J. J., Wortel, J., & Buijs, R. M. (1996). A diurnal rhythm of stimulatory input to the hypothalamo-pituitaryadrenal system as revealed by timed intrahypothalamic administration of the vasopressin V1 antagonist. Journal of Neuroscience, 16, 5555–5565. Kanabrocki, E. L., Hermida, R. C., Torossov, M., Haseman, M. B., Bettis, K., Young, R. M., et al. (2006). Chronotherapy of ovarian cancer: Effect on blood variables and serum cytokines. A case report. La Clinica Terapeutica, 157, 349–354. Karlsson, B., Alfredsson, L., Knutsson, A., Andersson, E., & Toren, K. (2005). Total mortality and cause-specific mortality of Swedish shift- and dayworkers in the pulp and paper industry in 1952–2001. Scandanavian Journal of Work and Environmental Health, 31, 30–35. Khalsa, S. B. S., Jewett, M. E., Duffy, J. F., & Czeisler, C. A. (2000). The timing of the human circadian clock is accurately represented by the core body temperature rhythm following phase shifts to a three-cycle light stimulus near the critical zone. Journal of Biological Rhythms, 15, 524–530. Kissileff, H. R. (1970). Free feeding in normal and “recovered lateral” rats monitored by a pellet-detecting eatometer. Physiological Behavior, 5, 163–173.

c04.indd Sec4:76

Kriegsfeld, L.J. & Silver, R. (2006). The regulation of neuroendocrine function: Timing is everything. Hormones & Behavior, 49, 557–574. Kriegsfeld, L. J., Silver, R., Gore, A. C., & Crews, D. (2002). Vasoactive intestinal polypeptide contacts on gonadotropin-releasing hormone neurones increase following puberty in female rats. Journal of Neuroendocrinology, 14, 685–690. Kubo, T., Ozasa, K., Mikami, K., Wakai, K., Fujino, Y., Watanabe, Y., et al. (2006). Prospective cohort study of the risk of prostate cancer among rotating-shift workers: Findings from the Japan collaborative cohort study. American Journal of Epidemiology, 164, 549–555. Kuriyama, K., Uchiyama, M., Suzuki, H., Tagaya, H., Ozaki, A., Aritake, S., et al. (2003). Circadian fluctuation of time perception in healthy human subjects. Neuroscience Research, 46(1), 23–31. Larsen, P. J., Enquist, L. W., & Card, J. P. (1998). Characterization of the multisynaptic neuronal control of the rat pineal gland using viral transneuronal tracing. European Journal of Neuroscience, 10, 128–145. Lavenex, P., Steele, M. A., & Jacobs, L. F. (2000). Sex differences, but no seasonal variations in the hippocampus of food-caching squirrels: A stereological study. Journal of Comparative Neurology, 425, 152–166.

8/18/09 5:11:36 PM

References 77 Lavery, D. J., Lopez-Molina, L., Margueron, R., Fleury-Olela, F., Conquet, F., Schibler, U., et al. (1999). Circadian expression of the steroid 15 alphahydroxylase (Cyp2a4) and coumarin 7-hydroxylase (Cyp2a5) genes in mouse liver is regulated by the PAR leucine zipper transcription factor DBP. Molecular and Cellular Biochemistry, 19, 6488–6499. Leak, R. K., & Moore, R. Y. (2001). Topographic organization of suprachiasmatic nucleus projection neurons. Journal of Comparative Neurology, 433, 312–334.

London, S. E., Monks, D. A., Wade, J., & Schlinger, B. A. (2006). Widespread capacity for steroid synthesis in the avian brain and song system. Endocrinology, 147, 5975–5987.

Lee, R., & Balick, M. J. (2006). Chronobiology: It’s about time. Explore, 2, 442–445.

Lowrey, P. L., Shimomura, K., Antoch, M. P., Yamazaki, S., Zemenides, P. D., Ralph, M. R., et al. (2000). Positional syntenic cloning and functional characterization of the mammalian circadian mutation tau. Science, 288, 483–492.

Lehman, M. N., Goodman, R. L., Karsch, F. J., Jackson, G. L., Berriman, S. J., & Jansen, H. T. (1997). The GnRH system of seasonal breeders: Anatomy and plasticity. Brain Research Bulletin, 44, 445–457.

Lucas, R. J., Freedman, M. S., Munoz, M., Garcia-Fernandez, J. M., & Foster, R. G. (1999). Regulation of the mammalian pineal by non-rod, non-cone, ocular photoreceptors. Science, 284, 505–507.

Lehman, M. N., Silver, R., Gladstone, W. R., Kahn, R. M., Gibson, M., & Bittman, E. L. (1987). Circadian rhythmicity restored by neural transplant. Immunocytochemical characterization of the graft and its integration with the host brain. Journal of Neuroscience, 7, 1626–1638.

Lucas, R. J., Hattar, S., Takao, M., Berson, D. M., Foster, R. G., & Yau, K. W. (2003). Diminished pupillary light reflex at high irradiances in melanopsin-knockout mice. Science, 299, 245–247.

Leibowitz, S. F., Weiss, G. F., Walsh, U. A., & Viswanath, D. (1989). Medial hypothalamic serotonin: Role in circadian patterns of feeding and macronutrient selection. Brain Research, 503, 132–140. Leonard, C., Fanning, N., Attwood, J., & Buckley, M. (1998). The effect of fatigue, sleep deprivation and onerous working hours on the physical and mental wellbeing of pre-registration house officers. Irish Journal of Medical Science, 167(1), 22–25.

Madison, D. M. (1984). Group nesting and its ecological and evolutionary significance in overwintering microtine rodents. Bulletin of the Carnegie Museum of Natural History, 10, 267–274. Mason, A. O., Greives, T. J., Scotti, M. A., Levine, J., Frommeyer, S., Ketterson, E. D., et al. (2007). Suppression of kisspeptin expression and gonadotropic axis sensitivity following exposure to inhibitory day lengths in female Siberian hamsters. Hormones and Behavior, 52, 492–498.

Levi, F. (1994). Chronotherapy of cancer: Biological basis and clinical application. Pathologie et Biologie, 42, 338–341.

Maywood, E. S., Bittman, E. L., & Hastings, M. H. (1996). Lesions of the melatonin- and androgen-responsive tissue of the dorsomedial nucleus of the hypothalamus block the gonadal response of male Syrian hamsters to programmed infusions of melatonin. Biology of Reproduction, 54, 470–477.

Levi, F. (2001). Circadian chronotherapy for human cancers. Lancet Oncol, 2, 307–315.

McDonald, M. J., & Rosbash, M. (2001). Microarray analysis and organization of circadian gene expression in Drosophila. Cell, 107, 567–578.

Levi, F. (2002). From circadian rhythms to cancer chronotherapeutics. Chronobiology International, 19, 1–19.

McShea, W. J. (1990). Social tolerance and proximate mechanisms of dispersal in winter groups of meadow voles (Microtus pennsylvanicus). Animal Behaviour, 39, (346–351).

Levi, F. (1987). [Chronobiology and cancer]. Pathologie et Biologie, 35, 960–968.

Lewis, D., Freeman, D. A., Dark, J., Wynne-Edwards, K. E., & Zucker, I. (2002). Photoperiodic control of oestrous cycles in Syrian hamsters: Mediation by the mediobasal hypothalamus. Journal of Neuroendocrinology, 14, 294–299.

Melnyk, R. B., Mrosovsky, N., & Martin, J. M. (1983a). Spontaneous obesity and weight loss: Insulin action in the dormouse. American Journal of Physiology, 245, R396–R402.

Lewy, A. J., Lefler, B. J., Emens, J. S., & Bauer, V. K. (2006). The circadian basis of winter depression. Proceedings of the National Academy of Sciences, USA, 103, 7414–7419.

Melnyk, R. B., Mrosovsky, N., & Martin, J. M. (1983b). Spontaneous obesity and weight loss: Insulin binding and lipogenesis in the dormouse. American Journal of Physiology, 245, R403–R407.

Li, X. C., Jarvis, E. D., Alvarez-Borda, B., Lim, D. A., & Nottebohm, F. (2000). A relationship between behavior, neurotrophin expression, and new neuron survival. Proceedings of the National Academy of Sciences, USA, 97, 8584–8589.

Messager, S., Hazlerigg, D. G., Mercer, J. G., & Morgan, P. J. (2000). Photoperiod differentially regulates the expression of Per1 and ICER in the pars tuberalis and the suprachiasmatic nucleus of the Siberian hamster. European Journal of Neuroscience, 12, 2865–2870.

Lincoln, G. A., Andersson, H., & Loudon, A. (2003). Clock genes in calendar cells as the basis of annual timekeeping in mammals: A unifying hypothesis. Journal of Endocrinology, 179, 1–13.

Meyer-Bernstein, E. L., Jetton, A. E., Matsumoto, S. I., Markuns, J. F., Lehman, M. N., & Bittman, E. L. (1999). Effects of suprachiasmatic transplants on circadian rhythms of neuroendocrine function in golden hamsters. Endocrinology, 140, 207–218.

Lincoln, G. A., & Clarke, I. J. (1997). Refractoriness to a static melatonin signal develops in the pituitary gland for the control of prolactin secretion in the ram. Biology of Reproduction, 57, 460–467. Lincoln, G. A., Johnston, J. D., Andersson, H., Wagner, G., & Hazlerigg, D. G. (2005). Photorefractoriness in mammals: Dissociating a seasonal timer from the circadian-based photoperiod response. Endocrinology, 146, 3782–3790. Lincoln, G. A., Messager, S., Andersson, H., & Hazlerigg, D. (2002). Temporal expression of seven clock genes in the suprachiasmatic nucleus and the pars tuberalis of the sheep: Evidence for an internal coincidence timer. Proceedings of the National Academy of Sciences, USA, 99, 13890–13895. Liu, Q. S., Li, J. Y., & Wang, D. H. (2007). Ultradian rhythms and the nutritional importance of caecotrophy in captive Brandt’s voles (Lasiopodomys brandtii). Journal of Comparative Physiology. Series B, 177, 423–432.

c04.indd Sec4:77

Lloyd, D., Lemar, K. M., Salgado, L. E., Gould, T. M., & Murray, D. B. (2003). Respiratory oscillations in yeast: Mitochondrial reactive oxygen species, apoptosis and time; a hypothesis. FEMS Yeast Research, 3, 333–339.

Mink, J. W., Blumenschine, R. J., & Adams, D. B. (1981). Ratio of central nervous system to body metabolism in vertebrates: Its constancy and functional basis. American Journal of Physiology, 241, R203–R212. Mistlberger, R. E., & Skene, D. J. (2004). Social influences on mammalian circadian rhythms: Animal and human studies. Biological Reviews of the Cambridge Philosophical Society, 79, 533–556. Mistlberger, R. E., & Skene, D. J. (2005). Nonphotic entrainment in humans? Journal of Biological Rhythms, 20, 339–352. Monnikes, H., Heymann-Monnikes. I., & Tache, Y. (1992). CRF in the paraventricular nucleus of the hypothalamus induces dose-related behavioral profile in rats. Brain Research, 574, 70–76. Moore, R. Y., & Eichler, V. B. (1972). Loss of a circadian adrenal corticosterone rhythm following suprachiasmatic lesions in the rat. Brain Research, 42, 201–206.

8/18/09 5:11:36 PM

78

Biological Rhythms

Moore, R. Y., & Klein, D. C. (1974). Visual pathways and the central neural control of a circadian rhythm in pineal serotonin N-acetyltransferase activity. Brain Research, 71, 17–33. Mori, T. Nagai, K., Hara, M., & Nakagawa, H. (1985). Time-dependent effect of insulin in suprachiasmatic nucleus on blood glucose. American Journal of Physiology, 249(1 Pt 2), R23–R30. Morikawa, Y., Nakagawa, H., Miura, K., Soyama, Y., Ishizaki, M., Kido, T., et al. (2005). Shift work and the risk of diabetes mellitus among Japanese male factory workers. Scandanavian Journal of Work and Environmental Health, 31, 179–183.

Ouyang, Y., Andersson, C. R., Kondo, T., Golden, S. S., & Johnson, C. H. (1998). Resonating circadian clocks enhance fitness in cyanobacteria. Proceedings of the National Academy of Sciences, USA, 95, 8660–8664. Panda, S., Provencio, I., Tu, D. C., Pires, S. S., Rollag, M. D., Castrucci, A. M., et al. (2003). Melanopsin is required for non-image-forming photic responses in blind mice. Science, 301, 525–527. Panda, S., Sato, T. K., Castrucci, A. M., Rollag, M. D., DeGrip, W. J., Hogenesch, J. B., et al. (2002). Melanopsin (Opn4) requirement for normal light-induced circadian phase shifting. Science, 298, 2213–2216.

Morin, L. P., & Allen, C. N. (2006). The circadian visual system, 2005. Brain Research Reviews, 51, 1–60.

Patel, D. (2006). Shift work, light at night and risk of breast cancer. Occupational Medicine, 56, 433.

Morin, L. P., Goodless-Sanchez, N., Smale, L., & Moore, R. Y. (1994). Projections of the suprachiasmatic nuclei, subparaventricular zone and retrochiasmatic area in the golden hamster. Neuroscience, 61, 391–410.

Perrot-Sinal, T. S., Kavaliers, M., & Ossenkopp, K. P. (1998). Spatial learning and hippocampal volume in male deer mice: Relations to age, testosterone and adrenal gland weight. Neuroscience, 86, 1089–1099.

Mosko, S. S., & Moore, R. Y. (1979). Neonatal suprachiasmatic nucleus lesions: Effects on the development of circadian rhythms in the rat. Brain Research, 164, 17–38.

Poole, C. J., Wright, A. D., & Nattrass, M. (1992). Control of diabetes mellitus in shift workers. British Journal of Industrial Medicine, 49, 513–515.

Munakata, M., Ichi, S., Nunokawa, T., Saito, Y., Ito, N., Fukudo, S., et al. (2001). Influence of night shift work on psychologic state and cardiovascular and neuroendocrine responses in healthy nurses. Hypertension Research, 24(1), 25–31. Musshoff, U., Riewenherm, D., Berger, E., Fauteck, J. D., & Speckmann, E. J. (2002). Melatonin receptors in rat hippocampus: Molecular and functional investigations. Hippocampus, 12(2), 165–173. Nagai, K., Nagai, N., Sugahara, K., Niijima, A., & Nakagawa, H. (1994). Circadian rhythms and energy metabolism with special reference to the suprachiasmatic nucleus. Neuroscience Biobehavioral Review, 18, 579–584. Nagai, K., Nishio, T., Nakagawa, H., Nakamura, S., & Fukuda, Y. (1978). Effect of bilateral lesions of the suprachiasmatic nuclei on the circadian rhythm of food-intake. Brain Research, 142, 384–389. Nagoshi, E., Saini, C., Bauer, C., Laroche, T., Naef, F., & Schibler, U. (2004). Circadian gene expression in individual fibroblasts: Cellautonomous and self-sustained oscillators pass time to daughter cells. Cell, 119, 693–705. Nelson, R. J. (1987). Photoperiod-nonresponsive morphs: A possible variable in microtine population density fluctuations. American Naturalist, 130, 350–369.

Popov, V. I., Bocharova, L. S., & Bragin, A. G. (1992). Repeated changes of dendritic morphology in the hippocampus of ground squirrels in the course of hibernation. Neuroscience, 48, 45–51. Pravosudov, V. V., & Clayton,N. S. (2002). A test of the adaptive specialization hypothesis: population differences in caching, memory, and the hippocampus in black-capped chickadees (Poecile atricapilla). Behavioral Neuroscience, 116, 515–522. Prendergast, B. J., Kriegsfeld, L. J., & Nelson, R. J. (2001). Photoperiodic polyphenisms in rodents: Neuroendocrine mechanisms, costs, and functions. Quarterly Review of Biology, 76, 293–325. Pyter, L. M., Reader, B. F., & Nelson, R. J. (2005). Short photoperiods impair spatial learning and alter hippocampal dendritic morphology in adult male white-footed mice (Peromyscus leucopus). Journal of Neuroscience, 25, 4521–4526. Pyter, L. M., Trainor, B. C., & Nelson, R. J. 2006. Testosterone and Photo period interact to affect spatial learning and memory in adult male whitefooted mice (Peromysus leucopus). European Journal of Neuroscience, 23, 3056–3062. Ralph, M. R., Foster, R. G., Davis, F. C., & Menaker, M. (1990). Transplanted suprachiasmatic nucleus determines circadian period. Science, 247, 975–978.

Nelson, R. J., Badura, L. L., & Goldman, B. D. (1990). Mechanisms of seasonal cycles of behavior. Annual Review of Psychology, 41, 81–108.

Ralph, M. R., & Menaker, M. (1988). A mutation of the circadian system in golden hamsters. Science, 241, 1225–1227.

Nelson, R. J., & Chiavegatto, S. (2001). Molecular basis of aggression. Trends in Neuroscience, 24, 713–719.

Rasika, S., Alvarez-Buylla, A., & Nottebohm, F. (1999). BDNF mediates the effects of testosterone on the survival of new neurons in an adult brain. Neuron, 22, 53–62.

Nelson, R. J., Demas, G. E., Huang, P. L., Fishman, M. C., Dawson, V. L., Dawson, T. M., et al. (1995). Behavioural abnormalities in male mice lacking neuronal nitric oxide synthase. Nature, 378, 383–386. Nelson, R. J., & Trainor, B. C. (2007). Neural mechanisms of aggression. NatureReview of Neuroscience, 8, 536–546. Newman, G. C., & Hospod, F. E. (1986). Rhythm of suprachiasmatic nucleus 2-deoxyglucose uptake in vitro. Brain Research, 381, 345–350. Nicholson, C. (1999). Signals that go with the flow. Trends of Neuroscience, 22(4), 143–145. Nottebohm, F. (1981). A brain for all seasons: Cyclical anatomical changes in song control nuclei of the canary brain. Science, 214, 1368–1370. Nottebohm, F. (2005). The neural basis of birdsong. PLoS Biology, 3, E164. Nunez, A. A., & Stephan, F. K. (1977). The effects of hypothalamic knife cuts on drinking rhythms and the estrus cycle of the rat. Behavioral Biology, 20, 224–234. O’Leary, E. S., Schoenfeld, E. R., Stevens, R. G., Kabat, G. C., Henderson, K., Grimson, R., et al. (2006). Shift work, light at night, and breast cancer on Long Island, New York. American Journal of Epidemiology, 164, 358–366.

c04.indd Sec4:78

Reddy, A. B., Karp, N. A., Maywood, E. S., Sage, E. A., Deery, M., O’Neill, J. S., et al. (2006). Circadian orchestration of the hepatic proteome. Current Biology, 16, 1107–1115. Refinetti, R., & Menaker, M. (1992). The circadian rhythm of body temperature. Physiological Behavior, 51, 613–637. Reiter, R. J., & Tan, D. X. (2002). Role of CSF in the transport of melatonin. Journal of Pineal Research, 33, 61. Revel, F. G., Ansel, L., Klosen, P., Saboureau, M., Pevet, P., Mikkelsen, J. D., et al. (2007). Kisspeptin: A key link to seasonal breeding. Reviews in Endocrine and Metabolic Disorders, 8(1), 57–65. Revel, F. G., Saboureau, M., Masson-Pevet, M., Pevet, P., Mikkelsen, J. D., & Simonneaux, V. (2006). Kisspeptin mediates the photoperiodic control of reproduction in hamsters. Current Biology, 16, 1730–1735. Ripperger, J. A., & Schibler, U. (2006). Rhythmic CLOCK-BMAL1 binding to multiple E-box motifs drives circadian Dbp transcription and chromatin transitions. Nature Reviews: Genetics, 38, 369–374. Robinson, N., Yateman, N. A., Protopapa, L. E., & Bush, L. (1990). Employment problems and diabetes. Diabetic Medicine, 7(1), 16–22.

8/18/09 5:11:37 PM

References 79 Roenneberg, T., Daan, S., & Merrow, M. (2003). The art of entrainment. Journal of Biological Rhythms, 18, 183–194. Roenneberg, T., & Merrow, M. (2002). “What watch? . . such much!” Complexity and evolution of circadian clocks. Cell Tissue Research, 309, 3–9. Rosenthal, N. E., Sack, D. A., Gillin, J. C., Lewy, A. J., Goodwin, F. K., Davenport, Y., et al. (1984). Seasonal affective disorder. A description of the syndrome and preliminary findings with light therapy. Archives of General Psychiatry, 41(1), 72–80. Roth, T. C., & Pravosudov, V. V. (2008). Hippocampal volumes and neuron numbers increase along a gradient of environmental harshness: A large-scale comparison. Proceedings of the Royal Society B, Published online doi:10.1098/rsbp2008.1184. Rowland, N. (1976). Endogenous circadian rhythms in rats recovered from lateral hypothalamic lesions. Physiological Behavior, 16, 257–266. Ruby, N. F., Brennan, T. J., Xie, X., Cao, V., Franken, P., Heller, H. C., et al. (2002). Role of melanopsin in circadian responses to light. Science, 298, 2211–2213. Rusak, B., & Zucker, I. (1979). Neural regulation of circadian rhythms. Physiological Review, 59, 449–526.

Sills, T. L., & Vaccarino, F. J. (1996). Individual differences in the feeding response to CCKB antagonists: Role of the nucleus accumbens. Peptides, 17, 593–599. Silver, R., Lehman, M. N., Gibson, M., Gladstone, W. R., & Bittman, E. L. (1990). Dispersed cell suspensions of fetal SCN restore circadian rhythmicity in SCN-lesioned adult hamsters. Brain Research, 525, 45–58. Silver, R., LeSauter, J., Tresco, P. A., & Lehman, M. N. (1996). A diffusible coupling signal from the transplanted suprachiasmatic nucleus controlling circadian locomotor rhythms. Nature, 382, 810–813. Simansky, K. J. (1996). Serotonergic control of the organization of feeding and satiety. Behavioral Brain Research, 73(1–2), 37–42. Skinner, D. C., & Caraty, A. (2002). Measurement and possible function of GnRH in cerebrospinal fluid in ewes. Reproduction Supplement, 59, 25–39.

Sahu, A. (2004). Minireview: A hypothalamic role in energy balance with special emphasis on leptin. Endocrinology, 145, 2613–2620.

Skinner, D. C., & Malpaux, B. (1999). High melatonin concentrations in third ventricular cerebrospinal fluid are not due to Galen vein blood recirculating through the choroid plexus. Endocrinology, 140, 4399–4405.

Sanborn, C., Currie, A. C., & Bailey, C. C. (1982). Shift work: How to adjust patterns of diabetes care. Occupational Health Nurses, 30(12), 25–28.

Skipper, J. K., Jr., Jung, F. D., & Coffey, L. C. (1990). Nurses and shiftwork: Effects on physical health and mental depression. Journal of Advanced Nursing, 15, 835–842.

Satoh, N., Ogawa, Y., Katsuura, G., Hayase, M., Tsuji, T., Imagawa, K., et al. (1997). The arcuate nucleus as a primary site of satiety effect of leption in rats. Neuroscience Letter, 224(3), 149–152.

Smith, G. P., & Gibbs, J. (1985). The satiety effect of cholecystokin. Recent progress and current problems. Annals of the New York Academy of Sciences, 448, 417–423.

Schick, R. R., Samsami, S., Zimmermann, J. P., Eberl, T., Endres, C., Schusdziarra, V., et al. (1993). Effect of galanin on food intake in rats: Involvement of lateral and ventromedial hypothalamic sites. American Journal of Physiology, 264(2 Pt 2), R355–R361.

Smulders, T. V., Sasson, A. D., & DeVoogd, T. J. (1995). Seasonal variation in hippocampal volume in a food-storing bird, the black-capped chickadee. Journal of Neurobiology, 27, 15–25.

Schlinger, B. A., Soma, K. K., & London, S. E. (2001). Neurosteroids and brain sexual differentiation. Trends in Neuroscience, 24, 429–431. Schulz, H., & Lavie, P. (1985). Ultradian rhythms in physiology and behavior. Berlin, Germany: Springer Verlag. Scott, C. J., Jansen, H. T., Kao, C. C., Kuehl, D. E., & Jackson, G. L. (1995). Disruption of reproductive rhythms and patterns of melatonin and prolactin secretion following bilateral lesions of the suprachiasmatic nuclei in the ewe. Journal of Neuroendocrinology, 7, 429–443. Scotti, M.-A., L., Belén, J., Jackson, J.E., & Demas, G.E. (2008). The role of androgens in the mediation of seasonal territorial aggression in male Siberian hamsters (Phodopus sungorus), Physiology & Behavior, 95(5), 633–640. Segawa, K., Nakazawa, S., Tsukamoto, Y., Kurita, Y., Goto, H., Fukui, A., et al. (1987). Peptic ulcer is prevalent among shift workers. Digestive Diseases and Sciences, 32, 449–453. Sherry, D. F., Forbes, M. R., Khurgel, M., & Ivy, G. O. (1993). Females have a larger hippocampus than males in the brood-parasitic brownheaded cowbird. Proceedings of the National Academy of Sciences, USA, 90, 7839–7843. Sherry, D. F., Jacobs, L. F., & Gaulin, S. J. (1992). Spatial memory and adaptive specialization of the hippocampus. Trends in Neuroscience, 15, 298–303. Shibata, S., Oomura, Y., Kita, H., & Hattori, K. (1982). Circadian rhythmic changes of neuronal activity in the suprachiasmatic nucleus of the rat hypothalamic slice. Brain Research, 247, 154–158. Shinohara, K., Honma, S., Katsuno, Y., Abe, H., & Honma, K. (1994). Circadian rhythms in the release of vasoactive intestinal polypeptide and arginine-vasopressin in organotypic slice culture of rat suprachiasmatic nucleus. Neuroscience Letter, 170, 183–186.

c04.indd Sec4:79

Sigmon, S. T., Pells, J. J., Schartel, J. G., Hermann, B. A., Edenfield, T. M., LaMattina, S. M., et al. (2007). Stress reactivity and coping in seasonal and nonseasonal depression. Behavioral Research Therapy, 45, 965–975.

Soma, K. K., Alday, N. A., Hau, M., & Schlinger, B. A. (2004). Dehydroepiandrosterone metabolism by 3beta-hydroxysteroid dehydrogenase/Delta5-Delta4 isomerase in adult zebra finch brain: Sex difference and rapid effect of stress. Endocrinology, 145, 1668–1677. Stanley, B. G., Daniel, D. R., Chin, A. S., & Leibowitz, S. F. (1985). Paraventricular nucleus injections of peptide, Y. Y., and neuropeptide Y preferentially enhance carbohydrate ingestion. Peptides, 6, 1205–1211. Stanley, B. G., Ha, L. H., Spears, L. C., & Dee, M. G. II. (1993). Lateral hypothalamic injections of glutamate, kainic acid, D., L-alpha-amino3-hydroxy-5-methyl-isoxazole propionic acid or N-methyl-D-aspartic acid rapidly elicit intense transient eating in rats. Brain Research, 613, 88–95. Stanley, B. G., & Leibowitz, S. F. (1985). Neuropeptide Y injected in the paraventricular hypothalamus: A powerful stimulant of feeding behavior. Proceedings of the National Academy of Sciences, USA, 82, 3940–3943. Stephan, F. K., Berkley, K. J., & Moss, R. L. (1981). Efferent connections of the rat suprachiasmatic nucleus. Neuroscience, 6, 2625–2641. Stephan, F. K., & Zucker, I. (1972). Rat drinking rhythms: Central visual pathways and endocrine factors mediating responsiveness to environmental illumination. Physiological Behavior, 8, 315–326. Stokkan, K. A., Yamazaki, S., Tei, H., Sakaki, Y., & Menaker, M. (2001). Entrainment of the circadian clock in the liver by feeding. Science, 291, 490–493. Storch, K. F., Lipan, O., Leykin, I., Viswanathan, N., Davis, F. C., Wong, W. H., et al. (2002). Extensive and divergent circadian gene expression in liver and heart. Nature, 417, 78–83. Swann, J. M., & Turek, F. W. (1985). Multiple circadian oscillators regulate the timing of behavioral and endocrine rhythms in female golden hamsters. Science, 228, 898–900.

8/18/09 5:11:37 PM

80

Biological Rhythms

Swann, J. M., & Turek, F. W. (1988). Transfer from long to short days reduces the frequency of pulsatile luteinizing hormone release in intact but not in castrated male golden hamsters. Neuroendocrinology, 47, 343–349.

Vasudevan, N., & Pfaff, D. W. (2007). Membrane-initiated actions of estrogens in neuroendocrinology: Emerging principles. Endocrine Reviews, 28, 1–19.

Takimoto, C. H. (2006). Chronomodulated chemotherapy for colorectal cancer: Failing the test of time? European Journal of Cancer, 42, 574–581.

Venuta, M., Barzaghi, L., Cavalieri, C., Gamberoni, T., & Guaraldi, G. P. (1999). [Effects of shift work on the quality of sleep and psychological health based on a sample of professional nurses]. Giornale Italiano di Medicina del Lavoro ed Ergonomia, 21, 221–225.

Tessonneaud, A., Locatelli, A., Caldani, M., & Viguier-Martinez, M. C. (1995). Bilateral lesions of the suprachiasmatic nuclei alter the nocturnal melatonin secretion in sheep. Journal of Neuroendocrinology, 7, 145–152.

Vielhaber, E., Eide, E., Rivers, A., Gao, Z. H., & Virshup, D. M. (2000). Nuclear entry of the circadian regulator mPER1 is controlled by mammalian casein kinase I epsilon. Molecular and Cellular Biology, 20, 4888–4899.

Thrun, L. A., Moenter, S. M., O’Callaghan, D., Woodfill, C. J., & Karsch, F. J. (1995). Circannual alterations in the circadian rhythm of melatonin secretion. Journal of Biological Rhythms, 10, 42–54.

Vitaterna, M. H., Takahashi, J. S., & Turek, F. W. (2001). Overview of circadian rhythms. Alcohol Research and Health, 25(2), 85–93.

Toh, K. L., Jones, C. R., He, Y., Eide, E. J., Hinz, W. A., Virshup, D. M., et al. (2001). An hPer2 phosphorylation site mutation in familial advanced sleep phase syndrome. Science, 291, 1040–1043. Trainor, B. C., Lin, S., Finy, M. S., Rowland, M. R., & Nelson, R. J. (2007). Photoperiod reverses the effects of estrogens on male aggression via genomic and nongenomic pathways. Proceedings of the National Academy of Sciences, USA, 104, 9840–9845. Tramontin, A. D., & Brenowitz, E. A. (2000). Seasonal plasticity in the adult brain. Trends in Neuroscience, 23, 251–258. Tricoire, H., Moller, M., Chemineau, P., & Malpaux, B. (2003). Origin of cerebrospinal fluid melatonin and possible function in the integration of photoperiod. Reproductive Suppl, 61, 311–321. Turek, F. W. (1977). The interaction of the photoperiod and testosterone in regulating serum gonadotropin levels in castrated male hamsters. Endocrinology, 101, 1210–1215. Turek, F. W., Joshu, C., Kohsaka, A., Lin, E., Ivanova, G., McDearmon, E., et al. (2005). Obesity and metabolic syndrome in circadian clock mutant mice. Science, 308. 1043–1045. Turek, F. W., & Zee, P. C. (1999). Regulation of sleep and circadian rhythms. In C. Lenfant (Ed.), Lung Biology in Health and Disease, (pp. 1–19) New York: Informa Health Care. Ueda, H. R., Hayashi, S., Chen, W., Sano, M., Machida, M., Shigeyoshi, Y., et al. (2005). System-level identification of transcriptional circuits underlying mammalian circadian clocks. Nature Genetics, 37(2), 187–192. Van Cauter, E., & Refetoff, S. (1985). Multifactorial control of the 24-hour secretory profiles of pituitary hormones. Journal of Endocrinological Investigation, 8, 381–391. Van den Pol. A. N., & Powley, T. (1979). A fine-grained anatomical analysis of the role of the rat suprachiasmatic nucleus in circadian rhythms of feeding and drinking. Brain Research, 160, 307–326. Van der Beek, E. M., Horvath, T. L., Wiegant, V. M., Van den Hurk, R., & Buijs, R. M. (1997). Evidence for a direct neuronal pathway from the suprachiasmatic nucleus to the gonadotropin-releasing hormone system: Combined tracing and light and electron microscopic immunocytochemical studies. Journal of Comparative Neurology, 384, 569–579. Van der Beek, E. M., Wiegant, V. M., van der Donk, H. A., van den Hurk, R., & Buijs, R. M. (1993). Lesions of the suprachiasmatic nucleus indicate the presence of a direct vasoactive intestinal polypeptide-containing projection to gonadotrophin-releasing hormone neurons in the female rat. Journal of Neuroendocrinology, 5, 137–144. Van der Veen, D. R., Minh, N. L., Gos, P., Arneric, M., Gerkema, M. P., & Schibler, U. (2006). Impact of behavior on central and peripheral circadian clocks in the common vole Microtus arvalis, a mammal with ultradian rhythms. Proceedings of the National Academy of Sciences, USA, 103, 3393–3398. Van Gelder, R. N. (2001). Non-visual ocular photoreception. Ophthalmic Genetics, 22, 195–205.

c04.indd Sec4:80

Voss, U. (2004). Functions of sleep architecture and the concept of protective fields. Review of Neuroscience, 15, 33–46. Vrang, N., Larsen, P. J., & Mikkelsen, J. D. (1995). Direct projection from the suprachiasmatic nucleus to hypophysiotrophic corticotropin-releasing factor immunoreactive cells in the paraventricular nucleus of the hypothalamus demonstrated by means of Phaseolus vulgaris-leucoagglutinin tract tracing. Brain Research, 684, 61–69. Wang, H., Ko, C. H., Koletar, M. M., Ralph, M. R., & Yeomans, J. (2007). Casein kinase I epsilon gene transfer into the suprachiasmatic nucleus via electroporation lengthens circadian periods of tau mutant hamsters. European Journal of Neuroscience, 25, 3359–3366. Wang, H., Sekine, M., Chen, X., & Kagamimori, S. (2002). A study of weekly and seasonal variation of stroke onset. International Journal of Biometeorology, 47, 13–20. Wang, Y., Levi, C. R., Attia, J. R., D’Este, C. A., Spratt, N., & Fisher, J. (2003). Seasonal variation in stroke in the Hunter Region, Australia: A 5-year hospital-based study, 1995–2000. Stroke, 34, 1144–1150. Watanabe, K., Koibuchi, N., Ohtake, H., & Yamaoka, S. (1993). Circadian rhythms of vasopressin release in primary cultures of rat suprachiasmatic nucleus. Brain Research, 624, 115–120. Watts, A. G., & Swanson, L. W. (1987). Efferent projections of the suprachiasmatic nucleus: II. Studies using retrograde transport of fluorescent dyes and simultaneous peptide immunohistochemistry in the rat. Journal of Comparative Neurology, 258, 230–252. Weaver, D. R. (1998). The suprachiasmatic nucleus: A 25-year retrospective. Journal of Biological Rhythms, 13, 100–112. Weaver, D. R., Carlson, L. L., & Reppert, S. M. (1990). Melatonin receptors and signal transduction in melatonin-sensitive and melatonininsensitive populations of white-footed mice (Peromyscus leucopus). Brain Research, 506, 353–357. Wehr, T. A. (1998). Effect of seasonal changes in daylength on human neuroendocrine function. Hormones and Research, 49(3–4), 118–124. Weiler, E. (1992). Seasonal changes in adult mammalian brain weight. Naturwissenschaften, 79, 474–476. Weingarten, H. P., Chang, P. K., & McDonald, T. J. (1985). Comparison of the metabolic and behavioral disturbances following paraventricularand ventromedial-hypothalamic lesions. Brain Research Bulletin, 14, 551–559. Weitzman, E. D., Fukushima, D., Nogeire, C., Roffwarg, H., Gallagher, T. F., & Hellman, L. (1971). Twenty-four hour pattern of the episodic secretion of cortisol in normal subjects. Journal of Clinical Endocrinology and Metabolism, 33, 14–22. Welsh, D. (2004). Single cell circadian rhythms of luminescence in cultures from MPER2-LUC knockin mice. 74, 18. SRBR annual meeting. British Columbia, Canada: Whistler. Welsh, D. K., Logothetis, D. E., Meister, M., & Reppert, S. M. (1995). Individual neurons dissociated from rat suprachiasmatic nucleus express independently phased circadian firing rhythms. Neuron, 14, 697–706.

8/18/09 5:11:38 PM

References 81 Wen, J. C., Hotchkiss, A. K., Demas, G. E., & Nelson, R. J. (2004). Photoperiod affects neuronal nitric oxide synthase and aggressive behaviour in male Siberian hamsters (Phodopus sungorus). Journal of Neuroendocrinology, 16, 916–921. Wingfield, J. C., Jacobs, J., & Hillgarth, N. (1997). Ecological constraints and the evolution of hormone-behavior interrelationships. Annal of New York Academy of Science, 807, 22–41. Woelfle, M. A., Ouyang, Y., Phanvijhitsiri, K., & Johnson, C. H. (2004). The adaptive value of circadian clocks: An experimental assessment in cyanobacteria. Current Biology, 14, 1481–1486. Wolk, R., Gami, A. S., Garcia-Touchard, A., & Somers, V. K. (2005). Sleep and cardiovascular disease. Current Problems in Cardiology, 30, 625–662. Wright, K. P., Jr., Hull, J. T., Hughes, R. J., Ronda, J. M., & Czeisler, C. A. (2006). Sleep and wakefulness out of phase with internal biological time impairs learning in humans. Journal of Cognitive Neuroscience, 18, 508–521. Xiong, J. J., Karsch, F. J., & Lehman, M. N. (1997). Evidence for seasonal plasticity in the gonadotropin-releasing hormone (GnRH) system of the ewe: Changes in synaptic inputs onto GnRH neurons. Endocrinology, 138, 1240–1250. Yagita, K., Tamanini, F., van Der Horst, G. T., & Okamura, H. (2001). Molecular mechanisms of the biological clock in cultured fibroblasts. Science, 292, 278–281.

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Yamamoto, H., Nagai, K., & Nakagawa, H. (1987). Role of SCN in daily rhythms of plasma glucose, FFA, insulin and glucagon. Chronobiology International, 4, 483–491. Yamazaki, S., Numano, R., Abe, M., Hida, A., Takahashi, R., Ueda, M., et al. (2000). Resetting central and peripheral circadian oscillators in transgenic rats. Science, 288, 682–685. Yan, L., Foley, N. C., Bobula, J. M., Kriegsfeld, L. J., & Silver, R. (2005). Two antiphase oscillations occur in each suprachiasmatic nucleus of behaviorally split hamsters. Journal of Neuroscience, 25, 9017–9026. Yaskin, V. (1984). Seasonal changes in brain morphology in small mammals. Pittsburgh, PA: Carnegie Museum of Natural History. Yoo, S. H., Yamazaki, S., Lowrey, P. L., Shimomura, K., Ko, C. H., Buhr, E. D., et al. (2004). PERIOD2::LUCIFERASE real-time reporting of circadian dynamics reveals persistent circadian oscillations in mouse peripheral tissues. Proceedings of the National Academy of Sciences, USA, 101, 5339–5346. Zucker, I., & Boshes, M. (1982). Circannual body weight rhythms of ground squirrels: Role of gonadal hormones. American Journal of Physiology, 243, R546–R551.

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Chapter 5

NEUROPHARMACOLOGY GARY L. WENK AND YANNICK MARCHALANT

given to people at this time produced more harm than good.) Finally, the term came to be associated with drugs that alter the mind in some manner, either positively or negatively.

This chapter is about drugs that affect the brain, and its purpose is to demonstrate that we can use our understanding of how drugs work in order to gain a better appreciation of how the brain works. Drugs alter brain function; therefore, it is possible to learn about normal brain function by studying how selected drugs with specific actions alter behavior and mental function (Wenk, 2003). Neuropharmacology is a discipline that uses drugs as tools to study the brain and better understand its functions. For our purposes, a drug is anything that we take into the body that ultimately affects brain function. This includes licit and illicit drugs, overthe-counter drugs, as well as nutrients such as vitamins, herbs, chocolate, and caffeine. The distinction between what is considered a nutrient, that is, something that the body needs, and a drug, that is, something that the mind might crave, has become quite blurred. Many people consider nicotine, chocolate, and caffeine essential parts of their daily diets. This chapter is organized according to the anatomy of the brain rather than according to the classes of the drugs. Each section begins with a brief discussion of the anatomy of the particular neurotransmitter system, the role of this system in specific brain regions, and how it is possible to manipulate brain function through the production, release, and inactivation of drugs.

Drugs that affect the brain are fundamental to most cultures and the routine use of stimulants and depressants is so omnipresent that most of us don’t even consider such substances to be drugs, but rather actual nutrients. Indeed, the distinction between drug and nutrient becomes more blurred with each generation. Many people cannot get through the day without the assistance of coffee, tea, tobacco, alcohol, cocoa, or marijuana. Throughout these pages, I will regard anything you take into your body as a drug, whether it’s obviously nutritious or not. As you will see, even molecules that are obviously nutrients, such as essential amino acids, have properties that can be ascribed to a psychoactive drug. Like nutrients, many drugs that affect brain function come from plants that grow all around us. Plant alkaloids (substances containing nitrogen and carbon)—the active ingredients of plants—are related structurally to the neurotransmitters your body uses, and so they can interact with the receptors on your neurons to influence brain function. This is a very important principle that will emerge as you read this chapter: A drug acts on the brain only if it in some way resembles an actual neurotransmitter, or if it is able to interact with an essential biochemical process in the brain that influences the production, release, or inactivation of a neurotransmitter. Plant alkaloids are essentially modified amino acids similar to those used by the brain and body. They resemble the natural chemicals produced and used by the brain. People in ancient cultures were very aware of these natural plant products and their unique properties; they often sought them out for remedies for a variety of illnesses. As a distinction, we will consider substances in plants that do not affect the brain either inert or nutritious.

All of the drugs discussed in this chapter affect the brain and therefore behavior. Because of this property, they are called psychoactive, a term that first appeared in 1548 in the title to a collection of prayers of comfort for the dead by Reinhard Lorichius, titled: “Psychopharmakon, Hoc Est: Medicina Animae.” Within these prayers, the term refers to a type of spiritual medicine that was used principally in miserable or hopeless situations near the end of life. The Greek word pharmakos referred to a human scapegoat—the person who was sacrificed, usually figuratively but sometimes literally by ritual stoning, as a remedy for the illness of another person, usually someone far more important in the society. Later, around 600 b.c. or so, the term came to refer to the drug, or poison, that was being given rather than the person. (Many of the psychoactive drugs

Ancient cultures’ use of plant extracts as medicines to affect the mind was likely the beginning of our concept of how our brain functions. The earliest cultures believed mental illness was something caused by evil spirits or was 82

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Principles of Neuropharmacology

a punishment delivered by an angry deity. In the middle part of the twentieth century, effective tranquilizers were introduced for the treatment of the mentally ill. The realization that it might be possible to cure mental illness in the same way that one cured physical illness was slow to gain general approval because of the wide-ranging, and for some quite frightening, implications about what this meant regarding the nature of the human brain. In the future, drugs will likely be used to do more than simply correct neurochemical imbalances but will actually be used to enhance our cognitive abilities. The reasons underlying the decision to take neuropharmaceuticals are likely as varied as the number of people using them. For example, people may take powerful antidepressant drugs when they cannot cope with the world as an imperfect place. Sometimes drugs are taken by people who are bored or distressed by tedious tasks. Soldiers have a long history of taking drugs to lessen the impact of long periods of intense terror. Before considering the neuropharmacology of specific neural systems and brain regions, some basic principles must be considered. First, drugs that affect the brain should not be viewed as being either good or bad. They are simply chemicals—no more, no less. They have actions within the brain that we desire or would like to avoid. Second, every drug has multiple effects. Because the brain is so complex and because drugs act in many different areas of the brain and body at the same time, they will often have many different effects on brain function and behavior. Third, the effects of a drug on the brain will always depend on the amount consumed or injected. Varying the dose of any particular drug will change the magnitude and the character of the effects of the drug. This principle is called the dose-response effect. In general, greater doses lead to greater, or sometimes completely opposite, effects. Finally, the effects of a drug on the brain are greatly influenced by the individual’s genetic and drug-taking experience and the expectations that the person has about the consequences of the drug-taking experience. For example, if you respond strongly to one drug, you’re likely to respond strongly to many drugs, and this trait is likely shared by one of your parents. Also, if you expect that a drug will act in a certain way on your brain and behavior, then it is much more likely to do so; this is referred to as the placebo effect. It’s ironic that the brain is the organ that decides for itself how it will experience the drug (for a more detailed discussion on placebo effects, see Chapter 63).

PRINCIPLES OF NEUROPHARMACOLOGY The part of the body where the drug acts to produce its effect is called the site of action. Drugs differ in their sites of action. This chapter focuses on those drugs that directly

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influence neural function. Often the behavioral effects of a drug provide clues to its site of action within the brain. For example, drugs that affect sleep usually alter activity in the reticular activating system. Another clue to site of action is afforded by the unequal distribution of neurotransmitters in the brain (Figure 5.1). For example, dopamine is highly concentrated in the basal ganglia, which controls movement. Therefore, administration of drugs that affect the dopamine system may affect the control of movement. Many drugs that might potentially influence brain function are never able to enter the central nervous system due to the presence of barriers; the most important is the blood-brain barrier, which is made up of many parts, the most important being a special type of capillaries. These capillaries have tight junctions between each other, have no fenestra, do not perform pinocytosis, exhibit a thick basement membrane made of an amorphous mucopolysaccharide and finally are covered by astrocyte processes that juxtapose on the basement membrane. These features allow the entry of drugs that are lipid soluble. The relative affinity of a drug for either lipid or water environments is known as its partition coefficient. The partition coefficient plays an important role in how drugs affect brain function. Very lipid-soluble drugs enter the brain very rapidly; they also tend to exit the brain rapidly, which limits the duration of their action (Meyer & Quenzer, 2005). Once a drug has entered the brain, its site of action is often a receptor protein within the synapse. The brain’s response to a drug is proportional to the fraction of receptors occupied. Drugs that bind to receptors and produce a pharmacological action are called agonists; drugs that bind to receptors but produce no pharmacological action are called antagonists. Receptors for several neurotransmitters, for example, acetylcholine, gamma-amino butyric acid (GABA), and glycine, have homologous structures and may share an evolutionary ancestor. An understanding of the evolution of the neurotransmitters and their receptors sometimes gives clues to their function. Once the drug has interacted with its respective receptor protein, its actions are terminated either enzymatically or by simple diffusion away from the synapse. Most neuroactive compounds are not transported into neuronal terminals. Some examples for selected neurotransmitter systems are discussed later. The removal of a drug from the brain is frequently accompanied by biological and behavioral changes that are opposite to those produced by the drug; that is, the brain always “pushes back.” For example, the euphoria induced by cocaine and amphetamine is often a prelude to severe depression. Many biological factors such as age and weight play a crucial role in how drugs affect the brain, and influence behavior and personality, an emergent property of the brain. This concept was probably best described by Wilder in his Law of Initial

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Neuropharmacology Corpus Callosum

P

A D N Brain Stem S

Cerebellum

Figure 5.1 Schematic anatomy of neurotransmitter systems. Note: A = Acetylcholine neurons within the basal forebrain region project topographically to the cortex, hippocampus, amygdala, and olfactory bulbs; D = Dopamine neurons within the rostral midbrain project into the ipsilateral striatum and frontal cortex; N = Norepinephrine neurons originate within a small region, the locus coeruleus, in the floor of the fourth ventricle and project into virtually all regions of the ipsilateral hemisphere, with the exception of the basal ganglia; S = Serotonin neurons originate with a scattered group of nuclei that lie along the midline of the pons and medulla and project both caudally into the brain stem and rostrally into all regions of the brain; P = Peptide-containing neurons tend to be more diffusely scattered as interneurons, although there are notable exceptions.

Value (Wilder, 1958). Each person has an initial level of excitation; the degree of response to a drug depends on this initial level. For example, euphoria is observed in patients suffering from pain, anxiety, or tension when they are given small doses of morphine. In contrast, a similar dose given to a happy, pain-free individual often precipitates mild anxiety and fear. Catatonic patients may respond with a burst of animation and spontaneity to an intravenous injection of barbiturates. Sedative drugs create more anxiety in outgoing, athletic people, as compared to passive, intellectual types. In the remainder of this chapter, we examine the intersection of neuroanatomy, neurochemistry, and neuropharmacology, beginning with the first neurotransmitter system discovered, acetylcholine.

ACETYLCHOLINE Acetylcholine is an important neurotransmitter for many species within all kingdoms. The precursors of acetylcholine synthesis, choline and coenzyme A, have been found in both prokaryotes and eukaryotes, including a strain of Pseudomonas fluorescens isolated from the juice of fermenting cucumbers as well as in the blue-green algae, Oscillatoria

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agardhii, where it may be involved with photosynthesis. Acetylcholine stimulates silk production in spiders and limb regeneration in salamanders (Venter et al., 1988). Therefore, it is difficult to ascribe a particular function to this molecule in nature. Acetylcholine neurons play an important role within the parasympathetic and sympathetic nervous system; their role in the periphery accounts for many significant side-effect profiles of drugs that also affect central cholinergic cells. Within the brain, there are numerous cholinergic systems; however, two are particularly important: the basal forebrain cholinergic system, which projects topographically to the cortex, hippocampus, and other limbic structures that influence memory, attention, and mood; and the intra-striatal collection of short-axon interneurons that influence control of movement (for review, see Wenk, 1997; see Chapter 17). Cholinergic neurons in the basal forebrain region that innervate the hippocampus and cortex are vulnerable to degenerative processes associated with Alzheimer ’s disease (AD) and may become dysfunctional during the early stages of the disease process (Davis et al., 1999; Whitehouse, Price, Clark, Coyle, & DeLong, 1981). The extent to which this neurotransmitter system is impaired may correlate with the severity of selected cognitive symptoms associated with dementia. For example, dysfunction of cholinergic input to the cortex may contribute to a deficit in attentional abilities (Sarter, Gehring, & Kozak, 2006); alterations in the projection to the central nucleus of the amygdala may underlie emotional changes (Power, Vazdarjanova, & McGaugh, 2003); and the dysfunction of cholinergic inputs to the hippocampus clearly underlies the presence of amnesia (Olton, Wenk, Church, & Meck, 1988; Wenk, 2007). A deficit in cholinergic biomarkers, including a decline in level of cholinergic synthetic enzyme, choline acetyltransferase activity, transmitter production, and release are commonly reported biochemical changes within the brains of patients with AD. It is important to recognize that the loss of these biomarkers does not herald the death of the neuron; an injured neuron will often reduce the production of its luxury systems related to neurotransmitter function in preference to biochemical processes that are essential for recovery. The persistence of the intact neurons offers an opportunity to rescue them from continued degeneration. As such, experimental manipulation of the functional integrity of cholinergic neurons in the basal forebrain of young rats has been used as an animal model for this component of AD pathology (Wenk, 2006; Wenk et al., 1994). Moreover, drug therapies designed to attenuate memory deficits associated with AD have focused on alleviating these impairments in the cholinergic synaptic function. Similar approaches have been used to compensate for presumed impairments in other neurotransmitter systems.

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Acetylcholine

These neurons synthesize acetylcholine from choline, obtained from the diet and acetyl groups that originate in mitochondria from the metabolism of glucose, and are transported into the cytoplasm attached to coenzyme A. The synthesis occurs within the cytoplasm, and the product is stored in synaptic vesicles or loosely bound to the cytoplasmic membrane for fast release. The vesicular pool is released only when the cytoplasmic pool is expended. Production via the enzyme choline acetyltransferase is controlled by end-production inhibition; the availability of choline and acetyl moiety is not rate limiting under normal conditions. Therefore, administration of choline via the diet does not increase acetylcholine production or release (Wild & Benzel, 1994). Once released, acetylcholine’s action within the synapse is terminated by the acetylcholinesterase enzyme; about 40% of the choline produced is actively taken up into the terminal to be reused again for synthesis of acetylcholine. The blockade of choline uptake by hemicholinium-3 is lethal because the reduced acetylcholine synthesis produces an imbalance in autonomic function and the loss of the ability of the motor neurons to contract the diaphragm; in contrast, this drug has no behavioral effects because it does not cross the blood-brain barrier. The release of acetylcholine from the presynaptic terminal can be inhibited by the toxin released from the Clostridium botulinum bacteria; death is caused by loss of diaphragmatic contraction, leading to asphyxiation. Once released, acetylcholine can act on two quite different protein receptors that have been designated (as have most neuropharmaceuticals) according to the compounds that were originally used to manipulate them, that is, nicotine and muscarine. Nicotinic receptors are directly coupled to a sodium-conducting channel and produce a rapid increase in sodium ion conductance and depolarization of the postsynaptic membrane. In contrast, muscarinic receptor stimulation leads to slower depolarization or hyperpolarization depending on the nature of the secondary messengers. Muscarinic receptors have been further subdivided (as have most other neurotransmitter receptors) according to their affinities for various newly investigated drugs. These receptors have not been found within kingdoms for protoctista and fungi although acetylcholine is produced by some members. These findings have led to some intriguing hypotheses regarding the evolution of receptors and the limitation of our chemical tools to investigate them. For example, nicotinic and muscarinic receptors have been found in peanut worms (whose fossils date back 500 million years), spoon worms, leeches, and earthworms. However, there is no evidence that the two receptors are related; muscarinic and nicotinic receptors differ in size, structure, and mechanism of action. Most of the acetylcholine receptors in the brain are muscarinic, while less

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than 10% are nicotinic (Cooper, Bloom, & Roth, 2002). Yet, stimulation of nicotinic receptors is clearly much more rewarding than stimulation of muscarinic receptors. The explanation for their divergent consequences underlies a basic principle in neuropharmacology that mimics the real estate industry: location is all that matters. Nicotine is a very potent agonist; as little as 60 mg can be fatal to an adult. Curare, a resinous extract of the plants Chondrodendron tomentosum and Strychnos toxifera from the Orinoco and Amazon basins in South America, is an antagonist at the nicotinic-type acetylcholine receptor. Because it does not cross the blood-brain barrier, its actions are expressed primarily on the autonomic ganglia and at the neuromuscular synapse. The drug is lethal because it blocks the neuromuscular nicotinic receptors located on the diaphragm that allow breathing; therefore, death is by asphyxiation. Muscarine, carbechol, and oxotremorine are agonists at the muscarinic-type acetylcholine receptors; atropine and scopolamine are antagonists at this receptor. Once acetylcholine is released into the synapse, its action is terminated by the enzyme acetylcholinesterase. This enzyme is produced by cholinergic neurons and released from the cytoplasm into the extracellular space where it is found in high concentration and sometimes taken up into noncholinergic nerve terminals. This enzyme can very quickly inactivate synaptic acetylcholine at the rate of approximately 25,000 molecules per second. Thus, even its partial inhibition will have a profound effect on synaptic levels of acetylcholine and postsynaptic stimulation of acetylcholine receptors. Physostigmine is a reversible inhibitor of this enzyme; because acetylcholine is usually inactivated by this enzyme, its levels increase quickly within the synapse. In the presence of physostigmine, or any other acetylcholinesterase inhibitor, acetylcholine will either diffuse out of the synapse or be catabolized by other esterase enzymes, such as butylcholinesterase. The widespread distribution of neuronal systems that use acetylcholine indicates that these systems play a significant role in many brain functions. Its role in neuroplasticity has been best studied. The blockade of the muscarinic receptors within the brain by scopolamine impairs memory and produces mental confusion due to its actions within the hippocampus and neocortex. (A not very amusing side note: Some news reports claim that thieves sometimes add scopolamine to chewing gum, chocolate, or drinks of unsuspecting people, or blow it into their faces, to immobilize and then rob them.) In contrast, drugs that enhance the action of acetylcholine at muscarinic receptors by preventing its catabolism, for example, physostigmine, may enhance memory and attentional abilities. Several plants that grow wild contain scopolamine, atropine, or related molecules, such as jimson weed (Datura stramonium),

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henbane (Hyoscyamus niger), and mandrake (Mandragora officinarum). The antagonism of muscarinic receptors within the neocortex slows neural activity and makes the user drowsy; this action within the hippocampus impairs plasticity. The deadly nightshade, Atropa belladonna, was given its name by Carl von Linné in the eighteenth century to indicate the poisonous nature of this plant. Atropos was one of the Greek “fates,” or “daughters of necessity” who also included Clotho, who spun the thread of life, and Lachesis, who allotted each man his portion of life. Atropos cut the thread of life at the appointed time. Muscarinic receptors are expressed by the smooth muscles that encircle the iris; their antagonism allows the pupils to dilate. The presence of muscarinic receptors within the motor cortex and the basal ganglia explains why scopolamine also produces slurred speech and generally impaired motor abilities. Higher doses of scopolamine can produce feelings of unpleasantness and visual and auditory hallucinations. The hallucinations are of ordinary objects and not mythic or other-worldly like those produced by drugs that directly affect serotonin receptors. This may provide insight into the function of acetylcholine and the influence of the location of muscarinic receptors within the normal brain. Stimulation of muscarinic receptors may produce euphoria and subjective sensory changes. The alkaloid muscarine is present in the mushroom Amanita muscaria; it can produce delirium and hallucinations when eaten. Another agonist of the muscarinic receptor is found in the areca nut of the betel pepper tree of Asia. The active ingredient is arecoline. The nut is used as a mild euphoriant and antitussive (cough suppressant) throughout Southeast Asia; these uses are consistent with the presence of muscarinic acetylcholine receptors with the limbic system and coughing centers of the brain. One of the best-studied agonists of the nicotinic acetylcholine receptor is, of course, nicotine. Nicotine occurs in more than 64 species of plants around the world, including the tobacco plant. The first use of tobacco was to treat persistent headaches, colds, and abscesses and sores on the head. Tobacco emetics were used to treat flatulence, and the smoke was inhaled deeply in order to lessen bad coughs. Jean Nicot sent some tobacco to Catherine de Medici, who was then queen to Henry II of France; she reported that it helped treat her migraines and the plant took on the title of herbe sainte or holy plant. Nicot got credit for the discovery and in 1565 Linnaeus named the genus Nicotiana in his honor. In the 1890s, the U.S. Pharmacopeia dropped nicotine from its list of useful therapeutic agents. The alkaloid nicotine is likely utilized by the tobacco plant as a defense against insects that would express this type of receptor in their body and be dose-dependently vulnerable to its toxicity. Tolerance and dependency develops from

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its chronic use. Most cigarettes contain about 1 to 2 mg of nicotine. Because nicotine is quite volatile and heat labile, only about 20% of this dose is actually inhaled into the body; however, due to its exceptional lipid solubility, at least 90% of the inhaled nicotine will be absorbed. Nicotine can be rapidly absorbed from mouth, lungs, or intact skin. Once the smoke is inhaled, it is absorbed via pulmonary alveoli and transported to the brain within 2 to 7 seconds. This makes smoking tobacco as efficient as an intravenous injection in terms of getting nicotine to its site of action within the brain. Nicotine is also quite toxic; 60 mg is considered a lethal dose for a human, and death takes only a few minutes to occur. The actions of nicotine at the synapse are complicated. Initially, at lower doses, nicotine stimulates the receptor; then, at higher doses it induces a depolarization blockade, or inactivation, of these receptors from further stimulation. The extent that desensitization occurs is influenced by the subunit composition of the receptor. Nicotine receptors are composed of five subunits that form an ion channel (Siegel, Albers, Brady, & Price, 2006). The brain expresses at least 12 different nicotinic receptor subunits. Thus, prolonged exposure to nicotine, for example, by continued smoking of tobacco products, would lead to a complex pattern of receptor stimulation and desensitization across brain regions. When this inactivation occurs in the periphery, it is associated with general muscle weakness as the function of the neuromuscular junction is impaired. Chronic exposure to nicotine leads to the rather paradoxical condition of an upregulation in the number of nicotinic receptors that may exist in a desensitized conformational state. The altered synaptic function within the brain leads to tremors. Once again, death is most often due to paralysis of the respiratory muscles. Nicotine affects cortical function in a complex dose-dependent fashion; low doses activate the left hemisphere and stimulate activity, while high doses activate the right hemisphere and are associated with sedative effects. Therefore, when doing boring tasks, a low dose of nicotine can increase subjective arousal. In contrast, during anxious or stressful situations, smokers may actually reduce subjective stress by activating the right hemisphere and producing sedation. Sixty percent of adults with attention deficit disorder are smokers, compared to less than 30% of the rest of the population, implying that these adults are finding a pharmacological substitute for their childhood medications. Nicotine also produces a dose-related self-report of euphoria that is most pronounced following overnight abstinence. This may explain why heavy smokers like to light up as soon as they awake. Throughout the day, smokers carefully, and probably unconsciously, control the amount of nicotine that reaches the brain by the number of cigarettes they use per hour, by altering the rate at which

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Catecholamines

they take a puff, and by the volume of their inhalation. This careful titration may optimize the amount of cholinergic receptor stimulation and cortical activation. In 1948, the Journal of the American Medical Association stated that “from a psychological point of view, in all probability more can be said in behalf of smoking as a form of escape from tension than against it.” Today, our perception of nicotine use has been altered principally by the consequences of the “vehicle” for nicotine administration, tobacco. Tobacco causes almost one U.S. death every minute or the equivalent of four major airline crashes daily.

CATECHOLAMINES The neurotransmitter systems considered in this section— dopamine and norepinephrine—are found in both the peripheral nervous system and central nervous system (Figure 5.2). Once again, I discuss the function of these

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two neurotransmitter systems by examining the consequences of stimulation and antagonism of their function by a rather large series of drugs. A consistent pattern of effects emerges demonstrating that dopamine is intimately related to reward and norepinephrine function underlies the components of arousal (for details, see Chapter 40). We know a great deal about the functions of these two neurotransmitter systems primarily because so many drugs have been discovered that can modify their function. Catecholamines are monoamines, that is, they contain at least one nitrogencontaining amine group. The term catechol refers to the presence of a six-carbon ring that has two hydroxy groups attached at adjacent positions to each other. Catecholamines occur extensively throughout nature and have been identified as neurotransmitters in animals as diverse as insects, crustacea, arachnids (spiders), and primates (Venter et al., 1988). Norepinephrine is predominant in the brain and peripheral nervous system in mammals, while dopamine is predominant in species that evolved prior to mollusks.

Blood-brain barrier Ca⫹⫹ 4 5 2 6

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Figure 5.2 The life cycle of a typical neurotransmitter. Note: (1) Nutrients, such as amino acids, glucose, fats, and dipeptides, from the diet are transported actively out of the arterial supply to the brain through the blood-brain barrier and, via astrocytes, into the neuron. (2) Enzymatic conversion, for example, occurs by hydroxylation, decarboxylation or esterification, and so on. (3) Active transport occurs into the synaptic vesicles for storage and later release. (4) Activation of the presynaptic neuron leads to the arrival of an action potential that induces the opening of voltage-controlled ion channels, in particular, calcium ion channels; (5) the entering calcium ions induce the fusion of the synaptic vessel to the presynaptic membrane and the release of the neurotransmitter into the synaptic space. (6) The neurotransmitter molecule briefly interacts with the specific proteins on the surface of the postsynaptic cell membrane and induces a large variety of potential responses, for example, opening or closing ion channels, de- or hyperpolarization of the membrane, and so on. (7) Secondary messengers may be produced due to the activation of enzymes that initiate a cascade of molecular processes. The consequences of this cascade are quite diverse, ranging from locally influencing the

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function of nearby receptors to alterations in the genome of the neuron itself. (8) The actions of the neurotransmitter must be terminated in order to permit the communication between two neurons. This is accomplished principally by actively reabsorbing the transmitter molecule back into the presynaptic terminal using dedicated protein complexes that are on the surface of the presynaptic membrane. (9) A secondary method of transmitter inactivation is by enzymatic conversion of the molecule so that it is no longer able to interact with its receptor. For example, the neurotransmitter molecule may be hydrolyzed, ionized, or conjugated onto a larger, and more water-soluble, molecule. (10) Once the neurotransmitter is enzymatically converted, it is removed from the brain and metabolites of neuronal function can be detected in the body fluids. (11) Drugs can interact with any of these processes and impair, or even sometimes enhance, the production, storage, release, receptor function, re-uptake, and inactivation processes. Fluctuations in the levels of the metabolites of different neurotransmitters can be monitored in order to judge the integrity of specific neural systems.

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In mammals, norepinephrine is distributed throughout the autonomic nervous system. Its presence there contributes significantly to the side effects, often undesired, of many drugs that alter the function of norepinephrine neurons in the brain. Norepinephrine is released from the postganglionic sympathetic neurons into the body organs as well as into blood vessels and hair follicles on skin. The activation of norepinephrine input to the skin is responsible for the goosebump response to frightening sights and sounds. Within the brain, almost all of the brain’s norepinephrine neurons are located in the locus coeruleus within the floor of the fourth ventricle. The name of this brain region is related to the fact that these norepinephrine neurons concentrate copper into a matrix of melanin pigment. Although copper is a required cofactor for one of the enzymes necessary for the synthesis of norepinephrine, the concentration of the copper far exceeds what is required for neurotransmitter synthesis. Unfortunately, the presence of this transition metal contributes to an age-associated vulnerability to oxidative stress. The axonal projections out of the locus coeruleus into the brain follow two ascending projections: The ventral pathway provides norepinephrine to the hypothalamus, septal area, pituitary, substantia nigra, and mammillary bodies. The dorsal pathway projects to the entire neocortex, hippocampus, thalamus, amygdala, superior and inferior colliculi, and the medial and lateral geniculate nuclei of the thalamus. Dopamine neurons exist within a small region of the midbrain and, although there are three to five times more dopamine neurons in the brain stem than norepinephrine neurons, they do not project as widely throughout the brain as do norepinephrine neurons. Although there are about 10 different groups of dopaminergic neurons within the brain, three general, long-axon pathways are recognized that ascend into the forebrain. The nigrostriatal pathway originates within the substantia nigra and projects to the striatum. The region was given the name substantia nigra, or dark substance, because it concentrates the transition metal iron that, similar to the situation seen in the locus coeruleus, is combined with a melanin pigment. The oxidation of iron contributes to the color of this brain region and also confers a degree of neuronal vulnerability to these cells (Molina-Holgado, Hider, Gaeta, Williams, & Francis, 2007). The degeneration of this pathway is associated with Parkinson’s disease and is characterized by tremors, spasticity, and akinesia. These symptoms provide insight into the normal responsibility of the release of dopamine within the striatum. Many drugs that interact with the function of the forebrain dopaminergic system have side effects that resemble those seen in people with Parkinson’s disease.

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Two other ascending pathways originate in the ventral tegmental area that lies just medial to the substantia nigra. The mesolimbic pathway projects to forebrain structures associated with the olfactory and limbic system. The mesocortical pathway projects to the frontal cortex. Too much activity in these pathways may underlie some of the symptoms associated with psychosis. The drugs that target this system will be discussed later. The many different catecholamine receptors that exist provide the opportunity for a large variety of drugs to have both selective and widespread effects on different parts of the peripheral and central nervous systems. These neurotransmitter receptor proteins have existed in the forms we find today for at least 600 million years. The actual point in evolution where receptor-mediated neurotransmission appears is not known. However, some of the oldest eukaryotes known respond to the same norepinephrine receptor–stimulating drugs as primates; for example, the beta subtype of norepinephrine receptors may have existed in annelids 500 million years ago during the Paleozoic era. Norepinephrine receptors were initially divided into two classes, alpha and beta, due to their distribution in the body and their selective responses to drugs that were available at that time. As newer drugs were studied, additional subclassifications were introduced to the standard nomenclature. Receptors are also categorized by their location on the neuron; for example, autoreceptors are found on the presynaptic membrane of many types of neurons; their purpose is to modulate the release of neurotransmitters from the axonal terminal. With continued investigations, subtypes of these subclassifications have led to the characterization of at least nine different receptors that are all linked to very similar primary signaling mechanisms. Pharmacological manipulation of these receptors, in order to understand their function, has been hindered by the lack of specific and selective agonists or antagonists. A second important feature of most receptors is that they are responsive to the presence of constant stimulation or constant blockade. For example, in response to constant blockade, receptors will become supersensitive. Receptor supersensitivity is a common behavior of receptors (Fleming, 1999) and may represent a compensatory mechanism by the postsynaptic cells in response to the absence of incoming signals via the receptor blockade. Exposure to receptor agonists can produce an uncoupling of the receptor from its intracellular signaling system, for example, a G-protein, leading to the stopped signaling. Ultimately, the receptor may be sequestered into intracellular compartments for later redeployment to the cell surface or degradation. The process of desensitization may involve genetic and posttranscriptional regulatory mechanisms (Heck & Bylund, 1998).

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Catecholamines

Many drugs that interact with the function of the neurotransmitter systems do so by affecting their metabolism. Three key enzymes are involved in the synthesis of dopamine and norepinephrine. These enzymes are structurally similar to each other, and the genes that encode them lay close together on the genome like beads on a string of DNA. These enzymes exist within the nervous system of all vertebrates and several invertebrate species but not all of these enzymes are expressed in every catecholamine neuron. When more than one enzyme is expressed in a neuron, their regulation is usually linked in a coordinated fashion. Occasionally during embryogenesis, selected enzymes are transiently expressed and then disappear. Evolutionary studies of these enzymes suggest a strong functional preservation of the catalytic site. The reason for this preservation may be related to the fact that the genes have a high degree of nucleotide homology, suggesting that they may have evolved from duplication of a common ancestral gene. Genetic analyses have shown that the enzymes that initiate the production of dopamine, norepinephrine, epinephrine, and serotonin, that is, phenylalanine hydroxylase, tyrosine hydroxylase, phenylethanolamine N-methyltransferase, and tryptophan hydroxylase, share considerable sequence homology (Baetge, Suh, & Joh, 1986; Grima, Lamouroux, Blanot, Biguet, & Mallet, 1985). The production of dopamine and norepinephrine begins with the amino acid tyrosine that is obtained from the diet and actively absorbed across the blood-brain barrier. Tyrosine is converted to l-dopa by the enzyme tyrosine hydroxylase. This enzyme is the rate-limiting step in the production of norepinephrine and dopamine. The activity of tyrosine hydroxylase is controlled by end-production inhibition and is also regulated by availability of precursors and cofactors. One very important cofactor is molecular iron. Without iron, this enzyme fails to function normally. People with anemia have reduced body levels of iron and, as a consequence, may have reduced tyrosine hydroxylase activity leading to reduced production of norepinephrine and dopamine. The reduced brain levels of these important neurotransmitters in the limbic system may lead to a slight depression. Tyrosine can also be acted on by the enzyme tyrosinase and converted into a melanin pigment. This enzyme is quite interesting to study because it is subject to a mutation that makes it heat labile, that is, it works only in the cooler areas of the body. The consequence of this mutation is a lack of pigmentation in humans; in cats it produces the Siamese breed. Apparently, this enzyme is critical for the normal decussation of visual tracts. The second critical enzymatic step in this pathway is L-Dopa decarboxylase. This enzyme converts the product of tyrosine hydroxylase, l-dopa, into dopamine. This enzyme is extremely

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efficient which may explain why brain levels of l-dopa tend to be so low and why providing substrate to it leads to a dramatic increase in the production of dopamine. The synthesis of dopamine occurs in the cytoplasm of neurons, and the dopamine produced is then transported into synaptic vesicles for storage until it is released with the passage of an action potential. This enzyme is rather nonspecific due to its evolutionary history and also produces serotonin in the presence of the amino acid tryptophan or converts tyrosine to tyramine, which is the precursor to the insect neurotransmitter octopamine. Octopamine is also present in low levels in the vertebrate brain. The third enzyme in this pathway is dopamine-beta-hydroxylase, and it converts dopamine into norepinephrine and is therefore not expressed by dopaminergic neurons. The enzyme is stored within the synaptic vesicles, where it lies in wait for the entry of dopamine molecules from the cytoplasm. The conversion occurs within the vesicle while it’s being transported to the terminals from the cell soma. The enzyme is released with the norepinephrine into the synapse with its principal cofactor, copper, and the intravesicle antioxidant ascorbic acid. The diet is important for the control of synthesis (Siegel et al., 2006). The shifting concentration of different precursor amino acids in the diet will alter their relative uptake and may limit or alter production. With a balanced diet, the uptake of all amino acids is fairly constant and in correct proportion. Too much of one amino acid will offset the uptake of the others and therefore alter the availability of dopamine or norepinephrine for release. Obviously, diet can affect a person’s mood, but the impact is usually more subtle than is typically produced by most psychoactive drugs. Once dopamine or norepinephrine is released, its actions within the synapse are terminated principally by re-uptake into the presynaptic terminal or by metabolic breakdown due to oxidative deamination by monoamine oxidase or transmethylation by catechol-O-methyltransferase. What if the vesicles are empty? One interesting drug, reserpine, prevents the transport of the neurotransmitters into the vesicles. Reserpine is found in the snake root plant. If dopamine and norepinephrine (and serotonin) cannot be stored safely in vesicles, they are caught in the cytoplasm, where the enzyme monoamine oxidase can catabolize them. Reserpine has a tranquilizing effect due to its ability to prevent the transfer of catecholamines into the synaptic vesicle. In addition, the reduction in the availability of these transmitters is associated with a severe depression that might explain why the snake root plant is called the insanity herb (pagla-kadawa) by Sherpas in the Far East. In contrast to reserpine, some drugs enhance the availability and release of catecholamines. One of the beststudied drugs is amphetamine. Amphetamine is taken up

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into the terminals disturbs the vesicular transport storage process, induces a presynaptic leakage of norepinephrine and dopamine, and also blocks their inactivation by reuptake and via monoamine oxidase (Sulzer, Sonders, Poulse, & Galli, 2005). The enhanced release of these catecholamines leads to heightened alertness, euphoria, lowered fatigue, decreased boredom, depressed appetite, insomnia, headaches, and tremors. The rebound symptoms from a drug’s effects on the brain are often proportional and in reverse to the effects of the drug. For example, amphetamine withdrawal produces extreme fatigue, dysphoria, and depression. Excessive exposure to amphetamine can produce a condition similar to paranoid schizophrenia, which is often the inevitable consequence of high-dose use. At the molecular level, these cognitive changes may be related to a reduction in the function or presence of dopamine transporters (Stanley, Pettergrew, & Keshavan, 2000). During World War II, forces on both sides of the battle lines used amphetamine to combat boredom, fatigue, and to increase endurance. Historians suggest that at the end of the war, Adolf Hitler ’s increasingly bizarre behavior may have been due to his excessive use of amphetamines. One of the basic principles of neuropharmacology is that lipid solubility is directly correlated with the speed of uptake of a drug into the brain (Meyer & Quenzer, 2005). Furthermore, the faster a drug enters the brain and alters its physiology, the greater the euphoria the drug is likely to induce. This principle has never been lost on illicit drug designers. Morphine becomes far more lipid-soluble and far more euphorigenic when two acetyl groups are added to produce heroin. Amphetamine has been modified many times in the past. The simplest manipulation was the addition of a methyl group to make methamphetamine, which is a very potent analog of amphetamine and far more lipidsoluble. Not surprisingly, its street name became “speed.” Over time, attempts to make amphetamine ever more lipidsoluble by the addition of carbon atoms has produce drugs that are more euphorigenic and/or hallucinogenic than amphetamine, for example, 3,4-methylenedioxyamphetamine and N-ethyl-3,4-methylenedioxyamphetamine were precursors to 2,5-dimethyl-4-methylamphetamine, and 3,4methylenedioxymethamphetamine; the last is known widely as “ecstasy.” Any chemical manipulation that makes amphetamine more lipid-soluble allows it to enter the brain faster; drugs that quickly enter the brain often become chosen as drugs of abuse. Although amphetamine does not occur naturally, some chemically similar molecules have been discovered. For example, asarone is chemically similar to amphetamine and is found in the plant Aacorus calamus that grows in Asia, Europe, and North America. Khat is an African plant, Catha edulis, that contains two phenylalkylamines called

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cathinone and cathine (d-norisoephedrine). The habit of chewing khat probably predates coffee drinking by centuries. The relative level of these naturally occurring analogs of amphetamine depends, as is true for most plant-derived psychoactive drugs, on where plant is grown, its age, and the time elapsed after collection. Cathinone is quite unstable, a proclivity that makes storage for distribution nearly impossible. Decoctions in hot water are called Abyssinian tea. Other compounds in this plant, as in so many others, include flavonoids that are anti-inflammatory. The cactus Lophophora williamsii is used to prepare a drink called peyote that contains 3,4,5-trimethoxyphenylethylamine (clearly a lipid-soluble molecule that is chemically similar to a catecholamine), also known as mescaline. It produces a dose-dependent range of effects that include euphoria at low doses and hallucinations at higher doses. Drugs that selectively block monoamine oxidase also occur naturally, for example harmaline and harmine can be found in a thick vine plant, Peganum harmala, which grows in the Amazon rain forest. The latency of onset is only 5 minutes after ingesting the plant, and the colorful visual hallucinations may last up to 8 hours. The catecholamine neurotransmitters are inactivated principally by re-uptake into the presynaptic nerve terminal (Siegel et al., 2006). Drugs that block this re-uptake process augment the effects of the neurotransmitter within the synaptic cleft. Most of these drugs have found clinical use as antidepressants. Depression is considered the common cold of psychiatric illness because each year more than 100 million people worldwide develop clinically recognizable depression (for a more detailed discussion of this topic, see Chapter 55). These agents block re-uptake of norepinephrine, dopamine, and serotonin into the terminals thus prolonging interaction in the synaptic cleft. However, all chemical action at the re-uptake site in clinical efficacy does not occur simultaneously. The drugs require 2 to 3 weeks to produce their antidepressant benefit. Their mechanism of action is believed to relate to the adaptive neural mechanisms following chronic use. Overall, the immediate effects block re-uptake and are offset by compensatory short- and long-term adjustments at catecholamine synapses. The blockade of re-uptake of dopamine and serotonin has profoundly euphorigenic effects, as do many current antidepressant drugs. In addition, many drugs that have this action are quite addicting, such as cocaine and amphetamine. At the other side of the synapse, drugs that block dopamine receptors have considerable therapeutic efficacy as antipsychotics. The fact that these drugs are capable of reducing some of the symptoms associated with psychosis does not prove that psychosis is due simply to a dysfunction in dopamine neurons. Indeed, this is a very important general point to consider when using drug actions to

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Serotonin 91

understand brain function. The knowledge that selective re-uptake inhibitors act on a few specific neurotransmitter systems in order to reduce the symptoms of depression does not prove that a dysfunction of one of these neural systems underlies either the mental disorder or its symptoms. It simply indicates that manipulation of re-uptake of a specific neurotransmitter molecule ultimately leads to an alteration in the presentation of symptoms. Indeed, compelling evidence exists that this action has very little to do with the therapeutic benefits of selective serotonin reuptake inhibitors (Santarelli et al., 2003). Furthermore, the alteration in dopamine function probably does not cause psychosis; rather, it is most likely just a secondary consequence of a complex array of alterations of one or more different neural systems in the brain. This may explain why the blockade of some dopamine receptors within certain brain regions reduces the severity of some symptoms but not others. The antagonism of dopamine receptors simply compensates for the presence of an error of brain chemistry or connectivity that may exist somewhere in the brain. There are five identified functional dopamine pathways within the brain (Cooper et al., 2002). The antagonism of each by antipsychotic drugs provides insight into their unique role in the brain. Two originate within the midbrain ventral tegmental area and project into many structures of the limbic system (mesolimbic) or into the frontal neocortex (mesocortical). The antipsychotic actions of dopamine receptor antagonists are thought to involve alterations in synaptic function of the mesolimbic and mesocortical dopamine pathways. A third dopaminergic pathway originates in an adjacent midbrain region, the substantia nigra, and projects into the striatum. Antagonism of dopamine receptors within the striatum by typical antipsychotic drugs leads to the appearance of a series of extrapyramidal side-effects that are similar to those seen in patients with Parkinson’s disease, including tremors when at rest, reduction of voluntary movement, spasticity, and dystonia. The level of dopamine within the frontal lobes and striatum has been correlated with smiling behavior in humans; consistent with this interesting pleasure-related role of dopamine are reports that the loss of the ability to smile is often seen in patients with Parkinson’s disease. Within the striatum, dopamine is likely released on acetylcholine interneurons; many of the extrapyramidal side-effects can be attenuated by drugs that block muscarinic acetylcholine receptors. Antipsychotic drugs also antagonize dopamine receptors within a dopaminergic pathway that originates within the hypothalamus and negatively controls the release of prolactin leading to the increased release of this hormone from the pituitary. It was once thought that antagonism of this pathway within the hypothalamus accounted for the significant weight gain associated with antipsychotic drugs;

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recent evidence suggests that the weight gain is related to the antagonism of histamine receptors. Ironically, the original clinical use of the first antipsychotic drug, Thorazine, occurred because of its ability to block histamine receptors and reduce symptoms of the common cold (Lindamood, 2005). Subsequently, its additional proclivities were recognized. In a manner similar to that observed following treatment with antidepressant drugs, the side effects of dopaminergic receptor blockade occur rather quickly but the clinical benefits require 2 to 3 weeks, or more, to fully develop.

SEROTONIN Serotonin was initially discovered in the serum and determined to have tonic effects on the vascular system, hence its name (Rapport, Green, & Page, 1948). It has been found in the venom of amphibians, wasps, and scorpions and within the nematocysts of sea anemone as well as in the nervous system of lobsters and parasitic flatworms. Serotonin immunoreactivity was not found in Coelenterata (Hydra magnipapillata), Echinodermata (Asterina pectinifera), or Protochordata (Halocynthia roretzi; Fujii & Takeda, 1988). In humans, approximately 90% of total body serotonin is found within the nervous system of the gastrointestinal system. About 8% is localized to platelets and mast cells and the remaining few percent is found within the brain, principally within the pineal gland—which is not generally considered part of the brain. Neurons that produce and release serotonin are organized into a series of nuclei that lie along the midline, or seam, of the reticular region of the brain stem; these are the raphe nuclei. The most caudal lying nuclei in the ventromedial pons send axonal projections into the spinal cord to control the sympathetic autonomic nervous system. The ascending pathways originate in the pons and midbrain raphe nuclei and pass through the medial forebrain bundle in the lateral hypothalamus to innervate the limbic system, hypothalamus, septal nuclei, cingulate gyrus, cerebellum, superior colliculi, hippocampus, and neocortex. Some fibers also make contacts with glial cells and blood vessels. The axonal terminals ramify very widely to topographically innervate most regions of the brain. Individual raphe neurons send projections into brain regions that have related functions. Raphe neurons have a regular, slow, spontaneous firing rate that varies little in response to sensory stimuli. Given the widespread ramifications of the individual neurons and the constant release rate of serotonin, it is likely that this neural system is involved in modulation of neural activity rather than actual information transfer. The production of serotonin requires the absorption of the amino acid tryptophan from the diet (Boadle-Biber, 1993).

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Transport of this large neutral amino acid is influenced by the level of other amino acids in blood. Reduced ingestion of tryptophan leads to reduced levels of brain serotonin; approximately 1% of ingested tryptophan is converted to serotonin within the brain and the remainder is used for protein synthesis. Tryptophan is converted to 5-hydroxytryptophan by tryptophan hydroxylase within the terminals; the enzyme is usually not saturated with substrate. Activity of enzyme can be inhibited by pchlorophenylalanine. 5-Hydroxytryptophan is then converted to serotonin by a decarboxylase and transported into vesicles by a reserpine-sensitive mechanism. End-product inhibition of serotonin synthesis is fairly minor. The enzymatic synthesis of serotonin is principally influenced by neuronal activity and availability of tryptophan in the blood; this may explain why depletion or supplementation of this amino acid in the diet can influence serotonin-controlled cognitive and neural processes such as mood and sleep (Wurtman & Wurtman, 1995; and see Chapter 24 for a detailed discussion). More than 12 serotonin receptors have been characterized. The action of serotonin at these receptors is terminated principally by re-uptake into the presynaptic terminal by a selective transporter protein. Astrocytes lack this transporter yet are able to take up serotonin. Catabolism of serotonin is performed only by monoamine oxidase; however, this enzyme has a relatively minor contribution to the overall inactivation of the action of serotonin. When serotonin is applied to autoreceptors on raphe neurons, principally the 5HT-1A subtype, cell firing is rapidly decreased (Hajós, Hajós-Korcsok, & Sharp, 1999). The firing rate of raphe neurons is likely regulated by a small neural loop (by autoregulation via somatodendritic autoreceptors) as well as by a long, negative feedback loop involving postsynaptic 5-HT-1A receptors (Dong, De Montigny, & Blier, 1999). Therefore, overall, serotonergic neurons negatively regulate their own activity. Drugs that influence serotonin release, re-uptake, or serotonin receptors will have profound affects on the stability of this control. In general, serotonin’s postsynaptic effect has a short latency and long-lasting inhibition. A pair of autoreceptors, 5-HT-1B and -1D, exist near the synapse on axon terminals and may regulate serotonin production and release in response to changes in the synaptic concentration of the transmitter. Exposure to the indole alkylamine d-lysergic acid diethylamide (LSD) also reduces raphe cell firing; however, the cognitive effects of this drug may far outlast the slowing of neuronal activity. The effects of LSD on serotonin neurons may only be the initial trigger that sets in motion a cascade of neural processes throughout the brain. Other hallucinogens, such as mescaline, do not affect raphe cell firing. Although significant

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evidence suggests an important agonist action at the 5HT2A receptor (Gonzalez-Maeso et al., 2007) in the initiation of hallucinations by LSD, the drug also has a high affinity for at least eight of the known serotonin receptors. Psilocybin, o-phosphoryl-4-hydroxy-N,N-dimethlytryptamine, obtained from the mushroom Psilocybe mexicana, is less potent than LSD but likely shares aspects of its actions on serotonin receptors (Spinella, 2001). Psilocybin is the only known naturally occurring indole to contain phosphorus and is chemically related to bufotenine, an interesting molecule that has been discovered in the skin and glands of a South American toad, bean pods from the South American tree Piptadenia peregrine, and the leaves and bark of the Central American mimosa, Acacia niopo. Genetically altered mice and positron emission tomography (PET) studies on humans have been very useful in demonstrating the potential role of specific serotonin receptors in the regulation of mood and control of anxiety. Mice lacking the 5HT-1A receptor show more anxiety-like behavior (Gobbi, 2005). In humans, ratings of religiosity and spirituality were inversely correlated with the number of 5HT-1A receptors (Borg, Andree, Soderstrom, & Farde, 2003). The potential effects of alterations in serotonin neuronal function in relation to spiritual experiences are consistent with the observations of the effects of hallucinogenic drugs such as LSD, psilocybin, and mescaline. In addition, these results have broad implications for understanding and treating several psychiatric conditions. Drugs that stimulate the 5HT-1A receptor have been shown to be clinically effective at reducing anxiety (Caliendo, Santagada, Perissutti, & Fiorino, 2005). Another common psychiatric disorder, depression, responds well to treatment with drugs that selectively block the re-uptake of serotonin into the presynaptic terminal. Long-term treatment with these drugs, and the gradual onset of clinical benefit, is associated with the progressive down-regulation of the number of 5HT-2C receptors (Millan, 2005). Recent evidence suggests that the clinical effectiveness of these antidepressant therapies may depend on their ability to induce neurogenesis within the hippocampus (Taylor, Fricker, Devi, & Gomnes, 2005).

ENDOCANNABINOIDS The very high potency of exogenous cannabinoids and their stereochemical and structural requirements for binding to brain tissue predicted the discovery of the brain’s endogenous cannabinoid system. Fifteen years ago, the first endocannabinoid was discovered and named anandamide, from the Sanskrit word ananda meaning “internal bliss.” Anandamide and a second endogenous cannabinoid,

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Amino Acids 93

2-AG (2-arachidonoyl-glycerol), are enzymatically produced within the postsynaptic neural membrane (Wilson & Nicoll, 2002). Their production is dependent on the entry of calcium ions following membrane depolarization. Therefore, unlike most classical transmitters, endocannabinoids are produced in response to neural activity and released immediately without being stored in vesicles. The endocannabinoid receptor CB1 is linked to a G-protein that inhibits the activation of adenylate cyclase and the formation of ATP (Mackie, 2006). The CB1 receptor is the most abundant G-protein coupled receptor in the brain; its abundance and presynaptic location on glutamate and GABA-releasing terminals gives an indication of its importance and potential role in the regulation of the brain’s principle excitatory and inhibitory neural systems. Anandamide inhibits the release of glutamate and acetylcholine within the cortex and hippocampus, an action that may underlie the effects of exogenous cannabinoids on memory. Anandamide also inhibits the release, and sometimes the re-uptake, of GABA; its complex actions with the basal ganglia and the presence of cannabinoid receptors in the cerebellum may underlie the ataxia occasionally seen in cannabis users (Hajos et al., 2000; Hajós, Ledent, & Freund, 2001; Tzavara, Wade, & Nomikos, 2003). Continued stimulation of CB1 receptors will produce the expected desensitization by G-protein uncoupling and the internalization of the receptor. Anandamide and 2-AG are inactivated by re-uptake into the presynaptic terminal by a specific transport protein and then hydrolyzed by specific enzymes (Fowler et al., 2005). Pharmacological antagonism of the endocannabinoid system may be therapeutic for major depressive disorders (Witkin, Tzavara, Davis, Li, & Nomikos, 2005). In addition, prolonged inhibition of the actions of these endocannabinoids may enhance the release of glutamate and increase the probability of excitotoxicity (Sang, Zhang, & Chen, 2007). Clinical trials using an antagonist of CB1 receptors demonstrated a reliable effect on appetite regulation, providing help to overweight patients. Interestingly, due to the relative omnipresence of CB1 receptors in the human brain, the use of endocannabinoid receptor antagonists might also prove helpful in relieving addiction to alcohol and nicotine. Endocannabinoids are catabolized by cyclooxygenase; which raises an interesting concern regarding the influence of long-term treatment with anti-inflammatory drugs that inhibit this enzyme, such as aspirin or ibuprofen, on mood and memory. We have speculated that the apparently complex actions of cannabinoids within the brain may be interrelated via the function of glutamate (an amino acid neurotransmitter that will be discussed later). Stimulation of endocannabinoid receptors may reduce brain inflammation in young and aged animals by restoring the proper calcium influx via glutamate

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receptor channels (Marchalant, Cerbai, Brothers, & Wenk, 2007). In addition to the role of the endocannabinoid system in neuroprotection and modulation of neuroplasticity and neurotoxicity, cannabinoid receptor stimulation may also modulate neurogenesis (Galve-Roperh, Aguado, Palazuelos, & Guzmán, 2007). Hippocampal neurogenesis declines with normal aging, and this correlates with the onset of depression (Olariu, Cleaver, & Cameron, 2007; Taylor et al., 2005); stimulation of the endocannabinoid system may provide clinical benefit by reversing this decline (Marchalant, Cerbai, Brothers, & Wenk, 2008).

AMINO ACIDS The brain holds relatively high concentrations of amino acids, as compared to other body tissues, which are used primarily for protein and neurotransmitter synthesis. Although glucose is utilized extensively by the brain for energy, it does not use amino acids for gluconeogenesis. Neurons respond to the amino acid neurotransmitters with either excitation or inhibition. The principal neurotransmitters in this group include glutamate, aspartate, gamma amino butyric acid (GABA), glycine, and N-acetyl aspartate. Glutamate, aspartate, and N-acetyl aspartate are the major excitatory amino acid neurotransmitters, while GABA and glycine are the major inhibitory amino acid neurotransmitters (Siegel et al., 2006). Glutamate and GABA have similar patterns of innervation and occur in greater concentration than the other amino acids. The excitatory glutamate and inhibitory GABA transmitter molecules differ by only the presence of a carbon dioxide group; a situation mimicked in nature. For example, the excitatory ibotenic acid and inhibitory muscimol occur together in the Amanita muscaria mushroom, differing only by the presence of a carbon dioxide group, and have been used extensively as pharmacological tools to study brain physiology. Glutamatergic neurotransmission is mediated through ionotropic glutamate receptors such as alpha-amino3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), kainate, and N-methyl-d-aspartate (NMDA) receptors. Additionally, glutamate activates G-protein coupled metabotropic glutamate receptors that are believed to have a more modulatory function. Most AMPA receptors are impermeable to calcium ions and contribute to fast synaptic transmission. In contrast, NMDA receptors are characterized by high permeability to calcium ions, voltagedependent blockade by magnesium ions, and slower gating kinetics. At normal resting potentials, the transmembrane electric field (negative on the inside of the cell) favors entry of positively charged Mg++ into the pore of

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the receptor so that the NMDA channel is blocked. Under such resting conditions, NMDA receptors do not conduct ions. However, with sufficient, and actually rather significant, postsynaptic depolarization within the neuronal membrane surrounding the channel, the magnesium ion is no longer strongly attracted into the pore of the channel and dissociates. Under such depolarized conditions, NMDA receptors activated by synaptically released glutamate are able to allow the influx of sodium ions and, in particular, calcium ions, and contribute to postsynaptic excitation and activation of second messenger systems. These features make NMDA receptors quite suitable for mediating plastic changes in the brain, such as learning and cognition (Castner & Williams, 2007). However, these features may also contribute to neurotoxicity due to excessive unregulated calcium ion influx through the channel. If, due to a variety of factors that may contribute to many neurodegenerative diseases, the NMDA channels remain open (Wenk, Danysz, & Roice, 1996), it is possible that ambient levels of glutamate associated with normal synaptic activity could activate NMDA receptors, allowing excessive calcium ion influx, which, if sufficiently prolonged, may trigger a cascade of events leading to neuronal injury and death. Pharmacological blockade of NMDA receptors can produce protection from excessive calcium ion influx; however, if the channel blockade is too complete, this could lead to a profound loss of neuroplasticity. Memantine is more potent and slightly less voltage-dependent than magnesium and it may serve as a more effective surrogate for magnesium ions (Rogawski & Wenk, 2003). As a result, of its somewhat less pronounced voltage-dependency, memantine is more effective than magnesium ions in blocking tonic pathological activation of NMDA receptors at moderately depolarized membrane potentials. However, following strong synaptic activation, memantine like magnesium ions can leave the NMDA receptor channel with voltage-dependent, fast-unblocking kinetics. In turn, memantine suppresses synaptic noise but allows the relevant physiological synaptic signal to be detected. This provides both neuroprotection and symptomatic restoration of synaptic plasticity by one and the same mechanism (Danysz & Parsons, 2003). Antagonists that have “too high” affinity for the channel or “too little” voltage-dependence, such as dizocilpine (5-methyl-10,11-dihydro-5H-dibenzocyclohepten-5,10-imine maleate, MK-801), do not have a favorable therapeutic profile and produce numerous side effects because they essentially act as an irreversible plug of the NMDA receptor channel and block both pathological and physiological function. Deficits in energy metabolism associated with aging play an important role in the vulnerability of neurons in neurodegenerative diseases (Emerit, Edeas, & Bricaire,

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2004; see Chapter 61). A defect in energy production would make neurons that express glutamatergic receptors more vulnerable to elevated synaptic levels of glutamate. Decreased levels of intracellular ATP would lead to a partial, and chronic, membrane depolarization, the relief of the voltage-dependent magnesium ion blockade at NMDA receptors, and a persistent increase in the influx of calcium ions into the cells; ultimately, the accumulation of intracellular calcium ions following the activation of NMDA receptors, by glutamate would lead to neuronal death. Chronic neuroinflammation, oxidative stress, or impaired intracellular calcium buffering may also result in impaired energy production, possibly leading to impaired function of the membrane ion pumps required for maintenance of the resting potential. In any of these situations, excessive calcium ion influx through NMDA receptors could activate a host of calcium ion–dependent signaling pathways and stimulate nitric oxide production through closely associated neuronal nitric oxide synthase. This gaseous neurotransmitter, nitric oxide, can react with a superoxide anion to form peroxynitrite, which disintegrates into extremely toxic hydroxyl free radicals that can further impair mitochondrial function and energy production. Intracellular calcium may become concentrated within the postsynaptic mitochondria further contributing to the impaired energy production within the region of the NMDA channels (Duchen, 2000). Mitochondrial dysfunction coupled with activation of glutamatergic receptors could underlie the selective vulnerability of neural systems during normal aging. Mitochondrial failure and neurochemical processes involving glutamate NMDA receptor in the presence of chronic neuroinflammation and oxidative stress may underlie the pathogenesis of many different neurodegenerative diseases (Barnham, Masters, & Bush, 2004; Wenk et al., 1996). GABA and glycine bind to their respective receptors that are primarily chloride ion channels; the opening of these channels allows chloride ions to move across the membrane to achieve an equilibrium that makes the membrane resistant to depolarization. The receptors for these two inhibitory amino acid transmitters are structurally similar and may share a common evolutionary history. The glycine receptor is antagonized by strychnine; the loss of this important inhibitory system leads to excitation of the glycine-sensitive neurons principally within the ventral (motor) spinal cord. GABA has two, or possibly three, receptors. The best studied is the GABAA receptor which is the site of action of many popular drugs, including alcohol and the benzodiazepines (Martin & Olsen, 2000; Wild & Benzel, 1994). When these drugs bind to the GABAA receptor, they enhance the ability of GABA to stabilize the membrane, altering the balance between excitation by

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Neuropeptides

glutamate and inhibition by GABA within local neuronal circuits. The GABAA receptor has been difficult to completely characterize because it is a multi-subunit, heteromeric ion channel: the receptor is essentially built using a combinatorial principle where the functional unit is not just multiple copies of a single polypeptide; rather, the channel is formed by many different polypeptides that contribute unique functional properties that can vary depending on its interactions with the other resident polypeptides. This multifunctional approach offers the opportunity for a more sophisticated and complex control that is common to other ionotropic receptors such as the NMDA glutamate and nicotinic acetylcholine receptors. Therefore, pharmacological manipulation of GABA receptor function has produced therapeutic benefit for a wide range of disorders, particularly anxiety and insomnia (Olsen, 2001).

ADENOSINE Adenosine is probably produced by all neurons in proportion with their firing rate (Siegel et al., 2006). It is the enzymatic product of ATP metabolism within the synapse. ATP can be stored within the synaptic vesicles and released with the neurotransmitter in an activity-dependent manner. Adenosine, in contrast, is not considered a classical neurotransmitter because it is not stored in vesicles and not released in quantal fashion. Adenosine acts as a local vasodilator within the extracellular space of the brain, providing a direct link between neuronal activity and regional blood flow. Extracellular levels must therefore be carefully controlled; adenosine is rapidly removed by re-uptake into cells and degraded to inosine by adenosine deaminase. Four adenosine receptor subtypes have been characterized; they are typical seven transmembranespanning G-protein coupled receptors that have been highly conserved throughout evolution and primarily produce a postsynaptic inhibition. Following tissue injury or the presence of inflammatory proteins, the extracellular level of adenosine will increase and likely plays an important role in neuroprotection. Stimulation of the A2A subtype has an anti-inflammatory effect. A1 and A2A adenosine receptors are the most common type found in the brain, and these receptors are blocked by caffeine at doses that are likely achieved by a person drinking a cup of coffee. Brain inflammation, hypoxia, and ischemia are associated with increased extracellular levels of adenosine. Adenosine may regulate aspects of the brain’s inflammatory processes, including the release of pro-inflammatory cytokines and modulation of microglial activation (Rosi, McGann, Hauss-Wegrzyniak, & Wenk, 2004) via A1 and

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A2A receptors, which may provoke neuroprotection or injury, respectively (Dunwiddie & Masino, 2001; Mayne et al., 2001); not surprisingly, the blockade of A2A receptors is neuroprotective (Kalda, Yu, Oztas, & Chen, 2006). Caffeine is a nonselective A1 and A2A adenosine receptor antagonist. Chronic caffeine intake may delay the onset of Alzheimer’s disease (Maia & de Mendonca, 2002) and Parkinson’s disease (Schwarzschild & Chen, 2002). Adenosine may direct both proinflammatory and antiinflammatory functions, depending on which subtype of adenosine receptor and which type of inflammatory cell are involved and the duration of the neuroinflammation (Dunwiddie & Masino, 2001; Farber & Kettenmann, 2006). The mechanism underlying the effect of adenosine A1 and A2A receptor antagonism on the level of microglial activation in the current study is unknown. A1 receptors on glutamatergic terminals form heteromeric complexes with A2A receptors; this A1–A2A receptor heteromer may provide a mechanism by which adenosine can dosedependently control glutamate release (Sitkovsky & Ohta, 2005). A2A receptors are located on glutamatergic terminals; A2A receptor antagonists can attenuate the release of glutamate in the presence of ischemia (Marcoli et al., 2003) possibly by acting on A2A receptors on astrocytes (Pintor et al., 2004). The reduced release of glutamate and the subsequent reduction in activation of the NMDA channel on neurons may underlie the effects of caffeine in the current study. Consistent with this hypothesis, we have recently shown that selective antagonism of NMDA receptors can reduce microglia activation in the DG (Rosi et al., 2003; Wenk, Parson, & Danysz, 2006) suggesting an influence of adenosine receptors on microglia activation that might be linked to the modulation of glutamate synaptic transmission or neuronal activity. The reduction in glutamatergic signaling may also contribute to the neuroprotective effects of caffeine (Kalda et al., 2006) and its therapeutic potential for preventing neurodegenerative diseases associated with neuroinflammation (Wenk & Hauss-Wegrzyniak, 2003; Wenk et al., 2006).

NEUROPEPTIDES The tissue concentration of neuropeptides is usually about three orders of magnitude lower than the classical neurotransmitters, such as acetylcholine and the catecholamines (Siegel et al., 2006). The synthesis, postproduction modifications, and inactivation of neuropeptides stand in contrast to the way the brain metabolizes the neurotransmitters discussed thus far. Neuropeptides are initially produced as large precursor molecules called proproteins that are further processes to preproproteins that are then further

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enzymatically processed to the final chemically stable polypeptide product that is stored in the axon terminal in large, dense-core vesicles until released. Once released, neuropeptides are hydrolyzed in the extracellular space to individual amino acids or small polypeptides; these are further catabolized rather than being recycled by re-uptake and then reused again. Therefore, the life cycle of neuropeptides is quite costly to the cell because the process is energetically inefficient. Neuropeptides commonly coexist within, and are released by, the same neuron however the rules governing their activity-dependent release may be different; for example, the release of the neuropeptide is usually from a different part of the terminal and may require a burst of high frequency stimulation and the subsequent influx of greater numbers of calcium ions. In addition, neuropeptides often coexist with other, structurally unrelated, neuropeptides, particularly within the hypothalamus and brain stem regions. Some small polypeptide products of the neuropeptides catabolism may be able to influence the interaction of coexisting neurotransmitters. Many neuropeptides found in invertebrates are insulinlike peptides, which suggests a shared evolutionary history, particularly since insulin has been found in protozoa, bacteria, fungi, invertebrates, and vertebrates. The neuropeptides growth hormone and prolactin may have diverged from a common ancestor about 350 million years ago (Miller et al., 1983). The primitive multicellular Hydra has a nervous system that uses neuropeptides as neurotransmitters, suggesting that neuropeptides were the first signaling molecules used by primitive nervous systems (Grimmelikhuijzen, Leviev, & Carstensen, 1996). Neuropeptides in primitive animals are constructed and stored in a manner similar to that found in vertebrates (Westfall, Sayyar, Elliott, & Grimmelikhuijzen, 1995); they produce excitatory or inhibitory actions when tested on vertebrate tissues. Postsynaptically, neuropeptides are more likely to produce a slow, longer-lasting change in membrane conductance than the classical amine transmitters. Pharmacological manipulation of neuropeptide systems has been most successful with the endogenous opiate neurotransmitter receptors, primarily because Mother Nature provided the first example, morphine, for the subsequent guidance of synthetic chemists. The number and variety of neuropeptide receptor agonists and antagonists for nonopiate systems remains comparatively limited. Pharmacological manipulation of neuropeptide receptors is also complicated by the fact that active neuropeptide receptors are not confined to the synapse and there is not always a direct corresponding relationship between the presence of a neuropeptide receptor and the neuropeptide itself.

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SUMMARY Given the limitations of space, the interested reader may want to examine some excellent texts on brain chemistry and pharmacology to further supplement their knowledge (Cooper et al., 1996; Meyer & Quenzer, 2005; Siegel et al., 2006; Wild & Benzel, 1994).

REFERENCES Albin, R. L., & Greenamyre, J. T. (1992). Alternative excitotoxic hypothesis. Neurology, 42, 733–738. Baetge, E. E., Suh, Y. H., & Joh, T. H. (1986). Complete nucleotide and deduced amino acid sequence of bovine phenylethanolamine Nmethyltransferase: Partial amino acid homology with rat tyrosine hydroxylase. Proceedings of the National Academy of Sciences, USA, 83, 5454–5458. Barnham, K. J., Masters, C. L., & Bush, A. I. (2004). Neurodegenerative diseases and oxidative stress. Nature Review, Drug Discovery, 3, 205–214. Boadle-Biber, M. C. (1993). Regulation of serotonin synthesis. Progress in Biophysics and Molecular Biology, 60, 1–15. Borg, J., Andree, B., Soderstrom, H., & Farde, L. (2003). The serotonin system and spiritual experiences. American Journal of Psychiatry, 160, 1965–1969. Caliendo, G., Santagada, V., Perissutti, E., & Fiorino, F. (2005). Derivatives as 5HT(1A) receptor ligands: Past and present. Current Medicinal Chemistry, 12, 1721–1753. Castner, S. A., & Williams, G. V. (2007). Tuning the engine of cognition: A focus on NMDA/D1 receptor interactions in prefrontal cortex. Brain Cognition, 63, 94–122. Cooper, J. R., Bloom, F. E., & Roth, R. H. (2002). The biochemical basis of neuropharmacology (8th ed.). New York: Oxford University Press. Daiello, L. A., Galvin, J. E., & Wenk, G. L. (2005). A case-based approach to management of Alzheimer ’s disease across the disease continuum. U.S. Pharmacist, 30, 2–5. Danysz, W., & Parsons, C. G. (2003). The NMDA receptor antagonist memantine as a symptomatological and neuroprotective treatment for Alzheimer ’s disease: Preclinical evidence. International Journal of Geriatric Psychiatry, 18, S23–S32. Davis, K. L., Mohs, R. C., Marin, D., Purohit, D. P., Perl, D. P., Lantz, M., et al. (1999). Cholinergic markers in elderly patients with early signs of Alzheimer disease. Journal of the American Medical Association, 281, 1401–1406. Dong, J. M., De Montigny, C., & Blier, P. (1999). Assessment of the serotonin re-uptake blocking property of YM992: Electrophysiological studies in the rat hippocampus and dorsal raphe. Synapse, 34, 277–289. Duchen, M. R. (2000). Mitochondria and calcium: From cell signalling to cell death. Journal of Physiology, 529, 27–68. Dunwiddie, T. V., & Masino, S. A. (2001). The role and regulation of adenosine in the central nervous system. Annual Review of Neuroscience, 24, 31–35. Emerit, J., Edeas, M., & Bricaire, F. (2004). Neurodegenerative diseases and oxidative stress. Biomedical Pharmacotherapy, 58, 39–46. Farber, K., & Kettenmann, H. (2006). Purinergic signaling and microglia. Pflugers Archives of European Journal of Physiology, 452, 615–621. Fleming, W. W. (1999). Cellular adaptation: Journey from smooth muscle cells to neurons. Journal of Pharmacology and Experimental Therapeutics, 291, 925–931.

8/17/09 2:00:15 PM

References 97 Fowler, C. J., Holt, S., Nilsson, O., Jonsson, K.-O., Tiger, G., & Jacobsson, S. O. P. (2005). The endocannabinoid signaling system: Pharmacological and therapeutic aspects. Pharmacology, Biochemistry and Behavior, 81, 248–262.

Olariu, A., Cleaver, K. M., & Cameron, H. A. (2007). Decreased neurogenesis in aged rats results from loss of granule cell precursors without lengthening of the cell cycle. Journal of Comparative Neurology, 501, 659–667.

Fujii, K., & Takeda, N. (1988). Phylogenetic detection of serotonin immunoreactive cells in the central nervous system of invertebrates. Comparative Biochemistry and Physiology, 89C, 233–239.

Olsen, R. W. (2001). GABA. In K. L. Davis, D. Charney, J. T. Coyle, & C. Nemeroff (Eds.), Neuropsychopharmacology: Fifth generation of progress (pp. 000–000). Philadelphia: Lippincott, Williams, & Wilkins.

Galve-Roperh, I., Aguado, T., Palazuelos, J., & Guzmán, M. (2007). The endocannabinoid system and neurogenesis in health and disease. Neuroscientist, 13, 109–114. Gobbi, G. (2005). Serotonin firing activity as a marker for mood disorders: Lessons from knockout mice. International Review of Neurobiology, 65, 249–272. Gonzalez-Maeso, J., Weisstaub, N. V., Zhou, M. M., Chan, P., Ivic, L., Ang, R., et al. (2007). Hallucinogens recruit specific cortical 5-HT2A receptor-mediated signaling pathways to affect behavior. Neuron, 53, 439–452. Grima, B., Lamouroux, A., Blanot, F., Biguet, N. F., & Mallet, J. (1985). Complete coding sequence of rat tyrosine hydroxylase mRNA. Proceedings of the National Academy of Sciences, USA, 82, 617–621. Grimmelikhuijzen, C. J. P., Leviev, I., & Carstensen, K. (1996). Peptides in the nervous systems of cnidarians: Structure, function and biosynthesis. International Review of Cytology, 167, 37–89. Hajós, M., Hajós-Korcsok, É., & Sharp, T. (1999). Role of the medial prefrontal cortex in 5-HT1A receptor-induced inhibition of 5-HT neuronal activity in the rat. British Journal of Pharmacology, 126, 1741–1750. Hajós, N., Katona, I., Naiem, S. S., Mackie, K., Ledent, C., Mody, I., et al. (2000). Cannabinoids inhibit hippocampal GABAergic transmission and network oscillations. European Journal of Neuroscience, 12, 3239–3249.

Pintor, A., Galluzzo, M., Grieco, R., Pezzola, A., Reggio, R., & Popoli, P. (2004). Adenosine A 2A receptor antagonists prevent the increase in striatal glutamate levels induced by glutamate uptake inhibitors. Journal of Neurochemistry, 89, 152–156. Power, A. E., Vazdarjanova, A., & McGaugh, J. L. (2003). Muscarinic cholinergic influences in memory consolidation. Neurobiology of Learning and Memory, 80, 178–193. Rapport, M. M., Green, A. A., & Page, I. H. (1948). Serum vasoconstrictor (serotonin): IV. Isolation and characterization. Journal of Biological Chemistry, 176, 1243–1251. Rogawski, M., & Wenk, G. L. (2003). The neuropharmacological basis for memantine in the treatment of Alzheimer ’s disease. CNS Drug Review, 9, 275–308. Rosi, S., McGann, K., Hauss-Wegrzyniak, B., & Wenk, G. L. (2003). The influence of brain inflammation upon neuronal adenosine A2B receptors. Journal of Neurochemistry, 86, 220–227. Sang, N., Zhang, J., & Chen, C. (2007). COX-2 oxidative metabolite of endocannabinoid 2-AG enhances excitatory glutamatergic synaptic transmission and induces neurotoxicity. Journal of Neurochemistry.

Hajós, N., Ledent, C., & Freund, T. F. (2001). Novel cannabinoid-sensitive receptor mediates inhibition of glutamatergic synaptic transmission in the hippocampus. Neuroscience, 106, 1–4.

Santarelli, L., Saxe, M., Gross, C., Surget, A., Battaglia, F., Dulawa, S., et al. (2003). Requirement of hippocampal neurogenesis for the behavioral effects of antidepressants. Science, 301, 805–809.

Heck, D. A., & Bylund, D. B. (1998). Differential down-regulation of alpha-2 adrenergic receptor subtypes. Life Sciences, 62, 1467–1472.

Sarter, M., Gehring, W. J., & Kozak, R. (2006). More attention must be paid: The neurobiology of attentional effort. Brain Research Reviews, 51, 145–160.

Kalda, A., Yu, L., Oztas, E., & Chen, J.-F. (2006). Novel neuroprotection by caffeine and adenosine A2A receptor antagonists in animal models of Parkinson’s disease. Journal of Neurological Sciences, 248, 9–15. Lindamood, W. (2005). Thorazine. Chemical Engineering News, 83, 126. Mackie, K. (2006). Cannabinoid receptors as therapeutic targets. Annual Review of Pharmacology and Toxicology, 46, 101–122. Maia, L., & de Mendonca, A. (2002). Does caffeine intake protect from Alzheimer ’s disease? European Journal of Neurology, 9, 377–382. Marchalant, Y., Cerbai, F., Brothers, H., & Wenk, G. L. (2007). Cannabinoid receptor stimulation is anti-inflammatory and improves memory in old rats. Neurobiology of Aging. Marchalant, Y., Cerbai, F., Brothers, H., & Wenk, G. L. (2008). Cannabinoid receptor stimulation partially restores age-associated decline in neurogenesis in the hippocampus. Neuroscience. Marcoli, M., Raiteri, L., Bonfanti, A., Monopoli, A., Ongini, E., Raiteri, M., et al. (2003). Sensitivity to selective adenosine A1 and A2A receptor antagonists of the release of glutamate induced by ischemia in rat cerebrocortical slices. Neuropharmacology, 45, 201–210. Martin, D. L., & Olsen, R. W. (2000). GABA in the nervous system: The view at 50 years. Philadelphia: Lippincott, Williams, & Wilkins. Meyer, J. S., & Quenzer, L. F. (2005). Psychopharmacology: Drugs, the brain and behavior. Boston: Sinauer Associates. Millan, M. J. (2005). Serotonin 5-HT2C receptors as a target for the treatment of depressive and anxious states: Focus on novel therapeutic strategies. Therapie, 60, 441–460. Molina-Holgado, F., Hider, R. C., Gaeta, A., Williams, R., & Francis, P. (2007). Metals ions and neurodegeneration. Biometals, 20, 639–654.

c05.indd Sec7:97

Olton, D. S., Wenk, G. L., Church, R. M., & Meck, W. H. (1988). Attention and the frontal cortical cortex as examined by simultaneous temporal processing. Neuropsychologia, 26, 307–318.

Schwarzschild, M. A., & Chen, J. F. (2002). Ascherio A. Caffeinated clues and the promise of adenosine A(2A) antagonists in, P. D. Neurology, 58, 154–160. Siegel, G. J., Albers, R. W., Brady, S. T., & Price, D. L. (2006). Basic neurochemistry: Molecular, cellular and medical aspects (7th ed.). New York: Elsevier. Sitkovsky, M. V., & Ohta, A. (2005). The ‘danger ’ sensors that STOP the immune response, the A2 adenosine receptors? Trends in Immunology, 26, 299–304. Spinella, M. (2001). The psychopharmacology of herbal medicines. Cambridge: MIT Press. Stanley, J. A., Pettergrew, J. W., & Keshavan, M. S. (2000). Magnetic resonance spectroscopy in schizophrenia: Methodological issues and findings: Part I. Biological Psychiatry, 48, 357–368. Sulzer, D., Sonders, M. S., Poulse, N. W., & Galli, A. (2005). Mechanisms of neurotransmitter release by amphetamines: A review. Progress in Neurobiology, 75, 406–433. Taylor, C., Fricker, A. D., Devi, L. A., & Gomnes, N. (2005). Mechanisms of action of antidepressants: From neurotransmitter systems to signaling pathways. Cellular Signalling, 17, 549–557. Tzavara, E. T., Wade, M., & Nomikos, G. G. (2003). Biphasic effects of cannabinoids on acetylcholine release in the hippocampus: Site and mechanism of action. Journal of Neuroscience, 23, 9374–9384. Venter, J. C., Di Porzio, U., Robinson, D. A., Shreeve, S. M., Lai, J., Kerlavage, A. R., et al. (1988). Evolution of neurotransmitter receptor systems. Progress in Neurobiology, 30, 105–169.

8/17/09 2:00:15 PM

98

Neuropharmacology

Wenk, G. L. (1997). The nucleus basalis magnocellularis cholinergic system: 100 years of progress. Neurobiology of Learning and Memory, 67, 85–95. Wenk, G. L. (2003). Neurotransmitters. In L. Nadel (Ed.), Encyclopedia of cognitive science (pp. 2414–2421). London: Nature Publishing, Macmillan Press. Wenk, G. L. (2006). Neuropathologic changes in Alzheimer ’s disease: Potential targets for treatment. Journal of Clinical Psychiatry, 67, 3–7. Wenk, G. L., Danysz, W., & Roice, D. D. (1996). The effects of mitochondrial failure upon cholinergic and glutamatergic toxicity within the nucleus basalis. NeuroReport, 7, 1453–1456. Wenk, G. L., & Hauss-Wegrzyniak, B. (2001). Animal models of chronic neuroinflammation as a model of Alzheimer ’s disease. In S. Bondy & A. Campbell (Eds.), Inflammatory events in neurodegeneration (pp. 83–87). Scottsdale: Prominent Press. Wenk, G. L., & Hauss-Wegrzyniak, B. (2003). Chronic intracerebral LPS as a model of neuroinflammation. In P. L. Wood (Ed.), Neuroinflammation: Mechanisms and management (2nd ed., pp. 137–150). Totowa, NJ: Humana Press. Wenk, G. L., Parson, C., & Danysz, W. (2006). Potential role of NMDA receptors as executors of neurodegeneration resulting from diverse insults: Focus on memantine. Behavioral Pharmacology, 17, 411–424.

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Westfall, J. A., Sayyar, K. L., Elliott, C. F., & Grimmelikhuijzen, C. J. P. (1995). Ultrastructural localization of Antho-RWamide, I., & II at neuromuscular synapses in the gastrodermis and oral sphincter muscle of the sea anemone. Calliactis parasitica. Biological Bulletin, 189, 280–287. Whitehouse, P. J., Price, D. L., Clark, A. W., Coyle, J. T., & DeLong, M. R. (1981). Alzheimer disease: Evidence for selective loss of cholinergic neurons in the nucleus basalis. Annals of Neurology, 10, 122–126. Wild, G. C., & Benzel, E. C. (1994). Essentials of neurochemistry. Boston: Jones and Bartlett. Wilder, J. (1958). Modern psychophysiology and the law of initial value. American Journal of Psychotherapy, 12, 199–207. Wilson, R. I., & Nicoll, R. A. (2002). Endocannabinoid signaling in the brain. Science, 296, 678–682. Witkin, J., Tzavara, E. T., Davis, R. J., Li, X., & Nomikos, G. G. (2005). A therapeutic role for cannabinoid CB1 receptor antagonists in major depressive disorders. Trends in Pharmacological Sciences, 26, 609–617. Wurtman, R. J., & Wurtman, J. J. (1995). Brain serotonin, carbohydratecraving, obesity and depression. Obesity Research, 3, S477–S480.

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Chapter 6

Neuroendocrinology: Mechanisms by Which Hormones Affect Behaviors DONALD W. PFAFF, MARC TETEL, AND JUSTINE SCHOBER

PRINCIPLE 1: IT IS POSSIBLE TO DISCERN MECHANISMS BY WHICH HORMONES AFFECT MAMMALIAN BEHAVIORS

The field of work that discovers how endocrine signaling agents affect the brain in order to regulate behavior has become highly developed during the past 50 years. As a result, it is possible to state principles of hormone/behavior relations and their mechanisms, rather than simply presenting a compendium of facts. For the latter, consult Hormones, Brain and Behavior, 5 volumes and 4,100 pages long, written by over 100 experts. Here we will try to make points that relate to the general features of neuroendocrine systems relevant for behavioral science. Most of our examples come from reproductive neuroendocrine mechanisms because progress has been most rapid in this area for four reasons: (1) Mating behaviors are specific, well-defined, and easily studied in the laboratory in their natural form; (2) the molecular biology of steroid sex hormones is best understood; (3) the stimuli eliciting these behaviors are relatively simple; and (4) the motoric responses themselves are stereotyped, do not need to be learned, and of relatively simple topography. Another very well-developed area of work covers mechanisms of stress hormones. For that topic, see Chapter 62. We have organized several principles of neuroendocrine mechanisms related to behavior, stated in the most general form we feel is justifiable. While this chapter is restricted in scope in order to maintain coherence, the range of hormone/behavior phenomena is breathtaking. Hormones do not “cause” behaviors; they alter the probabilities of given responses to fixed stimuli. And, the probabilities of a given response may be raised or lowered by hormone treatment. Further, the behavioral response to a hormone can depend on stage of development and environmental context. That said, the general principles discussed next remain true under a very wide variety of circumstances.

The proof that it is possible to work out detailed neuroanatomical, neurophysiological, and genomic mechanisms in a neuroendocrine system that regulates behavior came in four steps. 1. The localization of hormone target neurons in the brain was determined and estrogen-binding neurons in a limbic/hypothalamic system were discovered. The discovery initially was made in rat brain (Figure 6.1), but work on fish CNS through monkey CNS showed it to be a general vertebrate system. The neuroanatomical research was followed up by histochemical findings that demonstrated consequences of hormone binding for electrophysiological activity and neuronal growth. 2. The neural circuit for a hormone-dependent vertebrate behavior (Figure 6.2), the estrogen-dependent lordosis behavior (Pfaff, 1980), was worked out. 3. We found hormone-dependent genes in the brain (Figure 6.3). Their induction by estrogenic hormones has temporal, spatial, and gender specificities appropriate to reproductive behavior. 4. In turn, the products of some of these hormonedependent genes are required for hormone-dependent lordosis behavior (Pfaff, 1999). Taken together, these four findings showed that specific neurochemical reactions in specific parts of the brain determine a specific mammalian behavior.

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PRINCIPLE 2: THESE MECHANISMS WORK AT SEVERAL OVERLAPPING LEVELS OF CELLULAR FUNCTION, COVERING A WIDE RANGE OF PHYSICAL DIMENSIONS The four steps listed take a problem from genomic transcriptional alterations to physiological alterations to mammalian behavior. Starting with mechanisms at the level of the very smallest physical dimensions, it is known that lordosis behavior depends on estrogen receptor-alpha, but not on its gene duplication product, estrogen receptor-beta. The differences between the ligand-binding lipophilic pits of these two hormone receptors is measured in angstrom units. Hormone-dependent currents in the ventromedial hypothalamic neurons at the top of the circuit for lordosis behavior (Figure 6.2) are carried by ions such as sodium and potassium. Hormone-dependent gene expression is understood down to the single DNA nucleotide level, when the estrogen receptors bind to specific nucleotide sequences, estrogen response elements, on the chromosome. At the biochemical level, neuroendocrinologists understand many of the cellular pathways involved in transducing effects of sex hormones into cellular changes that underlie sex behavior. For example, estrogens not only affect the gene for the neuropeptide oxytocin, but also increase

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Figure 6.1 Looking at two schematic sagittal sections of the rat brain from the left side, the black dots indicate the distribution of nerve cells expressing estrogen receptors. Note. From Drive: Neurobiological and Molecular Mechanisms of Sexual Motivation, by D. W. Pfaff, 1999, Cambridge, MA: MIT Press. Reprinted with permission.

transduction for the oxytocin receptor (Figure 6.3). Thus, the neuropeptide, in order to be behaviorally active, must leave the rough endoplasmic reticulum where it is synthesized, directed toward the axon hillock, travel down the axon, be released from the presynaptic ending, and bind to its receptor. Likewise, molecular neuroendocrinologists have worked out some of the protein phosphorylation cascades that mediate membrane-initiated effects of steroid sex hormones (Rønnekleiv & Kelly, 2005). At the level of neuronal circuitry, the production of lordosis behavior is understood from the lumbosacral spinal cord up to the hypothalamus (Figure 6.2). The primary role of the telencephalon (e.g., the preoptic area, the septum, and the amygdala) is to inhibit lordosis behavior. What about the local environment of the female, and how might it affect lordosis? First and most obvious, signals from a reproductively competent conspecific are required. Among rodents, testosterone-dependent signals from a potential mating partner are likely to be carried by the olfactory or vomeronasal systems. Other animals might use other sensory systems, with the greatest variety and subtlety being used by the species with the highest capacity for information transfer, humans. In addition, however, the local environment has permissive and suppressive effects. The environment must afford the basis for

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Principle 2 Medial preoptic Medial ant. hypothalamus Hypothal Ventromedial Module nuclear hypothalamus

Estradiol Midbrain central gray

Midbrain reticular form

Figure 6.2 Drawing of the basic working circuit for the production of lordosis behavior in female quadripeds. Note. The circuitry is bilaterally symmetric and is plotted on just one side for visual clarity. From Drive: Neurobiological and Molecular Mechanisms of Sexual Motivation, by D. W. Pfaff, 1999, Cambridge, MA: MIT Press. Adapted with permission.

Lat. vestib. nuclear Lower Brainstem Module

Medullary reticular formation

Spinal cord

Midbrain Module

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Lateral vestibulosp. and reticulosp. tracts

Dorsal roots L1, L2

Stimuli

Spinal Module

L5, L6, S1

Pr Re essu cep re tor s

Flanks

Skin of rump tailbase perineum

Lat. longissimus and transverso – spinalis

Lordosis response

Figure 6.3 List of genes discovered to have two properties: that estrogens (E) having bound to estrogen receptors (ER) elevate their mRNA transcript levels; and that their gene products foster female reproductive behaviors.

Gene turned on (in hypothalamus) rRNA and growth

Note. The exception is prostaglandin D synthase, where hormones work to foster the behavior by disinhibition. From Drive: Neurobiological and Molecular Mechanisms of Sexual Motivation, by D. W. Pfaff, 1999, Cambridge, MA: MIT Press. Adapted with permission.

Progesterone receptor Nitric oxide synthase

ER-α E binds

ER-β

Adrenergic α1 receptor Muscarinic receptors

Female reproductive behaviors

Enkephalin X Opioid receptors Oxytocin X Oxytocin receptor

(in preoptic area) GnRH X GnRH receptor Prostaglandin-D synthase ( )

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(a)

(b)

IFF Testosterone: flank gland, scent marks

Olfaction (Long time, wide space)

IFF Estradiol: scent marks

E2

Testosterone Activity increased

Up the pheromone gradient

Activity increased

Ultrasound

Odor preference

(Limited t,s)

Cutaneous Stimuli

(Lordosis)

(Time)

Very rapid forward move and sudden stop

(Space)

Figure 6.4 Feed-forward mechanisms in reproductive behavior help to guarantee that reproductively competent conspecifics mate. Note. Signaling among hamsters over a long time period (A) and between rats over a short time period (B) has this character. In both cases, the “hormone-dependent behavioral funnels” help to move males and females into the same place at the same time so that they can mate. From Pfaff, Kow, Loose, and Flanagan-Cato (in preparation). Adapted with permission.

Move closer to burrow

. sion Ten port Sup ght. i We Lordosis

Follow

Encourage Proper mount

Mount

Time Fertilization

adequate nutrition of the female. Otherwise (a) she will not ovulate, and (b) electrical activity in her ventromedial hypothalamic neurons will not be high enough to trigger the rest of the lordosis behavior circuitry. The environment may also be a source of marked stress, which would inhibit lordosis behavior both directly and indirectly, the latter being shown by the female not leaving her home nest to engage in courtship behaviors that attract males. At the level of social behaviors, neuroendocrinologists have worked out how courtship behaviors by the female laboratory rat lead to successful mounting by the male and subsequent lordosis by the female. The female runs forward very rapidly then, equally suddenly, brakes to a full stop. This braking tenses her leg and postural muscles so that she is braced and ready to support the weight of the much larger male. Second, that very high degree of muscular tension itself primes lordosis behavior circuitry. Third, that topography of movement by the female causes the male that is following to bump into the female in the correct position for mounting so that even an inexperienced male will mount the braked female properly (Figure 6.4). Finally, reproduction in many animals is seasonal. It is important for offspring to be born in seasons that offer adequate food supplies. Since the distance to the sun is measured in light-years, this is the largest dimension bearing on neuroendocrine mechanisms regulating behavior. It is important to emphasize that all of these levels of mechanisms, from molecular reactions and ion flows

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measured in angstrom units to seasonal fluctuations dependent on day length, must work together for neuroendocrine regulation of reproduction to work in a biologically adaptive fashion—that is, for reproduction to occur successfully, but not to be attempted when it would be fruitless or dangerous.

PRINCIPLE 3: MOLECULAR ASPECTS OF HORMONE ACTION IN THE BRAIN USE THE SAME TYPES OF BIOCHEMISTRY AS IN OTHER HORMONE-DEPENDENT ORGANS The basic features of steroid hormone actions on neuroendocrine cells are indistinguishable from those discovered in tissues outside the brain. Nuclear receptors represent a superfamily of transcriptional activators that can be divided into subfamilies based on phylogenetic analysis (Evans, 1988; Mangelsdorf et al., 1995; Tsai & O’Malley, 1994). The classic steroid receptors represent the type I subfamily and include receptors for estrogens, progestins, androgens, glucocorticoids, and mineralocorticoids. Receptors for thyroid hormone, vitamin D3, all-trans retinoic acid, and 9-cis retinoic acid comprise the type II receptors. The third subfamily includes the orphan nuclear receptors, which have no known ligands (Benoit et al., 2006). Although this discussion focuses on ligand-dependent genomic mechanisms of action of the type I steroid receptors, studies are revealing an increasing role for nongenomic mechanisms

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Principle 3

via membrane-associated receptors in steroid action (Lange, 2007; Vasudevan & Pfaff, 2007). Mutagenesis studies reveal that steroid receptors share a modular domain structure (Evans, 1988; Figure 6.5). The amino terminal domain is highly variable and contains an activation function (AF-1) that regulates the level and specificity of activation of target genes (Tora, Gronemeyer, Turcotte, Gaub, & Chambon, 1988). The centrally located and conserved DNA-binding domain (DBD) contains two zinc fingers to facilitate receptor binding to DNA (Freedman & Luisi, 1993). The flexible hinge region is important in dimerization (Tetel et al., 1997) and in some receptors contains nuclear localization sequences (Ylikomi, Bocquel, Berry, Gronemeyer, & Chambon, 1992). The carboxyl-terminal ligand-binding domain (LBD) contains another activation function (AF-2) and is essential for ligand-dependent activation, dimerization, and binding of many cofactors (Lees, Fawell, & Parker, 1989; Oñate, Tsai, Tsai, & O’Malley, 1995; Shiau et al., 1998; Tetel et al., 1997). Some of the steroid receptors exist in two forms. For example, ER and ER are transcribed from different genes (Kuiper, Enmark, Pelto-Huikko, Nilsson, & Gustafsson, 1996), while the full-length PR-B and truncated PR-A are encoded by the same gene (Kastner et al., 1990). In both cases, the ER (Nomura et al., 2006) and PR

N

NTD

DBD

h

LBD

AF-1

AF-2

C Activation domains

Figure 6.5 Modular domain structure of steroid receptors. Note. AF  Activation function; DBD  DNA-binding domain; h  Hinge region; LBD  Ligand-binding domain; NTD  N-terminal domain.

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(Mani, Reyna, Chen, Mulac-Jericevic, & Conneely, 2006) subtypes have different biological functions. Since the discovery of estrogen receptors almost 4 decades ago (Gorski, Toft, Shyamala, Smith, & Notides, 1968; Jensen et al., 1968), a variety of in vitro and cell culture studies have elucidated much about the molecular mechanisms of steroid receptor action. The classic, ligand-dependent, genomic mechanism of action of steroid receptors is shown in Figure 6.6. In the absence of ligands, inactive steroid receptors are bound to heat-shock proteins and other immunophilins (Pratt, Galigniana, Morishima, & Murphy, 2004). Upon binding hormone, steroid receptors undergo a conformational change that causes dissociation of these heat-shock proteins and immunophilins, which allows receptors to dimerize (DeMarzo, Beck, Oñate, & Edwards, 1991). Activated receptor dimers bind preferentially to steroid response elements (SRE) in the promoter region of steroid-responsive target genes (Beato & Sánchez-Pacheco, 1996; Evans, 1988). These SREs consist of partial palindromic hexanucleotide sequences that are separated by an invariant three-nucleotide spacer (Beato & Sánchez-Pacheco, 1996). Binding of receptors to DNA increases or decreases gene transcription by altering the rate of recruitment of general transcription factors and influencing the recruitment of RNA polymerase II to the initiation site (Kininis et al., 2007; Klein-Hitpass et al., 1990). It is thought that steroids elicit many of their biological effects in brain by acting through their respective receptors to alter neuronal gene transcription, via mechanisms similar to those described previously, and cause changes in hormone-dependent behavior and physiology (Blaustein & Mani, 2006; Pfaff, 1997). For example, estrogens elevate transcription from the genes that encode the progesterone

Steroid p/CAF CBP SRCs

hsp 

SR

SR SR

Inactive steroid receptor

Active receptor dimers

Expression of steroid-induced genes

SR SR Steroid response element

Cytoplasm

Pol II

Nucleus

Figure 6.6 Ligand-dependent genomic mechanism of action of steroid receptors. Note. CBP  CREB-binding protein; hsp  Heat-shock proteins; p/CAF  p300/CBP-associated factor; Pol II RNA polymerase II; SR Steroid receptor; SRCs  Steroid receptor coactivator family (p160s); SRE  Steroid response element.

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receptor and the opioid peptide enkephalin in a manner that shows the neuroanatomical specificity, the temporal features, and the sex dimorphism that permits those gene products to foster the female sex behavior lordosis in the female but not the male (Pfaff, 1999). Coactivators of Steroid Receptors In contrast to the historical perspective of nuclear receptors acting alone by binding to their appropriate response elements on DNA, contemporary research reveals a wide set of nuclear proteins, called coactivators, that modulate behavioral and other physiological actions of hormones according to context. A critical component of efficient steroid receptor transcription is the recruitment of nuclear receptor coactivators, which dramatically enhance transcriptional activity (Lonard & O’Malley, 2006; O’Malley, 2006; Rosenfeld, Lunyak, & Glass, 2006). Under most conditions, steroid receptors interact with coactivators in the presence of an agonist, but not in the absence of ligands or in the presence of an antagonist (McInerney, Tsai, O’Malley, & Katzenellenbogen, 1996; Oñate et al., 1995; Shiau et al., 1998; Tanenbaum, Wang, Williams, & Sigler, 1998; but see also Dutertre & Smith, 2003; Oñate et al., 1998; Webb et al., 1998). It has been proposed that nuclear receptor coactivators influence receptor transcription through a variety of mechanisms, including acetylation of histones, methylation, phosphorylation, and chromatin remodeling (Lonard & O’Malley, 2006; Rosenfeld et al., 2006). The first steroid receptor coactivator to be cloned was steroid receptor coactivator-1 (SRC-1/NcoA-1; Oñate et al., 1995), which was later found to be a member of a larger family of p160 proteins that includes SRC-2 (also known as GRIP1, TIF2 and NCoA-2; Voegel, Heine, Zechel, Chambon, & Gronemeyer, 1996) and SRC-3 (AIB1, TRAM-1, p/CIP, ACTR, RAC3; Anzick et al., 1997). The SRC family of coactivators physically interacts with steroid receptors, including ER and PR, in a ligand-dependent manner (Oñate et al., 1995; Lonard & O’Malley, 2006; Rosenfeld et al., 2006). In cell culture, hormone-induced transactivation of PR is reduced by coexpression of ER, presumably due to squelching or sequestering of shared coactivators. This squelching effect can be reversed by over-expression of SRC-1, suggesting that coactivators are a limiting factor necessary for full transcriptional activation of receptors (Oñate et al., 1995). In further support of this concept, over-expression of SRC-1 relieves thyroid hormone receptor inhibition of ER-mediated transcription in a neuroendocrine model (Vasudevan et al., 2001). It has

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been suggested that the SRC family of coactivators acts as a platform to allow the recruitment of other coactivators, including CREB-binding protein (CBP) and p300/ CBP-associated protein (p/CAF), that possess histone acetyltransferase activity and aid in chromatin remodeling (Kamei et al., 1996; McKenna, Nawaz, Tsai, Tsai, & O’Malley, 1998; Smith, Oñate, Tsai, & O’Malley, 1996; Figure 6.2). Finally, cell culture studies provide evidence that steroid receptor-mediated recruitment of distinct coactivators (e.g., SRC-1 versus SRC-2) can result in different chromatin modifications and modulate the transcription of steroid-responsive genes (Li, Wong, Tsai, & O’Malley, 2003). Steroid Receptor Coactivator Function in the Brain While much is known about the molecular mechanisms of nuclear receptor coactivators from a variety of cell culture studies (Lonard & O’Malley, 2006; O’Malley, 2006; Rosenfeld et al., 2006), we are just beginning to understand their role in hormone action in brain (Molenda, Kilts, Allen, & Tetel, 2003). SRC-1 mRNA and protein are expressed at high levels in the cortex, hypothalamus, and hippocampus of rodents (Auger, Tetel, & McCarthy, 2000; Martinez de Arrieta, Koibuchi, & Chin, 2000; Meijer, Steenbergen, & de Kloet, 2000; Misiti, Schomburg, Yen, & Chin, 1998; Molenda, Griffin, Auger, McCarthy, & Tetel, 2002; Ogawa, Nishi, & Kawata, 2001; Shearman, Zylka, Reppert, & Weaver, 1999) and birds (Charlier, Lakaye, Ball, & Balthazart, 2002). In order for coactivators to function with steroid receptors, they must be expressed in the same cells. Indeed, SRC-1 is expressed in the majority of estrogeninduced PR cells in reproductively relevant brain regions, including the VMN, medial preoptic area, and arcuate nucleus (Tetel, Siegal, & Murphy, 2007). The expression of the SRC family of coactivators in brain appears to be regulated by a variety of factors, including hormones (Camacho-Arroyo, Neri-Gomez, Gonzalez-Arenas, & Guerra-Araiza, 2005; Charlier, Ball, & Balthazart, 2006; Iannacone, Yan, Gauger, Dowling, & Zoeller, 2002; Maerkel, Durrer, Henseler, Schlumpf, & Lichtensteiger, 2007; McGinnis, Lumia, Tetel, Molenda-Figuiera, & Possidente, 2007; Mitev, Wolf, Almeida, & Patchev, 2003; Ramos & Weiss, 2006), day length (Tetel, Ungar, Hassan, & Bittman, 2004), and stress (Bousios, Karandrea, Kittas, & Kitraki, 2001; Charlier et al., 2006; Meijer, van der Laan, Lachize, Steenbergen, & de Kloet, 2006). More recently, the function of nuclear receptor coactivators in hormone action in brain and behavior has been

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Principle 4

investigated. One clever set of experiments used “antisense” DNA oligomers. These are short sequences of DNA, usually 15 to 30 nucleotide bases, that are complementary to a chosen sequence in the messenger RNA being targeted. They ruin that messenger RNA’s function by two mechanisms: by rendering the messenger RNA too fat to fit into the ribosome to be translated, and by rendering the messenger RNA susceptible to breakdown by a degrading enzyme. In the developing rodent brain, antisense to SRC-1 reduced masculinization of the sexually dimorphic nucleus, indicating SRC-1 is involved in hormone-mediated sexual differentiation of the brain (Auger etal., 2000). This is important because this nucleus is one of a set of structures that shows hormonally and developmentally dependent sexual dimorphisms and may play an important role in the sexual differentiation and physiological regulation of brain and behavior. In the adult brain, SRC-1 and SRC-2 function in the VMN to modulate ER-mediated transactivation of the behaviorally relevant PR gene (Apostolakis, Ramamurphy, Zhou, Oñate, & O’Malley, 2002; Molenda et al., 2002). In addition, SRC-1 acts in the VMN to regulate both ER- and PR-dependent aspects of female sexual behavior in rats (Molenda-Figueira et al., 2006). In the adult quail brain, SRC-1 modulates hormone-dependent gene expression, brain plasticity, and behavior (Charlier, Ball, & Balthazart, 2005). Finally, the p160 coactivators function in glucocorticoid receptor action in glial cells (Grenier et al., 2005). Taken together, these findings indicate that the SRC family of coactivators has profound effects on hormone action in brain and the regulation of behavior. The mechanisms by which steroids act in a tissuespecific manner comprise a fundamental issue in steroid hormone action. The field of Neuroendocrinology is poised to make dramatic gains in understanding how steroids regulate gene expression in brain. Recent investigations indicate that, in addition to the bioavailability of hormone and receptor levels, nuclear receptor coactivators are critical regulatory molecules in hormone-dependent activation of genes in the brain and the regulation of behavior.

reverse(!)—the nature of the causal relation between a hormone-dependent gene and the behavior (Ogawa, Choleris, & Pfaff, 2004). Here are three examples:

PRINCIPLE 4: GENES CODING FOR HORMONE RECEPTORS DO NOT DRIVE BEHAVIOR DIRECTLY; THEY ARE MODULATED BY SEVERAL TYPES OF ORGANISMIC AND ENVIRONMENTAL FACTORS

Female mice tested for their vigor in nest defense showed a marked effect of the null deletion of ER-. At the beginning of each test, beta-ERKOs showed a much greater number of attacks than wildtype controls. On the other hand, when tested for testosterone-facilitated aggression beta-ERKOs responded with significantly less frequent aggression for a given dose of testosterone. This comparison suggests that the effect of a specific gene on aggressive behavior can depend on the type of aggression tested.

For sex behavior and for aggressive behaviors, factors such as the gender of the animal, the age of the animal, and the nature of the aggressive encounter may alter—and even

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1. Gender: Deleting the ER- gene permanently in a male mouse abolishes aggression (Ogawa, Washburn, Taylor, Lubahn, Korach, & Pfaff, 1998). However, in a female mouse, the same type of mutation increases aggression (Ogawa, Eng, et al., 1998). The results for the two sexes are opposite. Now consider deletion of the gene coding for ER-. In males, such a gene knockout can increase aggression, but in female mice, knocking out ER-beta reduces testosterone-facilitated aggression. From these two sets of contrasts between male and female mice, we infer that the effect of a given gene on aggression depends on the gender in which that gene is expressed. 2. Age: It is obvious that with respect to aggressive behaviors, the magnitude of the phenotype in the ER- knockout male mouse declines with age (Nomura et al., 2002). The strongest increase in aggression consequent to an ER-β gene deletion is just after puberty. While the mechanism for this is still obscure, nevertheless, it is clear that the effect of a specific gene on aggressive behaviors can depend on the age of the animals at which the behavioral assay is conducted. 3. Nature of the aggression: During the resident-intruder paradigm for testing aggression, ER- knockout female mice display high levels of increased aggression toward female intruders. Their aggression persists well beyond that shown by their wildtype littermate controls, whether the intruder is a female mouse treated with estrogens and progestins, or not. In contrast, if the intruder is an olfactory bulbectomized male mouse, the alpha-ERKO female’s aggression is at a very low level, not any different than the wildtype female mouse’s. This comparison shows that the effect of a specific gene on aggressive behavior can depend on the nature of the opponent.

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PRINCIPLE 5: BEHAVIORALLY RELEVANT HORMONE-SENSITIVE GENES GOVERN FUNCTIONAL MODULES FOR THE BIOLOGICALLY ADAPTIVE REGULATION OF BEHAVIOR Because the genes induced by sex hormones and important for lordosis behavior are not all of the same type—they include growth related genes, a transcription factor, genes for neurotransmitter receptors, neuropeptides and their receptors (Figure 6.3), a different kind of organizing principle had to be envisioned. Mong and Pfaff (2004) have conceived of functional modules governed by estrogens acting in the brain. The easiest to understand are the direct effects, from gene induction to neural circuit to behavioral change. Hormone effects on neurotransmitter receptors in ventromedial hypothalamic neurons directly trigger the rest of the lordosis circuit to operate, using the following modules. Noradrenergic -1b receptors, associated with generalized CNS arousal, are induced by estrogen treatment in ventromedial hypothalamic (VMH) cells which govern the rest of the lordosis behavior circuit. Noradrenergic (NA) ascending afferents come into the VMH from the ventral noradrenergic bundle, which originates in arousalrelated neuronal groups A1 and A2, and signals heightened arousal upon stimulation from the male. In biophysical studies, directly applied NA increases the electrical activity of VMH neurons. These VMH neurons are at the top of the lordosis behavior circuit (Figure 6.2), thus fostering reproductive behavior. Muscarinic cholinergic receptors responding to the neurotransmitter acetylcholine are also found on VMH neurons. Estrogen treatment increases their activities as well. Inputs to the VMH come from, among other places, the lateral dorsal nucleus of the tegmentum. Neurons there are part of the ascending arousal pathways, and would signal stimulation from the male upon mounting the female. In any case, inducing muscarinic receptors increases the VMH electrophysiological response to acetylcholine. The enhanced VMH output primes lower pathways in the circuit for lordosis behavior. There are also indirect effects, from gene induction to downstream genes to behavioral change. Some hormone effects occur early, long before the onset of reproductive behaviors, and set the stage for later developments. Neuronal Growth Growth promotion by estrogens in VMH neurons follows from the stimulation of synthesis of ribosomal RNA, which precedes the elaboration of dendrites and synapses on VMH neurons observed after hormonal treatment. The earliest

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estrogen effect is the increase of transcription of ribosomal RNA, followed rapidly by morphological effects, including those in the nucleolus itself and a striking elaboration of rough endoplasmic reticulum in the cytoplasm. Wooley and Cohen (2002) have shown, probably consequent to the phenomena described previously, a stimulatory effect of estrogen treatment on dendritic growth. In the hypothalamus, others have reported that estrogens foster dendritic growth and an increased number of synapses. Therefore, in VMH cells that control lordosis behavior circuitry, estrogens provide the structural basis for increased synaptic activity and, therefore, greater sex-behavior-facilitating output.

Amplification by Progesterone Administration of progesterone 24 or 48 hours after estrogen priming greatly amplifies the effect of estradiol on mating behavior. This effect requires the nuclear progesterone receptor (PR), as mating behavior disappears after antisense DNA against PR mRNA has been administered in the VMH. This behavior also disappears in PR knockout mice. Importantly, PR itself is a transcription factor, so it will be possible to explore downstream progesteronesensitive genes.

GnRH The physiological importance of estrogenic elevation of gonadotropin-releasing hormone (GnRH, LHRH) mRNA levels under positive feedback conditions—as well as elevation of the receptor mRNA for GnRH—must be to synchronize reproductive behavior with the ovulatory surge of luteinizing hormone (LH). The same GnRH decapeptide which stimulates the ovulatory release of gonadotropins also facilitates mating behavior. In many small animals, synchrony of sex behavior with ovulation would be biologically adaptive because it eliminates unnecessary exposure to predation. In this respect, the behavioral effect of this neuropeptide is consonant with its peripheral physiological action. The case of GnRH also brings up a rare, unambiguous proof of an individual gene causally related to a human social behavior. During development in vertebrates ranging from fish to humans, GnRH neurons migrate from their birth place in the olfactory placode into the brain. A human with damage at the Kallmann’s syndrome locus on the X chromosome did not fail to express the GnRH gene in the appropriate neurons. Instead, the neurons failed to migrate out of the olfactory placode (Schwanzel-Fukuda & Pfaff, 1989; Schwanzel-Fukuda, Bick, & Pfaff, 1989). A single gene for the Kall protein accounts for the deficit.

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Principle 6

It is for an extracellular matrix protein that is necessary for the GnRH neuronal migration and that, in fact, decorates the migration route. A striking feature of the phenotype in men is important to note. They have no libido. Here is the causal route. The men have no sexual drive (1) because they have little testosterone, (2) because they have little LH and FSH circulating from the pituitary gland, (3) because no GNRH is coming down the portal circulation to the pituitary from the hypothalamus, (4) because there is no GnRH in the hypothalamus, (5) because the GnRH neurons did not migrate during development into the brain and, (6) because of a mutation in the gene for the Kall protein. Therefore, we can causally connect, step-by-step, an individual gene to an important human social behavior, but at least six causal links are required. This causal route illustrates the complexity of gene/behavior relationships in humans. Some of the indirect effects have a causal route from gene induction to intermediate behaviors (Mong & Pfaff, 2004). That is, some of the genes affected by estrogens work by altering other behaviors which then prepare the animal for the behavior in question, in this case mating.

Analgesia The enkephalin gene is turned on rapidly by estrogens, within about 30 minutes, and this is proven to represent a hormone-facilitated transcriptional facilitation. The route of action on lordosis, of the enkephalin gene product, is indirect, through other behaviors. That is, we propose that, through the reduction of pain, enkephalins help to allow the female to engage in mating behavior despite the mauling she receives from the male. The strong somatosensory and interoceptive stimuli that ordinarily would be treated by the female as noxious are now tolerable and allow successful mating to proceed.

Anxiety Reduction The oxytocin gene and the gene for its receptor are both expressed by hypothalamic neurons at higher levels in the presence of estrogens. The indirect route of action of this multiplicative set of gene inductions, on mating behavior, is likely through a behavioral link: anxiety reduction allows courtship and mating. This proposal is consistent with previous formulations: oxytocin has been conceived as protecting instinctive behaviors connected with reproduction, maternity, and other social behaviors from the disruptive effects of stress. Indeed, oxytocin has an anxiolytic action in the presence of estrogens (which presumably elevate the oxytocin receptor gene product; McCarthy, McDonald, Brooks, & Goldman, 1996).

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Individual-Specific Olfactory Cues Nonvolatile Volatile Hypothalamus PVN and SON Vomeronasal organ

Main olfactory bulb/system

Accessory olfactory bulb/system

OT

ER β

OT E

OT

Blood stream OTR

Amygdala

ER α

E E

Social recognition

Ovaries: estrogen (E) production

Figure 6.7 Estrogens increasing oxytocin transcription in the hypothalamus and increasing oxytocin receptor transcription in the amygdala help to foster social recognition in mice. Note. Thus, four genes (estrogen receptor-alpha, estrogen receptor-beta, oxytocin, and oxytocin receptor) and their products form a functional module that supports social recognition. From “An Estrogen-Dependent Four-Gene Micronet Regulating Social Recognition: A Study with Oxytocin and Estrogen Receptor- and: Knockout Mice,” by E. Choleris, Gustafsson, et al., 2003, Proceedings of the National Academy of Sciences, p. 6196. Adapted with permission.

Social Recognition The induction of the oxytocin gene by estrogens is an ER- dependent, behaviorally significant phenomenon, reassuring since only ER- gene expression is found in oxytocinergic cells In turn, oxytocinergic projections to the amygdala, where oxytocin receptor gene transcription under the control of ER-alpha, are thought to be important for social recognition in mice, which helps to prevent aggression. Altogether, these data invoke the idea of a four-gene micronet (Choleris, Gustafsson, et al., 2003; Choleris, Little, etal., 2007) important for social behaviors (Figure 6.7). All of these modules support reproductive behaviors from the earliest estrogenic actions on the brain to the occurrences of mating behaviors themselves.

PRINCIPLE 6: TEMPORAL ASPECTS OF HORMONE ACTION IN THE BRAIN ARE IMPORTANT By temporal aspects, we mean questions of both duration and order of events. For some steroid sex hormone effects on a wide variety of behaviors, longer durations of hormone administration make

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for greater behavioral increases. A case in point is the effect of testosterone on male-typical sex behaviors in male rodents. Typically, for increased mounting behavior, and especially for the pelvic thrusting, penile insertions, and ejaculation, many days or several weeks of continuous testosterone treatment would be required for high levels of response. Hormone effects on female sex behaviors are more diverse. Consider first the role of estrogenic hormones. A long priming period is useful for high levels of lordosis behavior. If the female is cycling normally or has been exposed recently to substantial levels of estrogens, then 48 hours is enough. The longer the female is without ovarian estrogens, the longer the priming period required for female sex behavior. A molecular interpretation of the mechanism operating in this case is the following: That prolonged absence of estrogens allows the decline of nuclear coactivator protein levels (see Chapter 18), which are needed to transduce the nuclear binding of ERs into behaviorally relevant transcriptional facilitations. A surprising development with respect to long priming actions of estrogens came from biochemical work in the uterus. A priming period of 24 hours could be substituted for by two brief exposures to estrogen, suitably timed. We followed this up for the CNS and behavior. Again, an estrogen priming of 24 hours could be replaced by two 1-hour exposures, the first from hour 0 to 1 and the second beginning between 4 and 13 hours after the first. This pulse schedule was effective for inducing PR and as well as for lordosis behavior. Questions of Order of Administration In females, estrogen priming must be followed by progesterone for optimal behavioral facilitation. The progesterone, if it is timed correctly, amplifies the estrogenic effect. The required temporal parameters for progesterone are much different from those for estradiol—there is a biphasic action of progesterone both on pituitary release of LH and on behavior. In female rats, for example, 2 to 5 hours after progesterone injection, LH release and lordosis behavior are facilitated mightily. But the continued presence of progesterone, several hours later, actually inhibits both the endocrine output (LH) and the behavioral output (lordosis). In other neuroendocrine cases, brevity of hormone action is not only effective but also is actually required. Gonadotropin-releasing hormone (GnRH, also known as LHRH) is well known to show a pulsatile pattern of release. It is fascinating that populations of identical GnRH neurons can manage pulsatile output. In fact, pulsatile outputs of GnRH are absolutely necessary for the pituitary to respond with substantial gonadotropin release into the

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blood. A steady, high level of administration of GnRH actually turns off the gonadotropin system and thus can be used either (a) as a birth control device or (b) to ramp down the system in case of gonadal cancers. Some of these phenomena are beginning to be understood at the molecular level. The GnRH promoter is activated in an episodic fashion in GnRH neuronal cultures. A specific 410 base region of the GnRH promoter is required for pulsatile GnRH promoter activity. Within that region, a small five-base site that represents a binding site for the transcription factor Oct-1 is likewise required. In order to achieve fertility, LH has to be given in a pulsatile fashion.

PRINCIPLE 7: IN SOME CASES, A HORMONE REQUIRES METABOLISM OR NEEDS COMBINATIONS WITH OTHER HORMONE(S) TO BE EFFECTIVE BEHAVIORALLY First consider the case of testosterone as a “prohormone”— a steroid whose enzymatically regulated metabolism produces other steroids that are active in actually exerting the behavioral effect. Testosterone, produced mainly by the testis in men and the adrenals in women, is a potent androgen (male sex hormone), that influences many tissues throughout the body, including the CNS, in which testosterone receptors are present. Metabolites, of testosterone, however, also are important hormones and have specific effects that are different from the effects of testosterone itself. Two important testosterone metabolites are dihydrotestosterone (DHT) and estradiol (E2), which are male and female sex steroids, respectively. It is the conversion of testosterone to these two active hormonal metabolites that is critical for male sex behavior. There are specific receptors for each; for DHT these are in peripheral tissues related to the development of secondary sex characteristics, and for E2 these are in both peripheral tissues and the CNS. The major structural changes inherent in the enzymatic conversions of testosterone to DHT and E2 confer receptor specificity to these steroid compounds. The conversion of testosterone to E2 by the sequence of three enzymatic steps that are called aromatase, within the CNS, is considered to be important for the early development of the masculine brain. The aromatase enzyme is concentrated in areas of the brain related to sexual differentiation of the CNS, such as the hypothalamus. In the male, exposure of the developing brain to high concentrations of testosterone, and therefore to high concentrations of E2 converted from testosterone in specific regions, leads to, for example, the tonic secretion of gonadotropins in the adult male versus the cyclic secretion of these hormones in the adult

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Principle 8

female. Many differences in behavior, especially in aggressive behavior, also result from the differential exposure of the developing male and female CNS to testosterone and E2. While testosterone has many androgenic effects throughout the body and is responsible for virilization of internal structures, for example, testicular development, DHT is required for virilization of the external genitalia, that is, penile growth and scrotal development. If the enzyme converting testosterone to DHT, 5-alpha reductase, is genetically deficient, the external genitalia of the newborn infant appear ambiguous (male pseudohermaphroditism). Figure 6.4B schematically illustrates the genitalia of a normal man (left) and the genitalia of a 5-alpha reductase-deficient prepubertal boy. The female-appearing external genitalia belie the presence of an XY sex chromosome complement, functioning testes, although undescended, and a masculinized brain secondary to fetal testosterone exposure. The behavioral consequences of this anatomical alteration are obvious, and they also illustrate how hormone metabolites can influence behaviors through indirect routes. Before it was recognized that this syndrome is inherited and therefore is concentrated in certain families, affected infants were raised as girls. On reaching puberty, however, the increased secretion of testosterone from the pubertal testis led to some DHT being produced, so that many prepubertal “girls” developed a male phallus with erections, scrotal testes, male hair distribution, deepened voice, and male body habitus and psychological characteristics, to the initial consternation of parents and family members. However, the syndrome was quickly recognized to occur in affected families in several parts of the world, for example, in the Dominican Republic, so that newborns with ambiguous genitalia in these families were not forced to grow up as girls. Indeed, some individuals have entered into heterosexual relationships and have been able to function physically and emotionally as men. Others have led isolated lives or retained some female gender identity. The outward switch of sex and gender identity from female to male at puberty in 5-alpha reductase-deficient individuals was initially interpreted as the primacy of nature over nurture; that is, sex hormones being more influential than psychosocial factors imposed since infancy. However, the early recognition that newborn from affected families and with ambiguous genitalia might have masculine pubertal development led to these infants’ being raised either as boys or ambiguously as girls. The nature–versus– nurture dichotomy, therefore, is not as straightforward as some would believe. As already mentioned, for some hormone-dependent behaviors, one hormone is not enough: a combination is required. And for hormone combinations, order of administration can be crucial. Consider the combination of estrogens

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and progestins acting to facilitate reproductive and maternal behaviors. Even for the same pair of hormones, different temporal patterns of combinatorial action will be important for different behaviors. For example, in many female laboratory animals, a long priming treatment of estrogens—48 hours or more—followed by a brief progesterone exposure will promote female-typical sexual behaviors. The neural circuit and some of the genes supporting the mechanisms for this type of behavior have been worked out (see earlier discussion). In a biologically adaptive fashion, the requirement of E followed by P for female sex behavior synchronizes it with ovulation, which has the same combinatorial effects. However, following the estrogen priming, if the progesterone is allowed to stay around for a long time, the opposite effect is seen on sexual behavior. Finally, in a pregnant animal, estrogen levels are high and remain high during parturition. In contrast, progesterone levels start high but decline around the time of giving birth. High levels of estrogens and a declination in progestins make the optimal combination for maternal behavior (Numan & Insel, 2003). A different kind of combination of hormone actions underlies social recognition, motivation, and memory in female laboratory animals. Several labs have shown the powerful effects of oxytocin, and in some species vasopressin, on this cluster of behaviors. In addition, estrogens have an overriding effect in two ways, as noted in Figure 6.7. Working through estrogen receptor- estrogens turn on the oxytocin gene; and working through ER- they turn on the oxytocin receptor gene. Therefore, in this combination of hormones, estradiol and oxytocin, one has a superordinate relation to the other. Estrogens have to come first, to turn on the gene for the oxytocin receptor, for instance, and oxytocin later.

PRINCIPLE 8: SOME OF THE INFLUENCES ON SPECIFIC HORMONE-BEHAVIOR SYSTEMS ARE REMARKABLY NONSPECIFIC Decades of work have gone into the elucidation of the neuroanatomical pathways and neurophysiological mechanisms related to arousal. Mechanisms for generalized arousal of the CNS (Pfaff, 2006) are fairly well known at the neuroanatomical level. Their most important features emphasize multiplicity and redundancy of ascending arousal pathways in such a way as to prevent failure. Five major neurochemically distinct systems work together to increase arousal. They use norepinephrine, dopamine, serotonin, acetylcholine, and histamine as transmitters. They all begin in the brain stem and converge in the thalamus or in the basal forebrain (Figure 6.8). They overlap and cooperate. Their very multiplicity ensures against failure.

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Neuroendocrinology: Mechanisms by Which Hormones Affect Behaviors A

P

)

cts rA o t x Mo rte ed al co t c ire nt (D Fro

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Limbic cortex Striatum ACh Basal forebrain

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Figure 6.8 Classical ascending systems elevate CNS arousal. Note. From Brain Arousal and Information Theory, by D. Pfaff, 2006, Cambridge, MA: Harvard University Press. Adapted with permission.

Four sensory systems feed ascending arousal pathways in a straightforward fashion. These clearly show how vestibular stimuli, somatosensory, auditory, and taste stimuli on the tongue could arouse an animal or human being. Pain mechanisms further dramatize how a vastly amplified somatosensory signal from the skin or the viscera can wake up and alert an individual. Moreover, pain pathways and sexual cutaneous signals overlap, and share the ability to cause states of high arousal (Figure 6.9). In contrast, electrical impulses triggered by odor stimuli enter the brain through tracts in the basal forebrain, and project to the amygdala, a primary receiving zone which itself is connected with high degrees of arousal, during both sex and fear. Visual stimuli impact CNS arousal pathways both through the outer layers of the superior colliculus and through the reticular and medial cell groups of the thalamus. An important point to reiterate is that these various arousal-related transmitter systems and sensory signals converge. Whether in the basal forebrain or in the medial thalamus, a strong signal for cortical arousal is generated and must be distributed broadly in the cerebral cortex to command the attention of a wide variety of higher level perceptual processers and motor control cell groups. In terms of the general principles illustrated by CNS arousal pathways, it is eminently clear that arousal mechanisms impact neuroendocrine processes by their projections into the hypothalamus. They are bilateral. Unilateral damage in the animal brain or human brain has little effect on

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Heightened arousal Altered motivational state

Shared ascending pathways in A-L columns of spinal cord Stimulusproduced analgesia Sexually relevant

Painful

Somatosensory stimuli

Figure 6.9 Painful and sexually relevant cutaneous stimuli both increase CNS arousal but have different behavioral consequences.

generalized arousal or consciousness. Second, they are bidirectional. In addition to the classical aminergic ascending pathways just mentioned, there are crucial descending pathways (e.g., vasopressin, histamine, orexin). Third, these pathways have been conserved across a variety of species, including humans. Finally, these pathways always potentiate an animal’s or human’s behavioral responsivity.

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Principle 8

These responses may be active approach responses, but in the case of fearful or stressful inputs, these are avoidance responses. It has been hypothesized that some of the most important cells stimulating CNS arousal are medullary gigantocellular neurons that have bifurcating axons, both ascending and descending in the brain stem (Figure 6.10) and that likely contribute to cortical and to autonomic arousal, respectively. Electrical stimulation of these neurons in the rat medullary reticular formation stimulates EEG arousal, decreasing spectral power in the low-frequency delta range and increasing power in the high-frequency gamma range (Wu, Stavarache, Pfaff, & Kow, 2007). Even with these data in hand, further work will be necessary to sort out contradictions in the literature and to specify the exact molecular characteristics of these “master cells” for arousal and the exact mechanisms by which they influence CNS arousal. In neurophysiological terms, cells involved in generalized arousal of the CNS would be expected to respond to a variety of stimuli in several sensory modalities. During electrophysiological recordings from reticular and raphe neurons in the medulla, such neurons have been found (Hubscher & Johnson, 2002; Leung & Mason, 1998, 1999; Martin, Pavlides, & Pfaff, unpublished work). Moving anterior in the brain stem, certain ponto-medullary reticular neurons (Peterson, Anderson, & Filion, 1974) as well as

POA PVNp (OT, AVP) SCN TMN (HA) LHA (Orexin/ Hypocretin)

Brainstem

Autonomic Controls

Figure 6.10 CNS arousal depends on descending systems as well as ascending systems. Note. From Brain Arousal and Information Theory, by D. Pfaff, 2006, Cambridge, MA: Harvard University Press. Adapted with permission.

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the omnipause neurons of Phillips, Ling, and Fuchs (1999) recorded in the pons, also fit the requirement that cells be multimodal in their range of sensitivities and have firing rates correlated with arousal and visual attention. In the midbrain, the work of Horvitz, Stewart, and Jacobs (1997), recording from dopaminergic neurons, revealed responses that were correlated with the activation of behavior, that is, the initiation of motor responses directed toward salient stimuli. These and many other reports supply the neurophysiological basis of generalized arousal responses. Functional Genomics Data are accumulating rapidly with respect to neurochemical and genomic mechanisms for both arousal and stress. A large number of genes, more than 120, participate in regulating generalized CNS arousal. The large number is due to the inclusion of gene-encoding synthetic enzymes, receptors (for serotonin, alone, there are 14), transporters, and catabolic enzymes for both the relevant neurotransmitters and neuropeptides; both those increasing and those decreasing arousal (Pfaff, 2006). As might be expected, sex hormones are involved in CNS arousal. Disruption of the gene-encoding estrogen receptor alpha severely reduced arousal measures in female mice, compared to their wildtype littermate controls (Figure 6.11). Interestingly, disruption of the gene for estrogen receptor beta, a likely gene duplication product, had no significant effect (Garey et al., 2003). There are additional implications of having so many genes controlling arousal mechanisms. The heterogeneity among the genes involved presumably provides for great flexibility of response. The very multiplicity yields the possibility of large numbers of meaningful patterns of gene expression. In a neuroendocrine context, we have shown that one never could understand gene/behavior relations on a one-by-one basis. Moving beyond Beadle and Tatum’s concept from their work with the fungus Neurospora—their classical “one gene/one enzyme” concept—we reached the conclusion that different patterns of gene expression yield different patterns of sociosexual behaviors (Pfaff, Ogawa, Kia, Frohlich, & Kow, 2002). Finally, how do these generalized arousal forces impact specific neuroendocrine mechanisms? The answer lies in the exact neuroanatomical localizations of the receptors for arousal-related transmitters and neuropeptides in brain regions controlling the pituitary and hormone-dependent behaviors. For female sexual behavior, two arousalenhancing transmitters, norepinephrine and histamine, can influence sexual arousal and lordosis behavior by their excitatory effects on the electrical activity of ventromedial hypothalamic neurons (see earlier discussion). Conversely, mu-receptor opioid agonists (Devidze, Lee, Martin, &

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Note. There is no statistically significant effect of ERKO- gene deletion, compared to their respective WT controls. From “Genetic Contributions to Generalized Arousal of Brain and Behavior,” by Garey et al., 2003, Proceedings of the National Academy of Sciences, 100, 11019–11022. Adapted with permission.

Figure 6.11 Estrogen receptor alpha knockout (ERKO-) mice show decreased arousal responses to stimuli in several sensory modalities, compared to their wildtype (WT) littermate controls.

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Principle 9

Pfaff, submitted) and prostaglandin D (Mong et al., 2003), reduce generalized arousal, and as a consequence reduce lordosis behavior. The logical relations among generalized arousal, sexual arousal, and sexual behavior are shown in Table 6.1. More detail on CNS arousal and the activation of behavior can be found in Chapter 23.

PRINCIPLE 9: NEUROENDOCRINE MECHANISMS HAVE BEEN CONSERVED AND CONTINUE TO PROVIDE ADAPTIVE COORDINATION OF BRAIN WITH BODY TO REGULATE BEHAVIOR A modern neuroendocrinologist may not try to explain the psychological side of human mentation—the full range of mental, artistic, self-conscious expressions of the person in states of emotion that involve hormonal changes. However, a tremendous number of neuroanatomical, neurophysiological, genetic, and endocrine mechanisms related to sex behavior has been conserved from the animal brain into the human brain (Figure 6.12). To give just a few examples, the steroid hormones are the same in the human brain as in lower mammals. Steroid receptor chemistry, neuroanatomy, and molecular mechanisms are also conserved. Neuroendocrine system anatomy is much

TABLE 6.1

113

more highly conserved than other parts of the forebrain, and the tendency of hormone-responsive neurons to project to other hormone-responsive neurons is also preserved. The neuroendocrine neuropeptide par excellence is gonadotropin releasing hormone (GnRH, also called LHRH). Its human brain chemistry is identical to that in lower animals, its physiology, its release mechanisms, and its receptor physiology (e.g., in the anterior pituitary gland) are likewise conserved. Most strikingly, the unique migration during development of GnRH neurons from the olfactory pit to their final functional positions in the basal forebrain, discovered in mice (Schwanzel-Fukuda & Pfaff, 1989) also occurs in humans. In fact, as stated earlier, a failure of GnRH neuronal migration in men accounts for the loss of libido in X-chromosome-linked Kallmann’s disease (Schwanzel-Fukuda et al., 1989). Basic mechanisms have remained the same. Transcriptional biology, molecular biology of neurons, chemistries of a variety of neurotransmitters and neuropeptides, electrophysiological mechanisms, and many facets of cellular neuropharmacological effects all link human neuroendocrine systems to those of laboratory mammals. Finally, in terms of the neuroendocrinology of reproduction, the chromosomal biology of sex differences, the mechanisms of sex differentiation of the brain, and the basic requirements of behaviors necessary for sperm to meet egg and fertilize have remained very similar as we move in our

Relations among arousal mechanisms and sex behaviors (male and female). Requires

CNS Features

Mechanisms Require

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VMN/POA neuronal excitation

Sex hormones

Genes for nonspecific ascend systems

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Genes in CNS. molecular biology of neurons. Hypothalamic neuroanatomy Neuronal connections

Hormone receptors Chemistry neuroanatomy

GnRH neuron migration GnRH physiology

Neurotransmitters Neuropeptides Neurochemistry Neuroanatomy Cellular neuroanatomy Cellular neurophysiology Cellular neuropharmacology

Biology of sex differences Requirements for fertilization Steroid Hormones

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Figure 6.12 Hormone-dependent mechanisms conserved from animal to human brains (implications for understanding sexual arousal). Note. Since a remarkable number of neuroendocrine mechanisms have been conserved from animal brains into the human brain, modern neuroendocrine research contributes to the understanding of human neuroendocrine functions and their maladies. All of the structures and functions portrayed in this figure are identical or very similar in the human brain compared to a variety of laboratory animal mammalian subjects. From Drive: Neurobiological and Molecular Mechanisms of Sexual Motivation by D. W. Pfaff, 1999, Cambridge, MA: MIT Press.

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thinking from lower mammals to humans. Likewise, sex differences in aggression provide the same types of statistics among humans as in many lower mammals. Therefore, unless nature, having evolved a full set of working mechanisms for mammalian hormone-dependent behaviors, started an entire new set for humans, then we understand well the most primitive neuroendocrine mechanisms that drive hormone-influenced emotional expressions and behaviors in humans.

PRINCIPLE 10: HORMONE EFFECTS ON BRAIN ARE RELEVANT FOR HUMAN BEHAVIORAL PATHOLOGY Examples of disorders in neuroendocrine mechanisms that lead to serious medical conditions of behavioral pathology include hyperthyroid patients who are extremely jittery and nervous hypothyroid patients who are sluggish and dull, and patients with over-expression of CRF who “burn out” in a mixture of anxiety and depression. Many more examples could be given, but we choose to emphasize a neuroendocrine/behavior example that is so extreme that it constitutes a criminal condition. In the following example, the etiology of the disease is complex but its solution represents an application of neuroendocrine engineering. Pedophilia is classified as a psychosexual disorder. Strong obsession and compulsion components of pedophilia make incorporation of cognitive behavioral skills difficult. Medications that lower sex drive may enhance voluntary control (Berlin, 1983). Testosterone-lowering agents, serotonin re-uptake inhibitors, surgical castration, and stereotaxic neurosurgery have been used to reduce libido, deviant sexual arousal and fantasy, and the frequency of deviant sexual behavior. Pharmacotherapy has included testosterone-lowering agents such as medroxyprogesterone acetate (MPA), cyproterone acetate (CPA) and luteinizing hormone-releasing hormone (LHRH, aka GnRH) inhibitors, and gonadotropin-releasing hormone agonists (GnRH), as well as selective serotonin re-uptake inhibitors (SSRIs). Luprolide acetate (LA), one of several synthesized agonist analogs of LHRH, (aka GnRH), the hypothalamic factor that stimulates gonadotropin release from the pituitary (Vance & Smith, 1984) and produces a paradoxical effect on the pituitary, with initial stimulation of the release of luteinizing hormone (LH) and follicle-stimulating hormone (FSH), followed by inhibition after repeated administration (Belchetz, Plant, Nakai, Keogh, & Knobil, 1978; Bergquist, Nillius, & Wide, 1979; Evans, Doelle, Alexander, Uderman, & Rabin, 1984; Vilchez-Martinez et al., 1974) has been used for this type of therapy. LA causes a reduction in sex hormone

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release (a decrease in testicular steroidogenesis) that is probably secondary to a primary reduction in LH levels (Vance & Smith, 1984). Agonist analogs have also been shown to decrease the number of LH receptors in Leydig’s cells of hypophysectomized rats (Bambino, Schreiber, & Hsueh, 1980; Vance & Smith, 1984). Testosterone levels attained with continued administration of LA were lower than those attained with other medications and may result in LA being a more potent inhibitor of erectile responses compared with other agents. Patients generally become sexually impotent when plasma testosterone levels are less than one quarter of their initial value. A placebo controlled, blinded, multidisciplinary study of LA in pedophiles, detailed the objective effects of this drug on measurable aspects of the arousal response (Schober et al., 2005). One year of therapy on LA, followed by 1 year on a saline-placebo detailed testosterone levels during polygraph testing, plethysmography (PPG) with audio and visual stimuli, viewing time for visual stimuli, and selfreport of urges and masturbatory frequency toward children. Testosterone levels were reduced to castrate levels (less than 50 ng/dI) or one tenth the mean average level. By the second week of treatment, a sustained, profound suppression of testosterone, FSH, and LH remained for duration of treatment with LA. On saline placebo, testosterone levels rose slowly over a 3-month period to approach baseline. During the initial testosterone rise, no patient reported an increase in pedophilic urges, sex drive, or masturbation. Subsequently, levels fell to a mean of 11.6 ng/dL 1 month after the initial injection and remained low until LA was withdrawn. After testosterone fell to castrate levels, the study subjects reported decreased sex drive, decreased pedophilic sexual urges, and decreased masturbation frequency. When LA was replaced with saline placebo, all subjects initially reported no increase in sex drive, pedophilic sexual urges, or masturbation frequency. After 3 months on placebo, testosterone averaged 195 ng/dL, some subjects continued to report no increase in sex drive, pedophilic sexual urges, or masturbation frequency, but others expressed great distress that the medication was losing effectiveness and they were fearful of reoffense. At this time the placebo was revealed to the subjects. All chose to return to LA therapy. Throughout the study, modified visual reaction time results detected the subject’s self-reported choice of interest/preference, and indicated no consistent change in pedophilic interest preference as measured by visual reaction time. Overall, interest preference, as measured by PPG, indicated no consistent change in pedophilic interest preferences with LA therapy. However, the degree to which subjects responded decreased significantly, demonstrating that LA significantly decreased arousal. Penile plethysmography verified self-reported claims of lowered libido, in that LA therapy

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References

caused significant reductions in the magnitude of their sexual arousal pattern. LA significantly reduced pedophilic fantasies, urges, and masturbation on self-report. On all polygraphic assessments, at baseline, almost all responses were classified as deceptive. Deceptive responses about masturbation frequency, urges to initiate sexual contact, and sexual thoughts, decreased dramatically with LA therapy and deceptive responses increased dramatically on placebo. A direct and readily apparent correlation existed between deceptive responses and LA injection. When urges and masturbation frequency decreased on LA, a polygraph indicated almost no deceptive responses. Even with profound testosterone suppression, a complete suppression of arousal did not occur. Low levels of arousal and erectile ability persisted with sufficient tumescence to generate detectable levels on PPG. While on LA, subjects indicated they were better able to focus on employment, relaxation activities, and life planning without continual interruptions by sexual thoughts. All subjects noted a decrease in anxiety, better ability to regulate or control their actions, and increased motivation for work/school activities.

SUMMARY Considerable progress has been made in understanding the neuroanatomical, neurophysiological, and genomic mechanisms of neuroendocrine systems that regulate behavior. We expect that as our knowledge of neuroendocrine mechanisms deepens, it will be even easier to see how their functions contribute to behavioral regulation and their disorders lead to behavioral pathologies.

REFERENCES Anzick, S. L., Kononen, J., Walker, R. L., Azorsa, D. O., Tanner, M. M., Guan, X. Y., et al. (1997). AIB1, a steroid receptor coactivator amplified in breast and ovarian cancer. Science, 277, 965–968. Apostolakis, E. M., Ramamurphy, M., Zhou, D., Oñnate, S., & O’Malley, B. (2002). Acute disruption of select steroid receptor coactivators prevents reproductive behavior in rats and unmasks genetic adaptation in knockout mice. Molecular Endocrinology, 16, 1511–1523. Auger, A. P., Tetel, M. J., & McCarthy, M. M. (2000). Steroid receptor coactivator-1 mediates the development of sex specific brain morphology and behavior. Proceedings of the National Academy of Sciences, USA, 97, 7551–7555. Bambino, T. H., Schreiber, J. R., & Hsueh, A. J. (1980). Gonadotropinreleasing hormone and its agonist inhibit testicular luteinizing hormone receptor and steroidogenesis in immature and adult hypophysectomized rats. Endocrinology, 107, 908–917. Beato, M., & Sánchez-Pacheco, A. (1996). Interaction of steroid hormone receptors with the transcription initiation complex. Endocrine Reviews, 17, 587–609.

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115

Belchetz, P. E., Plant, T. M., Nakai, Y., Keogh, E. J., & Knobil, E. (1978). Hypophysial responses to continuous and intermittent delivery of hypopthalamic gonadotropin-releasing hormone. Science, 202, 631–633. Benoit, G., Cooney, A., Giguere, V., Ingraham, H., Lazar, M., Muscat, G., et al. (2006). International union of pharmacology. LXVI. Orphan nuclear receptors. Pharmacology Review, 58, 798–836. Bergquist, C., Nillius, S. J., & Wide, L. (1979). Intranasal gonadotropinreleasing hormone agonist as a contraceptive agent. Lancet, 2, 215–217. Berlin, F. S. (1983). Sex offenders: A biomedical perspective and a status report on biomedical treatment. In J. C. Greer & I. R. Stuart (Eds.), The sexual aggressor: Current perspectives on treatment (pp. 82–123). New York: Van Nostrand-Reinhold. Blaustein, J. D., & Mani, S. K. (2006). Feminine sexual behavior from neuroendocrine and molecular neurobiological perspectives. In J. D. Blaustein (Ed.), Handbook of neurochemistry and molecular neurobiology (pp. 95–150). New York: Springer. Bousios, S., Karandrea, D., Kittas, C., & Kitraki, E. (2001). Effects of gender and stress on the regulation of steroid receptor coactivator-1 expression in the rat brain and pituitary. Journal of Steroid Biocheminstry and Molecular Biology, 78, 401–407. Camacho-Arroyo, I., Neri-Gomez, T., Gonzalez-Arenas, A., & GuerraAraiza, C. (2005). Changes in the content of steroid receptor coactivator-1 and silencing mediator for retinoid and thyroid hormone receptors in the rat brain during the estrous cycle. Journal of Steroid Biocheminstry and Molecular Biology, 94, 267–272. Charlier, T. D., Ball, G. F., & Balthazart, J. (2005). Inhibition of steroid receptor coactivator-1 blocks estrogen and androgen action on male sex behavior and associated brain plasticity. Journal of Neuroscience, 25, 906–913. Charlier, T. D., Ball, G. F., & Balthazart, J. (2006). Plasticity in the expression of the steroid receptor coactivator-1 in the Japanese quail brain: Effect of sex, testosterone, stress and time of the day. Neuroscience, 172, 333–343. Charlier, T. D., Lakaye, B., Ball, G. F., & Balthazart, J. (2002). Steroid receptor coactivator SRC-1 exhibits high expression in steroid-sensitive brain areas regulating reproductive behaviors in the quail brain. Neuroendocrinology, 76, 297–315. Choleris, E., Gustafsson, J.-Å., Korach, K. S., Muglia, L. J., Pfaff, D. W., & Ogawa, S. (2003). An estrogen-dependent four-gene micronet regulating social recognition: A study with oxytocin and estrogen receptorand: Knockout mice. Proceedings of the National Academy of Sciences, 100, 6192–6197. Choleris, E., Little, S. R., Mong, J. A., Puram, S. V., Langer, R., & Pfaff, D. W. (2007). Microparticle-based delivery of oxytocin receptor antisense DNA in the medial amygdala blocks social recognition in female mice. Proceedings of the National Academy of Sciences, 104, 4670–4675. DeMarzo, A., Beck, C. A., Oñate, S. A., & Edwards, D. P. (1991). Dimerization of mammalian progesterone receptors occurs in the absence of DNA and is related to the release of the 90-kDa heat shock protein. Proceedings of the National Academy of Sciences, 88, 72–76. Devidze, N., Lee, A., Martin, E, & Pfaff. D. (Submitted). Molecular properties of medullary reticular neurons. Dutertre, M., & Smith, C. L. (2003). Ligand-independent interactions of p160/steroid receptor coactivators and CREB-binding protein (CBP) with estrogen receptor-alpha: Regulation by phosphorylation sites in the A/B region depends on other receptor domains. Molecular Endocrinology, 17, 1296–1314. Evans, R. M. (1988). The steroid and thyroid hormone receptor superfamily. Science, 240, 889–895. Evans, R. M., Doelle, G. C., Alexander, A. N., Uderman, H. D., & Rabin, D. (1984, May). Gonadotropin and steroid secretory patterns during chronic treatment with a luteinizing hormone-releasing hormone agonist analog in men. Journal of Clinical Endocrinology and Metabolism, 58, 862–867.

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Neuroendocrinology: Mechanisms by Which Hormones Affect Behaviors

Freedman, L. P., & Luisi, B. F. (1993). On the mechanism of DNA binding by nuclear hormone receptors: A structural and functional perspective. Journal of Cellular Biochemistry, 51, 140–150. Garey, J., Goodwillie, A., Frohlich, J., Morgan, M., Gustafsson, J.-A., Smithies, O., et al. (2003). Genetic contributions to generalized arousal of brain and behavior. Proceedings of the National Academy of Sciences, 100, 11019–11022. Gorski, J., Toft, D., Shyamala, G., Smith, D., & Notides A. (1968). Hormone receptors: Studies on the interaction of estrogen with the uterus. Recent Progress in Hormone Research, 24, 45–80. Grenier, J., Trousson, A., Chauchereau, A., Cartaud, J., Schumacher, M., & Massaad, C. (2005). Differential recruitment of p160 coactivators by glucocorticoid receptor between Schwann cells and astrocytes. Molecular Endocrinology, 20, 254–267.

Lonard, D. M., & O’Malley, B. W. (2006). The expanding cosmos of nuclear receptor coactivators. Cell, 125, 411–414. Maerkel, K., Durrer, S., Henseler, M., Schlumpf, M., & Lichtensteiger, W. (2007). Sexually dimorphic gene regulation in brain as a target for endocrine disrupters: Developmental exposure of rats to 4-methylbenzylidene camphor. Toxicology and Applied Pharmacology, 218, 152–165. Mangelsdorf, D. J., Thummel, C., Beato, M., Herrlich, P., Schütz, G., Umesono, K., et al. (1995). The nuclear receptor superfamily: The second decade. Cell, 83, 835–839. Mani, S. K., Reyna, A. M., Chen, J. Z., Mulac-Jericevic, B., & Conneely, O. M. (2006). Differential response of progesterone receptor isoforms in hormone-dependent and -independent facilitation of female sexual receptivity. Molecular Endocrinology, 20, 1322–1332.

Horvitz, J., Stewart, T., & Jacobs, B. (1997). Burst activity of ventral tegmental dopamine neurons is elicited by sensory stimuli in the awake cat. Brain Research Reviews, 759, 251–258.

Martinez de Arrieta, C., Koibuchi, N., & Chin, W. W. (2000). Coactivator and corepressor gene expression in rat cerebellum during postnatal development and the effect of altered thyroid status. Endocrinology, 141, 1693–1698.

Hubscher, C., & Johnson, R. (2002). Inputs from spinal and vagal sources converge on individual medullary reticular neurons. Society for Neuroscience, 271, 2.

McCarthy, M. M., McDonald, C. H., Brooks, P. J., & Goldman, D. (1996). An anxiolytic action of oxytocin is enhanced by estrogen in the mouse. Physiology and Behavior, 60, 1209–1215.

Iannacone, E. A., Yan, A. W., Gauger, K. J., Dowling, A. L. S., & Zoeller, R. T. (2002). Thyroid hormone exerts site-specific effects on SRC-1 and NCoR expression selectively in the neonatal rat brain. Molecular and Cellular Endocrinology, 186, 49–59.

McGinnis, M. Y., Lumia, A. R., Tetel, M. J., Molenda-Figuiera, H. A., & Possidente, B. (2007). Effects of androgenic steroids on the development and expression of running wheel activity and circadian rhythms in male rats. Physiology and Behavior, 92, 1010–1018.

Jensen, E. V., Suzuki, T., Kawasima, T., Stumpf, W. E., Jungblut, P. W., & de Sombre, E. R. (1968). A two-step mechanism for the interaction of estradiol with rat uterus. Proceedings of the National Academy of Sciences, USA, 59, 632–638.

McInerney, E. M., Tsai, M. J., O’Malley, B. W., & Katzenellenbogen, B. S. (1996). Analysis of estrogen receptor transcriptional enhancement by a nuclear hormone receptor coactivator. Proceedings of the National Academy of Sciences, 93, 10069–10073.

Kamei, Y., Xu, L., Heinzel, T., Torchia, J., Kurokawa, R., Gloss, B., et al. (1996). A CBP integrator complex mediates transcriptional activation and AP-1 inhibition by nuclear receptors. Cell, 85, 403–414.

McKenna, N. J., Nawaz, Z., Tsai, S. Y., Tsai, M. J., & O’Malley, B. W. (1998). Distinct steady-state nuclear receptor coregulator complexes exist in vivo. Proceedings of the National Academy of Sciences, 95, 11697–11702.

Kastner, P., Krust, A., Turcotte, B., Stropp, U., Tora, L., Gronemeyer, H., et al. (1990). Two distinct estrogen-regulated promoters generate transcripts encoding the two functionally different human progesterone receptor forms, A & B. EMBO Journal, 9, 1603–1614.

Meijer, O. C., Steenbergen, P. J., & de Kloet, E. R. (2000). Differential expression and regional distribution of steroid receptor coactivators SRC1 and SRC-2 in brain and pituitary. Endocrinology, 141, 2192–2199.

Kininis, M., Chen, B. S., Diehl, A. G., Isaacs, G. D., Zhang, T., Siepel, A. C., et al. (2007). Genomic analyses of transcription factor binding, histone acetylation, and gene expression reveal mechanistically distinct classes of estrogen-regulated promoters. Molecular Cell Biology, 27, 5090–5104. Klein-Hitpass, L., Tsai, S. Y., Weigel, N. L., Allan, G. F., Riley, D., Rodriguez, R., et al. (1990). The progesterone receptor stimulates cellfree transcription by enhancing the formation of a stable preinitiation complex. Cell, 60, 247–257. Kuiper, G. G. J. M., Enmark, E., Pelto-Huikko, M., Nilsson, S., & Gustafsson, J. (1996). Cloning of a novel estrogen receptor expressed in rat prostate and ovary. Proceedings of the National Academy of Sciences, 93, 5925–5930. Lange, C. A. (2007). Editorial: Membrane and nuclear steroid hormone receptors: Two integrated arms of the same signaling pathway? Steroids, 72, 105–106. Lees, J. A., Fawell, S. E., & Parker, M. G. (1989). Identification of two transactivation domains in the mouse oestrogen receptor. Nucleic Acid Research, 17, 5477–5487. Leung, C. G., & Mason, P. P. (1998). Physiological survey of medullary raphe and magnocellular reticular neurons in the anesthetized rat. Journal of Neurophysiology, 80, 1630–1646. Leung, C. G., & Mason, P. P. (1999). Physiological properties of raphe magnus neurons during sleep and waking. Journal of Neurophysiology, 81(2), 584–595. Li X., Wong, J., Tsai, M. J., & O’Malley, B. (2003). Progesterone and glucocorticoid receptors recruit distinct coactivator complexes and promote distinct patterns of local chromatin modification. Molecular and Cellular Biology, 23, 3763–3773.

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Meijer, O. C., van der Laan, S., Lachize, S., Steenbergen, P. J., & de Kloet, E. R. (2006). Steroid receptor coregulator diversity: What can it mean for the stressed brain? Neuroscience, 138, 891–899. Misiti, S., Schomburg, L., Yen, P. M., & Chin, W. W. (1998). Expression and hormonal regulation of coactivator and corepressor genes. Endocrinology, 139, 2493–2500. Mitev, Y. A., Wolf, S. S., Almeida, O. F., & Patchev, V. K. (2003). Developmental expression profiles and distinct regional estrogen responsiveness suggest a novel role for the steroid receptor coactivator SRC-l as a discriminative amplifier of estrogen signaling in the rat brain. Federation of American Societies for Experimental Biology. Journal, 17, 518–519. Molenda, H. A., Kilts, C., Allen, R. L., & Tetel, M. J. (2003). Nuclear receptor coactivator function in reproductive physiology and behavior. Biology of Reproduction, 69, 1449–1457. Molenda, H. A., Griffin, A. L., Auger, A. P., McCarthy, M. M., & Tetel, M. J. (2002). Nuclear receptor coactivators modulate hormone-dependent gene expression in brain and female reproductive behavior in rats. Endocrinology, 143, 436–444. Molenda-Figueira, H. A., Williams, C. A., Griffin, A. L., Rutledge, E. M., Blaustein, J. D., & Tetel, M. J. (2006). Nuclear receptor coactivators function in estrogen receptor- and progestin receptor-dependent aspects of sexual behavior in female rats. Hormones and Behavior, 50, 383–392. Mong, J. A., Devidze, N., Frail, D. E., O’Connor, L. T., Samuel, M., Choleris, E., et al. (2003). Estradiol differentially regulates lipocalintype prostaglandin D synthase transcript levels in the rodent brain: Evidence from high-density oligonucleotide arrays and in situ hybridization. Proceedings of the National Academy of Sciences, 100, 318–323.

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References Mong, J. A., & Pfaff, D. W. (2004). Hormonal symphony: Steroid orchestration of gene modules for sociosexual behaviours. Molecular Psychiatry, 9, 550–556. Nomura, M., Andersson, S., Korach, K. S., Gustafsson, J. A., Pfaff, D. W., & Ogawa, S. (2006). Estrogen receptor-beta gene disruption potentiates estrogen-inducible aggression but not sexual behaviour in male mice. European Journal of Neuroscience, 23, 1860–1868. Nomura, M., Durbak, L., Chan, J., Smithies, O., Gustafsson, J. A., Korach, K. S., et al. (2002). Genotype/age interactions on aggressive behavior in gonadally intact estrogen receptor beta knockout (betaERKO) male mice. Hormones and Behavior, 41, 288–296. Numan, M., & Insel, T. (2003). The neurobiology of parental behavior. Heidelberg: Springer Verlag. Ogawa, H., Nishi, M., & Kawata, M. (2001). Localization of nuclear coactivators p300 and steroid receptor coactivator 1 in the rat hippocampus. Brain Research, 890, 197–202. Ogawa, S., Choleris, E., & Pfaff, D. W. (2004). Genetic influences on aggressive behaviors and arousability in animals. Annals, New York Academy of Sciences, 1036, 257–266. Ogawa, S., Eng, V., Taylor, J., Lubahn, D., Korach, K., & Pfaff, D. (1998). Roles of estrogen receptor-alpha gene expression in reproductionrelated behaviors in female mice. Endocrinology, 139, 5070–5081. Ogawa, S., Washburn, T., Taylor, J., Lubahn, D., Korach, K., & Pfaff, D. (1998). Modifications of testosterone-dependent behaviors by estrogen receptor-alpha gene disruption in male mice. Endocrinology, 139, 5058–5069. O’Malley, B. W. (2006). Molecular biology. Little molecules with big goals. Science, 313, 1749–1750. Oñate, S. A., Tsai, S. Y., Tsai, M. J., & O’Malley, B. W. (1995). Sequence and characterization of a coactivator for the steroid hormone receptor superfamily. Science, 270, 1354–1357. Oñate, S. A., Boonyaratanakornkit, V., Spencer, T. E., Tsai, S. Y., Tsai, M. J., Edwards, D. P., et al. (1998). The steroid receptor coactivator-1 contains multiple receptor interacting and activation domains that cooperatively enhance the activation function 1 (AF1) and AF2 domains of steroid receptors. Journal of Biological Chemistry, 273, 12101–12108. Peterson, B. W., Anderson, M. E., & Filion, M. (1974). Responses of pontomedullary reticular neurons to cortical, tectal and cutaneous stimuli. Experimental Brain Research, 21, 19–44. Pfaff, D. (2006). Brain arousal and information theory. Cambridge, MA: Harvard University Press. Pfaff, D. (1999). Drive: Neurobiological and molecular mechanisms of sexual motivation. Cambridge, MA: MIT Press. Pfaff, D. (1980). Estrogens and brain function: Neural analysis of a hormone-controlled mammalian reproductive behavior. New York: Springer-Verlag. Pfaff, D. (1997). Hormones, genes, and behavior. Proceedings of the National Academy of Sciences, 94, 14213–14216. Pfaff, D., Ogawa, S., Kia, K., Frohlich, J. & Kow, L.-M. (2002). Genetic mechanisms in controls over female reproductive behaviors. In. D. Pfaff,. A. Arnold, S. Fahrbach, A. Etgen, R. Rubin (Eds.), Hormones, brain, and behavior (pp. 441–510). San Diego, CA: Academic Press/Elsevier. Phillips, J., Ling, L., & Fuchs, A. (1999). Action of the brainstem saccade generator during horizontal gaze shifts: Pt. I. Discharge patterns of omnidirectional pause neurons. Journal of Neurophysiology, 81, 1284–1295. Pratt, W. B., Galigniana, M. D., Morishima, Y., & Murphy, P. J. (2004). Role of molecular chaperones in steroid receptor action. Essays in Biochemistry, 40, 41–58. Ramos, H. E., & Weiss, R. E. (2006). Regulation of nuclear coactivator and corepressor expression in mouse cerebellum by thyroid hormone. Thyroid, 16, 211–216.

c06.indd 117

117

Rønnekleiv, O. K., & Kelly, M. J. (2005). Diversity of ovarian steroid signaling in the hypothalamus. Frontiers in Neuroendocrinology, 26, 65–84. Rosenfeld, M. G., Lunyak, V. V., & Glass, C. K. (2006). Sensors and signals: A coactivator/corepressor/epigenetic code for integrating signal-dependent programs of transcriptional response. Genes and Development, 20, 1405–1428. Schober, J. M., Kuhn, P. J., Kovacs, P. G., Earle, J. H., Byrne, P. M., & Fries, R. A. (2005). Leuprolide acetate suppresses pedophilic urges and arousability. Archives of Sexual Behavior, 34, 691–705. Schwanzel-Fukuda, M., Bick, D., & Pfaff, D. W. (1989). Luteinizing hormone-releasing hormone (LHRH)-expressing cells do not migrate normally in an inherited hypogonadal (Kallmann) syndrome. Brain Research: Molecular Brain Research, 6, 311–326. Schwanzel-Fukuda, M., & Pfaff, D. W. (1989). Origin of luteinizing hormone-releasing hormone neurons. Nature, 338, 161–164. Shearman, L. P., Zylka, M. J., Reppert, S. M., & Weaver, D. R. (1999). Expression of basic helix-loop-helix/PAS genes in the mouse suprachiasmatic nucleus. Neuroscience, 89, 387–397. Shiau, A. K., Barstad, D., Loria, P. M., Cheng, L., Kushner, P. J., Agard, D. A., et al. (1998). The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen. Cell, 95, 927–937. Smith, C. L., Oñate, S. A., Tsai, M. J., & O’Malley, B. W. (1996). CREB binding protein acts synergistically with steroid receptor coactivator-1 to enhance steroid receptor-dependent transcription. Proceedings of the National Academy of Sciences, 93, 8884–8888. Tanenbaum, D. M., Wang, Y., Williams, S. P., & Sigler, P. B. (1998). Crystallographic comparison of the estrogen and progesterone receptor ’s ligand binding domains. Proceedings of ther National Academy of Sciences, 95, 5998–6003. Tetel, M. J., Jung, S., Carbajo, P., Ladtkow, T., Skafar, D. F., & Edwards, D. P. (1997). Hinge and amino-terminal sequences contribute to solution dimerization of human progesterone receptor. Molecular Endocrinology, 11, 1114–1128. Tetel, M. J., Siegal, N. K., & Murphy, S. D. (2007). Cells in behaviourally relevant brain regions coexpress nuclear receptor coactivators and ovarian steroid receptors. Journal of Neuroendocrinology, 19, 262–271. Tetel, M. J., Ungar, T. C., Hassan, B., & Bittman, E. L. (2004). Photoperiodic regulation of androgen receptor and steroid receptor coactivator-1 in Siberian hamster brain. Molecular Brain Research, 131, 79–87. Tora, L., Gronemeyer, H., Turcotte, B., Gaub, M. P., & Chambon, P. (1988). The N-terminal region of the chicken progesterone receptor specifies target gene activation. Nature, 333, 185–188. Tsai, M. J., & O’Malley, B. W. (1994). Molecular mechanisms of action of steroid/thyroid receptor superfamily members. Annual Review of Biochemistry, 63, 451–486. Vance, M. A., & Smith, J. A. (1984). Endocrine and clinical effects of leuprolide in prostatic cancer. Clinical Pharmacology and Therapeutics, 36, 350–354. Vasudevan, N., & Pfaff, D. W. (2007). Membrane-initiated actions of estrogens in neuroendocrinology: Emerging principles. Endocrine Reviews, 28, 1–19. Vasudevan, N., Zhu, Y. S., Daniel, S., Koibuchi, N., Chin, W. W., & Pfaff, D. (2001). Crosstalk between oestrogen receptors and thyroid hormone receptor isoforms results in differential regulation of the preproenkephalin gene. Journal of Neuroendocrinology, 13, 779–790. Vilchez-Martinez, J. A., Coy, D. H., Arimura, A., Coy, E. J., Hirotsu, Y., & Schally, A. V. (1974). Synthesis and biological properties of (Leu-6)-LHRH and (D-Leu-6,desGly-NH210)-LH-RH ethylamide. Biochemical and Biophysical Research Communications, 59, 1226–1232. Voegel, J. J., Heine, M. J. S., Zechel, C., Chambon, P., & Gronemeyer, H. (1996). TIF2, a 160 kDa transcriptional mediator for the liganddependent activation function AF-2 of nuclear receptors. EMBO Journal, 15, 3667–3675.

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Webb, P., Nguyen, P., Shinsako, J., Anderson, C., Feng, W., Nguyen, M. P., et al. (1998). Estrogen receptor activation function 1 works by binding p160 coactivator proteins. Molecular Endocrinology, 12, 1605–1618. Wooley, C., & Cohen, R. (2002). Sex steroids and neuronal growth in adulthood. In D. Pfaff, A. Arnold, A. Etgen, S. Fahrbach, & R. Rubin (Eds.), Hormones brain and behavior (Vol. 4, pp. 717–778). San Diego, CA: Academic Press/Elsevier.

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Wu, H. Stavarache, M., Pfaff, D.W., & Kow, L. (2007). Arousal of cerebral cortex electroencephalogram consequent to high-frequency stimulation of ventral medullary reticular formation. Proceedings of the National Academy of Sciences, 104, 18292–18296. Ylikomi, T., Bocquel, M. T., Berry, M., Gronemeyer, H., & Chambon, P. (1992). Cooperation of protosignals for nuclear accumulation of estrogen and progesterone receptors. EMBO Journal, 11, 3681–3694.

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Chapter 7

Neuroimmunology STEVEN F. MAIER AND LINDA R. WATKINS

by the discoveries that the activity of immune processes can be classically conditioned (Ader & Cohen, 1975) and that exposure to stressors can alter immune responding (Solomon, 1969). Since learning and stress are “in the brain,” these discoveries suggested that the brain must be able to regulate immune processes. For this reason, this area of investigation has become known as psychoneuroimmunology. These seminal findings were soon followed by work indicating that immune organs are innervated by the sympathetic nervous system (Felten et al., 1987) and that immune cells express receptors for a variety of hormones controlled by the brain (Fahey, Gure, & Munck, 1981), thereby providing pathways by which the brain can contact immune organs and cells and mediate the effects of conditioning and stressors. More recently, it has become clear that communication between the brain and immune systems is bidirectional, with products of the immune system potently regulating the nervous system and phenomena that are the result of neural activity. We focus on this arm of the brain-immune interaction in this chapter because it is immune-to-nervous system communication that has the greatest implications for understanding behavior.

Disciplines within the biological and biomedical sciences have often been defined by organ systems: cardiology, audiology, endocrinology, are but a few examples. This has naturally led investigators to view the system that is the focus of their study as being disconnected from, and operating independently of, other systems. The tendency to think of systems as disconnected has nowhere been more apparent than in the fields of immunology and neuroscience. The immune system and the nervous system have been traditionally thought to operate independently of one another, and even after the research of the past several decades that has documented the close interplay and interaction between these two systems, the most recent texts in immunology do not even contain a reference to the nervous system in their indices, and conversely neuroscience texts typically do not contain even a reference to the immune system. However, living organisms are integrated wholes, and parts do not function in isolation. Because the nervous system is the command and integrative center of the organism, it would be most unlikely for important functions to operate without neural regulation. Functions such as digestion, excretion, the production of energy, and so on are all regulated by the nervous system. For example, the sympathetic nervous system regulates the rate of digestion, and adrenal hormones whose production is under the ultimate control of the hypothalamus regulate energy balance. Host defense against infection and injury is a key function needed for survival and involves widespread mechanisms throughout the body, therefore, it would be unlikely to operate without control from the nervous system. For the nervous system to exercise such control, it must “know” about the status and functioning of the immune system. Thus, it would be unlikely for neural activity to proceed independently of events in the immune system. These two systems are indeed in close communication and potently modulate each other ’s activities. The early research examining the possibility that the nervous and immune systems interact focused primarily on neural regulation of immunity. This emphasis was fueled

THE IMMUNE SYSTEM Defense against infection by microorganisms has been crucial for survival since the earliest periods of evolution. As a result, organisms have developed a complex array of defensive mechanisms to fight infection, and relatedly, to promote tissue repair after injury. If a microorganism succeeds in evading a set of passive bodily defenses (e.g., the epithelial surfaces of the body) it must first be recognized as “nonself ” and/or “dangerous.” You are most likely familiar with what is called “adaptive” or “acquired” immunity. Here, several types of leukocytes (T-cells and B-cells) recognize specific molecular sites on foreign invaders called antigenic sites (an antigen is defined as a molecule that can 119

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lead to the generation of antibodies). These leukocytes can do so because they express very specific antigen receptors on their surface. Because there are an enormous number of different antigens (roughly 106 for humans), there can be only a small number of T- or B-cells that express the receptor for any particular antigen. This small number of cells would not be sufficient for immune defense, and so the first step in adaptive immunity after antigen presentation to the T-cells is a clonal expansion of cells that express the antigen receptor in question. It takes 3 or 4 days to go through a large enough number of cell cycles to generate a sufficient number of T-cells that recognize the invading microbe, and only then can the effector processes that rid the organism of the invader develop. Innate Immunity Because it takes 3 or 4 days to even begin to generate the effectors of the adaptive immune system (cytotoxic T-cells, antibody, etc.) that can attack the bacteria, virus, and so on, the adaptive immune system cannot be the first line of immune defense. This more rapid defense is accomplished by the innate immune system. If a microorganism crosses an epithelial barrier, it rapidly encounters mononuclear phagocytes called macrophages (literally “big eaters”) that reside in submucosal tissues (see Figure 7.1). Macrophages mature continuously from monocytes that leave the circulation. As do T-cells and B-cells, they recognize nonself via surface receptors, but the macrophage receptors are quite different from those on the cells of the adaptive immune system. Instead of recognizing highly specific antigens, they instead recognize very general features of microorganisms called pathogen-associated molecular patterns (PAMPs). These PAMPs are general molecular motifs that are present on many microorganisms, but are not present in host tissue. For example, viruses almost always express double-stranded RNA, and so macrophages have a receptor (TLR-3) that is ligated by double-stranded RNA. There are only roughly 20 different receptors on macrophages that are used to recognize PAMPs and discriminate self from nonself, rather than the 106 on cells of the adaptive immune system. Thus, all macrophages express all of the receptors, and so clonal expansion to generate more macrophages is not necessary. That is, macrophages are ready right away to engage in immune defense. The binding of microorganisms by the macrophage receptors initiates a number of responses. First, some of the receptors lead the macrophage to engulf the microorganism, leading to its destruction. A variety of toxic substances are also produced (e.g., nitric oxide) that can kill microbes that are engulfed and that are near the macrophage. This process of phagocytosis is very rapid, beginning almost

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Figure 7.1 A macrophage extending toward a bacterium.

immediately after contact with an invader. Second, other receptors initiate signaling cascades that produce substances that initiate and maintain an inflammatory response at the site of the infection. Inflammation plays three roles: it (1) delivers additional cells (e.g., neutrophils) that can aid in killing, (2) produces a physical barrier that prevents the spread of infection to the bloodstream, and (3) aids in tissue repair. The most important of the substances that are produced are the pro-inflammatory cytokines, chemokines, and prostaglandins. These alter the local blood vessels and cooperate to lead a number of cell types to migrate to the site of infection, producing the redness, swelling, heat, and pain characteristic of local inflammation. Cytokines are small soluble proteins, first discovered to be released by immune cells, which diffuse away from the producing immune cell and serve as communication molecules between cells. The most important cytokines for producing local inflammation are interleukin-1 (interleukin stands for “between leukocytes”), interleukin-6, and tumor-necrosis factor alpha (IL-1, IL-6, TNF). Chemokines are chemoattractant proteins that stimulate the migration and activation of cells. There are many excellent textbooks you can consult to learn more about the immune system (e.g., Janeway, Travers, Walport, & Shlomchik, 2005). To this point, only local responses at the site of infection have been described. However, the very same cytokines that are critical for local innate immune reactions orchestrate a complex set of changes throughout the body that aid

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Role of the Brain in Host Defense

in protecting the host. This widespread set of changes is often called the acute-phase response (APR; Baumann & Gauldie, 1994). There are increases in the number of circulating leukocytes (leukocytosis), changes in plasma ions (e.g., iron), the liver shifts from producing the proteins that it normally makes (e.g., carrier proteins) to proteins that aid in fighting infection (e.g., haptoglobin), and so on. These changes are all adaptive. For example, plasma iron is reduced, and bacteria need iron in order to replicate. Again, these all occur within a few hours of infection.

ROLE OF THE BRAIN IN HOST DEFENSE The APR and Sickness Up to this point, none of the host responses to infection described have involved the brain. However, the fever that occurs during infection has long been considered to be part of the APR. Fever is an adaptive defensive mechanism because many microorganisms replicate poorly at elevated core body temperatures, and a number of enzymatic processes involved in the killing and removal of invading pathogens increase in activity at elevated temperatures (Kluger, Kozak, Conn, Leon, & Soszynski, 1996). The important point here, though, is that fever is mediated by the brain. Fever occurs because the set point of temperature-sensitive cells in the hypothalamus is raised, thereby leading to behaviors designed to drive up core body temperature (huddling to conserve heat, shivering to produce heat, etc.; Boulant, 2000). A number of other brain-mediated changes have come to be recognized as being part of the host response to infection. These include alterations in sleep patterns, reductions in activity, a loss of interest in social and sexual activities, increased sensitivity to pain, reduced food and water intake, and altered cognitive functions such as impaired hippocampal-dependent memory formation, among others (Dantzer, 2004). This pattern, which should be familiar to anyone who has had the flu, has been called “sickness.” In addition, there are several brain-mediated changes that are less obvious. The most important are activation of the hypothalamo-pituitary-adrenal (HPA) axis and the sympathetic nervous system (Berkenbosch, de Goeij, Rey, & Besedovsky, 1989). That is, there is a classic physiological “stress response” during sickness. Adaptiveness of Sickness It is important to understand that these sickness responses are not pathological or a reflection of weakness or debilitation produced by infection, but rather represent an adaptive evolved strategy to help combat the infection (Hart, 1988).

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For example, fever is quite energy intensive, requiring an extra 10% to 13% of metabolism for each degree rise in core body temperature. Thus, it would make sense for the sick organism to use its available energy stores to fight the infection. Reduced activity, reduced social behavior, and so on can be viewed as changes that reduce the energy used by behavior. Foraging for food would also use considerable energy and would expose the organism to an increased risk of predation during the illness, a time of decreased defensive abilities. Finally, the stress response that occurs can be understood in this context. The peripheral endpoints of the HPA axis and the sympathetic nervous system, cortisol and catecholamines, respectively, function to liberate energy from bodily stores, converting glycogen to glucose, and so forth. See Maier and Watkins (1998) for a more extended form of this argument. The manner in which interfering with hippocampaldependent memory formation could be adaptive is not obvious. It may not be adaptive per se. However, a plausible argument can be made. Some forms of memory depend critically on the hippocampus, and some do not. In particular, the hippocampus is involved in the formation of memories involving places or contexts, as opposed to discrete cues such as a tone or a light (Squire, 2004). Thus, for example, if a footshock is paired with a tone in some apparatus, fear will become conditioned to both the tone and the context of the apparatus so that fear responses will occur later to either the tone or the apparatus context. However, the memory of fear to the context requires the hippocampus, but memory of fear to the tone does not. It is thought that this is because the hippocampus is the part of the brain that forms representations of places and contexts (Rudy & Sutherland, 1995). Thus, animals and humans learn to be fearful of and, consequently, to avoid places where aversive events such as footshocks have occurred. This makes good adaptive sense. If a predator has been encountered or detected in a particular place, it is likely that the predator will be there again, and so the place is a good one to be wary about and perhaps avoid altogether. If an organism becomes ill or sick rather than being externally threatened, the symptoms will still appear while the individual is in some context or place, perhaps while it is foraging away from its home territory. However, it is likely that this place had nothing to do with producing the sickness or illness, particularly since the encounter with the microbe or poison will have been several hours before symptom manifestation. Thus, it would not be adaptive for the organism to become fearful of, or to avoid the place where sickness symptoms occurred. Indeed, organisms do not readily learn to avoid places where they became ill, but they readily learn to avoid tastes that precede illness (Garcia, Brett, & Rusiniak, 1989). This pattern

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would be accomplished if the hippocampus were taken “off line” during sickness. Motivational System Each of these aspects of sickness could be an independent brain-mediated response to microbial invasion of the body. However, it has been convincingly argued (Dantzer et al., 2005) that instead, they are the reflection of a common motivational change. That is, sickness is a motivational state (a central state that organizes the actions of the individual) that then drives these behaviors. That the innate immune response to infection produces brain-mediated changes that involve a shift in motivational state has a number of important implications. First, there must be mechanisms by which the innate immune system can signal the brain, informing the brain that viruses, bacteria, and so on, have entered the body. If there were not pathways by which the immune system could communicate to the brain, the brain would have no way to respond to peripheral infection. Second, discrete areas of the brain must change activity during sickness, otherwise the behavioral and other alterations would not occur. Regions of the brain almost never regulate only a single activity or process, and thus other behavioral and psychological activities not generally thought to be part of sickness may well be profoundly affected during sickness. A number of poorly understood phenomena may be understandable in this way (see the following discussion). Moreover, motivational states are often thought to compete with each other in a hierarchy. For example, fear competes with hunger—a person does not want to forage for food while a threat is present. Since fighting infection and promoting repair is a life and death issue, sickness should command the motivational stage and alter the expression of many other motivational systems. The next several sections examine these issues. Immune-to-Brain Communication Pathways As noted previously, the fact that a number of components of sickness are mediated by the brain implies that the immune system must have a way to “talk” to the brain. From this perspective, in addition to its other well-known functions, the immune system is a diffuse sense organ, scattered throughout the body, whose job it is to inform the nervous system about peripheral infection and injury (Blalock, 1984). This raises two key questions. First, what are the signals that arise from the immune system that serve to communicate to the CNS, and second, by what mechanism(s) do they communicate? The very same pro-inflammatory cytokines that participate in the orchestration of the peripheral inflammatory response also initiate immune communication to the CNS.

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This conclusion comes from findings indicating that (a) the peripheral administration of IL-1 in the absence of infection produces many of the brain-mediated sickness responses (Dinarello, 1991) and (b) peripheral blockade of pro-inflammatory cytokine receptors and the peripheral immunoneutralization and/or inhibition of synthesis of these cytokines, reduces or eliminates brain-mediated sickness responses as well as the activation of brain structures during infection (Bluthe, Dantzer, & Kelley, 1992). That is, the CNS does now not “know.” Of the cytokines, IL-1 appears to play the most prominent role, and the discussed manipulations are not always effective (Dunn & Swiergiel, 1998); other molecules may also be involved. This leaves the question of exactly how cytokines such as IL-1 signal the CNS. When considering this issue, a number of facts need to be kept in mind. First, mRNA for IL-1 and other cytokine receptors are widely distributed in the brain (Ericsson, Liu, Hart, & Sawchenko, 1995) and receptors are present on both neurons and glial cells. Second, activation of the innate immune response leads to the de novo synthesis of IL-1 within the brain. That is, cells within the brain, make (Buttini & Boddeke, 1995) and release (Ma, Chen, Oliver, Horvath, & Phelps, 2000) IL-1 in response to peripheral infection. Furthermore, simply injecting IL-1 peripherally leads cells within the brain to synthesize IL-1 (Hansen, Taishi, Chen, & Krueger, 1998). This brain IL-1 production is largely by microglial cells (Van Dam, Bauer, Tilders, & Berkenbosch, 1995; see discussion that follows). The nature of the signal(s) within the brain that leads microglia to become active in response to peripheral infection is still being explored, but adenosine triphosphate (ATP) and heat shock proteins (HSPs) are likely candidates (Mingam et al., 2007). Because IL-1 and other cytokine receptors are expressed on cells within the brain, the most obvious idea would be that cytokines accumulate in the bloodstream during infection, travel to the brain in the blood, then enter the brain parenchyma from the cerebral vasculature and then bind to their receptors. The difficulty is that cytokines are fairly large (IL-1 is roughly 15 kDa) and hydrophilic, and so would not be expected to be able to passively diffuse across the tight junctions involved in the cells of the cerebral blood vessels (the blood-brain barrier). However, a number of other mechanisms allow bloodborne cytokines to signal the brain: (a) Cytokines enter the brain from the blood at the circumventricular organs (Blatteis, 1990), such as the organum vasculosum lamina terminalis (OVLT), where the blood-brain barrier is absent or weak. Since cytokines cannot diffuse very far, they bind to receptors on astrocytes and other cells resident in the region, leading to the production of prostaglandins that can then travel to neural targets (Lin & Lin, 1996). Circulating

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pathogens themselves can bind to PAMP-recognizing receptors on macrophage-like cells residing in these structures (Lacroix, Feinstein, & Rivest, 1998). (b) Even though cytokines cannot passively diffuse across the blood-brain barrier, there are active transport mechanisms that carry cytokines across the barrier (Banks, 2005) where they can bind with receptors on cells near the blood vessels, such as perivascular macrophages. These macrophages then produce products, such as prostaglandins and nitric oxide, that can activate neurons. (c) Cytokines such as IL-1 bind to receptors on the luminal side of cerebral blood vessels, inducing the production of soluble mediators within the vessels, such as prostaglandins, which then diffuse into the brain parenchyma (Konsman, Vigues, Mackerlova, Bristow, & Blomqvist, 2004). Although the evidence for each of these blood-borne routes of communication is convincing, there are aspects of immune-to-brain signaling that these mechanisms cannot readily explain. First, the brain’s initial response to peripheral immune activation is rapid, with activation of brain structures. For example, Ericsson, Kovacs, and Sawchenko (1994) observed nuclear Fos protein (the gene c-fos is an immediate-early gene that is used as a neuronal activation marker) at 1 hour—the first time point tested. Since it takes considerable time for the Fos protein to be produced after the c-fos gene is activated, communication to the brain must have occurred within minutes. The bloodborne mechanisms almost certainly take longer than this to operate. Second, brain-mediated responses occur after the peripheral injection of quantities of immune activators that are too small to produce increases in blood levels of cytokines or other measured mediators. For example, the injection of very small quantities of lipopolysaccharide (LPS) that do not elevate peripheral circulating cytokines nevertheless produce fever (Hansen, O’Connor, Goehler, Watkins, & Maier, 2001). LPS is a constituent of the cell walls of gram-negative bacteria and is recognized by one of the PAMP-recognizing receptors (TLR-4). Third, it is difficult to understand how blood-borne cytokines can provide the brain with detailed information, such as the site of infection. All of these suggest that there ought to be neural as well as blood-borne mechanisms of immune-to-brain communication. Indeed, if the idea that the immune system functions as a sense organ is more than whimsical, then there ought to be communication from the immune system to the CNS over peripheral nerves—sense organs communicate to the CNS via nerves. The search for such a peripheral nerve can begin by inquiring as to whether there is a nerve that innervates sites in the body where immune responses happen (e.g., lymph nodes) and that sends afferent fibers to the brain. The vagus nerve is prominent in this regard

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because it innervates sites where pathogens enter the body (e.g., the lungs, the peritoneal cavity) and organs important for innate immunity (e.g., the liver). Indeed, peripheral immune activation, such as is produced by an intraperitoneal injection of LPS, activates afferent vagal nerves (Goehler, Gaykema, Hammack, Maier, & Watkins, 1998). Sensory structures associated with afferent vagal terminals (paraganglia) express IL-1 receptors, providing a mechanism by which peripheral cytokines can activate the vagus (Goehler et al., 1997). The vagus terminates in the nucleus tractus solitarius (NTS) in the brain stem, and during infection a neural cascade of activation spreads from the NTS to regions to which it projects (e.g., the parabrachial nucleus; Ericsson et al., 1994). Furthermore, electrical stimulation of afferent vagal fibers produces neural changes characteristic of sickness (Roosevelt, Smith, Clough, Jensen, & Browning, 2006). Supporting the importance of vagal signaling, severing the vagus often reduces or eliminates the brain activation (Wan, Wetmore, Sorensen, Greenberg, & Nance, 1994) and brain-mediated sickness responses (produced by injection of LPS, IL-1, and so on (Watkins et al., 1994). However, vagotomy does not always do so. For example, vagotomy blocks the fever produced by low, but not high, doses of LPS (Hansen et al., 2001). The most reasonable conclusion to date is that the vagus is an especially important signaling route early during infection, before high blood levels of cytokines have developed. Once blood levels are high, blood-borne routes are especially important. There also appears to be specificity of signaling to different regions of the brain, so that some behavioral endpoints lean more heavily on vagal signaling than do others. For example, Konsman, Luheshi, Bluthe, and Dantzer (2000) reported that severing the vagus blocks the reduction in social exploration, but not the fever produced by the very same dose of peripheral LPS. Finally, it should be noted that the vagus is not the only peripheral nerve that can signal the occurrence of immune activation. The vagus does not innervate regions such as the skin and oral cavity, and other nerves that innervate these regions may function to communicate immune activation to the CNS (Romeo, Tio, & Taylor, 2003). In sum, the immune system communicates to the CNS over multiple pathways. This multiplicity of communication routes likely functions to provide the CNS with detailed information concerning the immune status of the body, rather than simply that an infection is, or is not, present. Indeed, the pattern of brain activation differs after the injection of different immune activators. For example, Serrats and Sawchenko (2006) have reported that the peripheral injection of the bacterial superantigen staphylococcal enterotoxin B (SEB), which produces a peripheral cytokine pattern different from LPS, produces a different

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pattern of brain activation than does LPS. Thus, it may be that that the brain is able to construct an “image” of immune patterns in the periphery. The bidirectional pathways between the brain and immune system are depicted in Figure 7.2. Brain IL-1 Microglia As already noted, immune signals to the CNS lead cells in the brain to synthesize IL-1. That is, peripheral IL-1 “begets” central IL-1. This is a most unusual arrangement that has important implications. The IL-1 induction occurs largely in glial cells, with the initial response being most prominent in microglia (Van Dam et al., 1995). Because brain IL-1 is critical in immune-brain interactions (see the discussion that follows), microglia are key cells in these processes. This is entirely fitting because microglia

CRH

Pituitary

B NE, Enkephalins, SP, NPY Autonomic nervous system

β-Endorphin prolactin, GH other hormones Adrenal

ACTH

C NE Epi Enk

A CORT Spleen, thymus, and other immune organs

Brain IL-1 as a Mediator Immune cells (B & T cells, Mφ, etc.)

Proinflammatory cytokines (IL1, TNF, IL6)

Figure 7.2 Bidirectional pathways between the brain and the immune system. Note. A and B schematize the major outflow pathways from the brain to the immune system, the autonomic nervous and hypothalamo-pituitary-adrenal systems, respectively. C schematizes communication from the immune system to the brain.

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can be considered to be the brain’s resident immune cells, or at least “immune-like.” Microglia constitute 6% to 12% of all of the cells in the CNS and are of hematopoietic origin (Cuadros & Navascues, 2001), as are peripheral immune cells. In the adult, resident microglia arise from two sources. Resident microglia continue to divide throughout life, and peripheral blood monocytes migrate into the CNS and mature into microglia. Microglia express many or all of the same receptors as do peripheral macrophages, and thus can recognize bacteria, viruses, and so on. They are constantly monitoring the CNS environment and are responsible for immune surveillance within the CNS (Nimmerjahn, Kirchhoff, & Helmchen, 2005), and when they detect an invader, signs of cell damage via heat shock protein receptors, or other changes in the brain microenvironment, they change their morphology and function. When microglia are not stimulated, they have a highly ramified stellate morphology with little or no expression of activation markers such as CR3. They have often been described as appearing to be “damped” macrophages, held in check by the CNS microenvironment (Kreutzberg, 1996). When microglia detect danger, they change morphology, upregulate a variety of cell surface receptors, and synthesize and release a variety of pro-inflammatory mediators such as IL-1, prostaglandins, reactive oxygen species, and chemokines. That is, they initiate an inflammatory response in the CNS. Although it is common to think of microglia as being in either an inactivated or an activated state, microglia can actually be in a continuum of activational states. Of particular interest in the following discussion, microglia can be in a state that has been called “primed” (Perry, Newman, & Cunningham, 2003). In this state, they have a quiescent morphology and do not express upregulated activation markers. However, if stimulated the microglia overproduce inflammatory products such as IL-1. Microglia can remain in this primed or sensitized state for prolonged periods of time (Felton & Perry, 2005).

The fact that IL-1 is made in the CNS in response to peripheral immune activation is more than a curiosity. Importantly, the IL-1 that is synthesized in the brain is a key mediator of brain-mediated sickness responses. Many sickness responses can be prevented by injecting an IL-1 receptor antagonist (IL-1ra) into the brain (Maier, Watkins, & Nance, 2001). However, because IL-1 can enter the brain in small amounts, this finding could be explained by arguing that sickness responses are prevented by IL-1ra because it blocks the binding of this IL-1 that has entered from the outside. It is also the case that injecting IL-1 into the brain produces most of the brain-mediated sickness responses (Maier et al., 2001), but this also does not conclusively

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implicate brain-made IL-1. IL-1, like many peptides, is synthesized as a larger prohormone. Mature IL-1 is formed by cleavage of the prohormone by an enzyme called the IL-1 converting enzyme (ICE). Thus, inhibiting the actions of ICE would prevent new IL-1 from being manufactured. The injection of ICE inhibitors into the brain would prevent the brain from making new IL-1, but would have no effect on IL-1 that has entered from outside the brain. In the few experiments that have been conducted, intracerebral injection of ICE inhibitors has blocked or reduced brain-mediated sickness responses (Imeri, Bianchi, & Opp, 2006). More remarkably, IL-1 within the brain is also involved in the mediation of peripheral innate immune responses. For example, the blockade of IL-1 receptors in the brain reduces the gastrointestinal hypomotility produced by peripheral LPS (Plaza, Fioramonti, & Bueno, 1997). Conversely, elevating IL-1 in the brain can stimulate peripheral immune changes. Injecting IL-1 into the striatum leads the liver to manufacture acute phase proteins (Campbell et al., 2005). Since peripheral IL-6 is critical in regulating the liver APR, it would then be expected that brain IL-1 would lead to increases in blood levels of IL-6. Indeed, the intracerebral administration of IL-1 produces large increases in blood levels of IL-6, as well as IL-1. This increase in plasma IL-6 is not mediated by the injected IL-1 leaking to the periphery because a peripheral injection of the same small amount of IL-1 did not increase plasma IL-6. Furthermore, intracerebral administration of IL-1ra blocked the effects of intracerebral IL-1 (De Simoni et al., 1993), indicating that brain IL-1 receptors are the site at which the increase in blood IL-6 (as well as IL-1) is mediated. Thus, brain cytokines “beget” peripheral cytokines, as well as the other way around. Although the precise pathways are not well understood, it is clear that IL-1 in the brain initiates a communication process to the periphery

Sickness behaviors IL-1 Fever Liver IL-1

H I IL-1 P P

Hypo HPA activation IL-1 IL-1 IL-1 Paraganglia

LPS

NTS

s Vagu

Hyperalgesia

Macrophage

Figure 7.3 The complete brain-immune loop, with IL-1 in the brain regulating behavior and initiating an outflow from brain to the periphery.

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that signals the immune system. This arrangement is schematized in Figure 7.3. There is also an anti-inflammatory pathway from the brain to the immune system. The activation of efferent vagal fibers originating in the dorsal motor nucleus of the vagus can inhibit the release of peripheral pro-inflammatory cytokines via a cholinergic mechanism (Tracey, 2007). Finally, microglia can also release anti-inflammatory products (Rasley, Tranguch, Rati, & Marriott, 2006). Thus, there is truly bidirectional communication between the CNS and the innate immune system, involving both pro- and anti-inflammatory influences, with the balance between them doubtlessly critical. Downstream Mediators The fact that IL-1 and other cytokines within the brain are critical to the mediation of a variety of behavioral phenomena does not mean that IL-1 does so via a direct action on neuronal activity. Consider the memory impairments that are produced during peripheral infection. It is known that IL-1 induced in the hippocampus mediates the memory impairment because blocking IL-1 receptors in the hippocampus prevents the memory impairment from occurring (Pugh et al., 1999) and the microinjection of IL-1 into the hippocampus produces memory impairment (Barrientos et al., 2002). However, IL-1 may alter memory not because increases in IL-1 directly interfere with memory consolidation, but because IL-1 influences other molecules that are critical in memory formation. For example, the neurotrophin brain-derived neurotrophic factor (BDNF) within the hippocampus is critical in forming long-term hippocampal memories. Experiences such as contextual fear conditioning, that engage the hippocampus, induce the production of BDNF within specific layers of the hippocampus (Hall, Thomas, & Everitt, 2000). Moreover, this BDNF increase is necessary to form long-term hippocampally based memories (Alonso et al., 2002). This is noted because high levels of IL-1 in the hippocampus prevent the learning experience from increasing BDNF (Barrientos et al., 2004), and this may be the basis for the memory impairment, not the increased IL-1 per se. It is not known how IL-1 interferes with BDNF induction. However, the prostaglandins provide an intriguing possibility. The binding of IL-1 to its receptor activates the nuclear factor-kappa B (NF-kappaB) intracellular signaling pathway. NF-kappaB is a transcription factor that, among other things, increases the expression of the COX-2 enzyme. COX-2 induction, in turn, leads to the production of prostaglandins such as PGE2 (Hoozemans, Veerhuis, Janssen, Rozemuller, & Eikelenboom, 2001). There are four known subtypes of prostaglandin receptors (EP1–EP4), and one, the EP3 receptor, is primarily localized to neurons and is abundantly expressed in the hippocampus (Sugimoto & Narumiya, 2007).

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The EP3 receptor inhibits cAMP, and BDNF gene transcription is dependent on the cAMP-CREB pathway. Thus, PGE2 may decrease BDNF levels by binding to the EP3 receptor and reducing intracellular cAMP. The scenario would then be one in which IL-1 interferes with memory because it interferes with experience-dependent BDNF induction in the hippocampus, and IL-1 interferes with BDNF because it induces prostaglandins. Indeed, as this hypothesis would predict, infusion of PGE2 into the hippocampus after a learning experience interferes with both the induction of BDNF and the formation of memory, and the pharmacological blockade of prostaglandin production in the hippocampus prevents the memory impairment normally produced by intra-hippocampal injection of IL-1 (Hein et al., 2007). This discussion illustrates the complexity of the cascades involved. Indeed, the previous discussion is likely a gross oversimplification because IL-1 also activates mitogenactivated protein kinase pathways (MAPkinase), and these are also involved in the effects of IL-1 on plasticity (Kelly et al., 2003). There are doubtlessly many pathways and interactions initiated by IL-1 that mediate the behavioral changes that occur. It is likely that different behavioral endpoints (e.g., reduced food and water intake instead of memory formation) are mediated by different molecular cascades of IL-1-initiated events. However, it is interesting to note that the reduction in sexual behavior that is produced by immune activation is also blocked by prostaglandin inhibitors (Avitsur, Weidenfeld, & Yirmiya, 1999). Basal versus Elevated IL-1 The discussion to this point considered the impact of levels of IL-1 in the brain that are elevated above basal levels. However, basal IL-1 function may be necessary for the proper function of some of the very processes that are impaired by high levels. For example, memory is actually impaired if basal IL-1 function is removed, either by genetic deletion or by receptor blockade in the absence of manipulations that increase IL-1 above normal (Goshen & Yirmiya, 2005). Thus, the relationship between IL-1 and memory is really U shaped, and this may be the case for endpoints other than memory. Relationship between Peripheral and Brain IL-1 Before turning to a consideration of what all of this implies for phenomena of interest to behavioral scientists, it helps rationalize what is to follow to speculate on why there is this peculiar relationship between peripheral and brain IL-1. Elements of the innate immune system are phylogenetically quite old, whereas the adaptive immune system is a more recent evolutionary development. Even organisms as primitive as sponges (the most primitive multicellular

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organisms) can distinguish self from nonself, and contain phagocytic cells that can defend against invading microorganisms. Cells called amoebocytes in these invertebrates accomplish phagocytosis and PAMP recognition. These cells migrate to sites of infection in organisms such as mollusks and contain enzymes that are very similar to those in vertebrate macrophages. Importantly for the present discussion, amoebocytes synthesize and release cytokines such as IL-1 (Beck et al., 1993), and in vertebrates, they orchestrate innate immune defense (Clatworthy, 1998). In these organisms, the amoebocyte-produced cytokines communicate with and regulate neural processes, altering neural excitability (Clatworthy & Grose, 1999). This cytokine-to-neural tissue communication is involved not only in defense against pathogens, but also in defense against predators or external threats. For example, organisms such as mollusks identify predators or external threats via contact with their body surface. This body contact elicits withdrawal reflexes and locomotion, moving the body surface away from the threat. The cytokines released by amoebocytes can sensitize these withdrawal reflexes by increasing the excitability of the neurons involved (Clatworthy, 1998). The amoebocytes, in turn, receive signals from the neural tissue, providing bidirectional immune-neural communication even in these primitive organisms. Cells within the mollusk neural structures express cytokine-like molecules, and so cytokines might communicate in both directions. It is important to understand that organisms such as mollusks have a series of separate ganglia rather than a discrete brain and there is no blood-brain or blood-ganglia barrier. That is, immune-derived cytokines can communicate directly to neural tissue in the service of host defense in these organisms. With the development of a blood-brain barrier in vertebrates, IL-1 released by immune cells could no longer communicate directly with neural tissue. This may help to explain the very peculiar arrangement in vertebrates in which peripheral IL-1 induces the production of the very same molecule in the brain. Under this arrangement, when immune cells release IL-1, IL-1 still makes contact with neural tissue, but it is IL-1 that has been induced within the brain. One final point with regard to the role of cytokines in organisms such as mollusks: As previously noted, host defense requires the production of energy. In mollusks, the amoebocyte and cytokines are critical to this process. In mammals, the hypothalamus is key, with the hypothalamus being activated during infection by IL-1 (Schiltz & Sawchenko, 2007). The hypothalamus (a) releases corticotropin-releasing hormone (CRH) into the portal blood. The CRH travels to the pituitary gland where it stimulates the release of glucocorticoids into the bloodstream, the glucocorticoids then leading to energy production, and

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(b) initiates sympathetic activation that releases catecholamines into the blood, then leading to energy production. Mollusks do not have hypothalami, pituitaries, adrenals, or a sympathetic nervous system. However, all of these molecules are contained within the amoebocyte, and the amoebocyte releases glucocorticoids and catecholamines under the control of IL-1 (Ottaviani & Franchini, 1995).

IMPLICATIONS OF IMMUNE-BRAIN RELATIONSHIPS FOR BEHAVIOR In considering the implications of immune-brain relationships for behavior, we begin by discussing behavioral changes that occur during infection. We then move to a discussion of whether there are any implications of what will have been described for circumstances other than infection. Infection-Induced Changes As noted, microbial stimulation of innate immune cells initiates a cascade that communicates the presence of infection to the brain, with the brain then orchestrating a coordinated set of sickness responses that include increased sleep (particularly NREM sleep), social withdrawal, decreased food and water intake, impaired cognitive function, and so on. It is interesting that in other research domains many of these behaviors would be viewed as indicative of either depressed mood or anxiety. For example, the tendency to engage in social interaction with a conspecific is a well-validated “animal model of anxiety” (File & Seth, 2003). Manipulations that decrease anxiety in humans (e.g., anxiolytic drugs such as benzodiazepines) increase interaction, and manipulations that increase anxiety (e.g., anxiogenic drugs such as beta-carbolines) decrease interaction. Peripheral immune activation, as mentioned, decreases social interaction (Yirmiya, 1996). For these reasons, a number of investigators have explored whether peripheral immune activation might not also produce other behavioral changes that are used to implicate the presence of anxiety or depressed mood. Anxiety is often defined as fear in the absence of clearly threatening stimuli. Thus, assessments of anxiety typically involve the presentation of ambiguous or potentially threatening situations that are not overtly dangerous, with the behavioral measure being the animal’s tendency to avoid the potentially threatening circumstance. For example, in the elevated plus maze (EPM) there are four arms at right angles from a central area, with two being enclosed and two open (no sides or top). The entire maze is raised above the floor, so there is a potential drop from the open arms. Being in the open puts

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animals at greater risk, but animals also have a tendency to explore, so a typical subject (rat or mouse) spends considerable time in both open and closed arms when first placed in the apparatus. Manipulations that increase anxiety decrease the time that subjects spend in the open arms, and manipulations that decrease anxiety do the reverse (Carobrez & Bertoglio, 2005). The peripheral injection of either immune activators such as LPS, or the administration of IL-1 itself, decrease time spent in the open arms (Swiergiel & Dunn, 2007). As for depressed mood, a cardinal symptom of depression is adhedonia, a loss of interest in normal pleasures. Yirmiya and his colleagues have conducted an extensive series of studies exploring whether peripheral immune stimulation leads to behavioral changes that indicate a loss of pleasure. These involve an examination of sexual behavior and preference for sweet tastes. The peripheral administration of LPS and/or IL-1 reduces sexual behavior in females (Avitsur & Yirmiya, 1999) and the rat’s normal preference for sweet solutions (Yirmiya, 1996). Immune activation reduces motor activity and fluid intake in general. Simple reduction in motor activity doesn’t explain the reduced sexual behavior because the immune activation alters quite specific aspects of sexual behavior. A reduction in drinking cannot explain the sweet solution data because in these experiments the rats have two drinking tubes, one containing water and one the sweet solution. Immune stimulation causes a reduction in the percentage of total fluid intake that is the sweet solution. It can also be noted that these effects of immune activation can be prevented by chronic treatment with antidepressant drugs such as the SSRIs (Yirmiya et al., 2001). Nevertheless, the difficulty of disentangling effects that reflect anxiety or depressed mood from general reductions in motor activity and reduced food and water intake has been noted (Swiergiel & Dunn, 2007) and caution should be exercised. However, it has been demonstrated that lowlevel infection with the bacterium Campylobacter jejuni in mice, that does not produce overt signs of sickness, nevertheless leads to anxious behavior (Goehler, Lyte, & Gaykema, 2007). Research in this area with humans has the great advantage that anxiety and depressed mood can be assessed in ways that are not confounded with the behavioral changes, such as reduced motor activity characteristic of sickness. Very low doses of LPS, so low that humans cannot even discriminate whether they have been administered LPS or the vehicle, produce self-reports of increased anxiety (Krabbe et al., 2005). The most extensive studies here have been conducted in the context of interferon-alpha (IFN␣) administration. IFN␣ has antiviral and antitumor properties and is used in the treatment of hepatitis C infection and cancer.

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IFN␣ is itself a cytokine with properties that overlap those of the classic pro-inflammatory cytokines, and in addition, IFN␣ induces the production of IL-1 (Taylor & Grossberg, 1998). Chronic IFN␣ administration in these patients produces depressed mood that is so severe that roughly 50% of the patients come to meet the diagnostic criteria for major depression (Musselman et al., 2001). Importantly, prior administration of antidepressant drugs can prevent the depressogenic effect of the IFN␣ treatment (Raison et al., 2007). Anxiety and depressed mood are not mediated in the periphery, but in the brain. This means that immune activation produces these phenomena because it leads to neural changes. Because peripheral cytokines induce the production of cytokines within the brain, it is natural to inquire whether it is these cytokines induced in the brain that produce anxious and depressed behavior. IL-1 in the brain leads to alterations in serotonergic, noradrenergic, and dopaminergic function (Dunn, Wang, & Ando, 1999), thereby providing a tie to neurotransmitter systems traditionally thought to be involved in these processes. The research to date directed at determining the role of cytokines within the brain on anxiety- and depression-related behaviors produced by infection reveals a complex picture. For example, the reduction in social interaction produced by peripheral immune activation is prevented by blockade of IL-1 receptors in the brain, but the reduction in food and water intake is only partially blunted (Kent et al., 1992). The effects of brain IL-1 receptor blockade may also depend on the particular immune stimulus (Bluthe et al., 1992). This issue is the topic of current investigation and will be further discussed in the context of stress, rather than infection induced behavioral change. Findings that peripheral immune activation in animals and humans lead to depressed mood and anxiety lead naturally to an inquiry into whether peripheral immune activation and it’s consequent induction of brain cytokines might not be involved in mood and anxiety disorders. This issue is beyond the scope of this chapter and has been the subject of numerous reviews (e.g., Anisman, Merali, Poulter, & Hayley, 2005). Microglial Priming The research reviewed previously indicates that peripheral inflammatory events initiate a process that ultimately alters many aspects of behavior, mood, and cognition, and that the induction of cytokines within the brain is critical to the neural cascade that mediates these changes. Any organismic or other variable that would magnify this process would amplify the impact of infection or injury on these behavioral, emotional, and cognitive processes. It may

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well be that exaggerated immune-CNS interactions are involved in the mediation of a number of poorly understood phenomena of societal importance. It will be recalled that cytokines within the CNS are manufactured primarily by glial cells, with microglia being more reactive than astrocytes. As described previously, microglia can be in a continuum of activation states, passing from inactivated, to primed or sensitized, to fully activated. In the fully activated state, microglia synthesize and secrete high levels of pro-inflammatory molecules such as IL-1 on a continuous basis, whereas in the primed state they do not do so, but will produce exaggerated levels if they receive stimulating input. Aging Many of the changes that occur with aging are poorly understood. There has been considerable interest in the role of brain cytokines in the cognitive declines that occur with aging, largely because elevated levels of cytokines are associated with neurodegenerative disorders such as Alzheimer ’s disease (Cacquevel, Lebeurrier, Cheenne, & Vivien, 2004). However, with improvements in health care, many individuals are undergoing “normal healthy aging.” By the year 2030, roughly 20% of the population will be over 65 years of age. As life expectancy continues to increase, it is important to understand the factors underlying the decline in memory and cognition that occurs with normal aging, in addition to the processes associated with the more devastating pathological neurodegenerative disorders. Although there is disagreement about the extent to which memory and other cognitive functions decline during normal aging, there is agreement that the variability in individual performance increases with age, with some individuals suffering large declines (Laursen, 1997). In addition, vulnerability to cognitive declines associated with a variety of challenges, such as surgery and heart attacks, increases as people age. It is commonplace for an aging individual to be functioning at a high level, but then to display poor cognitive function after surgery, for example, hip replacement, that is seemingly unrelated to cognition or the brain. Indeed, the term postoperative cognitive dysfunction (POCD) has been used to identify this phenomenon (Bekker & Weeks, 2003). These precipitous declines in mental function have been difficult to understand. However, consider the possibility that glial cells are primed by normal healthy aging. Events such as surgery are inflammatory and produce high levels of circulating cytokines such as IL-1 (Carter & Whelan, 2001). The research reviewed previously indicates that these peripheral cytokines will communicate to the brain, leading to a neural cascade that includes the activation of glial cells. But if the glial cells are primed by aging,

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then the result should be exaggerated production of proinflammatory cytokines such as IL-1. This exaggerated production of brain IL-1 after peripheral inflammatory events such as surgery could then be triggering the memory and other cognitive impairments that follow. So, do glial cells change with age? With normal aging, there are indeed characteristic changes in microglial and astrocytic morphology, expression of activation makers, and function. For example, aging increases the microglial expression of complement type 3 receptor (CD11b), major histocompatibility complex (MHC) Class II, leukocyte common antigen (LCA; CD45), and CD4 (Perry et al., 2003). Functional changes such as increased phagocytic activity have also been reported (Sheng, Mrak, & Griffin, 1998). In general terms, this pattern can be described as a loss of the normal suppression that is produced by the CNS microenvironment. There are numerous possible sources for this loss of suppression, such as neuroendocrine dysregulation, cumulative oxidative stress, and mitochondrial changes. Neurons inhibit glial function via cell-to-cell contact (Neumann, 2001), and neuronal loss or reduced function may, itself, increase glial activation. The contact suppression of microglial activation by neurons is accomplished by a number of proteins that are expressed on the surface of neurons that bind to specific receptors for those proteins on microglia. For example, neurons express a glycoprotein identified as CD200 ligand, and it binds to a receptor, CD200R, that, within the brain, is expressed only by microglia (Hoek et al., 2000). Frank et al. (2006) reported that the expression of CD200 by neurons decreases in aging animals, providing a mechanism by which microglia are released from their normal inhibitory restraints. Whether CD200 is reduced because of neuronal loss or function is not known. It should be noted, however, that there are likely a number of mechanisms involved in age-related glial changes. For example, IL-10, an anti-inflammatory cytokine, decreases with age (Ye & Johnson, 2001), and IL-10 functions to inhibit glia. The important point is that glia do take on an activated morphology with age. This leaves the issue of whether the glial cells are fully activated or primed in animals that are aging, but not senescent. The results from studies that have examined pro-inflammatory cytokine expression with aging are somewhat inconsistent, but the available data suggest that PIC expression is at most elevated to only a minor degree in aged humans (Sheng et al., 1998) or rodents (Kyrkanides, O’Banion, Whiteley, Daeschner, & Olschowka, 2001). IL6 may be an exception because about a 20% increase in basal protein expression has been reported in older animals (Ye & Johnson, 2001). However, even 20% is a small fraction of the increases in brain IL-6 that occur after peripheral immune stimulation.

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The morphological changes in glia with age described here do not, in themselves, indicate that there would be an exaggerated pro-inflammatory response to peripheral immune stimulation. To examine this issue, Barrientos et al. (2006) injected both aging and young rats with live, replicating E. coli bacteria. This bacteria causes only a mild infection, and E. coli is cleared in both old and young rats in less than 24 hours. Importantly, E. coli did not produce a larger peripheral increase in pro-inflammatory cytokines in aging than in young rats, so any differences in brain responses could not be attributed to an augmented peripheral response. Protein levels of IL-1 within the hippocampus increased rapidly after infection in both young and old animals, but in the young rats levels returned to baseline between 4 and 24 hours after infection. In aging rats, IL-1 levels were elevated for 10, but not 14 days. This is an enormous difference in the duration of hippocampal IL-1 increase produced by peripheral infection. It is always possible that the IL-1 measured by Barrientos et al. (2006) did not derive from microglia. To further explore this issue, Frank et al. (2006) isolated microglia from the hippocampi of aging and young rats and studied them in vitro. The microglia from aging rats exhibited features of activation (e.g., increased expression of MHCII mRNA), but did not secrete more IL-1 than did microglia from young rats. However, when LPS was added to the culture to stimulate the microglia, the microglia from the aging animals produced much more IL-1 than did the microglia from the young subjects. Because there were no other cells in the culture, the microglia had to be the source of the IL-1, and because there were the same number of cells in culture for both groups, it had to be that individual microglia from aging rats secreted more IL-1 when stimulated. The foregoing makes clear that microglia can be primed or sensitized by aging. If the sensitized IL-1 response that results from primed glia produces cognitive impairments such as memory, then memory should be impaired in aging animals following infection or other inflammatory events for roughly the same duration as the IL-1 increase. To test this idea, aging and young rats of exactly the same ages and strain as used in the previous experiments were given either a peripheral infection with E. coli or vehicle injection. Learning tasks were conducted from 4 to 14 days later, with memory being tested 48 hours after learning. In young animals, exposure to E. coli had no effect at any of the E.coli-to-learning intervals. This was expected because the mild E. coli infection only increases hippocampal IL-1 for between 4 and 24 hours in young rats. In the aging animals, hippocampal memory formation was very poor when learning was 4 to 10 days after E. coli exposure. However, if 14 days intervened, memory was now normal. Thus, the

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time course of interference with the formation of longterm memory mirrored the time course of hippocampal IL-1 elevation. Importantly, memory for fear of the tone from the fear-conditioning task was not impaired, suggesting selectivity to the hippocampus. To determine whether these effects in the aging animals represented impairments of long-term memory formation, as opposed to deficits in learning or the processing of the information during the fearconditioning task, short-term memory 1 hour after training was also assessed. Short-term memory was unaffected by prior E. coli exposure in the aging subjects. That is, fear of the context was perfectly intact 1 hour after conditioning, supporting the idea that it is the consolidation of longterm memories, a process that requires the hippocampus, which is impaired. Finally, inhibition of prostaglandin synthesis within the hippocampus has been reported to block the effects of E. coli exposure on memory in aging animals (Hein et al., 2007), supporting the conclusion that the same cascade as described earlier is involved in the effects of peripheral immune stimulation on memory in aging. The effects of infection on memory and hippocampal IL-1 levels in aging animals persisted for only 10 days in the E. coli experiments. However, E. coli exposure in rats produces a very mild infection, fever persisting in the old animals for only 2 days. It may well be that a more powerful inflammatory stimulus would produce a much more prolonged increase in brain IL-1 in aging animals. There is also some evidence that inflammatory challenges may cumulate in aging animals—a “multiple hit” hypothesis. For example, surgery occurring 2 weeks after infection produces memory impairments for several months (Barrientos et al., unpublished data). Before leaving the discussion of aging it should be noted that there is no reason to expect that the impact of glial priming should be restricted to the effects of infection and inflammation on cognitive processes. Indeed, Huang, Henry, Dantzer, Johnson, and Godbout (2007) have reported that a central administration of LPS produces exaggerated sickness behavior in old animals. The only cells in the brain that express the receptor for LPS (TLR-4 receptor) are microglial cells and perivascular macrophages, supporting the priming hypothesis. It should also be noted that any factors that activate glia other than peripheral inflammation, should also have a potentiated effect in aged individuals. As discussed next, stressors may activate microglia, and brain IL-1 may mediate some of the effects of exposure to stressors. Thus, the experience of stressors would be expected to have an exaggerated impact on the aging individual. Early Infection Aging may be but one of many factors that prime glial cells. Maternal infection during the third trimester in

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humans has been associated with a number of neuropsychological difficulties in adulthood. Brain development in the rat and human are quite different, and the rat is born at a much more immature stage. At roughly postnatal day 4, the rat is considered to be equivalent in development to the third trimester in humans. Bilbo, Biedenkapp, et al. (2005) infected rats with E. coli on postnatal day 4 and examined their behavior in adulthood. The rats appeared normal until challenged with a peripheral injection of LPS under conditions that had no effect on control subjects. Now, the early-infected rats showed hippocampal-dependent memory impairments identical to those that occur in aging rats. An examination of both glial morphology and IL-1 in the brain indicated that the early infection did prime glia into adulthood, and inhibition of IL-1 synthesis by an ICE inhibitor in the adult subjects blocked the deleterious effects of peripheral LPS on memory (Bilbo, Levkoff, et al., 2005). Clearly, the existence of glial priming would predict that a broad range of behaviors would be altered by early infection. Whether this is so is under investigation. Stress It is not obvious how stress would be related to the processes so far discussed in this chapter. Several factors led a number of investigators to explore whether cytokines within the brain might be involved in mediating some of the consequences of exposure to stressors. First, some of the behavioral consequences of stressor exposure appear to be quite similar to those of infection (Maier & Watkins, 1998). Second, both infection and stressors activate the HPA axis and sympathetic nervous systems. This can be rationalized by considering that stressors or external threats evoke fight/flight, and fight/flight requires energy production, as does host defense against infection. As discussed previously, even the most primitive organisms engage in host defense against infection, and this involves the production of energy. Fight/flight evolved later, as it requires an organism complex enough to detect predators or threats at a distance, direct motor responses in complex ways, and integrate the two. Since evolution often works by cooptation of existing solutions to solve related problems (Gould, 1982), it may be that as the fight/flight response evolved it used the mechanism that was already present to produce energy—the sickness machinery. These considerations led a number of investigators to determine whether (a) stressors induce the production of cytokines in the brain as does infection, and (b) blockade of IL-1 or other cytokines in the brain would block the behavioral or endocrine effects of exposure to stressors. The literature is not extensive, but a variety of stressors do

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lead to IL-1 increases in specific regions of the brain (O’Connor et al., 2003). This suggests that stressors activate glial cells, and Nair and Bonneau (2006) found that restraint increased microglial proliferation. The literature on the blockade of IL-1 in the brain is even less extensive, but intracerebral injection of IL1-ra has been reported to reduce both the endocrine (Shintani et al., 1995) and behavioral (Maier & Watkins, 1995) effects of exposure to stressors. Stressors only induce IL-1 increases for a brief period of time (Nguyen et al., 2000). However, stressors might be able to prime microglia for a longer period of time. Microglia isolated from the hippocampi of rats stressed 24 hours earlier do not produce more IL-1 than do microglia from nonstressed controls, but produce exaggerated levels of IL-1 when LPS is added as a stimulus (Frank, Baratta, Sprunger, Watkins, & Maier, 2007). That is, stress produces the same pattern of microglial changes as does aging. The duration of this glial priming is not known. Cross-Sensitization The existence of stress-induced glial activation and priming has a number of implications (see Figure 7.4). First, if both stressors and peripheral inflammatory events activate and prime microglia, then there should be crosssensitization between stress and infection. That is, individuals that have experienced a stressor in the recent past should over-respond to infectious agents, and conversely, with the duration of such effects depending on the persistence of glial priming. Indeed, exposure to a stressor (inescapable tail shock in rats) has been shown to potentiate a variety of sickness-related responses to peripheral LPS (Johnson, O’Connor, Hansen, Watkins, & Maier, 2003), with the sensitization persisting for 4 to 10 days after only a single stressor session. For example, the HPA response to LPS is potentiated for 4, but not 10 days following tail shock (Johnson, O’Connor, Deak, Spencer, et al., 2002). Conversely, immune activation also senitizes responses to stresssors. For example, a single peripheral administration of IL-1 sensitizes the HPA response

Signal ATP, HSPs, etc.

Cytokines Cytokines

Behavior, mood, and cognition

Aging, early infection, stress

Figure 7.4 A variety of conditions can prime the glial response to normal input, thereby producing exaggerated cytokine responses and the products of brain cytokines.

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to novelty 22, but not 42 days later (Schmidt, Aguilera, Binnekade, & Tilders, 2003). The existence of cross-sensitization between stressors and immune stimuli does not indicate that glial priming and/or brain cytokines are key mediators of the process. There has been only a small amount of research directed at this issue. However, three findings do support this possibility. First, exposure to tail shock exaggerates the increase in brain IL-1 that is produced later by LPS (Johnson, O’Connor, Deak, Stark, et al., 2002). Second, the intracerebral administration of the IL-1 receptor antagonist blocks the sensitizing effect of tail shock to later LPS (Johnson, O’Connor, Watkins, & Maier, 2004), and third, the intracerebral injection of IL-1 produces sensitization to subsequent LPS (Johnson et al., 2004). Thus, IL-1 in the brain is both necessary and sufficient to produce cross-sensitzation, but the role of glia has not been explored. Stress and Peripheral Responses The idea that stressors and stimuli to the immune system ultimately act on overlapping neural circuitry involving IL-1 has another implication. This implication is that stressors should produce some of the same peripheral changes as occur during infection because the induction of IL-1 in the brain leads to signals to the periphery that impact on peripheral immune function (see previous discussion). Indeed, stressors as mild as exposure to a novel environment increase plasma IL-6 (LeMay, Vander, & Kluger, 1990), and more potent stressors lead to increases in plasma IL-1 as well (Johnson et al., 2003). As previously noted, peripheral IL-6 is a primary mediator of the acute phase response during infection. Thus, it should be the case that stressors, which induce IL-1 in the brain, should produce a peripheral acute phase response similar to that produced by infection. Not much research has been directed at this question, but it can be noted that exposure to a single session of tail shock produces various aspects of the APR including fever and shifts in liver function characteristic of infection—a reduction in the production of carrier proteins and an increase in production of acute phase proteins (haptoglobin and alpha 1-acid glycoprotein; Deak et al., 1997). This stressor-induced peripheral APR might have adaptive consequences. The period after a fight /flight emergency is likely a high probability period for infection from an injury, and exposure to tail shock has been shown to lead to enhanced recovery from a subcutaneous bacterial infection, as might occur during an injury (Deak, Nguyen, Fleshner, Watkins, & Maier, 1999). Thus, a number of hard-to-understand consequences of exposure to stressors may be understandable from the present perspective.

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SUMMARY In this chapter, we described the existence of a bidirectional immune-brain network with extensive communication pathways going in each direction. We focused on immuneto-brain communication because immune modulation of the brain has has the most important implications for behavior. We summarized a broad array of research indicating that processes within the immune system, via communication to the brain and consequent modulation of neural activity, potently influence and direct aspects of behavior, mood, and cognition. We concentrated on acute rather than chronic immune activation and related phenomena such as stress in order to highlight the adaptive nature of the processes involved, and it is likely that it is with regard to acute events that these processes are adaptive or beneficial. During evolution, organisms that experienced chronic infection or chronic stress would seem to have been less likely to reproduce or survive. The mechanisms that we described may not be beneficial when infection or stress becomes chronic. It is here that physiology may shade into pathology, with outcomes such as neurodegeneration and clinical depression. The existing experimental research has tended to employ immune activators that are quite potent, but it should be recognized that nonhuman and human animals frequently encounter molecules that are recognized as nonself by the immune system, but that are not infectious and of which the organism is unaware. These molecules might also initiate immune-to-brain signaling, and the types of behavioral changes described here. For example, Besedovsky, Sorkin, Keller, and Muller (1975) exposed subjects to sheep red blood cells (SRBC). SRBCs are not bacteria, viruses, or the like, but they are foreign proteins and thus will activate immune responses. At the peak of the immune response to the SRBCs, the subjects exhibited increased HPA responding, just as if there were a stressor present. Thus, the SRBCs initiated signaling to the brain, and the brain altered its pattern of activity. There is much unexplained variability across time in an individual’s behavior, mood, and cognition, and immune-to-brain signaling initiated by encounters with foreign proteins that are not overtly infectious could be an important source of such variation. Moreover, consider the implications of the crosssensitization phenomenon described combined with immune-to-brain signaling initiated simply by substances that are foreign. Individuals who have experienced stress in the recent past might now undergo a large change in behavior, mood, or cognition after exposure to a benign but foreign protein. Conversely, individuals who have recently been exposed to a substance of which they are unaware but is foreign might show exaggerated reactions to a stressful

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event. There are many poorly understood real-life phenomena to which these scenarios might apply. It is now commonplace to acknowledge hormonal influences on the brain, and immune modulation of the brain may be equally widespread. REFERENCES Ader, R., & Cohen, N. (1975). Behaviorally conditioned immunosuppression. Psychosomatic Medicine, 37, 333–340. Alonso, M., Vianna, M. R., Depino, A. M., Mello e Souza, T., Pereira, P., Szapiro, G., et al. (2002). BDNF-triggered events in the rat hippocampus are required for both short- and long-term memory formation. Hippocampus, 12(4), 551–560. Anisman, H., Merali, Z., Poulter, M. O., & Hayley, S. (2005). Cytokines as a precipitant of depressive illness: Animal and human studies. Current Pharmaceutical Design, 11, 963–972. Avitsur, R., Weidenfeld, J., & Yirmiya, R. (1999). Cytokines inhibit sexual behavior in female rats: II. Prostaglandins mediate the suppressive effects of interleukin-1beta. Brain Behavior and Immunity, 13, 33–45. Avitsur, R., & Yirmiya, R. (1999). The immunobiology of sexual behavior: Gender differences in the suppression of sexual activity during illness. Pharmacology Biochemistry and Behavior, 64, 787–796. Banks, W. A. (2005). Blood-brain barrier transport of cytokines: A mechanism for neuropathology. Current Pharmaceutical Design, 11, 973–984. Barrientos, R. M., Higgins, E. A., Biedenkapp, J. C., Sprunger, D. B., Wright-Hardesty, K. J., Watkins, L. R., et al. (2006). Peripheral infection and aging interact to impair hippocampal memory consolidation. Neurobiology of Aging, 27, 723–732. Barrientos, R. M., Higgins, E. A., Sprunger, D. B., Watkins, L. R., Rudy, J. W., & Maier, S. F. (2002). Memory for context is impaired by a post context exposure injection of interleukin-1 beta into dorsal hippocampus. Behavioral Brain Research, 134(1/2), 291–298. Barrientos, R. M., Sprunger, D. B., Campeau, S., Watkins, L. R., Rudy, J. W., & Maier, S. F. (2004). BDNF mRNA expression in rat hippocampus following contextual learning is blocked by intrahippocampal IL-1beta administration. Journal of Neuroimmunology, 155(1/2), 119–126. Baumann, H., & Gauldie, J. (1994). The acute phase response. Immunology Today, 15(2), 74–80. Beck, G., O’Brien, R. F., Habicht, G. S., Stillman, D. L., Cooper, E. L., & Raftos, D. A. (1993). Invertebrate cytokines. III: Invertebrate interleukin-1-like molecules stimulate phagocytosis by tunicate and echinoderm cells. Cellular Immunology, 146, 284–299. Bekker, A. Y., & Weeks, E. J. (2003). Cognitive function after anaesthesia in the elderly. Best Practice and Research: Clinical Anaesthesiology, 17, 259–272. Berkenbosch, F., de Goeij, D. E., Rey, A. D., & Besedovsky, H. O. (1989). Neuroendocrine, sympathetic and metabolic responses induced by interleukin-1. Neuroendocrinology, 50, 570–576. Besedovsky, H., Sorkin, E., Keller, M., & Muller, J. (1975). Changes in blood hormone levels during the immune response. Proceedings of the Society for Experimental Biology and Medicine, 150, 466–470. Bilbo, S. D., Biedenkapp, J. C., Der-Avakian, A., Watkins, L. R., Rudy, J. W., & Maier, S. F. (2005). Neonatal infection-induced memory impairment after lipopolysaccharide in adulthood is prevented via caspase-1 inhibition. Journal of Neuroscience, 25, 8000–8009. Bilbo, S. D., Levkoff, L. H., Mahoney, J. H., Watkins, L. R., Rudy, J. W., & Maier, S. F. (2005). Neonatal infection induces memory impairments following an immune challenge in adulthood. Behavioral Neuroscience, 119, 293–301.

8/18/09 4:58:15 PM

References 133 Blalock, J. E. (1984). The immune system as a sensory organ. Journal of Immunology, 132, 1067–1070.

interleukin-1 on stress-related neuroendocrine neurons. Journal of Neuroscience, 14, 897–913.

Blatteis, C. M. (1990). Neuromodulative actions of cytokines. Yale Journal of Biological Medicine, 63, 133–146.

Ericsson, A., Liu, C., Hart, R. P., & Sawchenko, P. E. (1995). Type 1 interleukin-1 receptor in the rat brain: Distribution, regulation, and relationship to sites of IL-1-induced cellular activation. Journal of Comparative Neurology, 361(4), 681–698.

Bluthe, R. M., Dantzer, R., & Kelley, K. W. (1992). Effects of interleukin-1 receptor antagonist on the behavioral effects of lipopolysaccharide in rat. Brain Research, 573, 318–320. Boulant, J. A. (2000). Role of the preoptic-anterior hypothalamus in thermoregulation and fever. Clinical Infectious Diseases, 31(Suppl. 5), S157–S161. Buttini, M., & Boddeke, H. (1995). Peripheral lipopolysaccharide stimulation induces interleukin-1 beta messenger RNA in rat brain microglial cells. Neuroscience, 65, 523–530. Cacquevel, M., Lebeurrier, N., Cheenne, S., & Vivien, D. (2004). Cytokines in neuroinflammation and Alzheimer ’s disease. Current Drug Targets, 5, 529–534. Campbell, S. J., Perry, V. H., Pitossi, F. J., Butchart, A. G., Chertoff, M., Waters, S., et al. (2005). Central nervous system injury triggers hepatic, CC, and CXC chemokine expression that is associated with leukocyte mobilization and recruitment to both the central nervous system and the liver. American Journal of Pathology, 166, 1487–1497. Carobrez, A. P., & Bertoglio, L. J. (2005). Ethological and temporal analyses of anxiety-like behavior: The elevated plus-maze model 20 years on. Neuroscience Biobehavioral Review, 29, 1193–1205. Carter, J. J., & Whelan, R. L. (2001). The immunologic consequences of laparoscopy in oncology. Surgical Oncology Clinics of North America, 10, 655–677. Clatworthy, A. L. (1998). Neural-immune interactions: An evolutionary perspective. Neuroimmunomodulation, 5(3/4), 136–142. Clatworthy, A. L., & Grose, E. (1999). Immune-mediated alterations in nociceptive sensory function in Aplysia californica. Journal of Expermintal Biology, 202(Pt. 5), 623–630. Cuadros, M. A., & Navascues, J. (2001). Early origin and colonization of the developing central nervous system by microglial precursors. Progress in Brain Research, 132, 51–59. Dantzer, R. (2004). Cytokine-induced sickness behaviour: A neuroimmune response to activation of innate immunity. European Journal of Pharmacology, 500(1/3), 399–411. Dantzer, R., Bluthe, R., Castanon, M., Kelley, K. W., Konsman, J., Laye, S., et al. (2005). Cytokines, sickness behavior, and depression. In R. Ader (Ed.), Psychoneuroimmunology (4th ed., pp. 281–317). San Diego, CA: Academic Press. Deak, T., Meriwether, J. L., Fleshner, M., Spencer, R. L., Abouhamze, A., Moldawer, L. L., et al. (1997). Evidence that brief stress may induce the acute phase response in rats. American Journal of Physiology, 273(6, Pt. 2), R1998–R2004. Deak, T., Nguyen, K. T., Fleshner, M., Watkins, L. R., & Maier, S. F. (1999). Acute stress may facilitate recovery from a subcutaneous bacterial challenge. Neuroimmunomodulation, 6(5), 344–354. De Simoni, M. G., De Luigi, A., Gemma, L., Sironi, M., Manfridi, A., & Ghezzi, P. (1993). Modulation of systemic interleukin-6 induction by central interleukin-1. American Journal of Physiology, 265(4, Pt. 2), R739–R742. Dinarello, C. A. (1991). Interleukin-1 and interleukin-1 antagonism. Blood, 77(8), 1627–1652. Dunn, A. J., & Swiergiel, A. H. (1998). The role of cytokines in infection-related behavior. Annals of the New York Academy of Science, 840, 577–585. Dunn, A. J., Wang, J., & Ando, T. (1999). Effects of cytokines on cerebral neurotransmission. Comparison with the effects of stress. Advances in Experimental Medicine and Biology, 461, 117–127. Ericsson, A., Kovacs, K. J., & Sawchenko, P. E. (1994). A functional anatomical analysis of central pathways subserving the effects of

c07.indd Sec3:133

Fahey, J. V., Gure, P. M., & Munck, A. (1981). Mechanisms of antiinflammatory actions of glucocorticoids. Inflammation Research, 2, 21–51. Felten, D. L., Felten, S. Y., Bellinger, D. L., Carlson, S. L., Ackerman, K. D., Madden, K. S., et al. (1987). Noradrenergic sympathetic neural interactions with the immune system: Structure and function. Immunology Review, 100, 225–260. Felton, L. M., & Perry, H. V. (2005). The interaction between brain inflammation and systemic infection. In R. Ader (Ed.), Psychoneuroimmunology (4th ed., pp. 429–449). San Diego, CA: Academic Press. File, S. E., & Seth, P. (2003). A review of 25 years of the social interaction test. European Journal of Pharmacology, 463(1/3), 35–53. Frank, M. G., Baratta, M. V., Sprunger, D. B., Watkins, L. R., & Maier, S. F. (2007). Microglia serve as a neuroimmune substrate for stressinduced potentiation of CNS pro-inflammatory cytokine responses. Brain Behavioral Immunity, 21, 47–59. Frank, M. G., Barrientos, R. M., Biedenkapp, J. C., Rudy, J. W., Watkins, L. R., & Maier, S. F. (2006). MRNA up-regulation of MHC II and pivotal pro-inflammatory genes in normal brain aging. Neurobiology of Aging, 27, 717–722. Garcia, J., Brett, L. P., & Rusiniak, K. W. (1989). Limits of Darwinian conditioning. In S. B. Klein & R. J. Mowrer (Eds.), Contemporary learning theories: Instrumental conditioning and the impact of biological constraints on learning (pp. 181–203). Hillsdale, NJ: Erlbaum. Goehler, L. E., Gaykema, R. P., Hammack, S. E., Maier, S. F., & Watkins, L. R. (1998). Interleukin-1 induces c-Fos immunoreactivity in primary afferent neurons of the vagus nerve. Brain Research, 804, 306–310. Goehler, L. E., Lyte, M., & Gaykema, R. P. (2007). Infection-induced viscerosensory signals from the gut enhance anxiety: Implications for psychoneuroimmunology. Brain Behavioral Immunity, 21, 721–726. Goehler, L. E., Relton, J. K., Dripps, D., Kiechle, R., Tartaglia, N., Maier, S. F., et al. (1997). Vagal paraganglia bind biotinylated interleukin-1 receptor antagonist: A possible mechanism for immune-to-brain communication. Brain Research Bulletin, 43, 357–364. Goshen, I., & Yirmiya, R. (2005). The role of pro-inflammatory cytokines in memory processes and neural plasticity. In R. Ader (Ed.), Psychoneuroimmunology (4th ed., pp. 337–370). San Diego, CA: Academic Press. Gould, S. J. (1982). Darwinism and the expansion of evolutionary theory. Science, 216, 380–387. Hall, J., Thomas, K. L., & Everitt, B. J. (2000). Rapid and selective induction of BDNF expression in the hippocampus during contextual learning. National Neuroscience, 3, 533–535. Hansen, M. K., O’Connor, K. A., Goehler, L. E., Watkins, L. R., & Maier, S. F. (2001). The contribution of the vagus nerve in interleukin-1beta-induced fever is dependent on dose. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 280, R929–R934. Hansen, M. K., Taishi, P., Chen, Z., & Krueger, J. M. (1998). Vagotomy blocks the induction of interleukin-1beta (IL-1beta) mRNA in the brain of rats in response to systemic IL-1beta. Journal of Neuroscience, 18, 2247–2253. Hart, B. L. (1988). Biological basis of the behavior of sick animals. Neuroscience Biobehavioral Review, 12, 123–137. Hein, A. M., Stutzman, D., Barrientos, R. M., Watkins, L. R., Rudy, J. W., et al. (2007). Prostaglandins are necessary and sufficient to induce contextual fear learning impairments after interleukin-1 beta injections into the dorsal hippocampus. Manuscript submitted for publication.

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Hoek, R. M., Ruuls, S. R., Murphy, C. A., Wright, G. J., Goddard, R., Zurawski, S. M., et al. (2000). Down-regulation of the macrophage lineage through interaction with OX2 (CD200). Science, 290, 1768–1771. Hoozemans, J. J., Veerhuis, R., Janssen, I., Rozemuller, A. J., & Eikelenboom, P. (2001). Interleukin-1beta induced cyclooxygenase 2 expression and prostaglandin E2 secretion by human neuroblastoma cells: Implications for Alzheimer ’s disease. Experimental Gerontology, 36, 559–570. Huang, Y., Henry, C. J., Dantzer, R., Johnson, R. W., & Godbout, J. P. (2007). Exaggerated sickness behavior and brain proinflammatory cytokine expression in aged mice in response to intracerebroventricular lipopolysaccharide. Neurobiology of Aging, 29, 1744–1753. Imeri, L., Bianchi, S., & Opp, M. R. (2006). Inhibition of caspase-1 in rat brain reduces spontaneous nonrapid eye movement sleep and nonrapid eye movement sleep enhancement induced by lipopolysaccharide. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 291, R197–R204. Janeway, C. A., Travers, P., Walport, M., & Shlomchik, M. (2005). Immunobiology: The immune system in health and disease (6th ed.). London: Garland Science. Johnson, J. D., O’Connor, K. A., Deak, T., Spencer, R. L., Watkins, L. R., & Maier, S. F. (2002). Prior stressor exposure primes the HPA axis. Psych oneuroendocrinology, 27(3), 353–365. Johnson, J. D., O’Connor, K. A., Deak, T., Stark, M., Watkins, L. R., & Maier, S. F. (2002). Prior stressor exposure sensitizes LPS-induced cytokine production. Brain Behavioral Immunity, 16, 461–476. Johnson, J. D., O’Connor, K. A., Hansen, M. K., Watkins, L. R., & Maier, S. F. (2003). Effects of prior stress on LPS-induced cytokine and sickness responses. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 284, R422–R432. Johnson, J. D., O’Connor, K. A., Watkins, L. R., & Maier, S. F. (2004). The role of IL-1beta in stress-induced sensitization of proinflammatory cytokine and corticosterone responses. Neuroscience, 127, 569–577. Kelly, A., Vereker, E., Nolan, Y., Brady, M., Barry, C., Loscher, C. E., et al. (2003). Activation of p38 plays a pivotal role in the inhibitory effect of lipopolysaccharide and interleukin-1 beta on long term potentiation in rat dentate gyrus. Journal of Biological Chemistry, 278, 19453–19462. Kent, S., Bluthe, R. M., Dantzer, R., Hardwick, A. J., Kelley, K. W., Rothwell, N. J., et al. (1992). Different receptor mechanisms mediate the pyrogenic and behavioral effects of interleukin 1. Proceedings of the National Academy of Sciences, USA, 89, 9117–9120. Kluger, M. J., Kozak, W., Conn, C. A., Leon, L. R., & Soszynski, D. (1996). The adaptive value of fever. Infectious Disease Clinic of North America, 10, 1–20. Konsman, J. P., Luheshi, G. N., Bluthe, R. M., & Dantzer, R. (2000). The vagus nerve mediates behavioural depression, but not fever, in response to peripheral immune signals; a functional anatomical analysis. European Journal of Neuroscience, 12, 4434–4446. Konsman, J. P., Vigues, S., Mackerlova, L., Bristow, A., & Blomqvist, A. (2004). Rat brain vascular distribution of interleukin-1 type-1 receptor immunoreactivity: Relationship to patterns of inducible cyclooxygenase expression by peripheral inflammatory stimuli. Journal of Comparative Neurology, 472, 113–129. Krabbe, K. S., Reichenberg, A., Yirmiya, R., Smed, A., Pedersen, B. K., & Bruunsgaard, H. (2005). Low-dose endotoxemia and human neuropsychological functions. Brain Behavioral Immunity, 19, 453–460. Kreutzberg, G. W. (1996). Microglia: A sensor for pathological events in the CNS. Trends in Neuroscience, 19, 312–318. Kyrkanides, S., O’Banion, M. K., Whiteley, P. E., Daeschner, J. C., & Olschowka, J. A. (2001). Enhanced glial activation and expression of specific CNS inflammation-related molecules in aged versus young rats following cortical stab injury. Journal of Neuroimmunology, 119, 269–277. Lacroix, S., Feinstein, D., & Rivest, S. (1998). The bacterial endotoxin lipopolysaccharide has the ability to target the brain in upregulating its

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membrane CD14 receptor within specific cellular populations. Brain Pathology, 8, 625–640. Laursen, P. (1997). The impact of aging on cognitive functions. An 11 year follow-up study of four age cohorts. Acta neurologica Scandinavia 172(Suppl.), 7–86. LeMay, L. G., Vander, A. J., & Kluger, M. J. (1990). The effects of psychological stress on plasma interleukin-6 activity in rats. Physiological Behavior, 47, 957–961. Lin, J. H., & Lin, M. T. (1996). Nitric oxide synthase-cyclo-oxygenase pathways in organum vasculosum laminae terminalis: Possible role in pyrogenic fever in rabbits. British Journal of Pharmacology, 118, 179–185. Ma, X. C., Chen, L. T., Oliver, J., Horvath, E., & Phelps, C. P. (2000). Cytokine and adrenal axis responses to endotoxin. Brain Research, 861, 135–142. Maier, S. F., & Watkins, L. R. (1995). Intracerebroventricular interleukin1 receptor antagonist blocks the enhancement of fear conditioning and interference with escape produced by inescapable shock. Brain Research, 695, 279–282. Maier, S. F., & Watkins, L. R. (1998). Cytokines for psychologists: Implications of bidirectional immune-to-brain communication for understanding behavior, mood, and cognition. Psychological Review, 105, 83–107. Maier, S. F., Watkins, L. R., & Nance, D. M. (2001). Multiple routes of action of interleukin-1 on the nervous system. In R. Ader, D. L. Felten, & N. Cohen (Eds.), Psychoneuroimmunology (3rd ed., pp. 563–585). San Diego, CA: Academic Press. Mingam, R., DeSmedt, V., Amedee, T., Bluthe, R. M., Kelley, K. W., Dantzer, R., et al. (2007). In vitro and in vivo evidence for a role of the P2X7 receptor in the release of IL-1 in the murine brain. Brain, Behavior, and Immunity, 22, 234–244. Musselman, D. L., Lawson, D. H., Gumnick, J. F., Manatunga, A. K., Penna, S., Goodkin, R. S., et al. (2001). Paroxetine for the prevention of depression induced by high-dose interferon alfa. New England Journal of Medicine, 344, 961–966. Nair, A., & Bonneau, R. H. (2006). Stress-induced elevation of glucocorticoids increases microglia proliferation through NMDA receptor activation. Journal of Neuroimmunology, 171(1/2), 72–85. Neumann, H. (2001). Control of glial immune function by neurons. Glia, 36, 191–199. Nguyen, K. T., Deak, T., Will, M. J., Hansen, M. K., Hunsaker, B. N., Fleshner, M., et al. (2000). Timecourse and corticosterone sensitivity of the brain, pituitary, and serum interleukin-1beta protein response to acute stress. Brain Research, 859, 193–201. Nimmerjahn, A., Kirchhoff, F., & Helmchen, F. (2005). Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo. Science, 308, 1314–1318. O’Connor, K. A., Johnson, J. D., Hansen, M. K., Wieseler Frank, J. L., Maksimova, E., Watkins, L. R., et al. (2003). Peripheral and central proinflammatory cytokine response to a severe acute stressor. Brain Research, 991, 123–132. Ottaviani, E., & Franchini, A. (1995). Immune and neuroendocrine responses in molluscs: The role of cytokines. Acta Biologica Academiae Scientiarum Hungaricae, 46(2/4), 341–349. Perry, V. H., Newman, T. A., & Cunningham, C. (2003). The impact of systemic infection on the progression of neurodegenerative disease. National Review of Neuroscience, 4, 103–112. Plaza, M. A., Fioramonti, J., & Bueno, L. (1997). Role of central interleukin-1 beta in gastrointestinal motor disturbances induced by lipopolysaccharide in sheep. Digestive Diseases and Sciences, 42, 242–250. Pugh, C. R., Nguyen, K. T., Gonyea, J. L., Fleshner, M., Wakins, L. R., Maier, S. F., et al. (1999). Role of interleukin-1 beta in impairment of contextual fear conditioning caused by social isolation. Behavioral Brain Research, 106, 109–118.

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References 135 Raison, C. L., Woolwine, B. J., Demetrashvili, M. F., Borisov, A. S., Weinreib, R., Staab, J. P., et al. (2007). Paroxetine for prevention of depressive symptoms induced by interferon-alpha and ribavirin for hepatitis C. Alimentary Pharmacology and Therapeutics, 25, 1163–1174. Rasley, A., Tranguch, S. L., Rati, D. M., & Marriott, I. (2006). Murine glia express the immunosuppressive cytokine, interleukin-10, following exposure to Borrelia burgdorferi or Neisseria meningitidis. Glia, 53, 583–592. Romeo, H. E., Tio, D. L., & Taylor, A. N. (2003). Effects of glossopharyngeal nerve transection on central and peripheral cytokines and serum corticosterone induced by localized inflammation. Journal of Neuroimmunology, 136(1/2), 104–111. Roosevelt, R. W., Smith, D. C., Clough, R. W., Jensen, R. A., & Browning, R. A. (2006). Increased extracellular concentrations of norepinephrine in cortex and hippocampus following vagus nerve stimulation in the rat. Brain Research, 1119, 124–132. Rudy, J. W., & Sutherland, R. J. (1995). Configural association theory and the hippocampal formation: An appraisal and reconfiguration. Hippocampus, 5, 375–389. Schiltz, J. C., & Sawchenko, P. E. (2007). Specificity and generality of the involvement of catecholaminergic afferents in hypothalamic responses to immune insults. Journal of Comparative Neurology, 502, 455–467. Schmidt, E. D., Aguilera, G., Binnekade, R., & Tilders, F. J. (2003). Single administration of interleukin-1 increased corticotropin releasing hormone and corticotropin releasing hormone-receptor mRNA in the hypothalamic paraventricular nucleus which paralleled long-lasting (weeks) sensitization to emotional stressors. Neuroscience, 116, 275–283. Serrats, J., & Sawchenko, P. E. (2006). CNS activational responses to staphylococcal enterotoxin B: T-lymphocyte-dependent immune challenge effects on stress-related circuitry. Journal of Comparative Neurology, 495, 236–254. Sheng, J. G., Mrak, R. E., & Griffin, W. S. (1998). Enlarged and phagocytic, but not primed, interleukin-1 alpha-immunoreactive microglia increase with age in normal human brain. Acta Neuropathologica, 95, 229–234. Shintani, F., Nakaki, T., Kanba, S., Sato, K., Yagi, G., Shiozawa, M., et al. (1995). Involvement of interleukin-1 in immobilization stress-induced increase in plasma adrenocorticotropic hormone and in release of

c07.indd Sec3:135

hypothalamic monoamines in the rat. Journal of Neuroscience, 15(3, Pt. 1), 1961–1970. Solomon, G. F. (1969). Stress and antibody response in rats. International Archives of Allergy and Applied Immunology, 35, 97–104. Squire, L. R. (2004). Memory systems of the brain: A brief history and current perspective. Neurobiology of Learning and Memory, 82(3), 171–177. Sugimoto, Y., & Narumiya, S. (2007). Prostaglandin E receptors. Journal of Biological Chemistry, 282, 11613–11617. Swiergiel, A. H., & Dunn, A. J. (2007). Effects of interleukin-1beta and lipopolysaccharide on behavior of mice in the elevated plus-maze and open field tests. Pharmacology Biochemistry and Behavior, 86, 651–659. Taylor, J. L., & Grossberg, S. E. (1998). The effects of interferonalpha on the production and action of other cytokines. Seminars in Oncology, 25(1 Suppl. 1), 23–29. Tracey, K. J. (2007). Physiology and immunology of the cholinergic antiinflammatory pathway. Journal of Clinical Investigation, 117, 289–296. Van Dam, A. M., Bauer, J., Tilders, F. J., & Berkenbosch, F. (1995). Endotoxin-induced appearance of immunoreactive interleukin-1 beta in ramified microglia in rat brain: A light and electron microscopic study. Neuroscience, 65, 815–826. Wan, W., Wetmore, L., Sorensen, C. M., Greenberg, A. H., & Nance, D. M. (1994). Neural and biochemical mediators of endotoxin and stressinduced c-fos expression in the rat brain. Brain Research Bulletin, 34, 7–14. Watkins, L. R., Wiertelak, E. P., Goehler, L. E., Mooney-Heiberger, K., Martinez, J., Furness, L., et al. (1994). Neurocircuitry of illnessinduced hyperalgesia. Brain Research, 639, 283–299. Ye, S. M., & Johnson, R. W. (2001). An age-related decline in interleukin10 may contribute to the increased expression of interleukin-6 in brain of aged mice. Neuroimmunomodulation, 9, 183–192. Yirmiya, R. (1996). Endotoxin produces a depressive-like episode in rats. Brain Research, 711, 163–174. Yirmiya, R., Pollak, Y., Barak, O., Avitsur, R., Ovadia, H., Bette, M., et al. (2001). Effects of antidepressant drugs on the behavioral and physiological responses to lipopolysaccharide (LPS) in rodents. Neuropsychopharmacology, 24, 531–544.

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Chapter 8

Neuroanatomy/Neuropsychology BRYAN E. KOLB AND IAN Q. WHISHAW

recognized that the body is complex and operates much like a machine, but he proposed that the mind was separate and was nonmaterial and nonspatial. Descartes argued that the mind and body are separate but interact to produce thought and behavior, a position referred to as dualism. The problem with the dualistic view is that for the mind to affect the body requires that energy be created, a proposition that violates fundamental laws of physics. Nonetheless, even today many people have a dualistic view of the mind and body and appear to believe that there is more to behavior than the brain. One example is the belief in a nonmaterial soul that influences behavior during life and exits after the body dies.

A challenge for science over the past 150 years has been to identify a general conceptual framework for how the human brain is organized to produce the amazing complexity of human behaviors ranging from movement to emotion to language. The human brain is composed of more than 180 billion cells, more than 80 billion of which are directly engaged in information processing. Given that each nerve cell receives up to 15,000 connections from other nerve cells, there is a challenge in understanding such complexity. The challenge is made simpler by understanding the brain’s underlying organization. Our understanding of brain organization starts with an examination of the historical development of current thinking. We then review how the brain is organized and identify rules that govern its operation. Finally, we consider how complex psychological functions emerge from the anatomical complexity.

The emergence of evolutionary theory in the mid-nineteenth century provided a different perspective: Rational behavior can be fully explained by the activity of the nervous system without the need for a nonmaterial mind. Darwin emphasized the important idea of “descent with modification” in which all living animals are descended from a common ancestor. He thus identified the principle that the workings of the human brain reflect a long history of adaptation of an early primitive brain. One implication of this view is that the human brain is not special but rather represents an elaboration of the brains of our nearest relatives such as the great apes and, likewise, the ape brains are elaborations of more primitive mammalian brains. The recognition that mammalian brains are fundamentally similar in general organization was an important step because it meant that the organization of the human brain could be studied using relatively simpler surrogate brains such as those of monkeys, carnivores, and rodents.

HISTORICAL IDEAS OF BRAIN ORGANIZATION People knew what the brain looked like long before they had any idea what it might do. Early humans must have noticed that all animals had a brain and that it was connected to other parts of the body by what we now know to be nerves. Understanding what the brain does requires the important philosophical leap, however, of first recognizing that the production of thought and behavior is based on biology rather than on some sort of “will” or energy force. Recognizing that behavior is related to biological activity is only the first step because there also needs to be a recognition that the nervous system, and not other organs such as the heart or liver, produces behavior. Although Alcmaeon of Croton (Greece) located mental processes in the brain about 2,500 years ago (and thus developed what is now called the brain hypothesis), the merit of this hypothesis has been hotly debated ever since and is still not universally accepted. The modern views were first clearly identified by Descartes in the seventeenth century. Descartes

Recognizing that the brain controls behavior was an important step, but the question of how the brain did this remained unanswered. One important philosophical issue that emerged in the late nineteenth century revolved around the question of whether the brain can be divided into separate parts representing separate functions or whether the brain operated as a whole and thus was indivisible. The identification of Broca’s area, and later Wernicke’s area, 136

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Methods of Studying Brain and Behavior Relations

137

Figure 8.1 Neurons. Note. A spiny stellate neuron from the nucleus accumbens showing the cell body and dendrites. The enlargement of a dendrite on the right shows the dendritic spines that provide the location of excitatory synapses.

as key to specific aspects of language suggested that functions were localized and led many neurologists to seek specific functions for every brain region. Parallel investigations revealed that the severity of cognitive loss was related to the extent of brain injury rather than the precise locus. Thus, other neurologists argued that functions like language could not be localized because so much of the brain was involved. One problem is that it is clear that behaviors such as emotion, memory, or language emerge from the activity of the entire nervous system and thus do not respect specific anatomical structures. This is not to say that specific regions do not play larger (or smaller) roles in different psychological functions but rather that all psychological functions require the contribution of many different neural systems, which by their nature are not precisely housed in single places. The final step in the historical development of current thoughts about brain organization and function was the recognition that the nervous system is composed of discrete autonomous units, neurons and glia, that are not physically connected but interact to generate nervous activity (Figure 8.1). The neuron hypothesis stipulates that neurons carry out the brain’s major functions whereas glia aid and modulate the neuron’s activities—for example, forming the fatty covering, or insulation, over neurons, as well as producing various chemicals that influence neuronal functions. The idea that neurons represented individual units in brain function dates back to Ramon y Cajal early in the twentieth century but it was only later that it was discovered that there are two distinctly different types of neurons that function to excite or inhibit the activity of other neurons. This dichotomy is philosophically very important because we now can see that not only does the brain produce thought and behavior but it also inhibits thoughts

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and behaviors. The recognition of this distinction is fundamental to understanding both brain function and its dysfunction.

METHODS OF STUDYING BRAIN AND BEHAVIOR RELATIONS The first insights into brain function were based on observations of naturally occurring injuries in people. It was not until the mid-1800s that experimental techniques, such as using electrical stimulation of the brain to determine functions, began to emerge. It was well into the twentieth century before a systematic science of brain function began to emerge both experimentally and clinically. We next review the principal methods used to outline the general anatomical and functional organization of the brain. Human Neuropsychology Human neuropsychology is the science that relates brain function to cognitive behavior. There is a rich history of clinical neurology dating well before Broca’s 1861 description of his patient Tan who had lost the ability to speak, but Broca’s patient was particularly important because it was the first time that a brain function was placed in a particular location in the brain—in this case “Broca’s area.” It was not until World War I that systematic descriptions of large numbers of cases of head-injured soldiers began to provide a basis for modern neuropsychology (e.g., Holmes, 1918). Soldiers with gunshot or shrapnel wounds to the brain showed specific symptoms, many of which were unexpected. For example, soldiers who were unable to respond to stimuli on their left side seemed unaware that

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there was any problem and would even deny that they had any difficulties. Neuropsychology became a systematic field of investigation only after World War II, however, and the term neuropsychology was first formally used in 1949 (Hebb, 1949). A study of an amnesic patient, who retained past memories but lost the ability to form new memories (Scoville & Milner, 1957), marked an important step in the development of modern neuropsychology because it led the systematic investigation of memory. The patient, referred to as H.M., had a selective removal of the hippocampus, a structure in the temporal lobe, to reduce his epileptic seizures. Immediately after the surgery, H.M. exhibited a severe amnesia in which he could recall virtually nothing that happened to him after his surgery—even though his memory of presurgical events appeared to be intact. Although amnesic patients had been described before, H.M. was the first case in which the symptoms could be attributed to a localized cerebral injury. Indeed, up until the description of H.M., studies on laboratory animals had been unable to find any injury that produced a specific memory loss, so H.M.’s condition was a major breakthrough. A further advance occurred with the studies of splitbrain patients by Sperry (1974). Patients who had the corpus callosum, which connects the two cerebral hemispheres, cut to relieve epileptic seizures revealed that each hemisphere makes complementary but different contributions to behavior. In particular, it became clear that not only was the left hemisphere verbal but that the left and right hemispheres had a different opinion about the world, and in the absence of the corpus callosum they acted pretty much independently of one another. The description of patient D.F. by Milner and Goodale (2006) led to another important shift in thinking about the organization of visual perception and action. D.F. suffered carbon monoxide poisoning and appeared to be essentially blind. Curiously, however, when she made arm and hand movements, she sometimes acted as though she could see. Thus, although she was unable to identify objects, she could reach and grasp the objects as if she knew what they were. Consider that our hand position in reaching for a glass is quite different from the position for picking up a pencil, but to make different movements would seem to require a recognition of the object. D.F.’s actions showed that some of our actions are conscious (such as identifying objects) and others are unconscious (such as making movements to manipulate objects). D.F. had no conscious vision but still had unconscious vision. Most neuropsychological studies in the past 50 years have not been case studies but rather studies of groups of patients with fairly circumscribed lesions, perhaps best exemplified by studies of patients with frontal or temporal

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lobectomies for the treatment of intractable epilepsy (e.g., Kolb & Whishaw, 2008). Sixty years of human neuropsychology have now clearly shown not only that functions are relatively localized in the cerebral cortex, but they have laid the groundwork for our current understanding of how the brain is organized. Electrophysiological Confirmation of Localization In 1870, Fristch and Hitzig (1956) described an extraordinary finding that electrical stimulation of the small portion of the cortex of a rabbit and a dog produced movements. Importantly, not only were there movements but the movements were selective such that stimulating different points led to different movements. Later studies showed similar effects in humans, other primates, and many laboratory animals (Woolsey, 1958). These studies led to the development of detailed somatosensory, motor, and language maps in the cerebral cortex (Penfield & Roberts, 1956). One of the difficulties of the early studies was that the cranial bones had to be opened to allow access to the brain, but in the past decade noninvasive techniques have been developed using magnetic stimulation on normal waking subjects. Laboratory Animal Studies of Brain Organization Although there were early isolated studies of animals such as dogs and birds with cerebral injuries, the first systematic studies of large numbers of animals were begun in the early part of the twentieth century. These studies had three distinct foci: (1) studies of how nerves sent messages using “simple” models such as the squid (e.g., Hodgkin & Huxley, 1952); (2) electrophysiological studies of spinal cord worked (e.g., Sherrington, 1948); and (3) behavioral studies of animals with discrete cerebral injuries (Lashley, 1960) directed toward studying how animals thought and remembered. By the 1960s, both the electrophysiological and behavioral techniques had evolved to a point that a new field of behavioral neuroscience began to emerge that paralleled the human studies described earlier. The recognition that the rodent is an excellent model for understanding basic principles of cerebral organization in primates allowed researchers to expand investigations of a wide range of topics ranging from motor control to memory, as well as allowing investigations of factors influencing recovery from brain injury such as stroke (e.g., Kolb & Tees, 1990; Whishaw & Kolb, 2006). Noninvasive Imaging One of the historical impediments to studying brain organization in humans has been the difficulty in studying brain

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function in normal volunteers. Although the electroenceophalogram (EEG), which measures electrical activity of the brain through the skull, was developed in the 1930s, its primary use was in identifying states of consciousness or brain pathology. The major advances in computer technology in the latter part of the twentieth century allowed EEG, to be used to identify discrete neural processing that is best exemplified by event-related potentials (ERPs). ERPs are signals that are correlated with specific forms of sensory processing, such as the recognition of some specific information. The difficulty with ERPs, however, is that although the temporal resolution of the electrical signal is good, the spatial localization of the signal is difficult because the recording is through the skull. The development of metabolic and blood flow measures of brain activity using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) provide a more precise view of brain function (see Kolb & Whishaw, 2008). The logic of these methods is that regions of the brain that are more active have a higher metabolic activity that can be seen by the brain’s use of glucose, oxygen, and blood. Thus, just as somatic muscles that are more active have higher metabolic demands than when they are less active, brain regions that are active use more resources than regions that are less active. It is now possible to do parallel studies using ERP and fMRI measures and thus allow better-refined temporal and spatial resolution. The development of computer-based data collection and sophisticated statistical procedures to average signals across subjects has allowed researchers to compare the brain activity of multiple subjects. For example, Hasson, Nir, Levy, Fuhrmann, & Malach (2004) allowed five subjects to freely view a 30-minute segment of a feature film, The Good, The Bad, and The Ugly, while cortical activity was monitored via fMRI. The authors reasoned that such a rich and complex visual stimulation would be far more similar to natural vision than the highly constrained visual stimuli normally used in the laboratory. Comparison of brain activation across the subjects showed the brains of different individuals tended to act in unison during the free viewing. This surprising activity coherence suggests that a large expanse of the human cortex is stereotypically responsive to naturalistic audiovisual stimuli. Furthermore, although overall there was widespread activity in the cerebrum during the viewing, there also were selective activations related to the precise moment-to-moment film content. For example, specific regions were activated by faces or places, suggesting relatively localized processing. The generalized and specific nature of the cerebral processing is clearly relevant to the

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debates regarding localization of functions in the brain discussed earlier. Molecular Studies With the description of the genome of both humans and laboratory animals in the early part of the twenty-first century, there has been a radical shift toward studying the genetic bases of brain function. Although these studies are likely to provide considerable insight into the detailed molecular mechanisms underlying neuronal functions, such studies have had little time to provide much impact on our general understanding of how the brain is organized functionally. One exception to this generalization comes from work showing that experience can modify gene expression, which in turn influences how behavior is expressed. For example, Weaver et al. (2004) showed that the amount of time a mother rat spends licking and grooming her infants can influence gene expression related to functions of the hypothalamic-pituitary-adrenal (HPA) stress axis. This genetic expression later influences the reactivity of the offspring to stress in adulthood but also determine how the female offspring interact with their pups.

GENERAL BRAIN ORGANIZATION Understanding the basic organization of the brain can be most easily seen in the evolutionary and ontogenetic development of the brain. Figure 8.2 shows a basic three-part structural plan that divides the brain into front, middle, and back components. The front and back components expand greatly in mammals and become further subdivided into five regions. Historically, embryologists gave rather cumbersome names to the various regions and these names remain, although they are seldom used in behavioral studies. The three regions of the primitive developing brain are first recognizable as enlargements at the front end of a fluid-filled tube. In the simple brain the front region (the prosencephalon) is responsible for olfaction and basic body functions such as feeding and drinking; the middle region (mesencephalon) for hearing and vision; and the back region (rhombencephalon) controls movement, balance, and breathing. The back end of the tube extends to form the spinal cord. As the brain enlarges in mammals and birds, cerebral hemispheres develop and existing functions are elaborated in the prosencephalon. The original tube also becomes elaborated to form pockets of fluid known as ventricles, as shown in Figure 8.3.

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(A) Fish, amphibian, reptile, human embryo at 25 days

(B) Mammals such as rat, human embryo at 50 days

(C) Fully developed human brain Telencephalon

Telencephalon Diencephalon Mesencephalon

Prosencephalon Mesencephalon

Myelencephalon

Spinal cord

Rhombencephalon

Diencephalon Mesencephalon Metencephalon Myelencephalon Spinal cord

Metencephalon

Spinal cord

Telencephalon (end brain)

Neocortex, basal ganglia, limbic system olfactory bulb, lateral ventricles

Diencephalon (between brain)

Thalamus, epithalamus, hypothalamus, pineal body, third ventricle

Mesencephalon

Tectum, tegmentum, cerebral aqueduct

Metencephalon (across-brain)

Cerebellum, pons, fourth ventricle

Myelencephalon (spinal brain)

Medulla oblongata, fourth ventricle

Spinal cord

Spinal cord

Prosencephalon (forebrain)

Mesencephalon (midbrain)

Figure 8.2 Steps in the ontogenetic development of the brain. Forebrain

Brainstem

Rhombencephalon (hindbrain) Spinal cord

Spinal cord

Note. (A) A three-chambered brain; (B) a five-chambered brain; (C) side view through the center of a human brain. From Fundamentals of Human Neuropsychology, fifth edition, by B. Kolb and I. Q. Whishaw, 2003, New York: Worth. Reprinted with permission.

Cerebral cortex Lateral ventricle Epithalamus Third ventricle Hypothalamus Thalamus Optic chiasm Fourth ventricle Cerebellum Medulla Spinal cord

Tegmentum Superior colliculus Pons

Inferior colliculus

Figure 8.3 Medial view through the center of the brain showing structures of the brain stem. Note. From Fundamentals of Human Neuropsychology, fifth edition, by B. Kolb and I. Q. Whishaw, 2003, New York: Worth. Reprinted with permission.

Cerebral aqueduct Reticular formation

The fluid in the ventricles, (cerebrospinal fluid or CSF) is produced by cells that line the ventricular walls. The CSF flows from the ventricles to eventually enter the circulatory system. The expanded prosencephalon is now referred to as the forebrain, and the remaining brain is referred to as the brain stem (Table 8.1). The brain stem receives nerves from all of the body’s senses and it sends nerves to control all of the body’s movements except the most complex movements of the fingers and toes of mammals. The forebrain acts to elaborate on the basic functions of the brain stem. In relatively primitive animals such as frogs, the entire brain is

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Tectum

essentially equivalent to the mammalian brain stem. Given that such animals have complex behavioral repertoires, it is obvious that the brain stem is quite a sophisticated piece of machinery. Brain Stem The brain stem can be divided into three functional regions: hindbrain, midbrain, and diencephalon. The diencephalon can be conceived as a “between brain” because it acts as a border between the lower (brain stem) and upper (forebrain) parts of the brain. Each brain stem region contains

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General Brain Organization 141 Table 8.1 Anatomical Divisions of the Central Nervous System Anatomical division

Functional division

Principal structures

Forebrain

Forebrain

Cerebral cortex Basal ganglia Limbic system

Brain stem

Diencephalon

Thalamus Hypothalamus

Midbrain

Tectum Tegmentum

Hindbrain

Cerebellum Pons Medulla oblongata Reticular formation

Spinal cord

Spinal nerves

Cervical nerves Thoracic nerves Lumbar nerves Sacral nerves

various subparts and thus performs more than a single task. Although all three regions have both sensory and motor functions, the hindbrain is more important for motor functions and the midbrain for sensory functions. The diencephalon plays a role in regulatory behaviors such as temperature regulation and the control of eating and drinking. Hindbrain The hindbrain has four major subregions (see Figure 8.3). The largest region of the hindbrain is the cerebellum, a structure that becomes progressively larger and more complex as behaviors become more complex as the forebrain expands. The pons and medulla contain substructures that control vital body functions such as breathing and the cardiovascular system. The final hindbrain region, the reticular formation, is a netlike mixture of neurons and nerve fibers with specialized roles in stimulating the forebrain. Midbrain The midbrain is composed of the tectum (roof of the ventricle) and tegmentum (floor of the ventricle). The tectum receives major inputs from the eyes and ears, with the optic nerve going to a region called the superior colliculus and the auditory nerve going to the inferior colliculus. The

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colliculi function both to analyze sensory input as well as to produce orienting movements related to sensory inputs, such as turning the eyes or head toward a sound. To allow such orientation the colliculi have a “map” of the external world so that the head and eyes can be directed correctly. The auditory and visual maps must overlap so that the two systems can work together. The tegmentum is composed of multiple structures, largely with movement-related functions. The main nuclei are the red nucleus (for control of limbs), substantia nigra (for the initiation and inhibition of movements), and the periacquiductal gray matter (for species typical behavior such as sexual behavior and for the modulation of pain). Diencephalon The integrating functions of the diencephalon require more anatomical parts than the rest of the brain stem. The two major structures are the hypothalamus and thalamus, both of which are composed of about 20 subnuclei (Figure 8.4). The hypothalamus contains nuclei associated with eating, drinking, thermal regulation, sexual behavior, emotional behavior, and hormone function. The hypothalamus is connected both directly and hormonally with the pituitary gland, which in turn produces hormones that travel to other organs such as the adrenal gland. In contrast to most of the brain stem, which is the same in males and females, there

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Diencephalon

Thalamus

Dorsomedial nucleus (connects to frontal lobe)

Lateral geniculate nucleus to visual cortex Medial geniculate nucleus to auditory cortex Auditory input

Optic tract from left eye

Hypothalamus and pituitary gland

Figure 8.4 Medial view of the diencephalon.

Hypothalamus

Note. (Right) Enlargement of the thalamus. (Bottom) Enlargement of the hypothalamus and pituitary. From Introduction to Brain and Behavior, second edition, by B. Kolb and I. Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

Pituitary stalk Pituitary gland

are sex differences in the anatomy of the hypothalamus that are presumably related both to sex-related hormones as well as sex-related differences in sexual and parental behaviors. The thalamus acts as a gateway for information traveling to the cortex (bark or outer portion of the forebrain). Each sense sends its input to a specific thalamic nucleus, which in turn sends information to specific cortical regions. For example, the lateral geniculate nucleus receives input from the optic nerve, and is thus visual, and the medial geniculate nucleus receives input from the auditory nerve and is auditory. Some thalamic regions have motor functions or act in an integrative fashion. The dorsomedial nucleus is an integrative region that receives input from many subcortical structures, as well as the olfactory system, and passes this integrated information to the frontal lobe of the cortex.

Forebrain The forebrain is the largest region of the mammalian brain and like the brain stem, it is composed of multiple regions, the principle ones being the cerebral cortex, basal ganglia, and limbic lobe. One striking characteristic of the forebrain is that it consists of two nearly symmetrical hemispheres, the left and the right, which have both overlapping and specialized functions.

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Cerebral Cortex There are two types of cerebral cortex, the new and the old. The new cortex (neocortex) has six layers of gray matter (cell bodies) atop a layer of white matter (fibers). It is the neocortex that is visible when we view the brain from the outside. The neocortex is unique to mammals and is central to the emergence of mental functions such as language, memory, attention, and so on. The old cortex (sometimes called limbic cortex) has three or four layers of gray over white matter and is considered to be more primitive than neocortex. Although the limbic cortex does play an important role in emotional states, its functions also include other mental functions and thus there normally is little reason to draw much functional distinction between neo and limbic cortex. The cerebral cortex is divided into four lobes (frontal, parietal, temporal, occipital) that are named by the cranial bones that overlie the brain rather than by any particular functional characteristics of the regions (Figure 8.5). The frontal and parietal lobes are divided by a deep fissure known as the central sulcus, and the temporal lobe is divided from the frontal lobe by another fissure known as the lateral (or Sylvian) fissure. Although the lobes each have multiple functions, we can ascribe some gross functions to each lobe. The three posterior lobes all have sensory functions, occipital for vision, temporal for audition, and parietal for somatosensation. In addition, visual functions importantly influence

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General Brain Organization 143 Cingulate cortex (limbic cortex)

Lateral View Central sulcus

Frontal lobe

Parietal lobe

Lateral Temporal fissure lobe

Occipital lobe

Figure 8.5 A lateral view of the human brain illustrating the lobes of the brain and the different lobes.

Temporal lobe

Note. The lateral fissure separates the temporal and frontal lobes whereas the central sulcus separates the frontal and parietal lobes. From Fundamentals of Human Neuropsychology, fifth edition, by B. Kolb and I. Q. Whishaw, 2003, New York: Worth. Adapted with permission.

Amygdala Hippocampus

Figure 8.7 Medial view of the human brain showing the principal regions of the limbic system. Note. From Introduction to Brain and Behavior, second edition, by B. Kolb and I.Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

each of these lobes. The extensive distribution of visual functions speaks to the large amount of the cerebral cortex that is involved in some form of visual processing. The frontal lobe has motor, olfactory, and gustatory functions as well as an important integrative role that is sometimes referred to as “executive” function. The cerebral cortex can also be divided on the basis of its cellular architecture (called cytoarchitecture). Brodmann first described a detailed map of cortical regions early in the twentieth century (Kolb & Whishaw, 2003), assigning some 50 numbers to distinctly different regions (e.g., 1, 2, 3; Figure 8.6). Each Brodmann region can be associated with specific subfunctions of the lobes. With the development of newer anatomical methods over the past 100 years, the Brodmann map has been refined and expanded further (e.g., Petrides, 2005) and the general idea of distinct cytoarchitectonic zones that correspond to distinct functions has been enhanced.

evolutionary origin of these structures, some anatomists referred to these regions as the reptilian brain, but the term limbic (meaning lining) is more widely recognized. The limbic lobe is also sometimes referred to as the limbic system but given that the limbic regions do not function as a unified structure, the term limbic system is really a misnomer. The principal structures of the limbic lobe include the amygdala, hippocampus, and cingulate cortex (Figure 8.7). The amygdala plays a central role in emotion, and especially in fear. Removal of the amygdala completely removes fear, whereas overactivation of the amygdala can render a person highly anxious. The hippocampus has a central role in certain kinds of memory as well as in spatial navigation. Limbic regions are also partly responsible for the rewarding properties of experiences, including psychoactive drugs. The cingulate cortex has been associated with pain, emotion, and memory, and it connects extensively with the amygdala and hippocampus.

Limbic Lobe As the brain of amphibians and reptiles evolved, structures lining the brain stem began to emerge. In view of the

Lateral View

9 10

8

3 5 1 2

9

46 45 47

11 38

43

44 52

8

7 19

39

22 37

33

18 11

42 19

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31 30

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38

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19 18 17

25

20 20

3 1 2 5

24

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41 40

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36

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6

Medial View

37

19

18

Figure 8.6 Brodmann’s areas of the cortex. Note. From Fundamentals of Human Neuropsychology, fifth edition, by B. Kolb and I. Q. Whishaw, 2003, New York: Worth. Adapted with permission.

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Figure 8.8 Frontal section of the cerebral hemispheres showing the basal ganglia relative to surrounding structures. Note. From Introduction to Brain and Behavior, second edition, by B. Kolb and I. Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

Basal Ganglia The basal ganglia are a collection of nuclei that lie just below the white matter of the anterior region of the cerebral cortex (Figure 8.8). The three principal structures are the caudate nucleus, putamen, and globus pallidus. All cortical regions send connections to the basal ganglia, allowing the basal ganglia to be well informed about the activities of the cortex. The basal ganglia in turn send connections to the motor system, thus influencing movement. The functions of the basal ganglia can be observed by analyzing the behavior of patients with the many diseases that interfere with normal functioning of these regions. Among the most common disorders are Parkinson’s disease, in which movement becomes more difficult, and Huntington’s chorea, in which unwanted tics and gestures interfere with normal movement. Thus, basal ganglia damage does not produce a disorder in producing movements as in paralysis but rather produce a disorder in controlling movements. This distinction is important because it shows that movements are produced at lower levels (such as in the brain stem) but modulated by the forebrain.

RULES OF BRAIN FUNCTION Having considered the general organization of the brain, we are now in a position to look at the general principles that guide how the various parts of the brain work together. The Brain Produces Movement within a Perceptual World It Creates The simplest summary of brain function is that it produces behavior. To do so, however, it must have information about the world. Movements are not made in a vacuum but are related to objects, places, memories, and so on. The representation of the world is dependent on the nature of the information sent to the brain, however. A person who is

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color blind has a very different representation of the world than those who perceive color. Similarly, a person who has perfect pitch has a different world than those who do not. Furthermore, animals such as dogs have a rich olfactory world that humans do not share. In contrast, dogs have poor color vision. Our failure to perceive smells and the dogs’ failure to perceive colors does not mean that they are not there—only that the reality we create is different. Although we tend to think that the world that we perceive is what is actually there, it is clear that individual realities (both between and within species) are rough approximations of what is actually present. A special function of the brain of each animal species is to produce a reality that is adaptive for that species. In other words, the behavior that the brain produces is directly related to the details of the world that the brain has created. Dogs and people behave differently toward smells (or colors) because of the nature of the perceptual world that their respective brains have created. The Brain Creates “Maps” of the World Sensory information is represented in the brain in an orderly manner. Consider the feelings from your skin when a fly is walking along your arm. You perceive a place on your body and you can orient to it. Further, when the fly moves along the hand to the arm, you perceive it to be in different body locations. Similarly, when you want to move a finger, you can do so without making movements elsewhere. This specificity in perception and movement is enabled by sensory and motor maps of the body in your brain. Indeed, it was these body maps that Fritsch and Hitzig (1956) first found when they electrically stimulated the cortex. But maps are not just about the body. When we wander about the world we can identify places by sight and sound, so there must be visual and auditory maps as well, and these maps must somehow be coordinated because sights and sounds subjectively appear to be in the same place.

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Rules of Brain Function

Each sensory system has more than one map of the world. This is because maps are often quite specific. Although we perceive shape and color of objects to be a single thing, they are represented by separate maps (color versus shape) in the brain. We can make a similar distinction between the sensations of touch and pain in the skin. Similarly, sounds differ in their pitch as well as their meaning (language versus musical sounds). We therefore can think of the brain’s creation of sensory experience as a series of maps of different aspects of sensory information. One major change in the brain during evolution is the creation of more and more maps as the brain grows larger. Furthermore, species differences in sensory capacities reflect not only differences in the number of maps but also in the nature of the maps. Jerison (1991) suggested that the intelligence of a given species is related to the number of maps. As the brain develops more maps, it is necessary to bind these maps together to form single percepts from equivalent maps. One way to do this is to label the equivalencies to organize them. The labels would designate objects by their place and time in the external world. Labels can thus act to organize information and therefore form the basis of thought.

Sensory and Motor Functions Are Relatively Separated One of the oldest established laws of nervous system function is the law of Bell and Magendie (nineteenthcentury Scottish and French anatomists). They noticed in four-legged animals that sensory input to the spinal cord entered the top (dorsal) part of the cord, whereas motor outputs left via the bottom (ventral) part. (In upright-walking animals like humans, the dorsal region becomes the back and the ventral region the front but the principle remains.) This distinction between sensory and motor regions is maintained in the brain as well. Recall that the midbrain has a sensory region, the colliculi, and a motor region, the tegmentum. The sensorymotor distinction is obvious in the forebrain as well. We have separate maps for skin sensation and muscle movements. It is obvious that although sensory and motor functions are separated, they must also be closely related or we could not organize our movements to specific places and things. Recall, for example, that the sensory and motor regions of the midbrain act together to allow the brain to orient the body to visual and auditory stimuli. The integration of sensory and motor functions is therefore a critical function of the brain.

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145

Inputs and Outputs of the Brain Are Crossed One peculiar feature of brain organization is that most of the inputs and outputs are “crossed.” For example, the sensory inputs from the right side of the body, and thus information from the right side of the world, go to the left side of the brain, and the motor outputs of the left side of the brain go back to the right side of the body. This crossed organization explains why people with brain injury to the left side of the brain may have difficulty in moving the right side of the body. Animals with eyes on the sides of their heads, such as horses, have an arrangement of visual input with nearly all input from the left eye going to the right side of the brain and the right eye to the left side. Animals with the eyes located side-by-side at the front of the face, such as humans and cats, have a slightly different arrangement in which only about half of the projection crosses. The input from the left side of each eye goes to the right brain, and that from the right side goes to the left side. This arrangement shows that it is the views of each side of the viewer ’s world that are projected to the opposite brain hemisphere. A crossed brain must somehow join the two sides of the perceptual world together. As a result, innumerable connections link the two sides of the brain. The most prominent connection in the human brain is the corpus callosum, which is a large bundle of about 200 million nerve fibers that joins the cortex of the left and right hemispheres of the brain.

Brain Anatomy and Function Display Both Symmetry and Asymmetry Although the left and right hemispheres look very similar, there are some asymmetrical features in both the gross anatomy as well as the details of cytoarchitecture. Asymmetry is critical for certain mental functions because we require a single representation of sensory or motor functions to make appropriate behaviors. Consider language. If language were represented on both sides of the brain, we would have the disconcerting ability to speak out of both sides of our mouth at the same time. A simple solution is to locate language on one side of the brain, the left. The same organization holds for bird song—it is also located on the left side of the bird’s brain. The problem in processing of spatial information is handled in the same way. If we want to make a movement in space, we need to direct both sides of the body to the same place, and so one hemisphere organizes spatial behavior. Note, however, that we still need to be able to move our arms to different places and so exert motor control on both sides of the brain for these movements. Thus, although the hemispheres appear symmetrical structurally, they are asymmetrically involved

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in behavior with language normally found in the left hemisphere and various aspects of spatial behavior, located in the right hemisphere. We can now see why patients with surgery to cut the corpus callosum have difficulties and essentially have two minds, one in the left and one in the right hemisphere. Because language is in the left hemisphere, only the left side can speak, but, similarly, because many spatial functions are organized in the right hemisphere, only the right hemisphere can control some visuospatial functions “normally.” The Brain Works by a Juxtaposition of Excitation and Inhibition Although we have emphasized the brain’s role in making movements, we must also recognize that the brain acts to prevent movements as well. In order to make a directed movement such as picking up a glass of water, we must also not make other movements such as moving the hand back and forth. Thus, in producing movement, the brain through excitation produces some action and through inhibition prevents other action. One of the best examples of the control of excitation and inhibition can be seen in patients with Parkinson’s disease. Parkinson’s patients have an uncontrollable shaking of the hands because they have a failure in the system that inhibits such movements. Paradoxically, they often have difficulty in initiating movements and appear frozen because they are unable to generate the excitation needed to produce movements. This juxtaposition of excitation and inhibition is central to how the brain produces behavior and can be seen at the level of individual neurons. All neurons have a spontaneous rate of activity that can be either increased (excitation) or decreased (inhibition). Additionally, some neurons act to excite others, whereas other neurons are inhibitory. These excitatory and inhibitory actions are produced by specific neurochemicals via which neurons communicate. The primary excitatory chemical in the brain is glutamate and the primary inhibitory chemical in the brain is gamma-aminobutyric acid (GABA). Just as individual neurons can act in an excitatory or inhibitory manner, so can brain regions. This distinction can be seen in the effects of brain disease or injury. A brain injury to a region that normally initiates speech may render the person unable to talk whereas those with an injury to a region that inhibits inappropriate language (such as swearing) may be unable to inhibit this form of talking. Thus, brain injury can produce either a loss or a release of behavior via changes in the balance of excitation and inhibition.

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The Nervous System Functions on Multiple Levels Sensory and motor functions are carried out at many places in the brain. For example, both sensory processing and motor control occur in the spinal cord, the brain stem, as well as the forebrain. This multiplicity of functions results from the nature of brain evolution. Simple animals such as worms have mainly a spinal cord, more complex animals such as fish have a brain stem as well, and more complex animals have added a forebrain (Figure 8.9). Each new addition to the brain added a new level of behavioral complexity but did not discard previous levels of control. For example, as animals evolved legs, they also had to add forebrain area to move them and later when they developed independent digit movements this too required more forebrain area. The addition of new brain areas can be viewed as adding new levels of nervous system control. The new levels are not autonomous, however, but must be integrated into the existing neural systems. Adding the capacity to move fingers must be related to the prior capacity to move the limbs. Each new level can be conceived as a way of refining and elaborating the control provided by the earlier levels. The idea of levels can not only be seen in the addition of forebrain areas to refine the control of the brain stem but also within the forebrain we can see the addition of new areas. As mammals evolved, they developed an increased capacity to represent the world in the cortex, an ability that is related to the addition of more maps. The new maps must be related to the older ones, however, and again simply reflect an elaboration of the sensory world that was there before.

Brain Systems Are Organized Both Hierarchically and in Parallel A complication with adding multiple levels of brain area is that the levels must be extensively interconnected. There are two different solutions to the wiring problem: serial and parallel circuits (Figure 8.10). A serial circuit hooks up a linear series of all regions concerned with a particular function. Consider vision. In a serial system the information from the eyes goes to regions that detect the simplest properties such as color or brightness. This information would then be passed to another region that determines shape and then to another region that measures movement and so on until at the most complex level the information is understood to be your grandmother. Information therefore flows in a hierarchical manner sequentially from simpler to more complex regions. In addition to this hierarchy, there are parallel systems. Recall in our earlier example that patient D.F. could not

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Rules of Brain Function

Highest Remaining Functional Area

147

Behaviors

Reflexes: Responds by stretching, withdrawal, support, scratching, paw shaking, etc. to appropriate sensory stimulation. Spinal cord (spinal)

Hindbrain (low decerebrate)

Midbrain (high decerebrate)

Hypothalamus, thalamus (diencephalic)

Postural support: Performs units of movement (hissing, biting, growling, chewing, lapping, licking, etc.) when stimulated; shows exaggerated standing, postural reflexes, and elements of sleepwalking behavior. Spontaneous movement: Responds to simple features of visual and auditory stimulation; performs automatic behaviors such as grooming; performs sunsets of voluntary movements (standing, walking, turning, jumping, climbing, etc.) when stimulated. Affect and motivation: Voluntary movements occur spontaneously and excessively but are aimless; shows well-integrated but poorly directed affective behavior; thermoregulates effectively.

Self-maintenance: Links voluntary movements and automatic movements sufficiently well for self-maintenance (eating, drinking) in a simple environment. Basal ganglia (decorticate)

Cortex (normal)

Control and intention: Performs sequences of voluntary movements in organized patterns; responds to patterns of sensory stimulation. Contains circuits for forming cognitive maps and for responding to the relationships between objects, events, and things. Adds emotional value.

consciously perceive objects but could reach for them, thus reflecting a parallel conscious and unconscious visual system. But even within these two systems, there is parallel processing. Our color vision is well suited to distinguishing form and texture, and so is useful for perceiving grandmother. Our black-and-white vision is better suited to detecting the movement of objects. Color vision and blackand-white vision are dependent on different receptors in the eye (cones versus rods), different pathways to the cortex, and different functional areas in the cortex, and eventually different cognitive and behavioral functions. Similar arrangements occur for other sensory systems; for example, the body senses of fine touch and pressure and pain and temperature are mediated by different parallel systems. The parallel/hierarchical organization of the brain is further reflected in the organization of complex mental

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Figure 8.9 The anatomical and behavioral levels of the nervous system. Note. Shading indicates the highest remaining functional area, in a hierarchy from spinal cord to cortex. From Introduction to Brain and Behavior, second edition, by B. Kolb and I. Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

processes, such as language and memory. Consider, for example, that the meaning of words can be influenced by tone of voice. The actual word is stored in the left hemisphere language zones but the tone of voice is a function of the right hemisphere, again reflecting parallel processing of auditory information. The various levels of neural organization for mental processes thus may be fairly widely distributed in the brain, leading to the concept that such functions are really a result of the activity of a complex network of connected regions rather than a simple serial network. Functions in the Brain Are Both Localized and Distributed The identification of specific language regions (i.e., Broca’s and Wernicke’s areas) led to the idea that functions

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Sensory Input to the Brain Is Divided for Object Recognition and Motor Control

(A) Secondary

Primary

Tertiary

(B) Level 4 Level 3 Level 4

Level 2 Primary

Level 3 Level 4

Level 2 Level 3

Level 4

Figure 8.10 Models of cortical processing. Note. A: Simple serial hierarchical model of cortical processing. B: Distributed hierarchical model. From Introduction to Brain and Behavior, second edition, by B. Kolb and I. Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

were localized—at least in the forebrain. The fundamental problem, however, is in defining a function. Consider language as an example. Language includes the processes of producing words orally, in writing, and by sign language, as well as constructing complex compositions such as poems, stories, songs, and so on. Language also includes the comprehension of written, oral, and sign language, and even touched letters (Braille). Language also may include the capacity to use multiple languages. It also includes the ability to sing and play musical instruments. Language is clearly not a single function and must require many different types of neural processing that are widely distributed in the brain. People with selective brain injuries may lose specific language abilities to produce words, read words, understand words, and so forth. They may lose the ability to name living things but not inanimate things and vice versa. Only if damage is extensive is language extensively compromised. Thus, we can see that language is distributed in the brain, with specific language-related skills found in relatively discrete locations. Other psychological functions such as memory, social/ emotional behavior, spatial behavior, and so on also show the same pattern of localization and distribution of function. It therefore would take massive disease or injuries to completely eliminate any complex function. Indeed, one of the characteristics of dementia diseases such as Alzheimer ’s is that people can withstand widespread deterioration of the cortex and yet maintain remarkable functions until the disease is well progressed.

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Sensory systems evolved first for controlling motion, not for the recognition of specific information. Simple organisms can detect information and move to or from it. It is not necessary to “perceive” an object to direct movements toward or away from it. As animals, and their behaviors, became more complex they began to evolve ways of representing their environment. In animals with complex brains, such as ourselves, there are distinct systems for producing movement toward objects and for recognizing objects. The visual system serves as an example as we discovered in the case of D.F. (Figure 8.11). Visual information goes from the eyes to the brain stem to visual regions of the occipital lobe where it becomes divided: one route, known as the ventral stream, is to the temporal lobe for object recognition whereas a second route, known as the dorsal stream, is to the parietal lobe for the guidance of movement relative to objects (Milner & Goodale, 2006). Evidence that these systems are independent can be seen in people with injuries to the ventral or dorsal stream, respectively (for a review see Milner & Goodale, 2006). People such as D.F. with ventral stream injuries are “blind” for the recognition of objects, yet they nevertheless shape their hand appropriately when asked to reach for the objects that they cannot identify. Consider reaching for a cup, for example. When normal subjects reach for a cup, their hand forms a shape that is different than when they reach for a spoon. People with ventral stream injuries can make appropriate hand shapes yet they do not consciously recognize the object. In contrast, people with dorsal stream injuries can recognize objects Parietal lobe

Occipital lobe

Temporal lobe

Figure 8.11 Two streams of visual processing. Note. The dorsal stream is an unconscious online control of movement. The ventral stream is a conscious system for object recognition. From Introduction to Brain and Behavior, second edition, by B. Kolb and I. Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

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but make clumsy reaching movements because they do not form appropriate hand postures until they contact objects and then shape their hand based on tactile information. The recognition that the perception for movement and perception for thought are independent processes has important implications for understanding brain organization. First, these two systems provide an excellent example of parallel processing. Second, although our impression may be that we are aware of our sensory world, it is clear that the sensory analysis required for some movements is not conscious. Third, the presence of nonconscious and conscious brain processing underlies an important difference in our cognitive functions. The unconscious movement system is always acting in the present and in response to online sensory input. In contrast, the recognition system allows us to escape the present and to bring to bear information from the past. Thus, the recognition systems form the neural basis of enduring memory.

The emergence of a large prefrontal region is correlated with the emergence of sophisticated behavioral abilities required to organize behavior in place and time. The temporal aspect includes the development of a concept of the role of the self over time—in the past, present, and the future. Such abilities require a system that can constrain the search for sensory information, a process often referred to as “attention.” Attentional systems require a mechanism for continuous monitoring of both external and internal events, which essentially is a system designed for shortterm, or working, memory. A loss of prefrontal function, such as in diseases like schizophrenia or drug addiction, result in a loss of executive control of behavior, leading to disorganized and maladaptive responses to sensory information. The complexity of prefrontal functioning requires that this cerebral region develop slowly and likely does not fully mature until about 20 years of age in humans.

Prefrontal Cortex Combines Object and Motor Control Systems

Individual Differences in Brain Organization

The division of sensory systems into an object-related and a motor-control system does not shed light on how animals can decide to do something before goal objects are present. This form of long-term planning and behavioral organization evolved in parallel to the sensory systems and resides in a region known as the prefrontal cortex (Figure 8.12). The prefrontal cortex is the cerebral region lying at the frontmost region of the frontal lobe and is found in all mammals (Kolb, 2006). In primitive mammals, the prefrontal cortex is modest in size, but as more sensory maps are added in evolution, there is a corresponding increase in volume of the prefrontal cortex such that in humans this cortex represents about 15% of the cerebral cortex.

Premotor cortex

Motor cortex

It is remarkable how different we can be from each other. In part that is because no two brains are identical. There are, however, several factors that increase the interindividual variation in the brain, two prominent ones being sex and handedness (for a review, see Kolb & Whishaw, 2008). Just as gonadal hormones produce differences in genitalia, these hormones also produce differences in brain structure and thus brain function. Sex-related differences can be seen both in the gross anatomy of brain regions such as in the hypothalamus, as well as in the details of cell structure in the forebrain. These anatomical differences lead to a wide range of behavioral differences, including the superior verbal ability of women and the superior spatial ability of men. The differences are not large, on the order of less

Central sulcus

Dorsolateral prefrontal cortex Prefrontal Cortex

Inferior prefrontal cortex

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Orbital cortex

Figure 8.12 The organization of the frontal lobe. Note. From Introduction to Brain and Behavior, second edition, by B. Kolb and I. Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

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Neuroanatomy/Neuropsychology

than a standard deviation, but they are consistent and are found across a wide range of populations and cultures. Similarly, there are differences in gross anatomy, cell structure, and connectivity in the right- and left-handed brain. Language provides a good example. At least 99% of right-handers have language in the left hemisphere but only about 67% of left-handers do. Although it is not known what anatomical differences predict which left-handers have left versus right hemisphere language, there is little doubt that there is some difference in neuronal organization that leads to the lateralization of language in the left versus the right.

Details of Brain Functioning Constantly Change Variability in neuronal organization is not only related to factors such as sex and handedness but is also related to experience. Experience-dependent variability reflects the brain’s capacity to alter its structure and function in reaction to environmental diversity, thus reflecting a capacity that is often referred to as brain plasticity. Although this term is now commonly used in psychology and neuroscience, it is not easily defined and is used to refer to changes at many levels in the nervous system ranging from molecular events, such as changes in gene expression, to behavior (e.g., Kolb & Gibb, 2008; Shaw & McEachern, 2001). Brain plasticity is required for learning and memory functions. In fact, information is stored in the nervous system only if there are changes in neuronal connectivity. Forgetting presumably reflects a loss of the connections that represented the memory. Brain plasticity is not just a characteristic of the mammalian brain but is found in the nervous system of all animals including even the simplest animals, such as the nematode C. elegans that is only a millimeter or so long (e.g., Rankin, 2005). Nonetheless, larger brains have more capacity for change and thus are likely to show more variability in neuronal organization. Brain plasticity is not always a good thing. Analysis of the brains of animals given addicting doses of drugs such as cocaine or morphine have shown large changes in neuronal connectivity that are suspected of underlying some of the maladaptive behaviors related to addiction (for a review, see Robinson & Kolb, 2004). There are many other examples of pathological plasticity including pathological pain (Baranauskas, 2001), pathological response to sickness (Raison, Capuron, & Miller, 2006), epilepsy (Teskey, 2001), and dementia (Mattson, Duan, Chan, & Guo, 2001).

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Psychological Functions Emerge from Extended Cerebral Networks Psychological functions such as memory, attention, emotion, and language can be described by words but they remain hypothetical constructs. A construct like memory is not a single thing but rather a reflection of many subprocesses, which we collectively refer to as memory. For example, we have memory for places, objects, faces, music, words, motor skills, and so on, and each of these requires a distinctive type of sensory processing. Furthermore, we have short-term memories of ongoing events and longterm memories of long past events. We also have memories of specific events as well as memories for which we can ascribe no single experience (e.g., knowing your own name). Nonetheless, although it should be no surprise that the memory of an old song or the rules of tennis are housed independently in the brain, there is a natural temptation to think that memory is found in a place in the brain. It is not. Thus, psychological constructs such as memory are widely distributed in both cortical and subcortical regions. The same is true of other psychological functions. The brain is not built on the concept of psychological functions but rather is built to support the processes that underlie different aspects of the functions. One example is language. We noted that for most people language is processed in the left hemisphere; however this is not because the brain evolved a place for language functions but rather that language requires certain types of auditory and motor processing that are housed in the left hemisphere.

SUMMARY The brain has a long evolutionary history and from a beginning of a few scattered neurons it has evolved in some species into a large, centrally located organ that represents the past, the present, and the future. Nevertheless, despite differences in size and complexity, the brains of different animal species are built on the same plan such that different regions are readily recognizable in different brains. This no doubt accounts for the many similarities displayed by diverse animal species and also allows us to generalize from the behavior of animals with simpler brains to ourselves. The brain has grown larger by the growth of existing structures and the addition of new structures, and, accordingly, behavior has become more complex by the expansion of some abilities and the addition of new behavioral strategies. The human brain has retained more primitive neural systems via which it makes online unconscious responses to the world but added systems via which it can represent that world consciously as a past, present,

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References 151

or future. In addition, despite having a basic anatomical plan, the brains of individuals vary enormously depending on their sex, handedness, and personal experience. Despite this plasticity, injury or disease that damages the brain reduces behavioral complexity by producing specific deficits if damage is limited, or generalized deficits if damage is extensive. Despite what neuroscience has learned about the brain, neuroscientists continue to study the factors that produce our conscious awareness of ourselves as individuals.

Kolb, B., & Whishaw, I. Q. (2008). Fundamentals of human neuropsychology (6th ed.). New York: Worth. Lashley, K. S. (1960). Functional determinants of cerebral localization. In F. A. Beach, D. O. Hebb, C. T. Morgan, & H. W. Nissen (Eds.), The neuropsychology of lashley (pp. 328–344). New York: McGraw-Hill. Mattson, M. P., Duan, W., Chan, S. L., & Guo, Z. (2001). Apoptotic and antiapoptotic signaling at the synapse: From adaptive plasticity to neurodegenerative disorders. In C. A. Shaw & J. McEachern (Eds.), Toward a theory of neuroplasticity (pp. 402–426). Philadelphia: Psychology Press. Milner, D., & Goodale, M. A. (2006). The visual brain in action (2nd ed.). New York: Oxford. Penfield, W., & Roberts, L. (1956). Speech and brain mechanisms. Princeton, NJ: Princeton University Press.

REFERENCES Baranauskas, G. (2001). Pain-induced plasticity in the spinal cord. In C. A. Shaw & J. McEachern (Eds.), Toward a theory of neuroplasticity (pp. 373–386). Philadelphia: Psychology Press. Fristch, G., & Hitzig, E. (1956). On the electrical excitability of the cerebrum. In G. von Bonin (Ed.), The cerebral cortex (pp. 73–96). Springfield, IL: Charles C Thomas. Hasson, U. Y., Nir, I. I., Levy, G., Fuhrmann, G. & Malach, R. (2004). Intersubject synchronization of cortical activity during natural vision. Science, 303, 1634–1640. Hebb, D. O. (1949). Organization of behavior. New York: McGraw-Hill. Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 116, 497–506. Holmes, G. (1918). Disturbances of vision by cerebral lesions. British Journal of Ophthamology, 2, 353–384. Jerison, H. (1991). Brain size and the evolution of mind. New York: American Museum of Natural History.

Raison, C. L., Capuron, L., & Miller, A. H. (2006). Cytokines sing the blues: Inflammation and the pathogenesis of depression. Trends in Immunology, 27, 24–31. Rankin, C. H. (2005). Nematode memory: Now, where was I? Current Biology, 15, R374–R375. Robinson, T. E., & Kolb, B. (2004). Structural plasticity associated with drugs of abuse. Neuropharmacology, 47(Suppl 1), 33–46. Scoville, W. B., & Milner, B. (1957). Loss of recent memory after bilateral hippocampal lesions. Journal of Neurology, Neurosurgery, and Psychiatry, 20, 11–21. Shaw, C. A., & McEachern, J. C. (Eds.). (2001). Toward a theory of neuroplasticity (pp. 176–192). Philadelphia: Psychology Press. Sherrington, C. S. (1948). The integrative action of the nervous system. New Haven, CT: Yale University Press. Sperry, R.W. (1974). Lateral specialization in the surgically separated hemispheres. In F.O. Schmitt and JF.G. Worden (Eds.). Neurosciences: Third study program (pp. 5–20). Cambridge, MA: MIT Press.

Kolb, B. (2006). Do all mammals have a prefrontal cortex. In J. Kaas (Ed.), Evolution of nervous systems: A comprehenive review (Vol. 3, pp. 443–450). New York: Elsevier.

Teskey, G. C. (2001). Using kindling to model the nreuoplastic changes associated with learning and memory, neuropsychiatric disorders, and epilepsy. In C. A. Shaw & J. C. McEachern (Eds.), Toward a theory of neuroplasticity (pp. 347–358). Philadelphia: Psychology Press.

Kolb, B., & Gibb, R. (2008). Principles of neuroplasticity and behavior. In D. Stuss, I. Robertson, & G. Winocur (Eds.), Brain plasticity and rehabilitation, pp. 6–21). New York: Oxford University Press.

Weaver, I. C., Cervoni, N., Champagne, F. A., D’Alessio, A. C., Sharma, S., Seckl, J. R., et al. (2004). Epigenetic programming by maternal behavior. Nature Neuroscience, 7, 847–854.

Kolb, B., & Tees, R. C. (1990). The cerebral cortex of the rat. Cambridge, MA: MIT Press.

Whishaw, I. Q., & Kolb, B. (2006). The behavior of the laboratory rat. New York: Oxford.

Kolb, B., & Whishaw, I. Q. (2003). Fundamentals of human neuropsychology (5th ed.). New York: Worth.

Woolsey, C.N. (1958). Organization of somatic sensory and motor areas of cerebral cortex. In H. F. Harlow & C. N. Woolsey (Eds.), Biological and biochemical basis of behavior (pp. 63–81). Madison: University of Wisconsin Press.

Kolb, B., & Whishaw, I. Q. (2005). Introduction to brain and behavior (2nd ed.). New York: Worth.

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Petrides, M. (2005). Lateral prefrontal cortex: Architectonic and functional organization. Philosophical Transactions of the Royal Society of London. Series B, 360, 781–795.

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Chapter 9

Essentials of Functional Neuroimaging TOR D. WAGER, LUIS HERNANDEZ, AND MARTIN A. LINDQUIST

There has been explosive interest in the use of brain imaging to study cognitive and affective processes in recent years (Wager, Hernandez, Jonides, & Lindquist, 2007). The neuroimaging data from functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies are central to the emerging fields of cognitive neuroscience, affective neuroscience, social cognitive neuroscience, neuroeconomics, and related neurobehavioral disciplines. fMRI and PET data are being combined with data on human performance and psychophysiology in increasingly sophisticated ways to yield models of human thought, emotion, and behavior. The best such models are informed by the rich histories of cognitive psychology and psychophysiology and—due largely to the integration of neuroimaging data—are grounded in brain physiology. This grounding permits stronger and more specific connections with the neurosciences and biomedical sciences, allowing behavioral scientists to leverage a vast and growing literature on brain systems developed in these fields. All methods used in the human neurobehavioral sciences have limitations, and neuroimaging is no exception. The current trend is toward increasingly interdisciplinary approaches that use multiple methodologies to overcome some of the limitations of each method used in isolation. Recent advances in engineering and signal processing allow electroencephalography (EEG) and fMRI data to be collected simultaneously (Goldman, Stern, Engel, & Cohen, 2000), which provides improved temporal precision, among other benefits. Combined fMRI and EEG/ magnetoencephalography (MEG) analyses are being developed that can provide better spatiotemporal resolution than either method alone (Dale et al., 2000; V. Menon, Ford, Lim, Glover, & Pfefferbaum, 1997). Neuroimaging data are also being combined with transcranial magnetic

stimulation to integrate the ability of neuroimaging to observe brain activity with the ability of transcranial magnetic stimulation (TMS) to manipulate brain function and examine causal effects (Bohning et al., 1997). The rapid pace of development and interdisciplinary nature of the neurobehavioral sciences presents an enormous challenge to researchers. Moving this kind of science forward requires a collaborative team with expertise in psychology, neuroanatomy, neurophysiology, physics, biomedical engineering, statistics, signal processing, and other disciplines depending on the research questions. True interdisciplinary collaboration is exceedingly challenging, because team members must know enough about the other disciplines to talk intelligently with experts in each field. Lead researchers on neuroimaging projects must know when to ask for help with various aspects of the project and what kind of expertise is needed. Supporting researchers must understand enough about the research questions and possibilities to bring their knowledge to bear in an optimal way. The goal in this chapter is to review the basic techniques involved in the acquisition and analysis of neuroimaging data—and some recent developments—in enough detail to highlight the most important issues and concerns. We also provide an overall road map of what kinds of study design and analysis options are available and some of their important limitations. The aspects of PET and fMRI methodology are organized here into four sections. The first section deals with what neuroimaging techniques measure, including the essentials of PET and fMRI data acquisition and the relationship between brain activity and observed signals in each modality. The second section describes the hierarchical structure of neuroimaging data and how these data are used to make psychological inferences. We emphasize two kinds of inferences: forward inferences about brain

Parts of this chapter are adapted from Wager, Hernandez, Jonides, and Lindquist (2007). Elements of functional neuroimaging. In Cacioppo, Tassinary, and Berntson (Eds.), Handbook of psychophysiology, fourth edition (pp. 19–55). Cambridge: Cambridge University Press. We would like to thank Dr. Doug Noll for providing Figure 9.3, and Matthew Davidson, Damon Abraham, Katherine Dahl, and Bryan Denny for helpful comments on the manuscript. 152

Handbook of Neuroscience for the Behavioral Science, edited by Gary G. Berntson and John T. Cacioppo. Copyright # 2009 John Wiley & Sons, Inc. c09.indd 152

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What Neuroimaging Techniques Measure

activity given a psychological experimental manipulation, and reverse inferences about psychology given patterns of brain activation. This section also deals with statistical inferences about populations and the localization of results from functional neuroimaging studies. The third section discusses experimental designs for neuroimaging experiments, including some considerations that are particular to neuroimaging data. The fourth section deals with neuroimaging data analysis, including sections on artifacts and signal processing before analysis (preprocessing), the general linear model (GLM), brain-behavior and brain-physiology relationships, and methods for investigating brain connectivity.

WHAT NEUROIMAGING TECHNIQUES MEASURE There are many ways to measure brain function, including fMRI, PET, single positron emission computerized tomography (SPECT), electroencephalography (EEG) with analysis of event-related potentials (ERP; Fabiani, Gratton, & Federmeier, 2007; Pizzagalli, 2007), magnetoencephalography (MEG; Hämäläinen, Hari, Ilmoniemi, Knuutila, & Lounasmaa, 1993), and near-infrared spectroscopy (Villringer & Chance, 1997). Each of these techniques provides a unique window into the functions of mind and brain. In this chapter, we mainly focus on PET and fMRI, because they are the most widely used and provide the

Table 9.1

153

most anatomically specific information across the entire brain. The relatively good spatial resolution of PET and fMRI complement the precise timing information provided by EEG and MEG. In addition, the ability of fMRI to measure activity over the entire brain every 2 s or so offers great potential for synergy with animal research. Whereas animal electrophysiology and lesion experiments are often focused on a single region, neuroimaging can assess global function and interactions across large-scale brain systems. PET and fMRI can be used in different ways, depending on the software and type of imaging chosen, to measure biological processes related to brain activity. Measures are generally obtained for each of a large number of local regions of brain tissue called “voxels” (three-dimensional pixels; imagine little cubes stacked together), providing 3-D brain maps. The partial list of popular measures and techniques shown in Table 9.1 includes measures of both brain structure and function. Structural measures may be divided into measures related to gray- and white-matter volume and density, and measures related to neurochemical receptors and other biomarkers. The most frequently used functional measures are those that measure processes related to overall neuronal/ glial activity, referred to here as “activation.” These measures include measures of glucose metabolism, blood flow or perfusion in PET and arterial spin labeling (ASL), and the Blood Oxygen Level Dependent (BOLD) signal in fMRI. Activation and deactivation in both PET and fMRI reflect changes in neural activity only indirectly, and they measure

Summary of PET and fMRI methods. Techniques for Studying Brain Structure

What Is Imaged?

Technique

Analysis

Gray/white matter/CSF distinctions

T1-weighted imaging (MRI)

Voxel-based morphometry (VBM), volume-based measures, surface-based measures (e.g., cortical thickness)

Gray/white matter/CSF distinctions

T2-weighted imaging (MRI)

Same as preceding

White-matter structure

Diffusion tensor imaging (DTI); Radioligand binding

Diffusion tractography

Neurochemical receptor occupancy

(PET); GABA-A: [C-11] flumazenil; dopamine D2:

Kinetic modeling, Logan-plot analysis

[C-11 raclopride]; Mu-opioids, [C-11] carfentanil; acetylcholine: [F-18] epibatidine, [C-11] scopolamine, serotonin: [C- 11] benzylamine; others

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Gene expression

PET radiolabeling; MR spectroscopy with kinetic modeling

Metabolites and various biomarkers

MR spectroscopy

Regional blood flow (perfusion)

[O-15] PET

Voxel-wise linear modeling; multivariate connectivity techniques

Relative Hb deoxygenation

Blood Oxygen Level Dependent (BOLD) signal, T2*-weighted image

Same as preceding

Glucose metabolism

[F-18]-fluorodeoxyglucose (FDG) PET

Same as preceding

Regional blood flow (perfusion)

Arterial spin labeling (ASL) fMRI

Same as preceding

Task-related neurochemistry

Radioligand binding (PET); see above

Kinetic modeling, Logan-plot analysis followed by linear modeling

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Table 9.2

Relative advantages of fMRI and PET.

Advantages of fMRI Cost and availability

fMRI has lower cost, more facilities available

Spatial resolution

fMRI has higher resolution, but new PET scanners can have same functional resolution for group studies

Temporal resolution

fMRI is superior, permitting event-related designs

Brain connectivity analyses

fMRI permits time-series connectivity analysis; PET and fMRI both permit individual differences analysis

Combination with other measures

Simultaneous time-series acquisition of fMRI and EEG provides most detailed mapping of relationships

Single-subject studies

fMRI permits detailed high-resolution studies of individuals

Repeatability

fMRI does not use radioactive substances, so frequent scans are considered safe

Measuring neurochemistry

PET is superior; can be used to directly investigate neurochemistry

Transparency of activation measures

PET provides more direct measures of blood flow or metabolism

Artifacts

PET does not suffer from magnetic susceptibility artifacts and gradient- or RF-related artifacts

Combination with other measures

PET is not magnetic and can be combined with simultaneous EEG, MEG, and TMS

Studying baseline activity

PET provides quantitative measure of baseline state; ASL fMRI also can, but is less commonly available

Naturalness of environment

PET is quieter and has more open physical environment; advantage for auditory and emotion tasks

different biological processes related to brain activity, which may be broadly defined as the energy-consuming activity of neurons and glia, and the electrical and chemical signals they produce. Thus, both PET and fMRI can be used to measure brain activity, though each has unique advantages. These are summarized in Table 9.2. Measures of Brain Structure Structural Scans MRI can provide detailed anatomical scans of gray and white matter with a spatial resolution well below 1 mm3. These images are used to localize functional results in individual or group-averaged brains. A growing set of measures related to brain structure allows for the analysis of changes with practice or development, effects of aging, and differences between healthy individuals and those with psychological disorders. A popular way of analyzing gray-matter density is the voxel-based morphometry (VBM) method (Ashburner & Friston, 2000; Good et al., 2001), which uses structural image intensity to measure gray- and white-matter density. Other methods use measures of cortical thickness derived from surface reconstruction and unfolding (Fischl, Sereno, & Dale, 1999; Van Essen & Dierker, 2007), or the volume of anatomically defined structures. A recent study reported that London taxi drivers, who had developed extensive expertise in spatial navigation, had larger posterior hippocampi (Maguire et al., 2000).

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Both structural and functional MRI images are obtained using the same scanner; the only difference is in how the scanner is programmed. A brief overview of the image acquisition process is as follows. A sample (e.g., a brain) is placed in a strong magnetic field and exposed to a radiofrequency (RF) electromagnetic field pulse. The nuclei absorb the energy only at a particular frequency band, which is strongly dependent on their electromagnetic environment, and become “excited” (they change their quantum energy state). The nuclei then emit the energy at the same frequency as they “relax.” The same antenna that produced the RF field detects the returned energy. Pulse sequences, or software programs that implement particular patterns of RF and gradient magnetic field manipulations (manipulations of the magnetic field’s shape), are used to acquire data that can be reconstructed into a map of the MR signal sources, that is, an image of the brain. Pulse sequence programming is the province of physicists and bioengineers; such divisions of labor among physicists, psychologists, neuroscientists, and statisticians are a hallmark of neuroimaging, which is highly interdisciplinary in nature. For more in-depth information, we recommend two approachable texts (Elster, 1994; Huettel, Song, & McCarthy, 2004), and more detailed texts for the advanced reader (Bernstein, King, & Zhou, 2004; Haacke, 1999). The relaxation process can be described by three values: T1, T2, and T2*. T1 and T2 are constants determined by the spin frequency, field strength, and tissue type (largely based on the hydrogen content, which depends in turn on

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What Neuroimaging Techniques Measure

A

155

B

C

Figure 9.1 The same slice of brain tissue can appear very different, depending on which relaxation mechanism is emphasized as the source of contrast in the pulse sequence. Note: Using long echo times emphasizes T2 differences among tissue types, and shortening the repetition time emphasizes T1 differences among tissue types. The same slice of the brain acquired as A: a T1weighted image and B: a T2-weighted image. C: Diffusion tensor imaging

how much water is in the tissue). T1 refers to the rate at which spins relax back to alignment with the main magnetic field, and T2 refers to the rate of attenuation of the magnetic field applied by the RF pulse. T2* is like T2, but depends additionally on local inhomogeneities in magnetic susceptibility that are caused by changes in blood flow and oxygenation, among other factors. T1 and T2 are constants determined by the spin frequency, field strength, and tissue type (largely based on the hydrogen content, which depends in turn on how much water is in the tissue). Different pulse sequences—patterns of RF excitations and data collection periods—produce images that are sensitive primarily to T1, T2, or T2*. Because T1 and T2 vary with tissue type but are otherwise constant, T1- and T2-weighted images can produce detailed representations of the boundaries between gray matter (mostly cell bodies), white matter (mostly axons), and cerebrospinal fluid (CSF). Because T2* is sensitive to flow and oxygenation, T2*-weighting is used to create images of brain function. An example of the same slice of tissue imaged with T1 and T2 weighting can be seen in Figure 9.1 A and B. The images look strikingly different. Changing the contrast mechanism can be very useful in differentiating brain structures or lesions, since some structures will be apparent in some kinds of images

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allows researchers to measure directional diffusion and reconstruct the fiber tracts of the brain. This provides a way to study how different brain areas are connected. Diffusion image is adapted from “Probabilistic Diffusion Tractography with Multiple Fibre Orientations: What Can We Gain?” by T. E. J. Behrens, H. J. Berg, S. Jbabdi, M. F. S., Rushworth, & M. W. Woolrich, 2007, NeuroImage, 34, pp. 144–155. Adapted with permission.

but not in others. Multiple sclerosis lesions are virtually invisible in T1-weighted images, but appear brightly in T2weighted images. Anatomical Connectivity MRI pulse sequences may also be tuned to be sensitive to directional (anisotropic) patterns of water diffusion, which may be used to track the course of axon (fiber) tracts. Water diffuses more readily along the axons that make up the brain’s white matter than across them. Diffusion tensor imaging (DTI) is an increasingly popular technique for measuring directional diffusion and reconstructing the fiber tracts of the brain (Figure 9.1 C; Denis Le Bihan et al., 2001). New tractography analyses for quantifying the thickness and connectivity of these tracts are being rapidly developed (Behrens et al., 2007). Such tools will increasingly allow researchers to analyze the relationships between structural connectivity and neuropsychological processes such as development, training, aging, cognitive and emotional function, and psychopathology (JohansenBerg & Behrens, 2006). DTI can be combined with other techniques, such as fMRI or other anatomical and neurochemical measures. One study used DTI to define adjacent subregions of the medial prefrontal cortex, and then used

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fMRI to show that the subregions responded differentially to different tasks (Johansen-Berg et al., 2004). Other Anatomical Measures PET imaging is complementary to MRI in an important way: It permits estimation of the density of a variety of neurochemical receptors across the brain. A radioactive label is chemically attached to a pharmacological agent and injected into the bloodstream. The agent is transported into the brain, where it binds to a specific class of receptors, depending on its biochemical nature. The PET camera detects the radiation emitted when the radioactive label decays, and so provides a 3-D map of the distribution of labeled substance across the brain. Kinetic models, which use systems of differential equations in conjunction with known kinetic properties of the pharmacological agent, can be used to quantify the label in extravascular space (tissue) and that bound to receptors. Related neurochemical measures, such as the rate of dopamine synthesis, can be obtained as well. This method is often used to study changes in endogenous neurochemical release, and we describe it more fully later in this chapter. In addition, MR spectroscopy provides a way of testing for the presence of biochemicals and some kinds of gene expression in a brain volume of interest, though this has not been widely applied yet in the cognitive neurosciences. Certain compounds produce well-defined peaks in the measured frequency spectrum, and can be readily detected, but many compounds of interest in neuroscience cannot.

quite fast, and their half-lives vary from a couple of minutes to a few hours, which means that a cyclotron must be available nearby to synthesize the radioactive tracer minutes before each PET scan. The tracer is injected into the subject’s bloodstream in either a bolus or a constant infusion that produces a steady-state concentration of tracer in the brain. As the tracer decays within the blood vessels and tissue of the brain, positrons are emitted. The positrons collide with nearby electrons (being oppositely charged, they attract), annihilating both particles and emitting two photons that shoot off in opposite directions. Photoreceptive cells positioned in an array around the participant’s head detect the photons. The fact that matched pairs of photons travel in exactly opposite directions and reach the detectors simultaneously is important for the tomographic reconstruction of the three-dimensional locations where the particles were annihilated. Note that the scanner does not directly detect the positrons themselves; it detects the energy that results from their annihilation. Depending on the design, most PET scanners are made up of an array of detectors that are arranged in a circle around the patient’s head, or in two separate flat arrays that are rotated around the patient’s head by a gantry. To detect simultaneously occurring pairs of photons, each pair of detectors on opposite sides of the participant’s head must be wired to a “coincidence detector” circuit, as illustrated in Figure 9.2. Small tubes (called septa or collimators) are placed around the detectors to shield them from radiation

Measures of Brain Activity Using Positron Emission Tomography Perhaps the most frequent use of both PET and fMRI is the study of metabolic and vascular changes that accompany changes in neural activity. With PET, we can separately measure glucose metabolism, oxygen consumption, and regional cerebral blood flow (rCBF). Each of these techniques allows us to make inferences about the localization of neural activity based on the assumption that neural activity is accompanied by a change in metabolism, in oxygen consumption, or in blood flow. The PET camera provides images by detecting positrons emitted by a radioactive tracer, the frequencies of which are reconstructed into three-dimensional volumes. Positrons are subatomic particles having the same mass but opposite charge as an electron—they are “antimatter electrons.” The most common radioactive tracers are 15O, “oxygen-15,” commonly used in blood-flow studies, 18F (fluorine), used in deoxyglucose mapping, and 13C (carbon) or 123I (iodine), used to label raclopride and other receptor agonists and antagonists. The decay rate of such isotopes is

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Positron emitted by isotope decay Neighboring electron

Scintillation counter

Scintillation counter Annihilation emits two photons in opposite directions

180 deg.

Coincidence detector

Computer

Display

Figure 9.2 A schematic diagram of the main components of a PET scanner.

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What Neuroimaging Techniques Measure

from the sides and help prevent coincidences due to background radiation. The injected tracer will be distributed throughout the blood vessels and tissue of the brain (indeed, throughout the rest of the body as well). Each pair of detectors counts photons emitted within the column of tissue between them. The density of photons that were emitted at each location can be calculated mathematically from the number of counts at each position or “projection.” PET images are simply maps of how many positron annihilation events occurred in the slice of interest. A more complete explanation of PET image formation, including a discussion of filtered back projection and other methods, can be found in several good texts (Bendriem & Townsend, 1998; Sandler, 2003). What do PET counts reflect? The answer depends on what molecule the label is attached to and where that molecule goes in the brain. Ideally, for 15O PET, counts reflect the rate of water uptake into tissue. 18-fluorodeoxyglucose (FDG) PET measures glucose uptake, whereas 13C Raclopride PET measures dopamine binding. In practice, the observed level of signal depends on several factors, including the concentration of the radiolabeled substance in the blood, the blood flow and volume, the presence of other endogenous chemicals that compete with the labeled substance, and kinetic properties such as the binding affinity of the substance to receptors, the rate of dissociation of the substance from receptors, and the rate at which the substance is broken down by endogenous chemicals. Accurate quantification of binding requires study of the kinetic properties of the substance in animals and the use of this information in kinetic models, which use differential equations to estimate the biological parameters of interest (e.g., ligand bound specifically to the receptor type of interest). Kinetic models have been developed to estimate how much tracer is contained in different categories, or compartments, of blood and tissue. Different forms of kinetic modeling have different numbers of compartments; a twocompartment model estimates how much of the radiolabeled compound is in the vasculature as opposed to in the brain. A three-compartment model used in receptor binding studies estimates tracer quantities in blood, free tracer in tissue, and label bound to receptors. Often a reference region with few or no receptors (the cerebellum for dopamine) is used to model the separation of free from bound tracer; this requires the assumption that none of the signal in the reference region comes from bound tracer. A fourcompartment model additionally separates tracer bound to receptors of a specific type (called specific binding) from those bound to other receptors (called nonspecific binding). For more details, we refer you to Frey (1999).

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Measures of Brain Activity Using Functional Magnetic Resonance Imaging Unlike PET, which can provide measures of overall activity or specific neurochemical systems, fMRI is principally used to obtain measures of regional brain activity (see Table 9.1). The most popular method is currently the Blood Oxygenation Level Dependent (BOLD) signal (Kwong et al., 1992; Ogawa et al., 1992), which is obtained using T2*-weighted images. Other methods are available but less widely used, including several varieties of Arterial Spin Labeling (ASL; Williams, Detre, Leigh, & Koretsky, 1992), which use pulse sequences sensitive to blood volume or cerebral perfusion. We focus here on BOLD physiology because it is overwhelmingly the most common method in current use. BOLD imaging takes advantage of the difference in T2* between oxygenated and deoxygenated hemoglobin. As neural activity increases, so does metabolic demand for oxygen and nutrients. Capillaries in the brain containing oxygen and nutrient-rich blood are separated from brain tissue by a lining of endothelial cells, which are connected to astroglia, a major type of glial cell that provides metabolic and neurochemical-recycling support for neurons. Neural firing signals the extraction of oxygen from hemoglobin in the blood, likely through glial processing pathways (Shulman, Rothman, Behar, & Hyder, 2004; Sibson et al., 1997). As oxygen is extracted from the blood, the hemoglobin becomes paramagnetic—iron atoms are more exposed to the surrounding water—which creates small distortions in the B0 field that cause a T2* decrease (a faster decay of the signal). Increases in deoxyhemoglobin can lead to a decrease in the BOLD signal, often referred to as the “initial dip.” The initial decrease in signal (whose existence is controversial) is followed by an increase, due to an over-compensation in blood flow that tips the balance toward oxygenated hemoglobin (and less signal loss due to dephasing), which leads to a higher BOLD signal. Initially, fMRI was performed by injection of contrast agents (such as iron) with paramagnetic properties, but the discovery that the T2* relaxation rate of oxygenated hemoglobin was longer than that of deoxygenated hemoglobin led to BOLD imaging as it is currently used with humans, without contrast agents (Kwong et al., 1992; Ogawa, Lee, Kay, & Tank, 1990). How well does the BOLD signal reflect increases in neural firing? The answer to this important question is complex, and understanding the physiological basis of the BOLD response is currently a topic of intense research (Buxton & Frank, 1997; Buxton, Uludag, Dubowitz, & Liu, 2004; Heeger & Ress, 2002; Vazquez & Noll, 1998). Some relationships among factors that contribute to BOLD signal are summarized in Figure 9.3.

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T2*-weighted Image Intensity ⫹

MR Properties

Decay Time (T2*) ⫹

Physical Effects

Other Factors Vessel Diameter Magnetic Field Vessel Orientation Uniformity (microscopic) Hematocrit Blood Volume Fraction ⫹ ⫺ Cerebral Blood Volume (CBV)

Blood Oxygenation

Vascular Physiological Effects

⫹ ⫹

⫹ ⫺

Cebral Blood Flow (CBF)

⫹ Metabolic Rates

Glucose and Oxygen Metabolism ⫹

Brain Function

Neuronal Activity

Figure 9.3 Influences on T2*-weighted signal in BOLD fMRI imaging. Note: Courtesy of Dr. Doug Noll.

The BOLD signal corresponds relatively closely to the local electrical field potential surrounding a group of cells—which is itself likely to reflect changes in postsynaptic activity—under many conditions. Demonstrations by Logothetis and colleagues have shown that high-field BOLD activity closely tracks the position of neural firing and local field potentials in cat visual cortex, even to the locations of specific columns of cells responding to particular line orientations (Logothetis, Pauls, Augath, Trinath, & Oeltermann, 2001). Under other conditions, however, neural activity and the BOLD signal may become decoupled (Disbrow, Slutsky, Roberts, & Krubitzer, 2000). For these reasons and others, the BOLD signal is only likely to reflect a portion of the changes in neural activity in response to a task or psychological state. Many regions may show changes in neural activity that is missed because they do not change the net metabolic demand of the region. Another important question is whether BOLD signal increases reflect neural excitation or inhibition. Some

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research supports the idea that much of the glucose and oxygen extraction from the blood is driven by glutamate metabolism, a major (usually) excitatory transmitter in the brain. Shulman and Rothman (1998) suggest that increased glucose uptake is controlled by astrocytes, whose end-feet contact the endothelial cells lining the walls of blood vessels. Glutamate, the primary excitatory neurotransmitter in the brain, is released by 60% to 90% of the brain’s neurons. When glutamate is released into synapses, it is taken up by astrocytes and transformed into glutamine. When glutamate activates the uptake transporters in an astrocyte, it may signal the astrocyte to increase glucose uptake from the blood vessels. Although it remains plausible that some metabolic (and BOLD) increases could be caused by increased inhibition of a region, in many tasks where both BOLD studies and neuronal recordings have been made, BOLD increases are found in regions in which many cells increase their activity. This is true in studies of visual processing, eye movements, task switching, working memory, food reward, pain, and other domains.

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What Neuroimaging Techniques Measure

Measures of Functional Neurochemistry Using Positron Emission Tomography The affinity of particular pharmacological agents for certain types of neurotransmitter receptors, such as raclopride for dopamine D2 receptors, provides a way to investigate the functional neurochemistry of the human brain. Radioactive labels such as C-11, a radioactive isotope of carbon, are synthesized in a cyclotron and attached to the pharmacological agent. Labeled compounds are injected into the arteries by either a bolus (a single injection) or continuous infusion, typically until the brain concentrations reach steady state. This method can be used to image task-dependent neurotransmitter release. As radioactively labeled neurotransmitters binds to receptors, the label degrades and gamma rays are emitted that are detected by the PET camera. When endogenous neurotransmitters are released in the brain, there is greater competition at receptors, and less binding of the labeled substance (referred to as specific binding). Thus, neurotransmitter release generally results in a reduction in radioactivity detected by the PET camera. The most common radioligands and transmitter systems studied are dopamine (particularly D2 receptors) using [11C]raclopride or [123I]iodobenzamide, muscarinic cholinergic receptors using [11C]scopolamine, opioids using [11C]carfentanil, and benzodiazepines using [11C]flumazenil. In addition, radioactive compounds that bind to serotonin, opioid, and several other receptors have been developed. As described earlier, because the dynamics of radioligands are complex, pharmacological agents must be carefully selected and tested in animals. Parameters from these studies are used in kinetic models to aid in quantifying how much labeled substance is bound to the receptor type of interest (Frey, 1999).

Limitations of Positron Emission Tomography and Functional Magnetic Resonance Imaging As you might expect, both PET and MRI have their share of pitfalls. You should consider the limitations of each technique not only when designing experiments, but also when examining the neuroimaging literature. Always ask the following question: “Are the activations caused by the experimental paradigm or by other unwanted sources?” Conversely, you should also ask: “Were there other active regions that were missed by the experimental paradigm?” Some of these errors may have occurred because of the spatial or temporal limitations of the technique, or they may be due to image artifacts or mischaracterized noise. Spatial Limitations Neither PET nor fMRI is well-suited for imaging small subcortical nuclei or cortical microcircuitry, though advances

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in high-field imaging and parallel acquisition methods are helping. The spatial resolution of PET is on the order of 1 to 1.5 cm3. fMRI resolution can be less than 1 mm3 in high-field imaging in animals, but is typically on the order of 27 to 36 mm3 or more for human studies. Thus, features such as cortical columns and even major subnuclei (e.g., there are 30 or so in each of the amygdala and thalamus) cannot typically be identified. The limiting factors in fMRI include signal strength and the point-spread function of BOLD imaging, which tends to extend beyond neural activation sites into draining veins (Duong et al., 2002). Careful work in individual participants has demonstrated the imaging of ocular dominance columns in humans (Cheng, Waggoner, & Tanaka, 2001). While this resolution does not sound all that bad, another factor seriously limits the spatial resolution in most studies. That is, making inferences about populations of subjects requires analyzing groups of individuals, each with a different brain. Usually, individual brains are aligned to one another through a registration or warping process (see Data Analysis: Implementation, later in this chapter), which introduces substantial blurring and noise in the group average. Thus, the effective resolution for group fMRI and PET studies is about the same. One estimate based on meta-analysis is that the spatial variation in the location of an activation peak among comparable group studies is 2 to 3 cm (Wager, Reading, & Jonides, 2004). Overcoming these limitations with high-resolution fMRI imaging is a challenging and developing research area. By focusing on particular regions and omitting data collection in much of the brain, voxels on the order of 1.5 mm per side can be acquired, yielding fMRI maps with resolution closer to the physical size of functional subregions (e.g., cortical fields within the hippocampus, or nuclei in the brain stem). This technique provides several advantages over standard mapping techniques. First, resolution can potentially be considerably enhanced, particularly when using high-field imaging and analysis techniques that remove some spread in fMRI signal due to draining veins (R. S. Menon, 2002). Second, collecting thinner slices can reduce susceptibility artifacts and improve imaging around the base of the brain (Morawetz et al., 2008). Finally, limitations in group studies related to interindividual variability can be partially overcome using identification of regions of interest on individual participants’ anatomical images or by advanced cortical unfolding and intersubject warping techniques (Zeineh, Engel, Thompson, & Bookheimer, 2003). However, there are costs as well. There is a substantial loss in signal due to the smaller volume of each voxel. In addition, coregistration techniques that ensure structureto-function correspondence and normalization techniques typically used to provide intersubject registration in group

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studies do not work very well when only a portion of the brain is imaged, because there are fewer functional landmarks for registration. High-resolution studies are promising when a small set of subcortical nuclei or nearby cortical regions are of primary interest. Acquisition Artifacts Artifactual activations (patterns of apparent activation arising from nonneural sources) and image distortions may arise from several sources, some unexpected. An early study found a prominent PET activation related to anticipation of a painful electric shock in the temporal pole (Reiman, Fusselman, Fox, & Raichle, 1989). However, it was discovered some time later that this temporal activation was actually located in the jaw—the subjects were clenching their teeth in anticipation of the shock. Important types of artifact include those related to magnetic susceptibility, reconstruction, head movement, heartbeat and breathing, instability in magnetic gradients used to acquire images, and radio-frequency interference from outside sources. Many of these artifacts apply only to or are more pronounced with fMRI; we provide more details on dealing with artifacts in analysis in the section Data Analysis: Implementation. Susceptibility artifacts in fMRI occur because magnetic gradients near air and fluid sinuses and at the edges of the brain cause local inhomogeneities in the magnetic field that affects the signal, causing distortion in echo-planar imaging (EPI) sequences and blurring and dropout in spiral sequences. These problems increase at higher field strengths and provide a significant barrier in performing effective high-field fMRI studies. Not all scanner/sequence combinations can reliably detect BOLD activity near these sinuses, which affects regions including the orbitofrontal cortex, inferior temporal cortex, hypothalamus, and amygdala. Signal may be recovered by using optimized sequences such as “z-shimming” (Constable & Spencer, 1999) or spiral in/out sequences (Glover & Law, 2001) or using a physical magnetic shim held in the mouth of the participant (Wilson & Jezzard, 2003). Signal loss and distortion may be further minimized by using improved reconstruction algorithms (Noll, Fessler, & Sutton, 2005) and “unwarping” algorithms that measure and attempt to correct EPI distortion (Andersson, Hutton, Ashburner, Turner, & Friston, 2001). Functional MRI also contains more sources of signal variation due to noise than does PET, including a substantial slow drift of the signal across time and higher frequency changes in the signal due to physiological processes accompanying heart rate and respiration. The lowfrequency noise component in fMRI can obscure results related to a psychological process of interest and it can

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produce false positive results, so it is usually removed statistically prior to analysis. A consequence of slow drift is that it is often impractical to use fMRI for designs in which a process of interest only happens once or unfolds slowly over time, such as drug highs or the experience of strong emotions, though some experimental/analysis approaches have been developed to facilitate such studies (Lindquist & Wager, 2007Lindquist, Waugh, & Wager, 2007). The vast majority of fMRI designs use discrete events that can be repeated many times over the course of the experiment (e.g., the most common method for studying emotion in fMRI is to repeatedly present pictures with emotional content). Temporal Resolution and Trial Structure Another important limitation of scanning with PET and fMRI is the temporal resolution of data acquisition. The details of this limitation are discussed in subsequent sections, but it is important to note here that PET and fMRI measure very different things, over different time scales. Because PET computes the amount of radioactivity emitted from a brain region, at least 30 seconds of scanning must pass before a sufficient sample of radioactive counts is collected. This limits the temporal resolution to blocks of time of at least 30 seconds, well longer than the temporal resolution of most cognitive processes. For glucose imaging (FDG) and receptor mapping using radiolabeled ligands, the period of data collection for a single condition is much longer, on the order of 30 to 40 minutes. Functional MRI has its own temporal limitation due largely to the latency and duration of the hemodynamic response to a neural event. Typically, changes in blood flow do not reach their peak until several seconds after local neuronal and metabolic activity has occurred. Thus, the locking of neural events to the vascular response is not very tight. Because of this limitation, a promising current direction is the estimation of the onset and peak latency of fMRI responses, and other parameters, averaged over many trials (Lindquist & Wager, 2007; R. S. Menon, Luknowsky, & Gati, 1998; Miezin, Maccotta, Ollinger, Petersen, & Buckner, 2000). We provide a more thorough discussion of this and related issues later in this chapter.

FROM DATA TO PSYCHOLOGICAL INFERENCE Goals of Data Analysis: Prediction and Inference A fundamental question in neuroimaging research is determining what the researcher hopes to achieve with the chosen method. Successful research requires a solid grasp of

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From Data to Psychological Inference

what kinds of imaging results constitute evidence for a psychological or physiological theory, and a grounded understanding of what kinds of results are likely to be obtainable. There are several potential inferential goals in neuroimaging studies. One goal is prediction of a psychological or disease state using neuroimaging data, which can be accomplished using regression or classification techniques (Norman, Polyn, Detre, & Haxby, 2006). More often, the psychologist would like to infer something about the structure of mental processes from imaging data. Making inferences about psychological states has been termed reverse inference, because it involves estimating the relative probabilities of different psychological hypotheses given the data, whereas what is observed in imaging studies is the probability of the data given a psychological state. Chapter 1 of this Handbook (Cacioppo & Berntson, in press) deals extensively with psychological inference from physiological data. In addition, several excellent papers review this issue in brain imaging (Poldrack, 2006; Sarter, Berntson, & Cacioppo, 1996) and physiological data generally (Cacioppo & Tassinary, 1990). Though we do not recapitulate this discussion here, we note that making psychological inferences based on activation in single brain regions is particularly problematic. Researchers have inferred that romantic love and retribution involve “reward system” activation because these conditions activate the caudate nucleus (Aron et al., 2005; de Quervain et al., 2004), that social rejection is like physical pain because it activates the anterior cingulate (Eisenberger, Lieberman, & Williams, 2003), among countless similar conclusions in the literature. These inferences are problematic because both these regions are involved in a wide range of tasks, including shifting of attention, working memory, and inhibition of simple motor responses, so their activation is not indicative of any particular psychological state (Bush, Luu, & Posner, 2000; Kastner & Ungerleider, 2000; Paus, 2001;

z ⫽ 16

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Note: All voxels identified show significant switch costs in at least two switch-no switch contrasts (p < .05 Family-wise error rate corrected in each). Thus, many regions not shown here may also show brain switch costs at less stringent thresholds. Regions colored in red are common activations that show no significant differences among costs for different types

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Van Snellenberg & Wager, in press; T. D. Wager, Jonides, & Reading, 2004; T. D. Wager, Jonides, Smith, & Nichols, 2005). There are other types of reverse inference that are less specific about the localization of psychological functions in the brain but are more defensible. These inferences fall into two major categories: those based on dissociations in activation among tasks, and those based on activation overlap across tasks. Both types involve studies that test two or more tasks in the same experiment. Dissociation occurs when a brain region is more active in Task A than in Task B. A double dissociation occurs when each task activates one region more than the other task. Double dissociations are a powerful tool because they imply that the two tasks utilize different processes, and that one task is not a subset of the other. A recent study in our laboratory illustrates this approach. We found that different types of task switching, or switching attention from one feature or object to another, differentially activate a set of regions thought to be involved in the control of attention (T. D. Wager, Jonides, Smith, & Nichols, 2005). Four types of switches were dissociable— each produced higher brain activity in some regions than the others—paralleling behavioral findings that performance switch costs are more highly correlated for similar types of switches (see Figure 9.4). The implication from this converging evidence is that different types of attention switching involve unique processes. Though double dissociations are potentially powerful, they have been criticized on several counts. For one thing, nonlinear relationships between task demands and activation can produce a double dissociation even if there are no processes unique to each task. Sternberg (2001) has proposed a stronger criterion for task separability called “separate modifiability,” which entails finding outcomes that are affected by each task but not the other task.

z ⫽ 10

Figure 9.4 (Figure C.1 in color section) Axial slices showing brain regions responsive to different types of switching and their overlap.

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Common activation Internal ⬎ External Enternal ⬎ Internal Object ⬎ Attribute Attribute ⬎ Object I ⬎ E and O ⬎ A

of switch (at p < .05 uncorrected). Other regions show evidence for greater activation in some switch types than others, as indicated in the legend. A ⫽ Attribute switch types; E ⫽ External; I ⫽ Internal; O ⫽ Object. From “Accounting for Nonlinear BOLD Effects in fMRI: Parameter Estimates and a Model for Prediction in Rapid Event-Related Studies,” by T. D. Wager, A. Vazquez, L. Hernandez, and D. C. Noll, 2005, NeuroImage, 25, pp. 206–218.

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A second type of psychological inference is based on the overlap in activation among tasks, which is often taken as evidence that the tasks share common processes (Sylvester et al., 2003). In the task switching study shown in Figure 9.4, even though there were quantitative dissociations in activation magnitude, all regions responded to at least two types of task switch, and some responded to all four. This implies at least some common processes across switch types, paralleled by significant performance correlations across types (T.D. Wager, Jonides, & Smith, 2006). Though the logic that activation overlap equals process overlap is commonly used, it provides weak support for shared neuronal processes: A single voxel in a neuroimaging study typically contains on the order of one million neurons, and it is entirely possible that different subsets of neurons in the same voxel are activated by different tasks. Paton, Belova, Morrison, and Salzman (2006) found different cells in the monkey amygdala that respond to either positive or negative predictions about upcoming rewards within the volume of a single neuroimaging voxel. Wang, Tanaka, and Tanifuji (1996), using optical imaging, found topographical maps of perceived head orientation in areas of temporal cortex that spanned only about 1 mm of cortex. fMRI-Adaptation Designs These issues have led to another method for assessing the use of common neural substrates across tasks. This method relies on repetition-suppression effects, or adaptation of fMRI responses to repeated events. It is possible to take advantage of this effect to tell whether two stimulus types (A and B) activate the same or different populations of neurons within a voxel (Grill-Spector & Malach, 2001). If a stimulus of type A is presented, then subsequent presentations of A will result in reduced signal (adaptation). The logic is that other stimuli, say of type B, that engage the same set of neurons will also evoke a reduced signal ([Balone– BafterA], cross-adaptation), whereas those that engage different neurons (even within the same voxel) will evoke a larger signal. Thus, small cross-adaptation effects may provide evidence that B engages different populations of neurons, whereas large cross-adaptation effects may be evidence that the circuitry for B and A overlap. However, caution in interpretation is in order, because habituation ([Balone– BafterA]) can be caused by the mechanical properties of the vascular bed (Vazquez et al., 2006; T. D. Wager, Vazquez, et al., 2005), and not to a neuronal habituation process. In fact, the response to A immediately after B is always likely to produce a reduced response compared with A alone because of the time it takes the vessels to regain their original shape after a BOLD response. This complicates the inference that similar adaptation and cross-adaptation implies overlapping neuronal populations. Another issue is that a recent

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electrophysiological study designed to test the validity of this paradigm reported differential habituation in single-cell recording to two stimuli, even though they both activated the same neuron (Sawamura, Orban, & Vogels, 2006). This finding challenges the inference that different adaptation and cross-adaptation effects imply different populations of neurons. Finally, though interpretations of fMRI-adaptation effects are often cast in terms of neuronal firing, more global processes related to memory may play an important role as well (Henson, 2003). The Hierarchical Structure of Neuroimaging Data Whichever type of inference is desired, inference is based on data, usually from multiple individuals. This section describes the structure of neuroimaging data, and the following sections describe some conceptual essentials of the steps that lead to psychological inference: valid group analysis, thresholding techniques, and localization of activated regions. Proper analysis of multisubject data in each voxel yields a statistical parametric map (SPM) of the reliability of contrast values—images that contain test statistic values (e.g., t-values) and p-values for the group analysis at each voxel. These statistic images are thresholded, with some provision for correcting for multiple comparisons across the many brain voxels tested, to obtain maps of suprathreshold or activated regions. Activated regions are localized relative to standard brain landmarks, often with the aid of brain atlases and norms, and interpreted in the context of other human and animal literature. Imaging data typically involves repeated observations over time—in fMRI as many as two thousand brain images can be collected in the course of a single imaging session for each participant. These images are nested within task conditions (e.g., tasks A and B, or “switch attention” [for a particular switch type] and “do not switch,” in our example study). Task conditions, in turn, are crossed with participant, meaning that they are assessed for each participant. Participants may be additionally nested within groups (e.g., patients versus controls, young versus elderly). Most often, a statistical model is specified for each participant that estimates the average response to each task condition of interest. Responses to different task conditions are compared by calculating contrasts across two or more conditions. Those measures are called contrast values, and they usually reflect a comparison of the activity levels between task conditions of interest (e.g., A minus B, “switch attention” minus “no switch”) that yields a single number for each participant. Contrast values for each voxel yield contrast images, three-dimensional maps of activation difference values for each participant. T-tests or comparable analyses can be performed for each voxel to discover where in the

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From Data to Psychological Inference

brain the difference is reliable. More detail on contrasts is provided in the section Data Analysis: Implementation. Analyzing contrast values has been referred to as the “subtraction method,” the logic of which is this: If you test two experimental conditions that differ by only one process, then a subtraction of the activations of one condition from those of the other should reveal the brain regions associated with the target process. Subtraction logic rests on a critical assumption that has been called the assumption of “pure insertion” (Sternberg, 1969). According to this assumption, changing one process does not change the way other processes are performed. Thus, by this assumption, the process of interest may be purely inserted into the sequence of operations without altering any other processes. Violations of subtraction logic have been demonstrated (Zarahn, Aguirre, & D’Esposito, 1997), and evoked activation depends on baseline cerebral blood flow in an area and other factors (Vazquez et al., 2006). However, subtractions remain widely used because comparisons among relative activity levels are central to the inferencemaking process. The assumption of pure insertion underlies the inference that more observed activity implies more intense neural and metabolic processes. In defense of the subtraction method, pure insertion need not be quantitatively or strictly true in all cases to yield useful comparisons across conditions. The contrast method applies to many comparisons other than the simple Task A – Task B subtraction, including incremental variations in task difficulty and factorial designs. It also applies to brain-performance correlation designs, in which activation contrast values are correlated with performance contrast values. These designs may employ multiple control or comparison conditions to strengthen the case for a relationship between activity in a particular brain region and a psychological process. They also extend beyond imaging of “activation” to studies that image neurochemical activity and other signals. Principles of Population Inference It is usually advantageous to design studies and statistical analyses in a way that permits inferences about a population of participants. Population inference is typical in all kinds of studies (e.g., when testing a new drug, researchers perform statistical tests that allow them to infer that the drug is likely to produce a benefit on average for individuals in a certain population). Even most studies of psychophysics and electrophysiology in monkeys, which often rely on only one or two participants for the entire study, need to be able to claim that their results apply beyond the particular individuals studied. They do so by invoking the additional assumption that all participants will behave the same way

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as the few observed in the study. In almost all domains of human neuropsychology, this is not a safe assumption, and statistics should be performed that permit population inference in a standard way. This can be achieved by considering the multilevel nature of neuroimaging data. A key to population inference is to treat the variation across participants as an error term in a group statistical analysis, which leads to generalizability of the results to new participants drawn from the same population. The most popular group analysis is the one-sample t-test on contrast estimates (e.g., Task A – Task B) at each voxel. This analysis tests whether the contrast of interest is nonzero on average for the population from which the sample was drawn, and it provides a starting point for our discussion on population inference. The principle, however, applies to any kind of statistical model, including more complex ANOVA and regression models and multivariate analyses such as group independent components analysis (ICA). Mixed versus Fixed Effects The one-sample t-test across contrast values treats the value of that contrast as a random variable with a normal distribution over subjects, and hence the error term in the statistical test is based on the variance across participants. Such an analysis has come to be known as a “random effects” analysis in the neuroimaging literature. Many early studies performed incorrect statistical analyses by lumping data from different participants together into one “supersubject” and analyzing the data using a single statistical model. This is called a “fixed effects” analysis because it treats the participant as a fixed effect, and assumes the only noise is due to measurement error within subjects. It is not appropriate for population inference because it does not account for individual differences. Collecting five hundred images each (250 of Task A and 250 of Task B) on two participants would be treated as the equivalent of collecting two images each (Task A and B) on 500 participants. Some researchers have argued that the fixed analysis allows researchers to make inferences about the brains of participants in the study, but not to a broader population. While this is technically true, inferences about particular individuals are seldom useful; such a lack of generalizability would be unacceptable in virtually any field, and we do not consider it appropriate for neuroimaging studies either. A more complete analysis is the mixed effects analysis, so termed because it estimates multiple sources of error, including measurement error within subjects and inter-individual differences between subjects. The one-sample t-test on contrast estimates described above is actually a simplified mixed-effects analysis that is valid if the standard errors of contrast estimates are the same for all participants.

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Full mixed-effects analyses use iterative techniques (such as the Expectation-Maximization [EM] algorithm) to obtain separate estimates of measurement noise and individual differences. They are implemented in popular packages such as Hierarchical Linear Modeling (HLM; Raudenbush & Bryk, 2002), R, and MLwiN (Rasbash, 2002). Neuroimaging data-friendly mixed-effects models are implemented in FSL (Beckmann, Jenkinson, & Smith, 2003; Woolrich, Behrens, Beckmann, Jenkinson, & Smith, 2004) and FMRISTAT (Worsley, Taylor, Tomaiuolo, & Lerch, 2004) software and are potentially implementable in SPM5. Thresholding and Multiple Comparisons The results of neuroimaging studies are often summarized as a set of “activated regions,” such as those shown in Figure 9.4. Such summaries describe brain activation by color-coding voxels whose t-values or comparable statistics (z or F) exceed a certain statistical threshold for significance. The implication is that these voxels are activated by the experimental task. A crucial decision is the choice of threshold to use in deciding whether voxels are active. In many fields, test statistics whose p-values are below .05 are considered sufficient evidence to reject the null hypothesis, with an acceptable false positive rate (alpha) of .05. In brain imaging, we often test on the order of 100,000 hypothesis tests (one for each voxel) at a single time. Hence, using a voxel-wise alpha of .05 means that 5% of the voxels on average will show false positive results. This implies that we actually expect on the order of 5,000 false positive results. Thus, even if an experiment produces no true activation, there is a good chance that without a more conservative correction for multiple comparisons, the activation map will show numerous activated regions, which would lead to erroneous conclusions. The traditional way to deal with this problem of multiple comparisons is to adjust the threshold so that the probability of obtaining a false positive is simultaneously controlled for every voxel (statistical test) in the brain. In neuroimaging, a variety of approaches toward controlling the false positive rate are commonly used—we discuss them in detail later. The fundamental difference between any method that is used is whether they control for the family wise error rate (FWER) or the false discovery rate (FDR). The FWER is the probability of obtaining any false positives in the brain, whereas the FDR is the proportion of false positives among all rejected tests. To illustrate the difference between FWER and FDR, imagine that we conduct a study on 100,000 brain voxels at alpha ⫽.001 uncorrected, and we find 300 significant voxels. According to theory, we would expect that

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100 (or 33%) of our significant discoveries, to be false positives, but which ones we cannot tell. Since 33% is a significant proportion of all active voxels, we may have low confidence that the activated regions are true results. Thus, it may be advantageous to set a threshold that limits the expected number of false positives to 5%. This is referred to as FDR control at the .05 level. In this case, we might argue that most of the results are likely to be true activations; however, we still cannot tell which voxels are truly activated and which are false positives. FWER, by contrast, is a stronger method for controlling false positives. Controlling the FWER at 5% implies that we set a threshold so that, if we were to repeat the previously mentioned experiment 100 times, only 5 out of the 100 experiments would result in one or more false positive voxels. Therefore, when controlling the FWER at 5%, we can be fairly certain that all voxels that are deemed active are truly active. The thresholds will typically be quite conservative, leading to problems with false negatives, or truly active voxels that are now deemed inactive. In our example, perhaps only 50 out of the 200 truly active voxels will give significant results. While we can be fairly confident that all 50 are true activations, we have still lost 150 active voxels, most of the true activity, which may distort our inferences and the usefulness of the experiment (see Figure 9.5). Most published PET and fMRI studies do not use either of these corrections; instead, they use arbitrary uncorrected thresholds, as shown in Figure 9.6, with a modal threshold of p < .001. A likely reason is that with the sample sizes typically available, corrected thresholds are so high that power is extremely low. This is extremely problematic when interpreting conclusions from individual studies, as many of the activated regions may simply be false positives. Imposing an arbitrary extent threshold for reporting based on the number of contiguous activated voxels does not necessarily correct the problem because imaging data are spatially smooth, and thus corrected thresholds should be reported whenever possible. Figure 9.6 shows the same activation map with spatially correlated noise thresholded at three different P-value levels. Due to the smoothness, the false-positive activation blobs (outside the squares) are contiguous regions of multiple voxels. Because achieving sufficient power is often not possible, it makes sense to report results at an uncorrected threshold and use meta-analysis or a comparable replication strategy to identify consistent results (T. D. Wager, Lindquist, & Kaplan, 2007), with the caveat that uncorrected results from individual studies cannot be strongly interpreted. Ideally, a study would report both corrected results and results at a reasonable uncorrected threshold (e.g., p < .001 and 10 contiguous voxels) for archival purposes.

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From Data to Psychological Inference

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(A) ␣ⴝ0.10, No correction

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Proportion of false positives FWER control at 10%

FWER Occurrence of false positive FDR control at 10%

0.0871

0.0952

0.0790

0.0908

0.0761

0.1090

0.0851

Proportion of active voxels that are false positives Figure 9.5 An overview of the effects of various approaches toward dealing with multiple comparisons. Note: (Top) Ten simulated t-maps were analyzed using an uncorrected threshold p < .10. True positives are indicated by white regions inside the gray squares. False positives are white pixels outside of the gray square. The proportion of false positives is listed under each image. They average 10%, as expected. (Middle) The same images with the threshold designed

␣ⴝ0.10

␣ⴝ0.01

␣ⴝ0.001

Figure 9.6. Multiple comparisons in the presence of spatially correlated noise. This figure shows simulated t-maps as in Figure 9.5, but with spatially “smooth” noise as is typical in actual fMRI experiments. In this case, imposing an arbitrary ‘extent threshold’ based on the number of contiguous activated voxels does not necessarily solve the problem of false positives. The same activation map, with spatially correlated noise, is thresholded at three different P-value levels. Due to the smoothness, the false-positive activation blobs (outside of the squares) are contiguous regions of multiple voxels, which can easily be misinterpreted as regions of activity.

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to control the Familywise error rate (FWER) at 10% using Bonferroni correction. There is only one false positive in the 10 images, at the cost of a significant increase in the number of false negatives. (Bottom) Similar results obtained using an FDR controlling procedure at the 10% level. The proportion of active voxels that are false positives is listed under each image. They average 10% as expected.

Family Wise Error Rate Correction The simplest way of controlling the FWER is to use Bonferroni correction. Here the alpha value is divided by the total number of statistical tests performed (voxels). If there is spatial dependence in the data—which is almost always the case, because the natural resolution and applied smoothing both lead to spatial smoothness in imaging data—this is an unnecessarily conservative correction that leads to a decrease in power to detect truly active voxels. Gaussian Random Field Theory (RFT; Worsley et al., 2004), used in SPM, FMRISTAT, and BRAINSTAT software (Taylor & Worsley, 2006), is another (more theoretically complicated) approach toward controlling the FWER. If the image is smooth and the number of subjects is relatively high (around 20), RFT is less conservative and provides control closer to the true false positive rate

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Essentials of Functional Neuroimaging Long-term Memory P-value thresholds used

60 # of Maps

50 0 ⫺50

40 20 0

50

0

⫺50

⫺100

0.0001 0.0005 0.001 0.005 Uncorrected

Figure 9.7 Common thresholds used in neuroimaging experiments. Note: A midline sagittal slice (left) shows the peak activations reported in 195 separate studies of long-term memory. The frequencies of P-value thresholds used for all statistical parametric maps in these studies are shown to the right. The most common threshold is P < .001, uncorrected for

than the Bonferroni method. With small samples, RFT is often more conservative than the Bonferroni method. It is acceptable to use the more lenient of the two, as they both control the FWER, which is what SPM currently does. In addition, RFT is used to assess the probability that k contiguous voxels are exceeding the threshold under the null hypothesis, leading to a “cluster-level” correction. Nichols and Hayasaka (2003) provide an excellent review of FWER correction methods, and they find that while RFT is overly conservative at the voxel level, it is somewhat liberal at the cluster level with small sample sizes. Both methods previously described for controlling the FWER assume that the error values are normally distributed, and that the variance of the errors is equal across all values of the predictors. As an alternative, nonparametric methods instead use the data to find the appropriate distribution. Using such methods can provide substantial improvements in power and validity, particularly with small sample sizes, and we regard them as the gold standard for use in imaging analyses. Thus, these tests can be used to verify the validity of the less computationally expensive parametric approaches. A popular package for doing nonparametric tests in group analyses, “Statistical Non-Parametric Mapping” (SnPM; Nichols & Holmes, 2002), is based on permutation tests. False Discovery Rate Control The false discovery rate (FDR) is a development in multiple comparison problems developed by Benjamini (1995) and Hochberg. While the FWER controls the probability of any false positives, the FDR controls the proportion of false positives among all rejected tests. The FDR controlling procedure is adaptive in the sense that the larger the signal, the lower the threshold. If all of the null hypotheses are true, the FDR will be equivalent to the FWER. Any procedure that controls the FWER will also control the FDR. Hence, any procedure that controls the FDR can only be

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0.01

0.05

0.05 Corr.

multiple comparisons. Corr: Corrected threshold. From “Meta-Analysis of Functional Neuroimaging Data: Current and Future Directions,” by T. D. Wager, M. Lindquist, and L. Kaplan, 2007, Social, Cognitive, and Affective Neuroscience, 2, pp. 150–158.

less stringent and lead to increased power. A major advantage is that since FDR controlling procedures work only on the p-values and not on the actual test statistics, it can be applied to any valid statistical test. Regions of Interest Analysis Because of the difficulty in preserving both false positive control and power in experiments with few subjects, researchers often specify regions of interest (ROIs) in which activation is expected before the study is conducted. ROI analyses are conducted variously over the average signal within a region, the peak activation voxel within a region, or preferably on individually defined anatomical or functional ROIs. Another technique involves testing every voxel within an ROI (e.g., the amygdala) and correcting for the number of voxels in the search volume. This is often referred to as a “small volume correction.” Two important cautions must be mentioned. First, conducting multiple ROI analyses increases the false positive rate. While it may be philosophically sound to independently test a small number of areas in which activation is expected, testing many such regions violates the spirit of a priori ROI specification and leads to an increased false positive rate. Small volume corrections in multiple ROIs also do not preserve the false positive rate across ROIs. Second, although activated regions can be used as ROIs for subsequent tests, the test used to define the region must be independent of the test conducted in that region. Acceptable examples include defining a region based on a main effect and then testing to see whether activity in that region is correlated with performance, or using the main effect of (A ⫹ B) to define a region and then testing for a difference (A – B). Problematic examples are defining a region activating in older subjects and then testing to see if its activity is reduced in younger subjects or defining a region based on activity in the first run of an experiment

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From Data to Psychological Inference

and then testing whether it shows less activity in subsequent runs. Both of these are not valid tests because they do not control for regression to the mean. Functional Localization and Atlases Accurately identifying the anatomical locations of activated regions is critical to making inferences about the meaning of brain imaging data. Knowing where activated areas lie permits comparisons with animal and human lesion and electrophysiology studies. It is also critical for accumulating knowledge across many neuroimaging studies. Localization is challenging for several reasons: First among them is the problem of variety; each brain is different, and it is not always possible to identify the same piece of brain tissue across different individuals (Thompson, Schwartz, Lin, Khan, & Toga, 1996; Vogt, Nimchinsky, Vogt, & Hof, 1995). Likewise, names for the same structures vary: The same section of the inferior frontal gyrus (IFG) can be referred to as IFG, inferior frontal convexity, Brodmann’s Area 47, ventrolateral prefrontal cortex, the pars orbitalis, or simply the lateral frontal cortex. Standard anatomical atlas brains differ as well, as do the algorithms used to match brains to these atlases. There is currently a wide and expanding array of available tools for localization and analysis. A database of tools is available from the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC; Table 9.3), and another useful list can be found at www.cma.mgh.harvard.edu/iatr/. The most accurate way to localize brain activity is to overlay functional activations on a co-registered, highresolution individual anatomical image. Many groups avoid issues of variability by defining anatomical regions of interest (ROIs) within individual participants and testing averaged activity in each ROI. Functional localizers— separate tasks or contrasts designed to locate functional regions in individuals—are also widely used, and functional and structural localizers can be combined to yield individualized ROIs. Structural ROIs are often used in detailed analysis of medial temporal regions in memory research; and retinotopic mapping, a functional localization procedure, to define individual visual-processing regions (V1, V2, V4, etc.) is now standard in research on the visual system (Tootell, Dale, Sereno, & Malach, 1996). However, the vast majority of studies are analyzed using voxel-wise analysis over much of the brain. In most applications, precise locations are difficult to define a priori within individuals, and often many regions as well as their connectivity are of interest. In such cases, atlas-based localization is used. Such localization can be performed using paper-based atlases (Duvernoy, 1995; Haines, 2000; Mai, Assheuer, & Paxinos, 2004), and there

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is no substitute for a deep knowledge of neuroanatomy. Automated atlases and digital tools are becoming increasingly integrated with analysis software. Some of the major ones are described next. Early approaches to atlas-based localization were based on the Talairach atlas (Talairach & Tournoux, 1988), a hand-drawn illustration of major structures and Brodmann’s Areas (BAs)—cortical regions demarcated according to their cytoarchitecture by Brodmann in 1909—from the left hemisphere of an elderly French woman. The brain is superimposed on a 3-D Cartesian reference grid whose origin is located at the anterior commissure. This allows brain structures to be identified by their coordinate locations. This stereotactic convention remains a standard today. Peak or center-of-mass coordinates from neuroimaging activations are reported in left to right (x), posterior to anterior (y), and inferior to superior (z) dimensions. Negative values on each dimension indicate locations at left, posterior, and inferior positions, respectively. The Talairach region labels were digitized, and a popular software program, the Talairach Daemon (Lancaster et al., 2000), allows researchers to map neuroimaging results onto Talairach’s labels. In addition, at least two popular software packages, AFNI (Cox, 1996) and BrainVoyager (Brain Innovation, Maastricht, Netherlands), allow researchers to align brains from neuroimaging studies to “Talairach space” using a few key landmarks identified on the brain and on the atlas. The alignment is performed by estimating 12 linear transformation parameters that include translation, rotation, zooms, and shears. Because the Talairach brain is not representative of any population and is not complete—only the left hemisphere was studied, and no histology was performed to accurately map BAs—Talairach coordinates and their corresponding BA labels should not be used (see Brett, Johnsrude, & Owen, 2002; Devlin & Poldrack, 2007, for discussion), as better alternatives are now available. Modern digital atlases based on group-averaged anatomy have largely replaced the Talairach brain. A current standard in the field is the Montreal Neurologic Institute’s (MNI’s) 305-brain average1 (Collins, Neelin, Peters, & Evans, 1994), shown in Figure 9.8 A, which is the standard reference brain for two of the most popular software packages, SPM and FSL (Smith et al., 2004) and the International Consortium for Brain Mapping project.

1

Called avg305T1 in SPM software. A higher-resolution template in the same space, called the ICBM-152 and named avg152T1 in SPM, is also available. It was created from the average of the 152 most prototypical images in the 305-brain set.

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Table 9.3

Current web sites for key resources.

Software Registries Neuroimaging Informatics Tools and Resources

www.nitrc.org

Internet Analysis Tools Registry

www.cma.mgh.harvard.edu/iatr/

Software Packages SPM

www.fil.ion.ucl.ac.uk/spm/software/

FSL

www.fmrib.ox.ac.uk/fsl/

AFNI

http://afni.nimh.nih.gov/

BrainVoyager

www.brainvoyager.com

FMRISTAT

www.math.mcgill.ca/keith/fmristat/

VoxBo

www.voxbo.org

FIASCO

www.stat.cmu.edu/˜fiasco/index.php?ref=FIASCO_home.shtml

Analysis Toolboxes SnPM, nonparametric analysis

www.sph.umich.edu/ni-stat/SnPM/

SPMd, image diagnostics

www.sph.umich.edu/ni-stat/SPMd/

Robust regression toolbox

www.columbia.edu/cu/psychology/tor/software.htm

Mediation analysis toolbox

www.columbia.edu/cu/psychology/tor/software.htm

GIFT (Group ICA)

http://icatb.sourceforge.net

MVPA toolbox: Classification

www.csbmb.princeton.edu/mvpa/

Netlab: Pattern classification

www.ncrg.aston.ac.uk/netlab/

Inverse logit HRF model

www.columbia.edu/cu/psychology/tor/software.htm

Atlases and Databases BrainMap

http://brainmap.org

ICBM

http://www.loni.ucla.edu/ICBM/

SUMS DB

http://sumsdb.wustl.edu:8081/sums/index.jsp

SPM Anatomy Toolbox

www.fz-juelich.de/inb/inb-3//spm_anatomy_toolbox

Wager lab meta-analyses

www.columbia.edu/cu/psychology/tor/MetaAnalysis.htm

Surface-Based Normalization/Warping FreeSurfer

http://surfer.nmr.mgh.harvard.edu

Caret/SureFit

http://brainmap.wustl.edu/caret/

Design Optimization Genetic Algorithm for fMRI

www.columbia.edu/cu/psychology/tor/software.htm

M-sequence toolbox

http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=3083

Digital atlases, including the MNI-305 template (not the Talairach template), permit fine-grained nonlinear warping of brain images to the template and can (if data quality is adequate) match the locations of gyri, sulci, and other local features across brains. A popular approach implemented in SPM software is intensity-based normalization. In this process, intensity values in a brain image are matched to a reference atlas image (template) by deforming the brain image in linear or nonlinear ways and using search algorithms to find the deformations that yield the best match. A preferred intensity-based method is the “unified segmentation

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and normalization” algorithm in SPM5 (Ashburner & Friston, 2005). A recent and promising alternative to intensity-based approaches is surface-based normalization, in which brain surfaces are reconstructed from segmented graymatter maps and inflated to a spherical shape or flattened (reviewed in Van Essen & Dierker, 2007). Features (e.g., gyri and sulci) are identified on structurally simpler 2-D or spherical brains, and the inflated brain is warped to an average spherical atlas brain. This approach has yielded better matches across individuals in comparison studies

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From Data to Psychological Inference (A) MNI 305 Average

(E)

(B) ICBM LBPA40 (SPM5)

(C) SPM Anatomy Toolbox

169

(D) Wager lab N18 (SPM5)

(F) ICBM MNI452 (Poly)

Anterior

Posterior

Anterior

Posterior

Medial

Lateral

Insula

Figure 9.8 (Figure C.2 in color section) Examples of atlas template images and a group-averaged, normalized structural image from a study. Note: A: The Montreal Neurologic Institute (MNI) 305-brain average as included with Statistical Parametric Mapping (SPM99, SPM2, or SPM5) software. The atlas brain is the same across software versions, though the algorithms for normalizing brains to the template have changed. B: The International Consortium for Brain Mapping (ICBM) LBPA atlas, based on manually labeled region from the Center for Morphometric Analysis at Harvard University. Each color represents a gross brain structure based on a consensus among 40 individually labeled brains. C: The singlesubject T1 brain co-registered with MNI space—the “colin brain” based on an average of 27 images of one individual—with overlaid consensus regions based on probabilistic cytoarchitecture. The probabilistic maps represented here are available in the SPM Anatomy toolbox, V1.5, and represent data from a series of studies on cytoarchitectural mapping of postmortem brains registered to the single-subject MNI template (Amunts et al., 2005; Eickhoff, Amunts, Mohlberg, & Zilles, 2006); see Table 9.3. Because the underlay brain is only a single brain, it may not be representative

(Fischl, Sereno, Tootell, & Dale, 1999; Van Essen & Dierker, 2007). Several free packages implement surfacebased normalization to templates registered to MNI space, including FreeSurfer (Table 9.3), Caret/SureFit software (Van Essen et al., 2001), and BrainVoyager. AFNI, using SUMA software (Saad, Reynolds, Argall, Japee, & Cox, 2004), and FSL have facilities for viewing and analyzing surface-based data with FreeSurfer and SureFit. Surfacebased add-ons in these packages permit surface-based registration to be performed after gross registration to the Talairach landmarks. Because the original BAs were not precisely or rigorously defined in a group, reporting of BAs using the Talairach atlas is not recommended (Devlin & Poldrack, 2007). Modern probabilistic cytoarchitectural atlases are being developed (Amunts, Schleicher, & Zilles, 2007), and some of these are

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Insula of anatomical locations in a study sample (compare the midbrain with that in D) and thus is not an ideal underlay image for localization of new study data. D: A trimmed average of 18 subjects’ T1 images initially warped to the MNI template using SPM and refined using a genetic algorithm based on custom code. This average brain shows good structural definition, indicating good intersubject registration, and is a suitable underlay image for functional activations. E: The ICBM 452-brain MNI average, with 5th-order polynomial warping to the standard space. The structural definition is excellent for the average of many brains, but the space is different from the MNI 305 space (e.g., the brain stem is much more anterior in the 452 brain), illustrating the need to report the specific atlas and procedures used in neuroimaging studies. F: Activations from a taskswitching paradigm (yellow; Wager, Jonides, Smith & Nichols, 2005a, 2005) superimposed on results from a meta-analysis of executive working memory (blue; Wager & Smith, 2003). Surface reconstruction was done with Caret software (Table 9.3) and shows a partially inflated left hemisphere (left) and a flattened cortical map of that hemisphere (right). Red and green arrows show the medial frontal gyrus and inferior frontal junction on each rendering.

available digitally either from the researchers or within FSL and SPM (as part of the SPM Anatomy Toolbox; Eickhoff et al., 2005; Figure 9.8B and C). In addition, software packages increasingly provide tools for visualizing activations relative to known functional and structural landmarks. Caret software allows study results to be mapped to a variety of atlases, including atlas brains included with SPM2, SPM99, and the Van Essen lab’s surface-based PALS atlas (see Figure 9.8F). Brain sections, surfaces, and flattened maps can be visualized, and digital overlays include probabilistic maps of visuotopic regions, orbitofrontal regions from a recent anatomical study (Ongur, Ferry, & Price, 2003), structural and functional landmarks, and a database of previous studies and reported peaks. The associated SumsDB database is a repository for study maps and peak coordinates (Table 9.3).

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Another way to localize functional activations is to compare them with the results of meta-analyses of other neuroimaging studies. Comparison with meta-analytic results can help identify functional landmarks and provide information on the kinds of tasks that have produced similar activation patterns. Whereas it was typical in early neuroimaging studies to claim consistency with previous studies based on activation in the same gross anatomical regions (e.g., activation of the anterior cingulate cortex), it is now recognized that many such regions are very large, and more precise correspondence is required to establish consistency across studies. Quantitative meta-analyses identify the precise locations that are most consistently activated across studies, and they thus provide excellent functional landmarks. Some meta-analysis maps are available on the SumsDB and BrainMap databases (Table 9.3), and a number are available on the web from individual researchers. Our lab currently has images from metaanalyses available on the web (Table 9.3), and these can be loaded into SPM, FSL, BrainVoyager, Caret, or other packages for visualization. The variety and heterogeneity of tools that are currently available is both a strength and an obstacle to effective localization. A few guidelines may aid in the process. First, it is preferable to overlay functional activations on an average of the actual anatomical brains from the study sample, after normalization (registration and/or warping) to a chosen template, instead of relying solely on an atlas brain (see Figure 9.8D). Normalization cannot be achieved perfectly in every region, and showing results on the subject’s actual anatomy is more accurate than assuming the template is a perfect representation. In addition, viewing the average warped brain can be informative about whether the normalization process yielded high co-registration of anatomical landmarks across participants, and can help identify problem areas. Single-subject atlases should not be taken as precise indicators of activation location in a study sample, and while they make attractive underlay images for activations, they should not be used for this purpose. Second, it is important to remember that atlas brains are different, and different algorithms used with the same atlas produce different results. Therefore, it is important to report which algorithm and which atlas were used. Also, it would be highly misleading to use a probabilistic atlas such as those in the SPM anatomy toolbox if the study brains were normalized to a different template (or with different procedures) than the one used to create the atlas (e.g., the SPM anatomy toolbox should not be used when normalizing to the ICBM-452 atlas; see Figure 9.8E). Regardless of the tools used, identifying functional activations on individual and group-averaged anatomy, collaborating with neuroanatomists when possible, and using print atlases to identify activations relative

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to structural landmarks are all essential components of the localization and interpretation process.

EXPERIMENTAL DESIGN FOR NEUROIMAGING EXPERIMENTS Types of Experimental Designs Designing a neuroimaging study involves a trade-off between experimental power and the ability to make strong inferences from the results. Some designs, such as the blocked design, typically yield high experimental power, but provide imprecise information about the particular psychological processes that activate a brain region. Event-related designs allow brain activation to be related more precisely to the particular cognitive processes engaged in certain types of trials, but suffer from decreased power. Researchers may also choose to focus intensively on testing one comparison of interest, and maximizing the power to detect this particular effect, or they may test multiple conditions to draw inferences about the generality of a brain region’s involvement in a class of similar psychological processes. In the following subsections, we describe several experimental designs and provide some discussion of the applications for which they are best suited. Blocked Designs Because long intervals of time (30 seconds or more) are required to obtain good PET images, the standard experimental design used in PET studies is the blocked design. In this design, different conditions in the experiment are presented as separate blocks of trials. To image a briefly occurring psychological process (e.g., the activation due to attention switching) using a blocked design, you might repeat the process of interest during an experimental block (A) and have the subject rest during a control block (B). The A – B (A minus B) comparison is the most basic type of contrast for this design. The blocked structure of PET designs (and blocked fMRI designs) imposes limitations on the interpretability of results. While activations related to slowly changing factors such as task-set or general motivation are well captured by blocked designs, they are not well suited if you want to image the neural responses to individual stimuli. In addition, the A – B contrast does not allow researchers to determine whether a region is activated solely in A, deactivated solely in B, or some combination of both effects. Multiple controls and comparison conditions can ameliorate this problem to some degree. The main advantage to using a blocked design is that it typically offers increased statistical power to detect a change. Under ideal conditions, blocked designs can be over 6 times

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Experimental Design for Neuroimaging Experiments

estimates typically come from studies of brief visual and motor events. In practice, the timing and shape of the HRF are known to vary across the brain, within an individual and across individuals (Aguirre et al., 1998; Schacter, Buckner, Koutstaal, Dale, & Rosen, 1997; Summerfield et al., 2006). Part of the variability is due to the underlying configuration of the vascular bed, which may cause differences in the HRF across brain regions in the same task for purely physiological reasons (Vazquez et al., 2006). Another source of variability is differences in the pattern of evoked neural activity in regions performing different functions related to the same task. Blocked designs are less sensitive to the variability of the HRF because they depend on the total activation caused by a train of stimulus events that makes the overall predicted response less sensitive to variations in the shape of responses to individual events. Predicted responses in block designs may still be inaccurate if the HRF model is inaccurate or if the density and time course of neural activity is not appropriately modeled (Price, Veltman, Ashburner, Josephs, & Friston, 1999). In a single-trial event-related design, events are spaced at least 20 to 30 seconds apart in time. fMRI signal can be observed on single trials if the eliciting stimulus is very strong (Duann et al., 2002), permitting the possibility of fitting models at the level of an individual trial (Rissman, Gazzaley, & D’Esposito, 2004). This promising technique enables the testing of relationships between brain activity and trial-level performance measures such as reaction time and emotion ratings for particular stimuli (Phan et al., 2004). Early studies frequently employed selective averaging of activity following onsets of a particular type (Aguirre, Singh, & D’Esposito, 1999; Buckner et al., 1998; Menon et al., 1998). However, even brief events (e.g., a 125 ms visual checkerboard display) have been shown to affect fMRI signal more than 30 s later (T. D. Wager, Vazquez,

as efficient as randomized event-related designs (T. D. Wager & Nichols, 2003). Generally, theory and simulations designed to assess experimental power in fMRI designs point to a 16 s to 18 s task / 16 to 18 s control alternating-block design as being optimal with respect to statistical power (Liu, 2004; Skudlarski, Constable, & Gore, 1999; T. D. Wager & Nichols, 2003). However, this is not always true as the relative power of a blocked design depends on whether the target mental process is engaged continuously in A and not at all in B, and whether imposing a block structure changes the task. Event-Related Functional Magnetic Resonance Imaging Event-related fMRI designs take advantage of the rapid dataacquisition capabilities of fMRI. They provide the ability to estimate the fMRI response evoked by specific stimuli or cognitive events within a trial (Rosen, Buckner, & Dale, 1998). In fMRI, the whole brain can be measured every 2 to 3 seconds (the “TR,” or repetition time of image acquisition), depending on the type of data acquisition and the spatial resolution of the images. The limiting factor in the temporal resolution of fMRI is generally not the speed of data acquisition, but rather the speed of the underlying evoked hemodynamic response to a neural event, referred to as the hemodynamic response function (HRF). A typical HRF begins within a second after neural activity occurs and peaks 5 to 8 seconds after that neural activity has peaked (Aguirre, Zarahn, & D’Esposito, 1998; Friston, Frith, Turner, & Frackowiak, 1995). Figure 9.9 shows the canonical HRF used in SPM software. While event-related designs are attractive because of their flexibility and the information they provide about individual responses, they rely more strongly on assumptions about the time course of both evoked neural activity and the HRF. It is common to assume a near-instantaneous neural response for brief events and a canonical HRF shape to generate linear models for statistical analyses (Figure 9.9; see also Data Analysis: Implementation). The canonical

Indicator functions A

Assumed HRF (Basis function)

Design Matrix (X)

Image plot of X A B C D

A Time

B

B

C

C 0

5 10 15 20

D

D 0

50 100 150 200 Time (s)

Figure 9.9 Construction of an event-related fMRI design matrix with four different event types (A-D), using the canonical SPM HRF.

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171

0

50 100 150 200 Time (s)

Note: Indicator functions corresponding to the four event types are convolved with the canonical HRF to create the regressors that make up the design matrix. An image of the design matrix is shown to the far right.

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et al., 2005). Because the selective averaging procedure does not take the stimulus history into account, it must be used with caution when responses to different events overlap in time. Because of this, the majority of analyses, including those that estimate the shapes of HRFs, are currently done within the GLM framework (see Data Analysis: Implementation). Reports that the fMRI BOLD response is linear with respect to stimulus history (Boynton, Engel, Glover, & Heeger, 1996) encouraged the use of more rapidly paced trials (Zarahn et al., 1997), spaced less than 1 s apart in the most extreme cases (Burock, Buckner, Woldorff, Rosen, & Dale, 1998; Dale & Buckner, 1997). Here linearity implies that the magnitude and shape of the HRF does not change depending on the preceding stimuli. Studies have found that nonlinear effects in rapid sequences (1 or 2 s) can be quite large (Birn, Saad, & Bandettini, 2001; Friston, Mechelli, Turner, & Price, 2000; Vazquez & Noll, 1998; T. D. Wager, Vazquez, et al., 2005), but that responses are roughly linear if events are spaced at least 4 s to 5 s apart (Miezin et al., 2000). If rapid designs are properly designed, they still allow us to discriminate the effects of different conditions. Key is incorporating “jitter,” or a variable interstimulus interval (ISI) between events, which is critical for comparing event-related responses with an implicit resting baseline to determine whether the events are “activations” or “deactivations” relative to rest. With a randomized and jittered design, sometimes several trials of a single type will occur in a row, and because the hemodynamic response to closely spaced events sums in a roughly linear fashion, the expected response to that trial-type will build to a high peak. Introducing jitter allows peaks and valleys in activation to develop that are specific to particular experimental conditions. If we care only about comparing event types (e.g., A – B), randomizing the order of events creates optimal rise and fall without additionally jittering the ISI. However, jittered ISIs are critical for comparing events to baseline activity and thus determining whether events activate or deactivate a voxel relative to that baseline (Josephs & Henson, 1999; Wager & Nichols, 2003). Suppose you have a rapid sequence with two types of trial—say, attention switch trials (S) and no-switch trials (N) as in the task-switching experiment described earlier (Figure 9.4). Randomly intermixing the trials with an ISI of 2 s will allow you to estimate the difference S – N. However, you cannot tell whether S and N activate or deactivate relative to some other baseline. If you vary the interstimulus intervals randomly between 2 and 16 s, you can compare S – N (with less power because there are fewer trials), but you also can test whether S and N show positive or negative activation responses. This ability comes from the inclusion of

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intertrial rest intervals against which to compare S and N, and the relatively unique signature of predicted responses to both S and N afforded by the random variation in ISIs. The advantages of rapid pacing—including faster trials and sometimes increased statistical efficiency—must be weighed against potential problems with nonlinearity, multicolinearity, and model misfitting. A current popular choice is to use jittered designs with interstimulus intervals of at least 4 s, with exponentially decreasing frequencies of delays up to 16 s. Optimized Experimental Designs What constitutes an optimal experimental design depends on the psychological nature of the task as well as on the ability of the fMRI signal to track changes introduced by the task manipulations over time. It also depends on the specific comparisons (contrasts) of interest in the study. To make matters worse, the delay and shape of the BOLD response (and ASL signals, and other blood flow-based methods), scanner drift, and nuisance factors such as physiological noise conspire to make experimental design for fMRI more complicated than for experiments that measure behavior alone. Not all designs with the same number of trials of a given set of conditions are equal, and the spacing and ordering of events is critical. Some intuitions and tests of design optimality follow from a deeper understanding of the statistical analysis of fMRI data and are elaborated on later. For a full treatment, there are several excellent papers (Josephs & Henson, 1999; Liu, 2004; Smith, Jenkinson, Beckmann, Miller, & Woolrich, 2007; T. D. Wager & Nichols, 2003). Several computer algorithms are available for constructing statistically optimized designs, including an approach based on m-sequences—mathematical sequences that are near-optimal for certain types of designs (Buracas & Boynton, 2002), and one based on a genetic algorithm (Wager & Nichols, 2003) that incorporates m-sequence designs as a starting point and considers the relative importance of various contrasts to the study goals in calculating optimality. Design Strategies for Enhanced Psychological Inference Thus far, we have alluded to a simple contrast between two conditions, the subtraction of a control condition (B) from an experimental one (A), or [A – B]. Such contrasts are critical because any task, performed alone, produces activation in huge portions of the brain. Though contrasts in event-related designs can usually be more readily interpreted as being evoked by specific psychological or physical events than those in blocked designs, a single contrast

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Experimental Design for Neuroimaging Experiments

leaves much room for incorrect inference. This is because there may be multiple psychological and physical differences between task conditions A and B. Imagine a study that compares a difficult version of a working memory task (A) with an easy one (B). Not only does the more difficult task require greater use of working memory, it may also elicit increases in heart rate, more frustration, more error-detection and correction processes, and more monitoring and adjustment of performance. The result is that the [A – B] contrast does not reveal activations associated only with working memory demand. Parametric Modulation Designs One way to constrain interpretation and strengthen the credibility of subtraction logic is to incrementally vary a parameter of interest across several levels (e.g., working memory demand), and perform multiple subtractions or linear contrasts across levels. An example is a study of the Tower of London task (Dagher, Owen, Boecker, & Brooks, 1999), which requires subjects to make a sequence of moves to transfer a stack of colored balls from one post to another in the correct order. The experimenters varied the number of moves incrementally from 1 to 6. Their results showed linear increases in activity in dorsolateral prefrontal cortex across all 6 conditions, suggesting that this area subserved the planning operations critical for good performance. Multiple Control Conditions and Conjunctions Another fruitful approach is to include multiple control conditions matched for various aspects of a target task of interest. In our working memory example, this might amount to including a control condition that produces comparable increase in heart rate without involving working memory, and another that is frustrating without involving working memory, and so on. If a brain region is more activated in the working memory task than each of the control tasks, then it strengthens the case that the region subserves working memory. A productive line of research using this approach is that of Kanwisher and colleagues in the study of face recognition (Kanwisher, McDermott, & Chun, 1997). In a long series of studies, they identified an area in the fusiform gyrus that responded to pictures of faces and drawings of faces, but not to houses, scrambled faces, partial faces, facial features, animal faces, and other control stimuli. By presenting a large number of control stimuli of various types, Kanwisher et al. ruled out many confounding variables and infer that the brain area they studied, which they called the fusiform face area (FFA), was specifically activated by the perception of faces. Though the interpretation of these results as evidencing a face-selective module

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in the cortex is still being debated, this line of research is an excellent example of using multiple control conditions to rule out alternative hypotheses for the cause of activation of a region. The fact that the ultimate implications for neuroscience are debated is a testament to the difficulty of conceptualizing and ruling out all the plausible confounds, and of making reverse inferences in general. A natural way of making comparisons using multiple control conditions is to use conjunction analysis, which is a logical “and” operator across multiple contrasts. You might want to identify voxels active in a [task A – task B] contrast and in a [task A – task C] contrast. In general, this question is approached by first calculating a statistical map for each contrast of interest, and then selecting those voxels that meet a chosen statistical threshold in both (or all) maps. In effect, the minimum statistic is compared with the conjunction null hypothesis, which specifies that all the contrasts must have significant effects for the conjunction to hold (T. Nichols, Brett, Andersson, Wager, & Poline, 2005). This logic holds generally for all kinds of conjunctions, for example, [A-B] and [C-D] and [E-F], whether or not they are independent. Care must be taken when considering the selection of a significance threshold for a conjunction of contrasts [A-B] and [A-C]: Earlier versions of conjunction analysis in SPM99 and SPM2 software (Price & Friston, 1997) tested the global null hypothesis that none of the effects are truly present. Rejecting this hypothesis implies a true effect in at least one contrast, which is actually an “or” rule: a significant conjunction result in this case implies true activation for contrast [A-B] or contrast [A-C] (T. Nichols et al., 2005). The current version of SPM offers the user a choice of which null hypothesis to test and also offers a range of intermediate alternatives, for example, the hypothesis that 2 or fewer of a series of contrasts have true effects (Friston, Penny, & Glaser, 2005). Unlike the other tests described, this hypothesis requires the assumption of independence among the contrasts, which is clearly violated in our example conjunction with two control conditions [A-B] and [A-C] because they share a baseline. Overall, if you want to test for the intersection (logical and) of multiple effects, then the conjunction null is the proper null hypothesis. In reporting results, the precise procedures and null hypothesis should always be stated; as with other aspects of data analysis, it is not sufficient to merely state that you performed a conjunction analysis with a particular software package. A Note on Baselines Whether a task produces activation or deactivation depends on the baseline condition with which it is compared. Over the past decade or so, Raichle and colleagues have argued for the idea that a quiet resting

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state provides a natural baseline condition against which to evaluate task-related activation (Gusnard, Raichle, & Raichle, 2001; Raichle et al., 2001). A source of support is that the oxygen extraction fraction, the ratio of oxygen use to oxygen supplied by blood, is relatively constant across the resting brain. The argument is that this ratio is one that we are equipped to maintain over long time periods, so it provides a natural physiological baseline. Due in large part to the evidence that Raichle has garnered, many researchers compare tasks with an open-eyed fixation or closedeye resting baseline condition. The intertrial intervals in an event-related design, if enough rest and temporal jitter are provided, can provide an estimate of task-evoked activation relative to baseline activity (though the baseline level itself cannot be quantified with BOLD fMRI); however, tasks may also elicit sustained activity during the intertrial intervals (Visscher et al., 2003). Others argue that the baseline state is just another type of cognitive state, albeit one that is poorly experimentally controlled or characterized. Stark and Squire (2001) found that activity in the medial temporal lobes was substantially higher during rest than during some low-level cognitive tasks. Whether a task of interest activated or deactivated the medial temporal lobes depended on the choice of baseline, begging the question of exactly what kind of mnemonic or other cognitive activity is happening during “rest.” Thus, some researchers choose to compare tasks of interest with low-level baseline tasks during which mental activity can be more precisely experimentally controlled (Johnson et al., 2005). Ultimately, the comparison between task states, including rest, is a comparison of activity evoked by different kinds of mental representations. These comparisons can only be psychologically meaningful if the mental processes involved in each task can be specified. This does not preclude the resting state as a baseline condition of interest. Proponents of the baseline state recognize it as an active state, and theories of mental activity during rest include simulation of situations, contingencies, and associated thoughts and feelings generally focused on the self (and likely involving memory retrieval and medial temporal lobe activation; Gusnard et al., 2001). Each investigator must consider these issues in relation to the particular goals of the study when designing the tasks and comparisons.

switches among objects. This design is a simple 2 ⫻ 2 factorial, with two types of trials (switch versus nonswitch) crossed with two types of judgments (object/attribute). This design permits the testing of three contrasts: (1) a main effect of switch versus nonswitch; (2) a main effect of task type; and (3) the interaction between the two, which tests whether the switch versus nonswitch difference is larger for one task-type than the other. Factors whose measurements and statistical comparisons are made within subjects, such as those described previously, are withinsubjects factors; and those whose levels contain data from different individuals (e.g., depressed patients versus controls) are between-subjects factors. Within-subjects factors generally offer substantially more power and have fewer confounding issues (e.g., differences in brain structure and HRF shapes) than between-subjects factors. Factorial designs allow us to investigate the effects of several variables on brain activations. They also permit a more detailed characterization of the range of processes that activate a particular brain region (e.g., attention switching in general, or switching more for one tasktype than the other. Factorial designs also permit us to discover double dissociations of functions within a single experiment. In our example (Figure 9.4), a factorial design was required to infer that a manipulation (e.g., object-switching) affected dorsolateral prefrontal cortex, but a second manipulation (e.g., attribute switching) did not. Factorial designs can also be used to test for violations of the critical assumption of pure insertion, and for a number of other processes. If the baseline process (e.g., task difficulty) can be manipulated independently of the target process (task-switching requirement), then researchers can test for interactions between task difficulty and switching, and test the notion that the switch process produces an additive increase in activation beyond the processes involved in the basic task.

Factorial Designs

PET and fMRI studies yield data in a format that requires substantial preprocessing before statistical analysis and inference can be performed in a valid and optimal way. The goals of preprocessing are (a) to minimize the influence of data acquisition and physiological artifacts; (b) to check statistical assumptions and transform data to meet the assumptions; (c) to standardize the locations of brain

Another extension of subtraction logic is the factorial design. The study of task switching presented in the introduction to this chapter serves as an example (T. D. Wager, Jonides, et al., 2005). A subset of conditions in the study compared switch versus nonswitch trials for each of two different types: switches among object attributes and

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DATA ANALYSIS: IMPLEMENTATION Data Preprocessing Artifacts, Assumptions, and the Need for Preprocessing

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Data Analysis: Implementation

regions across subjects to achieve validity and sensitivity in group analysis. Most analyses are based, first, on the assumption that all the voxels in any given image were acquired at the same time. Second, it is assumed that each data point in the time series from a given voxel was collected from that voxel only (the participant did not move in between measurements). Third, it is assumed that the residual variance will be constant over time and have a white noise distribution. Additionally, when performing group analysis and making population inference, all individual brains are assumed to be in register, so that each voxel is located in the same anatomical region for all subjects. Without any preprocessing, none of these assumptions hold and statistical analysis would not yield valid or interpretable results. In addition, as noted earlier (see Limitations of PET and fMRI: Acquisition Artifacts in this chapter), neuroimaging data contain artifacts that arise from many sources, including head movement, brain movement, and vascular effects related to periodic physiological fluctuations, and reconstruction and interpolation processes. fMRI data in particular often contain transient spike artifacts and slow drift over time related to a variety of sources, including

magnetic gradient instability, RF interference, and movement-induced inhomogeneities in the magnetic field. An example of transient artifacts as visualized in AFNI is shown in Figure 9.10. Spikes in the data during isolated volume acquisitions are apparent in some entire slices but not others, as shown by the bright bands in the sagittal slices at the bottom of Figure 9.10. This pattern suggests that gradient performance was affected during acquisition of some echo-planar images, which were acquired slice by slice in interleaved order in this experiment. These artifacts likely constitute a violation of the assumptions of normally and identically distributed errors; unless they are dealt with, the consequences include reduced power in group analysis and potentially increased false positives in single-subject inference. A first line of defense is, as with any kind of data analysis, to examine the data—in as raw a form as possible—and diagnose problems. This can be challenging given the massive proportions of neuroimaging data, and different packages provide different ways of looking at the data. As shown in Figure 9.10, AFNI provides an excellent facility for viewing time courses and images from one or more voxels (see Table 9.3 for a list of packages and web sites). Spike artifacts are often identified and problematic images

Figure 9.10 Transient spike artifacts as visualized in the software package AFNI.

This suggests that gradient performance was affected during acquisition of some echo-planar images, which were acquired slice by slice in interleaved order in this experiment.

Note: Spikes in the data during isolated volume acquisitions are apparent in certain slices, as shown by the bright bands in the sagittal slices (bottom).

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removed prior to or in the course of analysis, or minimized using trimming procedures, as in FIASCO software. VoxBo software also has good data-surfing capabilities. A popular approach implemented in FSL, FMRISTAT, and specialized packages such as GIFT (see later in this chapter) is to extract principal components or independent components from the whole-brain time series and visualize them. These components are increasingly used for artifact removal (Nakamura et al., 2006; Tohka et al., 2007), though if single-subject inference is desired, care must be taken not to bias the results by removing variance from the data without accounting for it in the statistical analysis. Apart from using the procedures described here, the effects of slow drift, the problem of intersubject registration, and some other artifacts can be minimized using preprocessing and analysis techniques to be described. In the rest of this chapter, we focus on fMRI analysis and briefly describe common preprocessing steps. Other neuroimaging methods, including PET, require different steps than those described here. Preprocessing Steps for Functional Magnetic Resonance Imaging The major steps in fMRI preprocessing are reconstruction, slice acquisition timing correction, realignment, coregistration of structural and functional images, registration or nonlinear warping to a template (also called normalization), and smoothing. Single-subject analyses do not require the warping step, which introduce spatial uncertainty in terms of anatomical locations and thus can provide much higher anatomical resolution. Group studies largely preclude false positives due to fMRI time series artifacts and permit population inference. Some group studies do not employ smoothing to increase spatial resolution. Reconstruction Images must be first reconstructed from the raw MR signal. Raw and reconstructed data are stored in a variety of formats, but reconstructed images are generally composed of a 3-D matrix of data, containing the signal intensity at each voxel or cube of brain tissue sampled in an evenly spaced grid, and a header that contains information about the dimensionality, voxel size, and other image parameters. A popular format is Analyze, also known as AVW, which uses a separate header file and image file for each brain volume acquired. Other formats, such as NIFTI, are also gaining popularity. A series of images describes the pattern of activity over the course of the experiment. It is also common to store images in a 4-D matrix, where the fourth dimension is time. Slice Timing Statistical analysis using a single hemodynamic reference function assumes that all the voxels in

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an image are acquired simultaneously. In reality, the data from different slices are shifted in time relative to each other because most BOLD pulse sequences collect data slice by slice—some slices are collected later during the volume acquisition than others. Thus, we need to estimate the signal intensity in all voxels at the same moment in the acquisition period. This can be done by interpolating the signal intensity at the chosen time point from the same voxel in previous and subsequent acquisitions. A number of interpolation techniques exist, from bilinear to sinc interpolations, with varying degrees of accuracy and speed. Sinc interpolation is the slowest, but generally the most accurate. Some researchers do not use slice timing, as it adds interpolation error to the data, and instead use more flexible hemodynamic models to account for variations in acquisition time. Realignment A major problem in most time-series experiments is movement of the subject’s head during acquisition of the time series. When this happens, the image voxels’ signal intensity gets contaminated by the signal from its neighbors. Thus, we must rotate and translate each individual image to compensate for the subject’s movements. Realignment is typically performed by choosing a reference image (popular choices are the first image or the mean image) and using a rigid body transformation of all the other images in the time series to match it, which allows the image to be translated (shifted in the x, y, and z directions) and rotated (altered roll, pitch, and yaw) to match the reference. The transformation can be expressed as a premultiplication of the “target” image spatial coordinates to be altered by a 3 ⫻ 3 affine matrix. The elements of this matrix are parameters to be estimated, and an iterative algorithm is used to search for the parameter estimates that provide the best match between a target image and the reference image. Usually, the matching process is done by minimizing sums of squared differences between the two images. Realignment corrects adequately for small movements of the head, but it does not correct for the more complex spinhistory artifacts created by the motion. The parameters at each time point are saved for later inspection and are often included in the analysis as covariates of no interest; however, even this additional step does not completely remove the artifacts created by head motion. Residual artifacts remain in the data and contribute to noise. Sometimes this noise is correlated with task contrasts of interest, which poses a problem, and can create false results in single-subject analyses. However, because these artifacts are expected to (and typically do) differ in sign and magnitude across subjects, group analysis is valid. Group analyses are usually robust to such artifacts in terms of false positives, but power can be severely compromised if large movement artifacts are present.

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Data Analysis: Implementation

Because of these issues, it is typical to exclude subjects that move their heads substantially during the scan. Subject motion in each of the 6 directions can be estimated using the magnitudes of the transformation required for each image during the realignment process, and time series of displacements are standard output for realignment algorithms. There are no hard-and-fast rules for how much movement is too much, but more than 1.5 mm displacement within a scanning session (while the scanner is running continuously) is typically considered problematic, and can usually be avoided with proper instructions to subjects and head restraints. Warping to Atlas (Normalization) For group analysis, each voxel must lie within the same brain structure in each individual subject. Individual brains have different shapes and features, but there are regularities shared by every nonpathological brain, and normalization attempts to register each subject’s anatomy with a standardized atlas space defined by a template brain (see Figure 9.11). Normalization can be linear, involving simple registration of the gross shape of the brain, or nonlinear, involving warping to match local features. In intensity-based normalization, matching is done using image intensities corresponding to gray/white matter/fluid tissue classes. Surface-based normalization uses extracted features such as gyral and sulcal boundaries explicitly (see Thresholding and Multiple Comparisons, earlier in this chapter). Here, we describe nonlinear intensity-based normalization as implemented in SPM software. Whereas the realignment and co-registration procedures perform a rigid body rotation, normalization can stretch and shrink different regions of the image to achieve the closest match. This warping consists of shifting the locations of pixels by different amounts depending on their original location. The function that describes how much to shift the voxels is unknown, but can be described by a set of cosine basis functions. The task is then to search for a set of coefficients (weights of each basis function) that minimize the least squares difference between the transformed image and the template. How closely the algorithm attempts to match the local features of the template depends on the number and spatial frequency of basis functions used. Often, warping that is too flexible (using many basis functions) can produce gross distortions in the brain, as local features are matched at the expense of getting the right overall shape, as shown in Figure 9.11B. This happens essentially because the problem space is too complex, and the algorithm can settle into a “local minimum” solution that is not close to the global optimal solution. Surface-based warping uses similar principles, but matches features on extracted cortical surface representations instead of image intensities.

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Intersubject registration is one of the largest sources of error in group analysis. Thus, it is important to inspect each normalized brain and, if necessary, take remedial measures. These include manually improving the initial alignment, using a mask to exclude problematic regions of atrophy or abnormality (e.g., a lesion), altering the number of basis functions and other fitting parameters, and in some cases developing specialized template brains (e.g., for children). Figure 9.11C shows a process of checking normalization for one subject. We have identified control points on the MNI ICBM152 template brain (left) that correspond to easily identifiable features. Then, we have taken those points and overlaid them on the subject’s normalized T1 image. For this subject, unlike the pathological case in Figure 9.11B, each of the control points matches the corresponding anatomical feature on the subject’s brain quite well. Such checking can be done in several ways, and though there are no hardand-fast rules for how to check and how much error is too much, each lab should develop a set of standardized procedures. Smoothing Many investigators apply a spatial smoothing kernel to the functional data, blurring the image intensities in space. This is ironic, given the push for higher spatial resolutions and smaller voxels—so why does anyone do it? One reason is to improve intersubject registration. A second reason is that Gaussian random field theory, a popular multiple-comparisons correction procedure, assumes that the variations across space are continuous and normally distributed. However, images are sampled on a grid of voxels, and neither assumption is likely to hold; smoothing can help meet these assumptions. Smoothing typically involves convolution with a Gaussian kernel, which is a 3-D normal probability density function often described by the full width of the kernel at half its maximum height (FWHM) in mm. One estimate of the smoothing required to meet the assumption is a FWHM of 3 times the voxel size (e.g., 9 mm for 3 mm voxels). An important consideration is that acquiring an image with large voxels and acquiring it with small voxels and smoothing an image are not the same thing. The signal-tonoise ratio during acquisition increases as the square of the voxel volume, so acquiring small voxels means that much signal is lost that can never be recovered. It is optimal in terms of sensitivity to acquire images at the desired resolution and not employ smoothing. Some recent acquisition schemes acquire images at the final functional resolution desired, which also permits much more rapid image acquisition as time is not spent acquiring information that would be discarded in analysis (M. Lindquist, Glover, & Shepp, in press).

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Figure 9.11 Normalization attempts to register each subject’s anatomy with a standardized template brain using an intensitybased warping procedure. Note: A: A schematic overview of the warping process. High resolution T1 images are warped onto a T1 template to give normalized images in a standard space. B: Incorrect warping can produce gross distortions in

Previously, many investigators applied temporal smoothing to the data as well as spatial smoothing. This procedure is another form of filtering like the high-pass filtering done in the course of model estimation; it removes high-frequency signals from the data, whereas high-pass filtering removes low-frequency signals. This procedure was implemented in SPM99 software (Table 9.3) primarily to facilitate accurate estimation of the degrees of freedom, which was assumed after smoothing to equal that implied by the kernel. This approach has largely been replaced by more standard time series models (e.g., autoregressive modeling). There is no expected benefit to temporal smoothing on sensitivity, as it further decreases the temporal resolution of the data, and it is not recommended. Co-registration Often, high-resolution structural images (T1 and/or T2) are used for warping and localization.

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the brain, as local features are matched at the expense of getting the correct overall shape. C: The normalization procedure can be checked by identifying control points on the MNI ICBM152 template brain (left) that correspond to easily identifiable features and overlaying them on the subject’s normalized T1 image. For this subject, each of the control points matches well with the corresponding anatomical feature.

The same transformations (warps) are applied to the functional images, which produce the activation statistics, so accurate registration of structural and functional images is critical. Co-registration aligns structural and functional images, or in general, different types of images of the same brain. Because functional and structural images are collected with different sequences and different tissue classes have different average intensities, using a least squares difference method to match images is often not appropriate. The signal intensity in gray matter (G), white matter (W), and ventricles are ordered W > G > V in functional T2* images, and V > G > W in structural T2 images (Figure 9.1). In such cases, an affine transformation matrix can be estimated by maximizing the mutual information among the two images, or the degree that knowing the intensity of one can be used to predict the intensity of the other (Cover & Thomas, 1991). Typically, a single structural image is co-registered to the first or mean functional image.

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Data Analysis: Implementation

Localizing Task-Related Activations with the GLM The GLM is the most common statistical method for assessing task–brain activity relationships in neuroimaging (Worsley & Friston, 1995). GLM is a linear analysis method that subsumes many basic analysis techniques, including t-tests, ANOVA, and multiple regression. The GLM can be used to estimate whether the brain responds to a single type of event, to compare different types of events, to assess correlations between brain activity and behavioral performance or other psychological variables, and for other tests. The GLM is appropriate when multiple predictor variables—which together constitute a simplified model of the sources of variability in a set of data—are used to explain variability in a single, continuously distributed outcome variable. In a typical neuroimaging experiment, the predictors are related to psychological events, and the outcome variable is signal in a brain voxel or region of interest. Analysis is typically “massively univariate,” meaning that the analyst performs a separate GLM analysis at every voxel in the brain, and summary statistics are saved in maps of statistic values across the brain. Because of the hierarchical structure of the data, an appropriate analysis for multisubject PET and fMRI studies is the mixed-effects GLM model. This is often approximated by performing a GLM model for each subject and using the resulting activation parameter estimates in a second-level group analysis. We refer to this as the unweighted summary statistic approach. FSL software currently performs a mixed-effects analysis, whereas the most typical analysis in SPM, AFNI, BrainVoyager, VoxBo, and other packages is the unweighted summary statistic approach. We describe the mechanics of a single-subject analysis and then the mixed-effects approach in the following subsections. Single-Subject GLM Model Basics For a single subject, the fMRI time course or series of PET values from one voxel is the outcome variable (y). Activity is modeled as the sum of a series of independent predictors (x variables, that is, x1, x2) related to task conditions and other nuisance covariates of no interest (e.g., head movement estimates). In fMRI analysis, for each task condition or event type of interest, a time series of the predicted shape of the signal response is constructed, usually using prior information about the shape of the vascular response to a brief impulse of neural activity. The vectors of predicted time series values for each task condition are collated into the columns of the design matrix, X, which contains a row for each of n observations collected (observations over time) and a column for each of k predictors. The GLM

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fitting procedure estimates the best-fitting amplitude (scaling factor) for each column of X, so that the sums of fitted values across predictors best fit the data. These amplitudes are regression slopes, and are denoted with the variable βˆ (the “hat” denotes an estimate of a theoretical constant value). It also estimates a time series of error values, εˆ, that cannot be explained by the model. The model is thus described by the equation: y ⫽ X ␤ ⫹ε

(9.1)

where β is a k⫻ 1 vector of regression slopes, X is an n⫻k model matrix, y is an n⫻ 1 vector containing the observed data, and ε is an n⫻ 1 vector of unexplained error values. The equation is in matrix notation, so that Xβ indicates the rise and fall in the data explained by the model, or the sum of each column of X multiplied by each element of β. Error values are assumed to be independent and to follow a normal distribution with mean 0 and standard deviation σ. The estimated βˆ s correspond to the estimated magnitude of activation for each psychological condition described in the columns of X. One advantage of the GLM is that there exists an algebraic solution for βˆ that minimizes the squared error: T -1 T βˆ ⫽ (X X) X y

(9.2)

where T indicates the transpose operator. Inference is generally conducted by calculating a t-statistic, which equals the βˆ s divided by their standard errors, and obtaining p-values using classical inference. The standard errors of the estimates are the diagonal elements of the matrix: se(βˆ ) ⫽ (X T X)-1σˆ

(9.3)

Notably, the error term comprises two separate terms from different sources. σ is the residual error variance, which depends on many factors, including scanner noise. (XTX)–1 depends on the design matrix itself, and reflects both the variability in the predicted signal and covariance among preditors (multicolinearity). Design optimization algorithms, described earlier, work on minimizing the design-related component of the standard error: (XTX)–1. An important additional feature of the data requires a further extension of the model. fMRI data are autocorrelated—signals are correlated with versions of themselves shifted in time and are not independent—and the autocorrelation must be removed for valid single-subject inference. This is typically done by estimating the autocorrelation in the residuals, after model fitting, and then removing the autocorrelation by “prewhitening.” Prewhitening works

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by premultiplying both sides of the general linear model equation (Equation 9.1) by the square root of a filtering matrix W, that will counteract the autocorrelation structure and create a new design matrix W1/2X and whitened data W1/2y. This process is incorporated into what is known as the generalized least-squares solution, so that: βˆ ⫽ (X T WX)-1X T Wy

(9.4)

Note that the standard errors and degrees of freedom change as well due to the whitening process. Because the estimation of W depends on βˆ , and vice versa, a one-step algebraic solution is not available, and the parameters are estimated using an iterative algorithm. There are many ways of designing W, ranging from estimates that make strong simplifying assumptions about the form of the data, such as the one-parameter autoregressive AR(1) model, to empirical estimates that use many parameters. As with any model-fitting procedure, a trade-off exists between using few and many parameters. Many-parameter models generally produce close fits to the observed data. However, models with few parameters—if they are chosen carefully—can produce more accurate estimates of the underlying true function because they are less susceptible to fitting random noise patterns in the data. Contrasts Contrasts across conditions can be easily handled within the GLM framework. Mathematically, a contrast is a linear combination of predictors. The contrast (e.g., A – B in a simple comparison, or A ⫹ B – C – D for a main effect in a 2 ⫻ 2 factorial design) is coded as a k⫻ 1 vector of contrast weights, which we denote with the letter c. For example, the contrast weights for a simple subtraction is c⫽ [1 – 1]T, while a single contrast for a linear effect across four conditions might be c⫽ [–3 – 1 1 3]T. Concatenating multiple contrasts into a matrix can simultaneously test a whole set. Thus, the main effects and interaction contrasts in a 2 ⫻ 2 factorial design can be specified with the following matrix: C⫽

[1 1 ⫺1 ⫺1

1 ⫺1 1 ⫺1

1 ⫺1 ⫺1 1]

Columns 1 and 2 test main effects, and the third tests their interaction. To test contrast values against a null hypothesis of zero—the most typical inferential procedure—contrast weights must sum to zero. If the weights do not sum to zero, then the contrast values partially reflect overall scanner signal intensity, and the resulting t-statistics are invalid. The analyst must take care to specify

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contrasts correctly, as contrast weights in neuroimaging analysis packages are often specified by the analyst, rather than being created automatically as in SPSS, SAS, and other popular statistical packages. The true contrast values Cβ can be estimated using CTβˆ , where βˆ is obtained using Equation 9.2. The standard errors of each contrast are the diagonals of: se (CT βˆ ) ⫽ CT (XT X)-1 XT Cσˆ

(9.5)

The whitening process is omitted here for simplicity, but can readily be incorporated. Most imaging statistics packages write a series of images to disk containing the betas for each condition throughout the brain, and another set of contrast images containing the values of CTβˆ throughout the brain. Contrast images are typically used in a group analysis. A third set of images contains t-statistics, or the ratio of contrast estimates to their standard errors. Assumptions The model-fitting procedure assumes that the effects due to each of the predictors add linearly and do not change over time (the system is linear and timeinvariant). The inferential process assumes that the observations are independent, that they all come from the same distribution, and that the residuals are distributed normally and with equal variance across the range of predicted values. All these assumptions are violated to a degree in at least some brain regions in a typical imaging experiment, which has prompted the development of important extensions, including diagnostic tools and robust model-fitting procedures (Loh, in press; Luo & Nichols, 2003; T. D. Wager, Keller, et al., 2005). Violations of the assumptions are not merely a theoretical nuisance. They can make the difference between a valid finding and a false positive result, or between finding meaningful activations in the brain and wasting substantial time and money. Diagnostic tools have been developed for exploring the data, looking for artifacts, and checking a number of assumptions about the data and model (Loh, 2008; Luo & Nichols, 2003), and like many tools developed by members of the neuroimaging community, they are freely available on the Internet. The quantity of data(e.g.,100,000 separate regressions on 1,000 data points per subject ⫻ 20 subjects) and the software and data structures that support its analysis make it difficult to examine assumptions and check the data, which makes such diagnostic tools all the more important. Another active area of research concerns strategies for dealing with some known violations of assumptions, described later. Violations of independence can be handled in a limited way using generalized least squares. Violations of equality and normality can be dealt with by using

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Data Analysis: Implementation

nonparametric permutation tests to make statistical inferences (Nichols & Holmes, 2002), or, if they result from the presence of outliers, by robust regression techniques (T. D. Wager, Keller, et al., 2005). Free implementations of each of these extensions are available (Table 9.3). GLM Model-Building in fMRI Perhaps the most challenging task in linear regression analysis is the creation of realistic predictions of taskrelated signals for the columns of X. PET images integrate across many psychological events, obviating the need for accurate models but also limiting the specificity with which activation can be linked to specific events or time periods. As discussed earlier, a popular method of forming predicted BOLD time series is to use a canonical HRF. The process is shown in Figure 9.9. To build the model, researchers start with an indicator vector representing the neuronal activity for each condition sampled at the resolution of the fMRI experiment, shown at the left of Figure 9.9 for four hypothetical event types (A – D). This vector has zero value except during hypothesized neural activation periods, when the signal is assigned a value of 1. Each indicator vector is convolved with the HRF to yield a predicted time course related to that event, which forms a column of the X. The rightmost panel shows X in image form, a common format for presentation in papers. If the canonical HRF fits the shape of the BOLD response to psychological events, then using the canonical HRF simplifies the analysis and has great sensitivity to detect differences. Consider two psychological events A and B that both activate a voxel, but with different amplitudes, as shown in the top left panel of Figure 9.12. Empirical time courses are shown in light lines, and the fitted responses (model fits) with the canonical HRF are shown in dark lines. The [A – B] contrast will appropriately reflect the different response amplitudes. The canonical HRF is a double-edged sword. If the canonical HRF does not fit, there is at best a drop in power, and at worst false positives and misinterpretation of results (Lindquist & Wager, 2007). Consider an example in which two conditions A and B produce responses of equivalent amplitude, but at different delays. This is shown in the top center panel of Figure 9.12, where the response to B is delayed by 3 s. Since the HRF shape is fixed, any difference in model fits will produce a difference in the only free parameter, amplitude. In this example, the estimated amplitude for A will be greater than for B. Without additional diagnostic tests, one might falsely infer that A activates the brain region more than B. This example illustrates the importance of visualizing the data and fits, rather than simply interpreting a statistically significant result at face value.

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Comparing groups of individuals (e.g., older versus younger adults, or patients and normal controls) can be especially problematic. If you find [A – B] amplitude differences, are those differences caused by differences in neural activity amplitude or the timing and shape of the vascular component of the BOLD response? Elderly subjects have reduced and more variable shapes of their HRFs compared with younger subjects (D’Esposito, Zarahn, Aguirre, & Rypma, 1999), making direct comparisons with a canonical HRF problematic. Alternate approaches include (a) measuring HRFs in visual and motor cortex for each individual subject using a separate task (Aguirre et al., 1998) or (b) using a more flexible model of the HRF by using a basis set. Basis Sets In the previous discussion, conditions are modeled by a single linear regressor that allows us to estimate only the amplitude of the predicted response (βˆ ) or contrast (CTβˆ ). Alternatively, the same neural indicator vector can be convolved with multiple canonical waveforms and entered into multiple columns of X for a single event type. These reference waveforms are basis functions, and the predictors for an event type constructed using different basis functions can combine linearly to better fit the evoked BOLD responses. An example is shown in the second row of Figure 9.12, in which a linear combination of the canonical HRF and its temporal derivative provide better fits to responses that look similar to the HRF (left panel), are shifted in time (center panel), or have extended activation durations (right panel). This basis set is the most popular current alternative to the canonical HRF alone among users of SPM software (Friston, Glaser, et al., 2002; Friston, Josephs, Rees, & Turner, 1998). Notice that the fits are better, but changes in delay and duration are far from perfectly modeled. The ability of a basis set to capture variations in hemodynamic responses such as those depicted in Figure 9.12 depends on both the number and shape of the reference waveforms. There is a fundamental trade-off between flexibility to model variations and power. This is because each parameter is estimated with error, and flexible models can tend to model noise and thus produce noisier parameter estimates. One of the most flexible models, a finite impulse response (FIR) basis set, contains one free parameter for every time point following stimulation in every cognitive event type that is modeled (Glover, 1999; Goutte, Nielsen, & Hansen, 2000; Ollinger, Shulman, & Corbetta, 2001). Using such a model makes minimal assumptions about the shape of the HRF because the βˆ s estimate the average response at each time point following the onset of an event. The FIR model is a preferred way to estimate and visualize

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Essentials of Functional Neuroimaging Basis functions True amplitude diff. [A - B] A

True delay diff. [A - B] A

True duration diff. [A - B]

B

Canonical HRF B

Canonical HRF ⫹ temporal derivative

Smooth FIR

Inverse Logit Model

Peri-stimulus Time

Figure 9.12 Basis sets differ in the flexibility they provide in modeling different HRF shapes. Note: Each column in the figure shows HRF estimates for an experiment where two conditions A and B produce responses of: (left) different amplitudes; (center) equivalent amplitude, but at different delays; and (right) equivalent amplitude, but different durations. The ability of four different basis sets to estimate the hemodynamic response function (HRF)

the shape of BOLD responses, and it is implemented in major software packages including AFNI, SPM, and FSL. An example of model fits using a smooth FIR model, which is constrained to produce smooth response functions, is shown in the third row of Figure 9.12. The model fits (dark black lines) fit the data reasonably accurately in all conditions, including those shifted in time (center) and extended in duration (right). Other choices of basis sets include those composed of principal components (Aguirre et al., 1998; Woolrich, Behrens, & Smith, 2004), cosine functions (Zarahn, 2002), radial basis functions (Riera et al., 2004), spectral basis sets (Liao et al., 2002), and other functions. The bottom row in Figure 9.12 shows fitted responses from a basis set recently developed in our lab that uses three superimposed inverse logit functions to model the rise, fall, and undershoot of the BOLD response (Lindquist & Wager, 2007). The model can handle both delays and variations in duration, making a single model appropriate for both brief events and prolonged epochs of stimulation. In addition, fits are as accurate as the FIR model fits for these data, and simulations showed that the model compares favorably with a range of other models in terms of statistical power. The model is freely available (see Table 9.3). Basis sets offer a major advantage—more accurate modeling of the HRF across subjects and across the brain—but they pose additional technical difficulties that make their use less common than perhaps it should be. First, it is not

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corresponding to each condition is shown in the four rows. The basis sets that were used are SPM software’s canonical HRF, the canonical HRF ⫹ its temporal derivative, a smooth finite impulse response (FIR) model and the inverse logit model. From “Validity and Power in Hemodynamic Response Modeling: A Comparison Study and a New Approach,” by M. A. Lindquist and T. D. Wager, 2007, Human Brain Mapping, 28, p. 776. Adapted with permission.

straightforward to calculate contrasts across conditions when there are multiple parameter estimates per condition. Leaving out some basis functions when calculating contrasts, though it is often done, is not generally advised. An alternative is to calculate one contrast per basis function for each contrast of interest. Group analysis can then be done using repeated measures analyses at the second level (in group analysis) rather than the usual one-sample t-test. However, there is a cost in power when basis functions are added, and in general whenever more parameter estimates are compared. Physiological Noise and Covariates of No Interest In both PET and fMRI designs, additional predictors are typically added to account for known sources of noise in the data. These nuisance covariates are included to reduce noise and to prevent signal changes related to head movement and physiological (e.g., respiration) artifacts from influencing the contrast estimates. In addition, covariates that implement high-pass filtering, or removal of signal frequencies below a specified cutoff, can also be added at this stage; this is the standard approach in SPM software. In PET, a common covariate is the global (whole-brain) mean signal value for each subject, included to control for differences in amount of radioactive tracer in circulation. In fMRI, the signal typically drifts slowly over time, so that the most power is in the lowest temporal frequencies. This characteristic has prompted the widespread use of

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Data Analysis: Implementation

high-pass filters that remove fluctuations below a specified frequency cutoff from the data. High-pass filtering is often performed in the GLM analysis by adding covariates of no interest (e.g., low-frequency cosines). Of course, care must be taken to ensure that the fluctuations induced by the task design are not in the range of frequencies removed by the filter. Design optimization algorithms can take this into account when constructing trial sequences (T. D. Wager & Nichols, 2003). Much of the autocorrelated noise and other noise variance in fMRI may come from aliased physiological artifacts (Lund, Madsen, Sidaros, Luo, & Nichols, 2006). Thus, it is increasingly popular to measure heartbeat and respiration during scanning and to use preprocessing algorithms for removing signals related to measured physiological fluctuations from the data prior to analysis (Glover, Li, & Ress, 2000). Programs for doing this are typically available from authors of research articles, but have not yet been incorporated as standard tools in neuroimaging analysis packages. Group Analysis The analysis described so far has been, for fMRI datasets, an analysis of data from a single subject. However, researchers are often interested in making inferences about a population, not just about a single subject or even a set of individual subjects, which requires a group analysis. Both PET and fMRI studies nearly always involve collecting more than one image per subject, and testing for the significance of effects in a group of subjects. In fMRI, typically, separate GLM analyses are conducted on the time series data for each subject at each voxel in the brain to estimate the magnitude of activation evoked by the task. This is called a “first level” analysis. These estimates are carried forward and tested for reliability across subjects in a “second level” group analysis. In PET, the first level analysis often consists of simple image subtractions, followed by the same type of second level analysis as for fMRI. The unweighted summary statistics approach referred to earlier consists of a simple one-sample t-test across contrast estimates for each subject. This analysis, like others discussed so far, is repeated at each voxel. It can be specified in the GLM framework, so Equations. 9.1 to 9.3 hold, and independence is typically assumed across subjects so no prewhitening is needed. The one-sample t-test for overall activation corresponds to a test of the model intercept in a GLM model. Additional covariates across subjects (e.g., average performance scores) can be specified and tested in simple or multiple regression. Two-sample and ANOVA designs to compare groups and related GLM variants can also be specified. Including covariates can improve statistical power for the test of overall activation, though

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care must be taken: The significance of the intercept can only be assessed if all other covariates are transformed to have a mean of zero. The unweighted summary statistic approach is valid if the contrast standard error is the same across all subjects, which implies identical design matrices and residual variances. This is rarely if ever true in practice, though the cost is mostly in the statistical power of the analysis and it is still widely used. Full mixed-effects models relax those stringent assumptions by considering the standard errors within each subject as well as contrast estimates. Mixedeffects analyses are standard in FSL and FMRISTAT software (see Mixed versus Fixed Effects, earlier in this chapter, and Table 9.3). Mixed-effects analyses essentially weight subjects when calculating group statistics. The larger a subject’s standard error, the less reliable their estimate, and the less that subject should contribute to the group results. This requires estimating variance components: One component is variance related to within-subject measurement error and model misfitting (σ2W), and another component is variance related to true interindividual differences among subjects (σ2B). Accurate estimation of the relative contribution of error within- and between-subjects allows for appropriate weighting. Restricted maximum likelihood (ReML) is a popular estimate of variance components based on the residuals. Since variance estimates and model fits (βˆ s) are interdependent, iterative algorithms such as EM are used to estimate ReML variance components. Statistical Power and Sample Size Statistical power depends on having either a large effect size (high contrast values) or a small standard error. The standard error in a group analysis is determined by both σ2W and σ2B. At the group level, σ2B can be reduced and power increased by increasing the sample size, more accurate normalization or more informed ROI selection, and increased control of strategies used and individual psychological responses to the task. σ2W can be reduced by improving modeling procedures and reducing acquisitionrelated scanner noise and physiological noise. A key question when designing a group study is determining an adequate sample size. The answer to this question depends on the effect size in the group, the amount of scanner noise and signal optimization, and it will be different for each task and each brain voxel (Desmond & Glover, 2002; Zarahn & Slifstein, 2001). Power analysis is difficult in fMRI because power depends on so many factors relating to psychology, task design and analysis, and hardware; however, by referring to standard effect sizes, you can obtain estimates of what sample sizes are needed in a group analysis.

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Figure 9.13 shows plots of power (y-axes) as a function of sample size (x-axes) for three effect sizes in two kinds of analysis. The effect sizes are Cohen’s d values, which is defined as mean activation magnitude divided by its standard deviation, for a simple one-sample t-test in group analysis. In behavioral sciences, d⫽ 0.3, 0.5, and 1 are considered small-, medium-, and large-effect sizes, respectively. Most activations reported in neuroimaging have effect sizes that are substantially larger—d⫽ 2 or more. However, this is partly because voxel-wise mapping capitalizes on chance due to selection bias: Voxels in which chance favors the evidence for activation have large effect sizes and tend to be reported. Whereas observed effect sizes in published reports are usually overestimated due to selection bias, the problem is exacerbated when many tests are performed. We show power curves here for effect sizes of 0.5, 1, and 2. Figure 9.13A shows results for a whole-brain search with 200,000 voxels, a typical number depending on acquisition and analysis choices, and FWE correction at p < .05 using the Bonferroni method. To achieve 80% power with a reasonable sample size, the effect size must be larger than 0.5, and around 40 subjects are required for d⫽ 1 and 18 subjects for d⫽ 2. Figure 9.13B shows the same results using nonparametric permutation testing, which takes into account the spatial smoothness in the data. We used nonparametric thresholds from 10 analyses from various studies reported in Nichols & Hayasaka, (2003) to estimate the effective number of independent

(A)

comparisons and thus power. With nonparametric analysis, around 25 subjects for d⫽ 1 and 11 subjects for d⫽ 2 provides 80% power. Design optimization procedures can be employed before data is ever collected to increase the effect size. For a fixed effect size and sample size, power depends on the within-subject standard error (se(CTβ)), which depends on both the design matrix, X, and the residual standard deviation, σ (Equation 9.5). The latter can be reduced by optimizing data collection (e.g., pulse sequences and hardware) and in the study design by ensuring the engagement of subjects in the tasks. Error related to X can be minimized during experimental design by carefully choosing the number, sequence, and spacing of events to minimize the design-related component of the standard error, CT (XTX)–1C. Effective minimization increases predictor variance and reduces predictor covariance (multicolinearity), and is particularly critical in event-related fMRI. It is possible to build an event-related fMRI design in which even large neuronal effects cannot be detected. For this reason, computer-aided design optimization can be very useful (Buracas & Boynton, 2002; T. D. Wager & Nichols, 2003). Finally, both theory and simulations show that there is a substantial trade-off in power between detecting activation differences between conditions using an assumed HRF shape and estimating the shape of evoked activations with a more flexible model (Liu, Frank, Wong, & Buxton,

(B) Power with nonparametric thresholding

Power with Bonferroni correction 1

1 d2 Power at p  05 corrected

Power at p  05 corrected

d2 0.8 d1 0.6

0.4 d  0.5

0.2

0

0

20

40 Sample Size

60

80

Figure 9.13 A: Power curves—calculated for effect sizes of 0.5, 1, and 2—for a whole-brain search with 200,000 voxels and FWE correction at p < .05 using the Bonferroni method. Note: The number of voxels would be typical of a whole-brain search through gray and white matter with a 2 ⫻ 2 ⫻ 2 mm sampling resolution.

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0.8 d1 0.6

0.4 d  0.5

0.2

0

0

20

40 Sample Size

60

80

B: The same power curves calculated based on the results of nonparametric permutation testing, which takes into account the spatial smoothness in the data. Based on the smoothness reported in Nichols and Hayasaka (2003) for 10 different statistic maps, we calculated an average of ˜750 effective independent comparisons. Correction across this number of comparisons was used in calculating power.

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Data Analysis: Implementation

2001). This trade-off is shown in Figure 9.14, in which shape-estimation power is shown on the x-axis and contrast-detection power is shown on the y-axis. The points in the model represent designs with different sequences and timing of events. Blocked designs have the highest [A – B] contrast detection power when the canonical HRF is used, but provide little information about the shape of the HRF. M-sequences, or sequences that are orthogonal to themselves shifted in time, provide optimal shape estimation power (the nonoptimality in the figure is due to truncation of the m-sequences—so they are not perfect), but low detection power (Buracas & Boynton, 2002). Random event-related designs fall somewhere in between. As the figure shows, designs optimized with a genetic algorithm (T. D. Wager & Nichols, 2003) can produce substantially better results than random designs on both measures. Bayesian Inference Bayesian methods have received a great deal of attention in fMRI literature. These inferential methods are now key 25 Block design 169 s on/off

Approximate theoretical limit

Contrast detection power Cor(1)(z)

20

Optimized designs (GA)

15

Event-related designs

185

components in several major fMRI analysis software packages (e.g., SPM and FSL). A full treatment of Bayesian methods is beyond the scope of this chapter, but an excellent overview can be found in Gelman et al. (2004). A key difference from the frequentist approach discussed previously (which subsumes classical inference in the GLM and its extensions) is that Bayesian analysis combines evidence from the data through priors—beliefs about the data specified as probabilities prior to data collection—to yield posterior probability values. This can be a big advantage in that estimates from data (e.g., of HRF shapes) can be easily regularized based on known information from other studies. Such prior constraints are also possible in frequentist analyses, though they require modifications and special procedures; lasso, ridge regression, and robust regression are examples. If you do not want to impose strong prior beliefs, then it is possible to use noninformative priors, which is implemented in the Bayesian approach in FSL software (Woolrich, Behrens, Beckmann, et al., 2004). For the single-level model, this leads to parameter estimates that are equivalent to those obtained using classical inference. Another way to choose prior beliefs is by estimating them from data. This is the “empirical Bayes” approach. It is a hybrid between classical and Bayesian inference that can provide some regularization without biasing the results of hypothesis tests, and is used in SPM software (Friston, Glaser, et al., 2002; Friston, Penny, et al., 2002).

10

Assessing Brain Connectivity 5 m-sequence designs 0 3.4

3.5

3.6 3.7 3.8 4 3.9 HRF shape estimation power

4.1

4.2

Figure 9.14 The trade-off between contrast detection (y-axis) and hemodynamic response function (HRF) shape estimation power (x-axis), and the performance of different types of designs on each. Note: Power on each axis is expressed here in terms of z-scores in a simulated group analysis (n⫽ 10, effect sizes estimated from visual cortex data in Wager et al., 2005). The double-circle shows a block design with roughly optimal task alternation frequency (16 s/task). The dark circles show power for a number of randomized event-related designs with roughly optimal parameters under linear modeling assumptions (randomized sequences with a stimulus every 2 s). The dark squares show truncated m-sequence designs with the same parameters as the randomized design. The open circles show results for genetic algorithm (GA) optimized designs with the same parameters. Each circle represents the results of one run of the optimization routine with different user-specified detection/shape estimation trade-off settings.

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Human brain mapping has been primarily used to provide maps that show which regions of the brain are activated by specific tasks. There has been an increased interest in augmenting this type of analysis with connectivity studies that describe how various brain regions interact and how these interactions depend on experimental conditions. It is common practice in the analysis of neuroimaging data to make the distinction between functional and effective connectivity (Friston, 1994). Functional connectivity is defined as the undirected association between two or more fMRI time series, while effective connectivity is the directed influence of one brain region on the physiological activity recorded in other brain regions; it implies both causality and directness. It implies causality because the models used to assess effective connectivity are usually directional, and directness in the sense that effective connectivity measures attempt to partial out indirect influences from other regions. Functional connectivity is a statement about observed associations among regions and other performance and physiological variables such as the correlation between time

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series in two regions (bivariate connectivity). Simple functional connectivity analyses usually compare correlations between ROIs, sometimes in a task-dependent fashion, or between a “seed” region of interest and voxels throughout the brain. Multivariate analysis methods are also used to reveal networks of multiple interconnected regions. Popular methods include Principal Components Analysis (PCA; Andersen & Avison, 1999), Partial Least Squares (PLS; McIntosh, Bookstein, Haxby, & Grady, 1996) and Independent Components Analysis (ICA; Calhoun, Adali, Pearlson, & Pekar, 2001a,b; McKeown & Makeig, 1998). Connectivity between two or more regions may result from direct influences (functional links between regions) or indirect effects due to common input from a third variable. None of these methods can address issues of causality or the common influences of other variables. Functional connectivity methods can be applied at different levels of analysis, with different interpretations at each level (see Figure 9.15). Connectivity across time series data can reveal networks that are dynamically coactivated over time (either intrinsically, regardless of task state, or in a task-dependent fashion), and is closest to the concept of communication among regions, though it does Time series/trial levels Region 1 Region 2

␤ˆ 1

Subject 1

not conclusively demonstrate that. Connectivity across single-trial response estimates (Rissman et al., 2004) can identify coherent networks of task-related activations. Whereas these levels are only accessible to fMRI and EEG/MEG, which provide relatively rich time series data, other levels of analysis may be examined in PET studies. Connectivity across subjects can reveal patterns of coherent individual differences, which may result from communication among regions but also from differences in strategy use or other genetically determined or learned differences among individuals. Finally, connectivity across studies can reveal tendencies for studies to coactivate within sets of regions, which may be influenced by any of the factors previously mentioned, and also differences among tasks or other study-level variables. An example is the finding that studies in which posttraumatic stress disorder (PTSD) patients showed increased amygdala activity tended to be the same studies in which patients showed decreased activation of the medial frontal cortex (Etkin & Wager, 2007). Regardless of the level of analysis, functional connectivity analyses can be useful for understanding that brain activations are part of coherent patterns that are separate, independent effects of task manipulations. Subject level Region 1 Region 2

Correlate magnitudes within condition or differences across conditions ␤ˆ ID

␤ˆ 2 Subject 2 ␤ˆ n

Subject n ⫹ ⫹ Artifacts Gradient drift Shot artifacts (spikes) Physiological effects Movement-related artifacts Arousal fluctuations

⫹ ⫹ Trial averaging/ model fitting Individual differences in . . . ⫺ ⫹ Neurovascular coupling differences Time series Trial resp. amplitudes

Figure 9.15 Functional connectivity methods can be applied at different levels of analysis, with different interpretations at each level. Note: (Left) Connectivity across time series data can reveal networks that are dynamically coactivated over time. The solid, dotted, and dashed lines indicate activation time series from three different subjects on the left, and average activation magnitudes for the same subjects (shown by hemodynamic response function [HRF] curves) at the right. Alternatively, measures of single-trial activation amplitude (black dots) can be extracted and used to estimate connectivity, which avoids some ambiguity with respect to the source of connectivity (task-related versus spontaneous).

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Artifacts Hematocrit, CO2 Vascular response (hemodyamic model fit) Gray-matter density Alertness

Brain system recruitment Strategy Performance Genetics

However, artifactual influences can make interpretation of both types of connectivity difficult (see list at the bottom). Distributed artifacts tend to create positive covariance, whereas neurovascular coupling differences —and resulting differences in HRF shapes between regions—tend to weaken covariance estimates. (Right) At the subject level, you can correlate magnitudes within condition or differences across conditions. This analysis is conducted on individual differences, rather than on time series data, and results may have different interpretations than time series connectivity data. Again, special care needs to be taken to limit the influence of artifacts, which are likely to be largely related to factors that create individual differences in model fits across the brain (see list).

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Generally, activation is only informative if it is restricted to specific brain regions (e.g., activation of the insula means little if every other brain region is activated to the same degree). Likewise, demonstrating that connectivity is greater within a set of regions than among other regions (e.g., for the cognitive control network of Cole & Schneider, 2007, or for demonstrating two or more separable sets of interconnected regions such as the multiple separate networks of coherent opioid release reported by Wager, Scott, & Zubieta, 2007) can provide valuable information about how brain regions function together. Demonstrating specificity of functional connectivity to a particular task state, as the psychophysiological interaction (PPI)/moderation analysis to be described later is designed to do, can be informative about how functional connectivity relates to psychological states. Reporting reciprocal activity (negative correlations) between ventromedial PFC and amygdala may be of limited usefulness if such correlations can be found in any task state; in that case, they may be a general feature of BOLD physiology or vasculature rather than an interesting instance of communication among brain regions. In contrast, effective connectivity analysis is modeldependent. Typically, a small set of regions and a proposed set of connections are specified a priori, and tests of fit are used to compare a small number of alternative models and assess the statistical significance of individual connections. Because connections may be specified directionally (with hypothesized causal influences of one area on another), the model implies causal relationships. Because there are many possible models, the choice of regions and connections must be anatomically motivated. Most effective connectivity depends on two models: a neuroanatomical model that describes which areas are connected, and a mathematical model that describes how areas are connected. Common methods include Structural Equation Modeling (SEM; McIntosh & Gonzalez-Lima, 1994) and Dynamic Causal Modeling (DCM; Friston, Harrison, & Penny, 2003). While effective connectivity methods have become increasingly popular, it is important to keep in mind that the conclusions about direct influences and causality obtained using these models are only as good as the specified models. Any misspecification of the underlying model will almost certainly lead to erroneous conclusions. In particular, the exclusion of important lurking variables (brain regions involved in the network but not included in the model) can completely change the fit of the model and thereby affect both the direction and strength of the connections. Great care always needs to be taken when interpreting the results of these methods. The distinction between functional and effective connectivity is not entirely clear (Horwitz, 2003). If the discriminating

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features are (a) a directional model in which causal influences are specified; and (b) the willingness to make claims about direct versus indirect connections, then many analyses, including multiple regression, might count as effective connectivity. Indeed, the PPI analysis referred to is typically described as an effective connectivity model, but it tests an interaction effect using linear regression (whether the slope of the linear association between two variables depends on the level of a third, moderating variable). The three-variable PPI model is actually a simple SEM though the criterion of assessing direct effects is not met, since no common indirect influences are accounted for. In the end, the difference between this model and more complicated SEMs is one of scale, and direct effects in any SEM can only be properly assessed if all relevant “3rd variables” have been included in the model and their connections modeled appropriately. While many researchers use both SEM and DCM with the goal of ascribing causality between different brain regions, the tests performed in both techniques are based on model fit rather than on the causality of the effect. Similarly, Granger causality (Roebroeck, Formisano, & Goebel, 2005) is another approach that is typically considered to test effective connectivity, though neither causal influences nor direct versus indirect effects are tested within the basic model framework. Causality is tested strictly in the sense of temporal relationships, rather than on whether activity in a brain region is necessary or sufficient for activity in another. In the end, it is not the label of “functional” or “effective” that is important, but the specific assumptions and robustness and validity of inference afforded by each method. When performing connectivity and correlation studies, it is tempting to make statements about causal links between different brain regions. The idea of causality is a very deep and important philosophical issue (Pearl, 2000; Rubin, 1974). A cavalier attitude frequently is taken in attributing causal effects, and the differentiation between explanation and causation is often blurred. Properly randomized experimental designs permit causal inferences of task manipulations on brain activity. In neuroimaging and EEG/MEG studies, all the brain variables are observed, and none are manipulated. We do not recommend making strong conclusions about causality and direct influences among brain regions using these methods because it is difficult to verify the validity of such conclusions. The combination of neuroimaging and TMS or related forms of brain stimulation (Bohning et al., 1997) may provide more reliable causal inferences about the effects of activating one brain region on another. By stimulating the brain, experimental manipulation of one brain area can be achieved and its causal effects on other brain regions thus examined. The problem

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remains of assessing which effects are direct as opposed to mediated by other intervening regions. Bivariate Connectivity Functional connectivity is a statement about the observed associations among regions and other performance and physiological variables. The simplest approach toward functional connectivity is to simply calculate the crosscorrelation between time series from two separate brain regions. The results can be used to determine whether the changes in activity in these regions are related to each other in a linear manner. This idea is expanded in seed analysis (Cordes et al., 2000; Della-Maggiore et al., 2000), where the cross-correlation between the time course from a predetermined region or cluster (the seed region) and all other regions of the brain is calculated. This allows researchers to search the brain for other regions that are positively (or negatively) correlated with the activity pattern found in the seed region. In addition to standard statistical assumptions, time series connectivity typically assumes that the connectivity is instantaneous, meaning that the time constants for neuronal and vascular effects are the same for each pair of regions, and the impulse response functions are thus the same. This assumption is often likely to be violated, and several approaches have been taken to account for variability in the neuronal activity—fMRI signal coupling, such as multivariate autoregressive modeling (Harrison, Penny, & Friston, 2003; Kim, Zhu, Chang, Bentler, & Ernst, 2007). Granger causality, a kind of autoregressive model discussed in more detail later, is a promising approach toward relaxing this assumption. Whatever method is used, functional connectivity is meaningful only to the degree that it is not driven by artifacts related to image acquisition and physiological noise; some artifactual influences are listed in Figure 9.15. Another approach that helps minimize issues of interregion neurovascular coupling differences and artifacts (but does not eliminate them) is the beta series approach (Rissman et al., 2004). In this technique, correlations are not estimated directly from the time series data. Instead, you obtain trial-by-trial estimates of event-related activity within the standard GLM framework. These trial-level activation parameter estimates (called beta values) are correlated across regions to obtain a measure of functional connectivity during each of the individual task components. Component Analysis: Principal Components Analysis, Independent Components Analysis, and Partial Least Squares Multivariate methods model brain imaging data by decomposing a large dataset (e.g., 1,000 time points ⫻ 100,000

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voxels ⫻ 20 subjects) into a smaller set of components and a series of weights. The components may be canonical patterns of activity across time and the weights their distribution across brain space, or vice versa. Principal components analysis (PCA), independent components analysis (ICA), and partial least squares (PLS) variations on this theme. These and related multivariate methods—canonical variates analysis (CVA), factor analysis, ordinal trends analysis (Habeck et al., 2005), and the multivariate linear model (MLM; Kherif et al., 2002)—are becoming an increasingly important part of the neuroimaging analyst’s toolbox. They all share the common core idea of decomposing the data into simpler components that maximize the variability explained by the model. The approaches differ in the criteria used to select components, and in whether the experimental design is included as part of the data to be modeled (inclusion is a defining feature of PLS). Each technique described in this section involves decomposing a data matrix, Y, into a set of spatial and temporal components. Let us define Y to be a t⫻v matrix, where t is the number of time points and v the number of voxels. Each column of Y is therefore a time series corresponding to one voxel in the brain, and each row is the collection of voxels that make up an image at a specific time point. Principal Components Analysis (PCA) decomposes the data matrix, Y, by finding linear combinations of time series, each of which make up a column in a matrix U (also of dimension t⫻v), such that each column of U is uncorrelated with every other column of U. The columns of U, called components, are arranged in order of variance explained: The first component explains the most variance possible in Y, the second component explains the maximal amount of remaining variance, and so forth. Together with their associated spatial maps and variances (to be described), these v components perfectly reproduce the data, but most of the total variance is usually captured in just the first few components of U. Thus, the first components can be considered a compressed representation of the data. Because each component is a weighted sum across time series of different voxels, another matrix V (of dimension voxel ⫻ component [v⫻v]) contains columns of voxel weights to create each component in U. The first column of V shows how to weight each of the v voxel time series to capture the most variance in Y and represents the spatial distribution of the first component. Thus, the columns of U are the temporal components (the “canonical” time series) and those of V are the spatial components (the maps across brain voxels) of these time series. In neuroimaging, the components are usually calculated through singular value decomposition (SVD) of the centered (mean-zero) data. SVD is a numerical technique that

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Data Analysis: Implementation

decomposes a data matrix, Y, into three simpler matrices (zeros make up at least half of the new matrices), while still representing the original data. In the case of neuroimaging data, these matrices can be interpreted as temporal components U and spatial components V such that: Y ⫽ USV ⌻

(9.6)

With centered (mean-zero) data, S is a diagonal matrix (only the diagonal elements are nonzero) whose entries are the “singular values,” the sums of squared deviations explained by each component. These are related to the eigenvalues such that λ ⫽ S2 /(t– 1). The columns of V are the eigenvectors, as in the eigendecomposition described earlier, and U*S are the component scores (components scaled by the amount of variability they explain), equal to Y*V in the eigendecomposition. The power of this technique lies in that the eigenvectors are orthogonal to each other. By decomposing the data into its eigenvectors and eigenvalues, we obtain a set of components (whether temporal or spatial) that are uncorrelated with each other. Furthermore, we also obtain coefficients of how heavily those components are represented in the original data. A thorough treatment of eigenvectors, eigenvalues, and SVD is provided by Strang (1980). Once you grasp the central idea of data decomposition into spatial and temporal components, you can understand many other techniques, such as ICA, as variations on this theme. Rather than maximizing the variance explained by each additional, orthogonal component, ICA components are chosen to maximize the statistical independence of the components in a more general sense. The components are not required to be orthogonal; rather, the constraint is that they be independent. The distribution of one component cannot be predicted from the values of the other, or more formally, the joint probability P(A,B) of components A and B is equal to P(A)P(B). In the Infomax variant of ICA, mutual information between components—a general measure of dependence that does not require the relationships between components to be linear or monotonic—is minimized (McKeown, 1998). ICA assumes that the data, Y, are a weighted sum of source signals (time series) contained in the source matrix X. The data Y is a linear mixture of these source components described by the weighting or mixing matrix of spatial weights M: Y ⫽ MX

(9.7)

Since both M and X are both unknown, there is no algebraic solution, so iterative search algorithms are used to estimate both M and X. An alternative decomposition is to transpose the data matrix and treat the spatial components as

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sources and the temporal components as mixing weights. (For more details, see McKeown & Sejnowski, 1998; Petersson, Nichols, Poline, & Holmes, 1999). At first glance, it appears close to impossible to solve Equation 9.7 for both M and X simultaneously. However, ICA makes crucial assumptions that allow you to obtain a solution. The main assumptions are that the data set consists of p statistically independent components, where at most one component is Gaussian. The independence assumption entails that the activations do not have a systematic overlap in time or space, while the non-Gaussiantity assumption is required for the problem to be well defined. In addition, it is assumed that the mixing matrix, M, is both square and invertible, which implies that the independent components can be expressed as a linear combination of the data matrix. Both PCA and ICA reduce the data to a simpler (lowerdimension than that of the v voxels) space by capturing the most prominent variations across the set of voxels. The components may reflect signals of interest or they may alternatively be dominated by artifacts, and it is up to the user to determine which are of interest (e.g., task-related). Both ICA and PCA assume all variability results from signal, as noise is not included in the model formulation. One issue involved with interpreting the results of an ICA analysis is that the sign of the independent components cannot be determined. In addition, the order of importance of the independent components cannot be determined. Therefore, it is necessary to sift through all the components to search for ones that are task-related or otherwise of interest. There is also no guarantee that a specific number of components can be used to explain most of the variation as is the case in PCA. A popular variant in the social sciences literature is factor analysis, which additionally fits a parameter for the noise variance at each voxel. A disadvantage of factor analysis is that the solution is rotationally indeterminate, and thus a number of combinations of spatial and temporal components can explain the same variability in the data. While both ICA and PCA are not rotationally indeterminate, there is some question as to what the “right” rotation is (in PCA it is determined by the amount of variance explained, which is not an index of meaningfulness since artifacts can create much variance). Interpreting thresholded component maps, as is commonly done, depends critically on establishing a rotation that is meaningful and reliable across studies. Multisubject Extensions As described so far, these techniques model only a single subject’s data. In a group study, there is the additional complexity of making population inference. It is not correct to treat all the data as coming from one supersubject and decomposing the group data matrix, for the same reasons that fixed effects

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analyses in the GLM are not appropriate. One approach is to decompose the group matrix, and subsequently “backreconstruct” or estimate spatial weights for each subject for a component of interest (Calhoun, Adali, Pearlson, & Pekar, 2001a). The spatial weights at each voxel across subjects are treated as random variables, and one-sample t-test is conducted to test whether that voxel loaded significantly on that component in the group. This approach is implemented in the Group Analysis of Functional Imaging toolbox (GIFT; Table 9.3). Another approach, called tensor ICA, is to use a three-way data decomposition with the group data to estimate temporal components and weights for each subject and each voxel (Beckmann & Smith, 2005). The subject weights at each voxel are then tested for significance. This approach is similar to related PCA-based techniques of PARAFAC (Bro, 1997) and INDSCAL/ALSCAL (Young, Takane, & Lewyckyj, 1978). It is implemented in the ICA tool (called MELODIC) in FSL software (Table 9.3). Structural Equation Modeling Structural equation modeling (SEM) has a rich history in the social sciences literature (Bollen, 1989). It was first applied to imaging data by McIntosh and Gonzalez-Lima (1994). In SEM, the emphasis lies on explaining the variance-covariance structure of the data. While SEM allows for the inclusion of latent variables (which is one of its major selling points in the social sciences), this option is not typically used by the neuroimaging community. An SEM without latent variables is typically called path analysis, but in this chapter we refer to methodology by the name structural equation modeling as this is the common practice in the neuroimaging literature. Structural equation models comprise a set of a priori determined regions and directed connections between these regions. A causal relationship is attributed a priori to the connections where an arrow from A to B implies that A causes B. Further path coefficients are defined corresponding to each link that represents the expected change in activity of one region given a unit change in the region influencing it. The path coefficient indicates the average influence across the time interval measured. Algebraically, we can express an SEM model as Y ⫽ MY ⫹εε

(9.8)

where Y is the data matrix, M is a matrix of coefficients that reflect the linear relationship between regions and e is independent and identically distributed normal noise. Typically, this model is rewritten: Y ⫽ (I ⫺ M)⫺1ε

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(9.9)

where I represents the identity matrix. The solution of the unknown coefficients in M is obtained by studying the empirical covariance matrix of Y. Like ICA, solving this model is not straightforward and typically users resort to iterative techniques. The covariance of the data represents how the activities in two or more regions are related. In SEM, we seek to minimize the difference between the observed covariance matrix and the one implied by the structure of the model. The parameters of the model are adjusted to minimize the difference between the observed and modeled covariance matrix. All inferences about the path coefficients rest on nested or stacked models. A hypothesis test on a single path coefficient may be performed by comparing the full model, with all path coefficients estimated, with a nested model in which the coefficient of interest is constrained to be zero.2 The two models are compared using a likelihood ratio test (LRT)—a statistical test of the goodnessof-fit between two models—to test whether a nonzero coefficient results in a significantly better model fit, and thus whether the coefficient is reliably different from zero. The LRT is only valid if it is used to compare nested models; that is, the more complex model must differ from the simple model only by the addition of one or more parameters. A similar approach can be taken when making inferences about changes in connectivity between different experimental conditions. This is done by first partitioning the data according to the different experimental conditions. Next, two models are specified. In the null model, path coefficients are constrained to be equal across conditions, and in the alternative model, coefficients of interest are allowed to vary. The LRT is used to test whether there is any significant difference between the models. If a significant difference exists, we reject the hypothesis that the path coefficients are equal in both conditions and a condition-dependent effect is declared. SEM makes some assumptions in setting up the model formulation. The data is assumed to be normally distributed and independent from sample to sample. An important consequence of the assumptions is that SEM discounts temporal information. Consequently, permuted data sets produce the same path coefficients as the original data, which is a weakness. The assumption of independence is violated in the analysis of a single subject. When looking at the individual differences level, this assumption is more reasonable.

2

Or another test value of interest.

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Dynamic Causal Modeling The measurements used in each of the connectivity approaches described so far are hemodynamic, and this limits the scope of the interpretation that can be made at the neuronal level. Dynamic causal modeling (Friston et al., 2003) is an attempt to move the connectivity analysis from the hemodynamic to the neuronal level. DCM uses standard linear systems analyses techniques, namely statespace design (Franklin, Workman, & Powell, 1997), and treats the brain as a deterministic nonlinear dynamic system that is subject to inputs and produces outputs. It makes inference about the coupling among brain areas and how the coupling is influenced by changes in experimental context. DCM models interactions at the neuronal rather than the hemodynamic level and is therefore more biologically accurate than many other models. However, the hemodynamic properties of the system must also be taken into account, as they can confound the measurements (e.g., a vascular delay could be interpreted as a neuronal delay). DCM is based on a neuronal model of interacting cortical regions, supplemented with a forward model describing how neuronal activity is transformed into the measured hemodynamic response. Effective connectivity is parameterized in terms of the coupling among unobserved neuronal activity in different regions. We can estimate these parameters by perturbing the system and measuring the response. Experimental inputs cause changes in effective connectivity at the neuronal level that in turn cause changes in the observed hemodynamics. DCM uses a bilinear model for the neuronal level and an extended balloon model (Buxton, Wong, & Frank, 1998) for the hemodynamic level. In a DCM model, the user specifies a set of experimental inputs (the stimuli) and a set of outputs (the activity in each region for each region). The task of the algorithm is then to estimate the parameters of the system, in this case, the “state variables.” Each region has five state variables; four correspond to the hemodynamic model and the fifth corresponds to neuronal activity. The estimation process is then carried out using Bayesian statistics: Normal priors are placed on the model parameters and an optimization scheme is used to estimate parameters that maximize the posterior probability. The posterior density is then used to make inferences about the significance of the connections between various brain regions. DCM is computationally demanding and is limited to eight regions in the current implementation of SPM. Granger Causality The main problem with methods such as SEM and DCM is that any misspecification of the underlying model will lead to erroneous conclusions. Granger causality takes a

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different approach to the problem. The technique was originally developed in economics (Granger, 1969) and has recently been applied to connectivity studies (Roebroeck et al., 2005). The benefit of Granger causality is that it does not rely on any a priori specification of a structural model, but rather is an approach for quantifying the usefulness of past values from various brain regions in predicting values in other regions. Granger causality provides information about the temporal precedence of relationships among two regions, but it is in some sense a misnomer because it does not actually provide information about causality. It is true that one variable (x) may precede a correlated variable (y) because x causes y. For example, hitting a baseball causes flight. However, there may be no causal relationship at all: A rooster may crow (x) every morning just before the sun rises (y), but it does not cause the sun to rise. For purposes of economic forecasting for which the technique was developed—or for making predictions based on fMRI data—the actual causal relationships may not matter, and Granger “causality” may be sufficient to be informative. However, it should not be taken as a measure of true causality. To illustrate the method, let x and y be two time courses of length N extracted from two brain regions or voxels. Each time course is modeled using a linear autoregressive model3 of the Mth order (where M≤N– 1), that is: M

x [ n ] ⫽ ∑ a [ i ]x [ n − m ] ⫹ εx [ n ] m⫽1

(9.10

M

y [ n ] ⫽ ∑ b [ i ] y [ n − m ] ⫹ εy [ n ] m⫽1

where both ex and ey are defined to be white noise. The vectors a and b are coefficients that describe how the current values of the time course depends on its past, and therefore it is clear from this formulation that both time courses depend immediately on their own past M values. As a second step of the analysis, we can expand each time course’s model using the autoregressive terms from the other signal. These additional autoregressive terms correspond to the directed influence (previous history) and not to the instantaneous signal; they can be written in the format: value_now ⫽ self_history ⫹ other_history ⫹ error More formally, the equations in our example can be expressed as: 3

Autoregressive models are used to represent processes whose “current” values can be written as a function of their own past values. The order of the model specifies how many steps back into the past the specified function goes.

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M

m⫽1

m⫽1

M

M

m⫽1

m⫽1

x [ n ] ⫽ ∑ a [ i ] x [ n − m ] ⫹ ∑ b [ i ] y [ n − m ] + εx [ n ] y [ n ] ⫽ ∑ b [ i ] y [ n − m ] ⫹ ∑ a [ i ] x [ n − m ] εy [ n ]

(9.11)

In this formulation, the current value of both time courses is assumed to depend both on the past M values of its own time course, but also the past M values of the other time course. By fitting each of these models (Equations 9.10 to 9.13), we can perform tests to determine whether the previous history of x has predictive value of the time course y (and vice versa). If the model fit is significantly improved by the inclusion of the cross-autoregressive terms, it provides evidence that the history of one of the time courses can be used to predict the current value of the other and a “Granger-causal” relationship is inferred. To test the influence between the two regions, we compare the fits to the model for each time course both with and without the additional “cross-autoregressive” terms (Roebroeck et al., 2005). The ratio of error sums of squares obtained from these fits are used to define a measure of the linear-directed influence from x to y, which is denoted Fx → y. If past values of x improve on the prediction of the current value of y, then Fx → y is large. A similar interpretation, but in the opposite direction, holds for Fy → x, which is defined in an analogous manner. The difference between these two terms can be used to infer which region’s history is more influential on the other. This difference is referred to as “Granger causality.” From this definition, it is clear that the idea of temporal precedence is used to identify the direction and strength of causality from information in the data. While it can reasonably be argued that temporal precedence is a necessary condition for causation, it is certainly not a sufficient condition. Therefore, to directly equate Granger causality and causality is a large leap of faith.

SUMMARY fMRI and PET are techniques for imaging the functioning human brain with increasingly precise temporal, spatial, and “neurochemical” resolution. Their major impact on psychology and neuroscience is to help establish a physical grounding in the brain for psychological concepts. However, inferences about psychological processes from brain activity are tricky to make. New kinds of analyses, such as pattern classification and meta-analysis, are helping researchers make inroads into this problem in limited ways. A second advantage of neuroimaging is that it allows researchers to study brain processes directly in humans,

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allowing links to be forged across animal and human neuroscientific research. Ultimately, this promotes the ability to integrate across fields and take advantage of a wealth of knowledge available from animal systems by establishing parallels with human brain processes. An obstacle in this process is the complexity of managing and processing neuroimaging data. To overcome this obstacle, researchers have developed a wide variety of tools, most of which are freely available to the research community, and centralized resources for distributing these tools over the internet. Finally, a third advantage of neuroimaging is that it allows full coverage of the brain, including—paticularly with fMRI—dynamic measures of activity every few seconds or less. This combination of spatial and temporal coverage is not available using other techniques, including those commonly used in animal models. This makes fMRI a particularly good tool for studying functional brain connectivity and large-scale distributed networks. A wide and growing array of connectivity analyses are being developed to exploit this feature, and the combination of advances in data collection, analysis methods, and methods for making psychological inferences are making fMRI a uniquely useful tool for neuroscientists.

REFERENCES Aguirre, G. K., Singh, R., & D’Esposito, M. (1999). Stimulus inversion and the responses of face and object-sensitive cortical areas. NeuroReport, 10, 189–194. Aguirre, G. K., Zarahn, E., & D’Esposito, M. (1998). The variability of human, BOLD hemodynamic responses. NeuroImage, 8, 360–369. Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., et al. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: Intersubject variability and probability maps. Anatomy Embryolgy, 210, 343–352. Amunts, K., Schleicher, A., & Zilles, K. (2007). Cytoarchitecture of the cerebral cortex: More than localization. NeuroImage, 37, 1061–1065. Andersen, A. H. G. D., & Avison, M. J. (1999). Principal component analysis of the dynamic response measured by fMRI: A generalized linear systems framework. Magnetic Resonance in Medicine, 17, 785–815. Andersson, J. L., Hutton, C., Ashburner, J., Turner, R., & Friston, K. (2001). Modeling geometric deformations in EPI time series. NeuroImage, 13, 903–919. Aron, A., Fisher, H., Mashek, D. J., Strong, G., Li, H., & Brown, L. L. (2005). Reward, motivation, and emotion systems associated with earlystage intense romantic love. Journal of Neurophysiology, 94, 327–337. Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry: The methods. NeuroImage, 11, 805–821. Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26, 839–851. Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2003). General multilevel linear modeling for group analysis in FMRI. NeuroImage, 20, 1052–1063. Beckmann, C. F., & Smith, S. M. (2005). Tensorial extensions of independent component analysis for multisubject fMRI analysis. NeuroImage, 25, 294–311.

8/18/09 5:25:41 PM

References 193 Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007). Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage, 34, 144–155.

Cheng, K., Waggoner, R. A., & Tanaka, K. (2001). Human ocular dominance columns as revealed by high-field functional magnetic resonance imaging. Neuron, 32, 359–374.

Bendriem, B., & Townsend, D. W. (1998). The theory and practice of 3D PET (Vol. 32). Boston: Kluwer Academic.

Cole, M. W., & Schneider, W. (2007). The cognitive control network: Integrated cortical regions with dissociable functions. NeuroImage, 37, 343–360.

Benjamini, Y. A. H. Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B, 57, 289–300. Bernstein, M. A., King, K. F., & Zhou, Z. J. (2004). Handbook of MRI pulse sequences. Burlington, MA: Elsevier Academic Press.

Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18, 192–205.

Birn, R. M., Saad, Z. S., & Bandettini, P. A. (2001). Spatial heterogeneity of the nonlinear dynamics in the FMRI BOLD response. NeuroImage, 14, 817–826.

Constable, R. T., & Spencer, D. D. (1999). Composite image formation in z-shimmed functional MR imaging. Magnetic Resonance in Medicine, 42, 110–117.

Bohning, D. E., Pecheny, A. P., Epstein, C. M., Speer, A. M., Vincent, D. J., Dannels, W., et al. (1997). Mapping transcranial magnetic stimulation (TMS) fields in vivo with MRI. NeuroReport, 8, 2535–2538.

Cordes, D., Haughton, V. M., Arfanakis, K., Wendt, G. J., Turski, P. A., Moritz, C. H., et al. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. American Journal of Neuroradiolgy, 21, 1636–1644.

Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley. Boynton, G. M., Engel, S. A., Glover, G. H., & Heeger, D. J. (1996). Linear systems analysis of functional magnetic resonance imaging in human V1. J Neurosci, 16, 4207–4221. Brett, M., Johnsrude, I. S., & Owen, A. M. (2002). The problem of functional localization in the human brain. National Reviews Neuroscience, 3, 243–249. Bro, R. (1997). PARAFAC. Tutorial and applications. Chemometrics and Intelligent Laboratory Systems, 38, 149–171. Buckner, R. L., Koutstaal, W., Schacter, D. L., Dale, A. M., Rotte, M., & Rosen, B. R. (1998). Functional-anatomic study of episodic retrieval: Pt. II. Selective averaging of event-related fMRI trials to test the retrieval success hypothesis. NeuroImage, 7, 163–175. Buracas, G. T., & Boynton, G. M. (2002). Efficient design of eventrelated fMRI experiments using M-sequences. NeuroImage, 16, 801–813. Burock, M. A., Buckner, R. L., Woldorff, M. G., Rosen, B. R., & Dale, A. M. (1998). Randomized event-related experimental designs allow for extremely rapid presentation rates using functional MRI. NeuroReport, 9, 3735–3739. Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences, 4, 215–222. Buxton, R. B., & Frank, L. R. (1997). A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. Journal of Cerebral Blood Flow and Metabolism, 17, 64–72. Buxton, R. B., Uludag, K., Dubowitz, D. J., & Liu, T. T. (2004). Modeling the hemodynamic response to brain activation. NeuroImage, 23 (Suppl. 1), S220–S233. Buxton, R. B., Wong, E. C., & Frank, L. R. (1998). Dynamics of blood flow and oxygenation changes during brain activation: The balloon model. Magnetic Resonance in Medicine, 39, 855–864. Cacioppo, J. T., & Berntson, G. G. (in press). Integrative neuroscience for the behavioral sciences: Implications for inductive inference. In Handbook of neuroscience for the behavioral sciences. [This Volume] Cacioppo, J. T., & Tassinary, L. G. (1990). Inferring psychological significance from physiological signals. American Psychologist, 45, 16–28. Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001a). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14, 140–151. Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001b). Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms. Human Brain Mapping, 13, 43–53.

c09.indd Sec5:193

Cover, T. M., Thomas, J. A., Wiley, J., & InterScience, W. (2006). Elements of Information Theory: Wiley-Interscience New York. Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance NeuroImages. Computers and Biomedical Research, 29, 162–173. D’Esposito, M., Zarahn, E., Aguirre, G. K., & Rypma, B. (1999). The effect of normal aging on the coupling of neural activity to the bold hemodynamic response. NeuroImage, 10, 6–14. Dagher, A., Owen, A. M., Boecker, H., & Brooks, D. J. (1999). Mapping the network for planning: A correlational PET activation study with the Tower of London task. Brain, 122, 1973–1987. Dale, A. M., & Buckner, R. L. (1997). Selective averaging of rapidly presented individual trials using fMRI. Human Brain Mapping, 5, 329–340. Dale, A. M., Liu, A. K., Fischl, B. R., Buckner, R. L., Belliveau, J. W., Lewine, J. D., et al. (2000). Dynamic statistical parametric mapping combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron, 26, 55–67. Della-Maggiore, V., Sekuler, A. B., Grady, C. L., Bennett, P. J., Sekuler, R., & McIntosh, A. R. (2000). Corticolimbic interactions associated with performance on a short-term memory task are modified by age. Journal of Neuroscience, 20, 8410–8416. Denis Le Bihan, M. D., Mangin, J. F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., et al. (2001). Diffusion tensor imaging: Concepts and applications. Journal of Magnetic Resonance Imaging, 13, 534–546. de Quervain, D. J., Fischbacher, U., Treyer, V., Schellhammer, M., Schnyder, U., Buck, A., et al. (2004, August 27). The neural basis of altruistic punishment. Science, 305, 1254–1258. Devlin, J. T., & Poldrack, R. A. (2007). In praise of tedious anatomy. NeuroImage, 37, 1033–1041; discussion 1050–1038. Disbrow, E. A., Slutsky, D. A., Roberts, T. P., & Krubitzer, L. A. (2000). Functional MRI at 1.5 tesla: A comparison of the blood oxygenation level-dependent signal and electrophysiology. Proceedings of the National Academy of Sciences, USA, 97, 9718–9723. Duann, J. R., Jung, T. P., Kuo, W. J., Yeh, T. C., Makeig, S., Hsieh, J. C., et al. (2002). Single-trial variability in event-related BOLD signals. NeuroImage, 15, 823–835. Duong, T. Q., Yacoub, E., Adriany, G., Hu, X., Ugurbil, K., Vaughan, J. T., et al. (2002). High-resolution, spin-echo BOLD, and CBF fMRI at 4 and 7 T. Magnetic Resonance in Medicine, 48, 589–593. Duvernoy, H. M. (1995). The human brain stem and cerebellum: Surface, structure, vascularization, and three-dimensional sectional anatomy with MRI. Wien, Germany: Springer-Verlag. Eickhoff, S. B., Amunts, K., Mohlberg, H., & Zilles, K. (2006). The human parietal operculum: Pt. II. Stereotaxic maps and correlation with functional imaging results. Cerebral Cortex, 16, 268–279.

8/18/09 5:25:41 PM

194

Essentials of Functional Neuroimaging

Eickhoff, S. B., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., et al. (2005). A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage, 25, 1325–1335. Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003, October 10). Does rejection hurt? An FMRI study of social exclusion. Science, 302, 290–292. Elster, A. D. (1994). Questions and answers in magnetic resonance imaging. St. Louis, MO: Mosby. Etkin, A., & Wager, T. D. (2007). Functional neuroimaging of anxiety: A meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. American Journal of Psychiatry, 164, 1476–1488. Fabiani, M., Gratton, G., & Federmeier, K. D. (2007). Event-related brain potentials: Methods, theory, and applications. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (4th ed., pp. 85–119). Cambridge: Cambridge University Press.

Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14, 21–36. Goutte, C., Nielsen, F. A., & Hansen, L. K. (2000). Modeling the haemodynamic response in fMRI using smooth FIR filters. IEEE Transactions of Medical Imaging, 19, 1188–1201. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37, 424–438. Grill-Spector, K., & Malach, R. (2001). FMR-adaptation: A tool for studying the functional properties of human cortical neurons. Acta Psychologica, 107, 293–321. Gusnard, D. A., Raichle, M. E., & Raichle, M. E. (2001). Searching for a baseline: Functional imaging and the resting human brain. National Reviews Neuroscience, 2, 685–694. Haacke, E. M. (1999). Magnetic resonance imaging: Physical principles and sequence design. New York: Wiley.

Fischl, B., Sereno, M. I., & Dale, A. M. (1999). Cortical surface-based analysis: Pt. II. Inflation, flattening, and a surface-based coordinate system. NeuroImage, 9, 195–207.

Habeck, C., Krakauer, J. W., Ghez, C., Sackeim, H. A., Eidelberg, D., Stern, Y., et al. (2005). A new approach to spatial covariance modeling of functional brain imaging data: Ordinal trend analysis. Neural Computation, 17, 1602–1645.

Fischl, B., Sereno, M. I., Tootell, R. B., & Dale, A. M. (1999). High-resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping, 8, 272–284.

Haines, D. E. (2000). Neuroanatomy: An atlas of structures, sections, and systems. Philadelphia: Lippincott Williams & Wilkins.

Franklin, G. F., Workman, M. L., & Powell, D. (1997). Digital control of dynamic systems. Boston: Addison-Wesley Longman. Frey, K. A. (1999). Positron emission tomography. In G. J. Siegel, B. W. Agranoff, R. W. Albers, S. K. Fisher, & M. D. Uhler (Eds.), Basic neurochemistry (6th ed., pp. 1109–1131). Philadelphia: Lippincott Williams & Wilkins. Friston, K. J. (1994). Functional and effective connectivity in neuroimaging: A synthesis. Human Brain Mapping, 2, 56–78. Friston, K. J., Frith, C. D., Turner, R., & Frackowiak, R. S. (1995). Characterizing evoked hemodynamics with fMRI. NeuroImage, 2, 157–165. Friston, K. J., Glaser, D. E., Henson, R. N., Kiebel, S., Phillips, C., & Ashburner, J. (2002). Classical and Bayesian inference in neuroimaging: Applications. NeuroImage, 16, 484–512. Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19, 1273–1302. Friston, K. J., Josephs, O., Rees, G., & Turner, R. (1998). Nonlinear eventrelated responses in fMRI. Magnetic Resonance in Medicine, 39, 41–52. Friston, K. J., Mechelli, A., Turner, R., & Price, C. J. (2000). Nonlinear responses in fMRI: The balloon model, volterra kernels, and other hemodynamics. NeuroImage, 12, 466–477. Friston, K. J., Penny, W. D., & Glaser, D. E. (2005). Conjunction revisited. NeuroImage, 25, 661–667. Friston, K. J., Penny, W., Phillips, C., Kiebel, S., Hinton, G., & Ashburner, J. (2002). Classical and bayesian inference in neuroimaging: Theory. NeuroImage, 16, 465–483. Glover, G. H. (1999). Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage, 9, 416–429. Glover, G. H., & Law, C. S. (2001). Spiral-in/out BOLD fMRI for increased, SNR, and reduced susceptibility artifacts. Magnetic Resonance in Medicine, 46, 515–522. Glover, G. H., Li, T. Q., & Ress, D. (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine, 44, 162–167. Goldman, R. I., Stern, J. M., Engel, J., Jr., & Cohen, M. S. (2000). Acquiring simultaneous, EEG, and functional MRI. Clinical Neurophysiology, 111, 1974–1980.

c09.indd Sec5:194

Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993). Magnetoencephalography: Theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics, 65, 413–497. Harrison, L., Penny, W. D., & Friston, K. (2003). Multivariate autoregressive modeling of fMRI time series. NeuroImage, 19, 1477–1491. Heeger, D. J., & Ress, D. (2002). What does fMRI tell us about neuronal activity? National Review of Neuroscience, 3, 142–151. Henson, R. N. (2003). Neuroimaging studies of priming. Progress in Neurobiology, 70, 53–81. Horwitz, B. (2003). The elusive concept of brain connectivity. NeuroImage, 19, 466–470. Huettel, S. A., Song, A. W., & McCarthy, G. (2004). Functional magnetic resonance imaging. Sunderland, MA: Sinauer. Johansen-Berg, H., & Behrens, T. E. (2006). Just pretty pictures? What diffusion tractography can add in clinical neuroscience. Current Opinion in Neurology and Neurosurgery, 19, 379–385. Johansen-Berg, H., Behrens, T. E., Robson, M. D., Drobnjak, I., Rushworth, M. F., Brady, J. M., et al. (2004). Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. Proceedings of the National Academy of Sciences, USA, 101, 13335–13340. Johnson, M. K., Raye, C. L., Mitchell, K. J., Greene, E. J., Cunningham, W. A., & Sanislow, C. A. (2005). Using fMRI to investigate a component process of reflection: Prefrontal correlates of refreshing a just-activated representation. Cognitive, Affective and Behavioral Neuroscience, 5, 339–361. Josephs, O., & Henson, R. N. (1999). Event-related functional magnetic resonance imaging: Modelling, inference and optimization. Philosophical Transactions of the Royal Society of London. Series B, 354, 1215–1228. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 4302–4311. Kastner, S., & Ungerleider, L. G. (2000). Mechanisms of visual attention in the human cortex. Annual Review of Neuroscience, 23, 315–341. Kherif, F., Poline J.-B., Flandin G., Benali H., Dehaene, S., & Worsley, K. J. (2002). Multivariate model specification for fMRI data. NeuroImage, 16, 795–815.

8/18/09 5:25:42 PM

References 195 Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007). Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Human Brain Mapping, 28, 85–93.

Menon, R. S., Luknowsky, D. C., & Gati, J. S. (1998). Mental chronometry using latency-resolved functional MRI. Proceedings of the National Academy of Sciences, USA, 95, 10902–10907.

Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., et al. (1992). Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Sciences, USA, 89, 5675–5679.

Menon, V., Ford, J. M., Lim, K. O., Glover, G. H., & Pfefferbaum, A. (1997). Combined event-related fMRI and EEG evidence for temporal-parietal cortex activation during target detection. NeuroReport, 8, 3029–3037.

Lancaster, J. L., Woldorff, M. G., Parsons, L. M., Liotti, M., Freitas, C. S., Rainey, L., et al. (2000). Automated talairach atlas labels for functional brain mapping. Human Brain Mapping, 10, 120–131.

Miezin, F. M., Maccotta, L., Ollinger, J. M., Petersen, S. E., & Buckner, R. L. (2000). Characterizing the hemodynamic response: Effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing. NeuroImage, 11, 735–759.

Liao, C. H., Worsley, K. J., Poline, J. B., Aston, J. A., Duncan, G. H., & Evans, A. C. (2002). Estimating the delay of the fMRI response. NeuroImage, 16, 593–606. Lindquist, M. A., Zhang, C. H., Glover, G., & Shepp, L. (2008). Rapid three-dimensional functional magnetic resonance imaging of the initial negative BOLD response. J Magn Reson, 191 (1), 100–111. Lindquist, M. A., & Wager, T. D. (2007). Validity and power in hemodynamic response modeling: A comparison study and a new approach. Human Brain Mapping, 28, 764–784. Lindquist, M., & Wager, T. D. (in press). Application of change-point theory to modeling state-related activity in fMRI. Applied Data Analytic Techniques for “Turning Points Research.” Lawrence Erlbaum/Taylor & Francis Group, Philadelphia. Lindquist, M. A., Waugh, C., & Wager, T. D. (2007). Modeling state-related fMRI activity using change-point theory. NeuroImage, 35, 1125–1141. Liu, T. T. (2004). Efficiency, power, and entropy in event-related fMRI with multiple trial types: Pt. II. Design of experiments. NeuroImage, 21, 401–413. Liu, T. T., Frank, L. R., Wong, E. C., & Buxton, R. B. (2001). Detection power, estimation efficiency, and predictability in event-related fMRI. NeuroImage, 13, 759–773. Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001, July 12). Neurophysiological investigation of the basis of the fMRI signal. Nature, 412, 150–157. Loh, J. M., Lindquist, M.A., Wager, T.D. (2008). Residual analysis for detecting mis-modeling in fMRI. Statistica Sinica, 18, 1421–1448. Lund, T. E., Madsen, K. H., Sidaros, K., Luo, W. L., & Nichols, T. E. (2006). Non-white noise in fMRI: does modelling have an impact? NeuroImage, 29 (1), 54–66. Luo, W. L., & Nichols, T. E. (2003). Diagnosis and exploration of massively univariate neuroimaging models. NeuroImage, 19, 1014–1032. Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S., et al. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences, USA, 97, 4398–4403. Mai, J. K., Assheuer, J., & Paxinos, G. (2004). Atlas of the human brain (2nd ed.). San Diego, CA: Elsevier Academic Press. McIntosh, A. R., Bookstein, F. L., Haxby, J. V., & Grady, C. L. (1996). Spatial pattern analysis of functional brain images using partial least squares. NeuroImage, 3, 143–157. McIntosh, A. R., & Gonzalez-Lima, F. (1994). Structural equation modeling and its application to network analysis in functional brain imaging. Human Brain Mapping, 2, 2–22. McKeown, M. J., Makeig, S., Brown, G. G., Jung, T. P., Kindermann, S. S., Bell, A. J., et al. (1998). Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp, 6 (3), 160–188.

c09.indd Sec5:195

Morawetz, C., Holz, P., Lange, C., Baudewig, J., Weniger, G., Irle, E., et al. (2008). Improved functional mapping of the human amygdala using a standard functional magnetic resonance imaging sequence with simple modifications. Magnetic Resonance Imaging, 26, 45–53. Nakamura, W., Anami, K., Mori, T., Saitoh, O., Cichocki, A., & Amari, S. (2006). Removal of ballistocardiogram artifacts from simultaneously recorded, EEG, and fMRI data using independent component analysis. IEEE Transactions of Biomedical Engineering, 53, 1294–1308. Nichols, T., Brett, M., Andersson, J., Wager, T., & Poline, J. B. (2005). Valid conjunction inference with the minimum statistic. NeuroImage, 25, 653–660. Nichols, T., & Hayasaka, S. (2003). Controlling the familywise error rate in functional neuroimaging: A comparative review. Statistical Methods in Medical Research, 12, 419–446. Nichols, T., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: A primer with examples. Human Brain Mapping, 15, 1–25. Noll, D. C., Fessler, J. A., & Sutton, B. P. (2005). Conjugate phase MRI reconstruction with spatially variant sample density correction. IEEE Transactions in Medical Imaging, 24, 325–336. Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10, 424–430. Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences, USA, 87, 9868–9872. Ogawa, S., Tank, D. W., Menon, R., Ellermann, J. M., Kim, S. G., Merkle, H., et al. (1992). Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging. Proceedings of the National Academy of Sciences, USA, 89, 5951–5955. Ollinger, J. M., Shulman, G. L., & Corbetta, M. (2001). Separating processes within a trial in event-related functional MRI. NeuroImage, 13, 210–217. Ongur, D., Ferry, A. T., & Price, J. L. (2003). Architectonic subdivision of the human orbital and medial prefrontal cortex. Journal of Comparative Neurology, 460, 425–449. Paton, J. J., Belova, M. A., Morrison, S. E., & Salzman, C. D. (2006, February 16). The primate amygdala represents the positive and negative value of visual stimuli during learning. Nature, 439, 865–870. Paus, T. (2001). Primate anterior cingulate cortex: Where motor control, drive and cognition interface. Natures Review Neuroscience, 2, 417–424. Pearl, J. (2000). Causality: Models, reasoning, and inference. New York: Cambridge University Press.

McKeown, M. J., & Sejnowski, T. J. (1998). Independent component analysis of fMRI data: examining the assumptions. Hum Brain Mapp, 6 (5–6), 368–372.

Petersson, K. M., Nichols, T. E., Poline, J. B., & Holmes, A. P. (1999). Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models. Philos Trans R Soc Lond B Biol Sci, 354 (1387), 1239–1260.

Menon, R. S. (2002). Postacquisition suppression of large-vessel BOLD signals in high-resolution fMRI. Magnetic Resonance in Medicine, 47, 1–9.

Phan, K. L., Taylor, S. F., Welsh, R. C., Ho, S. H., Britton, J. C., & Liberzon, I. (2004). Neural correlates of individual ratings of emotional salience: A trial-related fMRI study. NeuroImage, 21, 768–780.

8/18/09 5:25:42 PM

196

Essentials of Functional Neuroimaging

Pizzagalli, D. A. (2007). Electroencephalography and high-density electrophysiological source localization. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (4th ed., pp. 56–84). Cambridge: Cambridge University Press. Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Science, 10, 59–63. Price, C. J., & Friston, K. J. (1997). Cognitive conjunction: A new approach to brain activation experiments. NeuroImage, 5, 261–270. Price, C. J., Veltman, D. J., Ashburner, J., Josephs, O., & Friston, K. J. (1999). The critical relationship between the timing of stimulus presentation and data acquisition in blocked designs with fMRI. NeuroImage, 10, 36–44. Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, USA, 98, 676–682. Rasbash, J. (2002). A user ’s guide to MLwiN. London: University of London, Centre for Multilevel Modelling. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis (2nd ed.). Newbury Park, CA: Sage. Reiman, E. M., Fusselman, M. J., Fox, P. T., & Raichle, M. E. (1989, February 24). Neuroanatomical correlates of anticipatory anxiety. Science, 243, 1071–1074. (Published erratum appears in Science, 256, June 19, 1992, p. 1696) Riera, J. J., Watanabe, J., Kazuki, I., Naoki, M., Aubert, E., Ozaki, T., et al. (2004). A state-space model of the hemodynamic approach: Nonlinear filtering of BOLD signals. NeuroImage, 21, 547–567. Rissman, J., Gazzaley, A., & D’Esposito, M. (2004). Measuring functional connectivity during distinct stages of a cognitive task. NeuroImage, 23, 752–763. Roebroeck, A., Formisano, E., & Goebel, R. (2005). Mapping directed influence over the brain using Granger causality and fMRI. NeuroImage, 25, 230–242. Rosen, B. R., Buckner, R. L., & Dale, A. M. (1998). Event-related functional MRI: Past, present, and future. Proceedings of the National Academy of Sciences, USA, 95, 773–780. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701. Saad, Z. S., Reynolds, R. C., Argall, B., Japee, S., & Cox, R. W. (2004). SUMA: An interface for surface-based intra- and inter-subject analysis with AFNI. Paper presented at the Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium. Sandler, M. P. (2003). Diagnostic nuclear medicine. Philadelphia: Lippincott Williams & Wilkins. Sarter, M., Berntson, G. G., & Cacioppo, J. T. (1996). Brain imaging and cognitive neuroscience. Toward strong inference in attributing function to structure. American Psychology, 51, 13–21. Sawamura, H., Orban, G. A., & Vogels, R. (2006). Selectivity of neuronal adaptation does not match response selectivity: A single-cell study of the FMRI adaptation paradigm. Neuron, 49, 307–318.

Skudlarski, P., Constable, R. T., & Gore, J. C. (1999). ROC analysis of statistical methods used in functional MRI: Individual subjects. NeuroImage, 9, 311–329. Smith, S. M., Jenkinson, M., Beckmann, C., Miller, K., & Woolrich, M. (2007). Meaningful design and contrast estimability in FMRI. NeuroImage, 34, 127–136. Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208–S219. Stark, C. E., & Squire, L. R. (2001). When zero is not zero: The problem of ambiguous baseline conditions in fMRI. Proceedings of the National Academy of Sciences, USA, 98, 12760–12766. Sternberg, S. (1969). Memory-scanning: Mental processes revealed by reaction-time experiments. American Scientist, 57, 421–457. Sternberg, S. (2001). Separate modifiability, mental modules, and the use of pure and composite measures to reveal them. Acta Psychologica, 106, 147–246. Strang, G. (1980). Linear algebra and its applications: Academic Press New York. Summerfield, C., Greene, M., Wager, T., Egner, T., Hirsch, J., & Mangels, J. (2006). Neocortical connectivity during episodic memory formation. PLoS Biology, 4, E128. Sylvester, C. Y., Wager, T. D., Lacey, S. C., Hernandez, L., Nichols, T. E., Smith, E. E., et al. (2003). Switching attention and resolving interference: FMRI measures of executive functions. Neuropsychologia, 41, 357–370. Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system: An approach to cerebral imaging. Stuttgart, New York: Thieme Medical. Taylor, J. E., & Worsley, K. J. (2006). Inference for magnitudes and delays of responses in the FIAC data using BRAINSTAT/FMRISTAT. Human Brain Mapping, 27, 434–441. Thompson, P. M., Schwartz, C., Lin, R. T., Khan, A. A., & Toga, A. W. (1996). Three-dimensional statistical analysis of sulcal variability in the human brain. Journal of Neuroscience, 16, 4261–4274. Tohka, J., Foerde, K., Aron, A. R., Tom, S. M., Toga, A. W., & Poldrack, R. A. (2008). Automatic independent component labeling for artifact removal in fMRI. NeuroImage, 39 (3), 1227–1245. Tootell, R. B. H., Dale, A. M., Sereno, M. I., & Malach, R. (1996). New images from human visual cortex. Trends in Neurosciences, 19, 481–489. Van Essen, D. C., & Dierker, D. L. (2007). Surface-based and probabilistic atlases of primate cerebral cortex. Neuron, 56, 209–225. Van Essen, D. C., Drury, H. A., Dickson, J., Harwell, J., Hanlon, D., & Anderson, C. H. (2001). An integrated software suite for surfacebased analyses of cerebral cortex. Journal of the American Medical Informatics Association, 8, 443–459.

Schacter, D. L., Buckner, R. L., Koutstaal, W., Dale, A. M., & Rosen, B. R. (1997). Late onset of anterior prefrontal activity during true and false recognition: An event-related fMRI study. NeuroImage, 6, 259–269.

van Snellenberg, J. X., & Wager, T. D. (in press). Cognitive and Motivational Functions of the Human Prefrontal Cortex. In E. Goldberg & D. Bougakov (Eds.), Luria’s Legacy in the 21st Century. Oxford: Oxford University Press.

Shulman, R. G., & Rothman, D. L. (1998). Interpreting functional imaging studies in terms of neurotransmitter cycling. Proceedings of the National Academy of Sciences, USA, 95, 11993–11998.

Vazquez, A. L., Cohen, E. R., Gulani, V., Hernandez-Garcia, L., Zheng, Y., Lee, G. R., et al. (2006). Vascular dynamics and BOLD fMRI: CBF level effects and analysis considerations. NeuroImage, 32, 1642–1655.

Shulman, R. G., Rothman, D. L., Behar, K. L., & Hyder, F. (2004). Energetic basis of brain activity: Implications for neuroimaging. Trends in Neuroscience, 27, 489–495.

Vazquez, A. L., & Noll, D. C. (1998). Nonlinear aspects of the BOLD response in functional MRI. NeuroImage, 7, 108–118.

Sibson, N. R., Dhankhar, A., Mason, G. F., Behar, K. L., Rothman, D. L., & Shulman, R. G. (1997). In vivo 13C NMR measurements of cerebral glutamine synthesis as evidence for glutamate-glutamine cycling. Proceedings of the National Academy of Sciences, USA, 94, 2699–2704.

c09.indd Sec5:196

Villringer, A., & Chance, B. (1997). Non-invasive optical spectroscopy and imaging of human brain function. Trends in Neurosciences, 20, 435–442. Visscher, K. M., Miezin, F. M., Kelly, J. E., Buckner, R. L., Donaldson, D. I., McAvoy, M. P., et al. (2003). Mixed blocked/event-related designs separate transient and sustained activity in fMRI. NeuroImage, 19, 1694–1708.

8/18/09 5:25:43 PM

References 197 Vogt, B. A., Nimchinsky, E. A., Vogt, L. J., & Hof, P. R. (1995). Human cingulate cortex: Surface features, flat maps, and cytoarchitecture. Journal of Comparative Neurology, 359, 490–506.

Wang, G., Tanaka, K., & Tanifuji, M. (1996, June 14). Optical imaging of functional organization in the monkey inferotemporal cortex. Science, 272, 1665–1668.

Wager, T. D., Hernandez, L., Jonides, J., & Lindquist, M. (2007). Elements of functional neuroimaging. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (4th ed., pp. 19–55). Cambridge: Cambridge University Press.

Williams, D. S., Detre, J. A., Leigh, J. S., & Koretsky, A. P. (1992). Magnetic resonance imaging of perfusion using spin inversion of arterial water. Proceedings of the National Academy of Sciences, USA, 89, 212–216.

Wager, T. D., Jonides, J., & Reading, S. (2004). Neuroimaging studies of shifting attention: A meta-analysis. NeuroImage, 22, 1679–1693.

Wilson, J. L., & Jezzard, P. (2003). Utilization of an intra-oral diamagnetic passive shim in functional MRI of the inferior frontal cortex. Magnetic Resonance in Medicine, 50, 1089–1094.

Wager, T. D., Jonides, J., & Smith, E. E. (2006). Individual differences in multiple types of shifting attention. Memory and Cognition, 34, 1730–1743. Wager, T. D., Jonides, J., Smith, E. E., & Nichols, T. E. (2005). Towards a taxonomy of attention-shifting: Individual differences in fMRI during multiple shift types. Cognitive, Affective and Behavioral Neuroscience, 5, 127–143. Wager, T. D., Keller, M. C., Lacey, S. C., & Jonides, J. (2005). Increased sensitivity in neuroimaging analyses using robust regression. NeuroImage, 26, 99–113. Wager, T. D., Lindquist, M., & Kaplan, L. (2007). Meta-analysis of functional neuroimaging data: Current and future directions. Social, Cognitive, and Affective Neuroscience, 2, 150–158. Wager, T. D., & Nichols, T. E. (2003). Optimization of experimental design in fMRI: A general framework using a genetic algorithm. NeuroImage, 18, 293–309.

Woolrich, M. W., Behrens, T. E., & Smith, S. M. (2004). Constrained linear basis sets for HRF modelling using Variational Bayes. NeuroImage, 21, 1748–1761. Worsley, K. J., & Friston, K. J. (1995). Analysis of fMRI time-series revisited: Again. NeuroImage, 2, 173–181. Worsley, K. J., Liao, C. H., Aston, J., Petre, V., Duncan, G. H., Morales, F., et al. (2002). A general statistical analysis for fMRI data. NeuroImage, 15, 1–15. Worsley, K. J., Taylor, J. E., Tomaiuolo, F., & Lerch, J. (2004). Unified univariate and multivariate random field theory. NeuroImage, 23, S189–S195.

Wager, T. D., Reading, S., & Jonides, J. (2004). Neuroimaging studies of shifting attention: A meta-analysis. NeuroImage, 22, 1679–1693.

Young, F. W., Takane, Y., & Lewyckyj, R. (1978). ALSCAL: A nonmetric multidimensional scaling program with several difference options. Behavioral Research Methods and Instrumentation, 10, 451–453.

Wager, T. D. & Smith, E. E. (2003). Neuroimaging studies of working memory: a meta-analysis. Cognitive, Affective and Behavioral Neuroscience, 3, 255–274.

Zarahn, E. (2002). Using larger dimensional signal subspaces to increase sensitivity in fMRI time series analyses. Human Brain Mapping, 17, 13–16.

Wager, T. D., Scott, D. J., & Zubieta, J. K. (2007). Placebo effects on human mu-opioid activity during pain. Proceedings of the National Academy of Sciences, USA, 104, 11056–11061.

Zarahn, E., Aguirre, G., & D’Esposito, M. (1997). A trial-based experimental design for fMRI. NeuroImage, 6, 122–138.

Wager, T. D., Vazquez, A., Hernandez, L., & Noll, D. C. (2005). Accounting for nonlinear BOLD effects in fMRI: Parameter estimates and a model for prediction in rapid event-related studies. NeuroImage, 25, 206–218.

c09.indd Sec5:197

Woolrich, M. W., Behrens, T. E., Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2004). Multilevel linear modelling for FMRI group analysis using Bayesian inference. NeuroImage, 21, 1732–1747.

Zarahn, E., & Slifstein, M. (2001). A reference effect approach for power analysis in fMRI. NeuroImage, 14, 768–779. Zeineh, M. M., Engel, S. A., Thompson, P. M., & Bookheimer, S. Y. (2003). Dynamics of the hippocampus during encoding and retrieval of face-name pairs. Science, 299, 577–580.

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Chapter 10

Thalamocortical Relations S. MURRAY SHERMAN

The thalamus is a paired structure joined at the midline and located at the center of the brain (Figure 10.1). Each half is roughly the size of a walnut. The main part of the thalamus is divided into a number of discrete regions, known as relay nuclei. These contain the relay cells that project to the cerebral cortex. (In this chapter, cortex refers to neocortex, which does not include the hippocampal formation or olfactory cortex.) Lateral to this main body of the thalamus is the thalamic reticular nucleus (TRN in Figure 10.1), which fits like a shield alongside the body of the main relay nuclei of the thalamus. The thalamic reticular nucleus is comprised entirely of GABAergic neurons that do not innervate cortex but instead innervate thalamic relay cells. In Figure 10.1,

all but the front part of the thalamic reticular nucleus has been cut away to reveal the relay nuclei. Strictly speaking, the relay nuclei are the dorsal thalamus, while the thalamic reticular nucleus is part of the ventral thalamus; here, dorsal and ventral reflect embryonic origin rather than relative location in the adult, meaning that the relay nuclei and thalamic reticular nucleus have different developmental origins. Unless otherwise specified, thalamus refers to the relay nuclei of the dorsal thalamus. Virtually all information reaching the cortex must pass through and be relayed by the thalamus. Thus anything we are consciously aware of and all of our perceptions of the outside world depend on thalamic relays. This relay is dynamically controlled by behavioral states and processes, including attentional demands. Each of the main relay nuclei shown in Figure 10.1 innervate one or a small number of cortical areas and, as far as we know, every area of cortex receives a thalamic input. The thalamus is there not just to get peripheral information to the cortex, but it continues to play a vital role in the further processing of this information by the cortex. Where thalamocortical relationships are understood (e.g., the projection of the lateral geniculate nucleus to the primary visual cortex), the thalamic input plays a major role in determining the functional properties of the cortical target area. It has been shown that if retinal inputs in the ferret are diverted into the medial geniculate nucleus instead of the normal auditory inputs, the auditory cortex acquires visual responsiveness and organizes orientation-specific domains normally seen only in the visual cortex (Sharma, Angelucci, & Sur, 2000). Since all thalamic nuclei innervate cortex and all cortical areas are thus innervated, this might suggest that the functional properties of any cortical area follow its thalamic input rather than inputs from other cortical areas. This is a rather subversive idea that runs counter to the traditional dogma that cortical processing depends solely on direct cortico-cortical pathways. Cortico-thalamo-cortical pathways play a heretofore neglected and perhaps dominant role in cortical functioning.

Figure 10.1 Schematic view of the right thalamus of the human. Shown are the main relay nuclei plus the thalamic reticular nucleus (TRN), of which only the anterior portion is visible; the remainder has been removed to reveal the thalamic relay nuclei. Normally, the thalamic reticular nucleus extends the length of thalamus as a thin shield closely apposed to the lateral surface of the relay nuclei. Abbreviations: A, Anterior Nuclei; CM, Central Medial Nucleus; IL, Intralaminar Nuclei; LD, Lateral Dorsal Nucleus; LGN, Lateral geniculate Nucleus; LP or PO, Lateral Posterior or Posterior Nucleus; MD, Medial Dorsal Nucleus; MGN, Medial Geniculate Nucleus; MI, Midline Nuclei; P, pulvinar; TRN, thalamic reticular nucleus; VA, Ventral Anterior Nucleus; VL, Ventral Lateral Nucleus; VPL, Ventral Posterolateral Nucleus; VPM, Ventral Posteromedial Nucleus. 201

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To understand the functional relevance of the thalamus, it is necessary to understand some details about cell and circuit properties. Fortunately, these are mostly conserved throughout thalamus, so once we appreciate these for a model nucleus, we can extrapolate these properties for the entire thalamus. This is not to say that there are not important differences found among thalamic relay nuclei, but we concentrate in this chapter on those major properties that are common to the thalamus. The best-known and most thoroughly studied of the thalamic nuclei is the lateral geniculate nucleus, which relays retinal information to the visual cortex. We use this nucleus as our model template for cell and circuit properties. For details of the thalamus beyond the scope of this chapter, see recent books on this topic by Jones (2007) and Sherman and Guillery (2006).

BASIC CELL TYPES There are three major cell types involved in thalamic circuitry: relay cells, local interneurons, and thalamic reticular nucleus cells. The relay cells use glutamate as a neurotransmitter, whereas the other cell types are GABAergic. Relay Cells Although the evidence for different classes of relay cells is scattered and incomplete outside of the lateral geniculate nucleus (e.g., Li, Bickford, & Guido, 2003; Yen & Jones, 1983), the evidence is firm for several distinct types of geniculate relay cell. For example, the main geniculate relay cell classes in the cat are called X and Y (see Figure 10.2), and the equivalent types in the monkey are called parvocellular and magnocellular based on the geniculate laminae in which they are located (Casagrande & Xu, 2004; Hendry & Reid, 2000; Sherman, 1985). Another cell type, called W in the cat and K in the monkey, has also been described, but it is unclear if this is one or several distinct classes, and these cells are relatively poorly understood; further details can be found in Casagrande and Xu (2004; Hendry and Reid (2000); and Sherman (1985). These X and Y cell types in the cat’s lateral geniculate nucleus are innervated by equivalent, distinctive retinal cell types and thus represent an independent, parallel, retino-geniculo-cortical neuronal streams of information. X cells have smaller cell bodies and dendritic arbors that are largely bipolar and oriented perpendicular to the laminar borders of the lateral geniculate nucleus (Friedlander, Lin, Stanford, & Sherman, 1981; Stanford, Friedlander, & Sherman, 1983). Y cells have larger cell bodies and thicker dendrites arranged more or less in a spherical volume (Friedlander et al., 1981;

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Figure 10.2 Examples of thalamic cell types from lateral geniculate nucleus of the cat. These tracings were made from intracellular labeling during in vivo recording (Friedlander et al., 1981; Hamos et al., 1985). A: X relay cell. Note the grape-like appendages near primary branch points. Three examples are shown at higher magnification. B: Y relay cell. C: Interneuron. The dendrites have the appearance of an axonal terminal arbor, and the many boutons seen among the dendrites are indeed synaptic boutons known as F2. Examples are shown in the higher magnification. Scale: The scale bar is 50 µm for the cell drawings and 10 µm for the insets of A and C.

Stanford et al., 1983). For both cells, retinal inputs innervate relatively proximal dendrites, within about 100 µm from the cell body (Wilson, Friedlander, & Sherman, 1984). On Y cells, these retinal synapses are formed fairly simply onto dendritic shafts, but in X cells, these tend to contact curious grape-like appendages found near proximal dendritic branch points. Interneurons Interneurons are particularly interesting cells because, in addition to conventional axonal outputs, they also produce presynaptic terminals from their dendrites, and these dendritic outputs are more numerous than are the axonal (Friedlander et al., 1981; Hamos, Van Horn, Raczkowski, Uhlrich, & Sherman, 1985; Wilson et al., 1984). Figure 10.2C shows an example of an interneuron from the lateral geniculate nucleus of the cat. The dendrites look so much like an axonal terminal arbor that they have been called axoniform (Guillery, 1966). The axonal arbor distributes within the dendritic arbor, and they look so much alike that, with light microscopy, it is often impossible to distinguish the axon. However, because the axon is myelinated and the dendrites are not, they can readily be distinguished with an electron microscope. Also, much work at the electron microscopic level (Famiglietti & Peters, 1972; Guillery, 1969; Hamos et al., 1985; Ralston, 1971) has made it possible to distinguish the axonal terminals (called F1) from the dendritic terminals (called F2; see Figure 10.2C). One important distinction is that the axonal (F1) terminals are strictly presynaptic

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Cell Properties of Thalamic Relay Neurons

(to relay cells and other interneurons), whereas the dendritic (F2) terminals are both presynaptic (mostly to the grape-like clustered appendages of X cells; see Figure 10.2A and Wilson et al., 1984) and postsynaptic (mostly to retinal terminals). The F2 terminals are the only postsynaptic terminals so far described in the thalamus. The circuits entered into by these F2 terminals and the functional properties of interneurons are discussed further later in the chapter. Thalamic Reticular Nucleus Cells The final major cell type in the thalamus is the reticular cell, found in the thalamic reticular nucleus. These tend to have elongated dendritic arbors oriented parallel to the borders of the thalamic reticular nucleus (Figure 10.3; Uhlrich, Cucchiaro, Humphrey, & Sherman, 1991). Their axons project into the main relay nuclei of the thalamus and selectively target relay cells (Cucchiaro, Uhlrich, & Sherman, 1991), and local collaterals provide for contacts between reticular cells (Lam, Nelson, & Sherman, 2006; Sanchez-Vives, Bal, & McCormick, 1997). These cells are

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also functionally connected via gap junctions (Lam et al., 2006; Landisman et al., 2002).

CELL PROPERTIES OF THALAMIC RELAY NEURONS Thalamic relay cells, like cells throughout the central nervous system, have numerous voltage- and time-gated ionic channels in their membranes. The best known of these are the Na and K channels underlying the conventional action potential (see Figure 10.4). There are many others, including channels for other cations. One that is especially important to thalamic relay cells involves T-type Ca2 channels. (For details of T channel properties, see Huguenard & McCormick, 1994; Jahnsen & Llinás, 1984a, 1984b; and for other voltage gated channels in thalamic neurons, see Huguenard & McCormick, 1994; Sherman & Guillery, 2006.) The properties of these T channels are qualitatively the same as those of Na channels involved with the action potential. Figure 10.4 summarizes these properties, emphasizing the similarities with the T channels shown in Figure 10.5. Basic Properties of the T Channel

Figure 10.3 Example of cell in the thalamic reticular nucleus of Galago filled with neurobiotin. The star in the inset shows the location of the cell body. Redrawn from Figure II-12 of (Sherman and Guillery, 2006) from data supplied by P Smith, K Manning and D Uhlrich. Abbreviations: As in Figure 10.1, plus IC, internal capsule.

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Figure 10.4 is a review of the main properties of the Na (and K) channels underlying the action potential. When the Na channel is open, Na flows into the cell, producing a depolarizing current known as INa. However, the Na channel has two voltage sensitive gates—an activation gate and an inactivation gate—and both must be open for Na to flow into the cell. At a normal resting membrane potential (e.g., 65 mV), the inactivation gate is open, but the activation gate is closed, and thus there is no inward flow of Na (Figure 10.4A). Here INa is deactivated because the activation gate is closed, but it is also relieved of inactivation (or is de-inactivated) because the inactivation gate is open. When the membrane is depolarized to a certain level, the activation threshold for INa (Figure 10.4B), the activation gate pops open and so INa is both activated and de-inactivated; the result is that Na flows into the cell, producing the depolarizing upswing of the action potential. This depolarization, after a suitable period of 1 msec or so, leads to closing of the inactivation gate, and so while the Na channel remains activated, it is also inactivated (Figure 10.4C). This plus the opening of various slower K channels (channels that do not inactivate because they have only an activation gate), which produces a hyperpolarizing outward flow of K, repolarizes the membrane to near its starting position (Figure 10.1D). However, despite being repolarized, INa

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Figure 10.4 Schematic representation of voltage dependent Na+ and K+ channels underlying the conventional action potential. A–D show the channel events and E shows the effects on membrane potential. The Na+ channel has two voltage dependent gates: an activation gate that opens at depolarized levels and closes at hyperpolarized levels, and an inactivation gate with the opposite voltage dependency. Both must be open for the inward, depolarizing Na+ current (INa) to flow. The K+ channel (actually an imaginary combination of several different K+ channels) has a single activation gate, and when it opens at depolarized levels, an outward, hyperpolarizing K+ current is activated. A: At a resting membrane potential (roughly 60 to 65 mV), the activation gate of the Na+ channel is closed, and so it is deactivated, but the inactivation gate is open, and so it is de-inactivated. The single gate for the K+ channel is closed, and so the K+ channel is also deactivated. B: With sufficient depolarization

to reach its threshold, the activation gate of the Na+ channel opens, allowing Na+ to flow into the cell. This depolarizes the cell, leading to the upswing of the action potential. C: The inactivation gate of the Na+ channel closes after the depolarization is sustained for roughly 1 msec (“roughly,” because inactivation is a complex function of time and voltage), and the slower K+ channel also opens. These combined channel actions lead to the repolarization of the cell. While the inactivation gate of the Na+ channel is closed, the channel is said to be inactivated. D: Even though the initial resting potential is reached, the Na+ channel remains inactivated, because it takes roughly 1 msec (“roughly” having the same meaning as above) of hyperpolarization for de-inactivation. E: Membrane voltage changes showing action potential corresponding to the events in A to D. Redrawn from Figure IV-4 of (Sherman and Guillery, 2006).

remains inactivated because it takes roughly 1 msec of this hyperpolarization to open the inactivation gate, restoring the initial conditions of Figure 10.1A. Thus the two gates of the Na channel have opposite voltage dependencies and both respond relatively quickly to voltage changes. Finally, note that the roughly 1 msec of hyperpolarization needed to de-inactivate the Na channel provides a refractory period limiting firing rates for the action potential to 1000 Hz. While Figure 10.4 shows the basic voltage gated properties of the Na channel, one other feature is essential to propagating an all-or-none action potential. That is, the density of Na channels must be sufficiently high that, once threshold is reached, the further depolarization caused by the initial channels to open causes a self-regenerating,

explosive opening of other channels, and this propagates as an action potential. If the Na channel density were too low, the initial channels to open would lead only to a local depolarization that would decay exponentially. As shown in Figure 10.5, the voltage behavior of the T channel is qualitatively the same as that of the Na channel, with the same two types of voltage gate. At the starting position of Figure 10.5A, the activation gate is closed, but sufficient depolarization opens it (Figure 10.5B), allowing the inward IT that further depolarizes the cell. This depolarization eventually inactivates IT (Figure 10.5C) which, along with the activation of K channels, repolarizes the cell (Figure 10.5D). This repolarization eventually leads to de-inactivation of IT (Figure 10.5A).

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Figure 10.5 Schematic representation of actions of voltage dependent T (Ca2+) and K+ channels underlying low threshold Ca2+ spike; conventions as in Figure 10.4. Note the strong qualitative similarity between the behavior of the T channel here and the Na+ channel shown in Figure 10.4, including the presence of both activation and inactivation gates with similar relative voltage dependencies. A–D show the channel events and E shows the effects on membrane potential. A: At a relatively hyperpolarized resting membrane potential (roughly 70 mV), the activation gate of the T channel is closed, but the inactivation gate is open, and so the T channel is deactivated and de-inactivated. The K+ channel is also deactivated. B: With sufficient depolarization to reach its threshold, the activation gate of the T channel

opens, allowing Ca2+ to flow into the cell. This depolarizes the cell, providing the upswing of the low threshold spike. C: The inactivation gate of the T channel closes after roughly 100 msec (“roughly”, because, as for the Na+ channel in Figure 10.4, closing of the channel is a complex function of time and voltage), inactivating the T channel, and the K+ channel also opens. These combined actions repolarize the cell. D: Even though the initial resting potential is reached, the T channel remains inactivated, because it takes roughly 100 msec of hyperpolarization for de-inactivation. E: Membrane voltage changes showing low threshold spike corresponding to the events in A to D. Redrawn from Figure IV-5 of (Sherman and Guillery, 2006).

As in the case of Na channels, if a sufficiently high density of T channels exists, the threshold opening of the initial T channels leads to an explosive all-or-none spike. This is the case for thalamic relay cells, and the result is a spike-like depolarization of roughly 25 to 50 mV that propagates throughout the dendrites and soma. T channels are quite common in neurons throughout the central nervous system, but only in rare cells is the density high enough to support all-or-none Ca2 spikes. Thus, this property of all-or-none Ca2 spiking based on T channels is fairly unique to the thalamus. Every relay cell of every nucleus in every mammalian species so far tested shows this property (Sherman & Guillery, 2006). However, a further inspection of Figures 10.4 and 10.5 reveals certain important quantitative differences between the behavior of the Na and Ca2 channels. Perhaps most important are the temporal properties of

the inactivation gates. While the activation gates for both channels respond quickly to voltage changes, as does the inactivation gate of the Na channel, the inactivation gate of the T channel is much slower, requiring roughly 100 msec of a sustained polarization change to open or close. Actually, as is the case for the Na channel, the inactivation gate of the T channel has a complex voltage- and time-dependency, so that the greater the sustained polarization change, the more rapidly the gate opens or closes (Zhan, Cox, Rinzel, & Sherman, 1999). This temporal property for the T channel is important and will be considered further. Another quantitative difference is the functional voltage range: the T channel operates in a more hyperpolarized regime. In fact, because the T channel activates at a more hyperpolarized level, the resulting depolarization, which in thalamic relay cells is an all-or-none Ca2 spike, is also known as the “low threshold spike.”

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One other important difference not shown in Figures 10.4 and 10.5 is the distribution of these channels: T channels are effectively limited to the soma and dendrites, whereas Na channels, often found there as well, are notable for their distribution along the axon. This allows action potentials to travel from the soma to a target far away, and in the case of thalamic relay cells, this Na channel distribution permits action potentials to be delivered to cortical targets. While T channels underlie Ca2 spikes propagated in the dendrites and soma, these spikes do not propagate to the cortex. Thus the significance of these Ca2 spikes ultimately rests with their effect on conventional action potentials as described in the following section. This effect is dramatic and important. Burst and Tonic Firing Modes The primary functional significance of T channels for thalamic relay cells is that they are responsible for which of two very different response modes, called burst and tonic, characterize these cells’ responses (Jahnsen & Llinás, 1984a; Zhan et al., 1999). Figure 10.6 summarizes some of the features of these response modes. If the cell has been initially depolarized by just a few mV (5 mV) from rest, the T channels are inactivated and play no role in the response. This leads to tonic firing (Figure 10.6A) where a depolarizing current injection elicits a stream of unitary action potentials that lasts as long as the stimulus is suprathreshold. If, however, the cell has been hyperpolarized initially by 5 mV or so from rest, the T channels are de-inactivated and primed to respond to the next suitable depolarization, and the result is burst firing. This is shown in Figure 10.6B where the same depolarizing stimulus as in Figure 10.6A now evokes an all-or-none low threshold Ca2 spike with a burst of high frequency action potentials riding its crest. The exact same stimulus (think of this as the same excitatory postsynaptic potential or EPSP evoked from the same retinal input to a geniculate relay cell) creates a very different pattern of action potentials depending on the recent voltage history of the relay cell, and this pattern of firing is the only signal that reaches cortex. To summarize: The recent voltage history of a relay cells determines the inactivation state of its T channels, and this, in turn, determines whether the relay cell responds to its next, suprathreshold excitatory input in tonic or burst mode, a determination that dramatically affects the message relayed to the cortex. Significance of Response Mode for Thalamocortical Relays A major question for which we have only partial and largely hypothetical answers is: What is the functional significance

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Figure 10.6 Properties of IT and the low threshold Ca2+ spike. All examples are from relay cells of the cat’s lateral geniculate nucleus recorded intracellularly in an in vitro slice preparation. A, B: Voltage dependency of the low threshold spike. Responses are shown to the same depolarizing current pulse delivered intracellularly but from two different initial holding potentials. When the cell is relatively depolarized (A), IT is inactivated, and the cell responds in tonic mode, which is a stream of unitary action potentials to a suprathreshold stimulus. When the cell is relatively hyperpolarized (B), IT is de-inactivated, and the cell responds in burst mode, which involves activation of a low threshold Ca2+ spike (LTS) with multiple action potentials (8 in this example) riding its crest. C: Input-output relationship for another cell. The abscissa is the amplitude of the depolarizing current pulse, and the ordinate is the firing frequency of the cell for the first 6 action potentials of the response, since this cell usually exhibited 6 action potentials per burst in this experiment. The initial holding potentials are shown, and 47 mV and 59 mV reflects tonic mode, whereas 77 mV and 83 mV reflects burst mode. Redrawn from Figure IV-6 of Sherman and Guillery (2006).

of the burst and tonic response modes for thalamic relay performance? One answer comes from a consideration of the fact that the only message reaching the cortex is in the form of action potentials and they are evoked differently in the two response modes. During tonic firing, action potentials are

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Cell Properties of Thalamic Relay Neurons

directly evoked by an appropriate, suprathreshold depolarizing stimulus (e.g., an EPSP), and so a larger EPSP will evoke more firing. In other words, there is a relatively linear relationship between input (or EPSP) amplitude and firing rate. During burst firing, however, action potentials are not directly activated by the depolarizing input; instead they are activated by the large, depolarizing low threshold Ca2 spike. Because this Ca2 spike is all-or-none, a larger depolarizing input or EPSP will not evoke a larger Ca2 spike, and thus the input-output relationship during burst firing is highly nonlinear, approximating a step function. These differences are illustrated in Figure 10.6C (Zhan et al., 1999). Figure 10.7 shows related and additional effects of response mode. In this example, a geniculate relay cell is recorded intracellularly in an anesthetized cat while its responses to visual stimuli are monitored. These responses indicate how retinal input is relayed to the cortex. Because of the intracellular recording, it is possible to pass current into the cell either to depolarize its baseline level sufficiently to inactivate IT (e.g., baseline depolarized to –65 mV in Figure 10.7A) and promote tonic firing or to hyperpolarize it (e.g., baseline depolarized to –75 mV in Figure 10.7B) so as to deinactivate IT and promote burst firing. The visual stimulus in this case is a drifting sinusoidal grating, providing a visual stimulus in which contrast varies sinusoidally with time at 2 Hz. Figure 10.7A, lower, shows that the tonic response profile is sinusoidal and accurately reflects the contrast changes in the stimulus. The response in burst mode (Figure 10.7B, lower) does not accurately reflect the contrast changes, showing the sort of nonlinear distortion that can largely be predicted by the cellular properties shown in Figure 10.6C. This provides an obvious advantage for tonic firing because the nonlinear distortion caused by burst firing will limit the fidelity of the message relayed to the cortex. In other words, to faithfully reconstruct the visual world, the cortex is better served by tonic firing. What, then, is the purpose of burst firing? Two possible advantages have been suggested. One advantage is related to spontaneous firing, which is much lower during burst firing (Figure 10.7A, B, upper histograms; Guido, Lu, & Sherman, 1992; Guido, Lu, Vaughan, Godwin, & Sherman, 1995). Actually, the higher spontaneous activity helps preserve linearity during tonic firing because the raised level of activity allows inhibitory components of the visual stimulus to be represented; without this, the response would “bottom out,” reflecting rectification, which is itself a nonlinearity. There is another, perhaps more important, consequence of the difference in spontaneous activity levels. Spontaneous activity can be considered noise from the perspective of the cortex because, by definition, it represents firing in the geniculate relay cell that bears no relation to visual stimulation. The lower histograms in Figure 10.7A and B suggest that

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Figure 10.7 Responses of a representative relay cell in the lateral geniculate nucleus of a lightly anesthetized cat to a sinusoidal grating drifted through the cell’s receptive field. The trace at the bottom reflects the sinusoidal changes in luminous contrast with time. Current was injected into the cell through the recording electrode to alter the membrane potential. Thus in A, the current injection was adjusted so that the membrane potential without visual stimulation averaged 65 mV, promoting tonic firing, because IT is mostly inactivated at this membrane potential; in B, the current injection was adjusted to the more hyperpolarized level of 75 mV, permitting de-inactivation of IT and promoting burst firing. Shown are average response histograms to the visual stimulus (bottom histograms in A and B) and during spontaneous activity with no visual stimulus (top histograms), plotting the mean firing rate as a function of time averaged over many epochs of that time. The sinusoidal changes in contrast as the grating moves across the receptive field are also shown as a dashed, gray curve superimposed on the responses in the lower histograms. Note that the response profile during the visual response in tonic mode looks like a sine wave, but the companion response during burst mode does not. Note also that the spontaneous activity is higher during tonic than during burst firing. Redrawn from Figure VI-2 of Sherman and Guillery (2006).

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208 Thalamocortical Relations

the signal relayed during both response modes to visual stimulation are roughly equal in extent, and so the lower noise during burst firing suggests that the signal-to-noise ratio is higher during burst firing. A higher signal-to-noise ratio, in turn, suggests better detectability of the stimulus in the response of the geniculate relay cell, and this has been demonstrated (Guido, Lu, et al., 1995). Another advantage of burst firing is that it more powerfully affects the cortex (Swadlow & Gusev, 2001; Swadlow, Gusev, & Bezdudnaya, 2002). This is because the thalamocortical synapse shows strong paired-pulse depression (Abbott, Varela, Sen & Nelson, 1997; Castro-Alamancos & Connors, 1997; Chung, Li, & Nelson, 2002; Gil, Connors, & Amitai, 1999; Lee & Sherman, 2008, 2009) This is shown in Figure 10.8A, where a facilitating layer 6 corticothalamic synapse (Reichova & Sherman, 2004) is also shown for comparison. For a depressing synapse, action potentials

Figure 10.8 Examples of paired-pulse depression and pairedpulse facilitation. These recordings were made from in vitro slices of the mouse brain in which thalamocortical and corticothalamic projections are retained in the somatosensory system. When electrical stimulation is applied as a train of impulses at fixed frequency to afferents of a recorded cortical or thalamic cell, the resultant EPSPs decrease with stimulus number (paired-pulse depression) or increase (paired-pulse facilitation). A: Example of paired-pulse depression (upper trace; recording from a layer 4 cell and activating inputs from thalamus) and paired-pulse facilitation (lower trace; recording from thalamic relay cell and activating inputs from layer 6 of cortex). B: Time course of paired-pulse effects for the examples in A. The abscissa shows the interstimulus interval, and the ordinate, the measure of depression (left) or facilitation (right) expressed as the ratio of the amplitude of the second EPSP to the amplitude of the first. Unpublished data from laboratory of S.M. Sherman.

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arriving with interspike intervals of less than 50 to 150 msec (see Figure 10.8B) or so will depress the postsynaptic responses resulting in a smaller EPSP. During tonic firing, interspike intervals are sufficiently high to keep the thalamocortical synapses in more or less a constant state of depression. However, the dynamics of burst firing result in a synapse with no depression. This is because, to burst, a cell must be in a sustained state of hyperpolarization for 100 msec or so (to de-inactivate T channels) before responding to a depolarizing EPSP, and so there can be no action potentials during this period; this imposes a requisite silent period on a cell before each burst meaning that, when the burst is evoked, the thalamocortical synapse is free of depression. Elegant experiments by Swadlow and colleagues (Swadlow & Gusev, 2001; Swadlow et al., 2002) have directly confirmed this (Figure 10.9). Hypothesis for Burst and Tonic Firing To summarize the known functional consequences of firing mode (and there may be many other, undiscovered ones), tonic mode is associated with a more linear relay, while burst mode is associated both with superior signal detection and stronger cortical activation. This has led to the theory (Sherman, 1996, 2001; Sherman & Guillery, 2002, 2006) that burst mode may be involved in providing a strong “wake-up call” to the cortex that something has changed in the environment (e.g., the sudden appearance of a new visual stimulus), particularly in circumstances during which attention is not devoted to the relay under question (e.g., general drowsiness, or inattention, or for auditory thalamic relays while attention is diverted to visual stimuli). There are several very indirect lines of evidence in support of this. One is that bursting of thalamic relay cells increases with drowsiness (Massaux & Edeline, 2003; Ramcharan, Gnadt, & Sherman, 2000; Swadlow & Gusev, 2001). Another is that the initial cycle of a repeating visual tends more frequently to evoke bursting of geniculate relay cells (Guido & Weyand, 1995). Finally, studies of visual responses, including the use of natural visual scenes as stimuli, indicate that the best stimulus to evoke a burst is the replacement in the visual field of an inhibitory stimulus with an excitatory one, for instance, the replacement of a dark spot over the center of an on-center cell with a bright spot (Alitto, Weyand, & Usrey, 2005; Denning & Reinagel, 2005; Lesica & Stanley, 2004; Wang et al., 2007). For this hypothesis to make sense, thalamic circuitry must be arranged in a manner that can efficiently control response mode, promoting the transition between burst and tonic firing under appropriate conditions. That is, there must be inputs to relay cells that effectively control membrane potential for a sufficiently long period (i.e., for at least 100 msec or so) to control the inactivation state of IT. As the next section shows, this is indeed the case.

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Circuit Properties of Thalamic Relay Neurons

Figure 10.9 (Figure C.3 in color section) Current source density profiles in cortex generated from single spike in afferent thalamic neuron. Recordings were made simultaneously in an awake rabbit from a single neuron in the ventral posterior medial nucleus of the thalamus and from 16 probes at different depths along a column in the cortical target field of the recorded thalamic neuron. Spike triggered averaging was used to generate the synaptic sinks and sources as shown. A, B: Colorized current source density profile generated by tonic spike (A, ~120,000 thalamic action potentials) or first spike in burst (B, 2427 thalamic action potentials) in the thalamic afferent. The vertical orange line in each indicates the time of the action potential in the thalamic afferent. The red arrow in each shows the current source

evoked by the terminals of the thalamic afferent, and note that this is the same for the tonic and burst spike. The depths of layers 4 and 6 are also indicated. The vertical dashed white lines show the initial 1 msec of the postsynaptic responses, with large sinks in layers 4 and 6. Note the denser sinks for the burst spike (B) compared to the tonic spike (A). C, D: amplitude (peak peak) of the axon terminal response (C, indicated by the red arrows in A, B) and the magnitude of the initial 1 msec of the postsynaptic current sink (D) plotted at different recording sites for both the tonic spike and first spike in a burst. Note that there is no difference in the corticothalamic terminal responses for these two spikes but that the peaks in layers 4 and 6 are greater for the burst spike. Redrawn from Figure 3 of Swadlow et al. (2002).

CIRCUIT PROPERTIES OF THALAMIC RELAY NEURONS

Also intimately associated with relay cells are two types of local, GABAergic neurons that provide inhibitory input to relay cells: these are local interneurons and cells of the nearby thalamic reticular nucleus. Interneurons live among relay cells throughout the relay nuclei of the thalamus, and the ratio is roughly three relay cells to every interneuron across nuclei and species, with one curious exception (Arcelli, Frassoni, Regondi, De Biasi, & Spreafico, 1997). That is, while the lateral geniculate nucleus of the rat and mouse contain roughly 25% interneurons, the rest of the thalamus in these species contain almost no interneurons. This is not a rodent property because the thalamus of other rodent species, like squirrels, guinea pigs, and so on, contains normal numbers of interneurons. There are two major sources of extrinsic input to geniculate circuitry (see Figure 10.10). One is a feedback glutamatergic projection from layer 6 of the visual cortex, and the other is a mostly cholinergic input from

Fortunately, the detailed circuit properties of the thalamus are largely conserved among thalamic nuclei. To be sure, there are some differences in circuitry among thalamic nuclei. Certain ones will be discussed next. Because we know most about the lateral geniculate nucleus, this serves as a convenient model for all of the thalamus. Figure 10.10 schematically shows the main circuitry involving geniculate neurons, including the main transmitters and classes of postsynaptic receptor involved. (These circuit details are reviewed in Sherman & Guillery, 1996, 2004, 2006). Basic Anatomical Circuits Relay cells receive input from retinal axons and, in turn, project to visual cortex, mostly to layer 4 but also to layer 6.

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209

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210 Thalamocortical Relations

Figure 10.10 Schematic view of details of the main connections of the lateral geniculate nucleus. Indicated are the inhibitory or excitatory nature of the synapses, the postsynaptic receptors activated by each input on relay cells, and the neurotransmitters involved. Abbreviations: ACh, acetylcholine; GABA, -aminobutyric acid; Glu, glutamate; LGN, lateral geniculate nucleus; PBR, parabrachial region; TRN, thalamic reticular nucleus.

the parabrachial region of the midbrain. In both cases, individual axons branch to innervate all three thalamic cell classes: relay cells, interneurons, and reticular cells. Not shown for simplicity are various serotonergic, noradrenergic, GABAergic, and dopaminergic inputs from the brain stem and histaminergic inputs from the tuberomamillary nucleus of the hypothalamus. This is partly to avoid unnecessary complication and also because the functional significance of these other inputs is just beginning to be understood. (see Sherman & Guillery, 1996, 2004, 2006). Thus, not only do relay cells receive inputs from the retina, which represents the main input relayed to the cortex, but they also receive inputs from other sources as well. Postsynaptic Receptors on Relay Cells It is clear from Figure 10.10 that nonretinal inputs can influence retinogeniculate transmission. All of these inputs to relay cells operate via conventional chemical synapses, and thus their postsynaptic effects are largely controlled by postsynaptic receptors. These, too, are illustrated in Figure 10.10, and they are divided into two main groups: ionotropic and metabotropic. Examples of ionotropic receptors for the transmitter systems shown are AMPA receptors for glutamate, the GABAA receptor, and nicotinic receptors for acetylcholine; the equivalent metabotropic receptor examples are various metabotropic glutamate receptors, the GABAB receptor, and various muscarinic receptors for acetylcholine. Details of differences between these receptor classes are many (Brown et al., 1997; Conn & Pin, 1997; Mott & Lewis, 1991; Nicoll, Malenka, & Kauer, 1990; Pin &

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Duvoisin, 1995; Recasens & Vignes, 1995), but two major differences are particularly relevant here. First, excitatory and inhibitory postsynaptic potentials (EPSPs and IPSPs) generated via ionotropic receptors tend to be very brief, on the order of 10 msec or a few 10s of msec, whereas those via metabotropic are much more sustained, lasting 100s of msec to several seconds. Second, metabotropic receptors tend to be less sensitive in the sense that afferent firing rates usually need to be higher before they are activated; this is because these receptors tend to be a bit eccentrically located in the synapse with respect to ionotropic receptors (Lujan, Nusser, Roberts, Shigemoto, & Somogyi, 1996; Somogyi, Tamas, Lujan, & Buhl, 1998), and so more transmitter must be released to reach them. With these differences in mind, it is interesting that retinal input activates only ionotropic receptors (mostly AMPA), whereas all of the nonretinal inputs activate metabotropic receptors, often in addition to activation of ionotropic receptors. One input for which the postsynaptic receptor is not as clear is the input from interneurons to relay cells: clearly GABAA receptors are involved, but there have as yet been no definitive tests for the presence or absence of GABAB receptors for this input. Consequences of Type of Postsynaptic Receptor The fact that only ionotropic receptors are activated by retinal input is good for transfer of temporal information. That is, because the evoked EPSPs are brief, temporal summation does not occur until relatively high rates of firing in the retinal afferents, and thus it is possible to evoke a single EPSP for every retinal action potential for reasonable high rates of firing, thereby representing each input action potential as an EPSP in a one-to-one manner. Put another way, if retinal inputs activated metabotropic glutamate receptors, the sustained EPSPs would summate at lower firing rates, and no longer would postsynaptic responses ultimately relayed to the cortex be a precise copy of the retinal input. The representation of EPSPs evoked via metabotropic glutamate receptors would act like a low pass temporal filter, and temporal information would be lost. In this regard, the activation of metabotropic receptors, because of their long time course, would seem to provide a poor substrate for effective information transfer but an excellent one for modulation. In contrast, the sustained PSPs evoked by nonretinal inputs to relay cells means that sufficient activation of these inputs will provide rather lengthy effects on membrane potential, and thus excitability, of the relay cell. In this way, these nonretinal inputs will serve to modulate the gain or effectiveness of retinogeniculate transmission. Other consequences of these nonretinal inputs can be seen in their control of voltage gated ion channels, and a good

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Circuit Properties of Thalamic Relay Neurons

example of this is their ability to control response mode— burst or tonic—of the relay cell. Control of Response Mode Recall from the previous description of T channel behavior that inactivation or de-inactivation requires a change in membrane potential to be sustained for at least roughly 100 msec. PSPs activated via ionotropic receptors are poorly suited to this, because they are too brief. Thus, for instance, an AMPA- or nicotinic-mediated EPSP is too brief to inactivate many T channels for a cell in burst mode, and a GABAA-mediated IPSP is too brief to relieve many T channels of their inactivation for a cell in tonic mode. However, the sustained PSPs of metabotropic receptors, lasting 100s of msec, are ideally suited to control response mode. Thus activation of metabotropic glutamate receptors via layer 6 corticogeniculate input or muscarinic receptors via parabrachial input produces an EPSP sustained enough to inactivate T channels and switch relay cell firing mode from burst to tonic; likewise, activation of GABAB receptors produces an IPSP sustained enough to de-inactivate T channels and switch relay cell firing mode from tonic to burst. Further Details of Effects of Corticogeniculate or Parabrachial Inputs Another consequence of the postsynaptic receptor is that it often determines whether a given neurotransmitter acts in an excitatory or inhibitory manner. In the case of the circuitry shown in Figure 10.10, cholinergic inputs excite relay cells while they inhibit interneurons and reticular cells. This is achieved by two types of muscarinic receptors (McCormick, 1992). Those on relay cells are mostly of the M1 type, and activation of M1 receptors leads to closing of K channels, reducing the outward leakage of K ions and thereby resulting in an EPSP. Those on the GABAergic cells are mostly of the M2 type, and activation of M2 receptors leads to opening of K channels, increasing the outward leakage of K ions and thereby resulting in an IPSP. This allows cholinergic inputs to the thalamus to perform a neat trick: they directly excite relay cells while they indirectly disinhibit them. As a result, increasing activity of parabrachial neurons leads to more depolarized relay cells, making them more responsive to retinal input and biasing them toward the tonic firing mode. Indeed, parabrachial cells become more active with increasing vigilance (Datta & Siwek, 2002; Steriade & Contreras, 1995), and more vigilance is associated with increased retinogeniculate transmission and a shift toward tonic firing (Massaux & Edeline, 2003; Ramcharan et al., 2000; Swadlow & Gusev, 2001).

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211

The situation with corticogeniculate inputs is more complex. These inputs to relay cells and local GABAergic cells are all excitatory, and thus the circuitry shown in Figure 10.10 suggests that corticogeniculate input directly excites relay cells but indirectly inhibits them, and it is not clear from this perspective what purpose this serves or what effect corticogeniculate input has on the firing mode of relay cells. However, as Figure 10.11 indicates, Figure 10.10 may be misleading in terms of the specifics of corticogeniculate circuitry because it does not reveal important details. Figure 10.11 shows two distinct variants of this circuitry involving the thalamic reticular nucleus; one can imagine similar variants involving interneurons and, of course other variants are possible. The variant shown in Figure 10.11A is an example of classical feed forward inhibition. It might seem puzzling because increased activity leads to both depolarization and (indirect) hyperpolarization of the relay cell, with perhaps minimal effect on the relay cell’s membrane potential. This would have very little effect on T channel inactivation and thus little effect on response mode. However, the resultant increase in synaptic conductance would reduce input resistance of the relay cell, and this and other subtle effects pointed out by Chance, Abbott, and Reyes (2002) would result in a reduced retinogeniculate EPSP amplitude. In other words, this form of feedforward inhibition acts as an effective means of gain control for retinogeniculate transmission. The variant shown in Figure 10.11B has quite a different functional significance. This is no longer an example of feedforward inhibition, but instead, an active corticogeniculate axon will directly excite some relay cells and indirectly

Figure 10.11 Schematic view of different possible corticothalamic circuits involving the thalamic reticular nucleus that have quite different effects on relay cells. A: Feedback inhibitory arrangement. B: Arrangement in which activation of layer 6 cell monosynaptically excites some relay cells (e.g., cell 2) and disynaptically inhibits others (e.g., cells 1 and 3).See text for details.

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212 Thalamocortical Relations

inhibit others. In this specific example, increased activity in the corticogeniculate axon will depolarize geniculate cell 2, biasing it toward tonic firing, and hyperpolarize cells 1 and 3, biasing them toward burst firing. Evidence exists that activation of layer 6 corticogeniculate input can have dramatic effects on response mode, switching some relay cells from burst to tonic firing, and others, in the opposite direction (Wang, Jones, Andolina, Salt, & Sillito, 2006). Role of Interneurons Interneurons are particularly interesting cells because, among other properties, they have both axonal (F1) and dendritic (F2) output terminals. The axonal outputs seem to innervate both X and Y relay cells and other interneurons on proximal dendritic shafts with conventional, simple synapses. The dendritic outputs target relay X cells in complex synaptic arrangements known as triads (see Figures 10.12 and 10.13).

Figure 10.13 Schematic view of a synaptic triad. Arrows indicate direction of synaptic function, pointing from presynaptic to postsynaptic elements. The question marks indicate that the presence of the receptor indicated is unclear. Abbreviations: GABA, -aminobutyric acid; Glu, glutamate.

Triadic Circuits The F2 dendritic outputs of interneurons enter into a complex synaptic arrangement known as triads (see Figures 10.12 and 10.13). In the most common form, a retinal terminal

Figure 10.12 Electron micgrographs showing some properties of F2 terminals based on intracellular labeling with horseradish peroxidase of an interneuron in the cat’s lateral geniculate nucleus. A: F2 terminal appended to interneuron dendrite via long, thin process (arrow). B: Section through triad. A retinal terminal (R) synapses onto an F2 terminal and a relay cell dendrite (d), and the F2 terminal synapses onto the same dendrite. The arrows show the direction of the synapses, pointing from presynaptic to postsynaptic elements. Figure reassembled from Hamos et al. (1985).

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contacts an F2 terminal, and both of these terminals contact the same relay X cell, usually on a grape-like appendage (Hamos et al., 1985; Wilson et al., 1984). This would appear to be a form of simple feedforward inhibition, but a consideration of the postsynaptic receptors involved suggests a more interesting possibility. Release of GABA from the F2 terminal results in inhibition in the relay cell, and the rate of GABA release is strongly determined by the retinal input to the F2 terminal. The retinal input is glutamatergic. As noted, the retinal input to the relay cell acts via ionotropic receptors, but recent evidence (Cox & Sherman, 2000; Govindaiah & Cox, 2004) suggests that the retinal input to the F2 terminal operates mainly via metabotropic glutamate receptors (Figure 10.13). In Figure 10.13, arrows indicate the direction of synaptic function, pointing from presynaptic to postsynaptic elements. The question marks indicate that the presence of the receptor indicated is unclear. Also as noted, metabotropic receptor activation requires higher firing rates in the afferent. The implication here is that, at low firing rates, relay cells will be depolarized via the retinal input, but the feedforward circuit via the F2 terminal will not be activated, and so there will be no feedforward hyperpolarization. As the firing rate in the retinal afferent increases, more and more of the feedforward inhibition will be brought into play to offset the increasing, direct depolarization. There are two possible and related implications to this (Sherman, 2004). First, one function of this circuit is to extend the operating range of the retinogeniculate circuit.

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Drivers and Modulators 213

That is, if the retinal input fires at a high enough frequency to cause the relay cell to fire at its maximum frequency, thereby saturating its response, further increases in retinal firing cannot be represented in the relay. This triadic circuit would ensure that higher firing rates would be needed than without the circuit for the relay cell’s response to saturate. Second, this also means that as the firing rate in the retinal afferent increases, the gain of the retinogeniculate transmission is reduced, and furthermore, because the metabotropic response lasts so long, estimated to be several seconds in this example (Govindaiah & Cox, 2004), this reduced gain will continue for a period even if the retinal input reduces its firing level. Since retinal firing level generally increases monotonically with contrast in the visual stimulus, periods of higher stimulus contrast will produce a short, several-second period of reduced visual sensitivity. This phenomenon, known as contrast gain control, is a central feature of the visual system (Geisler & Albrecht, 1995; Määttänen & Koenderink, 1991; Ohzawa, Sclar, & Freeman, 1982). While there is evidence for contrast gain control having neuronal substrates in the retina and the cortex (Beaudoin, Borghuis, & Demb, 2007; Ohzawa et al., 1982; Bernardete, Kaplan, & Knight, 1992; Truchard, Ohzawa, & Freeman, 2000), this may also occur via thalamic processing (Sherman, 2004). Functioning of the Interneuron The F2 terminals are connected to each other and to the stem dendrite via long, thin processes (typically 10 m in length and 1 m in diameter; see Figure 10.12A). Modeling (Bloomfield & Sherman, 1989) suggests that, if there are not significant active processes in the membranes involved, a significant proviso, then any membrane potential changes generated in the F2 terminal (e.g., from activation of metabotropic glutamate receptors) would effectively decay before reaching the stem dendrite and thus have no discernable effects on other F2 terminals or on the cell body. This modeling further suggests that synaptic inputs that effectively control the axonal output are essentially limited to the soma itself and proximal dendrites. The hypothesis, then, is that the interneuron massively multiplexes, with an axonal output controlled in a conventional means via proximal inputs and dendritic outputs controlled locally and independently via direct inputs onto these F2 terminals (Sherman, 2004). Generality of Circuit Properties While Figures 10.10 through 10.13 refer specifically to the lateral geniculate nucleus, with minor exceptions, the principles they represent seem to be found throughout the thalamus. An important proviso is that these properties have

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been documented regarding thalamic nuclei for which sufficient information is available, but there are some that have not been much studied to date. Most of our knowledge is based on studies of thalamic nuclei that project mainly to layer 4 of the cortex, but some nuclei, such as the midline and interlaminar nuclei (see Figure 10.1) project largely to layer 1, and very little is known of their detailed cell and circuit properties.

DRIVERS AND MODULATORS A glance at Figure 10.10 reveals a common situation in brain circuitry that is often ignored or overlooked. That is, relay cells receive inputs from many different sources, but these do not act as some sort of anatomical democracy to equally affect relay cell responses. In fact, only one of these inputs, the retinal for the lateral geniculate nucleus and equivalent for other nuclei (e.g., lemniscal input for the ventral posterior nucleus and inferior collicular input for the medial geniculate nucleus), represents the actual input to be relayed to the cortex. In the case of the lateral geniculate nucleus, for example, the receptive fields of the relay cells represent the information relayed to the cortex, and these receptive fields have the same center/surround configuration as their retinal inputs but are very different from the orientation and direction selective receptive fields of layer 6 cells, not to mention the lack of clear visual receptive fields for parabrachial inputs (reviewed in Sherman & Guillery, 1996, 2006). The retinal input stands alone in terms of being the main information source to be relayed, but it also differs from nonretinal input along a number of anatomical, physiological, and pharmacological properties, and these differences extend to other thalamic nuclei. This has led to the conclusion that these form two different types of input exemplified by retinal and nonretinal input, and termed drivers (for the retinal equivalent because these provide a uniquely powerful drive of relay cells) and modulators (for the nonretinal equivalent because these chiefly modulate thalamic transmission of driver input; Sherman & Guillery, 1998). Table 10.1 summarizes these differences (reviewed in Sherman & Guillery, 1998, 2004, 2006); the 13 criteria in Table 10.1, in a roughly decreasing order of importance, are: 1. As already suggested for the lateral geniculate nucleus, drivers determine the main receptive field properties of the relay cell; modulator input does not. 2. Also as already noted, drivers activate only ionotropic receptors; modulators activate metabotropic as well as ionotropic receptors.

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214 Thalamocortical Relations TABLE 10.1 Criteria

Drivers and modulators in LGN plus layer 5 drivers

Retinal (Driver)

Layer 5 to HO (Driver) Modulator: Layer 6

Modulator: PBR

Modulator: TRN and Int

1

Determines relay cell receptive field

Determines relay cell receptive field*

Does not determine relay cell receptive field

Does not determine relay cell receptive field

Does not determine relay cell receptive field

2

Activates only ionotropic receptors

Activates only ionotropic receptors

Activates metabotropic receptors

Activates metabotropic receptors

TRN: Activates metabotropic receptors; Int:†

3

Large EPSPs

Large EPSPs

Small EPSPs

†

TRN: small IPSPs; Int:†

4

Large terminals on proximal dendrites

Large terminals on proximal dendrites

Small terminals on distal dendrites

Small terminals on proximal dendrites

Small terminals; TRN: distal; Int: proximal

5

Each terminal forms multiple contacts

Each terminal forms multiple contacts

Each terminal forms single contact

Each terminal forms single contact

Each terminal forms single contact

6

Little convergence on to target

Little convergence on to target*

Much convergence on to target

†

†

7

Very few synapses on to relay cells (5%)

Very few synapses on to relay cells (5%)

Many synapses on to relay cells (30%)

Many synapses on to relay cells (30%)

Many synapses on to relay cells (30%)

8

Often thick axons

Often thick axons

Thin axons

Thin axons

Thin axons

9

Glutamatergic

Glutamatergic

Glutamatergic

Cholinergic

GABAergic †

10

Synapses show pairedpulse depression (high p)

Synapses show paired-pulse depression (high p)*

Synapses show pairedpulse facilitation (low p)

†

11

Well localized, dense terminal arbors

Well localized, dense terminal arbors

Well localized, dense terminal arbors

Sparse terminal arbors

Well localized, dense terminal arbors

12

Branches innervate subtelencephalic targets

Branches innervate subtelencephalic targets

Subcortically known to innervate thalamus only

†

Subcortically known to innervate thalamus only

13

Innervates dorsal thalamus but not TRN

Innervates dorsal halamus but not TRN

Innervates dorsal thalamus and TRN

Innervates dorsal thalamus and TRN

TRN: both; Int: dorsal thalamus only

* †

Very limited data to date. No relevant data available.

3. Drivers evoke very large excitatory postsynaptic potentials; modulators generally evoke much smaller excitatory or inhibitory postsynaptic potentials. 4. Drivers form very large terminals on proximal dendrites; modulators usually form small terminals, and these can be on proximal or distal dendrites. 5. Each driver terminal forms multiple large synapses; each modulator terminal usually forms a single, small synapse. 6. Driver inputs show little convergence, meaning, for example, that one or a small number of retinal axons converge to innervate each geniculate relay cell; where evidence is available, modulator inputs show considerable convergence. 7. Driver inputs produce a small minority (5%) of the synapses onto relays cells; many modulator inputs produce

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8. 9. 10.

11.

larger synaptic numbers (e.g., the local GABAergic, cortical, and parabrachial modulator inputs in Figure 10.10 each produce about 30% to 40% of the synapses). Drivers have thick axons; modulators have thin axons. Drivers are glutamatergic; modulators can use a variety of neurotransmitters. Driver synapses show high release probability and paired-pulse depression; modulator synapses that have been tested so far show the opposite properties of low release probability and paired-pulse facilitation (see Figure 10.8). Driver terminal arbors are well localized with a dense array of terminals; modulator terminal arbors can be either well-localized and dense or relatively poorly localized and sparse.

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First and Higher Order Thalamic Relays 215

12. Branches of driver axons tend to innervate extrathalamic targets as well as the thalamus (e.g., many or all retinogeniculate axons branch and also innervate midbrain targets); those modulator inputs so far tested innervate the thalamus only. 13. Driver inputs innervate relay cells and interneurons in the dorsal thalamus but do not innervate the thalamic reticular nucleus; modulator inputs innervate relay cells, interneurons, and reticular cells. This driver/modulator distinction is clear not just in the lateral geniculate nucleus, but also in other thalamic relays for which sufficient information is available, such as the ventral portion of the medial geniculate nucleus (the primary auditory thalamic relay) and the ventral posterior nucleus (the primary somatosensory thalamic relay). The main point, again, is that not all anatomical pathways are functionally equivalent, and if we are to understand the functional organization of the thalamus and what it is that is being relayed, we must identify and characterize the driver input. This may also apply outside of the thalamus. This point is considered further in the next section.

FIRST AND HIGHER ORDER THALAMIC RELAYS There are two aspects of functional organization of thalamic nuclei that should be considered. One is the actual relay mechanisms, which are related to the cell and circuit properties defined earlier. The other is a determination of what, exactly, is being relayed by a given nucleus. This second functional property seems clearly defined for some nuclei, such as lateral geniculate nucleus, which relays retinal input, but until recently has not been so clear for many other nuclei, such as the pulvinar or medial dorsal nucleus. From the previous section, it should be clear that understanding this second property boils down to identifying the driver input to any particular nucleus. The recent ability to identify driver inputs to many heretofore rather mysterious nuclei, like the pulvinar or medial dorsal nucleus, has led to the further suggestion that, based on the origin of driver inputs, subcortical or cortical, thalamic nuclei can be identified as first order or higher order. Division of Thalamic Relays into First Order and Higher Order This distinction is well characterized by comparing the two main visual thalamic relays, the lateral geniculate nucleus and pulvinar (see Figure 10.14A). These two nuclei have the same general pattern of modulator inputs from local GABAergic neurons, the brain stem, and layer 6 of cortex.

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Data that have accumulated over the past few decades make it clear that the pulvinar receives its driver input from layer 5 of one cortical area and projects it to another (reviewed in Guillery, 1995; Guillery & Sherman, 2002a; Sherman & Guillery, 2006). This means that all thalamic nuclei receive a modulator projection from layer 6 that is mostly feedback but that some in addition receive a driver projection from layer 5 (instead of a subcortical driver, such as from the retina) that is feedforward (Van Horn & Sherman, 2004). As indicated in Figure 10.14A, this feedforward layer 5 input places these higher order thalamic nuclei in the middle of a cortico-thalamo-cortical route of information flow. The main sensory thalamic relays can be divided into first order and higher order. In addition to the lateral geniculate nucleus (first order) and the pulvinar (higher order) for vision, there is the ventral posterior nucleus (first order) and the posterior nucleus (higher order) for somesthesis, and the ventral division of the medial geniculate nucleus (first order) and its dorsal division (higher order) for hearing (reviewed in Guillery, 1995; Guillery & Sherman, 2002a; Sherman & Guillery, 2006). Other thalamic relays have also been so identified: The medial dorsal nucleus is mostly or wholly a higher order relay innervating prefrontal cortex; the ventral anterior and lateral nuclear complex, which innervates motor cortex, includes first order circuits based on cerebellar inputs and higher order circuits based on inputs from layer 5 of the motor cortex; and so on. While not all of the thalamus has been so identified yet as regards this division, it seems clear that most of the thalamic volume is involved in higher order relays. There is an important proviso to this, namely, that while first order nuclei seem fairly purely first order, those designated as higher order may have first order components as well. For instance, while most of the pulvinar receives layer 5 input from various regions of the visual cortex and thus appears to participate as a higher order relay in a corticothalamo-cortical circuit, parts of pulvinar are innervated by the superior colliculus. It is not entirely clear whether this colliculo-pulvinar pathway is a driver or modulator (or something else heretofore not described), but there is some anatomical evidence that at least some of the colliculopulvinar terminalis are quite large, suggesting that they are drivers (Kelly, Li, Carden, & Bickford, 2003). If so, then the pulvinar would represent a mixture of mostly higher order relays with some first order relays. Likewise, the posterior medial nucleus, which receives input from layer 5 of somatosensory cortex, also receives some direct spinothalamic input, but it is not known whether this latter input is a driver or modulator. A similar proviso exists for the dorsal portion of the medial geniculate nucleus, which we defined previously as a higher order nucleus: this receives input from the “belt” region of the inferior colliculus, but again,

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Figure 10.14 Schematic diagrams showing organizational features of first and higher order thalamic nuclei. A, B: Distinction between first order and higher order thalamic nuclei. A first order nucleus (A) represents the first relay of a particular type of subcortical information to a first order or primary cortical area. A higher order nucleus (B) relays information from layer 5 of one cortical area to another. This relay can be between first and higher order cortical areas as shown or between two higher order cortical areas. C: Role of higher order thalamic nuclei in cortico-cortical communication via cortico-thalamo-cortical circuits involving a

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projection from layer 5 of cortex to a higher order thalamic relay to another cortical area. As indicated, the role of the direct corticocortical projections, driver or modulator or other, is unclear. Note in A–C that the driver inputs, both subcortical and from layer 5, are typically from branching axons, the significance of which is elaborated in the text. Abbreviations: FO, first order; HO, higher order; LGN, lateral geniculate nucleus; MGNv or MGNd, ventral or dorsal division of medial geniculate nucleus; PO, posterior nucleus; TRN, thalamic reticular nucleus; VP, ventral posterior nucleus.

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it is not known if this is a driver or modulator. Finally, the medial dorsal nucleus, which has much layer 5 input from the prefrontal cortex also has input from the superior colliculus, and although this latter input is described as if it were a driver (Sommer & Wurtz, 2004a, 2004b), insufficient evidence exists as to its identity. Given the possibility that some thalamic nuclei defined here as higher order may also have first order components operating in parallel, we refer below to first order and higher order thalamic “relays” rather than “nuclei.” Implications for Cortical Functioning The concept of higher order relays offering a cortico-thalamo-cortical route for information processing should be seen in the context of the traditional view best expressed by Van Essen and colleagues (Felleman & Van Essen, 1991; Van Essen, Anderson, & Felleman, 1992), namely, that cortical areas communicate with one another via a plethora of direct cortico-cortical connections. In the visual cortex of rhesus monkeys, for instance, this view states that information is brought to the primary visual cortex by the lateral geniculate nucleus, and once it reaches the cortex, it stays there, being processed by the 30-odd visual areas of the cortex through a series of several parallel feedforward routes involving 4 or 5 hierarchical levels. This scheme also has feedback and lateral connections, and the direction of all of these pathways are defined by criteria dependent mostly on the laminar pattern of the cortico-cortical terminations. The cortico-thalamo-cortical pathways may be seen as a complementary or even alternate route for information processing by the cortex, and in this context means that the thalamus is not there just to bring information from the periphery to the cortex but also serves a central role in ongoing cortical processing. One way to try to gain insight into the functional significance of these various pathways is to recall the example of the lateral geniculate nucleus: not all inputs to relay cells are information bearing (i.e., drivers). It is interesting to speculate that the driver/modulator distinction that is so valuable in elucidating functional pathways through the thalamus might also apply beyond the thalamus, especially in the cortex. If so, then it would be appropriate to consider which of the direct cortico-cortical and indirect cortico-thalamo-cortical pathways, which are all glutamatergic pathways, are drivers or modulators.

the prototypical glutamatergic modulator. By these criteria, evidence exists that thalamocortical synapses, both from first order and higher order relay cells, have driver properties (Lee & Sherman, 2008, 2009). Likewise, the layer 5 corticothalamic synapses have driver properties (Guillery, 1995; Reichova & Sherman, 2004). Thus the cortico-thalamocortical pathways involving higher order thalamic relays appear to be functional information routes. In other words, as shown in Figure 10.14C, first order relays bring information of a certain type (e.g., visual) from a subcortical site (e.g., the retina) to the cortex for the first time, and higher order relays are used to pass on this information up the cortical hierarchy as it is processed. Less is known about the direct cortico-cortical synapses. These pathways have been defined almost strictly by anatomical criteria, and the assertion that all, or at least all of the feedforward cortico-cortical projections, are drivers and not modulators (or perhaps something entirely different) is not founded on empirical data. Evidence is now available that the driver/modulator classification works for at least one specific cortical circuit. Figure 10.15 shows that layer 4 cells in the visual cortex receive geniculate inputs with driver properties: these inputs provide the basic receptive field properties of their target cortical cells, and their synaptic properties, including paired-pulse depression of large EPSPs and lack of metabotropic receptor activation, are also driver characteristics (Lee & Sherman, 2008, 2009). These same layer 4 cells receive another glutamatergic input from branches of layer 6 corticogeniculate axons, and this synaptic input has modulator characteristics, including paired-pulse facilitation of small EPSPs and the presence of metabotropic receptor activation (Lee & Sherman, 2008, 2009). The numbers are also interesting because in both pathways the driver inputs to geniculate relay cells and layer

Drivers and Modulators in Various Thalamic and Cortical Circuits The retinogeniculate synapse can serve as the prototypical glutamatergic driver, and the layer 6 thalamocortical synapse,

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Figure 10.15 Schematic view of selected driver and modulator pathways, the percentages reflecting the relative number of synapses associated with each input.

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4 cortical cells operate over very few (but powerful) synapses, representing only ˜5% of the total (Ahmed, Anderson, Douglas, Martin, & Nelson, 1994; Latawiec, Martin, & Meskenaite, 2000; Van Horn, Eris¸ir, & Sherman, 2000), whereas the glutamatergic modulators inputs operate over many more (but weak) synapses, being about 35% of the input to relay cells and about 45%, to layer 4 cells (Ahmed et al., 1994; Ahmed, Anderson, Martin, & Nelson, 1997; Eris¸ir, Van Horn, & Sherman, 1997; Van Horn et al., 2000). Thus, while the thalamo-cortico-thalamic circuits involving higher order thalamic relays appears to be a functioning circuit to transmit information between cortical areas, it remains to be determined just what functional properties characterize the direct cortico-cortical projections. Nature of Information Relayed by the Thalamus As shown in Figure 10.14, a curious but potentially important fact is that many and perhaps all driver inputs to thalamic relay cells involve branching axons, with one branch innervating relay cells, and the other, extrathalmic subcortical targets (reviewed in Guillery, 2003, 2005; Guillery & Sherman, 2002b; Sherman & Guillery, 2006). Thus, many or all retinogeniculate axons branch to innervate the pretectum and superior collicus (Sur, Esguerra, Garraghty, Kritzer, & Sherman, 1987; Tamamaki, Uhlrich, & Sherman, 1994), and many or all layer 5 corticothalamic axons likewise branch to innervate other brain stem targets, sometimes reaching into the spinal cord (reviewed in Guillery, 2003, 2005; Guillery & Sherman, 2002b; Sherman & Guillery, 2006). Note that, unlike the layer 5 corticothalamic axons, which do not innervate the thalamic reticular nucleus but do branch innervate extrathalamic targets, layer 6 corticothalamic axons innervate the thalamic reticular nucleus but do not extend beyond the thalamus. Guillery (2003, 2005) reviewed these data and pointed out that the major extrathalamic targets of driver afferents to the thalamus appear to be motor targets, as if the messages actually sent to the cortex via the thalamus represent a sort of efference copy of motor commands, starting perhaps as very crude, preliminary commands that are updated and improved on as the message ascends the cortical hierarchy via the ascending cortico-thalamo-cortical circuits. The idea of efference copy is that a command sent to a motor center to initiate movement is copied to other brain areas, such as the cortex, so that these motor commands can be accounted for in the animal’s experience (for details, see Andersen, Snyder, Bradley, & Xing, 1997; Nelson, 1996; Thier & Ilg, 2005; Webb, 2004). Further details of these ideas of efference copy as regards thalamic circuitry can be found in Guillery (2003, 2005).

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Direct Cortico-Cortical versus Cortico-Thalamo-Cortical Circuits Figure 10.16 summarizes the main conclusions to be derived from an understanding of the existence of higher order thalamic relays. Figure 10.16A shows the conventional view. Here, information is relayed from the periphery by appropriate thalamic nuclei (e.g., the lateral geniculate or ventral posterior nuclei) to primary sensory cortex. From there, the information is processed by direct cortico-cortical connections through several hierarchical levels, including sensorimotor areas, and finally reaches motor cortex, from which a motor command is sent out of the cortex. This view has definite entry and exit points for information processing—the primary sensory cortex and motor cortex, respectively. It also has no definite role for most of the thalamus that we have identified as higher order (labeled by question marks).

Figure 10.16 Comparison of conventional view (A) with the alternative view proposed here (B). The question marks in A indicate higher order thalamic relays, for which no specific function is suggested. The question marks in B indicate uncertainty about the role of the direct corticocortical connections (see text for details). Abbreviations: FO, first order; HO, higher order. Further details in text.

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Summary

Figure 10.16B shows the alternative view offered here. By this view, from the beginning, the information from the periphery brought to first order thalamic relays is carried via branching axons that also innervate motor structures, suggesting the possibility that these primary messages relayed to the cortex are also some form of crude motor command. The further processing of information at the cortical level involves cortico-thalamocortical pathways using higher order thalamic relays. Here, too, the corticothalamic limb involves branching axons that also innervate motor structures as if the motor commands are being updated and refined by this cortical processing. There are two other points to notice about Figure 10.16B. First, there is no single entry to or exit from the cortex for information processing. Even the cortex regarded as solely sensory (e.g., primary visual cortex) has a layer 5 output to motor structures: indeed, as far as we know, all cortical areas have such a layer 5 output. Thus, electrical activation of the primary visual cortex in the monkey generates eye movements (Tehovnik, Slocum, & Schiller, 2003). In this regard, the very concept of a cortical area being either sensory or motor needs to be reconsidered. Second, Figure 10.16B raises the question of the direct cortico-cortical projections. Do they function as drivers, modulators, a combination, or something entirely different? One possibility is that a partial combination of panels A and B of Figure 10.16 is closer to the truth. That is, the cortico-cortical and cortico-thalamo-cortical circuits may represent two relatively independent, parallel streams of information processing. Perhaps the larger (anatomically) direct cortico-cortical route may reflect the major bulk of the basic information processing, while the cortico-thalamo-cortical route may be a means of each cortical area informing its upstream partner about motor commands it initiated so that this will not lead to confusion in how the outside world is represented. An example of this is the problem presented by eye movements: such movements create a visual stimulus on the retina of the visual environment moving in the opposite direction, and the the visual cortex must be able to distinguish between such self-generated stimuli and those actually initiated in the visual environment. The cortico-thalamo-cortical pathways may provide just this sort of information. However, the actual role of the various pathways, direct cortico-cortical and cortico-thalamo-cortical, remains unknown, and while there is some experimental evidence that the synapses in the cortico-thalamo-cortical circuit are all drivers, the actual synaptic function of direct cortico-cortical pathways remains to be determined.

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219

SUMMARY There are two main points to be made here. First, the thalamus is not a simple, machine-like relay, but instead its cell and circuit properties control the flow of information to the cortex in dynamic and state-dependent ways. Second, in addition to getting information to the cortex in the first place, the thalamus continues to play a role in processing that information via cortico-thalamo-cortical circuits involving higher order thalamic relays. One of the challenges to understanding how the cortex processes information is to understand the relative function of the direct corticocortical and indirect cortico-thalamo-cortical circuits. Thalamic Relay Functions The fact that relay cells receive 95% of their input from modulatory sources clearly indicates that many thalamic relay functions are under strong dynamic control. We are just beginning to understand this, and much of the control seems to be affected through control of membrane voltage. As is indicated in Figures 10.10 and 10.11, external modulatory inputs (e.g., feedback cortical and brain stem inputs) operate directly and indirectly via local GABAergic neurons to provide push-pull control of the membrane voltage. The example of how this interacts with the voltage—and time-gated Ca2 T channel has been detailed and, in addition to the ubiquitous Na channel underlying the classic action potential, this may be the best understood example of effects of membrane potential on relay cell functions. However, relay cells exhibit other voltage- and time-gated ion channels, including various K channels, other Ca2 and Na channels, and mixed cation channels, and these are understood much less well (for further details, see Huguenard & McCormick, 1994; Sherman & Guillery, 2006). This plus the fact that all of these channels likely have complex interactions with one another indicates that there is still much to learn about the effects of membrane voltage on thalamic relay cell functions. The synaptic triad involving dendritic outputs of interneurons provides another interesting but not well-understood relay function. A hypothesis has been advanced that this circuit helps to maintain a larger dynamic range of input/output relationships for the relay cell that involves controlling gain of the retinogeniculate synapse, a process that could also support the mechanism of contrast gain control. This is yet another idea that requires more data. Significance of Driver and Modulators and Higher Order Thalamic Relays The importance of the driver/modulator distinction in the thalamus seems fairly clear and straightforward. One can

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partly define the function of a thalamic relay by defining its driver input, and thus we can now argue that much of the function of heretofore rather mysterious nuclei like the pulvinar or medial dorsal nucleus is to relay information originating in layer 5 of the cortex. This, in, turn, defines higher order relays. Another more subtle implication of this distinction is related to the concept of labeled lines: Whatever the cause of a particular neuron firing, the result is always interpreted based on the most likely natural cause. For example, pressure applied to the side of the eyeball creates the perception of light and dark spots in the visual field because of the resultant effect on photoreceptors; it is not perceived as increased intraocular pressure. The cortex must always interpret the firing of relay cells as being due to driver input. Thus, for the lateral geniculate nucleus, every relay cell response must be interpreted as being due to retinal input and not cortical or brain stem. There is some evidence in anesthetized cats that practically every action potential seen in a geniculate relay cell can be attributed to a retinal spike (Cleland, Dubin, & Levick, 1971), so this concept is not so difficult to accept. A final and perhaps most profound implication of the driver/modulator concept is that it dictates that, not only are all inputs to a neuron not equal functionally, but in terms of information transfer versus modulation, only a very small subset of inputs to the thalamus are drivers. This distinction seems quite robust in the thalamus and offers a very different way of looking at information transfer. One important issue is the extent to which this distinction, so clear in the thalamus, can be extrapolated elsewhere, such as the cortex. Most cortico-cortical pathways, especially between areas, are glutamatergic, and it may be significant that metabotropic glutamate receptors are common in the cortex (Caleo et al., 2007). This means that some as yet undetermined subset of these pathways activate metabotropic glutamate receptors (Lee & Sherman, 2008, 2009), and as noted, this seems an important property of modulators. Thus, it seems plausible that many cortical pathways are modulatory. Nonetheless, such is our general ignorance of the functional properties of cortical circuitry and particularly of cortico-cortical projections between areas, that these pathways may require a classification scheme completely different from or in addition to the driver/modulator categories. First Order and Higher Order Thalamic Relays The major implication of the division of the thalamus into first and higher order relays is that, via the latter, corticocortical communication may depend heavily on the thalamus, a thalamic function previously unknown. It is possible that all cortico-cortical communication is via corticothalamo-cortical circuits and that all direct cortico-cortical

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pathways are modulatory. If so, this would mean that all information entering a cortical area, whether from the periphery (e.g., retina) or another cortical area, must pass through the thalamus. In other words, retinal information does not innervate the cortex directly but passes through a thalamic relay (i.e., the lateral geniculate nucleus) and this applies to cortico-cortical communication as well. A more plausible implication has been suggested earlier. That is, while some undetermined fraction of corticocortical pathways are not information bearing, many are, and the direct cortico-cortical and indirect cortico-thalamocortical circuits represent two parallel paths of information processing. More data are needed to sort this out. Nature of Driver Inputs to Thalamic Relay Cells A curious fact about many, and perhaps all, of the subcortical and layer 5 driver inputs to thalamic relay cells is that they are comprised of branching axons, with the extrathalamic branch innervating motor centers (see Figure 10.14; Guillery, 2003). The significance of this has been discussed in some detail by Guillery (2003, 2005) and will not be repeated here. Nonetheless, this anatomical fact does suggest that much of the evolution of the thalamus and the cortex has involved getting information to the cortex about motor commands and their updating. The thalamus has come a long way from when it was seen as an uninteresting structure whose only role was to relay information simply and consistently from the periphery to the cortex. We now understand that these relay functions are quite complicated and that the thalamus continues to play a role beyond simply getting information to the cortex from the periphery. Nonetheless, we are just beginning to understand these broader and more interesting functions of the thalamus. The challenge is to continue along these lines with more research focused on these subjects.

REFERENCES Abbott, L. F., Varela, J. A., Sen, K., & Nelson, S. B. (1997). Synaptic depression and cortical gain control. Science, 275, 220–224. Ahmed, B., Anderson, J. C., Douglas, R. J., Martin, K. A. C., & Nelson, J. C. (1994). Polyneuronal innervation of spiny stellate neurons in cat visual cortex. Journal of Comparative Neurology, 341, 39–49. Ahmed, B., Anderson, J. C., Martin, K. A. C., & Nelson, J. C. (1997). Map of the synapses onto layer 4 basket cells of the primary visual cortex of the cat. Journal of Comparative Neurology, 380, 230–242. Alitto, H. J., Weyand, T. G., & Usrey, W. M. (2005). Distinct properties of stimulus-evoked bursts in the lateral geniculate nucleus. Journal of Neuroscience, 25, 514–523. Andersen, R. A., Snyder, L. H., Bradley, D. C., & Xing, J. (1997). Multimodal representation of space in the posterior parietal cortex and its use in planning movements. Annual Reviews of Neuroscience, 20, 303–330.

8/17/09 2:06:22 PM

References 221 Arcelli, P., Frassoni, C., Regondi, M. C., De Biasi, S., & Spreafico, R. (1997). GABAergic neurons in mammalian thalamus: A marker of thalamic complexity? Brain Research Bulletin, 42, 27–37.

Gil, Z., Connors, B. W., & Amitai, Y. (1999). Efficacy of thalamocortical and intracortical synaptic connections: Quanta, innervation, and reliability. Neuron, 23, 385–397.

Beaudoin, D. L., Borghuis, B. G., & Demb, J. B. (2007). Cellular basis for contrast gain control over the receptive field center of mammalian retinal ganglion cells. Journal of Neuroscience, 27, 2636–2645.

Govindaiah, Cox, C. L. (2004). Synaptic activation of metabotropic glutamate receptors regulates dendritic outputs of thalamic interneurons. Neuron, 41, 611–623.

Bernardete, E. A., Kaplan, E., & Knight, B. W. (1992). Contrast gain control in the primate retina: P cells are not X-like, some M cells are. Visual Neuroscience, 8, 483–486.

Guido, W., Lu, S.-M., & Sherman, S. M. (1992). Relative contributions of burst and tonic responses to the receptive field properties of lateral geniculate neurons in the cat. Journal of Neurophysiology, 68, 2199–2211.

Bloomfield, S. A., & Sherman, S. M. (1989). Dendritic current flow in relay cells and interneurons of the cat’s lateral geniculate nucleus. Proceedings of the National Academy of Sciences, USA, 86, 3911–3914. Brown, D. A., Abogadie, F. C., Allen, T. G., Buckley, N. J., Caulfield, M. P., Delmas, P., et al. (1997). Muscarinic mechanisms in nerve cells. Life Sciences, 60, 1137–1144. Caleo, M., Restani, L., Gianfranceschi, L., Costantin, L., Rossi, C., Rossetto, O., et al. (2007). Transient synaptic silencing of developing striate cortex has persistent effects on visual function and plasticity. J Neurosci 27[17], 4530–4540. 2007. Casagrande, V. A. and X. Xu. (1994). Parallel visual pathways: A comparative perspective. In L. M. Chalupa & J. S. Werner (Eds.), Visual neurosciences (pp. 494–506). Cambridge, MA: MIT Press. Castro-Alamancos, M. A., & Connors, B. W. (1997). Thalamocortical synapses. Progress in Neurobiology, 51, 581–606. Chance, F. S., Abbott, L. F., & Reyes, A. (2002). Gain modulation from background synaptic input. Neuron, 35, 773–782. Chung, S., Li, X., & Nelson, S. B. (2002). Short-term depression at thalamocortical synapses contributes to rapid adaptation of cortical sensory responses in vivo. Neuron, 34, 437–446. Cleland, B. G., Dubin, M. W., & Levick, W. R. (1971). Sustained and transient neurones in the cat’s retina and lateral geniculate nucleus. Journal of Physiology, 217, 473–496. Conn, P. J., & Pin, J. P. (1997). Pharmacology and functions of metabotropic glutamate receptors. Annual Review of Pharmacology and Toxicology, 37, 205–237. Cox, C. L., & Sherman, S. M. (2000). Control of dendritic outputs of inhibitory interneurons in the lateral geniculate nucleus. Neuron, 27, 597–610. Cucchiaro, J. B., Uhlrich, D. J., & Sherman, S. M. (1991). Electronmicroscopic analysis of synaptic input from the perigeniculate nucleus to the A-laminae of the lateral geniculate nucelus in cats. Journal of Comparative Neurology, 310, 316–336. Datta, S., & Siwek, D. F. (2002). Single cell activity patterns of pedunculopontine tegmentum neurons across the sleep-wake cycle in the freely moving rats. Journal of Neuroscience Research, 70, 611–621. Denning, K. S., & Reinagel, P. (2005). Visual control of burst priming in the anesthetized lateral geniculate nucleus. Journal of Neuroscience, 25, 3531–3538. Eri¸sir, A., Van Horn, S. C., & Sherman, S. M. (1997). Relative numbers of cortical and brainstem inputs to the lateral geniculate nucleus. Proceedings of the National Academy of Sciences, USA, 94, 1517–1520. Famiglietti, E. V. J., & Peters, A. (1972). The synaptic glomerulus and the intrinsic neuron in the dorsal lateral geniculate nucleus of the cat. Journal of Comparative Neurology, 144, 285–334. Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1–47. Friedlander, M. J., Lin, C.-S., Stanford, L. R., & Sherman, S. M. (1981). Morphology of functionally identified neurons in lateral geniculate nucleus of the cat. Journal of Neurophysiology, 46, 80–129. Geisler, W. S., & Albrecht, D. G. (1995). Bayesian analysis of identification performance in monkey visual cortex: Nonlinear mechanisms and stimulus certainty. Vision Research, 35, 2723–2730.

c10.indd Sec2:221

Guido, W., Lu, S.-M., Vaughan, J. W., Godwin, D. W., & Sherman, S. M. (1995). Receiver operating characteristic (ROC) analysis of neurons in the cat’s lateral geniculate nucleus during tonic and burst response mode. Visual Neuroscience, 12, 723–741. Guido, W., & Weyand, T. (1995). Burst responses in thalamic relay cells of the awake behaving cat. Journal of Neurophysiology, 74, 1782–1786. Guillery, R. W. (1966). A study of Golgi preparations from the dorsal lateral geniculate nucleus of the adult cat. Journal of Comparative Neurology, 128, 21–50. Guillery, R. W. (1969). The organization of synaptic interconnections in the laminae of the dorsal lateral geniculate nucleus of the cat. Z Zellforsch, 96, 1–38. Guillery, R. W. (1995). Anatomical evidence concerning the role of the thalamus in corticocortical communication: A brief review. American Journal of Anatomy, 187, 583–592. Guillery, R. W. (2003). Branching thalamic afferents link action and perception. Journal of Neurophysiology, 90, 539–548. Guillery, R. W. (2005). Anatomical pathways that link action to perception. Progress in Brain Research, 149, 235–256. Guillery, R. W., & Sherman, S. M. (2002a) Thalamic relay functions and their role in corticocortical communication: Generalizations from the visual system. Neuron, 33, 163–175. Guillery, R. W., & Sherman, S. M. (2002b) The thalamus as a monitor of motor outputs. Philosophical Transactions of the Royal Society of London. Series B, 357, 1809–1821. Hamos, J. E., Van Horn, S. C., Raczkowski, D., Uhlrich, D. J., & Sherman, S. M. (1985, October 17). Synaptic connectivity of a local circuit neurone in lateral geniculate nucleus of the cat. Nature, 317, 618–621. Hendry, S. H. C., & Reid, R. C. (2000). The koniocellular pathway in primate vision. Annual Review of Neuroscience, 23, 127–153. Huguenard, J. R., & McCormick, D. A. (1994). Electrophysiology of the neuron. New York: Oxford University Press. Jahnsen, H., & Llinás, R. (1984a). Electrophysiological properties of guinea-pig thalamic neurones: An in vitro study. Journal of Physiology, 349, 205–226. Jahnsen, H., & Llinás, R. (1984b). Ionic basis for the electroresponsiveness and oscillatory properties of guinea-pig thalamic neurones in vitro. Journal of Physiology, 349, 227–247. Jones, E. G. (2007). The thalamus (2nd ed.). Cambridge: Cambridge University Press. Kelly, L. R., Li, J., Carden, W. B., & Bickford, M. E. (2003). Ultrastructure and synaptic targets of tectothalamic terminals in the cat lateral posterior nucleus. Journal of Comparative Neurology, 464, 472–486. Lam, Y.-W., Nelson, C. S., & Sherman, S. M. (2006). Mapping of the functional interconnections between reticular neurons using photostimulation. Journal of Neurophysiology. 96, 2593–2600. Landisman, C. E., Long, M. A., Beierlein, M., Deans, M. R., Paul, D. L., & Connors, B. W. (2002). Electrical synapses in the thalamic reticular nucleus. Journal of Neuroscience, 22, 1002–1009.

8/17/09 2:06:23 PM

222 Thalamocortical Relations Latawiec, D., Martin, K. A. C., & Meskenaite, V. (2000). Termination of the geniculocortical projection in the striate cortex of macaque monkey: A quantitative immunoelectron microscopic study. Journal of Comparative Neurology, 419, 306–319.

Sherman, S. M. (1996). Dual response modes in lateral geniculate neurons: Mechanisms and functions. Visual Neuroscience, 13, 205–213. Sherman, S. M. (2001). Tonic and burst firing: Dual modes of thalamocortical relay. Trends in Neuroscience, 24, 122–126.

Lee, C. C., & Sherman, S. M. (2008). Synaptic properties of thalamic and intracortical inputs to layer 4 of the first- and higher-order cortical areas in the auditory and somatosensory systems. J. Neurophysiol., 100, 317–326.

Sherman, S. M. (2004). Interneurons and triadic circuitry of the thalamus. Trends in Neuroscience, 27, 670–675.

Lee, C. C., & Sherman, S. M. (2009). Modulator property of the intrinsic cortical projection from layer 6 to layer 4. Front. Syst. Neurosci. 3:3. doi: 10.3389/neuro.06.003.2009.

Sherman, S. M., & Guillery, R. W. (1996). The functional organization of thalamocortical relays. Journal of Neurophysiology, 76, 1367–1395.

Lesica, N. A., & Stanley, G. B. (2004). Encoding of natural scene movies by tonic and burst spikes in the lateral geniculate nucleus. Journal of Neuroscience, 24, 10731–10740.

Sherman, S. M., & Guillery, R. W. (1998). On the actions that one nerve cell can have on another: Distinguishing “drivers” from “modulators.” Proceedings of the National Academy of Sciences, USA, 95, 7121–7126.

Li, J. L., Bickford, M. E., & Guido, W. (2003). Distinct firing properties of higher order thalamic relay neurons. Journal of Neurophysiology, 90, 291–299.

Sherman, S. M., & Guillery, R. W. (2002). The role of thalamus in the flow of information to cortex. Philosophical Transactions of the Royal Society of London [Biol] 357, 1695–1708.

Lujan, R., Nusser, Z., Roberts, J. D., Shigemoto, R., & Somogyi, P. (1996). Perisynaptic location of metabotropic glutamate receptors mGluR1 and mGluR5 on dendrites and dendritic spines in the rat hippocampus. Europena Journal of Neuroscience, 8, 1488–1500.

Sherman, S. M., & Guillery, R. W. (2004). Thalamus. In G. M. Shepherd (Ed.), Synaptic organization of the brain (pp. 311–359). Oxford University Press.

Määttänen, L. M., & Koenderink, J. J. (1991). Contrast adaptation and contrast gain control. Experimental Brain Research, 87, 205–212. Massaux, A., & Edeline, J. M. (2003). Bursts in the medial geniculate body: A comparison between anesthetized and unanesthetized states in guinea pig. Experimental Brain Research, 153, 573–578. McCormick, D. A. (1992). Neurotransmitter actions in the thalamus and cerebral cortex and their role in neuromodulation of thalamocortical activity. Progress in Neurobiology, 39, 337–388. Mott, D. D., & Lewis, D. V. (1991, June 21). Acilitation of the induction of long-term potentiation by GABAB receptors. Science, 252, 1718–1720. Nelson, R. J. (1996). Interactions between motor commands and somatic perception in sensorimotor cortex. Current Opinion in Neurobiology, 6, 801–810. Nicoll, R. A., Malenka, R. C., & Kauer, J. A. (1990). Functional comparison of neurotransmitter receptor subtypes in mammalian central nervous system. Physiology Review, 70, 513–565. Ohzawa, I., Sclar, G., & Freeman, R. D. (1982, July 15). Contrast gain control in the cat visual cortex. Nature, 298, 266–268. Pin, J. P., & Duvoisin, R. (1995). The metabotropic glutamate receptors: Structure and functions. Neuropharmacology, 34, 1–26. Ralston, H. J. (1971, April 30). Evidence for presynaptic dendrites and a proposal for their mechanism of action. Nature, 230, 585–587. Ramcharan, E. J., Gnadt, J. W., & Sherman, S. M. (2000). Burst and tonic firing in thalamic cells of unanesthetized, behaving monkeys. Visual Neuroscience, 17, 55–62. Recasens, M., & Vignes, M. (1995). Excitatory amino acid metabotropic receptor subtypes and calcium regulation. Annals of the New York Academy of Sciences, 757, 418–429. Reichova, I., & Sherman, S. M. (2004). Somatosensory corticothalamic projections: Distinguishing drivers from modulators. Journal of Neurophysiology, 92, 2185–2197. Sanchez-Vives, M. V., Bal, T., & McCormick, D. A. (1997). Inhibitory interactions between perigeniculate GABAergic neurons. Journal of Neuroscience, 17, 8894–8908. Sharma, J., Angelucci, A., & Sur, M. (2000, April 20). Induction of visual orientation modules in auditory cortex. Nature, 404, 841–847. Sherman, S. M. (1985). Functional organization of the W-,X-, and Y-cell pathways in the cat: A review and hypothesis. In J. M. Sprague & A. N. Epstein (Eds.), Progress in psychobiology and physiological psychology (Vol. 11, pp. 233–314). Orlando, FL: Academic Press.

c10.indd Sec2:222

Sherman, S. M., & Guillery, R. W. (2006). Exploring the thalamus and its role in cortical function. Cambridge, MA: MIT Press. Sommer, M. A., & Wurtz, R. H. (2004a). What the brain stem tells the frontal cortex: Pt. I. Oculomotor signals sent from superior colliculus to frontal eye field via mediodorsal thalamus. Journal of Neurophysiology, 91, 1381–1402. Sommer, M. A., & Wurtz, R. H. (2004b). What the brain stem tells the frontal cortex: Pt. II. Role of the SC-MD-FEF pathway in corollary discharge. Journal of Neurophysiology, 91, 1403–1423. Somogyi, P., Tamas, G., Lujan, R., & Buhl, E. H. (1998). Salient features of synaptic organisation in the cerebral cortex. Brain Research. Brain Research Reviews, 26, 113–135. Stanford, L. R., Friedlander, M. J., & Sherman, S. M. (1983). Morphological and physiological properties of geniculate W-cells of the cat: A comparison with X- and Y-cells. Journal of Neurophysiology, 50, 582–608. Steriade, M., & Contreras, D. (1995). Relations between cortical and thalamic cellular events during transition from sleep patterns to paroxysmal activity. Journal of Neuroscience, 15, 623–642. Sur, M., Esguerra, M., Garraghty, P. E., Kritzer, M. F., & Sherman, S. M. (1987). Morphology of physiologically identified retinogeniculate X- and Y-axons in the cat. Journal of Neurophysiology, 58, 1–32. Swadlow, H. A., & Gusev, A. G. (2001). The impact of ‘bursting’ thalamic impulses at a neocortical synapse. Nature of Neuroscience, 4, 402–408. Swadlow, H. A., Gusev, A. G., & Bezdudnaya, T. (2002). Activation of a cortical column by a thalamocortical impulse. Journal of Neuroscience, 22, 7766–7773. Tamamaki, N., Uhlrich, D. J., & Sherman, S. M. (1994). Morphology of physiologically identified retinal, X., & Y axons in the cat’s thalamus and midbrain as revealed by intra-axonal injection of biocytin. Journal of Comparative Neurology, 354, 583–607. Tehovnik, E. J., Slocum, W. M., & Schiller, P. H. (2003). Saccadic eye movements evoked by microstimulation of striate cortex. European Journal of Neuroscience, 17, 870–878. Thier, P., & Ilg, U. J. (2005). The neural basis of smooth-pursuit eye movements. Current Opinion in Neurobiology, 15, 645–652. Truchard, A. M., Ohzawa, I., & Freeman, R. D. (2000). Contrast gain control in the visual cortex: Monocular versus binocular mechanisms. Journal of Neuroscience, 20, 3017–3032.

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References 223 Uhlrich, D. J., Cucchiaro, J. B., Humphrey, A. L., & Sherman, S. M. (1991). Morphology and axonal projection patterns of individual neurons in the cat perigeniculate nucleus. Journal of Neurophysiology, 65, 1528–1541.

Wang, X., Wei, Y., Vaingankar, V., Wang, Q., Koepsell, K., Sommer, F. T., et al. (2007). Feedforward excitation and inhibition evoke dual modes of firing in the cat’s visual thalamus during naturalistic viewing. Neuron, 55, 465–478.

Van Essen, D. C., Anderson, C. H., & Felleman, D. J. (1992, January 24). Information processing in the primate visual system: An integrated systems perspective. Science, 255, 419–423.

Webb B. (2004). Neural mechanisms for prediction: Do insects have forward models? Trends in Neuroscience, 27, 278–282.

Van Horn, S. C., Eri¸sir, A., & Sherman, S. M. (2000). The relative distribution of synapses in the A-laminae of the lateral geniculate nucleus of the cat. Journal of Comparative Neurology, 416, 509–520. Van Horn, S. C., & Sherman, S. M. (2004). Differences in projection patterns between large and small corticothalamic terminals. Journal of Comparative Neurology, 475, 406–415. Wang, W., Jones, H. E., Andolina, I. M., Salt, T. E., & Sillito, A. M. (2006). Functional alignment of feedback effects from visual cortex to thalamus. Nature of Neuroscience, 9, 1330–1336.

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Wilson, J. R., Friedlander, M. J., & Sherman, S. M. (1984). Fine structural morphology of identified X- and Y-cells in the cat’s lateral geniculate nucleus. Proceedings of the Royal Society in London, Series B, 221, 411–436. Yen, C.-T., & Jones, E. G. (1983). Intracellular staining of physiologically identified neurons and axons in the somatosensory thalamus of the cat. Brain Research, 280, 148–154. Zhan, X. J., Cox, C. L., Rinzel, J., & Sherman, S. M. (1999). Current clamp and modeling studies of low threshold calcium spikes in cells of the cat’s lateral geniculate nucleus. Journal of Neurophysiology, 81, 2360–2373.

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Chapter 11

Vision DALE PURVES

The purpose of visual percepts is to generate successful behavior based on the information in retinal stimuli. When photoreceptors capture a sufficient number of photons, a series of processing steps is initiated in retinal circuitry; the outcome is then carried centrally by action potentials in the optic nerve to further processing stations in the thalamus and primary visual cortex, eventually reaching the visual association cortices. Perception—defined as what we actually see—is the result of this processing. Despite enormous progress in understanding the organization of visual circuitry over the past 50 years, how this circuitry generates percepts is not yet understood. The focus of this chapter is on perception as such, with the expectation that what we see can tell us much about what the underlying circuitry is seeking to accomplish. To help readers not familiar with the human visual system follow the relevant evidence, the chapter begins with a brief account of visual stimuli, how transduction by photoreceptors initiates the neural activity that leads to perception, and an overview of the subsequent visual pathways. The bulk of the chapter, however, discusses how and why we see the basic perceptual qualities that characterize visual experience: brightness, color, form, depth, and motion. Since perception is, by definition, a conscious phenomenon that humans report far more readily than experimental animals, most of the evidence derives from human studies.

ORGANIZATION OF THE VISUAL SYSTEM The primary visual pathway refers to the major route from retina to cortex that conveys the information in light stimuli that ultimately leads to perception (Figure 11.1; as indicated, centrally directed retinal pathways serve other functions as well). The primary visual pathway begins with the transduction of light energy by two types of retinal receptors, rods and cones, that define two overlapping but largely distinct light-level processing systems. The visual processing that rods initiate is primarily concerned

with perception at very low light levels, whereas cones only respond to greater light intensities and are responsible for the detail and color qualities that we normally think of as defining visual percepts. Subsequent to the retinal processing, the information arising from both rods and cones converges onto the retinal ganglion cells whose axons leave the retina in the optic nerve (see Figure 11.1). The major target of the retinal ganglion cells is the dorsal lateral geniculate nucleus in the thalamus, which comprises two magnocellular layers (so named because of the relatively large neurons in these layers) and four parvocellular layers containing smaller neurons. The distinct populations of neurons in these layers reflect substantially different functions that have perceptual consequences (Livingstone & Hubel, 1988). The magnocelluar layers and the larger retinal ganglion cells that innervate them tend to process information about changes in the stimulus, and thus perceptions of motion. In contrast, the parvocellular layers tend to process information about spatial detail and color. Neurons in both the magnocellular and parvocellular layers of the thalamus are also extensively innervated by axons descending from the cortex and other brain regions. Although the function of this descending innervation is not known, the geniculate nucleus is clearly a station for processing visual information and not simply passing it along to the cortex. The lateral geniculate neurons project in turn to the primary visual cortex, which is usually referred to as V1. Finally, the output neurons in the primary cortex project to additional visual cortical areas in the occipital, parietal, and temporal lobes. Because of the increasing integration of information from other brain regions in the visual cortical regions adjacent to V1, most investigators consider the primary visual cortex to be the terminus of the primary visual pathway. The higher-order cortical processing areas adjacent to V1 (Figure 11.2) are called cortical association areas; with respect to vision, these regions are called the extrastriate visual areas (V1 is also called the striate cortex because of an anatomically distinct layer that effectively creates

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Primary visual cortex Figure 11.1 (Figure C.4 in color section) The primary visual pathway (solid lines). The route that carries information centrally from the retina to those regions of the brain especially pertinent to what we see comprises the optic nerves, the optic tracts, the dorsal lateral geniculate nuclei in the thalamus, the optic radiations and the primary (or striate) and secondary (or extrastriate) visual cortices in the occipital lobes. The partial crossing of the

optic nerve axons at the optic chiasm means that the left occipital lobe processes information arising from the right visual field , and conversely (note the view of the brain in the diagram is from the ventral aspect). Other central pathways to targets in the brainstem (dotted lines) determine pupil diameter as a function of retinal light levels, help to organize and effect eye movements, and influence circadian rhythms. (From Purves & Lotto, 2003)

a striped appearance of cortical layer 4). Both clinical and experimental evidence has shown that these extrastriate regions of association cortex tend to process one or more of the qualities that define visual perception more extensively than others. Thus in humans and nonhuman primates, the area called V4 is especially important in processing information pertinent to color vision, and areas MT (for middle temporal) and MST (for middle superior temporal) are especially important for the generation of motion percepts (this

nomenclature derives from studies in nonhuman primates; the less distinct areas in humans is often referred to as MT⫹). A further general rule is that the flow of information in the extrastriate cortical areas is organized into two relatively separate information streams that eventually feed into areas in areas of association cortex in the temporal and parietal lobes, respectively (Figure 11.3; Ungerleider & Haxby, 1994; Ungerleider & Mishkin, 1982; see also Milner & Goodale, 1995). One of these loosely defined pathways,

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Figure 11.2 (Figure C.5 in color section) Higher order visual areas in the human brain determined by functional magnetic resonance imaging in normal human subjects. A, B: Lateral and medial surface views of the brain. The primary visual cortex is indicated in green; the additional colored areas are the extrastriate areas. C: To better see the relation of these areas, the cortex has been computationally “inflated” to flatten its highly convoluted surface. V1-V4 and MT⫹(V5) are indicated; VP is a ventral posterior area whose function is not well understood. (After Sereno et al., 1995)

of motion, orienting attention, and positional relationships between objects in the visual scene. Where? (Analysis of motion and spatial relations) 19 18 17 What? (Analysis of form and color)

Figure 11.3 (Figure C.6 in color section) The dorsal and ventral visual streams. The differential flow of information along these pathways has been documented in humans with functional magnetic resonance imaging (fMRI) and other methods. The ventral pathway conveys information to regions of the lateral and inferior temporal lobes and is especially important in the recognition of objects. The dorsal path runs to the adjacent areas of the parietal lobe and is more concerned with perception of the location of objects and orienting attention to them. These pathways are therefore referred to as the “where” and “what” streams, respectively. (From Blumenfeld, 2004)

called the ventral stream, leads from the striate cortex to the temporal lobe. The information carried in this pathway appears to be concerned with high-resolution vision and object recognition, a conclusion that accords with other evidence about functions of the temporal lobe. The dorsal stream leads from striate cortex and visually relevant areas into the parietal lobe. This pathway appears to be primarily responsible for spatial aspects of vision, such as the analysis

VISUAL SYSTEM FUNCTION To understand visual perception, some basic facts about visual function are necessary, beginning with the nature of visual stimuli. The photons to which the human eye is sensitive comprise only a miniscule fraction of the electromagnetic spectrum, namely photons with wavelengths of ⬃400 to 700 nm. Light is therefore defined by the fact that the human visual system has evolved to process this particular spectral range, presumably driven by the spectrum of sunlight at the surface of the earth, which has a strong peak at about 550 nm. Visual percepts can be elicited by amounts of light ranging from a few tens of photons/mm2 at the retinal surface to values a billion or more times greater. This enormous range of responsiveness allows the visual system to generate percepts in widely varying circumstances, in starlight as well as daylight. To function over such a broad range, the visual system— and other sensory systems—continually resets its sensitivity according to ambient conditions. The primary purpose of this adaptation is to ensure that, despite the limitations of nerve cells, useful signaling can occur with maximum efficiency over the full range of pertinent environmental conditions. The rate of neuronal firing conveys information about stimulus intensity (the more action potentials per unit time, the more intense the stimulus), and the maximum rate of signaling is only a few hundred action potentials per second. This biological range is obviously inadequate to

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Visual System Function

generate finely graded percepts that convey brightness values in response to a range of light intensities that spans 10 or more orders of magnitude. Thus, the sensitivity of the system is continually adjusted to match different levels of light intensity in the environment. Visual acuity is a second aspect of visual function pertinent to many aspects of perception. Acuity refers to the fineness of discrimination, as in distinguishing two points from one another in a visual scene (as in the standard test of acuity used by optometrists). Although the visual world seems to be seen quite clearly, visual acuity in humans actually falls off rapidly as a function of eccentricity. Consequently, vision outside the central few degrees of the visual field is extremely poor, and without a normally functioning central retina, vision operates at levels that qualify as legal blindness. Frequently moving the direction of gaze to different positions in visual space is essential for seeing objects in detail, which is what humans do during the normal inspection of a scene. On average, such eye movements, called saccades, occur 3 to 4 times a second. The reason for this difference in acuity according to where an image falls on the retina is largely due to the distribution of photoreceptors and the organization of the output to other retinal neurons (Figure 11.4). Cones, which as noted are responsible for detailed vision in daylight, greatly predominate in the central region of the retina, being most dense in a specialized region called the fovea. The fovea corresponds to the line of sight and the couple of degrees around it. The prevalence of cones falls off sharply in all directions as a function of distance from this locus; as a result, high acuity vision is limited to the fovea and its immediate surroundings. In contrast, rods are sparse in the fovea and absent altogether in the middle of it. The rod system has little acuity

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because many rods converge on the next level of neurons entailed in retinal processing; this arrangement enhances sensitivity, but at the expense of acuity. In consequence, sensitivity to a dim stimulus such as a spot of light is greater off the line of sight because of the paucity of rods in the fovea and their preponderance a few degrees away, even though acuity is less at this eccentricity. The reason people are generally unaware of the poor acuity of eccentric vision is simply because whenever it is important to see something clearly, the object of interest is brought onto the fovea by movements of the eyes, head, or body. A third basic aspect of visual physiology pertinent to perception is the receptive field characteristics of visual neurons. The receptive field of any sensory neuron is defined as the region of the receptor surface that, when stimulated, elicits a response in the neuron being examined. The receptive fields of visual neurons, whether in the retina, thalamus, or cortex, are typically defined by the region of visual space that corresponds to the stimulated region of the retinal surface (Figure 11.5). In addition to responsiveness measured in spatial terms, visual neurons are also sensitive to many other characteristics of a stimulus. In Figure 11.5, for example, the neuron illustrated responds vigorously to a moving bar oriented at some angles but not others. By testing a neuron’s sensitivity to a range of differently oriented stimuli, a tuning curve can be defined that indicates the maximal responsiveness of the cell to a given feature, orientation in this example (see Hubel & Wiesel, 2005, for a detailed account of this seminal work). The receptive fields of cortical neurons serving central vision in the primary visual cortex generally measure less than a degree of visual angle across, as do the receptive fields of the corresponding retinal ganglion cells and lateral

Figure 11.4 (Figure C.7 in color section) The acuity of the visual systems is determined primarily by the distribution of retinal receptors. Graph showing the density of rods and cones as a function of distance from the fovea. The poor resolution of vision a few degrees off the line of sight is a result of the relatively paucity of cones at eccentricities greater than a few degrees, the line of sight corresponding to the center of the cone rich fovea. (From Purves & Lotto, 2003)

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Figure 11.5 Example of the receptive field of a neuron recorded in the primary visual cortex of an experimental animal. As different stimuli are presented on a screen in front of the anesthetized animal, the neuron being recorded from fires in a variable way that defines both sensitivity to the stimulus location and the specific features to which it responds. In this instance, the neuron is especially sensitive to lines with a particular orientation. (After Howe, Purve, et al., 2005)

geniculate neurons. Even for cells serving the peripheral vision, the receptive fields in primary visual cortex measure only a few degrees. In extrastriate cortical areas, however, receptive fields often cover a substantial fraction of the entire visual field (which extends about 180˚ horizontally and 130˚ vertically). The location of retinal activity—and the corresponding topographical relationships in the primary visual pathway assumed to underlie the sense of where an object is in visual field—cannot be conveyed by neurons that respond to stimuli anywhere in such a large region of space. This relative mooting of retinal topography in higher-order visual cortical areas presents a problem for any rationalization of vision in terms of images and percepts based on image representation in the brain, a problem discussed later in the chapter. Finally, the organization of the visual system is hierarchical in the sense that lower-order stations lead anatomically and functionally to higher-order ones, albeit with much modulation and feedback at each stage. At each of the “lower” stations in the primary visual pathway, the receptive field characteristics of the relevant neurons can be understood reasonably well in terms of the cells that provide their input. Thus, the responses of retinal ganglion cells can be rationalized on the basis of the rods and cones that supply information to them via the bipolar and other retinal cells, geniculate cell responses can be understood in terms of the ganglion cells that innervate them, and that the responses of at least some classes of “lower-order” visual

cortical neurons make sense in terms of their geniculate inputs (reviewed in Hubel, 1988; Hubel & Wiesel, 2005). Beyond these initial processing levels, however, rationalizing the organization of the visual system in hierarchical terms—that is, in terms of lower-order neurons shaping the response properties of higher-order neurons—becomes difficult since the cells in question are increasingly driven by a variety of other higher-order neurons, including many conveying information that is not primarily visual. The problem with the idea of a hierarchy of visual processing, however, is not simply that it is hard to explain higher-order receptive field properties in terms of lowerorder ones. A more worrisome aspect of a visual hierarchy is the implication that there is some place in the brain where the various qualities processed in the primary and more specialized areas of cortex are brought together for purposes of perceiving them. This way of thinking has given rise to the idea that the activity of defined populations of visual neurons generates percepts by representing the various features of the world we actually see. This scenario, however, is not supported by present evidence about visual perception, and testing possible alternatives is the subject of much current research, as the following sections indicate.

BRIGHTNESS A good place to begin any consideration of visual percepts and how they are related to the structure and function of the visual system is the perception of light and dark elicited by different stimulus intensities. Light intensity is measured physically as luminance, whereas the ensuing sensory quality called is brightness. (Technically, brightness refers to the appearance of a light source, such as a light bulb, and lightness to the appearance of a surface such as a piece of paper; for present purposes, brightness is used in its more general inclusive sense.) Not the least of the reasons for starting with brightness is that such percepts are arguably the most fundamental visual qualities that humans see. Human vision cannot occur without this perceptual quality, whereas other qualities (color, for example) are expendable (some highly visual animals have well-developed color vision while others don’t). Psychophysical Measurements of Brightness Like all percepts, brightness is subjective and can only be evaluated indirectly. A conceptually straightforward but technically difficult perceptual determination is the least energetic retinal stimulation that can be perceived at all in dark-adapted subjects. By varying the amount of energy delivered, a psychophysical function can be obtained that

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Brightness

defines the stimulus threshold value. At the threshold, subjects have difficulty saying whether they saw something or not; therefore, such tests are usually carried out using a paradigm in which the observer must respond on each trial (called forced choice). Typically, a series of trials is presented in which stimuli of different energetic levels are randomly interspersed with trials that do not present a stimulus. Since 50% “correct” (i.e., saying “Yes, I saw something” or “No, I saw nothing” when a stimulus was or was not present, respectively) would be the average result obtained if subjects merely guessed on each trial, 75% correct responses is conventionally taken to be the criterion for establishing the threshold level of stimulus energy. The relative as opposed to the absolute sensitivity of the visual system can be measured somewhat more easily by asking how much physical change is needed to generate a perceptual change out at any level of luminance and at different stimulus wavelengths (Figure 11.6A). Such psychophysical functions have led to a number of important generalizations, one being the Weber-Fechner Law. This law states that the ability to notice a difference in a stimulus (called the just noticeable or equally noticeable difference) is determined by a fixed proportion of the stimulus intensity, the proportion referred to as the Weber fraction. The proportional relationship between just noticeable differences and stimulus magnitude expressed by the Weber-Fechner law makes good sense: Because of the limited number of action potentials that neurons can generate per second, the visual system must continually adjust their overall range of operation to provide subjects with information about energy levels of light that for

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humans span many orders of magnitude (see above); the Weber fraction presumably reflects this fact. Another psychophysical approach to evaluating brightness is called magnitude scaling and entails ordering percepts along a scale of subjective magnitude that covers the full range of the perceptual quality (Figure 11.6B). The most extensive studies of this sort were carried out by the Harvard psychologist Stanley Stevens, who worked on this issue from about 1950 to 1975 (Stevens, 1975). Stevens asked whether a light stimulus that is made progressively more intense elicits perceptions of brightness that linearly track physical intensity. In making such determinations, Stevens simply asked subjects to rate brightness on a number scale along which 0 represented the least sense of relative brightness in a test series, and 100 the greatest. In this manner, he determined that brightness scales as a power function with an exponent of ⬃0.5 under the standard conditions he used. The power functions found in such scaling experiments are sometimes referred to as reflecting Stevens’ Law. These observations show that the perception of light intensity is oddly nonlinear, a puzzling fact whose mystery is only deepened by the phenomena described in the next section. Further Discrepancies between Luminance and Brightness Given that brightness is the subjective experience of light intensity, a logical assumption would be that two objects in a scene that return the same amount of light to the eye should appear equally bright. Observations dating to the nineteenth century and earlier, however, showed that perceptions of

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far more sensitive to stimuli in the middle of the light spectrum. B: Illustration of magnitude scaling as a means of evaluating how human subjects perceive brightness. The results of such testing show that the relationship between the perception of brightness and the intensity of a light stimulus is a nonlinear power function (the exponent is ⬃0.5 in this instance). (After Purves & Lotto, 2003)

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brightness fail to meet this seemingly simple expectation. For example, two patches returning equal amounts of light to the eye are perceived as looking differently bright when placed on backgrounds that have different luminances (Figure 11.7A). Thus, a patch on a background of relatively low luminance appears somewhat brighter than the same patch on a background of higher luminance, a phenomenon called simultaneous brightness contrast. This effect can be made much more dramatic when the stimulus includes more information about the possible real-world conditions underlying the stimulus (Figure 11.7B). Until relatively recently, the explanation of this sort of effect most often given was based on the properties of neurons at the input level of the visual system (the retina) and the lateral interactions that demonstrably occur in retinal processing. Presumably as a means of enhancing the detection of luminance contrast boundaries (edges), the central region of the receptive fields of lower-order visual neurons has a surround of opposite functional polarity. The number of action potentials generated per unit of time by neurons whose receptive fields intersect a contrast boundary will therefore differ from the activity of neurons whose receptive fields fall entirely on one side of the boundary or the other (Figure 11.8). For example, the neurons whose receptive field centers lie just within the target on the dark background in Figure 11.7A will fire at a higher rate than the neurons whose receptive field centers lie just within the target on the light background (because the former are less inhibited by their oppositely disposed receptive field surrounds than the latter). As a result, many investigators supposed that the patch on the dark background looks

brighter than the patch on the light background because of this difference in the retinal output. The percepts elicited by other stimulus patterns, however, undermine the idea that simultaneous brightness contrast effects are an incidental consequence of an anomalous retinal output in response to edges (or indeed any other aspect of retinal processing). In Figure 11.9, for example, the target patches on the left are surrounded by a greater area of higher luminance (lighter) territory than lower luminance, and yet appear brighter than the targets on the right, which are surrounded by more lower luminance (darker) territory than higher. Although the average luminance values of the surrounds in this stimulus are effectively opposite those in standard simultaneous brightness stimulus in Figure 11.7A, the brightness differences elicited are about the same in both direction and magnitude as in the standard presentation. The perceptions of brightness elicited by the stimulus in Figure 11.7B are also difficult to explain in terms of a rule such as that illustrated in Figure 11.8. If the output of retinal neurons can’t account for the relative brightness values seen in response to stimuli such as the simple patterns in Figures 11.7 and 11.9, what then is the explanation? An alternative framework for thinking about this problem is based on the fact that the significance of the luminance in any part of a retinal image is unknowable by any direct operation on the stimulus as such. The reason for this counterintuitive statement is not hard to see. Three fundamental aspects of the physical world determine luminance: the illumination of objects, the reflectance of object surfaces, and the transmittance of the space between the objects and the observer. As indicated in Figure 11.10,

Figure 11.7 Simultaneous brightness contrast. A: Standard presentation of this effect; the two diamond shaped patches have the same luminance (see key), but the one in the dark surround looks somewhat brighter. B: Simultaneous brightness contrast effects

can be much greater when the scene contains more detailed contextual information; as shown in the key, the relevant patches that look very different in brightness although they again have the same luminance. (After Purves & Lotto, 2003)

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Brightness

Dark

A B C D E

Light

Edge “On”-center ganglion cells

Response rate

D E

C

A

Spontaneous level of activity

B

Position

Figure 11.8 Lateral interactions in the retina and their effect on retinal output. Diagram of the different firing rates of retinal ganglion cells as a function of their position with respect to a light-dark contrast boundary. See text for further explanation. (After Purves et al., 2008)

Figure 11.9 Stimulus pattern that elicits perceptual effects that cannot be explained in terms of lateral interactions arising from local contrast effects and any simple rule such as “a target surrounded by less luminant surfaces should appear brighter than the same target surrounded by more luminant surfaces”. The pattern is called “White’s illusion” after the psychologist who first described this effect. (After White, 1979)

the relative contributions of these factors are inevitably conflated in the retinal image. Thus, many different combinations of illumination, reflectance, and transmittance can give rise to the same value of luminance; as a result, there is no direct way that the visual system can know how these factors have actually been combined to generate a particular retinal luminance value. Because appropriate behavior requires responses that accord with the physical source of a stimulus, this uncertainty presents an enormous challenge in the evolution of vision; by the same token, this fact makes clear that if brightness percepts were simply

231

proportional to the luminance values in a stimulus, the percepts would not be a guide to successful behavior. A biologically useful approach would be possible, however, if brightness percepts were generated empirically according to the past success or failure observers had experienced interacting with different combinations of illumination, reflectance, and transmittance in natural scenes. In this framework, brightness would correspond to the relative frequency with which different possible combinations proved to be the source of the same or similar stimuli in the enormous number of visual scenes witnessed during the course of evolution (as well as by individual observers during their lifetimes). This general idea resonates to some degree with Hermann Helmholtz’s suggestion in the nineteenth century that empirical information might be needed to augment what he took to be the more or less veridical information supplied by peripheral sensory mechanisms (Helmholtz, 1866). As indicated in Figure 11.10, however, retinal receptors can’t provide any unambiguous information about the state of the world. Thus, the more radical idea now being examined is that visual processing is empirical from start to finish, the brightness seen by an observer being determined by the empirical significance of the stimulus for behavior, rather than the intensity of light falling on the retina (reviewed in Purves & Lotto, 2003; Purves, Williams, Nundy, & Lotto, 2004). The biological rationale for this way of seeing brightness is that by using the outcome of experience accumulated by trial and error during phylogenetic and ontogenetic experience (i.e., what worked as a percept in response to a given stimulus), percepts and the ensuing behaviors come to have an increasingly better chance of responding successfully to retinal images whose meaning is otherwise unknowable. Central Processing of Luminance How and where perceptions of brightness are generated centrally is not understood. There is no cortical region that is especially concerned with processing brightness in the way V4 cells are especially concerned with color or MT neurons with motion. Moreover, the close relationship between light intensity and the firing rate of retinal ganglion cells diminishes as neurons are tested in increasingly central stations of the visual system. Thus, cells in the lateral geniculate nucleus respond in more or less the same way to intensity as retinal ganglion cells, whereas most neurons in the visual cortex respond only weakly to changes in stimulus intensity as such. The key observations made by neurophysiologists Stephen Kuffler, David Hubel, and Torsten Weisel working first at Johns Hopkins and then at Harvard Medical School in the 1950s and 1960s showed that what neurons at the level of the primary visual cortex respond

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232 Vision

Illumination

Reflectance

Transmittance

Stimulus

Figure 11.10 (Figure C.8 in color section) The inevitable conflation in light stimuli of illumination, reflectance and transmittance, the factors that an observer must parse in order to respond appropriately to the pattern of luminance values in any visual stimulus. The difficulty for vision presented by this conflation of information in the retinal image is an example the “inverse optics problem.” (From Lotto & Purves, 2003)

to with respect to luminance is not the intensity of a light stimulus, but the contrast between light and dark regions in the stimulus (see Figure 11.5; Hubel, 1988; Hubel & Wiesel, 2005; Kuffler, 1953). Increasingly central neurons are more concerned with the configuration of the complex stimuli and less concerned with luminance levels per se. This key fact must be important in the way luminance is related to brightness, but it is not yet clear how.

(Figure 11.11B). The ability to see colors has presumably evolved in humans and many other mammals because perceiving spectral differences allows observers to distinguish surfaces in the natural world more effectively than distinctions made solely on the basis of luminance. The fact that color vision has not evolved to any great extent in many visual animals indicates that the ecological value of color perception is far less than the value of brightness perception, which is presumably essential to form vision.

COLOR

Initiation of Color Percepts

A second basic quality of human visual perception is color. Recall that brightness is defined as the perceptual category elicited by the overall amount of light in a visual stimulus. Color is the perceptual category generated by the distribution of that amount across the visible spectrum, that is, the relative amount of energy at short, middle, and longer wavelengths in a stimulus (Figure 11.11A). The experience of color actually comprises three perceptual qualities: (1) hue, which is the perception of the relative redness, blueness, greenness, or yellowness of a stimulus; (2) saturation, which is the degree to which the perception approaches a neutral gray (e.g., a highly unsaturated red is a percept that appears largely gray but nonetheless has an appreciable reddish tinge); and (3) color brightness, which is the same perceptual category described previously, but applied to a stimulus that elicits a discernible hue. Together these qualities describe a perceptual color space

Rods play little part in human color vision, as is apparent from the fact that we don’t see or color well or eventually at all in dim light where rod vision predominates. The reason is that all rods contain the same photopigment (rhodopsin), whereas three different cone photopigments (called cone opsins) characterize three different cone types. As a result, each cone type has a different absorption spectrum, and therefore responds best to a different portion of the visible spectrum (roughly speaking, to long, middle, and short wavelengths, respectively; see Figure 11.11A; Rodieck, 1998). The different responsiveness of the three cone types allows the cones to report information about the distribution of energy in a light stimulus, and thus to generate information leading eventually to percepts of hue and saturation as well as brightness. In contrast, rods can only report the amount of light they capture, and can thus generate only information pertinent to brightness.

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Color 233

Figure 11.11 The perception of color . A: (Figure C.9 in color section) The solid curves indicate the absorption properties of the three cone types in the human retina, showing their differential sensitivity to short, medium and long wavelength light (dotted curve shows rod absorption). B: (Figure C.10 in color section) Diagram of perceptual “color space” for humans. At any particular level of light intensity, movements around the perimeter of the relevant plane or “color circle” correspond to changes in hue (i.e., changes in the apparent contribution of red, green, blue or yellow to the percept), whereas movements along the radial axis correspond to changes in saturation (i.e., changes in the approximation of the color to the perception of a neutral gray). Each of the four primary color categories (red, green, blue and yellow) is characterized by a unique hue (indicated by dots) that has no apparent admixture of the other three (i.e., a color experience that cannot be seen or imagined as a mixture of any other colors). These four colors are considered primary is because of their perceptual uniqueness. (After Lotto & Purves, 2003)

The fact that human color vision is based on the different sensitivity of three cone types is called trichromacy; color vision in most other mammals that have significant color vision is based on only two cone types, and they are thus referred to as dichromats (Mollon, 1995; Rodieck, 1998). A common disorder of color perception is anomalous color vision based on a genetic defect in one (or sometimes more) of the three cone types, effectively creating human dichromats. The most common form of this sort of color blindness is deficiency of a single cone type, which affects about 5% of U.S. males (this inherited genetic defect is located on the X chromosome, which explains its overwhelming predominance in males; Nathans, 1987). Although such individuals cannot distinguish red and green hues, or less commonly blue and yellow ones, this inability presents little practical difficulty in daily life. Central Processing of Spectral Differences While successfully accounting for many aspects of color perception in the laboratory, explanations of color vision based on retinal output from the three human cone types

have long been recognized to be inadequate, in much the same way that retinal output determined by luminance does not explain the brightness values that people see. This realization dates back to the nineteenth century when Helmholtz’s contemporary, Ewald Hering, pointed out that some aspects of color perception cannot be fully understood simply on the basis of three cone types (Turner, 1994). For example, humans with normal color vision perceive red to be an opponent color to green, and blue to be an opponent color to yellow. Whereas observers can see and/or imagine a gradual transition from red to yellow through a series of intermediate colors (orange colors), there is no parallel perception—or conception—of how to get from red to green, or from blue to yellow except through gray or through one of the other primaries. Furthermore, in contrast to the way observers see orange as a mixture of red and yellow, or purple as a mixture of blue and red, humans perceive a particular hue of red, green, blue, and yellow to be unique in the sense of not being a mixture of any other colors (see Figure 11.11B). Because simply having three cone types offers no explanation of these perceptual phenomena, Hering argued correctly that the comparisons made by the

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three cone types provide only a partial account of the colors we end up seeing and how color sensations are generated. Central visual processing modulates the information generated by the retina to determine color percepts, just as central processing modulates luminance information to generate brightness percepts. The neural basis of opponent color percepts has been advanced by modern electrophysiological studies of wavelength-sensitive neurons at different stations of the visual system of nonhuman primates and other species with color vision. The majority of color-sensitive neurons in the retina and lateral geniculate nucleus of the thalamus have receptive fields that are organized in a color opponent fashion. Such cells are excited by light of one wavelength (e.g., long wavelength or “red” light) illuminating the center of their receptive field, and inhibited (or “opposed”) by another wavelength (e.g., middle wavelength or “green” light) falling in the region surrounding the center of the receptive field. In macaque monkeys (which have color vision nearly identical to humans), most (but not all) color opponent cells are antagonistic with respect to wavelengths that appear red and green, or blue and yellow (Hubel, 1988; DeValois & DeValois, 1993; Gegenfurtner, 2003). In addition to red/green and blue/yellow classes of opponent cells, other neurons are insensitive to differences in wavelength. These cells are often considered “white/ black” opponent neurons. The explanation usually given for the perceptual phenomena that Hering first noted is that perceptions of color are elicited by neurons comprising three color “channels” that operate in a push/pull fashion. For instance, when the neurons responsible for seeing red are excited, those responsible for green are inhibited, and vice versa. Color percepts clearly arise from processing at higher levels in the brain and not just from the presence of three cone types, even though the details and consequences of opponent color processing are not yet understood. Additional information about central color processing has come from studies in nonhuman primates carried out over the past 25 years (Zeki, 1983a, 1983b; 1989, 1993). This work and related clinical studies have shown without much doubt that extrastriate area V4 is important in color processing (see Figure 11.2). Neuropsychological and imaging studies of patients suffering from a condition called cerebral achromatopsia have confirmed the relative specialization of this cortical region. In effect, such individuals lose the ability to see the world in color, whereas other aspects of vision such as brightness and form vision remain intact. As indicated in Figure 11.12, these patients typically have damage to extrastriate visual areas, damage to V4 being the most common site. Further evidence that this general area of cortex is concerned with color processing comes from functional imaging studies of normal subjects, which

Achromatopsia (A)

Z ⫽ 36

Percent overlap 80 60 (B) 40 20

Figure 11.12 (Figure C.11 in color section) Damage to the ventral extrastriate occipital cortex (which includes area V4) can lead to an inability to see color (achromatopsia), despite being able to see brightness and form more or less normally. A: Degree of overlap in the location of lesions in a series of patients with achromatopsia as well as other neurological deficits. Given the anatomy of the primary visual pathway, such patients are often blind to stimuli of any sort in the contralateral visual field (see Figure 11.1). B: Degree of overlap in 11 patients with achromatopsia as the primary symptom. The narrower overlap in these patients is consistent with the conclusion that the integrity of V4 is important for color vision. Inset shows level of the horizontal sections shown. (From Bouvier & Engel, 2006)

show that much the same regional activation is elicited by color processing tasks. The neurologist and essayist Oliver Sacks (1995, 1996) had a patient who described objects in visual scenes as all being “dirty” shades of gray. When asked to draw objects from memory, he had no difficulty with the relevant shapes or with shading, but was unable to appropriately color the things he represented (e.g., he could draw a banana, but couldn’t color it yellow). As in any brain lesion study, however, some caution is warranted because of the great variability of cortical damage and uncertainty about the extent of neurological damage. Whereas V4 seems a key component of central color processing, a number of related extrastriate areas probably participate in generating color percepts as well. Color Contrast and Constancy Like perceptions of brightness, the colors we see are strongly influenced by the overall pattern of light in any

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Color 235

particular stimulus. For example, a stimulus patch generating exactly the same distribution of power at various wavelengths can appear quite different in color depending on its surroundings, a phenomenon called color contrast (Figure 11.13). Conversely, patches in a scene returning different spectra to the eye can appear to be much the same color, an effect called color constancy. Although these phenomena were well known to psychologists and vision scientists more than 100 years ago, they were emphasized by Edwin Land’s work in the late 1950s (Land, 1986). Land—an independent photochemist, who, among other achievements, invented polarizing filters and instant photography, and founded the Polaroid Corporation—used three adjustable lights generating short, middle, and long wavelength light respectively to illuminate a collage of colored papers. He used spectrophotometry to show that two patches that appeared to be different colors in white light (e. g., green and brown) continued to elicit much the same color percepts when the three illuminators were adjusted so that the light being returned from the “green” surfaces produced exactly the same readings on a spectrophotometer that had previously come from the “brown” surface—a striking demonstration of color constancy. Color contrast and constancy effects raise much the same problem for understanding color processing as the contextual

“Blue”

“Yellow”

Contrast

“Red”

“Red” Constancy

brightness effects do for understanding how achromatic percepts are generated. Together, these phenomena have led to a debate about brightness and color percepts that now spans more than a century. The issue is how global information about the spectral context in scenes is integrated with local spectral information to produce color percepts. Land tried to explain such effects by a series of algorithms that integrated the spectral returns of different regions over the entire scene (the implication being that color processing in the visual system implemented these algorithms). It was recognized even before Land’s death in 1991, however, that this so-called “retinex theory” did not work in all circumstances and was a description, not a physiological explanation. Other vision scientists have emphasized the opponent receptive field properties of central neurons, double opponent cells in particular, as possible neural substrates for such effects (these are neurons in which the activity of the surround is inhibited by activation of the receptive field center and vice versa; see Hubel, 1988, for a general description). Another approach has focused more specifically on the interaction of the three human cone types in responding to the foreground and background components of retinal images (e.g., Shevell & Monnier, 2006). Still others have provided evidence that, like achromatic brightness, perceptions of hue, saturation, and color brightness are generated

Figure 11.13 (Figure C.12 in color section) Demonstrations of color contrast and constancy. Color contrast. The four blue patches on the top surface of the cube in the left panel and the seven yellow patches on the cube in the right panel key are actually identical grey patches (see upper key below). Thus patches that are physically the same can be made to appear either blue or yellow by changing the spectral context in which they occur. Color constancy. Patches that have very different spectra (see the five different colored patches on the left and right in the lower key) can be made to look more or less the same color (red in this case) by contextual information. (From Purves & Lotto, 2003)

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according to the empirical significance spectral stimuli in past experience (Long, Yang, & Purves, 2006; Purves & Lotto, 2003; Purves et al., 2004). There is as yet no consensus about how central visual processing integrates local and global spectral information to produce the remarkable phenomenology of color perception.

PERCEPTION OF FORM A third basic quality of vision is the perception of form. In the simplest case, perceptions of form entail geometrical characteristics such as the length of lines, their apparent orientation, and the angles they make with other lines. Understanding the perception of such simple stimuli is a first step toward understanding how more complex objects are perceived. Seeing Simple Geometries A starting point in exploring how the visual system generates perceptions of form is examining how observers perceive the distance between two points in a visual stimulus, as in the perceived length of a line or the dimensions (size) of a simple geometrical shape. It is logical to suppose that the perception of a given line (e.g., a line drawn on a piece of paper or presented on a computer screen) should correspond more or less directly to its projected length in the retinal image. But as in the case of brightness and color, what we see does not correspond to physical reality. A wellstudied example is the variation in the perceived length of a line as a function of its orientation (Figure 11.14A). As investigators have repeatedly shown over the past 150 years, a line oriented more or less vertically in the retinal image appears to be significantly longer than a horizontal

(B)

1.15 Perceived line length

(A)

line of the same length, the maximum length being seen, oddly enough, when the stimulus is oriented about 30˚ from vertical (Figure 11.14B; Howe & Purves, 2005). This effect is a particular manifestation of a general tendency to perceive the extent of any spatial interval differently as a function of its orientation in the retinal image. For instance, as the psychologist Wilhelm Wundt (1862) first showed, the apparent distance between a pair of dots varies systematically with the orientation of an imaginary line between them, and a perfect square or circle appears to be slightly elongated along its vertical axis. There is a rich literature on the perceptions elicited by simple geometrical stimuli, showing that measurements made with rulers or protractors are typically at odds with the corresponding percepts (Robinson, 1972/1998). Some of the most familiar of these “geometrical illusions”—and the ones whose etiology has been most hotly debated—are illustrated in Figure 11.15. The first example is attributed to Hering, who showed that two parallel lines (indicated in red) appear bowed away from each other when presented on a background of converging lines (Figure 11.15A). In the Poggendorff illusion, the continuation of a line interrupted by a bar appears to be displaced vertically, even though the two line segments are actually collinear (Figure 11.15B). The inverted T illusion is effectively the phenomenon of vertical lines looking longer than horizontal ones described above (Figure 11.15C). In the more complex Müller-Lyer illusion, the line terminated by arrow tails looks longer than the same line terminated by arrowheads (Figure 11.15D). In the Ponzo illusion, the upper horizontal line appears longer than the lower one, despite the fact that they are again identical (Figure 11.15E). All these effects are apparent in natural scenes and, as in brightness and color, can be enhanced by more complex contextual information: the

1.10

1.05

1.00

Figure 11.14 Variation in apparent line length as a function of orientation. A) The horizontal line in this figure looks significantly shorter than the vertical or oblique lines, despite the fact that all the lines are identical in length. B) Quantitative assessment of the apparent length of a line reported by subjects as a function of its orientation in the stimulus (orientation is expressed as the angle between the line and the horizontal axis). The maximum

0

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length seen by observers occurs when the line is oriented approximately 30° from vertical, at which point it appears about 10–15% longer than the minimum length seen when the stimulus is horizontal (in this graph the reference is the apparent length of the line when horizontal, which is plotted as 1.00). (After Howe & Purves, 2005)

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Perception of Form 237 (A)

(C)

(B)

(D)

(E)

(F)

Rotate 90°

Figure 11.15 Examples of several much-studied geometrical illusions. A: The Hering illusion. B: The Poggendorff illusion. C: The inverted T illusion. D: The Müller-Lyer illusion. E: The Ponzo illusion. F: The table-top illusion. (From Purves & Lotto, 2003)

table top illusion created by psychologist Roger Shepard is a good example (Figure 11.15F). Several of these stimuli are effectively “size contrast” stimuli, similar in principle to brightness contrast and color contrast stimuli. That is, the same physical stimulus appears different when placed in different contexts. Neural Processing of Form Recall that receptive field properties define the physiological response of visual neurons. With respect to the orientation of a line, for example, neurons in V1 respond selectively to lines shown at different angles (see Figure 11.5). Related studies have shown that many V1 cells are also selective for particular line lengths (so called “end-stopped cells”). As a result, it is attractive to suppose that the activity of neurons in visual cortex that respond best to a stimulus feature, for example, a particular orientation or length of a line,

corresponds to a more or less direct representation of the perception of that stimulus feature in the retinal image. There are, however, a number of problems with this intuitively appealing view. The major obstacle is the uncertain relationship between retinal image features such as length and orientation, and the length and orientation of objects in the real world (Figure 11.16). The challenge of this ambiguous link between retinal stimuli and their physical sources has been recognized since the beginning of the eighteenth century, and is the same in principle as the conflation of information illustrated in Figure 11.10. As indicated in Figure 11.16, the problem is that images on the retina cannot, by their nature, uniquely specify the physical geometry of the objects in a scene. As in the case of brightness and color, the strategy needed to deal with this problem must somehow rely on empirical information because there is no logical solution of the quandary illustrated in Figure 11.16. As already noted, the idea that vision must in some way use empirical information goes back to Helmholtz’s writings in the nineteenth century (i.e., the idea that retinal information simply had to be given a helping hand by prior experience). Only recently, however, have vision scientists considered the possibility that percepts might be generated entirely on the basis of the empirical success or failure of visual experience during the evolution of vision. Evidence for a wholly empirical basis for the perception of simple forms comes from studies of the percepts elicited by the sort of stimuli shown in Figures 11.14 and 11.15 (reviewed in Howe & Purves, 2005). For example, laser range-scanning can provide a database of natural scene geometry that can serve as a proxy for accumulated human experience with the relationship between retinal projections at different orientations and their probable source (Figure 11.17). Such studies have shown that this cumulative information about the frequency with which the physical sources of lines generate retinal projections of a given length in different orientations correspond remarkably well with the way people see line lengths (Figure 11.18). The peculiar perceptual function in Figure 11.14B is accurately predicted on this basis (cf. Figure 11.18B). The other geometrical illusions shown in Figure 11.15 can likewise be explained in terms of the statistical link between retinal images and the sources of such geometries in the natural scenes that humans have always had to contend with in their behavior. This observation in the perception of geometry is consistent with empirical explanations of brightness and color percepts (see Purves & Lotto, 2003). How the visual system generates these statistical relationships is not known, but some recent evidence concerning the processing of forms is at least consistent with the idea that visual processing operates in this general way.

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Retinal projection

Figure 11.16 The inverse optics problem in geometry. The inherent ambiguity of retinal stimuli is illustrated here in terms of the perception of objects in space. The same linear projection on the retina can derive from an infinite number of linear objects at different distances, of different sizes, and in different spatial orientations. As a result, a retinal image cannot specify its physical source. (After Howe & Purves, 2005)

Figure 11.17 (Figure C.13 in color section) Relating the realworld geometry to retinal projections. A: Laser range scanning apparatus used to determine the physical geometry of scenes. The distances of object surfaces determined in this way are accurate to within a few millimeters. B: Representative images acquired

by the range scanning. Ordinary color images of the scenes are shown on the left and the corresponding range images acquired by the laser scanner on the right. Color-coding indicates the physical distance of each point in the scene from the origin of laser beam. (After Howe & Purves, 2005)

A key observation in this regard is that the activity of some visual cortical neurons cannot be understood in terms of their receptive field properties, at least as these properties have been conventionally defined (see Figure 11.5). For instance, the same pattern of neuronal activity in V1 can be elicited by differently oriented stimuli moving in different directions at different speeds (Figure 11.19; Basole, White, & Fitzpatrick, 2003). This result is contrary to what would be expected if the orientation of stimuli were represented simply by the activity of neurons selective for a given orientation. Although the finding illustrated in Figure 11.19 can be rationalized in several different ways, it raises doubts about the idea that receptive field properties are directly linked to perception.

Other observations have also challenged the conventional concept of receptive field properties by showing that the context of particular stimulus features modulates the relevant neuronal responses in a variety of ways. It is now generally recognized that the response properties of visual cortical neurons are influenced, often markedly, by stimuli presented outside the region of visual space that has traditionally defined extent of a neuron’s receptive field (reviewed in Fitzpatrick, 2000; Worgotter & Eysel, 2000). For instance, the response of orientation-selective cells in V1 to a moving bar is suppressed in varying degrees by the presence of moving bars outside the receptive field, even though the neurons show no response when the stimulus outside the field is presented alone (Knierim & Van Essen,

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Cumulative probability of occurrence of the physical sources

Perception of Depth 239 1

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Figure 11.18 The statistical relationship of oriented lines projected on the retina and their physical sources predicts the way observers see line lengths as a function of their orientation in the retinal image. A: Cumulative probability distributions of the occurrence of physical sources of differently oriented lines. B: Perception of line length as a function of orientation predicted by the data in (A). (After Howe & Purves, 2005)

Figure 11.19 Evidence that the same pattern of cortical activity in V1 can be elicited by different stimuli (the experimental animal in this case was a ferret). The optical imaging technique used here monitors cortical activity by virtue of activity-dependent changes in the light reflected from the primary visual cortex (the dark areas are more active; the view is looking down on V1,

1992). These findings are not particularly surprising, considering that neurons at different levels of the primary visual pathway receive a majority of their synaptic inputs from other neurons at the same level and/or feedback from neurons at higher levels of processing. They cast doubt, however, on the notion that a representation of the retinal image is in any sense reconstructed at some level of the visual system based on the combined receptive field properties of the relevant neurons. Several findings add to misgivings about the conventional interpretation of receptive field properties that have been expressed for some time now (e.g., Lennie, 1998). These countervailing observations about receptive fields should not be taken to mean that the evidence illustrated in Figure 11.5 is in any sense wrong. Rather, they simply imply that conventional thinking about the relationship between classical physiology and perception is incomplete. Finally, observations about how the visual cortex represents the perception of size imply that cortical processing that tracks perceptions rather than stimulus features as such (Figure 11.20). In this case the investigators took advantage of the retinotopic organization of the primary visual cortex and used fMRI to ask whether the area activated by a stimulus of a particular size corresponded to the actual size of the object in the retinal image or its perceived size. The active area in V1 appeared to track perceived rather than actual size, suggesting that cortical processing even at this initial stage is more closely related to perception than to the retinal representation of form. PERCEPTION OF DEPTH A fourth basic quality of vision is the perception of depth, that is, the perception of a three-dimensional world from

which has been surgically exposed). A: The same pattern of neuronal activity can be elicited by either of the two different stimuli in (B). B: Examples of stimuli comprising differently oriented line segments moving in different directions at different speeds; both elicited the pattern of activity shown in (A). (After Basole et al., 2003.)

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Figure 11.20 (Figure C.14 in color section) Representation of object size in the primary visual cortex. The two panels on the left show the stimulus used: the two checkered balls are the same size, but the one depicted as being further away looks larger than the one depicted as being closer. A: Inset showing the occipital region examined by fMRI study. B: Flickering stimulus used to define the region activated in the primary visual cortex of each subject. C: The numbers corresponding to the area of primary visual cortex activated in fMRI images by balls of different actual sizes. The results in several subjects indicated that active region in V1 varied with the perceived size of the balls in the stimulus rather than their actual size in the stimulus. (After Murray et al., 2006)

two-dimensional retinal images. Some aspects of depth are derived from information in the view of one eye alone, whereas another aspect is apparent only when both eyes are used together. Thus depth perception is generally discussed in terms of its monocular and binocular components. Monocular Depth Perception Monocular depth perception (the sense of three dimensionality when looking at the world with one eye closed) presumably derives from experience with the arrangement of objects in space. The most obvious fact learned from such experience is occlusion: When part of one object is obscured by another, the obstructing object is always closer to the observer than the obstructed object. Another universal experience pertinent to depth is the relationship of size and distance: a projection of the same object occupies progressively less space on the retina the further away it is, thus providing additional information about depth (and defining perspective). Additional monocular depth comes from motion parallax. When the position of the observer changes (by moving the head from side to side, for instance), the position of the background with respect to an object in the foreground changes more for nearby objects than distant ones. Finally, the fainter and fuzzier appearance of distant objects as a result of the Earth’s atmosphere

(referred to as aerial perspective) provides a further empirical indication of how far away things are. Moreover, because the atmosphere absorbs more long than short wavelength light (the interposed medium is effectively “sky”), distant objects also look bluer compared to their appearance nearby, as landscape artists know well. That monocular information about depth is largely learned accords with the fact human infants do not at first appreciate depth (newborn rhesus monkeys, however, do see depth quite well, as evidenced by skillful behavior as they leap from perch to perch). All of us gradually discover that more distant objects are often occluded, are smaller in appearance, tend to change position less with respect to the background when we move, and look fainter, fuzzier, and bluer. Since we are only dimly aware of these issues, if at all, it follows that the incorporation of this sort of information is unconscious, in keeping with the general idea that percepts are continually shaped by feedback from behaviors that work. Binocular Depth Perception A quite different sort of information about the arrangement of objects in space is available when scenes are viewed with both eyes. Binocular information about depth is called stereopsis and arises from the fact that the eyes are separated horizontally across the face by an average distance of 65 mm

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Perception of Depth 241

in adult humans, giving each eye a slightly different view of the same nearby objects (Figure 11.21A). This difference in the two images is called retinal disparity. The behavioral significance of stereo vision can be appreciated comparing the difficulty of bringing the points of two pencil points together in the frontal plane using both eyes and then only one. Making the tips of the pencils touch (or performing other, more consequential tasks) is much easier in binocular view. Other animals with frontally located eyes enjoy the same advantage in depth perception, and most mammals have some stereoscopic ability in the region of binocular overlap (the human overlap is about 140˚, whereas walleyed animals like horses have only about 15˚of overlap). As the English physicist and vision scientist Charles Wheatstone showed by his invention of stereoscope in the 1830s, the greater behavioral success with both eyes open in this and other tasks involving manipulation—and the corresponding enrichment in the perceptual sense of depth—arises from a “fusion” in visual perception of the somewhat different views of the two eyes (Figure 11.21B;

Wheatstone, 1838). Wheatstone also pointed out that stereoscopic information is limited to viewing objects relatively near the observer. The differences in the views of the right and the left eyes illustrated in Figure 11.21A decrease progressively as the lines of sight of the two eyes become increasingly parallel, causing the binocular disparity of objects in the image plane to eventually fall below the resolving power of the visual system (for all practical purposes, stereoscopy adds little to the success of visually guided behavior for objects more than a few meters away, and presumably evolved for its advantages in near tasks. Random Dot Stereograms Many studies of stereoscopic vision have used random dot stereograms (RDSs; Figure 11.22). In addition to their intrinsic interest, RDSs continue to fascinate because of basic issues they raise, in particular how locally randomly arranged right and left eye information can be put together

Figure 11.21 The different views of the two eyes. A: Viewing any nearby object with one eye and then the other makes obvious the difference in the views of the two eyes. B: The consequences for generating sensations of depth can be demonstrated with a stereoscope. If two pictures of a scene are taken from slightly different angles, then looking at the 2-D images binocularly produces a strong sensation of depth that is not present when the same images are viewed with one eye or the other, or identical images observed with both eyes. (From Purves & Lotto, 2003)

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242 Vision

Figure 11.22 Random dot stereograms and their construction. A–C: Construction and perceptual result of shifting a set of random dots to the left in the view of the right eye. D–F: Construction and perceptual result shifting a set of random dots

to the left in the view of the left eye. The diagrams of the resulting percepts in (C) and (F) assume that the observer is fusing the images “divergently” (i.e., by looking through the page). (From Purves & Lotto, 2003)

by the brain. These intriguing stimuli were introduced about 50 years ago and adapted for experimental work in vision by the late psychologist Bela Julesz working at Bell Labs (Julesz, 1995). Although the idea had been discovered some years earlier, the technique became widespread when Julesz showed how RDSs could be easily made and manipulated by a computer. RDSs are essentially stereograms of an object camouflaged so completely with respect to its background that the target can be seen only when the two monocular components of the random dot pair are viewed binocularly. Such stimuli thus eliminate all monocular depth cues (e.g., occlusion, perspective, motion parallax), and/or cognitive information (e.g., prior recognition of an object) that might surreptitiously affect neural processing specific to binocular depth perception. Figure 11.22 shows how such stimuli are typically made. A target object (a square in this case) within a field of randomly generated black and white “dots” (each comprising a few pixels on a computer screen) is selected and shifted a fraction of a degree over the background in the one member of the stereo pair (the corresponding set of dots in the view of the other eye remains

in place). The gap created by the shifted set is then filled in with additional random dots; note also that another set on the other side of the shifted set has been covered up in this process. As a result of this manipulation, the shifted square appears to be in front of or in back of the background array (depending on whether the shift was to the left or right) when the left and right random dot arrays are fused. Many people can, with a little practice, carry out such fusion by looking “through” the plane of the printed page (alternatively, the two components can be viewed in a stereoscope of the sort shown in Figure 11.21A). The perception of depth in response to RDSs is actually not as mysterious as it seems: the shifted pattern of dots in the two eye views simply mimics what would be seen if an object in this spatial arrangement were perfectly camouflaged by the texture of the background, and many natural situations approach this condition. The experience of looking at RDSs that lack an obvious frame (as in the “autostereograms” found in popular books and posters of such stimuli) suggests that the visual system determines how to put the two eye views together by trial and error.

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Perception of Motion 243

Binocular Neural Processing The fact that stereopsis depends on retinal disparity implies that the visual system must in some way compare the loci on the two retinas that are stimulated by light rays arising from the same points in visual space (Howard & Rogers, 1995). This idea is supported by the fact that many neurons in both the primary and extrastriate visual cortex of experimental animals have receptive fields that are “tuned” to specific disparities (Figure 11.23). This evidence, together with the knowledge that stereopsis can be elicited by random dot stereograms, suggests that the perception of binocular depth is generated by neural computations of the disparity at corresponding retinal points. Although this explanation is eminently logical, understanding how the nervous system implements the postulated geometrical comparison of a stereo pair has been a difficult challenge. There is as yet no agreement about an algorithm that could accomplish this feat. Nor is it agreed whether this interpretation adequately explains the further aspects of binocular vision: cyclopean fusion and binocular rivalry. Although we normally view nearby objects with both eyes open—and thus process two appreciably different retinal images (see Figure 11.21)—the perceived image of the nearby world is clearly a unified one (remember that for distant scenes the two retinal images are identical). Thus, what observers see in binocular view seems to have been generated by a single eye in the middle of the face, a subjective experience referred to as cyclopean vision. This union of two quite different monocular views into a coherent cyclopean percept is taken for granted. Yet like many other aspects of vision it presents a deep puzzle: How are the two independent views of any nearby

Tuned excitatory

Far

Neural activity

Near

scene conjoined to create a single percept having qualities (including stereoscopic depth) that are not present in the view of either eye alone? Most explanations of this puzzle depend on the fact that inputs from the two eyes converge on cortical neurons in the primary visual cortex (Figure 11.24). Although right and left eye inputs are kept apart in the thalamus and in cortical layer IV in V1 (which receives the afferents from the lateral geniculate nucleus; see previous discussion), many neurons in the deeper and more superficial cortical layers in the primary visual cortex of primates are binocularly driven. The prevalence of binocular cells in the primate visual cortex suggests that cyclopean vision arises from this demonstrable conjunction of right and left eye inputs at the level of common target cells in the visual cortex. Despite this attractive anatomical and physiological substrate for a perceptual union of the two monocular streams, the idea of “seeing” a cyclopean image by virtue of binocular neurons in the visual cortex, at least in any simple sense, is inconsistent with other evidence, the phenomenon of binocular rivalry in particular (Figure 11.25). Binocular rivalry refers to the fact that when a particular stimulus pattern (e.g., vertical stripes) is presented to one eye and a strongly discordant pattern (e.g., horizontal stripes) to the other, the same region of visual space is perceived to be alternately occupied by vertical stripes or horizontal stripes, but rarely (and only transiently) by both. If information from the two eyes were simply united in the visual cortex, observers would presumably see some stable integration of vertical and horizontal stripes in response to such stimuli (a grid in the most simplistic interpretation; see Figure 11.25A). Moreover, work by Randolph Blake and Nikos Logothetis (2002) have shown that it is not always the images on the two retinas that rival: at least in some circumstances it is the percepts themselves that seem to be the source of the competition, consistent with the idea that cortical activity is more concerned with percepts than image features (Figure 11.25B). There has been no consensus about the basis of binocular fusion and rivalry; how the visual system processes and unites the views of the two eyes is not yet understood.

PERCEPTION OF MOTION

Near

Fixation point

Far

Distance of stimulus relative to point of fixation

Figure 11.23 Disparity tuning in visual cortical neurons. Electrophysiological recording of the activity of single neurons in V1 of cats and monkeys shows that many cells respond selectively to binocular stimuli that have different disparities, leading to the concept of “near” and “far” cells. (After Poggio et al., 1995)

The final perceptual quality generated by visual processing considered here is motion, defined as the subjective experience elicited when a sequence of different but related images are presented to the retina over a brief span of time (physical motion can be either too fast or too slow to elicit the perception of motion; we don’t see the trajectory of a bullet or the motion of the hour hand on a clock). Much as a perceptual category like color that comprises the subsidiary qualities of

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244 Vision Primary visual cortex Cortical layer

I Monocular cells

II III IV

Binocular cells

V Left retina

Right retina

VI Lateral geniculate nucleus (thalamus) 6 5 4 3 2 1

Lateral geniculate nucleus (thalamus) Primary visual cortex

Figure 11.24 (Figure C.15 in color section) The anatomical conjunction of the two monocular streams of visual information in visual cortex. Inputs related to the right and left eyes first come together in the primary visual cortex, where half or more of the neurons in rhesus monkeys can be activated by a stimulus presented to either the left or right eye. Note that the afferents related

to the two eyes remain segregated at the level of the lateral geniculate nucleus in the thalamus and in the right eye/left eye cortical modules in layer IV illustrated in Figure 1; binocularly driven cells are found only above (and below; not shown) this thalamic input layer. (From Purves & Lotto, 2003)

(A) Monocular stimuli Left eye

Right eye

Binocular percept

(B)

Spikes s⫺1

40 20 0

Figure 11.25 Binocular rivalry. A: The phenomenon of binocular rivalry illustrated with vertical lines presented to the left eye and horizontal lines to the right eye. A grid pattern is not seen, indicating the views of the two eyes are not simply brought together by the activity of binocular neurons in the visual cortex. B: Electrophysiological recordings from individual visual cortical neurons in a monkey trained to report whether he was aware of the left or right eye image in a rivalry paradigm. The neuron shown in this example is active only when the right eye view was perceived (red bars). This result indicates that percepts compete in binocular rivalry. (B is from Blake & Logothetis, 2002)

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Perception of Motion 245

hue, saturation, and brightness, motion percepts entail the perception of speed and the perception of direction.

(A) Perceptual discrimination task Left?

Motion Right?

Evidence for Dedicated Motion Processing Areas

(B) Linking behavior to perception

% left choices

Just as more or less specific regions of the visual association cortices emphasize the processing of color (V4 and related areas), form (V1 and related areas) and depth (also V1 and related areas), so particular regions in the primate brain are especially concerned with motion processing. These regions in the occipital and posterior temporal lobes called MT for medial temporal and MST for medial superior temporal (see Figure 11.2; recall that these regions are called MT⫹ in humans to indicate that the MST component is less well defined in humans than in monkeys, in which most motion studies have been done). That these areas are specialized for motion processing was first determined by single unit recording in monkeys carried out in the 1970s and 1980s, which showed that many more cells within these areas are responsive to image sequences than cells in other visual cortical regions (Maunsell & Van Essen, 1983). Noninvasive brain imaging during the presentation motion stimuli has shown that the same general areas are active in humans viewing motion stimuli. The neurons in MT and MST in monkeys receive input from motion sensitive cells in V1, are arranged in columnar modules that have the same preference for oriented motion stimuli. Moreover, they respond to motion over large regions of a visual scene, a finding that accords with the large receptive field sizes of other neurons in extrastriate regions (see earlier discussion). Evidence that the activity of MT neurons is closely related to motion percepts has been provided by studies by William Newsome and his collaborators (Figure 11.26; Newsome, Britten, & Movshon, 1989; Sugrue, Corrado, & Newsome. 2005). Rhesus monkeys were shown a display of dots moving in different directions. If a sufficient proportion of the dots move coherently stimuli, humans or monkeys perceive and overall a direction of motion in the display (e.g., rightward). As indicated in the figure, monkeys can be trained to move their eyes in the direction of the movement of the dots. While a monkey trained in this way performed the task, action potentials were recorded from MT neurons. The recordings showed that the activity of single neurons was often correlated with the direction of dot motion. Indeed, the activity of neurons in the population was sometimes a better predictor of the direction of dot motion than the behavior of the monkey (i.e., its eye movements). To show that MT neurons play a causal role in such perceptual discriminations, it would be necessary to manipulate neuronal activity directly and then observe changes in behavior. To test this point, Newsome

Stimulus strength (left)

Figure 11.26 Relating motion sensitive neurons in MT to motion percepts. A: In this experiment a rhesus monkey was trained to report whether he perceived rightward or leftward motion in response to a pattern of moving dots (the report was made by shifting his eyes to either the right or left target). B: By changing the amount of coherent motion in the moving dot pattern, a psychophysical function was obtained that plots perceptual accuracy against the amount of motion coherence among the dots. Electrical stimulation of small populations of MT neurons (not shown) shifted this curve in a systematic way, showing that the activity of these neurons can influence motion perception. (After Sugrue et al., 2005)

and colleagues identified MT neurons that showed selective activity for a particular direction of motion. They then stimulated the neurons electrically. For about half of the electrode locations, such microstimulation increased the probability that the monkeys would move their eyes in the direction consistent with the directionally selective receptive field properties of the stimulated neurons. Like the evidence for the importance of V4 in color, the importance of these extrastriate temporal areas for motion processing has been underscored by a “motion-blind” patient (Zihl, von Cramon, & Mai, 1983). The patient is a 43-year-old woman known as LM who suffered a vascular lesion that caused bilateral damage in the general region of the MT⫹ motion areas. Although the lesion resulted in several neurological problems, a striking feature of her case was difficulty perceiving motion. She had difficulty following speech because she couldn’t pick up mouth movement cues, and was hesitant crossing a street because

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246 Vision

she couldn’t judge the movement of cars. Interestingly, LM is nonetheless able to perceive certain kinds of motion. For instance, when lights are attached to the key joints of the body and human movements observed in the dark, she can distinguish different types of common human movements such as walking. Consistent with this clinical evidence, transcranial magnetic stimulation of MT⫹ in normal human subjects can also interfere with motion percepts. Taken together, this evidence for specialized motion processing areas accords with the concept of a motion processing stream that conveys information from the magnocellular pathway that begins in the retina and is evident in the magnocellular layers of the thalamus and in V1. These areas are also components of the more broadly defined dorsal pathway concerned with object location and action, which contrasts with a ventral pathway that is more concerned with object recognition (see Figure 11.3). Some Problems Understanding Motion Perception Despite these advances, how motion percepts are generated by neural processing is far from understood. Because the movement of objects in three-dimensional space is projected onto the two-dimensional retinal surface, the changes in position that uniquely define motion in physical terms are always uncertain with respect to the possible sources of the retinal image sequence (see also Figure 11.16). A much-studied example that makes this point is the perception of a moving rod seen through an aperture that renders its ends invisible (Wallach, 1935; Wuerger, Shapley, & Rubin, 1996). As illustrated in Figure 11.27, the combinations of speeds and directions in this situation that could have given rise to the sequence of images falling on the retina is infinite. The challenge of explaining how the visual system generates quite definite perceptions of speed and direction in response to such stimuli is called the aperture problem, and remains to be solved. A further challenge in understanding motion percepts is the sense of entirely realistic motion generated from a series of static images, a phenomenon called apparent motion. The simplest stimulus sequence that could be used to study this phenomenon is the presentation of just two sequential flashed spots of light, which is what Max Wertheimer did nearly a century ago (Figure 11.28A; Wertheimer, 1912). For a spatial interval of one or a few degrees, Wertheimer found that if the temporal interval is less than ⬃2 ms, the two spots of light appear to come on simultaneously and no motion is seen; at the other extreme, if the interval is greater than ⬃450 ms, the two lights appear to come on sequentially and no motion is seen. Between these limits, subjects perceived some form of motion, the most “realistic” motion being in the middle of this range. The motion

Possible directions of motion for part of line in frame moving downwards

Actual direction of motion

Horizontally moving line

Circular aperture

Perceived direction of motion (discrepancy of ~ 45º)

Figure 11.27 The inherent ambiguity of motion stimuli. The stimulus sequence elicited by a rod moving behind an aperture can be generated by an infinite number of directions of physical motion, each associated with a different speed. Imagine, for example, that the linear object in the aperture is moving horizontally from left to right. The same stimulus sequence could have been generated by any of the directions of physical movement indicated by the other arrows around a limiting hemisphere, each coupled with an appropriate speed. In the absence of the aperture, such a line appears to be moving horizontally from left to right at a particular speed. The moment the aperture is applied, however, the line appears to be moving downward and to the right at a slower speed (arrow in circle). (From Purves & Lotto, 2003)

elicited by such stimuli is the basis movies and video, in which static images are presented at a high frame rate (96/s in movies; video frames are refreshed one line at a time such that the whole picture changes ⬃30 times each second, but the general idea is the same). Other intriguing percepts occur if additional lights are added to the simple sort of pattern studied by Wertheimer. For instance, if a quartet of lights is used, the apparent motion seen is horizontal and not diagonal, even though there is no obvious prohibition against seeing diagonal motion (Figure 11.28B). Explanations of apparent motion have tended to invoke rules or principles called heuristics that the visual system supposedly employs to guide perceptual “interpretations,” an approach derived from the gestalt school of psychology that Wertheimer founded. The basis of apparent motion is, however, unresolved and raises the general issue of how the visual system (or the brain more generally) parses visual information over time. The ongoing debate over how to rationalize perceived motion in the face of these problems is well beyond the scope of this chapter. In general, however, most theoretical explanations have been based on mathematical models of motion energy or on other nonlinear spatio-temporal

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Perception of Objects 247 (A) 1

2

1

2

1

2

(B)

Not

1

2

1

2

Figure 11.28 Apparent motion. A: When two fixed lights separated by an appropriate distance are turned on and off at an interval greater than 20ms but less than several hundred ms, observers see the first light (1) moving to the position of second light (2). B: Manipulating these variables, and varying the number of lights involved, elicits a variety of motion effects that are difficult to explain, such as why diagonal motion is not seen in this example.

filtering mechanisms. Another approach more in keeping with the approach that can rationalize aspects of brightness, color, and form is to suppose that the perceived directions and speeds of the moving objects are empirically determined by accumulated information about the possible sources of the inherently ambiguous stimuli. At present, however, there is no consensus about the strategy the visual system uses to generate motion percepts.

PERCEPTION OF OBJECTS The focus of the chapter has been on visual perceptual qualities as such—brightness, color, form, depth, and motion. No understanding of more complex percepts can emerge without first understanding these fundamental characteristics of visual percepts. It is obvious, however, that when we look at the world we see objects defined by these qualities. Moreover, objects that have particular significance for us—for example, human faces or symbols like letters

and numbers—are much more carefully inspected and attended to than other classes of objects. As already indicated, the recognition of objects by means of vision involves the ventral visual processing stream that eventually leads to the temporal lobe (see Figure 11.3). The region of the temporal lobe that supports object recognition is not uniform but is to some degree regionally specialized. Thus there is a relatively specific region on the inferior aspect of the temporal lobe where many neurons are responsive to faces (called the fusiform face area; Figure 11.29), another region that is involved in processing information about animals, another concerned with inanimate objects such as tools or houses, and still others concerned with recognizing words (Kanwisher, 2006). It is of course unlikely that every category of object we see has a dedicated area of temporal cortex underpinning recognition; thus how best to think about the organization of this part of the temporal lobe remains controversial. Similarly, whether object recognition entails the appreciation of significant parts (e.g., eyes, nose, mouth), a more global integration or some combination of these is much debated. Nonetheless, lesions of the inferotemporal cortex can clearly impair the ability to perform recognition tasks, sometimes quite selectively. Given that we perceive objects as unitary entities characterized by a variety of sensory attributes (size, weight, shape, color, texture, and so on), another issue is how these sensory qualities are brought together. This problem is generally referred to as the “binding problem” (Marr, 1982). There are several frameworks for thinking about a possible solution. One possible answer is fundamentally anatomical: The union of perceptual qualities being achieved by the convergence of information about various sensory properties in higher-order neurons whose activity would then represent a conjunction of the various qualities involved. This perspective is predicated on the idea that neurons representing percepts are at the apex of a processing pyramid. There are, however, logical obstacles to this interpretation, as already mentioned in discussing binocular fusion. Most neuroscientists have concluded that only the activity of a fairly large population of cells located in different brain regions could accomplish this feat. But if a dispersed population is involved, then how does the activity of this cohort of nerve cells become associated with the specific object in question? Some investigators have suggested that synchronized oscillatory activity among the relevant cortical neurons might serve this function. Another proposal is that the solution could lie in a rapid transition of attention to the activity of the various neurons representing different object qualities, the perception of unity being a result of this rapid transitioning. A more radical possibility is that neither physiological nor anatomical union is necessary.

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248 Vision (A)

(B) 1.00%

MR signal change

0.80%

White matter Face area

0.60% 0.40% 0.20% 0.00% ⫺0.20% ⫺5⫺4⫺3⫺2⫺1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time (s)

R

L

Figure 11.29 Functional magetic resonance imaging during a face recognition task. A: Face stimulus presented to a normal subject at time indicated by arrow. Graph shows activity change

in the relevant area of the right temporal lobe. B: Location of f MRI activity in right inferior termporal lobe. (Courtesy of Greg McCarthy; from Purves et al., 2004)

Whatever activity existed in the brain at a given moment that was consciously attended would constitute a percept, the binding following more or less automatically without any special mechanism being required. At present, all of these possibilities remain potential solutions to the conceptual puzzle of feature binding.

both the species and the individual. How the receptive field properties of visual neurons and the organization of neuronal populations can be understood in these terms is just beginning to be explored.

SUMMARY

Basole, A., White, L. E., & Fitzpatrick, D. (2003). Mapping multiple stimulus features in the population response of visual cortical neurons. Nature, 423, 986–990.

A great deal is now known about how information conveyed by light is processed in the primary visual pathway, including the primary visual cortex. How this information is processed in the higher order visual cortices is less well understood, however, and the generation of percepts remains a matter of debate. The basic challenge in understanding the strategy of neural processing in any of these regions is explaining how inherently uncertain retinal stimuli can give rise to definite perceptions and generally successful visually guided behavior. In each category of basic visual qualities—brightness, color, form, depth, and motion—the evidence points increasingly to an empirical strategy of vision as a means of contending with the inverse optics problem. The idea that what we see in response to retinal stimuli is a statistical manifestation of accumulated past experience rather than a logical analysis of the features of the retinal image runs counter to all our intuitions about vision. Nevertheless, the nature of the inverse problem and obvious discrepancies between what we actually see and physical reality are difficult to explain in any other way. The advantage of generating vision in this manner is that over evolutionary time, percepts—and the visual circuitry that underlies them—progressively incorporate the vast amount information derived from the experience of

REFERENCES

Blake, R., & Logothetis, N. K. (2002). Visual competition. Nature Review. Neuroscience, 3, 1–11. DeValois, R. L., & DeValois, K. K. (1993). A multistage color model. Vision Research, 33, 1053–1065. Fitzpatrick, D. (2000). Seeing beyond the receptive field in the primary visual cortex. Current Operations of Neurobiology, 10, 438–442. Gegenfurtner, K. R. (2003). Cortical mechanisms of color vision. Nature Review. Neuroscience, 4, 563–572. Helmholtz, H. L. F. V. (1866/1924). Helmholtz’s treatise on physiological optics. New York: Optical Society of America. Howard, I. P., & Rogers, B. J. (1995). Oxford psychology series: No. 29. Binocular vision and stereopsis. New York: Clarendon Press. Howe, C. Q., & Purves, D. (2005). Perceiving geometry: Geometrical illusions explained by natural scene statistics. New York: Springer. Hubel, D. H. (1988). Eye, brain, and vision, scientific American library series. New York: Freeman. Hubel, D. H., & Wiesel, T. N. (2005). Brain and visual perception: The story of a 25-year collaboration. Oxford: Oxford University Press. Julesz, B. (1995). Dialogues on perception. Cambridge, MA: MIT Press. Kanwisher, N. (2006, February 3). What’s in a face? Science, 311, 617–618. Knierim, J. J., & Van Essen, D. C. (1992). Neuronal responses to static texture patterns in area V1 of the alert macaque monkey. Journal of Neurophysiology, 67, 961–980. Kuffler, S. W. (1953). Discharge patterns and functional organization of mammalian retina. Journal of Neurophysiology, 16, 37–68. Land, E. H. (1986). Recent advances in retinex theory. Vision Research, 26, 7–21.

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7/14/09 5:14:10 PM

Additional Readings Lennie P. (1998). Single units and visual cortical organization. Perception, 27, 889–935. Livingstone, E. M., & Hubel, D. H. (1988, May 6. Segregation of form, color, movement, and depth: Anatomy, physiology, and perception. Science, 240, 740–749.

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Wheatstone, C. (1838). Contributions to the physiology of vision: Pt. I. On some remarkable and hitherto unobserved phenomena of binocular vision. Royal Society of London, 128, 371–394. Worgotter, F., & Eysel, U. T. (2000). Context, state and the receptive fields of striatal cortex cells. Trends in Cognative Science, 23, 497–503.

Long, F., Yang, Z., & Purves, D. (2006). Spectral statistics in natural scene predict hue, saturation, and brightness. Proceedings of the National Academy of Sciences, USA, 103, 6013–6018.

Wuerger, S., Shapley, R., & Rubin, N. (1996). On the visually perceived direction of motion by Hans Wallach: 60 years later. Perception, 25, 1317–1367.

Marr, D. (1982). Vision: A computational investigation into human representation and processing of visual information. San Francisco: Freeman.

Zeki, S. M. (1983a). Colour coding in the cerebral cortex: The reaction of cells in monkey visual cortex to wavelengths and colours. Neuroscience, 9, 741–766.

Maunsell, J., & Van Essen, D. (1983). Functional properties of neurons in the middle temporal visual area of the macaque monkey. Journal of Neurophysiology, 49, 1127–1147.

Zeki, S. M. (1983b). Colour coding in the cerebral cortex: The responses of wavelength-selective and colour-coded cells in monkey visual cortex to changes in wavelength composition. Neuroscience, 9, 767–781.

Milner, A. D., & Goodale, M. A. (1995). The visual brain in action. New York: Oxford University Press.

Zeki, S. (1989). A century of cerebral achromatopsia. Brain, 113, 1721–1777.

Mollon, J. D. (1995). Seeing colour. In T. Lamb & J. Bourriau (Eds.), Colour: Art and science (pp. 127–150). Cambridge: Cambridge University Press.

Zeki, S. (1993). A vision of the brain. Oxford: Blackwell Scientific Publications. Zihl, J., von Cramon, D., & Mai, N. (1983). Selective disturbance of movement vision after bilateral brain damage. Brain, 106, 313–340.

Nathans, J. (1987). Molecular biology of visual pigments. Annual Review of Neuroscience, 10, 163–194. Newsome, W. T., Britten, K. H., & Movshon, J. A. (1989, September 7). Neuronal correlates of a perceptual decision. Nature, 341, 52–54.

ADDITIONAL READINGS

Purves, D., Augustine, G. A., Fitzpatrick, D., Hall, W., LaMantia, A. S., McNamara, J. O., & Williams, S. M. (2008). Neuroscience (4th ed.). Sunderland, MA: Sinauer.

Barlow, H. B., & Mollon, J. D. (1982). The senses. Cambridge: Cambridge University Press.

Purves, D., Brannon, E. M., Cabeza, R., Huettel, S. A., LaBar, K. S., Platt, M. L., & Woldorff, M. (2008) Principles of cognitive neuroscience. Sunderland, MA: Sinauer. Purves, D., & Howe, C. Q. (2005) Perceiving geometry: Geometrical illusions explained by natural scene statistics. New York: Springer.

Berkeley, G. (1709/1976). A new theory of vision. Ayers, M.R., (Ed.) In Everyman’s library. London: Everyman/J.M. Dent. Bouvier, S. E., & Engel, S. A. (2006). Behavioral deficits and cortical damage in cerebral achromatopsia. Cerebral Cortex, 16, 183–191. Cornsweet, T. N. (1970). Visual perception. New York: Academic Press.

Purves, D., & Lotto, R. B. (2003). Why we see what we do: An empirical theory of vision. Sunderland, MA: Sinauer.

Courtney, S. M., & Ungerleider, L. G. (1997). What fMRI has taught us about human vision. Current Operations of Neurobiology, 7, 554–561.

Purves, D., Williams, S. M., Nundy, S., & Lotto, R. B. (2004). Perceiving the intensity of light. Psychology Review, 111(1), 142–158.

Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in primate cerebral cortex. Cerebral Cortex, 1, 1–47.

Robinson, J. O. (1998). The psychology of visual illusions. New York: Dover. (Original work published 1972)

Goodale, M. A., & Humphrey, G. K. (1998). The objects of action and perception. Cognition, 67, 179–205.

Rodieck, R. W. (1998). First steps in seeing. Sunderland, MA: Sinauer.

Horton, J. C. (1992). The central visual pathways. In W. M. Hart (Ed.), Adler’s physiology of the eye (pp. 728–772). St. Louis, MO: Mosby Yearbook.

Sacks, O. (1995). An anthropologist from Mars: Seven paradoxical tales. New York: Knopf. Sacks, O. (1996). The island of the colorblind. New York: Knopf.

Howe, C. Q., Lotto, R. B., & Purves, D. (2006). Empirical approaches to understanding visual perception. Journal of Theoretical Biology, 241, 866–875.

Shevell, S. H., & Monnier, P. (2006). Color shifts induced by S-cone patterns are mediated by a neural representation driven by multiple cone types. Visual Neuroscience, 23, 567–571.

Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology, 160, 106–154.

Stevens, S. S. (1975). Psychophysics. New York: Wiley.

Hubel, D. H., & Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195, 215–243.

Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2005). Choosing the greater of two goods: Neural currencies for valuation and decision making. Nature Reviews: Neuroscience, 6, 363–375. Turner, R. S. (1994). In the eye’s mind: Vision and the Helmholtz-Hering controversy. Princeton, NJ: Princeton University Press. Ungerleider, L. G., & Haxby, J. V. (1994). “What” and “where” in the human brain. Current Opinion in Neurobiology, 4, 157–165.

Hubel, D. H., & Wiesel, T. N. (1977). Functional architecture of macaque monkey visual cortex. Proceedings of the Royal Society, 198, 1–59. Kersten, D. (2000). High-level vision as statistical inference. In M. S. Gazzaniga (Ed.), The new cognitive neurosciences (pp. 353–363). Cambridge, MA: MIT Press. Knill, D. C., & Richards, W. (1996). Perception as Bayesian inference. New York: Cambridge University Press.

Ungerleider, J. G. , & Mishkin , M. ( 1982 ). Two cortical visual systems . In D. J. Ingle , M. A. Goodale , & R. J. W. Mansfield (Eds.), Analysis of visual behavior (pp. 549 – 586 ). Cambridge, MA : MIT Press .

Mountcastle, V. B. (1957). Modality and topographic properties of single neurons of cat’s somatic sensory cortex. Journal of Neurophysiology, 20, 408–434.

Wallach, H. (1935). Über visuell wahrgenommene Bewegungsrichtung. Psychologische Forscheung, 20, 325–380.

Mountcastle, V. B. (1998). Perceptual neuroscience: The cerebral cortex. Cambridge, MA: Harvard University Press.

Wertheimer, M. (1912/1950). Laws of organization in perceptual forms. In W. D. Ellis (Ed. & Trans.), A sourcebook of gestalt psychology (pp. 71–88). New York: Humanities Press.

Murray, S. O., Boyaci, H., & Kersten, D. (2006). The representation of perceived angular size in primary visual cortex. Nature Neuroscience, 9, 429–434.

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250 Vision Poggio, G. E. (1995). Mechanisms of stereopsis in monkey visual cortex. Cerebral Cortex, 3, 193–204. Poggio, G. F., & Poggio, T. (1984). The analysis of stereopsis. Annual Review of Neuroscience, 7, 379–412.

Sereno, M. I., Dale A. M., Reppas J. B., Kwong K. K., Belliveau J. W., Brady T. J., Rosen B. R., & Tootell R. B. (1995, May 12). Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science, 268, 889–893.

Pouget, A., Dayan, P., & Zemel, R. S. (2003). Inference and computation with population codes. Annual Review of Neuroscience, 26, 381–401.

Shepard, R. N., & Cooper, L. A. (1992). Representation of colors in the blind, color-blind and normally sighted. Psychology Science, 3, 97–103.

Rao, R. P. N., Olshausen, B. A., Lewicki, M. S. (Eds.). (2002). Probabilistic models of the brain: Perception and neural function. Cambridge, MA: MIT Press.

Shimojo, S., Paradiso, M., & Fujita, I. (2001). What visual perception tells us about mind and brain. Proceedings of the National Academy of Sciences, 98, 12340–12341.

Revonsuo, A., & Newman, J. (1999). Binding and consciousness. Consciousness and Cognition, 8, 123–127.

Simoncelli, E. P., & Olshausen, B. A. (2001). Natural images statistics and neural representation. Annual Review of Neuroscience, 24, 1193–1216.

Rock, I. (1984/1995). Perception. New York: Freeman.

Tootell, R. B., Reppas, J. B., Dale, A. M., Sereno, M. I., Malach, R., Brady, T. J., et al. (1995, May 11). Visual motion aftereffect in human cortical area MT revealed by functional magnetic resonance imaging. Nature, 375, 139–141.

Sakmann, B., & Creutzfeldt, O. D. (1969). Scotopic and mesopic light adaptation in the cat’s retina. Abteilung für neurophysiologie, Pflügers Arch, 313, 168–185. Salzman, C. D., Britten, K. H., & Newsome, W. T. (1990, July 12). Cortical microstimulation influences perceptual judgments of motion direction. Nature, 346, 174–177.

Wandell, B. A. (1995). Foundations of vision. Sunderland, MA: Sinauer.

Schiller, P. H. (1997). Past and present ideas about how the visual scene is analyzed by the brain. In Rockland K., Kaas J., Peters A., (Eds), Cerebral cortex and extrastriate cortex. New York: Plenum Press.

Yang, Z., & Purves, D. (2004). The statistical structure of natural light patterns determines perceived light intensity. Proceedings of the National Academy of Sciences, USA, 101, 8745–8750.

White, M. (1979). A new effect of pattern on perceived lightness. Perception, 8, 413–416.

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Chapter 12

Audition TROY A. HACKETT AND JON H. KAAS

ears produces a greater difference in sound arrival times (Sterbing-D’Angelo, 2007). Specializations for sound processing also occur at the cortical level. Across sensory systems, mammals with small brains, such as mice, process sensory information across limited arrays of only a few cortical areas. This is because cortical areas need to be large enough to contain a sufficient population of neurons to perform basic sets of functions (Kaas, 2000). For example, primary visual cortex needs to be of a certain minimal size in order to preserve the spatial information of the retinal image (Cooper, Herbin, & Nevo, 1993). Thus, mice and other small-brained mammals process auditory information at the cortical level over only a few cortical areas. However, mammals with large brains are released from this constraint. Some, such as the larger rodents, appear to have simply enlarged cortical areas without adding to the number of areas to any great extent. Others, especially primates, have increased the number of cortical processing stations, including both the number of cortical areas that are mainly involved with processing auditory information, and those areas involved in multisensory and higher-order processing. The focus of this chapter is on the elaborations and specializations of the auditory system of anthropoid primates, that is, monkeys, apes, and humans. Most of the elaborations of the auditory systems of anthropoids appear to be at the cortical level, in line with changes in other systems. But this, in part, may be because brain stem levels of processing have been studied in only a limited way in primates. Thus, some specializations may be discovered with further investigation. Much of what is known about cortical organization and function in anthropoid primates stems from physiological and anatomical studies of auditory cortex in Old World monkeys, although valuable results also have been obtained from New World monkeys. Very little is known about cortical organization in apes because invasive studies are no longer possible and noninvasive studies, such as fMRI, require some cooperation. What is known is from limited studies of cortical architecture, which can be

Audition, the process of hearing, depends on transforming perturbations in air pressure waves within a limited frequency range into nerve axon potentials by the receptor cells and the associated structures of the inner ear. Axons subserving these receptor cells then conduct these potentials to the auditory nuclei of the brain stem—the three cochlear nuclei where transformations of the neural signal already began. Further processing occurs over ascending brain stem pathways, involving a number of nuclei, terminating among subdivisions of the inferior colliculus in the midbrain. The subdivisions of the inferior colliculus project to subdivisions of the medial geniculate complex of the thalamus, which in turn relay to areas of auditory cortex. These areas of cortex provide feedback to previous stations, while distributing auditory information more broadly. The auditory system locates and identifies sounds, and it is especially important in humans as a means of mediating communication via speech. Variations in the organization and elaboration of the auditory system have allowed various species to succeed in a range of environments. For example, modification of the cochlear apparatus in a tunnel-living rodent, the mountain beaver, allows it to detect low-frequency changes in air pressure, possibly so that it can sense conspecifics and predators when they enter their underground tunnels (Merzenich, Kitzes, & Aitkin, 1973). In contrast, the auditory system has been altered in echo-locating bats so that it is sensitive to very high frequencies especially at and near the frequencies of the sounds that bats emit to reflect off prey and other objects (Dear, Simmons, & Fritz, 1993; Suga, 1990). As a result, bats have become the most successful of mammal orders next to rodents in terms of numbers of extant species. Other alterations of the auditory system occur more centrally. As a wellknown example, animals with large heads have an advantage over animals with small heads in locating the source of sounds, because the mass of the larger head better creates a difference in sound intensity for lateralizing the source of the sound, and the greater distance between the 251

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related to such studies in monkeys where other types of data have been collected. However, a greater understanding of the cortical regions involved in auditory processing in humans is rapidly emerging from fMRI studies. While comparative studies of cortical architecture suggest that the early stages of cortical processing are shared in monkeys and humans, monkeys clearly do not have the elaborations of the auditory system that are found in humans and allow for such proficiency in language. We start with a brief description of peripheral and brain stem levels of auditory processing, and then move on to thalamic and cortical levels in primates. In the first part of the review, relevant results come from a number of mammalian species, and even from other vertebrates because many anatomical investigations have concentrated on the auditory systems of cats, rodents, and nonmammals. The features described here are those that exist or are likely to exist in humans and other primates.

EARLY STAGES OF PROCESSING: EXTERNAL, MIDDLE, AND INNER EAR The auditory system detects oscillations of air pressure as they vary in time. The system is variably sensitive according to species-specific specializations, but generally sensitivity includes a middle range of oscillation frequencies while

excluding very low or very high frequencies. Human hearing extends from a low end of detecting oscillations in the range of 20 cycles per second (20 Hertz or 20 Hz) to around 20,000 Hz. Sensitivity in the higher range is reduced with age as damage to the inner ear accumulates. Sounds are the consequence of oscillations in air pressure. The auditory system is sensitive to the amplitudes of the oscillations, as well as the frequencies. Amplitude is usually specified by sound pressure levels (SPL) expressed in a logarithmic scale in decibels (dB), which covers the large range above threshold human hearing at about 0 dB and extends to around 120 dB, where sound becomes painfully loud. The transformations of air pressure oscillations to a neural code for sound begins in the cochlea of the inner ear (Figure 12.1). Before this stage, air pressure changes are reflected by the external ear, which varies in shape and in surface properties across mammalian species in ways that modulate the amplitude and frequency characteristics of air pressure waves as they are reflected off different parts of the external ear (pinna). The resulting small alterations in the oscillation pattern reflected off the upper or lower face of the pinna can provide subtle cues about the higher or lower source of the sound oscillation (Sterbing-D’Angelo, 2007). In mammals with movable ears, changes in ear position alter sensitivity to air pressure oscillations from various locations in ways that provide additional information about the location of sound sources. Humans, with Middle Ear

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Figure 12.1 The peripheral auditory system of humans includes the external ear, the middle ear, and the inner ear. Note: The external ear includes the pinna (auricle) and ear canal. The middle ear contains the three ossicles, and is separated from the external

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ear by the tympanic membrane. The auditory portion of the inner ear is the cochlea, which contains the sensory receptors (hair cells) of the auditory system.

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Early Stages of Processing: External, Middle, and Inner Ear

fixed external ears, depend more on head rotation, and the fixed position of the ears perhaps simplifies the computational task of sound localization. Air pressure changes are conducted via the external auditory canal to vibrate the tympanic membrane at the outer margin of the middle ear. An important function of the external auditory canal is to allow the tympanic membrane and the other components of the middle ear to be buried in the tissue of the head where they are less susceptible to traumatic damage. The role of the middle ear is to transfer oscillations in the air into oscillations of the fluid of the inner ear. Vibrations of the tympanic membrane (ear drum) via air pressure oscillations are transmitted across the middle ear space by a chain of three small bones (ossicles): The malleus (hammer), incus (anvil), and stapes (stirrup). The footplate of the stapes conducts pressure changes into the cochlea by its contact with the oval window (Figure 12.1). The round window is an open membrane in the bony cochlea that is displaced outward upon inward deflection of the oval window. Because the tympanic membrane is much larger than the footplate, a considerable gain in the force applied to the oval window occurs, which is sufficient to overcome the higher impedance of the fluid-filled cochlea. This force can be dampened for loud sounds by the reflexive contractions of small muscles attached to the ossicles. The cochlea of the inner ear is a complicated organ for the transduction of mechanical energy of displacements of the oval window into a neural code. The mammalian cochlea is a long tube of three compartments that is coiled

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like the shell of a snail, most likely to save space, but perhaps also to contribute to the transduction process. The three compartments constitute three parallel divisions of the coiled tube that are fluid filled. The middle compartment contains the organ of Corti, consisting of the sensory cells of the cochlea, the inner hair cells, as well as the outer hair cells and supporting cells (Figure 12.2). These cells are supported below by a basilar membrane and capped by the tectorial membrane that is attached to bone medial to the inner hair cells and makes contact with stereocilia that extend from upper surface of each hair cell. Vibrations of the middle ear are transmitted to the cochlear fluids via the oval window so that the basilar membrane and hair cells of the organ of Corti move relative to the tectorial membrane. The basilar membrane movement starts at the base of the cochlea at the oval window and proceeds various distances toward the apex. Vibrations of higher frequencies result in maximal membrane motion at the base of the cochlea, while lower frequencies displace the membrane more in more apical locations. Thus, sounds of high to low frequency maximally displace portions of the basilar membrane in a base to apex sequence along the basilar membrane. Sound frequency is thereby represented spatially along the length of the cochlea. The movements of the basilar membrane and hair cells result in the hairs of the hair cells being bent with a shearing action by the tectorial membrane, causing the hair cells to activate afferents of the auditory nerve that terminate in the cochlear nuclei of the brain stem. Single frequencies will activate

Scala vestibuli Organ Outer hair cells of Tectorial membrane Inner hair cells Corti

Scala media

Spiral ganglion Auditory nerve Basilar membrane Bony wall of Cochlea Scala tympani

Figure 12.2 A cross-section through the coiled cochlea showing the three fluid-filled compartments, or scalae.

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Note: The organ of Corti is housed within the scala media. It contains the inner and outer hair cells, tectorial and basilar membranes, and supporting cells.

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254 Audition

hair cells over limited extents of the base of the cochlea for high frequencies and over longer extents of the apex for low frequencies. Complex sounds of many frequencies will activate hair cells within a number of zones depending on the amplitudes of the component frequencies, as greater amplitudes activate more hair cells with great magnitude. Hair cell responses are influenced by two mechanisms intrinsic to the cochlea. First, the sensitivity of hair cells is increased by a metabolically active system that induces a difference in the ionic charge of the endolymphatic fluid of the scala media (about ⫹80 mV) compared to the cytoplasm within the hair cells (–40 to –70 mV). When the stereocilia of hair cells are deflected by oscillations in the cochlea, positively charged potassium ions flow into the hair cells, causing depolarization and release of neurotransmitters to the afferent neurons of the auditory nerve. Second, the outer cells become shorter or longer according to signals they receive from neurons in the superior olivary complex of the brain stem and intrinsic properties of the outer cells. This alters the mechanical properties of the cochlea to increase responses to sound. This effect is known as the cochlear amplifier, and it is likely the cause of weak sounds emitted by the inner ear—the otoacoustic emissions. The human cochlea has about 3,500 inner hair cells and about 14,000 outer hair cells. Hearing loss results from damage to hair cells, and hair cell loss is permanent in mammals.

THE AUDITORY NERVE Auditory nerve fibers are activated by mechanical stimulation of hair cells. Mechanical stimulation of the hair bundles (stereocilia) of the inner hair cells as they move relative to the overlying tectorial membrane causes hair bundles to pivot at their base and reduce or increase tension on elastic tip links that extend from the tip of one stereocilium to the side of its taller neighbor (Corey, 2007; Holt & Corey, 2000). Deflection toward the taller cilia increases tension and opens transduction channels, while deflection toward the shorter cilia allows channels to close. The current flow generated by the open channels activates the afferents of the auditory nerve that innervate the hair cells. The major functions of the auditory afferents are to encode the frequencies and intensities of sounds. Because the basilar membrane and the hair cells are displaced in a pattern that reflects the sound waveform, auditory nerve afferents are activated during the phase of the waveform during which the hair cells are displaced in the direction that opens transduction channels. When the sound waveform is repeated at a given frequency, producing the sensation of a pure tone, the auditory nerve afferents tend to

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discharge at the same phase of each waveform cycle. This feature of the responses of auditory nerve fibers, which is most apparent for lower frequency tones, is called phase locking. Phase locking means that a given afferent will discharge once or twice for each cycle of the waveform, and the temporal spacing of these discharges will be greater for low rather than high tones. Because the discharge pattern over a number of activated afferents corresponds to the frequency of the stimulating tone, this pattern provides a temporal code for sound frequency, at least for low frequencies where the phase locking is most precise. A second important source of information about sound frequency depends on the location along the organ of Corti where afferents are activated (a place code). As the traveling wave displaces the basilar membrane, hair cell activation peaks near the base of the cochlea for high-frequency sounds and near the apex for low-frequency sounds. Complex sounds composed of a combination of different frequencies activate different populations of afferents. Sound intensities reflect sound pressure levels. Low sound pressure levels activate few afferents at low levels within restricted portions of the cochlea. Higher sound pressure levels activate more afferents over longer portions of the cochlea and at higher discharge rates. These changes in neural discharge patterns provide information on sound intensity. Information on sound frequency and sound intensity is clearly reflected in the discharge patterns of single auditory nerve afferents. At low sound pressure levels, each afferent will be activated by sounds over a narrow range of frequencies. As the sound pressure level is lowered further, a level is reached where the afferent discharges just above spontaneous levels only at a particular sound frequency. This is called the characteristic or best frequency for that afferent. As sound pressure levels are systematically increased, the afferent will discharge at higher and higher rates for the characteristic sound frequency and will also discharge over a greater range of frequencies, typically with a sharp high-frequency cutoff. When afferent responses are plotted relative to tone frequency and sound pressure levels, the afferent response zone is called the frequency response area, or tuning curve (Figure 12.3), for that neuron. Many neurons at higher levels of the auditory system retain this information about sound intensity and frequency and have similar, but usually broader tuning curves. In addition to the signals sent by the large Type I afferents from the inner hair cells, thin afferents from the outer hair cells, the Type II afferents, send information of uncertain significance to the cochlear nuclei. Complex sounds add complexity to the simple summary of coding in the auditory nerve fibers because auditory neurons become less responsive (adapt) to a repeated

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Processing Auditory Signals in the Brain Stem

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Figure 12.3 A typical plot of the frequency response area, or tuning curve, of an auditory neuron. Note: The firing rate of the neuron is recorded as both the frequency of the sound (tone) and the intensity (sound pressure level) are systematically varied. For each combination of intensity and frequency, the normalized firing rate of the neuron is shown, where 1.0 ⫽ maximum firing rate. At the lowest effective intensity, the neuron responds to a single frequency (or a narrow range of frequencies) that is called the characteristic or best frequency. In this example, the best frequency is 1.2 kHz. The plot also reveals that the neuron is responsive to other frequencies at higher intensities. Plot courtesy of Corrie R. Camalier.

stimulus, and responses to one tone can reduce responses to another tone. When auditory nerve responses to one sound are reduced by a second sound, the first sound is said to mask or hide the second sound. Other complications arise from the roles of the three systems of brain stem efferent neurons that send information to the auditory periphery. As noted, olivocochlear efferents activate outer hair cells to alter their shapes and the mechanical properties of the organ of Corti. Other olivocochlear efferents terminate on the afferents of the inner hair cells to inhibit conduction. In addition, brain stem motor neurons activate the muscles of the middle ear, reducing sensitivity to intense sounds.

PROCESSING AUDITORY SIGNALS IN THE BRAIN STEM Auditory processing in the brain stem of mammals involves three divisions of the cochlear nucleus that receive direct inputs from the auditory nerve, nuclei of the superior olivary complex that combine information about sound sources (localization) from the two ears, nuclei of the lateral lemniscus (the ascending auditory pathway), and subdivisions of the inferior colliculus of the midbrain (Figure 12.4).

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C

C

Figure 12.4 The major ascending auditory pathways from the cochlea (C) to primary auditory cortex (AC). Note. Subdivisions of nuclear complexes and minor pathways are not shown. Major pathways and projections are indicated by thick lines. CN ⫽ cochlear nuclear complex; SOC ⫽ superior olivary complex; NLL ⫽ nuclei of the lateral lemniscus; IC ⫽ inferior colliculus; MGc ⫽ medial geniculate complex. From Hackett and Kaas, 2002. Adapted with permission.

The subdivisions of the inferior colliculus project in turn to the medial geniculate complex of the dorsal thalamus, where neurons relay auditory information to auditory cortex. Auditory information reaches the brain stem via large, rapidly conducting, myelinated type I axons in the auditory or eighth cranial nerve with cell bodies in the spiral ganglion of the cochlea. These axons terminate in the cochlear nuclei of the lower brain stem in spatial patterns that preserve their order of origin in the cochlea. Thereby, their terminations are cochleotopic or tonotopic in organization. The terminating axons branch to activate several different populations of neurons of different morphological types. Local circuits and synaptic arrangements result in these neuron types having different response properties, thus eighth nerve information is used in different ways by multiple systems of neurons that process information in parallel. Cochlear Nuclei The cochlear nuclear complex is commonly divided into a ventral part, which is divided further into an anterior ventral cochlear nucleus (AVCN), and a posterior ventral cochlear nucleus (PVCN), and a dorsal part, the dorsal

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cochlear nucleus (DCN). Auditory nerve axons entering the cochlear complex divide into an ascending branch that innervates the AVCN and a descending branch that innervates the PVCN and the DCN. These axons and their branches maintain the orderly cochleotopic arrangement of their origin in the organ of Corti, and thereby impose tonotopic patterns of organization on the three cochlear nuclei. Axons responsive to low frequencies terminate in the most ventral portions of the three nuclei, while axons responsive to progressively higher frequencies terminate in more and more dorsal portions. The cochlear nuclei contain small, intrinsic inhibitory neurons, an excititory, granule cell, and several types of relay neurons that project to other brain stem auditory structures. Each type preserves specific details of the discharge patterns of the afferent fibers of the auditory nerve (Romand & Avan, 1997). The output of these neurons is further altered in various ways by the circuitry within the complex. This is the start of a diversification of response types to permit a range of auditory functions.

SUPERIOR OLIVARY COMPLEX Encoding the cues used to localize sound is an important function of the superior olivary complex, which contains nuclei specialized for this purpose. The localization of sound sources in space largely depends on two cues resulting from differences in the information relayed from the two ears. As the waveforms of sounds located on one side of the head reach the closer ear first, there is a time difference in the activation of the eighth nerve afferents between the two ears. Because neural discharges are phased locked to the waveform, they are synchronized for stimuli with sources of equal distances from the two ears, or offset by various amounts with discharges from the closest ear occurring first. The phase locking information is most useful for sounds of low frequencies. The second important cue about sound location comes from intensity differences in the sound waves that reach the two ears, as the head dampens the air pressure wave so that air pressure levels are higher in the closest ear. Damping is greatest for higher frequencies. For both cues, a larger head provides better information in terms of phase and intensity differences. Other cues are subtle and depend on the reflective properties of the external ear and the environmental substrate so that the intensityfrequency spectrum of sounds from low or high sources sounds different. Only the processing of information from the phase and intensity difference is well understood. Sound location information is extracted from phase and intensity differences in the binaural signals by neurons in the superior olivary complex of the brain stem (Yin &

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Chan, 1990). Cells in the cochlear nuclear complex project to the medial superior olivary nuclei of both sides of the brain stem. Inputs to the ipsilateral medial superior olive course from ventral to dorsal, synapsing on a sequential line of medial superior olivary neurons. Those inputs from the contralateral cochlear complex synapse on the same sequence of neurons from the opposite direction. Because distance is also time in these sequences of activation (the axons are considered delay lines), neurons at different locations in the medial superior olive are simultaneously activated depending on the location of the sound source. Because activation by both ears is necessary for above threshold responses by medial superior olivary neurons, the array of neurons in the structure represents auditory space in the horizontal plane, and provides a place code for space. This has been called a computational map of auditory space (Knudsen, du Lac, & Esterly, 1987). Cells in the cochlear nucleus also project to the ipsilateral lateral superior olive and to the contralateral nucleus of the trapezoid body, which in turn projects to the adjacent lateral superior olive. As the projections to the lateral superior olive from the nucleus of the trapezoid body are inhibitory, neurons in the lateral superior olive of each side of the brain stem are highly activated when sound pressure levels are highest at the ipsilateral ear and they are most inhibited when the sound pressure levels are highest at the contralateral ear. As the sound pressure level differences increase in a positive manner at the ipsilateral ear, the olivary neurons increase in firing rate. Thus, they provide a rate code for sound location in the horizontal plane. This use of sound level differences as a cue to sound source location is most useful for high-frequency sounds. Nuclei of the Lateral Lemniscus The axons from the cochlear nuclei that cross the brain stem and ascend to the contralateral inferior colliculus are joined by axons from the superior olivary complex to form the pathway known as the lateral lemniscus (Figure 12.4). Axons of ipsilaterally projecting auditory neurons also join this pathway. In most mammals, collections of neurons form two nuclei within the axons of the lateral lemniscus, the ventral nucleus (VNLL), and the dorsal nucleus (DNLL) of the lateral lemniscus. The main inputs to the ventral nucleus are from the contralateral ventral cochlear nucleus and projections are mainly to the central nucleus of the inferior colliculus. The dorsal nucleus receives bilateral inputs from the anterior ventral cochlear nucleus and the lateral superior olive, an ipsilateral projection from the medial superior olive, and other inputs from the dorsal nucleus of the lateral lemniscus. The projections of the dorsal nucleus are to the central nuclei of the inferior

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Superior Olivary Complex

colliculus of both sides, and, more sparsely, to the deep layers of the superior colliculi of both sides. These connections suggest that the ventral nucleus is involved in processing signals from the contralateral ear, and the dorsal nucleus in binaural processing, but the precise roles of these nuclei remain unclear. Auditory Midbrain: Inferior Colliculus and Deep Superior Colliculus The relay of auditory information to the auditory thalamus and then to auditory cortex depends on the inferior colliculus (Ehret, 1997; Spangler & Warr, 1991). The inferior colliculus has been subdivided in several ways, but most investigators define a central nucleus, an external or lateral nucleus, a dorsal cortex, and sometimes a pericentral nucleus (Figure 12.5). The large central nucleus consists of a series of disk-shaped layers with rather indistinct boundaries that consist of sheets of neurons with dendritic arbors that are flattened in the plane of the layers and afferent axon arbors from the axons of the ascending auditory lemniscal pathway. Intrinsic connections course rostrocaudally within the plane of each layer, as do commissural connections connecting each layer with its counterpart within the opposite central nucleus. Each layer contains neurons that are maximally responsive to nearly the same characteristic frequency (isofrequency layers), and they project in a topographic manner to a tonotopically matched layer of (A)

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the ipsilateral ventral nucleus of the medial geniculate complex (a smaller contralateral projection also exists in at least some mammals). Thus, neurons within layers of the central nucleus receive frequency specific information from the cochlear complex and other brain stem auditory nuclei, share that information via intrinsic connections between neurons within layers and commissural connections between frequency matched layers of the two colliculi, and project as a unit to a layer of the ventral nucleus of the medial geniculate complex, thereby creating a sequence of frequency matched thalamic layers. These geniculate layers in turn have the topographic projections that provide primary areas of auditory cortex with their topotopic organization. The dorsolateral layers of the central nucleus of the inferior colliculus represent the lowest frequencies, while the ventromedial layers are devoted to the highest frequencies (Schreiner & Langner, 1988). The external nucleus of the inferior colliculus, or external cortex, forms a rostral cap on the colliculus where neurons receive ascending auditory inputs, inputs from nonprimary areas of cortex, and even ascending somatosensory inputs. Projections are to the medial and dorsal nuclei of the medial geniculate complex. Thus, the external nucleus appears to have a role in multisensory functions. The dorsal cortex of the inferior colliculus has three or four layers over most of its extent. The dorsal cortex caps the dorsal ends of the isofrequency layers of the central nucleus (Irvine, 1986). Descending inputs from primary

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Figure 12.5 A: Connections of subdivisions of the inferior colliculus with the auditory thalamus. B: Subdivisions of the thalamic medial geniculate complex with auditory cortex. Note. Proposed subdivisions of the inferior colliculus include the central nucleus (ICc), the lateral nucleus (LN), the dorsal cortex (DC), the dorsal medial nucleus (DM), and the ventral medial nucleus (VM). The subdivisions of the medial geniculate complex of the auditory thalamus include the ventral nucleus (V), the medial or magnocellular nucleus (M), and anterior (AD) and posterior (PD) divisions of the dorsal nucleus (D). The

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suprageniculate nucleus (Sg) also gets auditory inputs. In B, the lateral geniculate nucleus (LGN) and the substantia nigra (SN) are shown for reference. Auditory cortex, shown for a macaque monkey on the left in panel B, includes a core, belt, and parabelt (see text). Part of the parietal lobe has been cut away to reveal auditory cortex on the upper bank of the lateral sulcus. Areas surrounding auditory cortex include dysgranular insular cortex (Id), granular insular cortex (Ig), retroinsular cortex (Ri), pro, TpT and Ts2. CiS, circular sulcus.

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258 Audition

auditory cortex in monkeys (Fitzpatrick & Imig, 1978; Luethke, Krubitzer, & Kaas, 1989) and other mammals terminate within isofrequency contours in the dorsal cortex that extend into the dorsal ends of isofrequency layers in the central nucleus. The dorsal cortex receives some ascending auditory inputs, as well as connections that are mostly intrinsic to the layers of the central nucleus. Projections are to the dorsal nucleus of the medial geniculate complex. Because the major thalamic connections of the dorsal cortex differ from those of the central nucleus, and thereby contribute to a “nonprimary” auditory pathway, distinguishing the dorsal cortex from the central nucleus seems justified. Deep Layers of the Superior Colliculus The superior colliculus is predominately visual in function, with the superficial layers receiving direct inputs from the retina, as well as inputs from most or all of the areas of visual cortex (Kaas & Huerta, 1988). These inputs are retinotopically organized, and form a representation or map of the contralateral visual hemifield. Connections between the superficial and deeper layers instruct a motor map in the deeper layers so that visual inputs from any location in contralateral visual space help direct eye and head movements so that the source of the visual input is foveated (looked upon). The deeper layers of the superior colliculus also receive somatosensory and auditory input both from the brain stem and from the cortex, and these layers also have rather crude representations of auditory space and body surface location. Many of the deep neurons are multisensory. The auditory and somatosensory inputs contribute to the motor maps so that the eyes are directed toward imposing sights, sounds, and touches. While an auditory space map has been found in the homologue of the mammalian external nucleus of the inferior colliculus of owls (Cohen & Knudsen, 1999), the crude map of auditory space in the deep layers of the superior colliculus is the only auditory space map that has been found in the midbrain or at higher levels in mammals.

AUDITORY THALAMUS The auditory thalamus includes the medial geniculate complex, with inputs from the inferior colliculus, and several other nuclei with auditory and multisensory functions, the suprageniculate nucleus (Sg), the posterior nucleus (PO), the medial pulvinar (PM), and the auditory sector of the thalamic reticular nucleus (RT-aud). The medial geniculate complex is commonly divided into a ventral or principal nucleus (MGv), a medial or magnocellular nucleus (MGm), and a dorsal nucleus (MGd) that is sometimes divided into anterodorsal (MGad) and posterodorsal (MGpd) components

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(Figure 12.5A; Jones, 2006). Each of these divisions or nuclei has a distinctive histological appearance (histoarchitecture), pattern of connections, and populations of neurons with differing response properties. Ventral Nucleus (MGv) The ventral nucleus has a higher cell-packing density than other nuclei of the medial geniculate complex, making it easy to delimit in mammals, including humans and other primates (Hirai & Jones, 1989). The inferior colliculus, especially the central nucleus, projects densely to MGv. The inputs from the ipsilateral inferior colliculus are much denser than those from the contralateral inferior coliculus. In all studied mammals, MGv projects to the primary auditory cortex, A1. In addition, most, and perhaps all, mammals have more than one primary or primary-like area, and these areas of the auditory core receive MGv inputs. In monkeys, and probably other primates, MGv projects to three primary-like areas of the core of the auditory cortex, A1, the rostral (R) area, and the rostrotemporal (RT) area (Hashikawa, Molinari, Rausell, & Jones, 1995; Luethke et al., 1989; Morel, Garraghty, & Kaas, 1993; Morel & Kaas, 1992). Rodents and carnivores have a primary auditory area (A1), and anterior auditory field (AAF), both with MGv inputs. MGv receives topographically organized inputs from the tonotopically organized central nucleus of the inferior colliculus, and is thereby tonotopically organized (Imig & Morel, 1985). The inferior colliculus inputs are aligned in isofrequency sheets or layers in MGv, much like the layers of an onion, and these isofrequency layers project to isofrequency bands crossing the core auditory areas, A1, R, and RT. Neurons in MGv have frequency intensity tuning curves in that they respond at lowest sound pressure levels to a characteristic or best frequency, and to wider ranges of frequencies at higher sound pressure levels. Most of these neurons are responsive to both ears and are sensitive to interaural differences in sound intensity and time, thereby providing distributed information about sound source location, without forming a representation of sound location. The smaller medial, internal, or magnocellular nucleus of the medial geniculate complex (MGm) consists of large cells and groups of smaller cells along the medial aspect of the complex. MGm appears to get inputs from the external nucleus and parts of the central nucleus and the central nucleus of the inferior coliculus (Calford & Aitkin, 1983) as well as the deep layers of the superior colliculus (Kaas & Huerta, 1988). Other inputs from vestibular nuclei and spinothalamic pathway have been suggested, but also questioned (Jones, 2006). Inputs from the external nucleus of the inferior colliculus, the deep layers of the superior coliculus, and the spinothalamic pathway may account for

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Auditory Cortex in Mammals

the responsiveness of neurons in MGm to somatosensory, as well as auditory stimuli. MGm projects broadly to the auditory cortex, including the core and belt areas in primates. Reflecting the mixture of cell size in MGm, recordings indicate that some neurons are binaural and have short-latency responses to pure tones, while others have long-latency responses, broad frequency tuning, and often respond poorly to tones. The dorsal nucleus (MGd) caps the medial geniculate complex. MGd has larger and less densely packed neurons than MGv. MGd also is characterized by a reduced number of parvalbumin positive neurons and increased number of calbindin positive neurons (Molinari et al., 1995). In monkeys, the dorsal nucleus is sometimes divided into an anterodorsal nucleus with more densely packed neurons, as in MGv, and a posterodorsal nucleus that is more typical of MGd of other mammals in cytoarchitecture (Jones, 2006). Inputs to MGd have not been well determined for primates, but the main source appears to be from the external nucleus of the inferior colliculus, with little or no input from the central nucleus (Hashikawa et al., 1995). MGd projects broadly to areas of the auditory belt in monkeys (de la Mothe, Blumell, Kajikawa, & Hackett, 2006; Hackett, Stepniewska, & Kaas, 1998a, 1998b; Molinari et al., 1995; Morel et al., 1993; Morel & Kaas, 1992). In rodents, at least, neurons in MGd are broadly tuned to tone frequency and respond at a long latency (Zhang, Yu, Liu, Chan, & He, 2008). Other thalamic nuclei that are involved in auditory processing include the suprageniculate (Sg) nucleus, the limitans (Lim) nucleus, and the medial pulvinar (PM), which have widespread connections with belt, parabelt, and higher-order areas of auditory cortex in primates (Kaas & Hackett, 2000). These nuclei have multisensory properties and probably have a role in modulating the activities of neurons in these cortical areas. In addition, a portion of the reticular nucleus of the ventral thalamus receives inputs from collaterals of thalamocortical axons from the medial geniculate cortex and other inputs from corticothalamic axons projecting from the auditory cortex to the medial geniculate complex. The auditory portion of the reticular nucleus projects via GABAergic neurons to the medial geniculate complex, thereby adding a source of inhibition in the complex, in addition to that provided by intrinsic inhibitory neurons (Huang, Larue, & Winer, 1999).

AUDITORY CORTEX IN MAMMALS The auditory cortex includes areas of the cortex that are mainly or exclusively involved in the processing of auditory stimuli. These include the primary or primary-like

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areas, the surrounding second-level belt areas, and thirdlevel parabelt areas (Figure 12.5B). Across mammals, and even within a taxonomic group, proposals of how the auditory cortex is divided into areas have varied. Some of this variation undoubtedly reflects real differences between taxa, but some of the variation likely results from the sparseness and ambiguity of the collected data. Much of the earlier research on cortical organization was in cats, where a core of primary-like areas have been described, an anterior auditory field, an A1, and two posterior auditory fields that are less clearly primary (Winer & Lee, 2007). All three of the core areas have neurons that respond well to pure tones and are tonotopically organized. An anterior auditory field (AAF) and A1 are so much alike that either one could have been called A1. Likewise, in rodents, fields that resemble AAF, A1, and PAF have been described, with the AAF field sometimes being identified as A1, rather than the usually identified middle field as A1 (Kaas, 2008). In both cats and rodents, the primary-like core fields are surrounded by a number of less easily defined secondary auditory fields. Concepts of the auditory cortex in monkeys and other primates have gradually developed over years of research, and here again there is evidence for three primary fields, A1, a more rostral area (R), and a rostraltemporal area (RT). As for core areas in carnivores and rodents, these three areas are considered primary because they share a number of features. This includes direct topographically organized inputs from the tonotopically organized ventral nucleus of the medial geniculate complex. As a result of inputs, A1, R, and RT are tonotopically organized. These core areas also have the architectonic features of primary sensory areas of the cortex. This includes a layer 4 that is packed with small neurons, rather dense myelination, a high level of expression of cytochrome oxidase in layer 4, and a high level of expression of the calcium-binding protein, parvalbumin. In many mammals, auditory and other sensory areas also have high levels of AchE early in postnatal development, but monkeys and other anthropoid primates continue to express high AchE levels in the mature brain. Given that it has only become gradually apparent that most mammals have an auditory core of two to three primary areas, the question of the identities of areas termed A1 in cats, rodents, and primates has only recently emerged. In cats and rats, A1 represents high to low tones in a rostrocaudal gradient across cortex, the opposite of the direction of the tonotopic gradient in monkeys. This suggests that A1 in monkeys and other primates is not the same area (homologous) as A1 in rodents and carnivores. Instead, the rostral area, R, of primates has the caudorostral tonotopic gradient of high-to low-frequency representation expected for A1. Indeed, sometimes R appears to have been mistaken

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260 Audition

for A1 in monkeys. Alternatively, some (e.g., Jones, 2006) propose that the expansion of the temporal lobe in primates rotated A1 from its primitive position so that its caudal end actually became its rostral end, thus creating an apparent but false impression of a tonotopic organization reversed from primitive A1. However, while considerable rotation does occur, it is not obvious that the rotation is enough to reverse the tonotopic gradient. In addition, this proposed rotation would mean that a belt area on the caudal border of A1 in monkeys, area CM (see below) is in the relative position of the anterior auditory field of rats and cats, a core auditory area. Thus, the homology of primate A1 with A1 of other taxa remains an open question. Because the following review focuses on auditory cortex of primates, this question of homologies of primate and nonprimate areas can be avoided here.

AUDITORY CORTEX IN PRIMATES Auditory Core The auditory core includes those auditory areas that are primary or primary-like. Primary auditory areas have major activating inputs from the ventral nucleus of the medial geniculate complex (MGv), patterns of tonotopic organization, neurons that are highly responsive to pure tones, have frequency tuning curves with a characteristic or best frequency, histological features of primary sensory cortex, and projections that drive neurons in other cortical areas. In monkeys and probably other primates, the auditory core includes the classically defined primary auditory area, A1, and the more recently defined rostral area, R, and rostrotemporal area, RT. Primary area, A1, has a clearly defined gradient of tonotopic organization that proceeds from the caudomedial border to the rostrolateral border of A1 (Imig, Ruggero, Kitzes, Javel, & Brugge, 1977; Kosaki, Hashikawa, He, & Jones, 1997; Merzenich & Brugge, 1973; Morel et al., 1993; Morel & Kaas, 1992). Lines or strips of isofrequency representation cross this gradient in a rostromedial to caudolateral direction. The axon arbors of thalamocortical axons from MGv tend to elongate along the isofrequency contours, and intrinsic connections of A1 are elongated along these contours. The reciprocal cortical connections of A1 are most dense between A1 and adjacent cortical areas, including R of the core and ML, CL, CM, and MM of the auditory belt. A few connections are with more distant targets, including RT of the core and AL and RM of the belt. Remarkably, A1 has few if any connections with more distant auditory areas in the parabelt and beyond. Callosal connections are largely with A1 of the opposite

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hemisphere. Because neurons in A1 are sensitive to tone frequency, sound intensity, and binaural differences in stimulation, the neurons in A1 preserve those aspects of the response characteristics of MGv and ICc neurons that are important in identifying and localizing sounds. Architectonic studies have identified an auditory core in humans and even in chimpanzees (Hackett, Preuss, & Kaas, 2001; Rivier & Clarke, 1997; Sweet, DorphPetersen, & Lewis, 2005; Wallace, Johnston, & Palmer, 2002), and functional imaging (fMRI) studies in humans indicate that part of that core has the tonotopic organization of A1 (e.g., Talavage et al., 2004). The shape of the auditory core in chimpanzees and humans suggests that there is room for areas R and RT, and the fMRI results in humans provide direct evidence for R. Thus, all anthropoid primates (monkeys, apes, and humans) appear to have a similar auditory core that consists of A1, R, and possibly RT. Finally, judging from microelectrode mapping studies of tonotopic patterns in prosimian galagos (Brugge, 1982), prosimians have a core with at least two areas, A1 and R. Area R, somewhat smaller than A1, has a tonotopic organization that mirrors that of A1 (Figure 12.6). The response properties of neurons in R are highly similar to those of A1, although there may be some differences. As noted previously, major activating inputs are from MGv, while cortical connections are mainly with adjoining areas, A1, RT of the core, and RM and AL of the belt. Callosal connections involve mainly RT of the opposite hemisphere. Area RT is smaller than either R or A1, and, because of its size and rostroventral position, has received only limited attention in experimental studies. RT appears to have a tonotopic organization that is reversed from that of R so that low tones are represented in RT near the R border, while high tones are represented at the rostroventral border of RT (Figure 12.6C). The most extensive microelectrode mapping data from RT comes from studies on marmosets, a small New World monkey (Bendor & Wang, 2005), while limited, but supportive evidence comes from owl monkeys (Imig et al., 1977; Morel & Kaas, 1992; Recanzone, Schreiner, Sutter, Beitel, & Merzenich, 1999). Connections of RT have not been fully explored, but they include inputs from MGv and connections with adjacent core (R) and belt (RTL and RTM) areas. Auditory Belt The narrow 2 to 3 mm wide auditory belt consists of cortex that surrounds the core, but is clearly outside of the architectonically defined core. This means that the histological features of the primary sensory cortex are greatly muted or absent. Thus, the belt expresses less parvalbumin,

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Figure 12.6 Subdivisions of auditory cortex (A) and cortical connections of auditory cortex (B) in macaque monkeys. Note. The circular sulcus has been flattened to show medial auditory areas and the dorsal bank of the lateral sulcus has been removed to expose the lower bank. The core region (dark shading) contains three areas, A1, R, and RT (see text), the belt—a series of eight areas (CM, CL, ML, AL,

cytochrome oxidase, myelin, and acetylcholinesterase. In Nissl preparations, layer 4 is thinner and less densely packed with small neurons. More importantly, the belt is defined by dense interconnections with the fields of the auditory core. Because the belt receives only sparse inputs, at best, from the ventral nucleus of the medial geniculate complex, with most of its thalamic inputs coming from the dorsal and magnocellular divisions of the complex, belt areas likely depend on inputs from the core for most of their responsiveness to auditory stimuli. However, this has been tested only for the caudomedial area, CM, where lesions of A1 abolish the tuning of CM neurons to tone frequency, but some responses to auditory stimuli as well as to somatosensory stimuli remain (see the discussion that follows). The auditory belt appears to consist of eight auditory fields (Figure 12.7). In part, this conclusion stems from the evidence that each core area projects most densely to adjacent regions of the belt (Morel et al., 1993; Morel & Kaas, 1992; see Kaas Hackett, 2000). This suggests the existence of at least three belt areas medial to the core and three lateral to the core. Other belt areas could occupy the ends of the belt, and microelectrode recording and other evidence supports the conclusion that two areas, CM and CL, cap the caudal end of the core. Because neurons in the belt reflect a second level of cortical processing, these neurons are generally more sensitive to anesthetics and thus more difficult to activate in anesthetized animals. As a result, less is known about their response properties. In addition, these neurons are likely to have more complex response properties than neurons in the core, and they are typically less responsive to pure

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RTL, RTM, RM, and MM), and the parabelt, two divisions (RPB and CPS). Sulci are labeled for reference. Central sulcus, CS, lateral sulcus, LS, lunate sulcus, LuS, superior temporal sulcus, STS, intraparietal sulcus, STS, arcuate sulcus AS, principal PS. Ts2, Ts1, and pro are proposed subdivisions of the temporal lobe. Other abbreviations as in Figure 12.5. In B, arrows indicate connections between areas, with dotted lines marking less dense connections.

tones. Nevertheless, many neurons in the belt are responsive either to pure tones or frequency-centered narrow bands of noise so that best or favored frequency can be estimated for recorded neurons, and crude tonotopic patterns of organization within belt fields can be estimated (Rauschecker & Tian, 2004; Rauschecker, Tian, & Hauser, 1995). Such recordings indicate that areas AL and ML have tonotopic organizations that parallel those in adjoining areas R and A1, and that areas CM and CL may have tonotopic organizations that mirror reversals of that in A1 (Kajikawa, de la Mothe, Blumell, & Hackett, 2005). Other proposed belt fields may also contain representations of tone frequency. However, the generally broad tuning of belt neurons to frequency and the weak responsiveness of these neurons to pure tones suggests that the tonotopic patterns are not the dominant feature of their intrinsic organization, but rather a weakly preserved consequence of their connection patterns with A1. The belt areas are connected with core and parabelt areas. Adjacent areas have the strongest connections, so that the caudal belt areas (CM, CL, ML) are mainly connected with the A1 in the core and the caudal division of the parabelt (CPB; Hackett et al. 1998a; Jones, Dell’Anna, Molinari, Rausell, & Hashikawa, 1995; Morel et al., 1993; Morel & Kaas, 1992). Similarly, the rostral areas of the belt have stronger connections with the rostral divisions of the core and parabelt. This topographic pattern of connections extends to areas beyond the auditory cortex in the temporal, frontal, and parietal lobes (Romanski, Bates, & Goldman-Rakic, 1999; Romanski et al., 1999), so that caudal and rostral areas of the belt have connections with different areas in those regions.

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this region. The parabelt receives inputs from the MGd and MGm in thalamus and is broadly connected with all of the belt areas. As mentioned, the connections of the parabelt are topographic, so that the RPB and CPB are most strongly connected with rostral and caudal areas of the belt, respectively. The parabelt has extensive connections with areas beyond the auditory cortex. Those connection patterns match the rostrocaudal topography of the belt areas, but tend to be stronger than those in the belt (Hackett, Stepniewska, & Kaas, 1999; Romanski, Bates, et al., 1999, Romanski, Tian, et al., 1999). The distinctive connections of the rostral and caudal areas have given rise to the hypothesis that auditory information is processed in at least two functionally distinct streams (Rauschecker & Tian, 2000; Romanski, Bates, et al., 1999, Romanski, Tian, et al., 1999). Rostral auditory fields target rostral temporal and inferior, polar, and orbital prefrontal areas involved in processing related to auditory object recognition. Caudal auditory fields target dorsolateral and periarcuate prefrontal areas involved in multimodal spatial tasks. The segregation of pathways is not complete, however, because connections of rostral and caudal auditory areas overlap in the middle of the auditory cortex, and also in the dorsal superior temporal sulcus and dorsal prefrontal cortex. Thus, there appears to be substantial interaction between streams (Kaas & Hackett, 2000).

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AUDITORY CORTEX IN GREAT APES AND HUMANS

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Figure 12.7 Locations and tonotopy of auditory areas in macaque monkeys. Note. A: A lateral view of a monkey brain with parabelt areas sown on the surface of the superior temporal gyrus (STG), the lateral sulcus (LS), the central sulcus (CS), and the arcuate sulcus (AS) are shown for reference. B: The upper bank of the lateral sulcus has been removed to expose the lower bank and core and belt auditory areas (see text). (C) The tonotopic organization of the core auditory areas from low (L) to high (H) tones. Lines with A1 and R indicate lines of isorepresentation. See text for belt and parabelt areas.

Auditory Parabelt The auditory parabelt region is located along the lateral border of the belt region on the superior temporal gyrus. Two areas have been identified, one caudal (CPB) and one rostral (RPB; Hackett et al., 1998a, 1998b). Very little is known about the response properties of parabelt neurons because few studies have attempted to record from

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The expansion of the PT and STG in apes and especially humans is one of the clearest differences in the gross anatomy of the superior temporal lobe among primates. Because these structural differences are likely to underlie key differences in the capacity for speech and language, it is important to achieve a better understanding of the auditory cortex organization from studies of the human brain. Early descriptions of the human and nonhuman primate auditory cortex include the results of several histological studies conducted in the late-1800s and early-1900s (Beck, 1928, 1929; Brodmann, 1909; von Economo, 1925; von Economo & Horn, 1930). These classic studies localized the auditory cortex to the superior temporal gyrus of humans and nonhuman primates and still remain influential. The nomenclature derived from Brodmann’s map of the cerebral cortex, in particular, is widely used to denote areas of the brain in functional imaging, electrophysiology, and related clinical applications. The number of areas identified in human auditory cortex ranges from 3 (Brodmann’s areas 41, 42, 22) to over 30 (von Economo & Horn, 1930). There is no current consensus on the precise

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Auditory Cortex in Great Apes and Humans

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Brodmann (1909) (G)

Galaburda & Sanides (1980) (H)

Figure 12.8 The locations of the auditory cortex in the brains of macaque monkeys (A and D), chimpanzees (B and E), and humans (C and F). Note. A through C are views of the lower bank of the lateral sulcus (the superior temporal plane) of monkey, chimpanzee, and human brains with the auditory core outlined. Broken white lines designate sulcal landmarks, circular sulcus (Cis), anterior Heschl’s sulcus (HSa), posterior Heschl’s sulcus (HSp). Scale bars ⫽ 5 mm. D through F show the location of A1, the lateral belt area, ML, and the caudal parabelt (CPB) in frontal brain sections of monkey (D), chimpanzee (E), and human (F) brains. Question marks indicate uncertainties. Scale bar ⫽ 5 mm. G through I are previous descriptions of the organization of auditory cortex in humans shown on a view of the superior temporal plane as in C. Different parcellation schemes exist,

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Sweet et al (2005) (I)

and there are uncertainties. Areas 41, TC, and KA have been considered to be primary auditory cortex (A1). These proposed areas would contain all or most of the auditory core as presently defined. From “The Comparative Anatomy of the Primate Auditory Cortex” (pp. 199–219), by T. A. Hackett, in Primate Audition; Ethology and Neurobiology, A. A. Ghazanfar (Ed.), 2003 Boca Raton: CRC Press; “Organization and Correspondence of the Auditory Cortex of Humans and Nonhuman Primates” (pp. 109–119) by T. A. Hackett, in J. Kaas (Ed.), Evolution of Nervous Systems, 2007, Oxford: Elsevier; and “Architectonic Identification of the Core Region in Auditory Cortex of Macaques, Chimpanzees, and Humans,” by T. A. Hackett, T. M. Preuss, and J. H. Kaas, 2001, Journal of Comparative Neurology, 441, pp. 197–222. Adapted with permission. aBased on Brodmann (1909). bBased on Galaburda and Sanides (1980). cBased on Sweet, Dorph-Petersen, and Lewis (2005).

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number, although it appears that the divisions identified by Brodmann are large regions, which contain several subdivisions, as in other primates (Hackett, 2002, 2007). More recent studies have employed quantitative methods and studied the expression of enzymes, neurotransmitters, and receptors to identify areas and provide insight into their functional properties (Hackett et al., 2001; Morgan, Henderson, & Thompson, 1987; Morosan et al., 2001, Morosan, Schleicher, Amunts, & Zilles, 2005; Nakahara, Yamada, Mizutani, & Murayama, 2000; Rademacher, Caviness, Steinmetz, & Galabuda, 1993; Rivier & Clarke, 1997; Sweet et al., 2005; Wallace et al., 2002). Although interpretations vary, a common feature is that of a central core region flanked by belts of several nonprimary fields (Figure 12.8), similar to the pattern of organization found in monkeys and other animals. Core Region In monkeys, the core region is elongated along the anteriorposterior axis of the temporal lobe. In apes and humans, the core occupies the posteromedial two thirds of the TTG, which is oriented from posteromedial to anterolateral across the superior temporal plane (Hackett, 2002; Hackett et al., 2001). Therefore, the core region corresponds most closely to area 41 of Brodmann (1909), but there is much greater variability in apes and humans, compared to monkeys. The number of transverse temporal gyri varies between individuals and sometimes between hemispheres, and the position of the core region also varies relative to those gyri (Rademacher et al., 1993; Hackett et al., 2001). In humans, the most common expression is that of a single or paired TTG. In humans with a single TTG, the core occupies most of the gyrus and usually does not extend beyond its anterior and posterior sulcal boundaries. When the TTG is duplicated, the core usually occupies portions of both gyri.

BELT AND PARABELT REGIONS The homology of the belt and parabelt regions of monkeys and humans has not been firmly established, and so the observations included here remain speculative. Adjoining the core region on the anteromedial and posterolateral sides of the TTG are two bands that, at least in terms of relative position, appear to correspond to the belt region of monkeys. The anterior region most closely corresponds to the medial belt region of monkeys and area 52 of Brodmann. The posterior region occupies part of the planum temporale (PT) adjacent to the TTG, and corresponds most closely to area 42 of Brodmann and the lateral belt of monkeys (Galaburda & Sanides, 1980; Hackett, 2002; Sweet

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et al., 2005). On the more posterior portion of the PT are at least two other anatomically distinct regions. According to Brodmann (1909), part of area 22 extends onto the PT from the STG to flank the posterior border of area 42. This portion of area 22 may correspond to part of the parabelt region of monkeys, but in that case, the remainder of area 22 on the STG has no clear homologue in monkeys, apes, and humans. Further research is needed to clarify these relationships.

SUMMARY The mammalian auditory system is comprised of a highly complex network of peripheral and central structures that can extract, encode, and interpret acoustic signals in a dynamic acoustic environment. At each stage of processing, interconnected neurons in multiple parallel pathways contribute to the computation of information contained within the acoustic waveform. From these computations emerge cues about the location and identity of auditory objects used to guide reflexive and purposive behavior. Precisely how the auditory system accomplishes these tasks is only partially understood, but comparative studies in humans and other species are gradually uncovering the structural and functional mechanisms that underlie the perception of sound.

REFERENCES Beck, E. (1928). Die myeloarchitektonische felderung es in der Syvischen furche gelegenen teiles des menschlichen schläfenlappens. Journal of Psychiatric Neurology, 36, 1–21. Beck, E. (1929). Der myeloarchitektonische bau des in der Sylvischen furche gelegenen teiles des schläfenlappens beim schimpansen (Troglodytes niger). Journal of Psychiatric Neurology, 38, 309–428. Bendor, D., & Wang, X. (2005, August 25). The neuronal representation of pitch in primate auditory cortex. Nature, 436, 1161–1165. Brodmann, K. (1909). Vergleichende Lokalisationslehre der Grosshirnrhinde. Leipzig: Barth. Brugge, J. F. (1982). Auditory areas in primates. In C. N. Woolsey (Ed.), Cortical sensory organization (pp. 59–70). Clifton, NJ: Humana Press. Calford, M. B., & Aitkin, L. M. (1983). Ascending projections to the medial geniculate body of the cat: Evidence for multiple, parallel auditory pathways through the thalamus. Journal of Neuroscience, 3, 2365–2380. Cohen, Y. E., & Knudsen, E. I. (1999). Maps versus clusters: Different representations of auditory space in the midbrain and forebrain. Trends in Neurosciences, 22, 128–135. Cooper, H. M., Herbin, M., & Nevo, E. (1993). Visual system of a naturally microphthalmic mammal: The blind mole rat. (Spalax ehrenbergi.) Journal of Comparative Neurology, 328, 313–350. Corey, D. P. (2007). Stringing the fiddle: The inner ear’s two-part invention. Nature Neuroscience, 10, 1232–1233.

8/17/09 2:08:26 PM

References 265 Dear, S. P., Simmons, J. A., & Fritz, J. (1993, August 12). A possible neuronal basis for representation of acoustic scenes in auditory cortex of the big brown bat. Nature, 364, 620–623. de la Mothe, L., Blumell, S., Kajikawa, Y., & Hackett, T. A. (2006). Thalamic connections of the auditory cortex in marmoset monkeys: Core and medial belt regions. Journal of Comparative Neurology, 496, 72–96.

Kaas, J. H. (2008). The evolution of auditory cortex: The core areas. In J. A. Winer, C. E. Schreiner (Eds.), The auditory cortex. Berlin, Germany: Springer-Verlag.

Ehret, G. (1997). The auditory cortex. Journal of Comparative Physiology, 181, 547–557.

Kaas, J. H., & Hackett, T. A. (2000). Subdivisions of auditory cortex and processing streams in primates. Proceedings of the National Academy of Sciences, USA, 97, 11793–11799.

Fitzpatrick, K. A., & Imig, T. J. (1978). Projections of auditory cortex upon the thalamus and midbrain in the owl monkey. Journal of Comparative Neurology, 177, 573–655.

Kaas, J. H., & Huerta, M. F. (1988). Subcortical visual system of primates. In H. P. Steklis (Ed.), Comparative primate biology (Vol. 4, pp. 327–391). New York: Alan R. Liss.

Galaburda, A., & Sanides, F. (1980). Cytoarchitectonic organization of the human auditory cortex. Journal of Comparative Neurology, 190, 597–610.

Kajikawa, Y., de la Mothe, L., Blumell, S., & Hackett, T. A. (2005). A comparison of neuron response properties in areas A1 and CM of the marmoset monkey auditory cortex: Tones and broadband noise. Journal of Neurophysiology, 93, 22–34.

Hackett, T. A., Stepniewska, I., & Kaas, J. H. 1999) Prefrontal connections of the auditory parabelt cortex in macaque monkeys. Brain Research, 817, 45–58. Hackett, T. A., & Kaas, J. H. (2002) Auditory Processing in the Primate Brain. In M. Gallagher. & R. J. Nelson (Eds.), Handbook of Psychology, Vol. 3: Biological psychology. Wiley & Sons: New York. Hackett, T. A. (2003). The comparative anatomy of the primate auditory cortex. In A. A. Ghazanfar (Ed.), Primate audition: Behavior and neurobiology (pp. 199–226). Boca Raton: CRC Press. Hackett, T. A. (2007). Organization and correspondence of the auditory cortex of humans and nonhuman primates. In J. Kaas (Ed.), Evolution of nervous systems (pp. 109–119). Oxford: Elsevier. Hackett, T. A., Preuss, T. M., & Kaas, J. H. (2001). Architectonic identification of the core region in auditory cortex of macaques, chimpanzees, and humans. Journal of Comparative Neurology, 441, 197–222. Hackett, T. A., Stepniewska, I., & Kaas, J. H. (1998a). Subdivisions of auditory cortex and ipsilateral cortical connections of the parabelt auditory cortex in macaque monkeys. Journal of Comparative Neurology, 394, 475–495. Hackett, T. A., Stepniewska, I., & Kaas, J. H. (1998b). Thalamocortical connections of the parabelt auditory cortex in macaque monkeys. Journal of Comparative Neurology, 400, 271–286. Hashikawa, T., Molinari, M., Rausell, E., & Jones, E. G. (1995). Patchy and laminar terminations of medial geniculate axons in monkey auditory cortex. Journal of Comparative Neurology, 362, 195–208. Hirai, T., & Jones, E. G. (1989). A new parcellation of the human thalamus on the basis of histochemical staining. Brain Research Review, 14, 1–34.

Knudsen, E. I., du Lac, S., & Esterly, S. D. (1987). Computational maps in the brain. Annual Review of Neuroscience, 10, 41–65. Kosaki, H., Hashikawa, T., He, J. & Jones, E. G. (1997). Tonotopic organization of auditory cortical fields delineated by parvalbumin immunoreactivity in macaque monkeys. Journal of Comparative Neurology, 386, 304–316. Luethke, L. E., Krubitzer, L. A., & Kaas, J. H. (1989). Connections of primary auditory cortex in the new world monkey (Saguinus). Journal of Comparative Neurology, 285, 487–513. Merzenich, M. M., & Brugge, J. F. (1973). Representation of the cochlear partition on the superior temporal plane of the macaque monkey. Brain Research, 50, 275–296. Merzenich, M. M., Kitzes, L., & Aitkin, L. (1973). Anatomical and physiological evidence for auditory specialization in the mountain beaver (Aplodontia rufa). Brain Research, 58, 331–344. Molinari, M., Dell’Anna, M. E., Rausell, E., Leggio, M. G., Hashikawa, T., & Jones, E. G. (1995). Auditory thalamocortical pathways defined in monkeys by calcium-binding protein immunoreactivity. Journal of Comparative Neurology, 362, 171–194. Morel, A., Garraghty, P. E., & Kaas, J. H. (1993). Tonotopic organization, architectonic fields, and connections of auditory cortex in macaque monkeys. Journal of Comparative Neurology, 335, 437–459. Morel, A., & Kaas, J. H. (1992). Subdivisions and connections of auditory cortex in owl monkeys. Journal of Comparative Neurology, 318, 27–63. Morgan, J. E., Henderson, Z., & Thompson, I. D. (1987). Retinal decussation patterns in pigmented and albino ferrets. Neuroscience, 20, 519–535.

Holt, J. R., & Corey, D. P. (2000). Two mechanisms for transducer adaptation in vertebrate hair cells. Proceedings of the National Academy of Sciences, USA, 97, 11730–11735.

Morosan, P., Rademacher, J., Schleicher, A., M., Amunts, K., Schormann, T., & Zilles, K. (2001). Human primary auditory cortex: Cytoarchitectonic subdivisions and mapping into a spatial reference system. NeuroImage, 13, 683–701.

Huang, C. L., Larue, D. T., & Winer, J. A. (1999). GABAergic organization of the cat medial geniculate body. Journal of Comparative Neurology, 415, 368–392.

Morosan, P., Schleicher, A., Amunts, K., & Zilles, K. (2005). Multimodal architectonic mapping of human superior temporal gyrus. Anatomy and Embryology, 210, 401–406.

Imig, T. J., & Morel, A. (1985). Tonotopic organization in ventral nucleus of medial geniculate body in the cat. Journal of Neurophysiology, 53, 309–340.

Nakahara, H., Yamada, S., Mizutani, T., & Murayama, S. (2000). Identification of the primary auditory field in archival human brain tissue via immunocytochemistry of parvalbumin. Neuroscience Letters, 286, 29–32.

Imig, T. J., Ruggero, M. A., Kitzes, L. M., Javel, E., & Brugge, J. F. (1977). Organization of auditory cortex in the owl monkey (Aotus trivirgatus). Journal of Comparative Neurology, 171, 111–128. Irvine, D. R. F. (1986). The auditory brainstem. In D. Ottoson (Ed.), Progress in sensory physiology (pp. 1–279). Berlin, Germany: Springer-Verlag. Jones, E. G. (2006). The thalamus. Cambridge: Cambridge University Press. Jones, E. G., Dell’Anna, M. E., Molinari, M., Rausell, E., & Hashikawa, T. (1995). Subdivisions of macaque monkey auditory cortex revealed by calcium-binding protein immunoreactivity. Journal of Comparative Neurology, 362, 153–170.

c12.indd Sec7:265

Kaas, J. H. (2000). Why is brain size so important: Design problems and solutions as neocortex gets bigger or smaller. Brain and Mind, 1, 7–23.

Rademacher, J., Caviness, V. J., Steinmetz, H., & Galabuda, A. (1993). Topographical variation of the human primary cortices: Implications for neuroimaging, brain mapping, and neurobiology. Cerebral Cortex, 3, 313–329. Rauschecker, J. P., & Tian, B. (2000). Mechanisms and streams for processing of “what” and “where” in auditory cortex. Proceedings of the National Academy of Sciences, USA, 97, 11800–11806. Rauschecker, J. P., & Tian, B. (2004). Processing of band-passed noise in the lateral auditory belt cortex of the rhesus monkey. Journal of Neurophysiology, 91, 2578–2589.

8/17/09 2:08:26 PM

266 Audition Rauschecker, J. P., Tian, B., & Hauser, M. (1995, April 7). Processing of complex sounds in the macaque nonprimary auditory cortex. Science, 268, 111–114.

Sterbing-D’Angelo, S. (2007). Evolution of sound localization in mammals. In J. H. Kaas, (Ed.), Evolution of nervous systems (Vol. 3, pp. 253–260). London: Elsevier.

Recanzone, G. H., Schreiner, C. E., Sutter, M. L., Beitel, R. E., & Merzenich, M. M. (1999). Functional organization of spectral receptive fields in the primary auditory cortex of the owl monkey. Journal of Comparative Neurology, 415, 460–481.

Suga, N. (1990). Cortical computational maps for auditory imaging. Neurol Networks, 3, 3–21.

Rivier, F., & Clarke, S. (1997). Cytochrome oxidase, acetylcholinesterase, and NADPH-diaphorase staining in human supratemporal and insular cortex: Evidence for multiple auditory areas. NeuroImage, 6, 288–304. Romand, R., & Avan, P. (1997). Anatomical and functional aspects of the cochlear nucleus. In G. Ehret & R. Romand (Eds.), The central auditory system (pp. 97–191). New York: Oxford University Press. Romanski, L. M., Bates, J. F., & Goldman-Rakic, P. S. (1999). Auditory belt and parabelt projections to the prefrontal cortex in the rhesus monkey. Journal of Comparative Neurology, 403, 141–157. Romanski, L. M., Tian, B., Fritz, J., Mishkin, M., Goldman-Rakic, P. S., & Rauschecker, J. P. (1999). Dual streams of auditory afferents target multiple domains in the primate prefrontal cortex. Nature Neuroscience, 12, 1131–1136. Schreiner, C. E., & Langner, G. (1988). Periodicity coding in the inferior colliculus of the cat: Pt. II. Topographical organization. Journal of Neurophysiolog, 60, 1823–1840. Spangler, K. M., & Warr, W. B. (1991). The descending auditory system. In R. A. Altschuler, R. P. Bobbin, B. M. Clopton, & D. W. Hoffman (Eds.), Neurobiology of hearing: The central auditory system (pp. 27–45). New York: Raven Press.

c12.indd Sec7:266

Sweet, R. A., Dorph-Petersen, K. A., & Lewis, D. A. (2005). Mapping auditory core, lateral belt, and parabelt cortices in the human superior temporal gyrus. Journal of Comparative Neurology, 491, 270–289. Talavage, T. M., Sereno, M. I., Melcher, J. R., Ledden, P. J., Rosen, B. R., & Dale, A. M. (2004). Tonotopic organization in human auditory cortex revealed by progressions of frequency sensitivity. Journal of Neurophysiology, 91, 1282–1296. von Economo, C. (1925). Die cotyarchitectonik der hirnrinde des erwachsenen menschen. Berlin, Germany: Julius-Springer. von Economo, C., & Horn, L. (1930). Uber windungsrelief, masse und rindenarchitektonik der supratemporalflache, ihre individuellen und ihre seitenunterschiede. Journal of Neurology and Psychiatry, 130, 678–757. Wallace, M. N., Johnston, P. W., & Palmer, A. R. (2002). Histochemical identification of cortical areas in the auditory region of the human brain. Experimental Brain Research, 143, 499–508. Winer, J. A., & Lee, C. C. (2007). The distributed auditory cortex. Hearing Research, 229, 3–13. Yin, T. C. T., & Chan, J. C. K. (1990). Interaural time sensitivity in medial superior olive of cat. Journal of Neurophysiology, 64, 465–488. Zhang, Z., Yu, Y. Q., Liu, C. H., Chan, Y. S., & He, J. (2008). Frequency tuning and firing pattern properties of auditory thalamic neurons: An in vivo intracellular recording from the guinea pig. Neuroscience, 151, 293–302.

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Chapter 13

Chemical Senses SUSAN P. TRAVERS AND JOSEPH B. TRAVERS

sensations. In olfaction, the problem is magnified due to the huge number of compounds and resulting scents that can be discriminated. Yet for both senses, there is palpable excitement over recent discoveries of peripheral transduction mechanisms. In olfaction, each peripheral sensory neuron expresses just a single G-protein coupled receptor (GPCR), yet responds to a wide variety of stimuli. In taste, too, GPCRs for sweet and bitter tasting stimuli are segregated on different receptor cells but it is not uncommon to find peripheral fibers and central neurons that respond to more than one taste. Thus, the discovery of specific receptor and transduction mechanisms does not in itself solve the coding problem. How information is extracted from these receptors by the central nervous system, for example, how we achieve a singular sensation of sweet from neurons responsive to both sweet and salty compounds is hotly debated. Further fueling the debate are technical advances in the field of neurophysiology. The ability to record from more than one sensory-responsive neuron at a time means that temporal relations between neurons can be described. In addition to their classical “receptive fields,” any two neurons share a temporal space defined by the temporal relation of their respective spike trains. This adds additional degrees of freedom with which to define a coding scheme. Similarly with the advent of technical advances in functional imaging techniques, for example, c-fos immunohistochemistry, 2-DG, fMRI, and intrinsic imaging, interest in spatial topographical representations is often conceptually pitted against theories of dynamic coding. Here we present evidence from both perspectives. Our overview of the chemical senses focuses primarily on mammalian olfaction and taste. Our strategy is to present the systems independently, making comparisons when possible but not discussing the systems jointly until the orbital cortex, where there is clear convergence between taste and smell. Although we have tried to discuss similar aspects of the two senses, the coverage is uneven due to varying amounts of information. For example, analysis of olfactory bulb circuitry is much more advanced than is our

Identification of chemicals in the external world is assigned to the special senses of taste and smell. If we add the chemosensitivity of some somatosensory fibers, typically polymodal nociceptors responsive to compounds found in spices such as capsaicin, we can also include chemesthesis, the “common” chemical sense. Although activation of receptors associated with each of these senses produces unique, clearly differentiated sensations, the fusion of all three is the potent human sensation of flavor. It is standard textbook terminology to define flavor as the amalgamation of taste, olfaction, and chemesthesia, although temperature and texture are likely involved as well. This fusion of sensory modalities is unique to the chemical senses. A hedonic dimension further differentiates the chemical senses from other sensory modalities. Many tastes and smells are associated with strong innate preferences and aversions, a characteristic most likely related to senses so closely tied to the exigencies of survival. It is axiomatic in the field of taste to assert its primary role in food selection; that is, the selection of nutrients and the avoidance of toxins. There are strong preferences and aversions associated with odors, too. Odor is critical to mating and maternal function in many animals (survival of the species), and smell helps to avoid predators (survival of the individual). The hedonic responses to tastes and odors are also highly modifiable. Not all chemosensory preferences and aversions are innate, but instead are learned. In particular, taste and flavor aversions and preferences are strongly susceptible to modification by postingestive consequences. In the fields of both olfaction and taste there is vigorous debate about how stimuli are coded or represented in the nervous system. The chemical senses as a group pose a common problem compared to the other major senses, the lack of a stimulus dimension. The difficulty of the coding problem is thus exacerbated by problems in defining the stimulus to be transduced. Taste should be somewhat easier than smell because there is at least some agreement that the rather large range of chemicals that this system detects give rise to only four or five discrete qualitative 267

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understanding of central taste circuits. On the other hand, more studies in the taste system have focused on modulation of signals by homeostatic state. For both systems, however, a major emphasis is on neural representation of quality and how each system contributes to behavioral function. TASTE GPCR Transduction Mechanisms: Sweet, Bitter, and Umami The prototypic sweet tasting stimuli include simple and complex sugars, that is, caloric, nutritive compounds common in the diet of many animals. A few naturally occurring amino acids such as glycine also taste sweet. In addition, artificial sweeteners of varied chemical type and the D-isomers of several amino acids evoke this sensation as well. Despite this range of chemical structures, a single mechanism comprised of a heteromer of GPCR proteins, T1R2  T1R3, appears mostly responsible for the transduction of all sweet substances (X. Li et al., 2002; Nelson et al., 2001; G. Q. Zhao et al., 2003). When either, or especially both, proteins are genetically deleted in mice, preference for sweet stimuli is dramatically reduced, as are sweet-evoked peripheral nerve responses (Damak et al., 2003; G. Q. Zhao et al., 2003). In fact, it has long been evident that a certain mouse strain (B129) exhibits a reduced responsiveness to sweettasting compounds and the underlying basis for this difference is now known to be associated with a genetic variation in the T1R3 protein (Nelson et al., 2001). Variation in this receptor also explains the minimal sensitivity of wild and domestic cats to sugars, but in this case the culprit is the T1R2 element. Interestingly, pseudogenization of the T1R2 receptor accounts for the rare lack of a mammalian “sweet tooth” in these animals (X. Li et al., 2005). This loss of function has been hypothesized to result from the fact that cats are obligate carnivores and consequently, the ability to detect and prefer foods with sugars is of little use to them (reviewed in Boughter & Bachmanov, 2008). The transduction of L-amino acids also relies heavily on T1R family members (G. Q. Zhao et al., 2003). In fact, one of the same T1R proteins that comprises an element of the sugar receptor, T1R3, dimerizes with a different T1R family member, T1R1, to create a receptor that responds to L-amino acids, but not sugars (X. Li et al., 2002; Nelson et al., 2002). The human T1R1 variant results in an amino acid receptor highly preferential for L-glutamate, an amino acid found in particularly high concentrations in many foods. In fact, monosodium glutamate (MSG) is considered the prototypic umami substance. The name umami derives from the Japanese, and roughly translates to savory or delicious, but the rather subtle character of the sensation

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that it elicits has long made its status as a distinct taste quality subject to debate. The discovery of specifically tuned amino acid receptors has swayed the consensus of opinion in a positive direction though there is still not unanimous agreement. Species differences, even within mammals, confuse the issue. For example, the rodent T1R1-T1R3 heteromer responds to a much broader range of L-amino acids than in humans (Nelson et al., 2002). In addition, although deletion of either T1R1 or T1R3 profoundly diminishes peripheral nerve responses and behavioral preference for L-amino acids (G. Q. Zhao et al., 2003), a different knock-out of T1R3 revealed residual responsiveness (Damak et al., 2003). Indeed, with psychophysical tests evaluating threshold and discrimination rather than preference, T1R3 deletion produced only subtle alterations (Delay, Hernandez, Bromley, & Margolskee, 2006). These remaining capacities are consistent with data suggesting additional amino-acid receptor mechanisms, including a truncated form of a metabotropic glutamate receptor, tasmGLUr4. When heterologously expressed, this receptor responds to L-glutamate and the glutamate receptor agonist, AP4 (Chaudhari, Landin, & Roper, 2000). Bitter transduction likewise involves GPCRs, but these proteins comprise a distinct class, the T2Rs. Unlike sweet stimuli and amino acids, the number of family members is much larger, in rodents numbering over 35, and in humans only slightly fewer, though with a higher proportion of pseudogenes (reviewed in Boughter & Bachmanov, 2008). Presumably, the large number of T2R receptors is necessary to bind to diverse chemicals that elicit this taste quality. Although the tendency can be modified by learning, animals appear to be hard-wired to avoid consuming bitter tastants. This is adaptive because many of these compounds are toxins, often deriving from plants. For example, the alkyloids, quinine, nicotine, and morphine all taste bitter, as do the B-glucopyranosides such as salicin, a compound in willow bark closely related to aspirin. Heterologous expression has identified ligands for several T2Rs and a complex story is emerging. The receptor range varies for different family members, with some T2Rs apparently responding to just one or two ligands, others to multiple compounds within a chemical class, and yet others to a range of seemingly dissimilar compounds. For example, mT2R5 is a mouse T2R that responds mainly to cycloheximide, a bacterial toxin, and hT2R16 is a human T2R responsive to a large number of B-glucopyranosides (Bufe, Hofmann, Krautwurst, Raguse, & Meyerhof, 2002), and hT2R7 responds to several unrelated compounds including papaverine, chloroquine, quinacrine, and strychnine (Sainz et al., 2007). There is striking inter-intervidual sequence diversity in the genes encoding various T2Rs (reviewed in Max & Meyerhof, 2008). These differences this would be expected to lead to substantial variability in sensitivity for particular bitter

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Taste

substances, and ultimately, consequences for food choice. In fact, genetic diversity in bitter-sensing has been apparent since the early part of the twentieth century when Fox (1932) discovered that there was a bimodal distribution of thresholds among human subjects with regard to their ability to detect phenylthiocarbamide (PTC), a thioamide with thyroid toxicity. However, the basis for that difference only became clear when the identification of the T2R family allowed positional cloning to define the responsible receptor and genetic polymorphisms (Kim et al., 2003). So far, it appears that just one of the approximately 25 human T2Rs, hT2R38, detects PTC, and remarkably, substitution of just a few amino acids explains a major portion of the 1,000-fold individual variation in response threshold, as well as in the large differences in suprathreshold intensity of PTC and closely related compounds (Bufe et al., 2005; Kim et al., 2003). This variation in PTC sensitivity is not just a laboratory curiosity. Many vegetables contain PTClike glucosinolates. When humans are asked to rate bitterness, those foods containing glucosinolates, like mustard greens, turnips, and horseradish, taste more bitter to individuals with the sensitive hT2R38 halplotype, despite the fact that their ratings of other vegetables, such as radicchio or bitter melon, vary in a random way (Sandell & Breslin, 2006). A second example of genetic diversity explains some of the individual differences in perceiving the bitter side taste of the artificial sweetener, saccharin (Pronin et al., 2007). In contrast to PTC sensitivity, there appear to be (at least) two different members of the T2R family responsible for detecting saccharin’s bitterness, but just a single amino acid substitution in either receptor increases sensitivity to the bitter side taste. It has become increasingly clear that many of these oral T1R and T2R GPCRs also are expressed and functional in the lower GI tract, including the stomach, small intestine, and/or colon (reviewed in Rozengurt & Sternini, 2007). In the duodenum, one prominent location for T1R3 is within enteroendocrine cells in the duodenal villi (Bezencon, le Coutre, & Damak, 2007; Margolskee et al., 2007). T1R2 has likewise been identified in duodenal enteroendocrine cells, sometimes colocalized with T1R3 (Dyer, Salmon, Zibrik, & Shirazi-Beechey, 2005). The T1R2/T1R3 heterodimer in these enteroendocrine L-cells appears to underlie sugar-stimulated secretion of the gut hormone, glucagonlike peptide 1, and its downstream effects on the regulation of glucose-transporting enzymes in enterocytes; artificial sweeteners have similar effects (Margolskee et al., 2007). Thus, the intake of sweet foods can have profound effects on gastrointestinal physiology, regardless of caloric content. T1R1 is likewise observed in the duodenal villi, where it is hypothesized to detect amino acids (Bezencon et al., 2007). Perhaps more surprisingly, receptors for

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toxins, the T2Rs are also expressed in gut cells, including those in the stomach, small intestine (Wu et al., 2002) and colon (Rozengurt, 2006). Indeed, enteroendocrine cell lines expressing these receptors respond to bitter ligands, demonstrating a functional effect (Wu et al., 2002). That this information reaches the brain seems likely since intragastric administration of certain bitter tastants elicit c-fos immunoreactivty in the region of the nucleus of the solitary tract that receives information from the vagus nerve (e.g., Hao, Sternini, & Raybould, 2008; Yamamoto & Sawa, 2000). In fact, central Fos expression appears dependent on the secretion of two gut hormones, cholecystokinin and peptide YY (Hao et al., 2008). Secretion of these gut hormones, which have multiple effects including a profound depression of food intake, has long been known to be triggered by nutritive substances, but the recent data indicates that bitter tastants have the same effect. This common effect of sweet and bitter substances on the secretion of satiety hormones is in contrast to their opposite effects on intake when they interact with oral taste receptors. Transduction by Ion Channels: Salty and Sour A variety of salts can activate the taste system. The cation is the major determinant of stimulus quality but anions play a significant modulatory role. To humans, many salts evoke a characteristic salty sensation, considered one of four/five basic taste qualities. This quality is elicited in the purest form by sodium and lithium salts, especially sodium chloride and lithium chloride. Other salts, like ammonium chloride, potassium chloride, or even sodium salts in combination with other anions such as SO4, elicit a salty taste, but one that is mixed to different extents with other tastes, usually bitter and sour (van der Klaauw & Smith, 1995). An amiloride-sensitive channel (ENAC) located on the apical ending of specific taste receptor cells was actually the first taste receptor discovered. Its operation is elegantly simple; sodium ions preferentially flow into the cell so that the taste stimulus itself serves as the direct trigger for depolarization (Heck, Mierson, & DeSimone, 1984). This mechanism was the first example of a taste receptor used for other homeostatic functions. Indeed, ENAC was first cloned from the colon and is found in many epithelial tissues, including the nephron (kidney) where it plays a critical role in regulating extracellular fluid volume and blood pressure. In rodents, psychophysical studies using amiloride have established that ENACs are important for detecting sodium salts and critical for the ability to discriminate this cation from others (reviewed in Spector, 2003). However, in some other species, notably humans, ENACs are less important. In humans, amiloride blockade mainly reduces the slight sour taste elicited by sodium chloride (Smith & Ossebaard, 1995). Indeed, even in rodents, neither neural

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nor behavioral responses to sodium chloride are entirely eliminated by amiloride, and except for Li, responses to other cations such as NH4 and K are hardly affected (reviewed in S. C. Kinnamon & Margolskee, 2008). Thus, it is clear that additional mechanisms are involved in salt transduction. One strongly implicated mechanism is a variant of the TRPV1 ion channel (DeSimone et al., 2001; Lyall et al., 2004), a receptor first discovered in nociceptive afferents. In the pain system, this receptor is primarily responsible for detecting vanilloids like capsaicin, the spicy component of hot chile peppers, and partly responsible for detecting painful heat or protons (Caterina & Julius, 2001). The variant in taste receptor cells, TRPV1t, contributes to transducing both sodium and nonsodium salts, as assessed in TRPV1 knockout animals and pharmacological blockade. However, even in combination, ENAC and TRPV1 cannot entirely account for salt transduction. Acid transduction, leading to the perception of sourness, also involves mechanisms other than GPCRs. Although pH is a critical chemical feature of compounds that elicit sourness, it is significant that the magnitude of acid-evoked responses varies with intracellular, rather than extracelluar pH (Lyall et al., 2006). Organic acids are hypothesized to pass through the membrane in their undissociated state and become dissociated inside cells, whereas protons from (already dissociated) inorganic acids use ion channels to enter. These considerations are believed to account for the fact that, at a given pH, organic acids elicit larger responses and taste more sour than inorganic acids. Although acid exposure causes the intracellular pH of most taste bud cells to drop, only a subset exhibit an associated Ca response (T. A. Richter, Caicedo, & Roper, 2003). Thus, specificity is apparently conferred by the presence or absence of cellular machinery that performs this conversion. Several ion channels that permit the passage of hydrogen ions or which are modulated by pH are common to taste receptor and other cells, but there is no firm link between these channels and sour transduction (reviewed in S. C. Kinnamon & Margolskee, 2008). In contrast, recent work by three independent groups pinpointed two members of the TRPP ion channel family—PKD1L1 and PKD2L1—that are more specifically expressed in taste bud cells (A. L. Huang et al., 2006; Ishimaru et al., 2006; LopezJimenez et al., 2006). When cells with these proteins were ablated by expressing diphtheria toxin under the control of the PKD2L1 promoter, peripheral nerve responses to acids were abolished (at least for the one region of the mouth tested; A. L. Huang et al., 2006). Thus, it seems clear that the taste bud cells containing PKD1L1/PKD2L1 are critical to sour transduction although the role of the proteins, per se, remains to be established. Interestingly, besides taste bud cells, PKD1L1/PKD2L1 are found in just

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one other location—the cells lining the central canal of the spinal cord. These cells send processes into the cerebrospinal fluid and respond to pH changes. Detection of Fats Dietary fats are a dense source of energy and highly preferred by most mammalian species, but until recently the basis of how these nutrients are perceived has been obscure. The elucidation of transduction mechanisms for other tastants is also quite recent but there is an important difference in the case of fats. As discussed later, recording studies in peripheral taste fibers dating back to the 1940s demonstrated that compounds associated with sweet, salty, sour, and bitter sensations gave rise to neural responses in primary afferent taste fibers that were clearly different from responses elicited by the somatosensory (i.e., mechanical or thermal) properties of fluid flow alone. In contrast, fats have not been shown to elicit such differential responses in peripheral nerves or central recordings. Nevertheless, recent findings provide compelling evidence that taste bud cells possess specific molecular mechanisms for detecting free fatty acids—components of dietary fats that drive preference behavior. The initial studies that rekindled interest in the possibility that the taste system detects fats demonstrated that rodent taste bud cells contained delayed-rectifying potassium ion channels that could be blocked by polyunsaturated free fatty acids (Gilbertson, Fontenot, Liu, Zhang, & Monroe, 1997). The strongest evidence to date, however, is for CD36, a fatty acid transporter originally described in the lower GI tract, which has recently been located in taste bud cells (Fukuwatari et al., 1997; Gaillard et al., 2008; Laugerette et al., 2005). When the gene for CD36 is genetically ablated, preference for long-chain fatty acids is nearly obliterated. In addition, in these animals, the oral application of fatty acids fails to induce a typical cephalic phase response (Laugerette et al., 2005). Furthermore, when CD36 cells from the taste bud were immunomagnetically isolated and cultured, fatty acid stimulation could evoke calcium ion responses in these cells, but not in cells not expressing CD36 (Gaillard et al., 2008). Several groups have demonstrated that section of the oral taste nerves affects fatty acid-driven behavior, including preference and the ability to detect these compounds, as assessed in a conditioned taste aversion paradigm (Pittman, Crawley, Corbin, & Smith, 2007; Stratford, Curtis, & Contreras, 2006). Taste Bud Morphology and Processing The molecular machinery for taste transduction resides in modified epithelial cells organized into discrete onionshaped clusters called taste buds. Taste buds are found in

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Taste

the lingual, palatal, laryngeal, and pharyngeal epithelium. Oral taste buds are those considered to participate in taste perception, taste-driven appetitive behaviors, and consummatory reflexes. Laryngeal buds, on the other hand, mostly function in airway protection and little is known of the few taste buds populating the pharynx. These more caudal populations will not be considered further in this chapter. Oral taste buds are innervated by different cranial nerves or branches: the VIIth nerve innervates both the anterior tongue and palatal taste buds via the chorda tympani and greater superficial petrosal branches, respectively; the lingual branch of the glossopharyngeal nerve innervates posterior tongue taste buds (reviewed in Lundy & Norgren, 2004b; Pritchard & Norgren, 2004). At the light microscopic level, taste bud cells are similar to one another but a closer look reveals diversity (see J. Kinnamon & Yang, 2008, for a recent review of taste bud ultrastructure). The first hint came from electron microscopic studies demonstrating ultrastructural differences, the most obvious being a striking variability in electron density, suggesting three classes of cells, initially called “Dark,” “Light,” and “Intermediate” and now known as Types, I, II, and III. Because only Type III cells synapse with the primary afferent nerve, they were initially considered to be the primary receptor cells and the other types were relegated to supporting roles. However, this model is undergoing radical revision. Details are still murky, but two important changes in how the bud is viewed are nearly certain: Synapses are probably not required for a taste bud cell to communicate directly with primary afferent fibers and there is considerable communication among taste bud cells themselves. Recent studies have provided strong evidence that ATP is a critical transmitter between the taste bud and afferent nerves. P2X receptors for ATP are expressed in primary taste afferents (Bo et al., 1999), ATP is released by taste buds, and double knockout of P2X2/P2X3 proteins obliterates taste responses in both the chorda tympani and glossopharyngeal nerves, as well as behavioral responses to most stimuli (Finger et al., 2005). Because taste bud cells can release ATP nonsynaptically through pannexin 1 hemichannels (Y. J. Huang et al., 2007), the conundrum raised by earlier findings demonstrating that T1R/T2R receptors are only expressed by Type II cells, that is, those cells without synapses, appears partly resolved. Rather than playing a supporting role, Type II cells are clearly receptor cells that probably communicate directly, via nonsynaptic ATP release, with primary afferent nerves. However, there are further complications. Type II cells are not the only receptor cells because other types contain receptors for ionic stimuli (Kataoka et al., 2008; Vandenbeuch, Clapp, & Kinnamon, 2008). In addition, Type III cells likely use a different a transmitter, perhaps

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serotonin, to modulate activity in primary afferent nerves (Y. J. Huang et al., 2007; Kaya, Shen, Lu, Zhao, & Herness, 2004). Furthermore, several other neurotransmitters and modulators, including NPY (F. L. Zhao et al., 2005), CCK (Herness, Zhao, Lu, Kaya, & Shen, 2002), serotonin (Kaya et al., 2004), and ATP (Y. J. Huang et al., 2007) have been implicated in within-bud communication. The families of receptors for sweet or umami (T1Rs) and bitter (T2Rs) tastants are both expressed in Type II cells but it is important to realize that they occur in different cells, as revealed by double situ hybridization and immunohistochemistry (Nelson et al., 2001; G. Q. Zhao et al., 2003). Likewise, as shown in Figure 13.1, PKD1L1/ PKD2L1 proteins, indicative of sour-sensing cells, are expressed in yet a different group, probably Type III cells (Kataoka et al., 2008). In fact, even the different T1Rs underlying sweet and umami taste (T1R1 and T1R2) are at least partially segregated (Nelson et al., 2001). In contrast, despite evidence for some independence (Behrens, Foerster, Staehler, Raguse, & Meyerhof, 2007), many T2Rs detecting varied bitter stimuli are co-expressed (Adler et al., 2000). These patterns of quality-specific expression may seem intuitively satisfying but they were initially somewhat surprising. Receptor segregation implies response specificity, but much previous physiological work had implied the opposite (e.g., Gilbertson, Boughter, Zhang, & Smith, 2001; Herness, 2000). This apparent conflict between the molecular and physiological data rekindled a decades-old argument about whether taste quality is coded by specifically tuned neural elements, that is, a labeled line, or by an ensemble code. The mechanisms downstream of transduction provided a further opportunity to probe the coding question. Even though distinct receptors detect sweet, umami, and bitter stimuli, these GPCRs share a common (Y. Zhang et al., 2003), though perhaps

PKD2L1

T1R3

PKD2L1

T2Rs

Figure 13.1 (Figure C.16 in color section) Double in situ hybridization. One probe was for putative sour-detecting cells expressing PKD2L1; the second probe stained receptors for other qualities. Note: (Left) Co-localization of PKD2L1 with T1R3, a common component of the receptor for sweet and umami compounds. (Right) Co-localization with a mixture of 20 T2Rs, receptors for bitter tastants. Note that PKD2L1 never co-localizes with either of the other receptors. From figure 1 in “The Cells and Logic for Mammalian Sour Taste Detection,” by A. L. Huang et al., 2006, Nature, 442, pp. 934–938. Adapted with permission.

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not sole (Damak et al., 2003) transduction cascade utilizing PLCβ2 and the TRP channel, TRPm5. When PLCβ2 or TRPm5 were genetically deleted, neural and behavioral responses to sweet, bitter, and amino acid stimuli were profoundly diminished, but responses to salty and sour tastants remained (Y. Zhang et al., 2003). Significantly, when PLCβ2 expression was rescued under the control of a single T2R promoter, bitter responsiveness to a broad array of bitter tastants, but not responsiveness to sweet and umami stimuli was restored, suggesting functional segregation of sweet versus bitter and umami pathways (Mueller et al., 2005). These findings are somewhat at odds with the broader tuning detected using physiological techniques. However, the discrepancy should not be exaggerated, since physiological data indicate that most nonspecific taste neurons are broadly tuned to electrolytes; in particular few respond to both bitter and sweet stimuli (reviewed in Spector & Travers, 2005). Nevertheless, experiments that deleted taste bud cells expressing the PKD2L1 molecule told a similar story; in this case, chorda tympani responses to sour stimuli were obliterated, but responses to other tastants, including sodium chloride, remained (A. L. Huang et al., 2006). This is more surprising since, beyond the taste bud, broadly tuned acid/sodium chloride neurons are commonly observed. Even this, however, would be partly explicable by convergence of receptor cells onto primary afferents or by virtue of interaction within the bud. As depicted in Figure 13.2, recent studies suggest that some of this interaction may entail convergence of qualityspecific information from Type II cells onto more broadly tuned Type III cells (Tomchik, Berg, Kim, Chaudhari, & Roper, 2007). One neurotransmitter mediating communications between Type II and Type III cells is ATP, the same transmitter responsible for communication between Type II cells and the primary afferent nerves (A. L. Huang et al., 2006). Thus, it seems likely that Type II cells communicate both directly and indirectly with primary afferents (Tomchik et al., 2007). Indeed, the complex actions of the many neurotransmitters and modulators in the taste bud suggest that we have only begun to glimpse the tip of the iceberg with regard to how taste bud processing modifies signals (e.g., Roper, 2006; F. L. Zhao et al., 2005). Finally, there are hormonal influences at the level of the taste bud. Leptin, a polypeptide secreted by fat cells, that influences hypothalamic mechanisms to suppress appetite, also directly influences taste receptor cells, in particular it inhibits responses to sweet stimuli, tastants that potently promote feeding behavior (Shigemura et al., 2004). Indeed, the lack of the leptin receptor in the db/db mouse appears to account for the fact that this strain exhibits increased peripheral nerve responses to sweet, but not other stimuli (Ninomiya, Sako, & Imai, 1995).

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Sweet

Bitter

??Salt Sour

??Salty Umami ATP IIT1R2

IIIPKD2L1 IIT2R

I IIT1R1 ?

?

ATP

Primary afferents (Vii&ix)

Figure 13.2 Schematic diagram of taste bud processing. Note: Separate sets of Type II cells in the taste bud contain GPCRs for substances perceived as sweet, umami, or bitter. Cells expressing PKD2L1 are important for sour detection and have the distinct ultrastructural characteristics of Type III cells. Type II cells secrete ATP in response to stimulation with sweet, bitter, and umami tastants. Both primary afferent neurons and Type III cells are capable of responding to the ATP signal. Type III cells are the only taste bud cells that make classic synapses with primary afferent nerves; the transmitter is unknown. Type I cells resemble glia and have long been thought to play a purely supporting role, but recent data suggest that they may respond to salts. Based on figure 7 in Tomchik et al. (2007) and additional data from Y. J. Huang et al. (2007), Finger et al. (2005), and Vandenbeuch et al. (2008).

Primary Afferent Responsiveness and Function Information from taste buds is transmitted to primary afferent gustatory fibers. Individual fungiform papillae are separated from each other and contain a limited number of taste buds, making it possible to gain insight into the degree of convergence onto primary afferents. In fact, rodents have just a single taste bud per fungiform papilla, providing an opportunity to derive rather precise estimates for chorda tympani fibers. In the mouse, a recent study elegantly demonstrated that a single afferent chorda tympani fiber usually receives input from just one taste bud (Zaidi & Whitehead, 2006). In other species, there is apparently more but still relatively limited convergence (e.g., Nagai, Mistretta, & Bradley, 1988). It would be interesting to know if this pattern generalizes to posterior tongue taste

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Taste

buds, but this information is not available because taste buds are so densely packed in the circumvallate and foliate papillae. In addition, because individual taste buds express varied receptors it is difficult to extrapolate the amount of convergence from a given receptor type to a primary afferent as has been possible for convergence of olfactory receptors onto neurons in the bulb (see discussion that follows). However, the relatively small receptive fields for single anterior tongue fibers, along with the narrow response profiles of many single fibers in the chorda tympani, greater superficial petrosal, and glossopharyngeal nerves, implies considerable specificity. The popular notion of a lingual taste map, with various regions devoted to one or another quality is obviously in error. Indeed, in humans, it has been demonstrated that stimulation of a single fungiform papilla can give rise to enough sensory information to elicit recognition of multiple qualities (Bealer & Smith, 1975). Nevertheless, there are regional differences in relative responsiveness to different taste qualities. One common pattern is for the posterior tongue to exhibit a greater degree of responsiveness to bitter substances, although this may not be true in primates (see Spector & Travers, 2005, table 1). There are additional sensitivity differences as well, but these are more idiosyncratic across species. Initial recordings from single taste fibers were performed in cats (Pfaffmann, 1941) and revealed a lack of absolute specificity for a given quality. This basic result has endured scores of replications but has been refined in important ways. Even using moderate concentrations, there are peripheral fibers that respond nearly equally to stimuli evoking entirely distinct qualities. Other fiber types, however, are quite narrowly tuned. In fact, even broad fibers are far from a hodgepodge of sensitivities. One cogent scheme considers narrowly tuned fibers to be “specialists” and those more broadly tuned as “generalists” (e.g., Breza, Curtis, & Contreras, 2007; Frank, 1973; Lundy & Contreras, 1999). There are multiple reports of specialist fibers that respond several-fold more robustly to sweeteners, bitters, or sodium salts than to contrasting qualities. Fibers strongly responsive to acids, on the other hand, usually are generalists that respond to other electrolytes, including sodium salts, nonsodium salts, and ionic bitters like quinine hydrochloride. In fact, the electrolyte generalist category is probably divisible into a number of subtypes (Breza et al., 2007; Lundy & Contreras, 1999; Sollars & Hill, 2005). It is important to recognize that an apparent lack of narrowly tuned fibers for a particular quality in a given nerve and species can be misleading. For example, in rat chorda tympani fibers or their cell bodies in the geniculate ganglion, responses to the bitter stimulus, quinine, are observed mostly in neurons that also respond to other electrolytes

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(Breza et al., 2007; Frank, Contreras, & Hettinger, 1983; Lundy & Contreras, 1999). However, this is not true in the glossopharyngeal nerve, which contains a large population of neurons highly and specifically responsive to bitter tastants (Frank, 1991). Likewise, in the rat, narrowly tuned fibers that respond preferentially to sweeteners like sucrose comprise just a small group of neurons in the chorda tympani nerve but are prominent among geniculate ganglion neurons innervating the palate (Sollars & Hill, 2005) and in the glossopharyngeal nerve (Frank, 1991). Indeed, they are plentiful in the chorda tympani of most other species (see Spector & Travers, 2005, table 1). Figure 13.3 shows representative recordings from specialist and generalist fibers, taken from geniculate ganglion neurons innervating the anterior tongue of the rat. How amino and fatty acids fit into the picture is not clear. Receptor mechanisms for these compounds have been defined but these stimuli have been used infrequently when assessing peripheral nerve (and central) responses. For amino acids (reviewed in Spector & Travers, 2005), most information is available for glutamate, usually its sodium salt, monosodium glutamate (MSG). Many electrolyte-sensitive neurons respond to MSG, including sodiumspecialists and electrolyte-generalists, apparently due to the sodium cation, but this stimulus also drives neurons robustly responsive to sweeteners (see Figure 13.3). Even less is known about other amino acids, although those that taste sweet to humans, for example, the D-isomers and certain L-amino acids, drive the sweet-specialist fibers. Thus, in contrast to the segregation of T1R2 and T1R3 receptors in taste bud cells, there is not much evidence for a differential response to umami stimuli in peripheral fibers though there is one report of single fibers in the mouse glossopharyngeal nerve that respond more specifically to MSG (Ninomiya, Sako, & Funakoshi, 1989). There is even less information on fatty acid responses. A recent study of rat chorda tympani fibers used linoleic acid (a fatty acid present in corn oil). Not one of the 52 fibers assessed responded to this stimulus (Breza et al., 2007), despite the fact that transection of this nerve blunts behavioral recognition of linoleic acid (Stratford et al., 2006). Because the CD36 receptor is expressed more heavily in the posterior tongue, it is possible that responses would be more evident in the glossopharyngeal nerve. Indeed, a brief report of whole nerve recordings from the pharyngeal branch of the glossopharyngeal nerve demonstrated responses to oleic acid but not a substance with a similar texture, mineral oil. Further, it was interesting that the oleic acid responses were blunted by intraveneous injection of a satiety-producing agent, leptin (Kitagawa et al., 2007). Additional work using both amino and fatty acids is clearly warranted to determine if these classes of chemicals evoke clear taste signals.

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Chemical Senses Sucrose-specialist

15 s

NaCl-specialist

NaCl-generalistI

NaCl-generalistII

Acid-generalist

0.5 M Sucrose

0.1 M NaCl

0.01 M citric acid

Figure 13.3 Representative single-unit recordings from two types of specialist and three types of generalist neurons recorded from rat geniculate ganglion neurons that send their peripheral processes into the chorda tympani nerve to supply the anterior tongue. Note: The sodium chloride-specialist and electrolyte generalist neurons were the most common types, typical of rat chorda tympani recordings. The sucrose-specialist neuron was one of just a few cells of this type, but such neurons are plentiful in afferent fibers innervating the palate or posterior tongue in this species. Note that monosodium glutamate (MSG) drove both the specialist neurons. It is likely that sodium moiety was responsible

The claim that some peripheral fibers are specialists, specifically tuned for a given quality, must be tempered by the critical issue of stimulus concentration. Parametric data on concentration is sparse, but available information indicates that higher concentrations generally give rise to broader tuning. At the same time, it is clear that the response-concentration functions in a given cell have stimulus-specific slopes, which are often shallower for stimuli eliciting nonoptimal responses. In fact, in some cells, certain stimuli never elicit an above-threshold response, regardless of concentration (e.g., Hanamori, Miller, & Smith, 1988). Neurons with both types of responseconcentration functions can be seen in Figure 13.4. The broader tuning with higher concentrations would seem to make an ensemble code a necessity for disambiguating concentration and quality. However, the precise nature of that code, for example, the critical size of the ensemble, is unclear. The flip side of the concentration issue is that lower concentrations activate cells more selectively. Under these conditions, the ensemble may consist of only a single, selectively responsive cell type more akin to a sparse code

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0.02 M QHCl

0.1 M MSG

for activating sodium chloride-specialist, glutamate for activating the sucrose specialist. Thus, similar to the few other available reports, there is little evidence that umami stimuli elicit differential responses upstream, despite a notable degree of segregation of T1R1 and T1R2 receptors in taste bud cells. Also note the absence of quinine hydrochloride/bitterspecialists. Although such neurons are rare or absent in the chorda tympani, they are plentiful in the glossopharyngeal nerve. From figure 1 in “Monosodium Glutamate but Not Linoleic Acid Differentially Activates Gustatory Neurons in the Rat Geniculate Ganglion,” by J. M. Breza et al., 2007, Chemical Senses, 32, pp. 833–846. Reprinted with permission.

or labeled-line. Critical tests of these hypotheses require a larger amount of coordinated behavioral and neural data. In addition to the relative differences in chemosensitivity, other functional distinctions characterize taste nerves. This has been studied most thoroughly in rat. In light of the fact that the glossopharyngeal nerve innervates several times as many taste buds as the chorda tympani, it is surprising that loss of glossopharyngeal input generally has little effect on tasks that measure taste quality discrimination, whereas chorda tympani section, and especially combined section of the chorda tympani and greater superficial petrosal nerve, has a profound effect (reviewed in Spector, 2003). In these experiments, an animal is required to make a correct choice between different qualities to receive a reward or avoid a punishment; that is, taste quality is a discriminative stimulus. That this difference between the VIIth and IXth nerves represents a functional distinction with regard to task type rather than a stimulus-specific effect was particularly evident when rats were challenged to distinguish between quinine and potassium chloride (St. John & Spector, 1998). In the rat, the chorda tympani nerve

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Taste

275

500

Impulses/10 s

400

Q

Fiber 66 QHCI - best

Fiber 68 QHCI - best Q

H

300 200

S 100

H

N

N

S

0 0.0003

0.003

0.03

0.3 0.0003 Stimulus concentration (M)

Figure 13.4 Concentration-response functions for two neurons in the glossopharyngeal nerve of the hamster. Note: These neurons were both classified as “QHCl (quinine)-best,” based on the fact that the total response to quinine summed across concentrations elicited the largest response. These two fibers, however, had very different response-concentration functions; the fiber on the left maintained specificity over a range of concentrations; that on the right

is weakly responsive to these tastants, particularly quinine, and more important, most responses to these stimuli cooccur in neurons broadly responsive to electrolytes (e.g., Frank et al., 1983). In contrast, the glossopharyngeal nerve contains separate fiber groups robustly and selectively responsive to potassium chloride and other electrolytes versus those responsive to quinine and other bitter stimuli (Frank, 1991). Even so, chorda tympani section compromises potassium chloride/quinine discrimination more severely than section of the glossopharyngeal nerve (St. John & Spector, 1998). On the other hand, the glossopharyngeal nerve provides a much more effective afferent signal for triggering “gaping,” an oromotor rejection reflex preferentially elicited by bitter-tasting stimuli (e.g., J. B. Travers, Grill, & Norgren, 1987). When voluntary licking, driven by the hedonic nature of the stimulus is measured, effects are more varied and dependent on the sensitivity of a given primary afferent nerve. Even more important, as Spector (2003) has proposed, there appears to be synergy between different nerves in contributing to these tasks. The relative differences between primary gustatory nerves in subserving reflexive, hedonic, and discriminative behaviors bear some resemblance to the distinctive roles of olfactory axons innervating the main olfactory epithelium and vomeronasal organ; however, both the functional distinctions and sensitivity differences are more pronounced in the olfactory domain. Central Pathways and Hierarchy of Function Cranial nerves carrying gustatory information make their first synapse in the major visceral sensory nucleus of the

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0.003

0.03

0.3

responded nearly as well to hydrogen chloride at higher concentrations. Nevertheless, the cell remained poorly responsive to sucrose and sodium chloride over the entire intensity range. H  Hydrogen chloride; N  Sodium chloride; Q  Quinine; S  Sucrose. From figure 5 in “Gustatory Responsiveness of Fibers in the Hamster Glossopharyngeal Nerve,” by T. Hanamori et al., 1988, Journal of Neurophysiology, 60, p. 485. Adapted with permission.

medulla, the nucleus of the solitary tract (NST), with the special visceral afferents terminating rostral to their general visceral counterparts that convey signals from the gastrointestinal tract, respiratory, and cardiovascular systems (reviewed in Lundy & Norgren, 2004b; Pritchard & Norgren, 2004). One class of efferent projections from NST terminate locally, in the subjacent parvocellular reticular formation including regions close to preganglionic parasympathetic neurons, and more lightly in oromotor nuclei and the caudal NST (Beckman & Whitehead, 1991; Halsell, Travers, & Travers, 1996; J. B. Travers, 1988). These local pathways underlie cephalic phase and somatic reactions to taste stimuli (Figure 13.5). Ascending pathways from NST are somewhat different in rodent and primate. In both species, information ultimately reaches gustatory cortex and limbic structures, but the routes diverge across species. In rodents, the gustatory NST projects densely to the parabrachial nucleus (PBN) of the pons (Norgren & Leonard, 1971, 1973), and PBN neurons give rise to a thalamocortical pathway, including the most medial, parvicellular portion of the ventroposteromedial thalamic nucleus (VPMpc), which in turn projects to the primary gustatory cortex in the insula (Kosar, Grill, & Norgren, 1986a, 1986b). PBN projections along a second trajectory reach various nuclei in the ventral forebrain, most prominently the central nucleus of the amygdala and bed nucleus of the stria terminalis as well as a contiguous set of structures extending from the lateral hypothalamus caudally to the ventral pallidum rostrally (Bernard, Alden, & Besson, 1993; Halsell & Frank, 1992; Norgren, 1974; Norgren & Leonard, 1973). These two pathways are commonly thought to contribute to sensory/discriminative versus motivational functions (e.g., see

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Chemical Senses Interneurons Reticular formation

Sensory Taste

Motor Jaws & tongue

Muscle Ingestion & Rejection

Glu Lick nNMDA 

rNST

Gape

mV o

GABA

AD Glycine

QHCI  r 



nNMDA

STY mXII

GEN p Glu

Figure 13.5 Connectional model of a neural substrate for switching between oromotor responses of ingestion and rejection produced by palatable and unpalatable gustatory stimuli. Note: Projections from rostral (gustatory) nucleus of the solitary tract (rNST) synapse on populations of preoromotor interneurons in the subjacent reticular formation. Excitatory projections from the rNST produce the ingestive sequence of licking, characterized by an alternating sequence of tongue protrusion and retraction, with tongue protrusion coincident with jaw opening. Rejection responses to a bitter stimulus quinine mono-hydrochloride (QHCl) are induced by disinhibition of the network that recruits additional preoromotor interneurons or increases their response frequency

Spector & Travers, 2005), but this is an oversimplication because many complex behaviors require both cortical and limbic structures. In primate, ascending gustatory efferents appear to bypass PBN and ascend directly to VPMpc (Beckstead, Morse, & Norgren, 1980), then reach the insular/opercular cortex (Pritchard, Hamilton, Morse, & Norgren, 1986). Projections from the insula reach many of the same limbic structures, but the direct brain stemlimbic taste pathway appears missing in the primate (T. Pritchard & Norgren, 2004). This latter conclusion, however, is not ironclad. Although it is clear that there is a direct NST-VPMpc pathway in the primate not present in the rodent, the data for the lack of a PBN gustatory relay in the primate is less conclusive as it is based only on studying efferents from the rostral pole/anterior tongue region of NST. Thus, it remains possible that posterior mouth taste information does reach the PBN, as is certainly the case for efferents from the general visceral NST (Beckstead et al., 1980). In fact, human imaging data hints at a PBN taste relay (Topolovec, Gati, Menon, Shoemaker, & Cechetto, 2004). If so, a direct taste pathway from the brain stem to the limbic system is a possibility since the primate PBN has ventral forebrain projections similar to the rodent (Pritchard, Hamilton, & Norgren, 2000).

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QHCI to produce larger amplitude EMG responses. Phase switching during the rejection response is apparent as a three-phase sequence of initial tongue retraction followed by tongue protrusion coincident with jaw-opening and followed by a second tongue retraction. Dynamic modeling suggests that this phase switching can be accomplished by differences in the decay kinetics of inhibitory synapses between interneurons (see Venugopal et al., 2007, for details). AD  Anterior digastric (jaw-opener); GABA  Gamma-aminobutyric acid; GEN  Genioglossus muscle (tongue protrudor); Glu  Glutamate; nNMDA  Non-N-methyl-D-aspartate; STY  Styloglossus muscle (tongue retractor).

Classic experiments done 20 years ago revealed key features of gustatory functional anatomy. When rats are stimulated with small amounts of tastants delivered through intraoral cannulae, they exhibit one of two stereotyped sets of responses indicating the acceptability of the fluid: sweet, salty, umami, and sour stimuli mainly elicit lick-like movements and swallowing; bitter-tasting stimuli evoke a dramatically different response consisting of a very wide mouth opening with a unique tongue/jaw coordination (the “gape”) that leads to fluid rejection (Grill & Norgren, 1978b). Similar oral reactions occur in humans, including newborn infants (Steiner, 1973), and nonhuman primates (Steiner & Glaser, 1984). These reactions of rats to experimenter-delivered taste stimuli resemble those observed during voluntary intake, but experimentercontrolled stimulation made it possible to test these consummatory reactions after a key experimental manipulation where appetitive behavior does not occur- decerebration. Using these techniques, Grill and Norgren (1978c) demonstrated that these responses are preserved in decerebrate rats. Others have shown that similar behaviors persist in anencephalic human infants (Steiner, 1973) and decerebrate cats (Miller & Sherrington, 1916). These findings indicate that the gustatory brain stem is capable of making

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the basic distinction between acceptable and toxic fluids. Furthermore, the animal retains the regulatory capacity to respond appropriately to changes in stimulus concentration and to adjust behavior in response to certain postingestive signals. Thus, the animal exhibits longer licking bouts with increases in sucrose concentration; more gaping with higher quinine concentrations (Grill & Norgren, 1978b), and less licking to sucrose when testing is preceded by gastric fill (Grill & Norgren, 1978a) or cholycystokinin injection (Grill & Smith, 1988). However, other aspects of regulatory control are absent. In intact animals, food or sodium deprivation augment licking to sucrose and sodium, respectively, but these changes are lost in decerebrates (Grill, Schulkin, & Flynn, 1986; Seeley, Grill, & Kaplan, 1994). Likewise, when an animal is given a conditioned taste aversion (CTA) by inducing illness after the consuming a novel, preferred stimulus, oromotor reactions switch from licking to gaping. This type of plasticity is also demolished by decerebration (Grill & Norgren, 1978a).

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Brain Stem Topography and Circuitry There is a distinct topography of primary afferent fibers in the first-order gustatory relay, though it is much less dramatic than the precise topography established by olfactory bulb afferents discussed later. The chorda tympani, greater superficial petrosal, and glossopharyngeal nerves synapse in an orderly sequence roughly from rostrolateral to caudomedial (reviewed in Lundy & Norgren, 2004b; T. Pritchard & Norgren, 2004). Despite this orderly sequence, there is significant overlap between primary afferents. Figure 13.6 shows data from a recent triple-label tracing study that illustrates this overlapping topography (May & Hill, 2006). The overlap between nerves is particularly pronounced between the greater superficial petrosal and the two other nerves (Hamilton & Norgren, 1984; May & Hill, 2006), presumably forming the basis of the convergence between apposing regions of the tongue and palate observed neurophysiologically (Ogawa, Hayama, & Ito, 1982; S. P. Travers, Pfaffmann, & Norgren, 1986). Although less striking, there is also overlap between chorda tympani and glossopharyngeal endings, but neurophysiological evidence for convergence is apparent mostly with intracellular (Grabauskas & Bradley, 1996), not extracelluar recordings (S. P. Travers & Norgren, 1995), suggesting weaker connections. In vivo neurophysiological studies of the gustatory NST that map receptive fields find an orderly orotopic representation, with significant segregation of the anterior and posterior oral cavity (Dickman & Smith, 1989; S. P. Travers & Norgren, 1995). The topography apparent in mammals is emphasized greatly in certain

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Figure 13.6 (Figure C.17 in color section) Fluorescent photomicrographs showing triple-labeled terminal fields from the chorda tympani (CT, red), greater superficial petrosal (GSP, green) and glossopharyngeal (IX, blue) nerves in the nucleus of the solitary tract (NST) in the horizontal plane. Note: The approximate border of the nucleus is outlined in white. Sections are arranged from dorsal (A) to ventral (D). Because of the orientation of the nucleus, dorsal sections are also more caudal; ventral sections more rostral. The IXth nerve projection extends most caudally and medially (A); the chorda tympani most rostrally and laterally. Despite this orotopic organization, there is obvious overlap, particularly between the CT and GSP (yellow region in C) and between the GSP and IX (blue-green region in B). From figure 3 in “Gustatory Terminal Field Organization and Developmental Plasticity in the Nucleus of the Solitary Tract Revealed through Triple-Fluorescence Labeling,” by O. L. May and D. L. Hill, 2006, Journal of Comparative Neurology, 497, p. 661. Adapted with permission.

fish that possess taste buds on the exterior body innervated by the VIIth nerve, and in the mouth by the Xth cranial nerve. This topography is visible at a gross anatomical level where the separate representations are associated with definable lobes protruding from the medullary surface (reviewed in Whitehead & Finger, 2008). Although gustatory orotopy remains evident at the cortical level (Benjamin & Pfaffmann, 1953; Hanamori, Kunitake, Kato, & Kannan, 1997; Yamamoto, Matsuo, & Kawamura, 1980),

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it is clearest at the first-order relay. Even at the second synaptic relay in rodents, PBN neurons exhibit greater convergence from the anterior and posterior oral cavity (Halsell & Travers, 1997) and descriptions of orotopic organization exhibit more variability between studies, suggestive of a greater overlap and complexity (discussed in Lundy & Norgren, 2004b). Although orotopy is obvious, an ordered organization of what is arguably the most functionally salient feature of the system, taste quality, is more debatable. In contrast to the well-documented quality-specific spatial patterns in the olfactory bulb, with a few exceptions (e.g., Geran & Travers, 2006; Halpern, 1965; T. R. Scott, Yaxley, Sienkiewicz, & Rolls, 1986b), neurophysiological studies have not reported a chemotopic organization in NST. To some extent, this is excusable on the basis of technical difficulties. Gustatory neurons are small, relatively difficult to isolate, and along two of three anatomical axes, distributed over just a couple hundred microns, making it difficult to build up topographical maps. To a limited extent, these difficulties have been overcome by using Fos immunohistochemistry, which succeeded in uncovering a rough chemotopy for bitter tastants that activated cells restricted more medially than those activated by sweet (Harrer & Travers, 1996) and sour (S. P. Travers, 2002) stimuli. Fos immunohistochemistry has also revealed evidence for chemotopy in the PBN. In this higher order relay, there was a dichotomy between stimuli preferred (sucrose and sodium chloride) and avoided (acid and quinine) in appetitive tasks (Yamamoto, Shimura, Sakai, & Ozaki, 1994). As in other systems (Hunt, Pini, & Evan, 1987), however, the cells that express Fos in the gustatory system seem to represent just a subpopulation of all activated cells. In fact in NST, the prototypic and neurophysiologically potent stimulus, sodium chloride, does not elicit any Fos at all (S. P. Travers, 2002). Thus, though providing a strong hint of a systematic underlying organization, Fos staining cannot reveal the full story on a putative brain-stem gustatory chemotopy. As discussed later, recent intrinsic imaging in the gustatory cortex provides further evidence that an overlapping chemotopy indeed characterizes the organization of the system (Accolla, Bathellier, Petersen, & Carleton, 2007). Both the rostral NST and gustatory PBN have differentiated morphologies indicative of subnuclei/subdivisions, identifiable on the basis of fiber staining and the different concentrations of varied cell types (Fulwiler & Saper, 1984; Whitehead, 1988). The circuitry has been best studied in NST. Glutamate, acting through nonNMDA and NMDA receptors is a critical neurotransmitter for conveying signals from primary afferent fibers to NST (C. S. Li & Smith, 1997; Wang & Bradley, 1995). These primary afferents synapse most densely in a dorsal/central

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region, the rostral central subnucleus (Whitehead & Frank, 1983); the same subnucleus from which the largest proportion of ascending projections arise. PBN projection neurons have morphologies described as “elongate” and “stellate” (J. B. Travers, 1988; Whitehead, 1990). The prominent dendritic orientation of elongate cells in the mediolateral plane is parallel to the trajectory of incoming fibers; stellate cells have dendrites radiating in all directions. Local medullary projections, on the other hand, arise most prominently from the lateral and especially the ventral subnucleus, which contains a higher concentration of larger stellate neurons. These relationships suggest the possibility of a more direct pathway from primary afferents to the ascending pathway. Indeed, electron microscopic reconstructions of identified PBN projection cells show that synapses resembling those from primary afferents terminate on dendritic shafts or spines of these neurons (Whitehead, 1986). However, parallel studies are not available for local medullary projection cells and this conjecture is further complicated by the extensive dendritic arborizations of NST neurons (Davis & Jang, 1988; King & Bradley, 1994; Whitehead, 1988). There is also a prominent class of small ovoid neurons throughout the various subnuclei; these are considered to be inhibitory interneurons and some contain GABA (Davis & Jang, 1988; Lasiter & Kachele, 1988; Whitehead, 1990). EM reconstructions show that symmetric, putative inhibitory synapses terminate on both dendritic and somal compartments (Whitehead, 1986, 1993), in distinction to the preferential dendritic terminations of primary afferents (May, Erisir, & Hill, 2007; Whitehead, 1986). Response Properties Chemosensitive profiles of NST and PBN neurons resemble, to a first approximation, those observed in primary afferent fibers; that is, there are a limited number of types definable on the basis of an optimal or “best” stimulus quality. However, due at least in part to convergence, on average, brain-stem neurons are more broadly tuned than their peripheral counterparts (reviewed in Spector & Travers, 2005). A striking example was reported in the rat, when it was observed that single NST neurons that received convergent input from the anterior tongue and anterior palate often responded to sodium chloride applied to the tongue, but sucrose applied to the palate (S. P. Travers et al., 1986). A series of studies systematically compared tuning profiles using the same species (hamster), stimuli, and method of stimulation and found that the breadth of responsiveness of certain cell types increased at each successive relay (Smith, Van Buskirk, Travers, & Bieber, 1983). Because of this broadening, many have concluded that a sparse code or labeled line is not a viable option for coding taste

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intravenous injections of glucose, glucacon, or insulin preferentially decrease multiunit responses elicited by sweet stimuli (Giza, Deems, Vanderweele, & Scott, 1993; Giza & Scott, 1983, 1987). Gastric distention likewise suppresses NST taste responses (Glenn & Erickson, 1976). Another satiety-mimicking treatment, intraduodenal lipid, diminished PBN gustatory responses, with particularly potent effects for sucrose responses in neurons narrowly tuned to sweet stimuli (Hajnal, Takenouchi, & Norgren, 1999). Gastric distension also influences PBN neurons, though, as shown in Figure 13.7, gastric-induced taste response enhancements as well as suppressions were observed (Baird, Travers, & Travers, 2001). These changes are assumed to

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quality in the CNS (see Simon, de Araujo, Gutierrez, & Nicolelis, 2006, for a contemporary exposition of this opinion). Moreover, work in NST has noted considerable variability in responses over multiple trials (Di Lorenzo & Victor, 2003). Interestingly, this analysis also showed that, for some cells, the temporal characteristics of the spike train were more reliable suggesting that spike timing could make a contribution to quality coding. Despite the problem of response variability and the average increase in breadth of responsiveness, some brain stem neurons remain narrowly tuned (reviewed in Spector & Travers, 2005). For example, in a series of studies using an awake, behaving preparation Norgen’s group explicitly subdivided best-stimulus types of NST (Nakamura & Norgren, 1991) and PBN neurons (Nishijo & Norgren, 1990), demonstrating that there were specifically and broadly tuned neurons within a given type. Other work makes it clear that it is important to consider receptive field when making generalizations about tuning curves. For example, one investigation that recorded from the NST region most responsive to anterior mouth stimulation concluded that responses to bitter-tasting stimuli occurred mainly within electrolyte-generalist cells, and that salty, sour, and bitter stimuli were nearly equipotent in driving this class of neurons (Lemon & Smith, 2005). A second study that sampled the entire NST concluded instead that there was a class of neurons optimally and narrowly tuned to bitter tastants; however, all of these neurons had posterior tongue-receptive fields (Geran & Travers, 2006). The significance of these varied tuning curves is unknown but it is possible that they relate to the varied functions of the taste system. At a minimum, taste has discriminative, motivational, and reflex functions, and it is possible that these operations function to some extent in parallel (Spector & Travers, 2005). In fact, not only are NST projections to the medullary reticular formation and PBN concentrated in different subnuclei, double-labeling studies reveal that very few neurons project to both locations (Halsell et al., 1996). Likewise in PBN, there is some evidence for segregation between thalamic and ventral forebrain outputs (Voshart & van der Kooy, 1981). These anatomical studies support the notion of parallel processing in the taste system, although a coherent story regarding the relationship between response properties and function has yet to emerge (discussed in Spector & Travers, 2005).

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Brain stem gustatory responses are far from static. Instead they are subject to modulation by homeostatic state and prior history, presumably reflecting the markedly different behavioral reactions to taste evident in deprived and sated states and as a result of past experience. In the rodent NST,

Note: Average time course of responses that were suppressed (A, n  18) or enhanced (B, n  7) by gastric distension. Modulation occurred in both directions but suppression was over twice as common as enhancement. From figure 4 in “Integration of Gastric Distension and Gustatory Responses in the Parabrachial Nucleus,” by J. P. Baird et al., 2001, American Journal of Physiology: Regulatory, Integrative, and Comparative Physiology, 281, p. R1587. Adapted with permission.

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be part of the substrate for the reduced preference for palatable stimuli in satiated animals. Another powerful manipulation that affects taste preference is the induction of a sodium appetite in response to deprivation or hormonal treatment. In nondeprived animals, isotonic and lower concentrations of sodium are preferred and higher concentrations are rejected. However, sodium deprived animals avidly prefer even hypertonic salt, as long as it contains the sodium cation (C. Richter, 1956). Neurally, this manipulation usually (but not always, see Tamura & Norgren, 1997) reduces sodium chloride-evoked responses in the brain stem (Jacobs, Mark, & Scott, 1988; McCaughey & Scott, 2000; Shimura, Komori, & Yamamoto, 1997; Tamura & Norgren, 2003). Indeed, this phenomenon was first observed in the chorda tympani nerve and noted to be most dramatic for sodium-specialist fibers with high firing rates (Contreras, 1977), a specificity maintained centrally. The reduction in sodium chloride responses in sodium-specialist neurons is hypothesized to be part of the mechanism responsible for blunting the aversiveness of hypertonic saline and thus promoting consumption in the deprived state. Induction of a conditioned taste aversion (CTA) also influences brain stem taste cells. The influence of CTA is also evident in neurophysiological responses recorded from both NST (Chang & Scott, 1984) and PBN (Shimura, Tanaka, & Yamamoto, 1997; Shimura, Tokita, & Yamamoto, 2002). Increases and decreases in responsiveness have both been reported, the direction apparently dependent on the nature of the conditioned stimulus. The neural substrate for brain stem plasticity most likely includes local connections and forebrain influences. In NST, gut afferents in the vagus project to an almost entirely separate region caudal and medial to gustatory projections (e.g., Beckstead & Norgren, 1979; Hamilton & Norgren, 1984). However, anatomical data suggest a possible intranuclear projection from the caudal to the rostral NST (Karimnamazi, Travers, & Travers, 2002). The gustatory and visceral NST send projections to PBN that maintain significant topography, but also exhibit considerable overlap (Hermann & Rogers, 1985; Karimnamazi et al., 2002). Thus, the effects of the intravenous satiety factors, gastric distension, and intraduodenal lipids could be mediated in part through activation of vagal afferents or chemosensitive neurons in caudal NST/area postrema, with subsequent transmission to the rostral NST and/or to the gustatory PBN. Such a pathway would be consistent with the sufficiency of the brain stem for exhibiting behavioral changes after similar manipulations. On the other hand, alterations after sodium appetite or conditioned taste aversion are more likely to be mediated by forebrain projections or hormonal influences. Indeed, decerebration alters responses of brain stem taste neurons (Di Lorenzo,

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1988; Mark, Scott, Chang, & Grill, 1988), and abolishes changes induced by CTA, as measured neurophysiologically (Tokita, Karadi, Shimura, & Yamamoto, 2004) or by Fos expression (Tokita, Shimura, Nakamura, Inoue, & Yamamoto, 2007). Several forebrain regions, including the insular cortex, lateral hypothalamus, central nucleus of the amygdala, and bed nucleus of the stria terminalis send robust, direct projections to the rostral NST (M. C. Whitehead, Bergula, & Holliday, 2000) and gustatory areas of the PBN (Saggu & Lundy, 2008). Furthermore, recent studies demonstrate that activation of forebrain regions powerfully modulates taste responses (e.g., Cho, Li, & Smith, 2003; Di Lorenzo & Monroe, 1992, 1995; C. S. Li, Cho, & Smith, 2005; R. F. Lundy & Norgren, 2004a; Smith, Ye, & Li, 2005). The neurochemical substrate for modulation is unknown, but in addition to the ubiquitous neurotransmitters, GABA and glutamate, the gustatory NST and PBN are supplied by fibers containing a variety of neuromodulatory substances including several neuropeptides and their receptors (see Bradley, 2008; R. F. Lundy & Norgren, 2004b, for recent reviews). Forebrain Lemniscal Pathway Similar to other sensory systems, except olfaction (see later discussion), ascending gustatory information terminates in a highly focused projection in a first-order (see Chapter 10) thalamic nucleus, the VPMpc, and VPMpc neurons in turn, synapse in a circumscribed cortical region, the dysgranular insular (rodents) or insular/opercular (primates) cortex. Presumably, VPMpc serves an important processing and gating role, as is apparent for homologous thalamic regions for other sensory systems. However, only a handful of careful studies have described thalamic responses (e.g., T. R. Scott & Erickson, 1971) and a clear picture of thalamic function has yet to emerge. Likewise, knowledge of thalamic circuitry is sparse, although, as for other first-order thalamic relays, the thalamic reticular nucleus provides a notable input (Hayama, Hashimoto, & Ogawa, 1994). More is known about gustatory cortex and the pace of discovery has accelerated in the past few years, particularly with the increasing use of chronic recording using microwire bundles that allow observations over many trials and simultaneously recorded cells. Earlier studies used either acute, anesthetized preparations or chronic recording with limited trials recorded on a cell-by-cell basis. Perhaps not surprisingly, the pictures that emerge from these two perspectives are quite different. When studied on a cell-by-cell basis with limited trials, most primary cortical taste neurons have been grouped into a limited number of types according to their optimal

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Figure 13.8 Poststimulus time histograms (upper traces) for a gustatory-responsive neuron recorded from the insular cortex in an awake, freely moving rat. Note: This cell responded relatively specifically to sodium chloride, similar to the sodium chloride-specialist geniculate ganglion neuron shown in Figure 13.5. Increasing concentrations of sodium chloride produced systematic increases in the neural response. Responses in A were evoked when the animal drank from a sipper spout, licks are shown on the lower

taste quality (Figure 13.8). This is true, regardless of whether acute/anesthetized or chronic/awake rodents (e.g., Ogawa, Hasegawa, & Murayama, 1992; Yamamoto, Matsuo, Kiyomitsu, & Kitamura, 1989; Yamamoto, Yuyama, Kato, & Kawamura, 1985) or primates (e.g., Scott, Sienkiewicz, Rolls, & Yaxley, 1986a) are studied. Also like their brain stem counterparts, cortical neurons exhibit a range of tuning. There is no clear trend for tuning to sharpen or broaden (reviewed in Spector & Travers, 2005). Moreover, unlike the somatosensory (Chapter 14), visual (Chapter 11), or olfactory system (see later), there is little evidence that more complex stimulus configurations, for example, mixtures (Plata-Salaman, Smith-Swintosky, & Scott, 1996), become particularly potent. However, the intermingling of responsive and unresponsive neurons in much of primate cortex leaves a lingering suspicion that more appropriate stimuli or behavioral contexts may still be discovered. Also in contrast to other cortical regions, there have been no reports of a functional columnar organization. However, this has not been studied sufficiently because of the difficulty of making penetrations perpendicular to the gustatory cortical surface, inconveniently located very far laterally, and in primate, buried in the Sylvian fissure. Despite providing an overall picture similar to that in brain stem, these cell-by-cell studies reveal some cortical cells with novel characteristics. Ogawa and colleagues (Ogawa et al., 1992) reported that a subset of cortical neurons were “double-peaked.” These neurons did not show

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trace. Responses in B were elicited by infusing stimuli though an intraoral cannula; jaw muscle activity is shown in the lower trace. Note that there was a minimal response to quinine under both conditions, demonstrating that the small response in A is not due to a lack of adequate stimulation resulting from less licking. From “Taste Responses of Cortical Neurons in Freely Ingesting Rats,” by T. Yamamoto et al., 1989, Journal of Neurophysiology, 61, p. 1246. Reprinted with permission.

the orderly response profiles typical throughout the gustatory system; that is, a systematic decline to either side of the optimal stimulus when the four standard stimuli are arranged from most to least preferred: sucrose, sodium chloride, hydrogen chloride, quinine. These novel profiles were taken as evidence for convergence. In addition, Yamamoto and his group (Yamamoto et al., 1989) reported that, although most cortical neurons had response profiles resembling those in the brain stem (Type I; see Figure 13.8), there was also a smaller group of cells (Type II) with opponent processing characteristics; that is, a response increment to preferred stimuli and a decrement to aversive stimuli, or vice versa. At lower levels of the neuraxis, not only are such on/off type responses rare, even frank response suppressions are uncommon. The more recent, multisite experiments reach starkly different conclusions. These studies find a strong trend toward very broad tuning; in fact overall, taste neurons are so broad that defining an optimal stimulus on the basis of response magnitude is not attempted (e.g., Fontanini & Katz, 2006; Stapleton, Lavine, Wolpert, Nicolelis, & Simon, 2006). A graphic example is depicted in Figure 13.9. This different perspective partly derives from the increased power gained by analyzing multiple trials and focusing on the temporal properties of the response. Indeed, in the seminal paper in this field, a major point was that the population of cortical gustatory neurons expands dramatically when the fine grain of spike timing is taken into account

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(Katz, Simon, & Nicolelis, 2001). Further potential for coding is apparent in temporal relationships between simultaneously recorded cell ensembles (Jones, Fontanini, Sadacca, Miller, & Katz, 2007). The precise nature of the putative spatiotemporal code is still emerging and appears to bear little resemblance to the obvious rhythmicity and synchrony exhibited by neurons in the olfactory system, as discussed later. One proposal is that the cortical spike train evolves in time in order to code different aspects of the fluid stimulus; that is, somatosensory (0 to 500 ms), taste quality (500 to 1,000 ms), and taste hedonics (1,000 to 2,500 ms; Katz et al., 2001). However, as shown in Figure 13.9, other studies find that taste quality is coded in the very early (200 ms) time period (Stapleton et al., 2006). Some of these apparent differences may relate to the varying behavioral paradigms under which animals are tested. Indeed, one study showed that gustatory cortical responses were labile, dependent on the rat’s state of “engagement” (Fontanini & Katz, 2006).

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be much more broadly tuned than the cell in Figure 13.10 and in fact, a differential response to taste questionable, since the cell also responds robustly to a nongustatory stimulus, water. However, when multiple trials were used to model the Poisson distribution of the fine grain of the spike counts (15 ms bins), a differential response emerged. Importantly, this information was available in the interval occurring during a single lick. From figure 7 in “Rapid Taste Responses in the Gustatory Cortex during Licking,” by J. R. Stapleton et al., 2006, Journal of Neuroscience, 26, p. 4132. Reprinted with permission.

Another novel approach that sheds a different perspective on cortical processing focuses on the spatial domain. Accolla and colleagues (Accolla et al., 2007) performed a rigorously controlled experiment that demonstrated a convincing chemotopy using intrinsic optical imaging in anesthetized rats. Responses to sucrose, sodium chloride, hydrochloric acid, and quinine were arranged in an overlapping rostral to caudal sequence (Figure 13.10). Because these investigators mainly stimulated the anterior tongue and palate, this chemotopy was not merely secondary to orotopy; in fact, if more regions of the mouth had been stimulated an even more striking cortical map of taste quality might have been observed (Yamamoto et al., 1980). This spatial organization is similar to that observed in earlier neurophysiological investigations in rats (Yamamoto et al., 1985) and monkeys (T. R. Scott, Yaxley, Sienkiewicz, & Rolls, 1986a). How this orderly chemotopy relates to the very broadly tuned neurons observed in the multisite studies has yet to be explained.

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Figure 13.10 (Figure C.19 in color section) Spatial mapping of taste quality in the rat insular cortex using intrinsic optical imaging. Note: Tastants were applied (mainly to the anterior mouth) in anesthetized animals. (Top Panels) (blue background): Population maps for sodium chloride, sucrose, citric acid, and quinine based on 27 animals over 8 to 18 total presentations with each stimulus. (Bottom Panels) (black background): Comparison of each stimulus pair. For each pair, both overlap

As noted, cortical responses are plastic, depending on behavioral state (Fontanini & Katz, 2006) and insular responses in the rat also reflect satiety (de Araujo et al., 2006), similar to the brain stem. In the monkey, however, as in the medulla (Yaxley, Rolls, Sienkiewicz, & Scott, 1985), responses in the primary gustatory cortex seem unchanged by feeding the animal to satiety (Rolls, Scott, Sienkiewicz, & Yaxley, 1988). Because satiety does modulate higher-order gustatory neurons in the orbitofrontal cortex (Rolls, Sienkiewicz, & Yaxley, 1989, see below), initial interpretations suggested that this reflected a perceptual/ motivational split from primary to higher-order cortex. However, functional imaging studies in humans suggest that insular gustatory neurons are indeed modulated by satiety (Small, Zatorre, Dagher, Evans, & Jones-Gotman, 2001). Thus, further investigations may be necessary to reveal the full story on how homeostatic state affects gustatory responsiveness in the primate primary taste cortex. Learning also alters insular taste responses. A pioneering study (Yamamoto et al., 1989) observed the same cortical taste neurons in awake animals prior to and following induction of a CTA. Pairing a particular stimulus with subsequent intraperitoneal injection of a nausea-inducing agent produced varied changes. About 20% of the Type I cortical taste cells exhibited enhanced responses specific to the conditioned stimulus; that is, both response increments and decrements were magnified. In contrast, almost all of the Type II (hedonic) neurons were affected and in these cells

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and segregation are apparent. The representation of sucrose and quinine are the most separate. For each panel, the white vertical line indicates the location of the middle cerebral artery; posterior is to the right; dorsal (medial) at the top of each figure. Scale bars  500 uM. From figure 5 in “Differential Spatial Representation of Taste Modalities in the Rat Gustatory Cortex,” by R. Accolla et al., 2007, Journal of Neuroscience, 27, p. 1401. Adapted with permission.

the CTA caused response direction to change, excitatory responses became inhibitory and vice versa. A more recent multisite investigation also reported conditioning-induced changes in about one third of cortical taste neurons, but in contrast to the earlier work, there was nearly ubiquitous suppression of the preconditioning responses. Furthermore, these suppressions were confined to the later, “hedonic” period of firing (Grossman, Fontanini, Wieskopf, & Katz, 2008). Intriguingly, the temporal aspects of this effect resembled one reported in a much earlier study of CTA effects on brain stem neurons, though the direction of the modulation was opposite. In the NST, a CTA produced an increase in the response to the conditioned stimulus, but one that occurred only with a “long” (1 second) latency (Chang & Scott, 1984). Finally, another recent investigation employed optical imaging to demonstrate that a CTA to saccharin changed the spatial topography of the response to one resembling that elicited by quinine. In other words, the shift entailed increases of activity in one region and decreases in another (Accolla & Carleton, 2008). Thus, there is little doubt that CTA produces profound cortical changes. However, further work is necessary not only to clarify the nature of those alterations, but also to discern their synaptic basis and contribution to behavioral changes. Limbic The limbic forebrain and hypothalamus are critical substrates for ingestion and other motivated behaviors. Eating

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and drinking culminate in relatively fixed, species-specific patterns of oromotor behaviors. However, these patterns are subject to modifications induced by learning and physiological needs and more complex environmental controls. Even more so, appetitive behaviors necessary for consumption must be flexible to correspond with alterations in internal state, environment, and past experience. Gustatory signals influence the ventral forebrain through both direct and indirect pathways. As discussed, in the rodent there are direct projections from the gustatory PBN to the amygdala, bed nucleus of the stria terminalis, lateral hypothalamus, and ventral pallidum (Bernard et al., 1993; Norgren, 1974; Norgren & Leonard, 1973). These connections are supplemented by projections from the insular cortex; in primates, the cortex is their main source (reviewed in Lundy & Norgren, 2004b; Pritchard & Norgren, 2004; Whitehead & Finger, 2008). Gustatory signals reach other limbic regions, of particular significance, the nucleus accumbens, a structure strongly implicated in reward and motivation. There are a variety of potential indirect routes through which taste signals could influence nucleus accumbens. One is a projection from the insular cortex (Reynolds & Zahm, 2005). However, this projection does not appear to originate in the primary taste cortex, as it does not arise from the dysgranular or granular regions where taste responses are most prominent (Kosar et al., 1986a; Ogawa, Ito, Murayama, & Hasegawa, 1990). Instead, the insularaccumbens projection arises from the juxtaposed agranular insular cortex (Reynolds & Zahm, 2005), which in turn receives inputs from the dysgranular (Shi & Cassell, 1998). On the other hand, functional work suggests that taste information may primarily influence accumbens neurons via ventral forebrain projections since sucrose-elicited dopamine release in accumbens was dampened by lesion of the parabrachial nucleus but not changed by thalamic lesions (Hajnal & Norgren, 2005). There are many descriptions of presumptive taste responses in limbic structures during consumption of food or fluid rewards in operant tasks. These same cells are often modulated by other unconditioned sensory stimuli, including olfaction, and by the conditioned stimulus or the operant response itself. Studies that focus on the details of gustatory processing are fewer. Compared to the thalamocortical pathway, gustatory-modulated neurons in limbic regions exhibit more convergence from nongustatory sources and exhibit more decrements in response to gustatory stimuli (reviewed in Spector & Travers, 2005). Lateral hypothalamic (Yan & Scott, 1996) and amygdalar (Nishijo, Uwano, Tamura, & Ono, 1998; T. R. Scott et al., 1993), neurons respond differentially to tastants but the critical variable appears to be hedonics instead of quality. This hypothesis is supported by the nearly ubiquitous influence

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of satiety on taste responses in these regions (e.g., Burton, Rolls, & Mora, 1976; Yan & Scott, 1996). Contemporary studies have also turned their attention to the nucleus accumbens (Roitman, Wheeler, & Carelli, 2005; Taha & Fields, 2005) and ventral pallidum (Tindell, Smith, Pecina, Berridge, & Aldridge, 2006). Both hedonically positive (sucrose) and negative (quinine) stimuli delivered through intraoral cannulas drive accumbens neurons and responses are apparent on the very first presentation, demonstrating that gustatory responsiveness is innate (Roitman et al., 2005). Novel responses subsequently develop to the conditioned visual and auditory stimuli signaling tastant delivery further demonstrating the plasticity of these neurons. Similar to the hypothalamus and amygdala, palatability appears to be a critical stimulus dimension. In accumbens, sucrose and quinine not only drive different sets of cells, but the two sets of neurons respond differentially, that is, with response decrements and increments, respectively. A recent investigation (Figure 13.11) made the intriguing observation that these palatable flavor-elicited responses could be altered based on whether they served as a signal for the availability to self-deliver cocaine or saline (Wheeler et al., 2008). In this experiment, sweet saccharin was mixed with different flavors of Kool-Aid. After training, the flavor which served as the discriminative stimulus for saline was unaltered, but somewhat counterintuitively, the flavor that predicted cocaine now elicited response decrements, a switch concurrent with a change in the oromotor response to rejection (gaping) (Figure 13.11). These alterations in behavioral and neural responsiveness were hypothesized to be caused by a dysphoric state induced by the anticipation of drug withdrawal. This experiment not only illustrates the plasticity of accumbens responses but also emphasizes convergence between modalities since the different Kool-Aid flavors are largely distinguishable on the basis of olfaction. Additional insight regarding accumbens responses was derived from studying responses that occurred when waterdeprived rats licked water and varying concentrations of sucrose (Taha & Fields, 2005). Similar to what Roitman and colleagues observed, most sucrose responses were inhibitory. However, these inhibitory responses appeared more directly related to licking than to sensory parameters. Thus, varying sucrose concentration did not modulate the magnitude of the response decrements, and licking water or even a dry spout also produced suppressions. These neural suppressions were interpreted as “gating”; that is, allowing, an ingestive response to be made (Taha & Fields, 2005), a conclusion consistent with the feeding-stimulatory effects of inhibiting accumbens neurons pharmacologically (e.g., Pecina & Berridge, 2000). However, in agreement with Roitman’s report, Taha and Fields (Taha & Fields, 2005)

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Figure 13.11 Effects of discriminative stimulus training for cocaine availability on flavor-induced responses of accumbens neurons. Note: (Top Panels) A and B: Under naive conditions, when saccharin is mixed with orange or grape flavoring, intraoral delivery of either stimulus primarily elicits firing decrements in accumbens neurons as is typical for palatable stimuli. C and D: After training, the stimulus that has been paired with the opportunity to self-deliver saline (intraveneously) still elicits response decrements, but the stimulus paired with cocaine elicits

also observed a small population of sucrose-driven excitatory responses and these cells exhibited positive concentration-response functions. Furthermore, a given sucrose concentration elicited larger responses when paired with water than with a higher sugar concentration. Such a “contrast” paradigm is well known to change the reward value of a gustatory stimulus (Flaherty, Turovsky, & Krauss, 1994). Thus, it seems reasonable to hypothesize that the sucrose-driven accumbens excitatory cells are more directly related to stimulus palatability. The ventral pallidum, a target of accumbens projections (reviewed in Zahm, 2000) also contains gustatoryresponsive neurons whose firing rates are modulated by

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mostly response increments. (Lower Panels) Behavioral changes in stimulus-induced oromotor responses under the two conditions, when the stimulus signals saline (left) or cocaine (right). Upper insets show still video frames of licking (left) and gaping (right); lower insets depict associated electromyographic recordings from a jaw-opening muscle. From figures 1 and 3 in “Behavioral and Electrophysiological Indices of Negative Affect Predict Cocaine Self-Administration,” by R. A. Wheeler et al., 2008, Neuron, 57, pp. 774–785. Reproduced with permission.

the hedonics. The effects of salt appetite are particularly compelling (Tindell et al., 2006). Before and after recovery from sodium deprivation, ventral pallidal neurons responded with higher firing rates to a preferred stimulus, 0.5 M sucrose, than to a stronger (1.5 M), but very aversive sodium chloride concentration. However, during the deprived state, firing rates to salt were equal to those elicited by sucrose. This neural change occurred in parallel with a switch in oromotor responses from gaping to licking. It is informative to compare these results to lower levels of the gustatory neuraxis, where sodium deprivation produced decreases in neural responses. It seems possible that the peripheral and brain stem decreases in responsiveness may

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function to decrease the aversiveness of high sodium concentrations, whereas the increases in accumbens responses are responsible for driving salt-seeking behavior. OLFACTION Transduction The Nobel Prize winning work of Buck and Axel (1991) demonstrated that olfactory transduction involves a (uniquely) large superfamily of G-protein coupled receptors (GPCRs), and that each olfactory receptor neuron expresses only one receptor type (but see Mombaerts, 2004c). The number of such genes varies greatly across species, ranging from 82 in chickens (Niimura & Nei, 2005) to several hundred in humans (Niimura & Nei, 2003), to well over a thousand in rodents and dogs (Quignon et al., 2003; Young et al., 2003; reviewed in Ache & Young, 2008). Despite the fact that a large number of these genes, including well over half in humans, are nonfunctional pseudogenes (Glusman, Yanai, Rubin, & Lancet, 2001; Zozulya, Echeverri, & Nguyen, 2001; reviewed in Mombaerts, 2004b), olfactory genes in all vertebrates represent a considerable proportion of the genome, perhaps 4% in mice, 1.4% in humans. Despite the remarkable achievement of characterizing the olfactory receptor genome, relatively little is known about the specificity of the ligands that activate these receptors. The majority of mammalian olfactory receptors are “orphans,” lacking an identified ligand (reviewed in Malnic, 2007). Unlike taste receptor GPCRs, in which specific receptors are associated with distinct classes of perception, for example, sweet, bitter, olfactory receptors respond to a wide variety of odorants with different chemical structures. This suggests that olfactory receptors only recognize specific parts of a given odorant and that it is the combination of activated receptors that produces the afferent signal to the brain. Many vertebrates also possess a second olfactory epithelium located in the vomeronasal organ (VNO) and the discovery of GPCRs on sensory neurons in the main olfactory epithelium was soon followed by the discovery of two additional unique superfamilies of G-protein coupled receptor in the VNO (Dulac & Axel, 1995; Herrada & Dulac, 1997; Matsunami & Buck, 1997). In addition, there are several smaller, less well-characterized olfactory sensory cells in the septal organ of Masera and the Grueneberg ganglion. Furthermore, not all olfactory receptors are GPCRs that rely exclusively on cAMP (Spehr, Spehr, et al., 2006). In addition, there is a class of vomeronasal olfactory receptors associated with members of the transient receptor protein superfamily (TRPC2; Kelliher, Spehr, Li, Zufall, & Leinders-Zufall, 2006; Liman, Corey, & Dulac, 1999).

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The large number of genes and pseudogenes associated with olfaction engenders genetic variability as a ready explanation for the considerable variation in olfactory sensitivity within and between species. It is likely that polymorphisms in OR genes contribute to olfactory thresholds in mice, for example, that span four orders of magnitude (Joshi, Volkl, Shepherd, & Laska, 2006; Laska, Joshi, & Shepherd, 2006). In humans, variations in olfactory sensitivity range from specific anosmias (Whissell-Buechy & Amoore, 1973) to hyposmia to hyperosmia. The origins of these variations likely reflect genetic polymorphisms. A study in humans suggested that single nucleotide polymorphisms that are particularly susceptible to genetic variation could account for hyperosmia associated with the perspirant odorant isovalaric acid (Menashe et al., 2007). Across a population of 377 individuals, there was a close association between those individuals with hypersensitivity to isovalaric acid and a particular gene for the olfactory receptor OR11H7P. Similarly, estimates of the proportion of individuals perceiving the urinary metabolites of asparagus as malodorous range from 10% to 24% (Lison, Blondheim, & Melmed, 1980; reviewed in Mitchell, 2001). Genetic polymorphisms may also explain why only certain individuals produce the offending odor; with estimates ranging from 40% to 79% (Mitchell, Waring, Land, & Thorpe, 1987). Olfactory Bulb Processing Afferent fibers from olfactory receptor neurons (ORN) located in the main olfactory epithelium (MOE) enter the brain through the cribiform plate where they synapse in the glomerular layer of the main olfactory bulb (MOB, Figures 13.12 and 13.13; reviewed in Shepherd, Chen, & Greer, 2004; Shipley, Ennis, & Puche, 2004). The glomerulus is a spheroid concentration of neuropil demarcated by a shell of surrounding glia, that ranges from 100 um to 200 um dia in mammals. Within the neuropil is a dense matrix of synaptic connections between ORNs making contact with dendrites of olfactory bulb output neurons, the mitral and tufted cells (M/T), as well as the dendrites of juxtaglomerular neurons surrounding each glomerulus. The glomerular structure is conserved across phyla and can be recognized in the antennal lobe of many insects, as well as in the olfactory bulb of different classes of vertebrates, for example, amphibia and fishes (Hildebrand & Shepherd, 1997). The similarity of this structure in so many different species belies its importance in sensory processing and it is considered a fundamental, universal unit of olfactory processing. In vertebrates, there are multiple topographical relationships between the MOE and the glomeruli in the main olfactory bulb. The MOE itself can be divided into four zones, arranged dorsal to ventral based on the expression

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Olfaction (A)

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287

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AOB MOB

Glomerulus Olfactory sensory MOE neurons VNO

Figure 13.12 (Figure C.20 in color section) Olfactory receptor neurons expressing the same GPCR in the main olfactory epithelium (MOE) converge to one or a few glomeruli in the main olfactory bulb (MOB).

ORN

GL

PG ET

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ET PG

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MC MCL

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Figure 13.13 Schematic representation of the olfactory bulb indicating major layers, cell types, and circuits. Note: Excitatory synapses shown with solid arrows, inhibitory synapses with small filled circle. Major layers: EPL  External plexiform layer, GCL  Granule cell layer; GL  Glomerular layer; MCL  Mitral cell layer. Major cell types: ET  External tufted cell; GC  Granule cell; MC  Mitral cell; PG  Periglomular cell; SA  Short-axon cell. From figure 3 in “Coding and Synaptic Processing of Sensory Information in the Glomerular Layer of the Olfactory Bulb,” by M. Wachowiak and M. T. Shipley, 2006, Seminars in Cell and Developmental Biology, 17, pp. 411–423. Adapted with permission.

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Note: In contrast, GPCRs located in the vomeronasal organ (VNO) epithelium converge to multiple glomeruli in the accessory olfactory bulb (AOB). From figure 4 in “Genes and Ligands for Odorant, Vomeronasal, and Taste Receptors,” by P. Mombaerts, 2004a, Nature Reviews: Neuroscience, 5, 263–278. Reprinted with permission.

of odorant receptor genes (Sullivan, Adamson, Ressler, Kozak, & Buck, 1996), and olfactory neurons from these zones map loosely onto the olfactory bulb along the dorsal to ventral axis. Within each MOE zone, however, the 6,000 to 10,000 ORNs, each expressing a unique receptor, are more or less randomly distributed. The loose topography between the zones of MOE and MOB and the random distribution of classes of ORNs within each zone, gives way to a visually stunning molecular topography when genetic markers for a particular ORN are visualized (see Mombaerts et al., 1996, figure 4). The landmark achievement of two groups working independently (Ressler, Sullivan, & Buck, 1994; Vassar et al., 1994) demonstrated that each of the 6,000 to 10,000 ORNs expressing a unique G-protein coupled receptor converged onto only a few glomeruli, numbering in some cases as few as two, one located in the medial half of the bulb, the other in the lateral half (Figure 13.12a). What is the significance of this molecular specificity with regard to odorant quality coding? Despite the receptor specificity of ORNs, most respond to a wide range of odorants, implying that the receptor is responsive to only a part of a chemical compound, one that might be shared by odorants with vastly different perceptual qualities. Thus, odorant quality specificity is not to be found in the molecular specificity of the receptor. Rather, because

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“Relative” response

“Absolute” response

AlphaBenzaldehyde L-carvone phellandrene

Figure 13.14 (Figure C.21 in color section) Quantitative analysis of the uptake of 2-DG in the olfactory bulb in response to different odorants. Note: (Top Row) Absolute levels of activity measured against nonresponsive tissue. (Bottom Row) When relative values of activity are calculated

cells with identical receptors converge in discrete glomeruli, it is now axiomatic that it is the “combinatorial” pattern of activated glomeruli that is unique for a given odorant. Dependent measures of physiological activity, unit recording, 2-DG, optical imaging and c-fos expression all confirm that a given odorant, particularly at low concentration, activates only a small number of glomeruli (reviewed in Johnson & Leon, 2007) (Figure 13.14). The high degree of spatial convergence of ORNs onto specific glomeruli, and the fact that a unique spatial array of glomeruli are activated by a given odorant, however, does not in itself dictate that there is a spatial code for odor quality per se, that is, that location matters. Although the degree to which these maps have significance for quality coding is not universally accepted, they reveal important organizational properties of the olfactory bulb itself. Odorant activated maps are clearly not random. Other studies, however, have demonstrated strong dynamic, temporal components to olfactory responses that may contribute to quality discrimination and learning (reviewed in Kepecs, Uchida, & Mainen, 2006; Laurent et al., 2001; Schaefer & Margrie, 2007). Nevertheless, the anatomical singularity of glomeruli across phyla, together with their quantal-like innervation by a single receptor type has led to wide acceptance of the glomerulus as a functional unit of olfactory processing. As a functional unit, two major levels of organization are relevant to the question of olfactory coding: first, are glomeruli spatially organized?, and second, what processing takes place within the glomerulus?

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1-pentanol

Santalol

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GL/SEZ 3.03.5 2.53.0 2.02.5 1.52.0 1.01.5 0.51.0 0.00.5 0.50.0 1.00.5 1.51.0

Z score 45 34 23 12 01 10 21

by a Z transformation, different activated regions are more pronounced as indicated by yellow From figure 8 in “Chemotopic Odorant Coding in a Mammalian Olfactory System,” by B. A. Johnson and M. Leon, 2007, Journal of Comparative Neurology, 503, p. 56. Reprinted with permission.

Spatial Organization of Glomeruli: Maps Although there is no single physicochemical dimension to odorant stimuli, odorants with similar structural characteristics activate similar “clusters” of glomeruli that may be organized into “modules” or “domains” (reviewed in Johnson & Leon, 2007). These modules, and even in some cases individual glomeruli, appear relatively invariant across individuals, perhaps indicative of a spatial map for quality representation. Johnson and Leon have identified approximately nine such modules including those responsive to carboxylic acids, primary alcohols, aromatic hydrocarbons or compounds with high water solubility. Within these modules, there is further spatial organization. Systematically increasing the carbon number of a compound, for example, progressively alters the location of the activated glomeruli from dorsal to ventral within the module. Increasing odorant concentration recruits additional glomeruli, frequently outside the confines of a module (Stewart, Kauer, & Shepherd, 1979). Although in some instances, this increase in intensity may be associated with a change in odorant quality, this is not always the case Thus, the spatial code for odorant quality is not necessarily restricted to a single domain within the bulb, and activation of glomeruli across the entire olfactory bulb, may reflect a (second) more “global” level of spatial mapping (Johnson & Leon, 2007). However, even across concentrations there is still a degree of spatial specificity, and it is possible to look across the entire glomerular layer of

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Olfaction

the bulb and observe that similarly structured compounds activate similar patterns of glomeruli. The observation that odorants with similar chemical structures (or qualities) activate neighboring glomeruli draws attention to the possibility of functional interactions. Functional interactions between adjacent glomeruli, such as lateral inhibition that might serve to sharpen the glomerular map to a given odorant, would certainly imply that location is important in the representation of odorant quality. Glomerular Processing Incoming afferents from ORNs form monosynaptic glutamatergic connections with the apical dendrites of the ≈15 mitral and tufted cells that populate a given glomerulus (Shipley et al., 2004). In addition, incoming afferents form axodendritic synapses on juxtaglomerulur neurons within glomeruluar neuropil (Figure 13.13). These classical axodendritic synapses, however, are only a fraction of the synaptic contacts within the glomerulus. Fundamental to glomerular processing is the presence of an intricate assemblage of more unusual connections, excitatory and inhibitory dendro-dendritic synapses between and among juxtaglomerular and M/T neurons that shape olfactory bulb output. Thus, many of the putative circuits involving intra-glomerular processing require the backpropagation of somatic action potentials for dendritic release of excitatory and inhibitory neurotransmitters (Chen & Shepherd, 2005; G. Scott et al., 2003; Shepherd et al., 2004). Three classes of juxtaglomerular neurons contribute to glomerular processing (reviewed in Hayar, Karnup, Ennis, & Shipley, 2004; Hayar, Karnup, Shipley, & Ennis, 2004; Figure 13.13). Periglomular neurons (PG) constitute the largest class and have a primarily local (intraglomerular) GABAergic inhibitory function; external tufted neurons (ET) have a local intraglomerular excitatory function, and short axon neurons (SA) provide excitatory interglomerular connections (Aungst et al., 2003). There are extensive dendrodendritic connections among these juxtaglomerular neurons and together with afferent ORN terminals there is a bewildering array of potential synaptic glomerular interactions from which numerous putative circuits can be extracted (Figure 13.13). These intraglomerular circuits appear designed to both amplify the sensory signal and improve the signal to noise ratio (Chen & Shepherd, 2005; Wachowiak & Shipley, 2006). One form of signal amplification comes from the massive convergence of the 6,000 to 10,000 ORNs, all with the same receptor profile, that each release glutamate onto M/T neuron dendrites within a given glomerulus. This convergence combined with the presence of gap junctions

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between M/T neurons (Kosaka & Kosaka, 2005) provides a highly focused input. But this intense input is not without regulation and glomerular processing is subject to presynaptic, feedforward and lateral inhibition. Following depolarization from afferent stimulation, M/T cells display a delayed hyperpolarization, indicative of inhibitory processes (Wachowiak & Shipley, 2006). One source of feed-forward inhibition is from direct ORN activation of PG neurons that form inhibitory dendro-dendritic synapses on M/T neurons. However, most ORN terminals are onto ET (excitatory) interneurons, such that a indirect source of feedforward inhibition is via ORN projections to external tufted (ET) that excite PG (inhibitory) connections to M/T dendrites (Hayar, Karnup, Ennis, et al., 2004). Other types of inhibition that serve to limit overall excitability within a glomerulus include presynaptic inhibition, perhaps originating from PG neurons, that activate GABAB and dopamine receptors on ORN terminals (AroniadouAnderjaska, Zhou, Priest, Ennis, & Shipley, 2000; Ennis et al., 2001; Murphy, Darcy, & Isaacson, 2005). Lateral inhibition between glomeruli can potentially occur in both the glomerular and the external plexiform layers (EPL; Figure 13.13). Adjacent glomuruli are synaptically linked in the EPL via reciprocal synapses between the apical dendrites of granule cells and the lateral dendrites of M/T cells (Shipley et al., 2004). Recording from M/T neurons in vivo, Yokoi demonstrated that iontophoresis of either inhibitory (GABAA) or excitatory (AMPA) receptor antagonists broadened the response profile of neurons to odorants, both by increasing excitatory responses to odorants and by suppressing inhibitory responses (Yokoi, Mori, & Nakanishi, 1995). This effect was interpreted as interfering with dendrodendritic synapses between M/T neurons and granule cells in the EPL, that is AMPA receptor antagonists blocked the excitatory M/T—granule cell synapse, thus preventing granule cell inhibition of M/T dendrites, whereas the GABAA antagonist directly blocked the inhibitory synapse between granule cells and M/T neurons. Lateral inhibition between glomeruli can also occur within the glomerular layer. In vitro slice recordings in which the contribution of the external plexiform layer was eliminated with microcuts, showed that inhibitory postsynaptic potentials could be evoked from a neighboring glomerulus in response to excitation of a nearby glomerulus. Anatomically, it was demonstrated that SA neurons were the major source of interglomerular interactions but because these neurons are excitatory, it was postulated that they contact GABAergic PG neurons to effect the inhibition (Aungst et al., 2003). Because ORN axons do not directly synapse on short axon neurons, it is likely that this lateral inhibition is mediated via ORN projections onto

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ET neurons that, in turn drive SA neurons (Wachowiak & Shipley, 2006). A third source of lateral inhibition between glomeruli could involve presynaptic inhibition. Calcium imaging of an intact olfactory bulb in response to natural odorants showed that GABAB antagonists differentially affected the excitability of highly activated glomeruli compared to more weakly activated surround glomeruli (Vucinic, Cohen, & Kosmidis, 2006). Specifically, when the GABAB antagonist CGP46381 was applied to the exposed bulb, weakly activated glomeruli were more excited compared to glomeruli that were highly activated under control conditions. The implication is that highly activated glomeruli excite inhibitory PG neurons via the ET-SA—PG pathway, and that “spillover” of PG GABA activates GABAB receptors on ORN terminals. One of the more intriguing characteristics of ET neurons is that they have intrinsic bursting properties (Hayar et al., 2004), a characteristic that could account for the long lasting depolarizations in mitral cells (Carlson, Shipley, & Keller, 2000; Wachowiak & Shipley, 2006). At a more speculative level, it has been proposed that the resonant, bursting property of ET cells could facilitate a properly timed phasic sensory input, that is, an animal sniffing. However, the bursting activity of ET cells is also likely to lead to phasic inhibition of M/T neurons via ET projections onto inhibitory PG neurons. Thus, ET phasic activity could modulate M/T excitability to produce a “window of opportunity” that further enhances a given sensory input (Wachowiak & Shipley, 2006). Thus, intense activation of a glomerulus with an odorant can suppress adjacent glomerular activity that is more weakly stimulated. This suppression of adjacent glomeruli, together with the several mechanisms that serve to amplify activity within a glomerulus, massive ORN convergence, synchronization of intraglomerular mitral cells via gap junctions, or ET bursting, serve to heighten activity of a given glomerulus glomerular while “sharpening” glomerular output relative to more weakly activated surrounding glomeruli. This “winner take all” scenario (Chen & Shepherd, 2005; Wachowiak & Shipley, 2006) based on lateral inhibition is a powerful argument in favor of a spatial code, and one might therefore assume that lesions restricted to one part of the bulb or another would leave a hole in the olfactory map, that is, produce specific anosmias. However, this does not appear to be the case and specific deficits following olfactory bulb lesions are hard to demonstrate. Rather, a “mass action” effect is more descriptive of lesions, in which progressively larger lesions increasingly blunt olfactory discrimination in general (Johnson & Leon, 2007). In a recent review, however, Johnson and Leon (2007) argue that lesion studies have not yet adequately tested spatial

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specificity. For example, they note the lack of an effect of olfactory bulb lesions on the discrimination of different enantiomers of carvone. These lesions were restricted to the dorsal bulb and no problems with discrimination were detected (Slotnick & Bisulco, 2003) Based on 2-DG mapping studies, however, different enantiomers of carvone produced a more differentiated pattern of glomerular activation in the posterior ventromedial glomerular layer compared to the highly similar glomerular patterns in the dorsal aspect of the bulb; that is, the locations where lesions were made. Thus, Johnson and Leon predict that lesions made in the posterior ventromedial layer might well specifically impair carvone discrimination. In short, despite the overwhelming anatomical and physiological evidence for lateral inhibition in the olfactory bulb, evidence that this inhibition actually increases or sharpens behavioral discriminability awaits further investigation. Temporal Processing Much as recognition of a high degree of spatial organization in the olfactory system (e.g., glomerular structure) dates back to the earliest anatomical studies of Cajal, observations of temporal patterning date back to early recordings from the olfactory bulb where oscillating field potentials were observed at the respiratory rhythm (Adrian, 1950). A relationship between the respiratory rhythm and olfactory neuron responsiveness is clearly evident in M/T cells (Macrides & Chorover, 1972; Walsh, 1956), but can also be observed across the olfactory epithelium (Chaput, 2000). Within the olfactory bulb, M/T neurons show clear respiratory phase locking (Macrides & Chorover, 1972; Walsh, 1956) and a variety of patterns of excitation and inhibition have been described (reviewed in Buonviso, Amat, & Litaudon, 2006). Some cells show simple action potential burst patterns of excitation or inhibition, while others show more complex mixtures of these responses. Some of this activity clearly relates to the peripheral input. In vivo whole cell recording from M/T neurons revealed bursts of action potentials riding on rhythmic excitatory post synaptic potentials (EPSPs) locked to the respiratory rhythm in response to odorants (Cang & Isaacson, 2003). In those cells that showed odorant related suppression of action potential bursts, subthreshold EPSPs were still present, suggesting that glomerular inhibitory circuits were suppressing afferent excitation (Figure 13.15). In animals that don’t sniff, other forms of phasic modulation are present, for example, fish “cough” and insects “flick” (Dethier, 1987). Thus, much as a glomerulus represents a spatial unit of olfactory processing, so too might a sniff represent a dynamic unit (Kepecs et al., 2006). Although no one questions the fundamental phasic relationship between olfactory neuron activity

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Olfaction (A)

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Figure 13.15 Whole-cell recordings from mitral or tufted cells in the olfactory bulb show bursts of activity in phase with respiration in response to odorant stimulation. Note: Respiratory rhythm is shown below each cell response. (A) Response of one cell to amylacetate produces bursts of activity in phase with respiration. (B) A cell that was inhibited by amyl acetate stimulation shows subthreshold, respiratory-timed responses, suggesting that inhibition is of central, presumably glomerulus origin. From figure 2 in “In Vivo Whole-Cell Recording of Odor-Evoked Synaptic Transmission in the Rat Olfactory Bulb,” by J. Cang and J. S. Isaacson, 2003, Journal of Neuroscience, 23, p. 4110. Reprinted with permission.

and respiration, the origin and functional significance of this relationship is still open to question. Superimposed on the question of the origin and significance of the relationship between respiration and olfactory responses, are experiments demonstrating that odor discrimination takes place within a sniff cycle (Abraham et al., 2004; Rinberg, Koulakov, & Gelperin, 2006; Uchida & Mainen, 2003), thus putting some constraints on a temporal code. With regard to mechanisms, a recent study (Grosmaitre, Santarelli, Tan, Luo, & Ma, 2007) suggests that approximately 50% of olfactory sensory neurons have a mechanical sensitivity that could underlie respiratory entrainment. Patch recordings revealed a cAMP-dependent sensitivity that tracked the strength of odorless puffs delivered to the cell. A knock-out mouse lacking a cyclic-nucleotide-gated channel lacked this sensitivity and failed to show respiratory entrainment in the main olfactory bulb. Although peripheral input clearly plays an important role in olfactory bulb entrainment with respiration, it is likely that centrifugal influences also play a role because dissociation of the bulb from the rest of the brain reduces olfactory bulb respiratory synchronization (M. Chaput, 1983; Potter & Chorover, 1976). Further contributions to respiratory synchronization come from intrinsic circuitry within the bulb as well as intrinsic membrane properties of glomerular neurons since rhythmic activity can be recorded in slice preparations, devoid of both phasic peripheral input and centrifugal influences. Electrical stimulation of olfactory afferents elicited 2 Hz oscillations in M/T cells (Schoppa & Westbrook, 2001) and ET cells burst in the theta range due to intrinsic membrane properties (Hayar et al., 2004). Synchronization via excitatory intraglomerular processing is suggested by the tight cross-correlations obtained

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between M/T neurons belonging to the same glomerular unit (Buonviso, Chaput, & Berthommier, 1992). The functional significance of respiratory time-locked activity in the theta range (4 to 9 cycles/sec) has been the subject of much speculation (A. T. Schaefer & Margrie, 2007; J. W. Scott, 2006). Does it simply reflect the active pursuit of odorants that occurs when an animal modulates its respiration rhythm to engage in sniffing, or does synchronized activity reflect an underlying dynamic principle of temporal coding? One of the constraints on a theory of temporal coding is evidence that animals can make odorant discriminations within one sniff cycle (Abraham et al., 2004; Rinberg et al., 2006; Uchida & Mainen, 2003) reviewed in (Kepecs et al., 2006; Uchida, Kepecs, & Mainen, 2006). Estimates from these studies suggest that odor discrimination can take place in less than 100 msec, although difficult tasks might take somewhat longer. But even within a single sniff cycle, temporal properties of the spike train can potentially provide information. Response latency is potentially one such property. Increasing odorant concentration shortens the latency of the first spike in a mitral cell burst and increases the number of action potentials within the burst; however, the instantaneous firing rate remains relatively constant at 40 Hz (Cang & Isaacson, 2003; Margrie & Schaefer, 2003). Because latency is a reflection of stimulus strength, it might be argued that a given odorant will produce a variety of latencies in different glomeruli depending on the affinity of the ORN to that odorant. The shortest latencies might occur with an oscillating postsynaptic membrane, such that a stronger input reaches threshold earlier (Hopfield, 1995). Thus, a spatio-temporal pattern is produced when strongly activated glomeruli also have shorter latencies (A. T. Schaefer & Margrie, 2007). A potential advantage of a latency code is that it can be “read” earlier than the time required for assessing the cumulative action potentials emitted over a sniff cycle. Stronger stimuli that result in earlier latencies can thus ensure rapid discrimination. More subtle discrimination might require the inclusion of more weakly activated glomeruli recruited later in the cycle. Although low-frequency phasic activity in the olfactory bulb can contribute to information processing to the extent threshold influences latency, other studies point to yet higher-frequency oscillations as a source or reflection of temporal coding. Superimposed over theta range activity are higher-frequency single-cell responses in the beta range, 20 to 35 Hz and an even higher gamma range, 35 to 100 Hz. These higher frequencies can also be observed recording local field potentials where gamma activity can be seen riding on theta activity (Barrie, Freeman, & Lenhart, 1996). Gamma activity might originate from

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when oscillatory activity in the antennal lobe of the honeybee was blocked with perfusion of GABAA antagonists, discrimination was impaired (Stopfer, Bhagavan, Smith, & Laurent, 1997). Using a GABAA receptor beta 3 subunit knockout mouse, there was enhanced gamma oscillatory activity in the olfactory bulb but the change in performance on correlated behavioral discrimination tasks appeared somewhat ambiguous (Nusser, Kay, Laurent, Homanics, & Mody, 2001).

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Figure 13.16 Olfactory bulb oscillatory activity in the gamma range increases in power during a difficult odor discrimination task (B) compared to a simple discrimination task (A). Note: Arrow indicates increase in gamma activity just after the odorant stimulus-evoked potential (top trace in A and B). From figure 2 in “Olfactory Bulb Gamma Oscillations Are Enhanced with Task Demands,” by J. Beshel et al., 2007, Journal of Neuroscience, 27, p. 8360. Reprinted with permission.

mitral cells that fire at a preferred 40 Hz frequency (A. T. Schaefer & Margrie, 2007). Although evidence for temporal coding in the olfactory system of invertebrates has been more extensively investigated (Laurent et al., 2001), several studies provide indirect evidence for a functional role for beta and gamma activity in vertebrate olfactory learning and memory. Local field potentials recorded in the olfactory bulb of rats learning an olfactory discrimination showed enhanced beta but relatively little gamma activity when rats had mastered the task (Martin, Gervais, Hugues, Messaoudi, & Ravel, 2004). A somewhat opposite conclusion was reached in an experiment that compared easy (coarse) versus difficult (fine) olfactory discriminations (Beshel, Kopell, & Kay, 2007). Here, it was evident that the more difficult discrimination was associated with enhanced olfactory bulb gamma activity but changes in beta activity were not observed (Figure 13.16). These studies are correlational and the investigators concede that the oscillatory activity may not be necessary to the task; for example, gamma activity was not always observed in the early trials of a block. Several studies have tried to observe changes in discrimination ability when oscillatory activity is disrupted. For example,

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The vomeronasal epithelium lies at the dead-end of a tube in the nasal septum. The difficulty of stimuli reaching these receptors is overcome by a sympathetically mediated rhythmic vasoconstriction that pumps chemicals into the lumen (Meredith, 1994) and thus the conventional view that vomeronasal stimulation requires “contact,” as occurs during conspecific exploration for potential mates. However, there is evidence that vomeronasal receptors can respond to “traditional,” volatile olfactory stimuli and that the main olfactory system is also involved in pheromone detection (Brennan & Zufall, 2006; Mombaerts, 2004a; Zufall & Leinders-Zufall, 2007). The more recent formulation is that there are multiple olfactory systems with overlapping functions that operate in parallel (Breer, Fleischer, & Strotmann, 2006; Spehr, Spehr, et al., 2006). Two superfamilies of GPCRs are segregated within the vomeronasal epithelium (Figure 13.12B). One class (V1s) is located in the superficial (apical) zone of the epithelium, and a second class (V2rs) is located in the deep (basal) zone. The receptor systems differ in structure and receptivity to ligands. More recent work suggests that V1rs consist of 191 functioning genes (out of 308 total; X. Zhang, Zhang, & Firestein, 2007) that are primarily responsive to small organic compounds (Leinders-Zufall et al., 2000). Of the 280 V2 receptor genes, 120 appear functional (Young & Trask, 2007). Both families are GPCRs but V2rs are unique among olfactory receptors in that they possess a very long extracellular N-terminus. Axons from VNO neurons expressing these receptors project to the accessory olfactory bulb located caudal to the main olfactory bulb. Unlike neurons projecting to the main olfactory epithelium, VNO neurons expressing the same receptor project to multiple glomeruli in the accessory olfactory bulb (Belluscio, Koentges, Axel, & Dulac, 1999; Rodriguez, Feinstein, & Mombaerts, 1999; Figure 13.12B). One of the more intriguing aspects of V2rs is that they respond to MHC class 1 peptides (Leinders-Zufall et al., 2004). These nonvolatile compounds that are associated with immune function are found in body fluids such as urine and milk and provide a unique, genetic-based

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Figure 13.17 (Figure C. 22 in color section) Quantitative analysis of c-fos expression in the olfactory bulb of a female mouse in response to urine from male mice with modified MHC gene. Note: Wild-type mouse with H-2b gene and its spontaneous mutant H-2bm8 produce class I glycoproteins that impart variation in body scent. From figure 3 in “Olfactory Fingerprints for Major Histocompatibility Complex-Determined Body Odors II: Relationship among Odor Maps, Genetics, Odor Composition, and Behavior,” by M. L. Schaefer, K. Yamazaki, K. Osada, D. Restrepo, and G. K. Beauchamp, 2002, Journal of Neuroscience, 22, p. 9516. Reprinted with permisison.

identification of an individual. Among other functions, receptors sensitive to MHC peptides mediate the Bruce effect in mice, a condition in which pregnancy is terminated when a pregnant female is exposed to the urine of a nonparent male (Bruce, 1959). Thus, when the urine of the mating male is adulterated with foreign MHC peptides, the previously benign urine is now effective (LeindersZufall et al., 2004). MHC sensitive receptors are not limited to the vomeronasal system and are evident in the main olfactory system as well (Spehr, Kelliher, et al., 2006; Figure 13.17). However lesions of the VNO alone are effective in suppressing the Bruce effect (Kelliher et al., 2006). MHC peptides are likely candidates to mediate other olfactory-based social and reproductive functions as well. For example, mice are more likely to mate with conspecifics with dissimilar MHCs, and dams are more likely to retrieve pups with similar MHCs (reviewed in Brennan & Zufall, 2006). Pheromone detection in general, and responsiveness to MHC peptides in particular, is not restricted to the vomeronsal system, and numerous studies now confirm a role of the MOB in pheromone identification (reviewed in Shepherd, 2006; see also Brennan & Zufall, 2006; Spehr, Kelliher, et al., 2006). Mating preference, for example, may utilize MHC receptors in the MOB. Mutant mice lacking CNga2, a membrane channel associated with cAMP receptors in the main olfactory bulb, but not found in the vomeronasal organ, showed deficits in MHC stimulusevoked field potentials in the olfactory bulb as well as deficits in mating behavior (Mandiyan, Coats, & Shah, 2005; Spehr, Kelliher, et al., 2006). Anatomical tracing studies further suggest that the MOB has specific hypothalamic projections that initiate classical pheromone-induced behavior. Luteinizing hormone-releasing hormone (LHRH)

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is essential for female reproduction and mating and is secreted by specific populations of hypothalamic neurons. Using the transynaptic tracing properties of viruses, Yoon, Enquist, and Dulac (2005) engineered a pseudorabies virus that was only expressed by LHRH neurons. They determined that LHRH cells received second or third order projections from the main olfactory bulb but no projections from the accessory olfactory bulb. The detection and functional effects of pheromones would seem to epitomize labeled line coding in that a specific receptor/ligand interaction leads to activation or release of a single, often stereotyped, behavior. Although both the VNO and the main olfactory systems contribute to pheromone detection, evidence is accumulating that they process olfactory signals differently. Unlike sensory neurons in the main olfactory sensory epithelium, vomeronasal sensory neurons appear highly specific. Using confocal imaging and patch recording, Leinders-Zufall et al. (2000) demonstrated that each of six putative pheromones from male or female urine elicited responses from a very small subset of VNO sensory cells and at very low concentrations. Individual cells were responsive to only one stimulus and coded the intensity of the stimulus over a range of concentrations, but higher concentrations did not recruit new cells. Neither did these cells respond to control stimuli in which the stimulating peptide was structurally altered. The location of these cells in the dorsal epithelium suggested that they were V1 receptors. Similarly, in response to two different MHC peptides, calcium imaging of V2 receptors in the ventral epithelium showed narrow tuning properties and a high degree of specificity (Leinders-Zufall et al., 2004). Only about 1.6% of the cells were responsive to the MHC peptides, and of these, only 0.4% responded to both. The main olfactory epithelium responds to MHC peptides as well, but with less specificity (Spehr, Kelliher, et al., 2006). Here, sensory responses had a threshold about two orders of magnitude higher than their vomeronasal counterparts and also responded at higher concentrations to the control peptides. Chronic unit recording also suggests that pheromone detection is processed differently in the AOB compared to the MOB (Luo, Fee, & Katz, 2003). The response properties of single cells in the AOB of mature mice were tested with stimuli originating from a lightly anesthetized female or male mouse of the same or different strain placed in the testing chamber, or with volatile olfactory stimuli presented on cotton swabs. Unlike neurons in the MOB, AOB neurons did not respond to the volatile stimuli, and instead, required physical contact with the mouse “stimulus.” Responses in the AOB were highly specific with the majority of neurons responding to only a single male/female X strain/nonstrain pair. AOB responses had very long latencies compared to volatile stimulus-evoked

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responses in the MOB and may reflect the trade-off of speed for a highly specific “labeled line” system. The complimentary and overlapping sensitivity of the MOB and AOB to pheromones extends to more central connections. Although the immediate projections of the MOB and AOB show little overlap, secondary projections converge in the medial amygdala that forms an important “nexus” for integration of these signals with projections to the hypothalamus and other structures mediating neuroendocrine and motivated behavior (Brennan & Zufall, 2006). Olfactory Cortex Output from the olfactory bulb is carried by projections from M/T axons that reach a number of rostral telencephalic structures collectively referred to as the olfactory cortex (Neville & Haberly, 2004; Price, 1973; Shipley et al., 2004). The list is long and includes the anterior olfactory cortex, tenia tecta, dorsal peduncular cortex, piriform cortex, olfactory tubercle, cortical amygdala, agranular insula, and entorhinal cortex. These structures have been parsed based on their anterior, medial, or lateral location (Shipley et al., 2004), whether they are of cortical or striatal origin (Wilson, Kadohisa, & Fletcher, 2006), or whether they have the architectonic structure characteristic of paleocortex (Neville & Haberly, 2004). Although the agranular insula and entorhinal cortex represent a transitional form of the cortex between the three-layered paleocortex and the sixlayered neocortex (Neville & Haberly, 2004), unambiguous olfactory neocortical representation in the orbitofrontal cortex originates from secondary olfactory cortex projections and from (tertiary) dorsal medial thalamic projections that are the target of cortical amygdala and piriform efferents. Piriform cortex (PC) is the largest of the olfactory cortex structures and is the best characterized. Mitral and tifted cell axons leave the olfactory bulb and form the lateral olfactory tract (LOT) that serves as a boundary between several PC subdivisions. Piriform cortex coincident with the tract is anterior PC (aPC), piriform cortex posterior to the tract is posterior PC (pPC). The LOT further demarcates a dorsal anterior PC, dorsal to the LOT, from a ventral anterior PC ventral to the tract (Neville & Haberly, 2004). There is a high degree of segregation between the pyramidal cell dendritic targets of olfactory bulb efferents and intracortical (association) projections. The former synapse on distal dendrites in layer Ia; the latter on more proximal sites in layer Ib. This pattern of innervation may form a fundamental substrate for olfactory learning and memory. Both types of synapses support NMDA-mediated long-term potentiation, that is, a short theta frequency burst of stimulation applied to either class of afferent fibers increases the magnitude of the postsynaptic response (Jung, Larson, &

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Lynch, 1990; Kanter & Haberly, 1990) and both rodent (e.g., Staubli, Fraser, Faraday, & Lynch, 1987) and human lesion studies (e.g., Dade, Zatorre, & Jones-Gotman, 2002) support a role for the piriform cortex in olfactory learning. Nor has it gone unnoticed that the theta frequency for eliciting long-term potentiation (LTP) corresponds roughly to the range of olfactory sniffing. As with olfactory bulb activity, PC neurons produce bursts of olfactory induced activity in phase with respiration (Neville & Haberly, 2004; Wilson et al., 2006). Further controlling the induction of some forms of LTP is the removal of inhibition. Long-term potentiation involving simultaneous activation of both OB afferent and association fiber inputs requires the blockade of GABAA receptors (Kanter & Haberly, 1993). Local GABAergic neurons within PC may mediate this action and several classes of GABAergic interneurons in PC provide a substrate for both feedforward or feedback inhibition of pyramidal cell responses (reviewed in Neville & Haberly, 2004; Suzuki & Bekkers, 2007). Local inhibitory interneurons could also play a role in hyperpolarizations observed between respiratory bursts (Wilson et al., 2006) as well as beta oscillatory activity (Neville & Haberly, 2003). The high degree of molecular specificity conferred by the convergence of olfactory receptor cells onto discrete olfactory bulb glomeruli does not appear to be maintained in the olfactory cortex. Functional mapping studies of PC using 2-DG and Fos expression indicate that individual odors evoke activity over large areas (Cattarelli, Astic, & Kauer, 1988; Illig & Haberly, 2003). Within the dorsal aPC, there is a rostral to caudal gradient such that higher concentrations of olfactory stimuli recruit successively caudal regions (Sugai, Miyazawa, Fukuda, Yoshimura, & Onoda, 2005). Studies are beginning to show more explicitly how the olfactory cortex processes sensory input in the service of higher cognitive function such as olfactory learning and memory. Several studies indicate that M/T neurons from different OB glomeruli converge in the olfactory cortex. Lei, Mooney, and Katz (2006) directly compared single unit responses from M/T neurons and anterior olfactory nucleus neurons to the same battery of olfactory stimuli consisting of binary olfactory mixtures and the mixture components. Overall, cortical neurons appeared more broadly tuned than OB neurons. Although OB neurons responded to single mixtures about as often as their cortical counterparts, individual cortical neurons responded to a variety of mixtures compared to OB neurons and were more responsive to the individual components of a mixture. Thus, many more OB neurons were responsive to just one of the components of a mixture compared to cortical neurons, and many cortical neurons responded to four or even five of the individual

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mixture components, an uncommon response profile for OB neurons. In addition, some olfactory cortical neurons responded to mixtures of stimuli whose components were previously shown to activate distinctly different OB glomeruli. For example, some olfactory cortical neurons responded to a mixture of 6, 20 hexamone and heptanal. These were stimuli that had been previously shown to activate different glomeruli, based on 2-DG studies by Leon and colleagues. (Discussed in Lei et al., 2006). Mixture suppression and facilitation were also common in the cortical neurons (Lei et al., 2006). Another study in PC using natural food odors reached a similar conclusion (Yoshida & Mori, 2007). A large proportion of PC neurons responded to more than one of a number of core odorants that have been used to characterize foods. These core odorants would be expected to activate different patterns of OB glomeruli, as a result of having different chemical structures (e.g., sulfides, esters). Thus, multiply responsive PC neurons imply a pattern of convergence. A large proportion of anterior PC neurons with nonlinear mixture effects further suggest cortical processing. Neither of these studies can differentiate between convergence mediated by OB afferents onto the distal dendrite of pyramidal neurons and convergence via intracortical pathways. Multisite recording from anterior PC, however, indicate both types of convergence (Rennaker, Chen, Ruyle, Sloan, & Wilson, 2007). In particular, 15% of the neurons showed a cross-correlogram indicative of direct cell-to-cell interaction, that is, intracortical convergence. Further evidence of convergence also comes from an analysis of olfactory adaptation (Kadohisa & Wilson, 2006a). Mitral and tufted cells adapt rapidly to continuous olfactory stimulation. Recording from anterior PC, however, showed that a novel olfactory stimulus applied once the adaptation had occurred reactivated the neuron, presumably via OB efferents acting indirectly through intracortical collaterals. Thus, the aPC effectively filters out background odors to enhance olfactory “figure-ground” relationships. The differential pattern of PC innervation in which OB efferents dominate in aPC and intracortical assocation fibers dominate in pPC suggests differential roles with respect to associative processing. Neurons in pPC become more broadly tuned as a function of odor experience compared to neurons in aPC that become more narrowly tuned (Kadohisa & Wilson, 2006b). The narrow tuning of aPC neurons is also associated with an experience-dependent lowering of response correlations between a mixture and its components, suggesting that aPC has synthesized the mixture and given it its own “identity,” that is, uncorrelated with component odors. In contrast, as neurons become more broadly tuned in pPC, individual neuron responses between a mixture and its binary components

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become more highly correlated(i.e., a mixture and its components are more similar and presumably now share a common percept, for example, quality). Similar conclusions were reached in the human piriform cortex using fMRI (Gottfried, Winston, & Dolan, 2006). Perhaps the most striking difference between aPC and pPC comes from studies comparing neural response profiles during associative learning (Calu, Roesch, Stalnaker, & Schoenbaum, 2007; Roesch, Stalnaker, & Schoenbaum, 2007). Neurons in aPC and pPC were recorded as rats were conditioned to discriminate between two odors—one associated with a positive (sucrose) reinforcer and the other associated with a negative (QHCl) reinforcer. In general, neurons in both structures showed a high degree of associative activity, that is, (population) odorant responses became larger following the conditioning procedure. When the odor-conditioning pairs were reversed, however, such that an odor previously paired with sucrose was now paired with quinine, neurons in aPC continued to respond as they had during the prior conditioning but neurons in the pPC altered their response characteristic and neurons previously responsive only to one odor became responsive to the other when positively reinforced.

ORBITAL FRONTAL CORTEX AND FLAVOR Food in the mouth stimulates gustatory, olfactory (via a retronasal pathway), and somatosensory afferents that fuse into the perception of flavor. This fusion is primarily associated with specific regions of the orbital frontal cortex (OFC), although evidence for convergence has been described at lower levels in rodents. For example, a few neurons in the insular cortex of rats responded to both olfactory and gustatory stimulation (Yamamoto et al., 1989). But by and large, the neural substrate for flavor, and its close conceptual linkage with experience-dependent phenomena, has been best characterized in lateral orbital cortex, based on neural recordings from nonhuman primates and functional imaging studies in humans. The path for olfactory signals to the orbitofrontal cortex is both direct and indirect. Because some PC neurons project to OFC, the first neocortical representation of odorants is only two synapses removed from an odorant in a best-case scenario. A second pathway from the endopiriform nucleus (olfactory cortex) to the mediodorsal thalamus, and then to the OFC represents a second pathway which has the virtue of dignifying the olfactory system with a thalamic relay as found in all other major sensory systems. Gustatory projections reach the orbital cortex from the insula, although the pathway may involve more synapses that originally proposed (discussed in Pritchard & Norgren, 2004).

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Figure 13.18 (Figure C.23 in color section) Functional magnetic resonance imaging of human insular and orbitofrontal cortex show enhanced activity when taste and smell are congruent (e.g., vanilla/sweet) compared to incongruent stimuli (e.g., vanilla/salty). Note: Color coding of graphs does not correspond to enhanced activity in the images. From figures 4 and 5 in “Experience-Dependent Neural Integration of Taste and Smell in the Human Brain,” by D. M. Small et al., 2004, Journal of Neurophysiology, 92, pp. 1896 & 1899. Reprinted with permission.

Sensory-responsive cells in OFC of monkeys include those responsive to olfaction, vision, and taste, as well as all binary combinations (Rolls & Baylis, 1994). Some of these neurons appear to have response profiles consistent with the concept of flavor, for example, responses to both a sweet taste and fruit odor. Somewhat more recently, cortical neurons responsive to fat have been described, but most of this responsiveness appears to be due to textural rather than chemosensory properties. Nevertheless, it is noteworthy that some of these neurons also responded to congruent olfactory stimuli, for example, the odor of cream (Rolls, Critchley, Browning, Hernadi, & Lenard, 1999). Functional MRI studies in humans support the existence of chemosensory convergence in OFC and have identified specific areas of the rostral or caudal OFC where these interactions take place. Figure 13.18 illustrates the important finding that this convergence resulted in synergistic activity only when a particular taste-olfactory combination was one that would be expected to occur in food; for example, sweet and vanilla, but not when the components of the combination were incongruent, for example, salty and vanilla (Small et al., 2004). A strong case can be made that the sensory convergence (pairing) of olfactory and gustatory signals that produces flavor depends on experience (Small et al., 2007). There is ample evidence from single cell studies in both rodent and

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primate, as well as functional imaging studies in human, that show experience-dependent changes in OFC associated with satiety, habituation, classical conditioning, and learned reversal learning. Thus, over the course of a meal and the onset of satiety, food becomes less preferred. This lack of preference is associated with a specific loss of sensitivity in monkey OFC neurons that were initially responsive to the taste of a particular food. Thus, a neuron responsive to a sweet beverage looses its responsiveness to that beverage as it is consumed to satiety, but retains gustatory responsiveness to other stimuli, for example, salt (Rolls et al., 1989). Odorant-responsive neurons in OFC show similar satiety effects (Critchley & Rolls, 1996a) as do fMRI images in human OFC (O’Doherty et al., 2000). Yet other studies in both human and subhuman primates also show experience-dependent changes in chemosensory, particularly olfactory-responsive OFC. For example, olfactory response profiles of single OFC units change when an odor, initially paired with a positively reinforcing taste stimulus, is subsequently paired with a negatively reinforcing taste stimulus (Critchley & Rolls, 1996b). Thus, these neurons reflect the meaning or hedonic valence of the olfactory signal, rather than chemical structure. Even without associative pairing, fMRI studies in humans demonstrated that experience (exposure) alone was sufficient to induce changes in OFC odorant-induced activity correlated with perceptual changes (Gottfried, 2007). Exposure to a (target) odorant for 3.5 minutes increased the subject’s ability to differentiate the odor from a closely related odorant chosen to be similar in either quality (e.g., minty or floral) or chemical group (alcohol or ketone). Thus, OFC activity in response to the related compound increased after exposure to the target stimulus in parallel with the change in discriminability. Odorant compounds unrelated to the target stimulus showed no such change in activity. In a second experiment, Gottfried demonstrated that pairing an aversive shock to one of two initially indistinguishable odorants increased their perceptual discriminability as well as OFC activity associated with the CS (Gottfried, 2007). Outputs of the OFC to subcortical structures involved with behavioral choice (e.g., striatum) and food intake (lateral hypothalamus) confers on these multimodal chemosensory neurons an important role in adaptive, flexible behavior (Krettek & Price, 1977; McDonald, 1991; Ongur, An, & Price, 1998). Although the simple decision to ingest sweets and avoid bitters is made at the brain stem level in both humans and other animal species, the selection of many foods, particularly in humans that appears as an “acquired taste” is probably an acquired flavor, with which we can give thanks to our frontal cortex.

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References 297

REFERENCES Abraham, N. M., Spors, H., Carleton, A., Margrie, T. W., Kuner, T., & Schaefer, A. T. (2004). Maintaining accuracy at the expense of speed: Stimulus similarity defines odor discrimination time in mice. Neuron, 44, 865–876.

Bezencon, C., le Coutre, J., & Damak, S. (2007). Taste-signaling proteins are coexpressed in solitary intestinal epithelial cells. Chemical Senses, 32, 41–49.

Accolla, R., Bathellier, B., Petersen, C. C., & Carleton, A. (2007). Differential spatial representation of taste modalities in the rat gustatory cortex. Journal of Neuroscience, 27, 1396–1404.

Bo, X., Alavi, A., Xiang, Z., Oglesby, I., Ford, A., & Burnstock, G. (1999). Localization of ATP-gated P2X2 and P2X3 receptor immunoreactive nerves in rat taste buds. NeuroReport, 10, 1107–1111.

Accolla, R., & Carleton, A. (2008). Internal body state influences topographical plasticity of sensory representations in the rat gustatory cortex. Proceedings of the National Academy of Sciences, USA, 105, 4010–4015.

Boughter, J. J., & Bachmanov, A. A. (2008). Genetics and evolution of taste. In S. Firestein & G. K. Beauchamp (Eds.), The senses: A comprehensive reference (Vol. 4, pp. 371–390). San Diego, CA: Academic Press.

Ache, B., & Young, J. M. (2008). Phylogeny of chemical sensitivity. In A. Basbaum, A. Kaneko, G. Shepherd, & G. Westheimer (Eds.), The senses: A comprehensive reference (Vol. 4, pp. 1–26). San Diego, CA: Academic Press.

Bradley, R. (2008). Neurotransmitters in the taste pathway. In S. Firestein & G. K. Beauchamp (Eds.), The senses: A comprehensive reference (pp. 261–270). San Diego, CA: Academic Press.

Adler, E., Hoon, M. A., Mueller, K. L., Chandrashekar, J., Ryba, N. J., & Zuker, C. S. (2000). A novel family of mammalian taste receptors. Cell, 100, 693–702. Adrian, E. D. (1950). The electrical activity of the mammalian olfactory bulb. Electroencephalography and Clinical Neurophysiology, 2, 377–388. Aroniadou-Anderjaska, V., Zhou, F. M., Priest, C. A., Ennis, M., & Shipley, M. T. (2000). Tonic and synaptically evoked presynaptic inhibition of sensory input to the rat olfactory bulb via GABA(B) heteroreceptors. Journal of Neurophysiology, 84, 1194–1203. Aungst, J. L., Heyward, P. M., Puche, A. C., Karnup, S. V., Hayar, A., Szabo, G., et al. (2003, December 11). Centre-surround inhibition among olfactory bulb glomeruli. Nature, 426, 623–629. Baird, J. P., Travers, S. P., & Travers, J. B. (2001). Integration of gastric distension and gustatory responses in the parabrachial nucleus. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 281, R1581–R1593. Barrie, J. M., Freeman, W. J., & Lenhart, M. D. (1996). Spatiotemporal analysis of prepyriform, visual, auditory, and somesthetic surface EEGs in trained rabbits. Journal of Neurophysiology, 76, 520–539. Bealer, S. L., & Smith, D. V. (1975). Multiple sensitivity to chemical stimuli in single human taste papillae. Physiology and Behavior, 14, 795–799. Beckman, M. E., & Whitehead, M. C. (1991). Intramedullary connections of the rostral nucleus of the solitary tract in the hamster. Brain Research, 557(1–2), 265–279. Beckstead, R. M., Morse, J. R., & Norgren, R. (1980). The nucleus of the solitary tract in the monkey: Projections to the thalamus and brain stem nuclei. Journal of Comparative Neurology, 190, 259–282. Beckstead, R. M., & Norgren, R. (1979). An autoradiographic examination of the central distribution of the trigeminal, facial, glossopharyngeal, and vagal nerves in the monkey. Journal of Comparative Neurology, 184, 455–472. Behrens, M., Foerster, S., Staehler, F., Raguse, J. D., & Meyerhof, W. (2007). Gustatory expression pattern of the human TAS2R bitter receptor gene family reveals a heterogenous population of bitter responsive taste receptor cells. Journal of Neuroscience, 27, 12630–12640. Belluscio, L., Koentges, G., Axel, R., & Dulac, C. (1999). A map of pheromone receptor activation in the mammalian brain. Cell, 97, 209–220. Benjamin, R., & Pfaffmann, C. (1953). Cortical localization of taste in the albino rat. Journal of Neurophysiology, 18, 56–64. Bernard, J. F., Alden, M., & Besson, J. M. (1993). The organization of the efferent projections from the pontine parabrachial area to the amygdaloid complex: A phaseolus vulgaris leucoagglutinin (PHA-L) study in the rat. Journal of Comparative Neurology, 329, 201–229.

c13.indd 297

Beshel, J., Kopell, N., & Kay, L. M. (2007). Olfactory bulb gamma oscillations are enhanced with task demands. Journal of Neuroscience, 27, 8358–8365.

Breer, H., Fleischer, J., & Strotmann, J. (2006). The sense of smell: Multiple olfactory subsystems. Cellular and Molecular Life Sciences, 63, 1465–1475. Brennan, P. A., & Zufall, F. (2006, November 16). Pheromonal communication in vertebrates. Nature, 444, 308–315. Breza, J. M., Curtis, K. S., & Contreras, R. J. (2007). Monosodium glutamate but not linoleic acid differentially activates gustatory neurons in the rat geniculate ganglion. Chemical Senses, 32, 833–846. Bruce, H. M. (1959, July 11). An exteroceptive block to pregnancy in the mouse. Nature, 184, 105. Buck, L., & Axel, R. (1991). A novel multigene family may encode odorant receptors: A molecular basis for odor recognition. Cell, 65, 175–187. Bufe, B., Breslin, P. A., Kuhn, C., Reed, D. R., Tharp, C. D., Slack, J. P., et al. (2005). The molecular basis of individual differences in phenylthiocarbamide and propylthiouracil bitterness perception. Current Biology, 15, 322–327. Bufe, B., Hofmann, T., Krautwurst, D., Raguse, J. D., & Meyerhof, W. (2002). The human TAS2R16 receptor mediates bitter taste in response to beta-glucopyranosides. Nature Genetics, 32, 397–401. Buonviso, N., Amat, C., & Litaudon, P. (2006). Respiratory modulation of olfactory neurons in the rodent brain. Chemical Senses, 31, 145–154. Buonviso, N., Chaput, M. A., & Berthommier, F. (1992). Temporal pattern analyses in pairs of neighboring mitral cells. Journal of Neurophysiology, 68, 417–424. Burton, M. J., Rolls, E. T., & Mora, F. (1976). Effects of hunger on the responses of neurons in the lateral hypothalamus to the sight and taste of food. Experimental Neurology, 51, 668–677. Calu, D. J., Roesch, M. R., Stalnaker, T. A., & Schoenbaum, G. (2007). Associative encoding in posterior piriform cortex during odor discrimination and reversal learning. Cerebral Cortex, 17, 1342–1349. Cang, J., & Isaacson, J. S. (2003). In vivo whole-cell recording of odorevoked synaptic transmission in the rat olfactory bulb. Journal of Neuroscience, 23, 4108–4116. Carlson, G. C., Shipley, M. T., & Keller, A. (2000). Long-lasting depolarizations in mitral cells of the rat olfactory bulb. Journal of Neuroscience, 20, 2011–2021. Caterina, M. J., & Julius, D. (2001). The vanilloid receptor: A molecular gateway to the pain pathway. Annual Review of Neuroscience, 24, 487–517. Cattarelli, M., Astic, L., & Kauer, J. S. (1988). Metabolic mapping of 2-deoxyglucose uptake in the rat piriform cortex using computerized image processing. Brain Research, 442, 180–184. Chang, F. C., & Scott, T. R. (1984). Conditioned taste aversions modify neural responses in the rat nucleus tractus solitarius. Journal of Neuroscience, 4, 1850–1862.

8/17/09 2:09:14 PM

298

Chemical Senses

Chaput, M. A. (1983). Effects of olfactory peduncle sectioning on the single unit responses of olfactory bulb neurons to odor presentation in awake rabbits. Chemical Senses, 8, 161–177. Chaput, M. A. (2000). EOG responses in anesthetized freely breathing rats. Chemical Senses, 25, 695–701. Chaudhari, N., Landin, A. M., & Roper, S. D. (2000). A metabotropic glutamate receptor variant functions as a taste receptor. Journal of Neuroscience, 3, 113–119. Chen, W. R., & Shepherd, G. M. (2005). The olfactory glomerulus: A cortical module with specific functions. Journal of Neurocytology, 34(3/5), 353–360. Cho, Y. K., Li, C. S., & Smith, D. V. (2003). Descending influences from the lateral hypothalamus and amygdala converge onto medullary taste neurons. Chemical Senses, 28, 155–171.

Ennis, M., Zhou, F. M., Ciombor, K. J., Aroniadou-Anderjaska, V., Hayar, A., Borrelli, E., et al. (2001). Dopamine D2 receptor-mediated presynaptic inhibition of olfactory nerve terminals. Journal of Neurophysiology, 86, 2986–2997. Finger, T. E., Danilova, V., Barrows, J., Bartel, D. L., Vigers, A. J., Stone, L., et al. (2005, December 2). ATP signaling is crucial for communication from taste buds to gustatory nerves. Science, 310, 1495–1499. Flaherty, C. F., Turovsky, J., & Krauss, K. L. (1994). Relative hedonic value modulates anticipatory contrast. Physiology and Behavior, 55, 1047–1054. Fontanini, A., & Katz, D. B. (2006). State-dependent modulation of time-varying gustatory responses. Journal of Neurophysiology, 96, 3183–3193. Fox, A. (1932). The relationship between chemical constitution and taste. Proceedings of the National Academy of Sciences, USA, 18, 115–120.

Contreras, R. J. (1977). Changes in gustatory nerve discharges with sodium deficiency: A single unit analysis. Brain Research, 121, 373–378.

Frank, M. E. (1973). An analysis of hamster afferent taste nerve response functions. Journal of General Physiology, 61, 588–618.

Critchley, H. D., & Rolls, E. T. (1996a). Hunger and satiety modify the responses of olfactory and visual neurons in the primate orbitofrontal cortex. Journal of Neurophysiology, 75, 1673–1686.

Frank, M. E. (1991). Taste-responsive neurons of the glossopharyngeal nerve of the rat. Journal of Neurophysiology, 65, 1452–1463.

Critchley, H. D., & Rolls, E. T. (1996b). Olfactory neuronal responses in the primate orbitofrontal cortex: Analysis in an olfactory discrimination task. Journal of Neurophysiology, 75, 1659–1672.

Frank, M. E., Contreras, R. J., & Hettinger, T. P. (1983). Nerve fibers sensitive to ionic taste stimuli in chorda tympani of the rat. Journal of Neurophysiology, 50, 941–960.

Dade, L. A., Zatorre, R. J., & Jones-Gotman, M. (2002). Olfactory learning: Convergent findings from lesion and brain imaging studies in humans. Brain, 125(Pt. 1), 86–101.

Fukuwatari, T., Kawada, T., Tsuruta, M., Hiraoka, T., Iwanaga, T., Sugimoto, E., et al. (1997). Expression of the putative membrane fatty acid transporter (FAT) in taste buds of the circumvallate papillae in rats. FEBS Letters, 414, 461–464.

Damak, S., Rong, M., Yasumatsu, K., Kokrashvili, Z., Varadarajan, V., Zou, S., et al. (2003). Detection of sweet and umami taste in the absence of taste receptor T1r3. Science, 301, 850–853.

Fulwiler, C. E., & Saper, C. B. (1984). Subnuclear organization of the efferent connections of the parabrachial nucleus in the rat. Brain Research, 319, 229–259.

Davis, B. J., & Jang, T. (1988). A golgi analysis of the gustatory zone of the nucleus of the solitary tract in the adult hamster. Journal of Comparative Neurology, 278, 388–396.

Gaillard, D., Laugerette, F., Darcel, N., El-Yassimi, A., Passilly-Degrace, P., Hichami, A., et al. (2008). The gustatory pathway is involved in CD36-mediated orosensory perception of long-chain fatty acids in the mouse. Federation of American Societies for Experimental Biology, 22, 1458–1468.

de Araujo, I. E., Gutierrez, R., Oliveira-Maia, A. J., Pereira, A., Jr., Nicolelis, M. A., & Simon, S. A. (2006). Neural ensemble coding of satiety states. Neuron, 51, 483–494. Delay, E. R., Hernandez, N. P., Bromley, K., & Margolskee, R. F. (2006). Sucrose and monosodium glutamate taste thresholds and discrimination ability of T1R3 knockout mice. Chemical Senses, 31, 351–357. DeSimone, J. A., Lyall, V., Heck, G. L., Phan, T. H., Alam, R. I., Feldman, G. M., et al. (2001). A novel pharmacological probe links the amilorideinsensitive NaCl, KCl, and NH(4)Cl chorda tympani taste responses. Journal of Neurophysiology, 86, 2638–2641. Dethier, V. G. (1987). Sniff, flick, and pulse, an appreciation of interruption. Proceedings of the American Philosophical Society, 131, 159–174. Dickman, J., & Smith, D. (1989). Topographic distribution of taste responsiveness in the hamster medulla. Chemical Senses, 14, 231–247. Di Lorenzo, P. M. (1988). Taste responses in the parabrachial pons of decerebrate rats. Journal of Neurophysiology, 59, 1871–1887. Di Lorenzo, P. M., & Monroe, S. (1992). Corticofugal input to tasteresponsive units in the parabrachial pons. Brain Research Bulletin, 29, 925–930. Di Lorenzo, P. M., & Monroe, S. (1995). Corticofugal influence on taste responses in the nucleus of the solitary tract in the rat. Journal of Neurophysiology, 74, 258–272. Di Lorenzo, P. M., & Victor, J. D. (2003). Taste response variability and temporal coding in the nucleus of the solitary tract of the rat. Journal of Neurophysiology, 90, 1418–1431. Dulac, C., & Axel, R. (1995). A novel family of genes encoding putative pheromone receptors in mammals. Cell, 83, 195–206. Dyer, J., Salmon, K. S., Zibrik, L., & Shirazi-Beechey, S. P. (2005). Expression of sweet taste receptors of the T1R family in the intestinal tract and enteroendocrine cells. Biochemical Society Transactions, 33(Pt. 1), 302–305.

c13.indd 298

Geran, L. C., & Travers, S. P. (2006). Single neurons in the nucleus of the solitary tract respond selectively to bitter taste stimuli. Journal of Neurophysiology, 96, 2513–2527. Gilbertson, T. A., Boughter, J. D., Jr., Zhang, H., & Smith, D. V. (2001). Distribution of gustatory sensitivities in rat taste cells: Whole-cell responses to apical chemical stimulation. Journal of Neuroscience, 21, 4931–4941. Gilbertson, T. A., Fontenot, D. T., Liu, L., Zhang, H., & Monroe, W. T. (1997). Fatty acid modulation of K channels in taste receptor cells: Gustatory cues for dietary fat. American Journal of Physiology, 272, C1203–C1210. Giza, B. K., Deems, R. O., Vanderweele, D. A., & Scott, T. R. (1993). Pancreatic glucagon suppresses gustatory responsiveness to glucose. Journal of General Physiology, 265, R1231–R1237. Giza, B. K., & Scott, T. R. (1983). Blood glucose selectively affects taste-evoked activity in rat nucleus tractus solitarius. Physiology and Behavior, 31, 643–650. Giza, B. K., & Scott, T. R. (1987). Intravenous insulin infusions in rats decrease gustatory-evoked responses to sugars. Journal of General Physiology, 252, R994–R1002. Glenn, J. F., & Erickson, R. P. (1976). Gastric modulation of gustatory afferent activity. Physiology and Behavior, 16, 561–568. Glusman, G., Yanai, I., Rubin, I., & Lancet, D. (2001). The complete human olfactory subgenome. Genome Research, 11, 685–702. Gottfried, J. A. (2007). What can an orbitofrontal cortex-endowed animal do with smells? Annals of the New York Academy of Sciences, 1121, 102–120. Gottfried, J. A., Winston, J. S., & Dolan, R. J. (2006). Dissociable codes of odor quality and odorant structure in human piriform cortex. Neuron, 49, 467–479.

8/17/09 2:09:14 PM

References 299 Grabauskas, G., & Bradley, R. M. (1996). Synaptic interactions due to convergent input from gustatory afferent fibers in the rostral nucleus of the solitary tract. Journal of Neurophysiology, 76, 2919–2927.

Hayar, A., Karnup, S., Ennis, M., & Shipley, M. T. (2004). External tufted cells: A major excitatory element that coordinates glomerular activity. Journal of Neuroscience, 24, 6676–6685.

Grill, H. J., & Norgren, R. (1978a, July 21). Chronically decerebrate rats demonstrate satiation but not bait shyness. Science, 201, 267–269.

Hayar, A., Karnup, S., Shipley, M. T., & Ennis, M. (2004). Olfactory bulb glomeruli: External tufted cells intrinsically burst at theta frequency and are entrained by patterned olfactory input. Journal of Neuroscience, 24, 1190–1199.

Grill, H. J., & Norgren, R. (1978b). The taste reactivity test: Pt. I. Mimetic responses to gustatory stimuli in neurologically normal rats. Brain Research, 143, 263–279. Grill, H. J., & Norgren, R. (1978c). The taste reactivity test: Pt. II. Mimetic responses to gustatory stimuli in chronic thalamic and chronic decerebrate rats. Brain Research, 143, 281–297. Grill, H. J., Schulkin, J., & Flynn, F. W. (1986). Sodium homeostasis in chronic decerebrate rats. Behavioral Neuroscience, 100, 536–543. Grill, H. J., & Smith, G. P. (1988). Cholecystokinin decreases sucrose intake in chronic decerebrate rats. Journal of General Physiology, 254, R853–R856. Grosmaitre, X., Santarelli, L. C., Tan, J., Luo, M., & Ma, M. (2007). Dual functions of mammalian olfactory sensory neurons as odor detectors and mechanical sensors. Journal of Neuroscience, 10, 348–354. Grossman, S. E., Fontanini, A., Wieskopf, J. S., & Katz, D. B. (2008). Learning-related plasticity of temporal coding in simultaneously recorded amygdala-cortical ensembles. Journal of Neuroscience, 28, 2864–2873. Hajnal, A., & Norgren, R. (2005). Taste pathways that mediate accumbens dopamine release by sapid sucrose. Physiology and Behavior, 84, 363–369.

Hermann, G. E., & Rogers, R. C. (1985). Convergence of vagal and gustatory afferent input within the parabrachial nucleus of the rat. Journal of the Autonomic Nervous System, 13(1), 1–17. Herness, S. (2000). Coding in taste receptor cells: The early years of intracellular recordings. Physiology and Behavior, 69(1/2), 17–27. Herness, S., Zhao, F. L., Lu, S. G., Kaya, N., & Shen, T. (2002). Expression and physiological actions of cholecystokinin in rat taste receptor cells. Journal of Neuroscience, 22, 10018–10029. Herrada, G., & Dulac, C. (1997). A novel family of putative pheromone receptors in mammals with a topographically organized and sexually dimorphic distribution. Cell, 90, 763–773. Hildebrand, J. G., & Shepherd, G. M. (1997). Mechanisms of olfactory discrimination: Converging evidence for common principles across phyla. Annual Review of Neuroscience, 20, 595–631. Hopfield, J. J. (1995, July 6). Pattern recognition computation using action potential timing for stimulus representation. Nature, 376, 33–36.

Hajnal, A., Takenouchi, K., & Norgren, R. (1999). Effect of intraduodenal lipid on parabrachial gustatory coding in awake rats. Journal of Neuroscience, 19, 7182–7190.

Huang, A. L., Chen, X., Hoon, M. A., Chandrashekar, J., Guo, W., Trankner, D., et al. (2006, August 24). The cells and logic for mammalian sour taste detection. Nature, 442, 934–938.

Halpern, B. P. (1965, October 23). Chemotopic organization in the bulbar gustatory relay of the rat. Nature, 208, 393–395.

Huang, Y. J., Maruyama, Y., Dvoryanchikov, G., Pereira, E., Chaudhari, N., & Roper, S. D. (2007). The role of pannexin 1 hemichannels in ATP release and cell-cell communication in mouse taste buds. Proceedings of the National Academy of Sciences, USA, 104, 6436–6441.

Halsell, C. B., & Frank, M. E. (1992). Organization of taste-evoked activity in the hamster parabrachial nucleus. Brain Research, 572(1/2), 286–290. Halsell, C. B., & Travers, S. P. (1997). Anterior and posterior oral cavity responsive neurons are differentially distributed among parabrachial subnuclei in rat. Journal of Neurophysiology, 78, 920–938. Halsell, C. B., Travers, S. P., & Travers, J. B. (1996). Ascending and descending projections from the rostral nucleus of the solitary tract originate from separate neuronal populations. Neuroscience, 72, 185–197. Hamilton, R. B., & Norgren, R. (1984). Central projections of gustatory nerves in the rat. Journal of Comparative Neurology, 222, 560–577. Hanamori, T., Kunitake, T., Kato, K., & Kannan, H. (1997). Convergence of afferent inputs from the chorda tympani, lingual-tonsillar and pharyngeal branches of the glossopharyngeal nerve, and superior laryngeal nerve on the neurons in the insular cortex in rats. Brain Research, 763, 267–270. Hanamori, T., Miller, I. J., Jr., & Smith, D. V. (1988). Gustatory responsiveness of fibers in the hamster glossopharyngeal nerve. Journal of Neurophysiology, 60, 478–498. Hao, S., Sternini, C., & Raybould, H. E. (2008). Role of CCK1 and Y2 receptors in activation of hindbrain neurons induced by intragastric administration of bitter taste receptor ligands. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 294(1), R33–R38. Harrer, M. I., & Travers, S. P. (1996). Topographic organization of foslike immunoreactivity in the rostral nucleus of the solitary tract evoked by gustatory stimulation with sucrose and quinine. Brain Research, 711(1/2), 125–137. Hayama, T., Hashimoto, K., & Ogawa, H. (1994). Anatomical location of a taste-related region in the thalamic reticular nucleus in rats. Neurosciences Research, 18, 291–299.

c13.indd 299

Heck, G. L., Mierson, S., & DeSimone, J. A. (1984, January 27). Salt taste transduction occurs through an amiloride-sensitive sodium transport pathway. Science, 223, 403–405.

Hunt, S. P., Pini, A., & Evan, G. (1987, August 13). Induction of c-fos-like protein in spinal cord neurons following sensory stimulation. Nature, 328, 632–634. Illig, K. R., & Haberly, L. B. (2003). Odor-evoked activity is spatially distributed in piriform cortex. Journal of Comparative Neurology, 457, 361–373. Ishimaru, Y., Inada, H., Kubota, M., Zhuang, H., Tominaga, M., & Matsunami, H. (2006). Transient receptor potential family members PKD1L3 and PKD2L1 form a candidate sour taste receptor. Proceedings of the National Academy of Sciences, USA, 103, 12569–12574. Jacobs, K. M., Mark, G. P., & Scott, T. R. (1988). Taste responses in the nucleus tractus solitarius of sodium-deprived rats. Journal of Physiology, 406, 393–410. Johnson, B. A., & Leon, M. (2007). Chemotopic odorant coding in a mammalian olfactory system. Journal of Comparative Neurology, 503, 1–34. Jones, L. M., Fontanini, A., Sadacca, B. F., Miller, P., & Katz, D. B. (2007). Natural stimuli evoke dynamic sequences of states in sensory cortical ensembles. Proceedings of the National Academy of Sciences, USA, 104, 18772–18777. Joshi, D., Volkl, M., Shepherd, G. M., & Laska, M. (2006). Olfactory sensitivity for enantiomers and their racemic mixtures: A comparative study in CD-1 mice and spider monkeys. Chemical Senses, 31, 655–664. Jung, M. W., Larson, J., & Lynch, G. (1990). Long-term potentiation of monosynaptic EPSPs in rat piriform cortex in vitro. Synapse, 6, 279–283. Kadohisa, M., & Wilson, D. A. (2006a). Olfactory cortical adaptation facilitates detection of odors against background. Journal of Neurophysiology, 95, 1888–1896.

8/17/09 2:09:15 PM

300

Chemical Senses

Kadohisa, M., & Wilson, D. A. (2006b). Separate encoding of identity and similarity of complex familiar odors in piriform cortex. Proceedings of the National Academy of Sciences, USA, 103, 15206–15211.

Laska, M., Joshi, D., & Shepherd, G. M. (2006). Olfactory sensitivity for aliphatic aldehydes in CD-1 mice. Behavioural Brain Research, 167, 349–354.

Kanter, E. D., & Haberly, L. B. (1990). NMDA-dependent induction of long-term potentiation in afferent and association fiber systems of piriform cortex in vitro. Brain Research, 525, 175–179.

Laugerette, F., Passilly-Degrace, P., Patris, B., Niot, I., Febbraio, M., Montmayeur, J. P., et al. (2005). CD36 involvement in orosensory detection of dietary lipids, spontaneous fat preference, and digestive secretions. Journal of Clinical Investigation, 115, 3177–3184.

Kanter, E. D., & Haberly, L. B. (1993). Associative long-term potentiation in piriform cortex slices requires GABAA blockade. Journal of Neuroscience, 13, 2477–2482. Karimnamazi, H., Travers, S. P., & Travers, J. B. (2002). Oral and gastric input to the parabrachial nucleus of the rat. Brain Research, 957, 193–206. Kataoka, S., Yang, R., Ishimaru, Y., Matsunami, H., Sevigny, J., Kinnamon, J. C., et al. (2008). The candidate sour taste receptor, PKD2L1, is expressed by Type III taste cells in the mouse. Chemical Senses, 33, 243–254. Katz, D. B., Simon, S. A., & Nicolelis, M. A. (2001). Dynamic and multimodal responses of gustatory cortical neurons in awake rats. Journal of Neuroscience, 21, 4478–4489. Kaya, N., Shen, T., Lu, S. G., Zhao, F. L., & Herness, S. (2004). A paracrine signaling role for serotonin in rat taste buds: Expression and localization of serotonin receptor subtypes. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 286, R649–R658. Kelliher, K. R., Spehr, M., Li, X. H., Zufall, F., & Leinders-Zufall, T. (2006). Pheromonal recognition memory induced by TRPC2independent vomeronasal sensing. European Journal of Medicine, 23, 3385–3390. Kepecs, A., Uchida, N., & Mainen, Z. F. (2006). The sniff as a unit of olfactory processing. Chemical Senses, 31, 167–179. Kim, U. K., Jorgenson, E., Coon, H., Leppert, M., Risch, N., & Drayna, D. (2003, February 21). Positional cloning of the human quantitative trait locus underlying taste sensitivity to phenylthiocarbamide. Science, 299, 1221–1225. King, M. S., & Bradley, R. M. (1994). Relationship between structure and function of neurons in the rat rostral nucleus tractus solitarii. Journal of Comparative Neurology, 344, 50–64. Kinnamon, J., & Yang, R. (2008). Ultrastructure of taste buds. In S. Firestein & G. K. Beauchamp (Eds.), The senses: A comprehensive reference (Vol. 4, pp. 000–000). San Diego, CA: Academic Press. Kinnamon, S. C., & Margolskee, R. F. (2008). Taste transduction. In S. Firestein & G. K. Beauchamp (Eds.), The senses: A comprehensive reference (Vol. 4, pp. 219–236). San Diego, CA: Academic Press. Kitagawa, J., Shingai, T., Kajii, Y., Takahashi, Y., Taguchi, Y., & Matsumoto, S. (2007). Leptin modulates the response to oleic acid in the pharynx. Neuroscience Letters, 423, 109–112. Kosaka, K., & Kosaka, T. (2005). Synaptic organization of the glomerulus in the main olfactory bulb: Compartments of the glomerulus and heterogeneity of the periglomerular cells. Anatomical Science International, 80(2), 80–90. Kosar, E., Grill, H. J., & Norgren, R. (1986a). Gustatory cortex in the rat: Pt. I. Physiological properties and cytoarchitecture. Brain Research, 379, 329–341. Kosar, E., Grill, H. J., & Norgren, R. (1986b). Gustatory cortex in the rat: Pt. II. Thalamocortical projections. Brain Research, 379, 342–352. Krettek, J. E., & Price, J. L. (1977). Projections from the amygdaloid complex and adjacent olfactory structures to the entorhinal cortex and to the subiculum in the rat and cat. Journal of Comparative Neurology, 172, 723–752. Lasiter, P. S., & Kachele, D. L. (1988). Organization of GABA and GABAtransaminase containing neurons in the gustatory zone of the nucleus of the solitary tract. Brain Research Bulletin, 21, 623–636.

c13.indd 300

Laurent, G., Stopfer, M., Friedrich, R. W., Rabinovich, M. I., Volkovskii, A., & Abarbanel, H. D. (2001). Odor encoding as an active, dynamical process: Experiments, computation, and theory. Annual Review of Neuroscience, 24, 263–297. Lei, H., Mooney, R., & Katz, L. C. (2006). Synaptic integration of olfactory information in mouse anterior olfactory nucleus. Journal of Neuroscience, 26, 12023–12032. Leinders-Zufall, T., Brennan, P., Widmayer, P., Chandramani, S. P., Maul-Pavicic, A., Jager, M., et al. (2004, November 5). MHC class I peptides as chemosensory signals in the vomeronasal organ. Science, 306, 1033–1037. Leinders-Zufall, T., Lane, A. P., Puche, A. C., Ma, W., Novotny, M. V., Shipley, M. T., et al. (2000, June 15). Ultrasensitive pheromone detection by mammalian vomeronasal neurons. Nature, 405, 792–796. Lemon, C. H., & Smith, D. V. (2005). Neural representation of bitter taste in the nucleus of the solitary tract. Journal of Neurophysiology, 94, 3719–3729. Li, C. S., Cho, Y. K., & Smith, D. V. (2005). Modulation of parabrachial taste neurons by electrical and chemical stimulation of the lateral hypothalamus and amygdala. Journal of Neurophysiology, 93, 1183–1196. Li, C. S., & Smith, D. V. (1997). Glutamate receptor antagonists block gustatory afferent input to the nucleus of the solitary tract. Journal of Neurophysiology, 77, 1514–1525. Li, X., Li, W., Wang, H., Cao, J., Maehashi, K., Huang, L., et al. (2005). Pseudogenization of a sweet-receptor gene accounts for cats’ indifference toward sugar. PLoS Genetics, 1(1), 27–35. Li, X., Staszewski, L., Xu, H., Durick, K., Zoller, M., & Adler, E. (2002). Human receptors for sweet and umami taste. Proceedings of the National Academy of Sciences, USA, 99, 4692–4696. Liman, E. R., Corey, D. P., & Dulac, C. (1999). TRP2: A candidate transduction channel for mammalian pheromone sensory signaling. Proceedings of the National Academy of Sciences, USA, 96, 5791–5796. Lison, M., Blondheim, S. H., & Melmed, R. N. (1980). A polymorphism of the ability to smell urinary metabolites of asparagus. British Medical Journal, 281, 1676–1678. LopezJimenez, N. D., Cavenagh, M. M., Sainz, E., Cruz-Ithier, M. A., Battey, J. F., & Sullivan, S. L. (2006). Two members of the TRPP family of ion channels, Pkd1l3 and Pkd2l1, are co-expressed in a subset of taste receptor cells. Journal of Neurochemistry, 98, 68–77. Lundy, R. F., Jr., & Contreras, R. J. (1999). Gustatory neuron types in rat geniculate ganglion. Journal of Neurophysiology, 82, 2970–2988. Lundy, R. F., Jr., & Norgren, R. (2004a). Activity in the hypothalamus, amygdala, and cortex generates bilateral and convergent modulation of pontine gustatory neurons. Journal of Neurophysiology, 91, 1143–1157. Lundy, R. F., Jr., & Norgren, R. (2004b). Gustatory system (3rd ed.). San Diego, CA: Elsevier Academic Press. Luo, M., Fee, M. S., & Katz, L. C. (2003, February 21). Encoding pheromonal signals in the accessory olfactory bulb of behaving mice. Science, 299, 1196–1201. Lyall, V., Heck, G. L., Vinnikova, A. K., Ghosh, S., Phan, T. H., Alam, R. I., et al. (2004). The mammalian amiloride-insensitive nonspecific salt taste receptor is a vanilloid receptor-1 variant. Journal of Physiology, 558(Pt. 1), 147–159.

8/17/09 2:09:15 PM

References 301 Lyall, V., Pasley, H., Phan, T. H., Mummalaneni, S., Heck, G. L., Vinnikova, A. K., et al. (2006). Intracellular pH modulates taste receptor cell volume and the phasic part of the chorda tympani response to acids. Journal of General Physiology, 127, 15–34. Macrides, F., & Chorover, S. L. (1972, January 7). Olfactory bulb units: Activity correlated with inhalation cycles and odor quality. Science, 175, 84–87.

Mombaerts, P. (2004c). Odorant receptor gene choice in olfactory sensory neurons: The one receptor-one neuron hypothesis revisited. Current Opinion in Neurobiology, 14, 31–36.

Malnic, B. (2007). Searching for the ligands of odorant receptors. Molecular Neurobiology, 35(2), 175–181.

Mombaerts, P., Wang, F., Dulac, C., Chao, S. K., Nemes, A., Mendelsohn, M., et al. (1996). Visualizing an olfactory sensory map. Cell, 87, 675–686.

Mandiyan, V. S., Coats, J. K., & Shah, N. M. (2005). Deficits in sexual and aggressive behaviors in Cnga2 mutant mice. Journal of Neuroscience, 8, 1660–1662.

Mueller, K. L., Hoon, M. A., Erlenbach, I., Chandrashekar, J., Zuker, C. S., & Ryba, N. J. (2005, March 10). The receptors and coding logic for bitter taste. Nature, 434, 225–229.

Margolskee, R. F., Dyer, J., Kokrashvili, Z., Salmon, K. S., Ilegems, E., Daly, K., et al. (2007). T1R3 and gustducin in gut sense sugars to regulate expression of Na-glucose cotransporter 1. Proceedings of the National Academy of Sciences, USA, 104, 15075–15080.

Murphy, G. J., Darcy, D. P., & Isaacson, J. S. (2005). Intraglomerular inhibition: Signaling mechanisms of an olfactory microcircuit. Journal of Neuroscience, 8, 354–364.

Margrie, T. W., & Schaefer, A. T. (2003). Theta oscillation coupled spike latencies yield computational vigour in a mammalian sensory system. Journal of Physiology, 546(Pt. 2), 363–374.

Nagai, T., Mistretta, C. M., & Bradley, R. M. (1988). Developmental decrease in size of peripheral receptive fields of single chorda tympani nerve fibers and relation to increasing NaCl taste sensitivity. Journal of Neuroscience, 8, 64–72.

Mark, G. P., Scott, T. R., Chang, F. C., & Grill, H. J. (1988). Taste responses in the nucleus tractus solitarius of the chronic decerebrate rat. Brain Research, 443(1–2), 137–148.

Nakamura, K., & Norgren, R. (1991). Gustatory responses of neurons in the nucleus of the solitary tract of behaving rats. Journal of Neurophysiology, 66, 1232–1248.

Martin, C., Gervais, R., Hugues, E., Messaoudi, B., & Ravel, N. (2004). Learning modulation of odor-induced oscillatory responses in the rat olfactory bulb: A correlate of odor recognition? Journal of Neuroscience, 24, 389–397.

Nelson, G., Chandrashekar, J., Hoon, M. A., Feng, L., Zhao, G., Ryba, N. J., et al. (2002, March 14). An amino-acid taste receptor. Nature, 416, 199–202.

Matsunami, H., & Buck, L. B. (1997). A multigene family encoding a diverse array of putative pheromone receptors in mammals. Cell, 90, 775–784. Max, M., & Meyerhof, W. (2008). Taste receptors. In S. Firestein & G. K. Beauchamp (Eds.), The senses: A comprehensive reference (Vol. 4, pp. 197–217). San Diego, CA: Academic Press. May, O. L., Erisir, A., & Hill, D. L. (2007). Ultrastructure of primary afferent terminals and synapses in the rat nucleus of the solitary tract: Comparison among the greater superficial petrosal, chorda tympani, and glossopharyngeal nerves. Journal of Comparative Neurology, 502, 1066–1078. May, O. L., & Hill, D. L. (2006). Gustatory terminal field organization and developmental plasticity in the nucleus of the solitary tract revealed through triple-fluorescence labeling. Journal of Comparative Neurology, 497, 658–669. McCaughey, S. A., & Scott, T. R. (2000). Rapid induction of sodium appetite modifies taste-evoked activity in the rat nucleus of the solitary tract. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 279, R1121–R1131. McDonald, A. J. (1991). Topographical organization of amygdaloid projections to the caudatoputamen, nucleus accumbens, and related striatallike areas of the rat brain. Neuroscience, 44, 15–33. Menashe, I., Abaffy, T., Hasin, Y., Goshen, S., Yahalom, V., Luetje, C. W., et al. (2007). Genetic elucidation of human hyperosmia to isovaleric acid. PLoS Biology, 5(11), e284. Meredith, M. (1994). Chronic recording of vomeronasal pump activation in awake behaving hamsters. Physiology and Behavior, 56, 345–354. Miller, F. R., & Sherrington, C. S. (1916). Some observations on the buccopharyngeal stage of reflex deglutition in the cat. Quarterly Journal of Experimental Physiology, 9, 147–186.

Nelson, G., Hoon, M. A., Chandrashekar, J., Zhang, Y., Ryba, N. J., & Zuker, C. S. (2001). Mammalian sweet taste receptors. Cell, 106, 381–390. Neville, K. R., & Haberly, L. B. (2003). Beta and gamma oscillations in the olfactory system of the urethane-anesthetized rat. Journal of Neurophysiology, 90, 3921–3930. Neville, K. R., & Haberly, L. B. (2004). Olfactory cortex. In G. M. Shepherd (Ed.), The synaptic organization of the brain (5th ed., pp. 415–454). New York: Oxford University Press. Niimura, Y., & Nei, M. (2003). Evolution of olfactory receptor genes in the human genome. Proceedings of the National Academy of Sciences, USA, 100, 12235–12240. Niimura, Y., & Nei, M. (2005). Evolutionary dynamics of olfactory receptor genes in fishes and tetrapods. Proceedings of the National Academy of Sciences, USA, 102, 6039–6044. Ninomiya, Y., Sako, N., & Funakoshi, M. (1989). Strain differences in amiloride inhibition of NaCl responses in mice, mus musculus. Journal of Comparative Physiology: A, 166, 1–5. Ninomiya, Y., Sako, N., & Imai, Y. (1995). Enhanced gustatory neural responses to sugars in the diabetic db/db mouse. Journal of General Physiology, 269, R930–R937. Nishijo, H., & Norgren, R. (1990). Responses from parabrachial gustatory neurons in behaving rats. Journal of Neurophysiology, 63, 707–724. Nishijo, H., Uwano, T., Tamura, R., & Ono, T. (1998). Gustatory and multimodal neuronal responses in the amygdala during licking and discrimination of sensory stimuli in awake rats. Journal of Neurophysiology, 79, 21–36. Norgren, R. (1974). Gustatory afferents to ventral forebrain. Brain Research, 81, 285–295. Norgren, R., & Leonard, C. M. (1971, September 17). Taste pathways in rat brainstem. Science, 173, 1136–1139.

Mitchell, S. C. (2001). Food idiosyncrasies: Beetroot and asparagus. Drug Metabolism and Disposition, 29, 539–543.

Norgren, R., & Leonard, C. M. (1973). Ascending central gustatory pathways. Journal of Comparative Neurology, 150, 217–237.

Mitchell, S. C., Waring, R. H., Land, D., & Thorpe, W. V. (1987). Odorous urine following asparagus ingestion in man. Experientia, 43, 382–383.

Nusser, Z., Kay, L. M., Laurent, G., Homanics, G. E., & Mody, I. (2001). Disruption of GABA(A) receptors on GABAergic interneurons leads to increased oscillatory power in the olfactory bulb network. Journal of Neurophysiology, 86, 2823–2833.

Mombaerts, P. (2004a). Genes and ligands for odorant, vomeronasal and taste receptors. Nature Reviews: Neuroscience, 5, 263–278.

c13.indd 301

Mombaerts, P. (2004b). Love at first smell: The 2004 Nobel Prize in physiology or medicine. New England Journal of Medicine, 351, 2579–2580.

8/17/09 2:09:16 PM

302

Chemical Senses

O’Doherty, J., Rolls, E. T., Francis, S., Bowtell, R., McGlone, F., Kobal, G., et al. (2000). Sensory-specific satiety-related olfactory activation of the human orbitofrontal cortex. NeuroReport, 11, 399–403. Ogawa, H., Hasegawa, K., & Murayama, N. (1992). Difference in taste quality coding between two cortical taste areas, granular and dysgranular insular areas, in rats. Experimental Brain Research, 91, 415–424. Ogawa, H., Hayama, T., & Ito, S. (1982). Convergence of input from tongue and palate to the parabrachial nucleus neurons of rats. Neuroscience Letters, 28, 9–14. Ogawa, H., Ito, S., Murayama, N., & Hasegawa, K. (1990). Taste area in granular and dysgranular insular cortices in the rat identified by stimulation of the entire oral cavity. Neurosciences Research, 9, 196–201. Ongur, D., An, X., & Price, J. L. (1998). Prefrontal cortical projections to the hypothalamus in macaque monkeys. Journal of Comparative Neurology, 401, 480–505. Pecina, S., & Berridge, K. C. (2000). Opioid site in nucleus accumbens shell mediates eating and hedonic “liking” for food: Map based on microinjection fos plumes. Brain Research, 863(1–2), 71–86. Pfaffmann, C. (1941). Gustatory afferent impulses. Journal of Cellular and Comparative Physiology, 17, 243–258. Pittman, D., Crawley, M. E., Corbin, C. H., & Smith, K. R. (2007). Chorda tympani nerve transection impairs the gustatory detection of free fatty acids in male and female rats. Brain Research, 1151, 74–83. Plata-Salaman, C. R., Smith-Swintosky, V. L., & Scott, T. R. (1996). Gustatory neural coding in the monkey cortex: Mixtures. Journal of Neurophysiology, 75, 2369–2379. Potter, H., & Chorover, S. L. (1976). Response plasticity in hamster olfactory bulb: Peripheral and central processes. Brain Research, 116, 417–429. Price, J. L. (1973). An autoradiographic study of complementary laminar patterns of termination of afferent fibers to the olfactory cortex. Journal of Comparative Neurology, 150, 87–108. Pritchard, T. C., Hamilton, R. B., Morse, J. R., & Norgren, R. (1986). Projections of thalamic gustatory and lingual areas in the monkey, macaca fascicularis. Journal of Comparative Neurology, 244, 213–228.

Rinberg, D., Koulakov, A., & Gelperin, A. (2006). Speed-accuracy tradeoff in olfaction. Neuron, 51, 351–358. Rodriguez, I., Feinstein, P., & Mombaerts, P. (1999). Variable patterns of axonal projections of sensory neurons in the mouse vomeronasal system. Cell, 97, 199–208. Roesch, M. R., Stalnaker, T. A., & Schoenbaum, G. (2007). Associative encoding in anterior piriform cortex versus orbitofrontal cortex during odor discrimination and reversal learning. Cerebral Cortex, 17, 643–652. Roitman, M. F., Wheeler, R. A., & Carelli, R. M. (2005). Nucleus accumbens neurons are innately tuned for rewarding and aversive taste stimuli, encode their predictors, and are linked to motor output. Neuron, 45, 587–597. Rolls, E. T., & Baylis, L. L. (1994). Gustatory, olfactory, and visual convergence within the primate orbitofrontal cortex. Journal of Neuroscience, 14, 5437–5452. Rolls, E. T., Critchley, H. D., Browning, A. S., Hernadi, I., & Lenard, L. (1999). Responses to the sensory properties of fat of neurons in the primate orbitofrontal cortex. Journal of Neuroscience, 19, 1532–1540. Rolls, E. T., Scott, T. R., Sienkiewicz, Z. J., & Yaxley, S. (1988). The responsiveness of neurones in the frontal opercular gustatory cortex of the macaque monkey is independent of hunger. Journal of Physiology, 397, 1–12. Rolls, E. T., Sienkiewicz, Z. J., & Yaxley, S. (1989). Hunger modulates the responses to gustatory stimuli of single neurons in the caudolateral orbitofrontal cortex of the macaque monkey. European Journal of Medicine, 1(1), 53–60. Roper, S. D. (2006). Cell communication in taste buds. Cellular and Molecular Life Sciences, 63, 1494–1500. Rozengurt, E. (2006). Taste receptors in the gastrointestinal tract: Pt. I. Bitter taste receptors and alpha-gustducin in the mammalian gut. American Journal of Physiology: Gastrointestinal and Liver Physiology, 291, G171–G177. Rozengurt, E., & Sternini, C. (2007). Taste receptor signaling in the mammalian gut. Current Opinion in Pharmacology, 7, 557–562.

Pritchard, T. C., Hamilton, R. B., & Norgren, R. (2000). Projections of the parabrachial nucleus in the old world monkey. Experimental Neurology, 165, 101–117.

Saggu, S., & Lundy, R. F. (2008). Forebrain neurons that project to the gustatory parabrachial nucleus in rat lack glutamic acid decarboxylase. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 294, R52–R57.

Pritchard, T. C., & Norgren, R. (2004). Gustatory system. In G. Paxinos & J. Mai (Eds.), The human nervous system (pp. 000–000). Boston: Elsevier.

Sainz, E., Cavenagh, M. M., Gutierrez, J., Battey, J. F., Northup, J. K., & Sullivan, S. L. (2007). Functional characterization of human bitter taste receptors. Biochemical Journal, 403, 537–543.

Pronin, A. N., Xu, H., Tang, H., Zhang, L., Li, Q., & Li, X. (2007). Specific alleles of bitter receptor genes influence human sensitivity to the bitterness of aloin and saccharin. Current Biology, 17, 1403–1408.

Sandell, M. A., & Breslin, P. A. (2006). Variability in a taste-receptor gene determines whether we taste toxins in food. Current Biology, 16, R792–R794.

Quignon, P., Kirkness, E., Cadieu, E., Touleimat, N., Guyon, R., Renier, C., et al. (2003). Comparison of the canine and human olfactory receptor gene repertoires. Genome Biology, 4, R80.

Schaefer, A. T., & Margrie, T. W. (2007). Spatiotemporal representations in the olfactory system. Trends in Neurosciences, 30, 92–100.

Rennaker, R. L., Chen, C. F., Ruyle, A. M., Sloan, A. M., & Wilson, D. A. (2007). Spatial and temporal distribution of odorant-evoked activity in the piriform cortex. Journal of Neuroscience, 27, 1534–1542.

Schaefer, M. L., Yamazaki, K., Osada, K., Restrepo, D., & Beauchamp, G. K. (2002). Olfactory fingerprints for major histocompatibility complex-determined body odors: Pt. II. Relationship among odor maps, genetics, odor composition, and behavior. Journal of Neuroscience, 22, 9513–9521.

Ressler, K. J., Sullivan, S. L., & Buck, L. B. (1994). Information coding in the olfactory system: Evidence for a stereotyped and highly organized epitope map in the olfactory bulb. Cell, 79, 1245–1255.

Schoppa, N. E., & Westbrook, G. L. (2001). Glomerulus-specific synchronization of mitral cells in the olfactory bulb. Neuron, 31, 639–651.

Reynolds, S. M., & Zahm, D. S. (2005). Specificity in the projections of prefrontal and insular cortex to ventral striatopallidum and the extended amygdala. Journal of Neuroscience, 25, 11757–11767.

Scott, G., Westberg, K. G., Vrentzos, N., Kolta, A., & Lund, J. P. (2003). Effect of lidocaine and NMDA injections into the medial pontobulbar reticular formation on mastication evoked by cortical stimulation in anaesthetized rabbits. European Journal of Medicine, 17, 2156–2162.

Richter, C. (1956). Salt appetite of mammals: Its dependence on instinct and metabolism. In L’Instinct dans le comportement des animaux et de l’homme (pp. 577–629). Paris: Masson et cie editeurs.

Scott, J. W. (2006). Sniffing and spatiotemporal coding in olfaction. Chemical Senses, 31, 119–130.

Richter, T. A., Caicedo, A., & Roper, S. D. (2003). Sour taste stimuli evoke Ca2 and pH responses in mouse taste cells. Journal of Physiology, 547, 475–483.

Scott, T. R., Jr., & Erickson, R. P. (1971). Synaptic processing of tastequality information in thalamus of the rat. Journal of Neurophysiology, 34, 868–883.

c13.indd 302

8/17/09 2:09:16 PM

References 303 Scott, T. R., Karadi, Z., Oomura, Y., Nishino, H., Plata-Salaman, C. R., Lenard, L., et al. (1993). Gustatory neural coding in the amygdala of the alert macaque monkey. Journal of Neurophysiology, 69, 1810–1820. Scott, T. R., Yaxley, S., Sienkiewicz, Z. J., & Rolls, E. T. (1986a). Gustatory responses in the frontal opercular cortex of the alert cynomolgus monkey. Journal of Neurophysiology, 56, 876–890. Scott, T. R., Yaxley, S., Sienkiewicz, Z. J., & Rolls, E. T. (1986b). Gustatory responses in the nucleus tractus solitarius of the alert cynomolgus monkey. Journal of Neurophysiology, 55, 182–200. Seeley, R. J., Grill, H. J., & Kaplan, J. M. (1994). Neurological dissociation of gastrointestinal and metabolic contributions to meal size control. Behavioral Neuroscience, 108, 347–352. Shepherd, G. M. (2006, January 12). Behaviour: Smells, brains and hormones. Nature, 439, 149–151. Shepherd, G. M., Chen, W. R., & Greer, C. A. (2004). Olfactory bulb. In G. M. Shepherd (Ed.), The synaptic organization of the brain (5th ed., pp. 165–216). New York: Oxford University Press. Shi, C. J., & Cassell, M. D. (1998). Cascade projections from somatosensory cortex to the rat basolateral amygdala via the parietal insular cortex. Journal of Comparative Neurology, 399, 469–491. Shigemura, N., Ohta, R., Kusakabe, Y., Miura, H., Hino, A., Koyano, K., et al. (2004). Leptin modulates behavioral responses to sweet substances by influencing peripheral taste structures. Endocrinology, 145, 839–847. Shimura, T., Komori, M., & Yamamoto, T. (1997). Acute sodium deficiency reduces gustatory responsiveness to NaCl in the parabrachial nucleus of rats. Neuroscience Letters, 236, 33–36. Shimura, T., Tanaka, H., & Yamamoto, T. (1997). Salient responsiveness of parabrachial neurons to the conditioned stimulus after the acquisition of taste aversion learning in rats. Neuroscience, 81, 239–247. Shimura, T., Tokita, K., & Yamamoto, T. (2002). Parabrachial unit activities after the acquisition of conditioned taste aversion to a non-preferred HCl solution in rats. Chemical Senses, 27, 153–158. Shipley, M. T., Ennis, M., & Puche, A. C. (2004). Olfactory system. In G. Paxinos (Ed.), The rat nervous system (3rd ed., pp. 923–964). San Diego, CA: Elsevier Academic Press. Simon, S. A., de Araujo, I. E., Gutierrez, R., & Nicolelis, M. A. (2006). The neural mechanisms of gustation: A distributed processing code. Nature Reviews: Neuroscience, 7, 890–901. Slotnick, B., & Bisulco, S. (2003). Detection and discrimination of carvone enantiomers in rats with olfactory bulb lesions. Neuroscience, 121, 451–457. Small, D. M., Bender, G., Veldhuizen, M. G., Rudenga, K., Nachtigal, D., & Felsted, J. (2007). The role of the human orbitofrontal cortex in taste and flavor processing. Annals of the New York Academy of Sciences, 1121, 136–151. Small, D. M., Voss, J., Mak, Y. E., Simmons, K. B., Parrish, T., & Gitelman, D. (2004). Experience-dependent neural integration of taste and smell in the human brain. Journal of Neurophysiology, 92, 1892–1903. Small, D. M., Zatorre, R. J., Dagher, A., Evans, A. C., & Jones-Gotman, M. (2001). Changes in brain activity related to eating chocolate: From pleasure to aversion. Brain, 124, 1720–1733. Smith, D. V., & Ossebaard, C. A. (1995). Amiloride suppression of the taste intensity of sodium chloride: Evidence from direct magnitude scaling. Physiology and Behavior, 57, 773–777. Smith, D. V., Van Buskirk, R. L., Travers, J. B., & Bieber, S. L. (1983). Coding of taste stimuli by hamster brain stem neurons. Journal of Neurophysiology, 50, 541–558. Smith, D. V., Ye, M. K., & Li, C. S. (2005). Medullary taste responses are modulated by the bed nucleus of the stria terminalis. Chemical Senses, 30, 421–434. Sollars, S. I., & Hill, D. L. (2005). In vivo recordings from rat geniculate ganglia: Taste response properties of individual greater superficial

c13.indd 303

petrosal and chorda tympani neurones. Journal of Physiology, 564, 877–893. Spector, A. C. (2003). The functional organzation of the gustatory system: Lessons from behavior. Progress in Psychobiology and Physiological Psychology, 18, 101–161. Spector, A. C., & Travers, S. P. (2005). The representation of taste quality in the mammalian nervous system. Behavioral and Cognitive Neuroscience Reviews, 4, 143–191. Spehr, M., Kelliher, K. R., Li, X. H., Boehm, T., Leinders-Zufall, T., & Zufall, F. (2006). Essential role of the main olfactory system in social recognition of major histocompatibility complex peptide ligands. Journal of Neuroscience, 26, 1961–1970. Spehr, M., Spehr, J., Ukhanov, K., Kelliher, K. R., Leinders-Zufall, T., & Zufall, F. (2006). Parallel processing of social signals by the mammalian main and accessory olfactory systems. Cellular and Molecular Life Sciences, 63, 1476–1484. St. John, S. J., & Spector, A. C. (1998). Behavioral discrimination between quinine and KCl is dependent on input from the seventh cranial nerve: Implications for the functional roles of the gustatory nerves in rats. Journal of Neuroscience, 18, 4353–4362. Stapleton, J. R., Lavine, M. L., Wolpert, R. L., Nicolelis, M. A., & Simon, S. A. (2006). Rapid taste responses in the gustatory cortex during licking. Journal of Neuroscience, 26, 4126–4138. Staubli, U., Fraser, D., Faraday, R., & Lynch, G. (1987). Olfaction and the “data” memory system in rats. Behavioral Neuroscience, 101, 757–765. Steiner, J. E. (1973). The gustofacial response: Observation on normal and anencephalic newborn infants. Symposium on Oral Sensation and Perception, 4, 254–278. Steiner, J. E., & Glaser, D. (1984). Differential behavioral responses to taste stimuli in hon-human primates. Journal of Human Evolution, 13, 709–723. Stewart, W. B., Kauer, J. S., & Shepherd, G. M. (1979). Functional organization of rat olfactory bulb analysed by the 2- deoxyglucose method. Journal of Comparative Neurology, 185, 715–734. Stopfer, M., Bhagavan, S., Smith, B. H., & Laurent, G. (1997, November 6). Impaired odour discrimination on desynchronization of odourencoding neural assemblies. Nature, 390, 70–74. Stratford, J. M., Curtis, K. S., & Contreras, R. J. (2006). Chorda tympani nerve transection alters linoleic acid taste discrimination by male and female rats. Physiology and Behavior, 89, 311–319. Sugai, T., Miyazawa, T., Fukuda, M., Yoshimura, H., & Onoda, N. (2005). Odor-concentration coding in the guinea-pig piriform cortex. Neuroscience, 130, 769–781. Sullivan, S. L., Adamson, M. C., Ressler, K. J., Kozak, C. A., & Buck, L. B. (1996). The chromosomal distribution of mouse odorant receptor genes. Proceedings of the National Academy of Sciences, USA, 93, 884–888. Suzuki, N., & Bekkers, J. M. (2007). Inhibitory interneurons in the piriform cortex. Clinical and Experimental Pharmacology and Physiology, 34, 1064–1069. Taha, S. A., & Fields, H. L. (2005). Encoding of palatability and appetitive behaviors by distinct neuronal populations in the nucleus accumbens. Journal of Neuroscience, 25, 1193–1202. Tamura, R., & Norgren, R. (1997). Repeated sodium depletion affects gustatory neural responses in the nucleus of the solitary tract of rats. Journal of General Physiology, 273, R1381–R1391. Tamura, R., & Norgren, R. (2003). Intracranial renin alters gustatory neural responses in the nucleus of the solitary tract of rats. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 284, R1108–R1118. Tindell, A. J., Smith, K. S., Pecina, S., Berridge, K. C., & Aldridge, J. W. (2006). Ventral pallidum firing codes hedonic reward: When a bad taste turns good. Journal of Neurophysiology, 96, 2399–2409.

8/17/09 2:09:16 PM

304

Chemical Senses

Tokita, K., Karadi, Z., Shimura, T., & Yamamoto, T. (2004). Centrifugal inputs modulate taste aversion learning associated parabrachial neuronal activities. Journal of Neurophysiology, 92, 265–279. Tokita, K., Shimura, T., Nakamura, S., Inoue, T., & Yamamoto, T. (2007). Involvement of forebrain in parabrachial neuronal activation induced by aversively conditioned taste stimuli in the rat. Brain Research, 1141, 188–196. Tomchik, S. M., Berg, S., Kim, J. W., Chaudhari, N., & Roper, S. D. (2007). Breadth of tuning and taste coding in mammalian taste buds. Journal of Neuroscience, 27, 10840–10848. Topolovec, J. C., Gati, J. S., Menon, R. S., Shoemaker, J. K., & Cechetto, D. F. (2004). Human cardiovascular and gustatory brainstem sites observed by functional magnetic resonance imaging. Journal of Comparative Neurology, 471, 446–461. Travers, J. B. (1988). Efferent projections from the anterior nucleus of the solitary tract of the hamster. Brain Research, 457, 1–11. Travers, J. B., Grill, H. J., & Norgren, R. (1987). The effects of glossopharyngeal and chorda tympani nerve cuts on the ingestion and rejection of sapid stimuli: An electromyographic analysis in the rat. Behavioural Brain Research, 25, 233–246. Travers, S. P. (2002). Quinine and citric acid elicit distinctive fos-like immunoreactivity in the rat nucleus of the solitary tract. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 282, R1798–R1810. Travers, S. P., & Norgren, R. (1995). Organization of orosensory responses in the nucleus of the solitary tract of rat. Journal of Neurophysiology, 73, 2144–2162. Travers, S. P., Pfaffmann, C., & Norgren, R. (1986). Convergence of lingual and palatal gustatory neural activity in the nucleus of the solitary tract. Brain Research, 365, 305–320. Uchida, N., Kepecs, A., & Mainen, Z. F. (2006). Seeing at a glance, smelling in a whiff: Rapid forms of perceptual decision making. Nature Reviews: Neuroscience, 7, 485–491. Uchida, N., & Mainen, Z. F. (2003). Speed and accuracy of olfactory discrimination in the rat. Journal of Neuroscience, 6, 1224–1229. Vandenbeuch, A., Clapp, T. R., & Kinnamon, S. C. (2008). Amiloridesensitive channels in type I fungiform taste cells in mouse. BMC Neuroscience, 9, 1. van der Klaauw, N. J., & Smith, D. V. (1995). Taste quality profiles for fifteen organic and inorganic salts. Physiology and Behavior, 58, 295–306. Vassar, R., Chao, S. K., Sitcheran, R., Nunez, J. M., Vosshall, L. B., & Axel, R. (1994). Topographic organization of sensory projections to the olfactory bulb. Cell, 79, 981–991. Venugopal, S., Travers, J. B., Terman D. H. (2007 April). A computational model for motor pattern switching between taste-induced ingestion and rejection oromotor behaviors. Journal Comput Neurosci, 22, 223–38. Voshart, K., & van der Kooy, D. (1981). The organization of the efferent projections of the parabrachial nucleus of the forebrain in the rat: A retrograde fluorescent double-labeling study. Brain Research, 212, 271–286. Vucinic, D., Cohen, L. B., & Kosmidis, E. K. (2006). Interglomerular center-surround inhibition shapes odorant-evoked input to the mouse olfactory bulb in vivo. Journal of Neurophysiology, 95, 1881–1887. Wachowiak, M., & Shipley, M. T. (2006). Coding and synaptic processing of sensory information in the glomerular layer of the olfactory bulb. Seminars in Cell and Developmental Biology, 17, 411–423. Walsh, R. R. (1956). Single cell spike activity in the olfactory bulb. Journal of General Physiology, 186, 255–257. Wang, L., & Bradley, R. M. (1995). In vitro study of afferent synaptic transmission in the rostral gustatory zone of the rat nucleus of the solitary tract. Brain Research, 702(1–2), 188–198.

c13.indd 304

Wheeler, R. A., Twining, R. C., Jones, J. L., Slater, J. M., Grigson, P. S., & Carelli, R. M. (2008). Behavioral and electrophysiological indices of negative affect predict cocaine self-administration. Neuron, 57, 774–785. Whissell-Buechy, D., & Amoore, J. E. (1973, March 23). Odour-blindness to musk: Simple recessive inheritance. Nature, 242, 271–273. Whitehead, M. C. (1986). Anatomy of the gustatory system in the hamster: Synaptology of facial afferent terminals in the solitary nucleus. Journal of Comparative Neurology, 244, 72–85. Whitehead, M. C. (1988). Neuronal architecture of the nucleus of the solitary tract in the hamster. Journal of Comparative Neurology, 276, 547–572. Whitehead, M. C. (1990). Subdivisions and neuron types of the nucleus of the solitary tract that project to the parabrachial nucleus in the hamster. Journal of Comparative Neurology, 301, 554–574. Whitehead, M. C. (1993). Distribution of synapses on identified cell types in a gustatory subdivision of the nucleus of the solitary tract. Journal of Comparative Neurology, 332, 326–340. Whitehead, M. C., Bergula, A., & Holliday, K. (2000). Forebrain projections to the rostral nucleus of the solitary tract in the hamster. Journal of Comparative Neurology, 422, 429–447. Whitehead, M. C., & Finger, T. (2008). Gustatory pathways in fish and mammals. In S. Firestein & G. K. Beauchamp (Eds.), Handbook of the senses: A comprehensive reference (Vol. 4, pp. 237–258). Whitehead, M. C., & Frank, M. E. (1983). Anatomy of the gustatory system in the hamster: Central projections of the chorda tympani and the lingual nerve. Journal of Comparative Neurology, 220, 378–395. Wilson, D. A., Kadohisa, M., & Fletcher, M. L. (2006). Cortical contributions to olfaction: Plasticity and perception. Seminars in Cell and Developmental Biology, 17, 462–470. Wu, S. V., Rozengurt, N., Yang, M., Young, S. H., Sinnett-Smith, J., & Rozengurt, E. (2002). Expression of bitter taste receptors of the T2R family in the gastrointestinal tract and enteroendocrine STC1 cells. Proceedings of the National Academy of Sciences, USA, 99, 2392–2397. Yamamoto, T., Matsuo, R., & Kawamura, Y. (1980). Localization of cortical gustatory area in rats and its role in taste discrimination. Journal of Neurophysiology, 44, 440–455. Yamamoto, T., Matsuo, R., Kiyomitsu, Y., & Kitamura, R. (1989). Taste responses of cortical neurons in freely ingesting rats. Journal of Neurophysiology, 61, 1244–1258. Yamamoto, T., & Sawa, K. (2000). Comparison of c-fos-like immunoreactivity in the brainstem following intraoral and intragastric infusions of chemical solutions in rats. Brain Research, 866(1/2), 144–151. Yamamoto, T., Shimura, T., Sakai, N., & Ozaki, N. (1994). Representation of hedonics and quality of taste stimuli in the parabrachial nucleus of the rat. Physiology and Behavior, 56, 1197–1202. Yamamoto, T., Yuyama, N., Kato, T., & Kawamura, Y. (1985). Gustatory responses of cortical neurons in rats: Pt. II. Information processing of taste quality. Journal of Neurophysiology, 53, 1356–1369. Yan, J., & Scott, T. R. (1996). The effect of satiety on responses of gustatory neurons in the amygdala of alert cynomolgus macaques. Brain Research, 740(1–2), 193–200. Yaxley, S., Rolls, E. T., Sienkiewicz, Z. J., & Scott, T. R. (1985). Satiety does not affect gustatory activity in the nucleus of the solitary tract of the alert monkey. Brain Research, 347, 85–93. Yokoi, M., Mori, K., & Nakanishi, S. (1995). Refinement of odor molecule tuning by dendrodendritic synaptic inhibition in the olfactory bulb. Proceedings of the National Academy of Sciences, USA, 92, 3371–3375.

8/17/09 2:09:17 PM

References 305 Yoon, H., Enquist, L. W., & Dulac, C. (2005). Olfactory inputs to hypothalamic neurons controlling reproduction and fertility. Cell, 123, 669–682.

Zhang, X., Zhang, X., & Firestein, S. (2007). Comparative genomics of odorant and pheromone receptor genes in rodents. Genomics, 89, 441–450.

Yoshida, I., & Mori, K. (2007). Odorant category profile selectivity of olfactory cortex neurons. Journal of Neuroscience, 27, 9105–9114.

Zhang, Y., Hoon, M. A., Chandrashekar, J., Mueller, K. L., Cook, B., Wu, D., et al. (2003). Coding of sweet, bitter, and umami tastes: Different receptor cells sharing similar signaling pathways. Cell, 112, 293–301.

Young, J. M., Shykind, B. M., Lane, R. P., Tonnes-Priddy, L., Ross, J. A., Walker, M., et al. (2003). Odorant receptor expressed sequence tags demonstrate olfactory expression of over 400 genes, extensive alternate splicing and unequal expression levels. Genome Biology, 4, R71. Young, J. M., & Trask, B. J. (2007). V2R gene families degenerated in primates, dog and cow, but expanded in opossum. Trends in Genetics, 23, 212–215. Zahm, D. S. (2000). An integrative neuroanatomical perspective on some subcortical substrates of adaptive responding with emphasis on the nucleus accumbens. Neuroscience and Biobehavioral Reviews, 24, 85–105. Zaidi, F. N., & Whitehead, M. C. (2006). Discrete innervation of murine taste buds by peripheral taste neurons. Journal of Neuroscience, 26, 8243–8253.

c13.indd 305

Zhao, F. L., Shen, T., Kaya, N., Lu, S. G., Cao, Y., & Herness, S. (2005). Expression, physiological action, and coexpression patterns of neuropeptide Y in rat taste-bud cells. Proceedings of the National Academy of Sciences, USA, 102, 11100–11105. Zhao, G. Q., Zhang, Y., Hoon, M. A., Chandrashekar, J., Erlenbach, I., Ryba, N. J., et al. (2003). The receptors for mammalian sweet and umami taste. Cell, 115, 255–266. Zozulya, S., Echeverri, F., & Nguyen, T. (2001). The human olfactory receptor repertoire. Genome Biology, 2, RESEARCH0018, Epub June 1, 2001. Zufall, F., & Leinders-Zufall, T. (2007). Mammalian pheromone sensing. Current Opinion in Neurobiology, 17, 483–489.

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Chapter 14

Somatosensory Processes STEVEN S. HSIAO AND PRAMODSINGH H. THAKUR

rapid. Blind subjects can read Braille at approximately 100 words per minute that translates to a recognition rate of about 100 ms for a single Braille character (Foulke, 1991). This is extremely fast considering the slow rate at which action potentials are generated and transmitted. One hundred milliseconds includes the time for the impulses to be encoded in the peripheral responses, conveyed to the cortex (which takes about 25 ms alone), transformed at several processing stages and matched against all stored memories. This suggests that the mechanisms underlying tactile pattern perception involve relatively few serial processing stages and that the cortical processing of sensory information uses parallel processing mechanisms. In this chapter, we first give a brief discussion on the psychophysics of tactile perception. We concentrate our discussion on the perception of tactile shape and texture from the hand. We then discuss our present understanding of how stimuli that activate the mechanoreceptive afferents are represented in the periphery and in the responses of neurons in primary and secondary somatosensory cortical areas, briefly discuss how these representations are affected by higher cognitive functions such as selective attention, and conclude by speculating on how sensory inputs are integrated to form coherent representations of the size and shape of objects.

A fundamental question across all sensory systems is to understand the neural mechanisms that underlie the perceptions that we experience as we interact and move about in our environment. The question is particularly interesting and challenging in the somatosensory system, which dynamically integrates, in a seamless manner, a wide range of sensory inputs that guide motor outputs. The sensory inputs include the appreciation of exteroceptive stimuli or perceptions that are produced by environmental stimuli, which include the perception of temperature, pain, itch, and tactile inputs such as the form and texture of objects. The sensory inputs also include the appreciation of interoceptive stimuli or perceptions, which include the perception of body position, body movement, and body force. Anatomical and neurophysiologic evidence suggests that each of these sensory inputs are initially segregated, with each modality having a unique set of peripheral input receptors that are initially processed along separate ascending and cortical pathways. However, perceptually, these inputs are processed in parallel, which results in a single unified percept of the environment. An example of the close interplay between the different modalities of touch and motor actions is the act of making of a snowball. While the task seems simple, in reality it is quite complex and involves recognizing the local shape, texture, and temperature of the snow at each location where the fingers contact the snow and then recognizing how the global and local shape of the snow changes as the snow is compressed into a ball. The recognition process begins with the activation of arrays of peripheral receptors embedded in the skin, muscles, tendons, and joints which provide an initial peripheral representation of the sensory input. This initial representation is in the form of spatial and temporal patterns of action potentials that travel along the spinal cord, thalamus, and to various processing stages in cortex where the information is transformed and integrated into a central representation that is matched against stored memories. The sensory information is then used to guide the motor outputs. The entire process is extremely

PSYCHOPHYSICS Tactile Perception Our ability to perceive the world through our hands is rich and diverse and in many ways analogous to the visual and auditory perceptual experience. The richness of touch is eloquently described by Helen Keller, “My world is built of touch-sensations, devoid of physical color and sound; but not without color and sound it breathes and throbs with life. Every object is associated in my mind with tactual qualities which, combined in countless ways, give me a 306

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Psychophysics

sense of power, of beauty, or of incongruity: for with my hand I can feel the comic as well as the beautiful in the outward appearance of things.” (Keller, 1919, p. 7). Two kinds of psychophysical studies have typically been performed. The first are objective perceptions, which include attributes of the stimuli that can be objectively quantified and as such can be objectively rated as being correct or wrong. One example of an objective study is subjects performing a tactile letter recognition task. In these studies, the subjects scan their fingers over an embossed letter and state what letter they feel. In this experiment, the subjects’ performance can be objectively judged by the experimenter to be right or wrong. The second are subjective perceptions for which there is no right or wrong answers. An example of a subjective task is one where subjects feel a textured surface and give a subjective magnitude estimate of the roughness of the surface. The task is subjective because the response that the subjects give cannot be judged as right or wrong by the experimenter.

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of confusions that are made in vision and touch, when the letters are scaled in height to the receptor densities in the two systems, are similar. The performance is independent of whether the letters are indented or scanned across the finger pad or whether the letters are actively or passively presented to the skin (Phillips, Johnson, & Browne, 1983; Vega-Bermudez, Johnson, & Hsiao, 1991). Recent studies (Bensmaia, Denchev, Dammann, Craig, & Hsiao, 2008), however, suggest that although perception may be similar, the acuity for distinguishing complex shapes may be different. In those studies, subjects were asked to discriminate the relative orientations of bars and edges presented to the distal finger pad. They find that while human observers are visually able to discriminate the orientation of bars that differ by fractions of a degree, the comparable threshold in touch is closer to 20 degrees. This tactile orientation discrimination threshold is independent of whether the bars are indented or scanned across the finger pad. These results demonstrate that although the mechanisms of 2D form process are similar between the two systems, they are not identical.

Psychophysics of Two-Dimensional Spatial Form

100

100

90

80

80

60

70

40

60

20

Chance

50

Percent Correct (letter identification)

Percent Correct (gap and grating tasks)

A number of studies have shown that we have a high capacity to discriminate two-dimensional (2D) patterns that are scanned or indented into the distal finger pads. In touch, the threshold for perception of 2D patterns is determined by the density of the receptors innervating the skin. In the finger pad, this translates to a spatial acuity of about 1 mm (Figure 14.1). Furthermore, studies in which subjects are presented with complex 2D patterns suggest that the mechanisms of form processing in vision and touch are similar (see Hsiao, 1998, for a review). The similarity in form processing between the two systems is exemplified by studies where subjects are asked to discriminate embossed letters of the alphabet. Those studies show that the patterns

0 0.0

0.5

1.0 1.5 2.0 Element Width (mm)

2.5

Figure 14.1 Tactile spatial acuity is about 1.0 mm on the distal finger pad measured using three psychophysical tasks: gap detection, grating orientation, and letter recognition.

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Psychophysics of Three-Dimensional Size and Shape While the mechanisms underlying 2D form processing are similar between vision and touch, the mechanisms underlying three-dimensional (3D) form are different. The ability to recognize 3D shapes improves as the number of digits used to contact objects increases (Davidson, 1972; Kappers & Koenderink, 1996), demonstrating that 3D form processing involves the integration of tactile inputs across fingers. Further, the ability of subjects to rapidly recognize common objects without visual input is greater than 96% (Klatzky, Lederman, & Metzger, 1985). Three-dimensional object recognition typically involves dynamic exploratory movements where subjects enclose the object in their hands or systematically moving their fingers around the object (Lederman & Klatzky, 1987). The results from a variety of studies suggest that the mechanisms differ for discriminating small and large objects. The discrimination of small shapes depends on cutaneous inputs and the discrimination of large shapes depends on the integration of cutaneous inputs with inputs from proprioceptors that signal hand conformation. Davidson (1972) showed that subjects are less successful at identifying 3D curved surfaces when tracing them with a single finger than when they contact surfaces simultaneously with multiple fingers, demonstrating that simultaneous input from the different digits is important for object recognition. The importance of proprioceptive input to object recognition is illustrated by the Aristotle illusion. In this illusion, a continuous edge feels like two separate edges (with different orientations) when touched with the fingers crossed

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Somatosensory Processes

Psychophysics of Texture Psychophysical studies show that the perception of texture is multidimensional in nature with the main components captured along three dimensions. The first dimension is determined by the degree that surfaces vary in height and is related to the percepts of rough and smooth. The second dimension is determined by the degree that surfaces conform to pressures normal to the surface and is related to the percepts of hard and soft. The third dimension is determined by surface friction and is related to the percepts of sticky and slippery. These dimensions capture over 90% of the variance in multidimensional scaling studies (Hollins, Faldowski, Rao, & Young, 1993). Studies where surfaces are scanned with the bare finger and with probes that are held in the hand show that the three textural dimensions are similar but not identical in the two scanning modes, which suggests that the neural mechanisms underlying texture perception with a bare finger and probe are different (Yoshioka, Bensmaia, Craig, & Hsiao, 2007).

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Synergy 9 (S9)

Synergy 8 (S8)

Synergy 7 (S7)

Synergy 6 (S6)

Synergy 5 (S5)

Synergy 4 (S4)

Synergy 3 (S3)

Synergy 2 (S2)

Synergy 1 (S1)

(A)

(B)

Postural changes along a synergy

Actual

Constructed for synergies Synergy 1

Synergies 1⫹6 Synergies 1⫹2⫹6 Synergy 1 to 6

Front view

(Benedetti, 1985; Craig, 2003). These results demonstrate that proprioceptive inputs modify the way that cutaneous inputs are interpreted and provide us with a working hypothesis that the coding of 3D shape depends on the interpretation of local cutaneous features in the context of hand conformation and movements. If this hypothesis is correct, then there should be specific hand conformations that are used when grasping shapes. The human hand is a complex structure with multiple joints with more than 20 degrees of freedom. However, the movements of the different joints are not independent and tend to be correlated. A recent study (Thakur, Bastian, & Hsiao, 2008) revealed that during haptic manipulation of everyday objects, subjects evoke hand postures along a limited set of nine patterns of hand motions, referred to as synergies (Figure 14.2). These synergies are consistent across various subjects and across different sets of objects explored. We believe that these synergies provide a reduced basis set over which proprioceptive information from the hand is processed in the cortex. Moreover, these synergies are also used during simpler tasks such as reach-to-grasp, which may provide a simple experimental paradigm to parametrically sample the space of proprioception used during haptic manipulation. The perception of object size also requires the integration of cutaneous and proprioceptive inputs. Berryman, Yau, and Hsiao (2006) showed that the perceived size of an object is independent of contact force or contact area and is based on an integration of information about the compliance of the objects that is derived from the cutaneous inputs with information of the spread between the fingers that is derived from proprioceptive inputs.

Side view

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Figure 14.2 A: Postures along the nine synergies. Each row illustrates the postural changes as the hand moves along a particular synergy. B: Examples of how synergies are combined to form a pinch grip between digits 1 and 2. Note: Column 1 is the actual pinch movement. Columns 2, 3, 4 are the contribution of synergy 1 alone, synergy 1 and 6, and synergies 1, 2 and 5. Column 5 is the sum of the first 6 synergies. From “Multi-Digit Movement Synergies of the Human Hand in an Unconstrained Haptic Exploration Task,” by P. H. Thakur, A. J. Bastian, and S. S. Hsiao, 2008, Journal of Neuroscience, Volume 28, p. 1275. Reprinted with permission.

PERIPHERAL RECEPTORS The peripheral afferent system consists of 13 types of afferents traditionally classified based on the class of information they carry (Table 14.1). These include four mechanoreceptive afferents, four proprioceptive afferents,

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Peripheral Receptors 309 Table 14.1

Peripheral receptor types.

Receptor

Fiber Group

Receptors Respond To

Function

Cutaneous, low-threshold mechanoreceptors Merkel (SAI)

Aβ

Steady deformation and motion (10x greater)

Form perception (e.g., Braille); texture perception (roughness, hardness)

Ruffini (SAII)

Aβ

Skin stretch

Perception of skin stretch, hand shape, force acting on an object in the hand

Meissner (RA1)

Aβ

Skin movement (glabrous skin only)

Perception of local movement, detection of slip, grip control

Pacinian (RAII)

Aβ

High frequency vibration

Perception of distant events through objects held or grasped in the hand

I

Muscle length and velocity

Joint angle

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two thermoreceptive afferents (for cooling and warming, respectively), two nociceptive or pain afferents, and one afferent related to itch. The receptors underlying temperature, pain, and itch send their outputs to the cortex via axons that are small, approximately 1 to 2 microns in diameter that are either unmyelinated (warm, burning pain, and itch) or myelinated (cold, pricking pain). The neural mechanisms underlying inputs from these afferents are complex and will not be discussed further in this chapter. Peripheral Receptors Underlying Mechanoreception The four mechanoreceptive afferents are further classified based on their response to a sustained indentation. Two types of mechanoreceptors respond with a brief burst followed by a sustained discharge that decays gradually over time and are named slowly adapting types I and II (SAI and SAII). The other two types are characterized by responding with a transient burst of impulses to the onset and offset of the stimulus that decays rapidly to zero and hence are rapidly adapting and are called RA and Pacinian (PC). Extensive neurophysiological studies in humans and nonhuman primates of these afferents (except the SAII afferents, which do not exist in nonhuman primates) have

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shown that there are minimal interspecies differences in their response properties. SAI afferents branch repeatedly toward their end, lose their myelin, and end in several closely packed dermal ridges at the base of the epidermis where the branch endings are enclosed by epidermal merkel cells (Iggo & Andres, 1982). Although, the merkel cells contact the afferent ending via glutamergic synapses, it is unclear what role they play in the transduction of mechanical stimuli. Action potentials in the afferents appear to arise due to mechanosensitive channels at the tips of the axonal endings (Diamond, Mills, & Mearow, 1988; Ogawa, 1996). SA1 afferents have high innervation density and small receptive field (RF) sizes (about 100 afferents per cm2 at the fingertip for both man and monkey, diameters of about 2.5 mm; see Johnson, 2001, for a review). As a result they transmit a high-resolution spatial image of the stimulus indenting the skin (Figure 14.3) and are able to resolve spatial details of stimuli down to about 1.0 mm (Phillips & Johnson, 1981a). Indenting the RF of an SAI afferent reveals a suppressive region surrounding the excitatory core of the RF (Vega-Bermudez & Johnson, 1999). Surround suppression provides feature selectivity to edges in the neuronal responses. A given stimulus elicits differential responses depending on the relative strengths of excitation and suppression encountered in different

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Figure 14.3 Spatial event plots of peripheral SA1 (Top), RA (Middle), and PC (Bottom) neurons for embossed letters 6.0 mm high. Note: SA1 afferents show high spatial acuity, RA show poorer acuity and PCs are unable to discriminate between the different letters. From Phillips et al. (1988). Reprinted with permission.

directions. The suppression also makes the neuron insensitive to uniform indentation. Unlike the sensory cortical neurons, where surround inhibition is thought to arise due to lateral inhibition, surround inhibition in SAI afferents is caused by skin mechanical effects, which render the neuron sensitive to specific component of strain or a closely related variable (Dandekar, Raju, & Srinivasan, 2003; Phillips & Johnson, 1981b; Sripati, Bensmaia, & Johnson, 2006). RA afferents also branch repeatedly as they approach the skin and end in broadly stacked discs in the Meissner ’s corpuscles. The Meissner ’s corpuscles occur in the dermal pockets between the sweat glands and the adhesive ridges, thus located as close to the epidermis as possible (Guinard, Usson, Guillermet, & Saxod, 2000). Their proximity to the surface, together with their extremely large density, makes them very sensitive to minute deformations of the skin. The effective operating range of indentations for an RA afferent is 4 to 400 microns, whereas the equivalent range for SAI afferents is 15 to 1,500 microns (Blake, Johnson, & Hsiao, 1997; Johansson & Vallbo, 1979). Although extremely sensitive, RAs resolve the spatial details of stimuli poorly as compared to SAI afferents (Figure 14.3), partly due to larger receptive field sizes owing to the greater degree of convergence and divergence between the Meissner corpuscles and the afferent fiber to which they project, which results in these afferents having relatively uniform receptive field profiles. In addition, the filtering provided by the stacks of corpuscles renders the RAs insensitive to sustained deformations. Thus, RA responses are best suited to signal responses related to small changes in inputs, such as those encountered in low amplitude, low frequency vibrations (flutter), or very fine slips of objects held in the hand. The second class of rapidly adapting mechanoreceptors end in the deeper layers of dermis in bodies comprising of concentric layers of fluid filled sacs known as Pacinian corpuscles (Bell, Bolanowski, & Holmes, 1994). The multiple layers concentric shells act as a high pass filter making

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PC afferents insensitive to static indentation but extremely sensitive to high frequency vibrations (Talbot, DarianSmith, Kornhuber, & Mountcastle, 1968). These afferents can detect indentations as small as 1 nm applied directly to the corpuscle or 10nm applied to the skin (Brisben, Hsiao, & Johnson, 1999). Because of their extreme sensitivity, the receptive field boundaries for PC afferents are hard to define and they sometimes encompass the entire hand or arm. Because of their large receptive fields and extreme sensitivity to high frequencies, they are effective at transmitting vibrations through hand-held objects and play a dominant role in transmitting information regarding surfaces explored through hand-held probes. The SAII afferents have larger receptive field sizes than SAI afferents. Both SAI and SAII respond to forces orthogonal and parallel to the skin surface. However, between the two, SAI are more sensitive to orthogonal forces, while SAII are sensitive to forces parallel to the skin surface (Macefield, Hager-Ross, & Johansson, 1996). This makes SAII afferents insensitive to simple indentations and more sensitive to skin stretch as compared to SAI. Due to their larger receptive field sizes and sensitivity to skin stretch, SAII afferents are thought to transmit a dynamic neural image of hand conformation. A striking peculiarity of SAII afferents is their absence in some of the mammals. Studies have documented the presence of SAII afferents in cats and humans, but they are absent in nonhuman primates and mice. In cats, SAII afferent axons terminate in Ruffini endings. However in humans, Ruffini endings have been reported only in the bed of the fingernails and as such there is at present uncertainty as to the receptor ending for these afferent fibers. Evidence from studies that combine psychophysics and neurophysiology suggest that each of these afferent fibers play a different role in perception. SAI afferents are the spatial system. They are the only afferents with a spatial acuity that is sufficient to support the psychophysical studies described previously. Further, in a series of studies (Figure 14.4), Hsiao and Johnson showed that only these afferents can account for subjective estimates of the roughness of surfaces when scanned with the bare finger. These studies show that roughness is coded by a central mechanism that computes the spatial variation in firing rates among the population of SAI afferents separated by about 2.0 mm. Studies by LaMotte and his colleagues suggest that SAI afferents are also responsible for the perception of surface hardness and softness (Srinivasan & LaMotte, 1995). The working hypothesis is that hardness is related to the overall spatial pattern of activation of the SAI afferents. The peripheral neural coding of stickiness has not been investigated. The SAI system is analogous to the parvocellular system in the visual system (Hsiao, 1998).

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Note: Results from four studies showing the relationship between psychophysical study where subjects were asked to give subjective magnitude estimates of the roughness of surfaces and neurophysiological recordings from peripheral SAI afferents of the monkey. The four studies are labeled A–D. At the bottom of each graph is a picture of the stimulus pattern.

Plotted in each graph are the normalized psychophysical estimates and the mean spatial variation in firing rates of SAI afferents separated by about 2.0 mm. The correlations for all four studies are greater than .97. a Based on Blake, Hsiao, and Johnson (1997). b Based on Connor and Johnson (1992). c Based on Connor, Hsiao, Phillips, and Johnson (1990). d Based on Yoshioka, Gibb, Dorsch, Hsiao, and Johnson (2001).

The RA afferents are the motion system. The brief transient onset and offset responses to transient stimuli make them particularly sensitive to low frequency vibrations (called flutter) and the detection of moving stimuli (Gardner & Palmer, 1990; Talbot et al., 1968). In addition, studies in humans suggest that these afferents play an important role in signaling slip on the

skin, which is particularly important for adjusting grip force when grasping objects (Westling & Johansson, 1987). As stated earlier, the PC system is the vibration system and it plays an important role in using tools and signaling information corresponding texture information with tools. The SAII afferents signal local skin stretch and

Figure 14.4 Peripheral neural coding of roughness.

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may be important for signaling joint angle (Edin & Abbs, 1991). Peripheral Receptors Underlying Proprioception The four types of proprioceptive afferents are classified based on their targets in the periphery. Two of them, namely the group Ia afferents and group II afferents terminate in the primary and secondary muscle spindle receptors. The other two terminate in the Golgi tendon organs and receptors located in the joints. Muscle spindles are located in the fleshy part of the muscle and innervate 3 to 10 intrafusal muscle fibers. They are positioned in parallel to the extrafusal muscle fibers, such that the ends of the intrafusal muscle fibers make lateral connection with the perimyesium of the muscle fascicle. Since the length of muscle spindles is constant across different muscles, a change in muscle length induces a stretch in the spindle receptors that is proportional to the relative change in the length or velocity of the muscle fascicle irrespective of the length or size of the muscle (Proske, Wise, & Gregory, 2000). Thus, spindle afferents signal the relative change in the length and velocity of the muscle during movements. Unlike spindle afferents, which are placed in parallel with the muscles, Golgi tendon receptors are arranged in series with the muscle fascicles. Over 90% of the tendon receptors are located at the musculotendinous junctions, while the remainder are located in the tendons (Barker, Emonet-Denand, Laporte, Proske, & Stacey, 1973). Due to their serial location, tendon receptors are stretched in response to muscle fascicle contraction, and the tendon afferents are sensitive to the force generated by the contracting muscle fascicles. Together with the spindle afferents, the tendon afferents form a complementary signaling system. Muscle contraction causes an increase in force that results in the activation of the tendon afferent simultaneously, the spindle afferents fall silent due to the decrease in the muscle length. Similarly, when the muscle relaxes, spindle afferents are activated due to the lengthening of the muscle, while the tendon afferents reduce their firing due to the reduction in the muscular force. Joint afferents include both small and large diameter afferents that innervate all intra-articular structures such as ligaments, discs, and menisci. The slowly adapting type of joint afferents terminate in Golgi organs or Ruffini endings, while the rapidly adapting type terminate in Pacinian or smaller Paciniform endings. As compared to the cutaneous mechanoreceptors, joint receptives are less sensitive to light cutaneous stimuli applied to the skin but respond well to compressive forces. Golgi afferents respond to compressive forces normal to the capsular surface, while the Ruffini afferents respond to planar forces (Grigg & Hoffman, 1982).

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ASCENDING PATHWAYS The information from these peripheral afferent fibers ascends along two main pathways to the cortex. One pathway is the spinalthalamic tract that carries information from the small diameter fibers and conveys information from the nociceptive, thermoreceptive, and itch afferents. The other pathway is the dorsal-column medial leminiscal pathway that conveys information from the large diameter afferents, namely the mechanoreceptive and proprioceptive afferents. Neurophysiological studies suggest that intermediate neurons along this pathway function mainly as relay stations and that there is little convergence or divergence of information. Those studies show that there is a tight correspondence between the firing of neurons in the dorsal column nuclei (DCN) and their peripheral afferent counterparts (Coleman, Zhang, & Rowe, 2003; Gynther, Vickery, & Rowe, 1995) with afferent fibers able to directly generate spikes in DCN neurons. Similarly, studies in neurons in the ventroposterior lateral neurons of the thalamus (VPL) suggest that these neurons also have small receptive fields that suggest that there is little convergence of afferent input in VPL as well (Wang, Merzenich, Sameshima, & Jenkins, 1995). Thus, the first processing station of tactile information appears to be in areas 3a, 3b, 1, and 2, which are the four areas that comprise primary somatosensory (SI) cortex (Figures 14.5, 14.6, and 14.7).

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Figure 14.5 Block diagram of the anatomical pathways of the somatosensory cortex.

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(B) Postcentral gyrus (first somatosensory cortex) Intralaminar and posterior groups of thalamic nuclei

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Figure 14.6 Ascending pathways of the dorsal-column-medial leminiscal pathway (A) that carries information about mechanoreceptive and proprioceptive inputs and the spinal thalamic pathway (B) that carries information from pain, temperature, and itch afferents.

CORTICAL PROCESSING OF TACTILE INFORMATION Primary Somatosensory Cortex The neural responses of the SI cortex, which is the main target of thalamic VPL neurons, are in the initial stages of being understood. Neurons in 3a receive their inputs from the shell region surrounding VPL and respond primarily to deep input, which suggests that 3a is responsible for processing proprioceptive information, particularly joint position and joint velocity (Gardner, 1988). This area may play an important role in sensory prosthetic devices where electrical stimulation is used to signal limb and hand position. Perhaps the most extensively studied region of SI is area

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Note: From “Somatic Sensation” (p. 775), in Fundamental Neuroscience Academic, M. J. Zigmond, F. E. Bloom, S. C. Landis, J. L. Roberts, and L. R. Squire (Eds.), 1999, San Diego, CA: Academic Press. Reprinted with permission.

3b, which receives its primary input from the core region of the thalamic neurons in VPL. Area 3b is an important processing stage for tactile information and is responsible for extracting fundamental features of the cutaneous inputs. This is demonstrated in ablation studies in which animals are unable to perform tactile tasks that require cutaneous inputs, including texture and form discrimination tasks (Randolph & Semmes, 1974). The responses of neurons in 3b suggest that it may function like neurons in VI cortex. As shown in Figure 14.8, neurons in 3b typically have receptive fields composed of a central excitatory region flanked by one or more inhibitory regions which, like simple cells in the visual system, provide these neurons with feature selectivity to spatial stimuli. As shown, the excitatory and

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Figure 14.7 A: Lateral view of the brain showing the locations of primary (SI) and secondary (SII) cortex. B: Cross section of the postcentral gyrus showing the locations of the four areas that make up the SI cortex. (C) Projection pattern from the ventrolateral complex of the thalamus to SI cortex. (A) Area 3b

n⫽89 (36%) (D)

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Linear receptive fields of neurons in area 3b of SI

Note: Black represents excitatory regions, white inhibitory regions. From “Structure of Receptive Fields in Area 3b of Primary Somatosensory Cortex in the Alert Monkey,” by J. J. DiCarlo, K. O. Johnson, and S. S. Hsiao, 1998, Journal of Neuroscience, 18, pp. 2626–2645. Adapted with permission.

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Note: From “Somatic Sensation” (p. 781), in Fundamental Neuroscience Academic, M. J. Zigmond, F. E. Bloom, S. C. Landis, J. L. Roberts, and L. R. Squire (Eds.), 1999, San Diego, CA: Academic Press. Reprinted with permission.

inhibitory subregions of the receptive field are oriented and, as such, many neurons in 3b show selectivity to oriented bars and edges (Figure 14.9). Neurometric functions that were computed by estimating the population response to oriented bars that differ by different degrees suggest that neurons in 3b have thresholds of about 20 degrees which is similar to human psychophysical thresholds (Bensmaia, Hsiao, Denchev, Killebrew, & Craig, 2008; Figure 14.10). These results suggest that cutaneous form processing begins with the extraction of information about orientation in area 3b of somatosensory cortex. Neurons in 3b have an additional inhibitory component that lags behind the initial response. Figure 14.11 illustrates the spatial-temporal receptive field of typical tactile neurons and demonstrate that the initial spatial response is replaced by in-field inhibition that inhibits the response after about 34 to 40 ms. This replacing inhibition is thought to play an important role in providing these neurons with velocity invariance to scanned stimuli (DiCarlo & Johnson, 1999). Neurons in area 1 have responses that in many ways are similar to those found in area 3b. For example the responses in both areas are almost exclusively cutaneous,

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Figure 14.9 Orientation tuning properties of area 3b neuron. Note: Top left shows raster plots of the response of a neuron to scanned and indented bars. Bottom left shows that the orientation tuning is similar for scanned and indented bars. From “The Representation of Stimulus Orientation in the Early Stages of Somatosensory Processing,” by S. J. Bensmaia, P. V. Denchev, J. F. Dammann III, J. C. Craig, and S. S. Hsiao, 2008, Journal of Neuroscience, 28, pp. 776–786. Adapted with permission.

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Figure 14.11 Spatiotemporal Receptive fields of peripheral SAI, RA and cortical neurons in areas 3b and 1. Note: Shows the evolution of areas of excitation and inhibition over time following stimulus onset. There is a initial spatial response followed by a period of replacing inhibition. The inhibition in the initial SAI response is due to skin mechanics. From “Spatiotemporal Receptive Fields of Peripheral Afferents and Cortical Area 3b and 1 Neurons in the Primate Somatosensory System,” by A. P. Sripati, T. Yoshioka, P. Denchev, S. S. Hsiao, S. S. and K. O. Johnson, 2006, Journal of Neuroscience, 26, pp. 2101–2114. Reprinted with permission.

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and neurons in area 1 have linear receptive fields consisting of excitatory and inhibitory subregions followed by replacing inhibition. Further, neurons in area 1 show similar tuning to orientation as area 3b neurons (Bensmaia, Denchev, et al., 2008). However, there are several studies suggesting that it plays a different role in perception. First, animals seem to show different behavioral deficits following ablation of area 1. In contrast to area 3b where animals are devastated by the loss of area 3b input, behavioral effects following the loss of area 1 are relatively mild and seem to mainly affect the ability of animals to detect changes in texture. Furthermore, neurons in area 1 have larger RFs that span multiple fingers and tend to have larger inhibitory areas. Perhaps most significantly, area 1 neurons have responses that are poorly explained by linear mechanisms.

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These findings suggest that area 1 is at a higher stage of processing than 3b and plays a role in extracting information about more complex spatially invariant features of tactile stimuli. Area 2 is the first place where proprioceptive and mechanoreceptive inputs converge. Although there have been relatively few studies of area 2, our current understanding suggests that it plays an important role in extracting features related to 3D object recognition. Animals who have area 2 ablated are unable to discriminate large shapes that require integration of cutaneous and proprioceptive inputs (Murray & Mishkin, 1984), and neurophysiological and imaging studies suggest that neurons in area 2 respond selectively to objects that differ in 3D shape (Bodegard, Geyer, Grefkes, Zilles, & Roland, 2001; Iwamura & Tanaka, 1978). Parietal Operculum (SII cortex) There are two main projections from SI cortex. One is directed caudally to areas 5 and 7, and the other is directed dorsally to the SII cortex, which is located in the upper bank of the parietal operculum. Several studies suggest that areas 5 and 7 may not be related directly to tactile discrimination. Murray and Mishkin (1984) demonstrated

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that animals with lesions of these areas show only mild deficits in making tactile discrimination judgments, suggesting that areas 5 and 7 may not be directly involved in object shape discrimination. An alternative hypothesis is that these areas are involved in the perception of the immediate extra-personal space and are responsible for directing attention to where the body is located in space and with guiding the body and hand to targets in that space (Gardner et al., 2007, Gardner, Ro, Babu, & Ghosh, 2006; Jeannerod, Arbib, Rizzolatti, & Sakata, 1995; Mountcastle, Lynch, Georgopoulos, Sakata, & Acuna, 1975; Sakata & Iwamura, 1978). The other projection is directed ventrally toward the second somatosensory cortex (SII). Although SII also receives a direct projection from VPL, there is strong evidence suggesting that it is also important for object discrimination and is the next processing stage beyond SI cortex. As with area 3b, animals with SII ablated are unable to discriminate the shapes and textures of objects. Neurophysiological studies of SII show that it is not a single area but, like SI cortex, is composed of a minimum of three areas in monkeys and four areas in humans (Figure 14.12; Eickhoff, Schleicher, Zilles, & Amunts, 2006; Fitzgerald, Lane, Thakur, & Hsiao, 2004; Hinkley, Krubitzer,

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Figure 14.12 Somatotopic map of the SII cortex. Note: SII is composed of three fields that contain three complete maps of the body—an anterior field (SIIa), central field (SIIc), and a posterior field (SIIp). The left graph shows an unfolded map of the upper bank of the lateral sulcus (UBLS). From “Receptive field properties

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of the macaque second somatosensory cortex: Evidence for multiple functional representations,” by P. J. Fitzgerald, J. W. Lane, P. H. Thakur, and S. S. Hsiao, 2004, Journal of Neuroscience, 24, p.11202. Reprinted with permission.

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Nagarajan, & Disbrow, 2006). While the roles and functions of these areas in SII are unknown, evidence from studies in nonhuman primates suggests that they play different roles in perception. Two areas, namely the anterior and posterior parts of SII (SIIa and SIIp), have neurons that respond well to both cutaneous and proprioceptive inputs, which suggests that these areas are involved in 3D object perception. In contrast, the central area of SII (SIIc) contains mainly neurons that respond to cutaneous inputs, suggesting that it may be important for processing inputs related to 2D form and texture (Fitzgerald et al., 2004). Neurons in SII tend to have much larger and more elaborate receptive fields than neurons in the SI cortex and in contrast to area 3b and 1, where 50% to 70% of the neurons show orientation tuned responses (Bensmaia, Denchev, et al., 2008; Hsiao, Lane, & Fitzgerald, 2002), only about 20% to 30% of the neurons

show orientation tuning with more tuned neurons in SIIc (Fitzgerald, Lane, Thakur, & Hsiao, 2006b). Showing that there are fewer orientation-tuned neurons, however, does not imply that SII is less important for processing object shape. The RF structures of SII neurons are particularly intriguing. Although some neurons have simple receptive fields confined to a single finger pad, most neurons have receptive fields with complex combinations of finger pads with some pads showing orientation tuned responses, others having untuned excitatory responses and still others having untuned inhibitory responses (Figure 14.13; Fitzgerald, Lane, Thakur, & Hsiao, 2006a). These receptive fields are not spatially homogeneous, and we believe that they are well suited for coding features of large objects that span multiple fingers and may underlie the neural representation of 3D shape (Fitzgerald, Lane, Thakur, & Hsiao, 2006b; Haggard, 2006).

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Figure 14.13 (Figure C.24 in color section) Receptive fields of neurons in the SII cortex. Note: The receptive fields of neurons that had one or more tuned pads (left). Each set of squares represents the RF of a single neuron. Within each set are the responses from the distal, middle, and proximal pads of digits 2 through 5 (see top left neuron for an example). Red are pads that showed excitation, blue-inhibition. Pads that were orientation tuned have a bar oriented at the preferred orientation. Right graph shows the raster plot from a single SII neuron, illustrating that the tuning is similar across

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pads. From “Receptive Field (RF) Properties of the Macaque Second Somatosensory Cortex: RF Size, Shape, and Somatotopic Organization,” by P. J. Fitzgerald, J. W., Lane, P. H., Thakur, and S. S. Hsiao, 2006a, Journal of Neuroscience, 26, p. 6490; and “Receptive Field Properties of the Macaque Second Somatosensory Cortex: Representation of Orientation across Finger Pads,” by P. J. Fitzgerald, J. W. Lane, P. H. Thakur, and S. S. Hsiao, 2006b, Journal of Neuroscience 26, p. 6475. Reprinted with permission.

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There are two additional lines of evidence supporting the hypothesis that SII is important for 2D and 3D shape processing. One comes from studies showing that the orientation tuning of neurons with receptive fields that contain multiple tuned pads are similar across pads, suggesting that oriented edges of objects that span multiple fingers are integrated by these neurons. The others are studies showing that most neurons in SII show position invariant responses to oriented edges placed on a single finger pad (Thakur, Fitzgerald, Lane, & Hsiao, 2006). This is important because invariant responses are a hallmark of higher stages of sensory processing where the neural representations are matched to stored memories. Processing of Touch beyond the SII Cortex The processing of tactile information beyond SII is not well understood: however, other areas of the cortex have been shown to be important for processing information about form (Prather, Votaw, & Sathian, 2004) and vibration (Romo & Salinas, 2003). Of particular importance are studies by Romo and his colleagues who have systematically mapped the neural pathways that underlie the decision process in animals making vibratory discriminations. Further, neural imaging and studies using transmagnetic stimulation (TMS) in humans suggest that other areas of the cortex may play important roles in processing tactile form and texture. Candidate areas include the anterior parts of the intraparietal cortex (IPA), supramarginal gyrus (Bodegard et al., 2001), right intraparietal sulcus (pIPS; Stilla, Deshpande, LaConte, Hu, & Sathian, 2007), and possibly visual areas (Merabet et al., 2004; Sadato et al., 1996; Zangaladze, Epstein, Grafton, & Sathian, 1999).

EFFECTS OF ATTENTION A discussion of the mechanism underlying tactile perception would be incomplete without reference to the perception of touch in relation to higher cognitive functions. Several studies have now shown selective attention has a major affect on the way that tactile stimuli are perceived. Furthermore, studies in monkeys have shown that neurons in the thalamus, and primary and especially secondary somatosensory cortex are affected by selective attention (see Hsiao & Vega-Bermudez, 2002, for a review). In the SII cortex, about 90% of the neurons are affected by attention. Attention effects on firing rate are both additive and multiplicative (Chapman & Meftah, 2005; Sripati & Johnson, 2006). Furthermore, attention affects not only the firing rate but also the degree of synchronous firing across the population of neurons (Roy, Steinmetz, Hsiao,

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Johnson, & Niebur, 2007; Steinmetz et al., 2000; Figure 14.14). Studies in humans using ECoG recordings suggest that the effects are particularly pronounced in the high gamma range (Ray, Niebur, Hsiao, Sinai, & Crone, 2008) and that neurons in the frontal lobe play a role in the cognitive but not the sensory aspects of touch. The results suggest that sensory inputs are continuously being modulated by higher cognitive functions as they are processed along the pathways leading to sensation and perception.

SUMMARY Tactile perception is rich and multidimensional in nature. It is through the sense of touch that we directly experience and interact with the environment around us. For example, consider what happens when you reach down to pick up a baseball. Initially, the hand is guided by visual inputs that tells the somatosensory system where the ball is in relation to your body. This information is used to guide the arm to the correct location of the ball. As the hand reaches out to grasp the ball, the synergies between the muscles, nerves, and joints define a set of movements that shape the hand in a way that is appropriate for grasping it securely. As the fingers close around the ball, contact at each of the finger pads activates all of the mechanoreceptors and thermoreceptors in the skin. The nociceptors are silent as are the itch receptors because of a lack of an adequate stimulus. The thermoreceptors provide information about the temperature while the SAI afferents provide an isomorphic representation of the local spatial patterns of stimulation. The activity from the SAI afferent discharge is used by cortical circuits in areas 3b, 1, and SII that extract information about the local features of the surface at the points of contact. As the ball is lifted, microslips of the ball relative to the skin are detected by the RA afferents that act as feedback and result in increases in grip force until it is above the safety margin to prevent slipping. The PC afferents are firing but most likely provide no useful information for the task and as such the inputs from these afferents are suppressed in the cortex. Meanwhile, inputs from the proprioceptive inputs from the skin, joints, and muscles provide information about hand conformation. These inputs from the different digits are combined, perhaps in area 3a, to form representations of the synergistic movements of the hand and digits. The cutaneous input and proprioceptive inputs are then integrated by neurons in area 2 (Bodegard et al., 2001; Ostry & Romo, 2001). The processing then proceeds to the three areas that make up SII cortex where information about local features from the different fingers are combined to form representations of the size and shape of the ball. Neurons in SII then project to

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References 319

X (A) Tactile hit Tactile miss Visual task

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Figure 14.14 (Figure C.25 in color section) Effects of attention on the responses of neurons in SII cortex. Note: Left graph shows the change in firing rate in an animal performing a tactile letter discrimination task (letter is 6.0 mm high, scanned at 20 mm/sec across the finger). Bottom solid line represents the rate when the animals attention is distracted away from the letter and is performing a visual task, Middle dashed line and top solid line are the rates evoked when the animal performed the tactile task and correctly (hit) or incorrectly (miss) identified the letter. Right graph shows the change in synchronous firing between pairs of neurons recorded simultaneously while

multimodal areas in the parietal, occipital, and inferotemporal cortex where the tactile representations are compared and matched against previously stored representations of objects. It is after the match is made with the stored memories that the observer perceives the shape and texture of the baseball.

REFERENCES Barker, D., Emonet-Denand, F., Laporte, Y., Proske, U., & Stacey, M. J. (1973). Morphological identification and intrafusal distribution of the endings of static fusimotor axons in the cat. Journal of Physiology, 230, 405–427.

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Coincidences s⫺1

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the animal performed the visual task (right) or tactile task (left). Blue dots are when the neurons fired synchronously. Below each plot are the firing rates of the two neurons and a plot or the expected (red) and actual (blue) number of synchronous events one would expect based on the changes in firing rate. From “Effects of Selective Attention of Spatial Form Processing in Monkey Primary and Secondary Somatosensory Cortex,” by S. S. Hsiao, D. M. O’Shaughnessy, and K. O. Johnson, 1993, Journal of Neurophysiology, 70, p. 446; and “Attention Modulates Synchronized Neuronal Firing in Primate Somatosensory Cortex,” by P. N. Steinmetz et al., 2000, Nature, 404, p. 187.

Bensmaia, S. J., Denchev, P. V., Dammann, J. F., III, Craig, J. C., & Hsiao, S. S. (2008). The representation of stimulus orientation in the early stages of somatosensory processing. Journal of Neuroscience, 28, 776–786. Berryman, L. J., Yau, J. M., & Hsiao, S. S. (2006). Representation of object size in the somatosensory system. Journal of Neurophysiology, 96, 27–39. Blake, D. T., Hsiao, S. S., & Johnson, K. O. (1997). Neural coding mechanisms in tactile pattern recognition: The relative contributions of slowly and rapidly adapting mechanoreceptors to perceived roughness. Journal of Neuroscience, 17, 7480–7489. Blake, D. T., Johnson, K. O., & Hsiao, S. S. (1997). Monkey cutaneous SAI & RA responses to raised and depressed scanned patterns: Effects of width, height, orientation, and a raised surround. Journal of Neurophysiology, 78, 2503–2517. Bodegard, A., Geyer, S., Grefkes, C., Zilles, K., & Roland, P. E. (2001). Hierarchical processing of tactile shape in the human brain. Neuron, 31, 317–328.

Bell, J., Bolanowski, S. J., & Holmes, M. H. (1994). The structure and function of pacinian corpuscles: A review. Progress in Neurobiology, 42, 79–128.

Brisben, A. J., Hsiao, S. S., & Johnson, K. O. (1999). Detection of vibration transmitted through an object grasped in the hand. Journal of Neurophysiology, 81, 1548–1558.

Benedetti, F. (1985). Processing of tactile spatial information with crossed fingers. Journal of Experimental Psychology: Human Perception and Performance, 11, 517–525.

Chapman, C. E., & Meftah, E. (2005). Independent controls of attentional influences in primary and secondary somatosensory cortex. Journal of Neurophysiology, 94, 4094–4107.

Bensmaia, S., Hsiao, S. S., Denchev, P., Killebrew, J. H., & Craig, J. C. (2008). The tactile perception of stimulus orientation. Somatosensory and Motor Research 25, 49–59.

Coleman, G. T., Zhang, H. Q., & Rowe, M. J. (2003). Transmission security for single kinesthetic afferent fibers of joint origin and their target cuneate neurons in the cat. Journal of Neuroscience, 23, 2980–2992.

8/17/09 2:09:46 PM

320

Somatosensory Processes

Connor, C. E., Hsiao, S. S., Phillips, J. R., & Johnson, K. O. (1990). Tactile roughness: Neural codes that account for psychophysical magnitude estimates. Journal of Neuroscience, 10, 3823–3836. Connor, C. E., & Johnson, K. O. (1992). Neural coding of tactile texture: Comparisons of spatial and temporal mechanisms for roughness perception. Journal of Neuroscience, 12, 3414–3426. Craig, J. C. (2003). The effect of hand position and pattern motion on temporal order judgments. Perception and Psychophysics, 65, 779–788. Dandekar, K., Raju, B. I., & Srinivasan, M. A. (2003). 3-D finite-element models of human and monkey fingertips to investigate the mechanics of tactile sense. Journal of Biomechanical Engineering, 125, 682–691. Davidson, P. W. (1972). Haptic judgments of curvature by blind and sighted humans. Journal of Experimental Psychology, 93, 43–55. Diamond, J., Mills, L. R., & Mearow, K. M. (1988). Evidence that the merkel cell is not the transducer in the mechanosensory merkel cellneurite complex. Progress in Brain Research, 74, 51–56. DiCarlo, J. J., & Johnson, K. O. (1999). Velocity invariance of receptive field structure in somatosensory cortical area 3b of the alert monkey. Journal of Neuroscience, 19, 401–419. DiCarlo, J. J., Johnson, K. O., & Hsiao, S. S. (1998). Structure of receptive fields in area 3b of primary somatosensory cortex in the alert monkey. Journal of Neuroscience, 18, 2626–2645. Edin, B. B., & Abbs, J. H. (1991). Finger movement responses of cutaneous mechanoreceptors in the dorsal skin of the human hand. Journal of Neurophysiology, 65, 657–670. Eickhoff, S. B., Schleicher, A., Zilles, K., & Amunts, K. (2006). The human parietal operculum: Pt. I. Cytoarchitectonic mapping of subdivisions. Cerebral Cortex, 16, 254–267. Fitzgerald, P. J., Lane, J. W., Thakur, P. H., & Hsiao, S. S. (2004). Receptive field properties of the macaque second somatosensory cortex: Evidence for multiple functional representations. Journal of Neuroscience, 24, 11193–11204. Fitzgerald, P. J., Lane, J. W., Thakur, P. H., & Hsiao, S. S. (2006a). Receptive field (RF) properties of the macaque second somatosensory cortex: RF size, shape, and somatotopic organization. Journal of Neuroscience, 26, 6485–6495. Fitzgerald, P. J., Lane, J. W., Thakur, P. H., & Hsiao, S. S. (2006b) Receptive field properties of the macaque second somatosensory cortex: Representation of orientation on different finger pads. Journal of Neuroscience, 26, 6473–6484.

Gynther, B. D., Vickery, R. M., & Rowe, M. J. (1995). Transmission characteristics for the 1:1 linkage between slowly adapting type II fibers and their cuneate target neurons in cat. Experimental Brain Research, 105, 67–75. Haggard, P. (2006). Sensory neuroscience: From skin to object in the somatosensory cortex. Current Biology, 16, R884–R886. Hendry, S. H. C., Hsiao, S. S., & Bushnell, M. C. (1999). Somatic sensation. In M. J. Zigmond, F. E. Bloom, S. C. Landis, J. L. Roberts, & L. R. Squire (Eds.), Fundamental neuroscience (pp. 761–789). San Diego, CA: Academic Press. Hinkley, L. B., Krubitzer, L., Nagarajan, S., & Disbrow, E. A. (2006). Sensorimotor integration in S2, PV, and the parietal rostroventral areas of the human Sylvian fissure. Journal of Neurophysiology, 97, 1288–1297. Hollins, M., Faldowski, R., Rao, S., & Young, F. (1993). Perceptual dimensions of tactile surface texture: A multidimensional-scaling analysis. Perception and Psychophysics, 54, 697–705. Hsiao, S. S. (1998). Similarities between touch and vision. In J. W. Morley (Ed.), Neural aspects of tactile sensation (pp. 131–165). Amsterdam: Elsevier. Hsiao, S. S., Lane, J. W., & Fitzgerald, P. (2002). Representation of orientation in the somatosensory system. Behavioural Brain Research, 135, 93–103. Hsiao, S. S., O’Shaughnessy, D. M., & Johnson, K. O. (1993). Effects of selective attention of spatial form processing in monkey primary and secondary somatosensory cortex. Journal of Neurophysiology, 70, 444–447. Hsiao, S. S., & Vega-Bermudez, F. (2002). Attention in the somatosensory system. In N. J. Nelson (Ed.), The somatosensory system: Deciphering the brain’s own body image (pp. 197–217). Boca Raton: CRC Press. Iggo, A., & Andres, K. H. (1982). Morphology of cutaneous receptors. Annual Review of Neuroscience, 5, 1–31. Iwamura, Y., & Tanaka, M. (1978). Postcentral neurons in hand region of area 2: Their possible role in the form discrimination of tactile objects. Brain Research, 150, 662–666. Jeannerod, M., Arbib, M. A., Rizzolatti, G., & Sakata, H. (1995). Grasping objects: The cortical mechanisms of visuomotor transformation. [Review]. Trends in Neurosciences, 18, 314–320.

Foulke, E. (1991). Braille. In M. A. Heller & W. Schiff (Eds.), The psychology of touch (pp. 219–233. Hillsdale, NJ: Erlbaum.

Johansson, R. S., & Vallbo, Å. B. (1979). Detection of tactile stimuli: Thresholds of afferent units related to psychophysical thresholds in the human hand. Journal of Physiology, 297, 405–422.

Gardner, E.P. (1988). Somatosensory cortical mechanisms of feature detection in tactile and kinesthetic discrimination. Can J Physiol Pharmacol, 66, 439–454

Johnson, K. O. (2001). The roles and functions of cutaneous mechanoreceptors. Current Opinion in Neurobiology, 11, 455–461.

Gardner, E. P., Babu, K. S., Reitzen, S. D., Ghosh, S., Brown, A. S., Chen, J., et al. (2007). Neurophysiology of prehension: Pt. I. Posterior parietal cortex and object-oriented hand behaviors. Journal of Neurophysiology, 97, 387–406. Gardner, E. P., & Palmer, C. I. (1990). Simulation of motion on the skin: Pt. III. Mechanisms used by rapidly adapting cutaneous mechanoreceptors in the primate hand for spatiotemporal resolution and two-point discrimination. Journal of Neurophysiology, 63, 841–859. Gardner, E. P., Ro, J. Y., Babu, K. S., & Ghosh, S. (2007). Neurophysiology of prehension: Pt. II. Response diversity in primary somatosensory (S-I) and motor (M-I) cortex. Journal of Neurophysiology, 97, 1656–1670. Grigg, P., & Hoffman, A. H. (1982). Properties of ruffini afferents revealed by stress analysis of isolated sections of cat knee capsule. Journal of Neurophysiology, 47, 41–54. Guinard, D., Usson, Y., Guillermet, C., & Saxod, R. (2000). PS-100 and NF 70–200 double immunolabeling for human digital skin meissner corpuscle 3D imaging. Journal of Histochemistry and Cytochemistry, 48, 295–302.

c14.indd Sec4:320

Kappers, A. M. L., & Koenderink, J. J. (1996). Haptic unilateral and bilateral discrimination of curved surfaces. Perception, 25, 739–749. Keller, H. (1919). The world I live in. New York: Century. Klatzky, R. L., Lederman, S. J., & Metzger, V. A. (1985). Identifying objects by touch: An “expert system.”Perception and Psychophysics, 37, 299–302. Lederman, S. J., & Klatzky, R. L. (1987). Hand movements: A window into haptic object recognition. Cognitive Psychology, 19, 342–368. Macefield, V. G., Hager-Ross, C., & Johansson, R. S. (1996). Control of grip force during restraint of an object held between finger and thumb: Responses of cutaneous afferents from the digits. Experimental Brain Research, 108, 155–171. Merabet, L., Thut, G., Murray, B., Andrews, J., Hsiao, S., & Pascual-Leone, A. (2004). Feeling by sight or seeing by touch? Neuron, 42, 173–179. Mountcastle, V. B., Lynch, J. C., Georgopoulos, A. P., Sakata, H., & Acuna, C. (1975). Posterior parietal association cortex of the monkey: Command functions for operations within extrapersonal space. Journal of Neurophysiology, 38, 871–908.

8/17/09 2:09:47 PM

References 321 Murray, E. A., & Mishkin, M. (1984). Relative contributions of SII and area 5 to tactile discrimination in monkeys. Behavioural Brain Research, 11, 67–85.

Sripati, A. P., Bensmaia, S. J., & Johnson, K. O. (2006). A continuum mechanical model of mechanoreceptive afferent responses to indented spatial patterns. Journal of Neurophysiology, 95, 3852–3864.

Ogawa, H. (1996). The merkel cell as a possible mechanoreceptor cell. Progress in Neurobiology, 49, 317–334.

Sripati, A. P., & Johnson, K. O. (2006). Dynamic gain changes during attentional modulation. Neural Computation, 18, 1847–1867.

Ostry, D. J., & Romo, R. (2001). Tactile shape processing. Neuron, 31, 173–174.

Sripati, A. P., Yoshioka, T., Denchev, P., Hsiao, S. S., & Johnson, K. O. (2006). Spatiotemporal receptive fields of peripheral afferents and cortical area 3b and 1 neurons in the primate somatosensory system. Journal of Neuroscience, 26, 2101–2114.

Phillips J.R., Johnson, K.O., Hsiao, S.S. (1988). Spatial pattern representation and transformation in monkey somatosensory cortex. Proc Natl Acad Sci, 85, 1317–1321. Phillips, J. R., & Johnson, K. O. (1981a). Tactile spatial resolution: Pt. II. Neural representation of bars, edges, and gratings in monkey primary afferents. Journal of Neurophysiology, 46, 1192–1203. Phillips, J. R., & Johnson, K. O. (1981b). Tactile spatial resolution: Pt. III. A continuum mechanics model of skin predicting mechanoreceptor responses to bars, edges, and gratings. Journal of Neurophysiology, 46, 1204–1225. Phillips, J. R., Johnson, K. O., & Browne, H. M. (1983). A comparison of visual and two modes of tactual letter resolution. Perception and Psychophysics, 34, 243–249. Prather, S. C., Votaw, J. R., & Sathian, K. (2004). Task-specific recruitment of dorsal and ventral visual areas during tactile perception. Neuropsychologia, 42, 1079–1087. Proske, U., Wise, A. K., & Gregory, J. E. (2000). The role of muscle receptors in the detection of movements. Progress in Neurobiology, 60, 85–96. Randolph, M., & Semmes, J. (1974). Behavioral consequences of selective ablations in the postcentral gyrus of macaca mulatta. Brain Research, 70, 55–70. Ray, S., Niebur, E., Hsiao, S. S., Sinai, A., & Crone, N. E. (2008). High-frequency gamma activity (80–150Hz) is increased in human cortex during selective attention. Clinical Neurophysiology, 119, 116–133.

Stilla, R., Deshpande, G., LaConte, S., Hu, X., & Sathian, K. (2007). Posteromedial parietal cortical activity and inputs predict tactile spatial acuity. Journal of Neuroscience, 27, 11091–11102. Talbot, W. H., Darian-Smith, I., Kornhuber, H. H., & Mountcastle, V. B. (1968). The sense of flutter-vibration: Comparison of the human capacity with response patterns of mechanoreceptive afferents from the monkey hand. Journal of Neurophysiology, 31, 301–334. Thakur, P. H., Bastian, A. J., & Hsiao, S. S. (2008). Multi-digit movement synergies of the human hand in an unconstrained haptic exploration task. Journal of Neuroscience. 28, 1271–1281. Thakur, P. H., Fitzgerald, P. J., Lane, J. W., & Hsiao, S. S. (2006). Receptive field properties of the macaque second somatosensory cortex: Nonlinear mechanisms underlying the representation of orientation within a finger pad. Journal of Neuroscience, 26, 13567–13575. Vega-Bermudez, F., & Johnson, K. O. (1999). Surround suppression in the responses of primate SA1 and RA mechanoreceptive afferents mapped with a probe array. Journal of Neurophysiology, 81, 2711–2719. Vega-Bermudez, F., Johnson, K. O., & Hsiao, S. S. (1991). Human tactile pattern recognition: Active versus passive touch, velocity effects, and patterns of confusion. Journal of Neurophysiology, 65, 531–546.

Romo, R., & Salinas, E. (2003). Flutter discrimination: Neural codes, perception, memory and decision making. Nature Reviews: Neuroscience, 4, 203–218.

Wang, X., Merzenich, M. M., Sameshima, K., & Jenkins, W. M. (1995, November 2). Remodelling of hand representation in adult cortex determined by timing of tactile stimulation [see comments]. Nature, 378, 71–75.

Roy, A., Steinmetz, P. N., Hsiao, S. S., Johnson, K. O., & Niebur, E. (2007). Synchrony: A neural correlate of somatosensory attention. Journal of Neurophysiology, 98, 1645–1661.

Westling, G. & Johansson, R. S. (1987). Responses inglabrous skin mechano receptors during precision grip in humans. Experimental Brain Rearch, 66, 128–140.

Sadato, N., Pascual-Leone, A., Grafman, J., Ibanez, V., Delber, M. P., Dold, G. R., et al. (1996, April 11). Activation of the primary visual cortex by Braille reading in blind subjects. Nature, 380, 526–528.

Yoshioka, T., Bensmaia, S. J., Craig, J. C., & Hsiao, S. S. (2007). Texture perception through direct and indirect touch: An analysis of perceptual space for tactile textures in two modes of exploration. Somatosensory and Motor Research, 24, 53–70.

Sakata, H., & Iwamura, Y. (1978). Cortical processing of tactile information in the first somatosensory and parietal association areas in the monkey. In G. Gordon (Ed.), Actice touch: The mechanism of recognition of objects by manipulation: A multi-disciplinary approach (pp. 55–72). Oxford: Pergamon Press. Srinivasan, M. A., & LaMotte, R. H. (1995). Tactual discrimination of softness. Journal of Neurophysiology, 73, 88–101.

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Steinmetz, P. N., Roy, A., Fitzgerald, P. J., Hsiao, S. S., Johnson, K. O., & Niebur, E. (2000, March 9). Attention modulates synchronized neuronal firing in primate somatosensory cortex. Nature, 404, 187–190.

Yoshioka, T., Gibb, B., Dorsch, A. K., Hsiao, S. S., & Johnson, K. O. (2001). Neural coding mechanisms underlying perceived roughness of finely textured surfaces. Journal of Neuroscience, 21, 6905–6916. Zangaladze, A., Epstein, C. M., Grafton, S. T., & Sathian, K. (1999, October 21). Involvement of visual cortex in tactile discrimination of orientation. Nature, 401, 587–590.

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Chapter 15

Personal and Extrapersonal Spatial Perception GIUSEPPE VALLAR AND ANGELO MARAVITA

fundamental classes of representations of objects in space, including the subjects’ own body. In egocentric coordinate frames, the position of objects is coded with reference to the whole body of the subject, or of body parts (e.g., the arm, the hand), giving rise to representations, which may be head-centered (in the visual domain, resulting from the combination of the retinotopic map with information about eye position), trunk-centered (based also on information about the position of the head, and about posture), arm-centered, and so forth (Lacquaniti, 1997). In allocentric coordinate frames, objects are primarily coded with reference to their spatial and configurational properties, such as the relationships between their component parts, and among different objects present in the environment. Egocentric representations may be used for the organization of goal-directed movements, such as reaching a target or avoiding a harmful stimulus (Figure 15.1). Allocentric representations, encoding the configurational properties of objects and the relationships among them, may be useful for their identification and for navigation in space. In ecological conditions, objects are typically perceived from a variety of egocentric (observer-based) perspectives, suggesting a close interaction between these two types of frames of reference (Vallar, 2003). The neuropsychological findings from brain-damaged patients provide, through selective patterns of impairment, definite evidence for a fractionation of the internal map of space into a number of discrete, though interrelated, components. In humans, there is a well-established hemispheric asymmetry that attaches to the right hemisphere a main role for spatial processing, with the left hemisphere being mainly concerned with language (Milner, 1971). As for spatial cognition, this is definitely suggested by the neuropsychological evidence that spatial impairments, such as unilateral neglect, are most frequently associated with right brain damage (Bisiach & Vallar, 2000). The hemispheric asymmetry in spatial processing has been characterized as a “left hemisphere deficit rather than as a right hemisphere specialization”: during evolution, language and other

MULTIPLE REPRESENTATIONS AND FRAMES OF SPATIAL REFERENCE Human beings, as well as animals, live in a complex environment. They continuously receive and process signals concerning objects in space and the spatial position of their body through different sensory modalities (visual, auditory, somatosensory, and vestibular). They continuously move and are able to keep track of the position of their body and of the location of objects in the space around them. These complex skills, essential to survival, comprise the perceptual processing of different sensory inputs from a continuously changing environment and the programming and execution of motor acts. These include pointing to and reaching for objects through grasping and locomotion (Vallar, 2003). The subjective, phenomenal, experience of space is largely unitary (Rizzolatti, Fadiga, Fogassi, & Gallese, 1997). However, when the experience of the world, namely of the space around us, is considered with reference to a person, who perceives objects and makes movements, the space may be conceived as the medium whereby the position of things, including the body, becomes possible (MerleauPonty, 1945). Accordingly, our body, with the objects around us, gives rise to relationships such as “top” and “bottom,” “left” and “right,” “near” and “far.” Our unitary phenomenal experience of space involves the integration of sensory and motor information that builds up internal representations of the body-in-space and of the space around us. First, the processing of sensory inputs produces representations of the stimulus in primary sensory cortices, that are specific to each sensory modality, retinotopic in vision (see Chapter 11), somatotopic in the tactile domain (see Chapter 14) (while for audition the primary sensory cortex has a tonotopic, nonspatial representation; Chapter 12). The integration of visual, auditory, and somatosensory information with signals (eye position, vestibular, proprioceptive) concerned with the position of the body and of body parts in space results in two 322

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Personal and Extrapersonal Space 323 Z axis (Yaw) Midfrontal (coronal) plane

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Figure 15.1 The midsagittal plane of the body, which divides the extrapersonal space and the body into the left and the right sides, and other body coordinate systems and axes of rotation. Note. An object with its intrinsic axes. c.g. ⫽ center of gravity. From figure 1.3, pg. 7, “Human Spatial Orientation,” by I. P. Howard and W. B. Templeton, 1966, London: Wiley. Adapted with permission.

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cognitive processes may have co-opted left-hemisphere neural tissue, previously devoted to visuo-spatial processing (Funnell, Corballis, & Gazzaniga, 2000).

PERSONAL AND EXTRAPERSONAL SPACE In the context of the spatial reference frames illustrated in Figure 15.1, a main distinction has been drawn between a personal space, namely the space of the self (ego-space), that is normally located within the limits of the body space, and an extrapersonal space. Phenomenologically, and with reference to the actions that can be performed in it, the extrapersonal space has been further subdivided into a number of components. The grasping space may include, in turn, a number of subcomponents, with reference to the motor effector that is used, in order to perform actions: the whole body (general grasping space), the mouth (perioral and intraoral space), and the hand (manual space). The grasping space, by using instruments such as a rake, can be extended (instrumental grasping space, see section on “tools and mirrors”). The space beyond grasping has been further fractionated. The near-distant action space would amount to a few meters (6 to 8) around the

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body. Its limits are shown by the finding that subjects, after a few steps when blindfolded, become more and more unsure about their position in space. Beyond the near-distant action space, the far-distant space is mainly a visual space that is phenomenologically perceived as non-Euclidean, with a nonliner scaling of distance (Grüsser & Landis, 1991). On the basis of visual parameters, such as ordinal depththreshold functions, Cutting and Vishton (1995) hypothesize three different classes of distance around an observer: personal space (generally within arm’s reach and slightly beyond), action space, and vista space (beyond about 30 m). A distinction between discrete representations of peripersonal space (mainly concerned with visuomotor operations in near-body space), and of more distant extrapersonal spaces also characterizes the three-dimensional (3D) model of Previc (1998). The representations of extrapersonal spaces, in turn, include a focal component (mainly concerned with visual search and object recognition), an action component (orienting in topographically defined space, such as in navigation), and a most distant ambient extrapersonal component (mainly concerned with orienting in earth-fixed space). Perhaps, the first scientific piece of evidence of a dissociation between personal and extrapersonal space comes

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from the observation that patients suffering from peripheral disorders of the vestibular system (labyrinthitis) may experience a disproportionate increase (hyperschematia) or decrease (hyposchematia) of the subjective size of their whole body, or of body parts, or a pathological displacement of them (paraschematia) (Bonnier, 1905; see a reappraisal of Bonnier ’s observations in Vallar & Papagno, 2003). A few years later, on the basis of observations in brain-damaged patients, the suggestion was made that an internal representation of the body exists (the body schema), largely nonconscious, a combined standard, resulting from previous postures and movements, and mainly concerned with keeping track and updating the position of the body and of body parts, for the purpose of forthcoming postures and movements. Another schema, more superficial, was supposed be involved in the localization of tactile stimuli (Head & Holmes, 1911). To summarize, there is consensus that the internal representation of space is not unitary. A main drawn distinction concerns personal (i.e., the body) versus extrapersonal (i.e., the space around us) representations. Within extrapersonal space, a peripersonal (i.e., within hand/arm reach) near space is distinguished from a more distant space. This far space, in turn, has been further subdivided into subcomponents, that may differ in some specific aspects, according to each particular model.

NEUROPSYCHOLOGICAL DISSOCIATIONS Extrapersonal versus Personal Space The syndrome of unilateral spatial neglect has provided definite information as to the existence and independence of discrete representations of different sectors of space. Spatial neglect is a multicomponent disorder, whereby patients fail to explore the side of space contralateral to the cerebral lesion (contralesional), and do not report sensory events (e.g., visual stimuli, touches delivered to the contralesional hand) occurring in that sector of space. The disorder is more frequent and severe after damage to the right cerebral hemisphere and concerns the left side of personal and extrapersonal space (Bisiach & Vallar, 2000). Typically, patients show left spatial neglect for both extrapersonal and personal space (i.e., their body). However, dissociations have been reported between these two manifestations of the disorder. Early observations showed that right-brain-damaged patients may present with extrapersonal visual neglect, without neglect for the left side of their body (Paterson & Zangwill, 1944, patient #1). Other patients may present with neglect for the left side of the body, with no evidence of extrapersonal neglect

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(Bisiach, Perani, Vallar, & Berti, 1986). Guariglia and Antonucci (1992) studied in more detail a right-braindamaged patient who had no extrapersonal neglect in cancellation, reading, drawing, and perceptual tasks, but “was unable to look at his own left leg while walking with a cane and was unable to utilize the residual movement skills of the left side of the body.” These selective patterns of impairment (double dissociation, see Vallar, 2000) suggest that discrete neural systems involved in the representation of personal and extrapersonal space exist in the brain. These representations are likely to be built up, modulated, and updated though the integration of different sensory inputs. One piece of illustrative evidence for this modulation and integration comes from studies showing that muscular vibration may illusorily modify the perceived image of the body, such as the so-called Pinocchio’s illusion (Lackner, 1988), or the displacement of visual targets (Biguer, Donaldson, Hein, & Jeannerod, 1988). In the neuropsychological domain, a variety of sensory stimulations may improve or worsen many manifestations of the neglect syndrome (Kerkhoff, 2003; Rossetti & Rode, 2002; Vallar, Guariglia, & Rusconi, 1997). The selective impairments described here, however, cannot be traced back to sensory disorders, which may nevertheless contribute to shape the deficit (Bisiach & Vallar, 2000). Extrapersonal neglect may occur without any associated visual or somatosensory impairment (Bisiach et al., 1986). Personal neglect, conversely, is much more closely associated with sensory (vision, tactile perception, position sense) impairments (Bisiach et al., 1986). However, individual case studies show that sensory deficits may be absent, or mild, in patients with personal neglect (Guariglia & Antonucci, 1992; Ortigue, Mégevand, Perren, Landis, & Blanke, 2006). These findings suggest that neglect for the left side of the body cannot be entirely traced back to defective sensory inputs, but reflects the impairment of higher-order representations of the body. Extrapersonal Space: The Far versus Near Distinction Brain (1941) described three right-brain-damaged patients who were impaired in localizing objects by pointing in the contralesional half-field, both within arm reach, and, two of them, at a greater distance. One patient (case #3), however, did not run into objects, and “his defective localization appeared to be limited to objects within arm’s length.” On the basis of these observations, Brain suggested a distinction between processes involved in the estimation of “walking distance,” and processes concerned with the estimation of “grasping distance,” with possible discrete neural correlates. With explicit reference to a fractionation

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Neuropsychological Dissociations

of spatial representations related to the motor effectors used to perform actions, Brain distinguished a “manual,” and a “brachial space,” as well as a far space, where objects can be reached through locomotion. Many years later, Halligan and Marshall (1991) reported a right-brain-damaged patient who showed contralesional left neglect in near peripersonal space, and, particularly, in line bisection, both manual, and with a projection light pen. The patient’s rightward error was however greatly reduced when the bisection by the light pen took place in far space. In fact, the patient was well able to play a traditional pub game, namely throwing darts at a circular target hung on a wall, from a distance of about 2.5 m. Actually, the patient was consistently more accurate than Peter Halligan, and always hit the dart board. The spatial pattern of the darts thrown did not show any discernable spatial deviation and they were often close to the center (John Marshall and Peter Halligan, personal communication). Cowey, Small, and Ellis (1994) in five right-brain-damaged patients found the opposite dissociation, namely a greater impairment for lines well beyond reach. To summarize, the study of right-brain-damaged patients with left neglect suggests that different right-hemispherebased neural systems are involved in the representation of near or peripersonal (within hand/arm reach) versus far, distant, sectors of extrapersonal space. One interpretation of this dichotomy, that prima facie clashes with our phenomenal experience of the unity of extrapersonal space, is related to the different effectors (e.g., an arm-reaching movement, a saccade), that may be recruited to perform actions toward specific objects, located in different sectors of extrapersonal space, with respect to the body (Berti & Rizzolatti, 2002). Neuropsychological studies, however, have also shown that the near/far dichotomy may be revealed through perceptual paradigms that do not require motor actions toward a target (Pitzalis, Di Russo, Spinelli, & Zoccolotti, 2001). Furthermore, another fractionation of sectors of space (top versus bottom, see Figure 15.1) is revealed by brain damage: vertical or altitudinal neglect for the lower (Rapcsak, Cimino, & Heilman, 1988), or the upper (Shelton, Bowers, & Heilman, 1990) peripersonal sectors of space. The more frequent report of neglect for the lower sector of space is in line with the finding that left spatial neglect also has an altitudinal component, being more severe in the left lower sector of extrapersonal space (Halligan & Marshall, 1989; Pitzalis & Di Russo, 2001). The top/bottom dichotomy is more difficult to accommodate with reference to different effectors such as the arm-hand system, even though different directions of eye movements may be a relevant factor. A similar argument is provided by the observation of neglect confined to front or back space (Vallar, Guariglia,

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Nico, & Bisiach, 1995). Finally, the representations of these considered sectors of extrapersonal space (far versus near and upper versus lower) may be distinguished in terms of both motor, effector-related (Berti & Rizzolatti, 2002), and sensory, perceptual, factors (Cutting & Vishton, 1995; Previc, 1998), that contribute to their building up and updating. Spatial Coding of Touch: Localizing Tactile Sensations When Crossing the Hands When we look straight ahead, there is an exact correspondence between the left and the right for both the objects in the space around us, and our own left and right body parts. For example, the left shoulder corresponds to the left side of extrapersonal space relatively to the egocentric reference frame (see Figure 15.1). However, for mobile body parts, such as the hands, that are typically displaced in different spatial positions, during most actions, this exact correspondence does not always hold. In particular, when the hands cross the midline, there may be a left hand in right peripersonal space and vice versa. This means that a somatosensory stimulus may need to be coded in different spatial reference frames at the same time. One such frame is an egocentric code basically corresponding, in the example mentioned earlier, to the right- or to the left-hand-side with respect to the midsagittal plane of the trunk. Other frames are centered on the position of each single body part in space, with respect to both other body parts and near objects. In this view, the localization of a tactile stimulus on a hand takes into account not only the somatosensory input (which impinges directly on the somatosensory cortex), but also the position of the hand in space. As we discuss later, the integration of signals coming from vision, proprioception, and touch is essential to localize somatosensory stimuli delivered to mobile body parts. While in most instances of the daily life this integration is perfectly efficient, hand crossing may not be fully compensated by the brain in some specific situations. In a seminal study, Yamamoto and Kitazawa (2001) showed that temporal order judgment of tactile stimuli on the hands was much impaired when the hands were crossed. This result suggests that the localization of the stimulus takes into account not only the hand that is being stimulated, but also the spatial location of the stimulus in external space. Gathering this information may have a cost when the hands are crossed and the anatomical and spatial features of the stimulus do not coincide. The effect of hand crossing in patients affected by disorders of tactile perception may also disclose the relative role of somatosensory and spatial mapping of external stimuli for touch perception. Right brain-damaged patients with left spatial neglect or extinction may show defective

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Personal and extrapersonal Spatial Perception

awareness of contralesional touches. However, when the hands are crossed, so that the contralesional hand is moved in the ipsilesional side of space, the perception of contralesional touches improves (Aglioti, Smania, & Peru, 1999; but see Bartolomeo, Perri, & Gainotti, 2004, for partially different results, although with a rather different task; Smania & Aglioti, 1995). Indeed, this effect may be useful in clinical practice, where it may help distinguish primary sensory deficits (not, or only scarcely, modulated by postural changes) from contralesional inattention. In a logically related experiment with a patient with right parietal damage and left neglect, Valenza, Murray, Ptak, and Vuilleumier (2004) found that a double stimulation of the right ipsilesional forearm (touches delivered to the elbow and the hand) induced a correct perception of both stimuli when the arm was in an anatomical, uncrossed posture, namely to the right of the body’s midline. However, when the right forearm was placed across the midline, the patient failed to report touches given to the hand. Apparently, the patient extinguished the more distal stimulus that was delivered in a left spatial position, with respect to the body’s midsagittal plane. These results suggest that the perception of a tactile stimulus may be determined not only by somatosensory factors, but also by the spatial position of the touched skin, with reference to egocentric coordinate frames, centered on the individual body parts. This cross-talk between different coordinate systems allows us to keep track of our body parts in space and of any single stimulus delivered to them, supporting a high degree of flexibility during motor acts. The role of egocentric spatial coordinate frames in perceptual awareness of sensory events is not confined to the somatosensory domain. In the visual domain, Kooistra and Heilman (1989) found that the left hemianopia of one right-brain-damaged patient improved when her eyes were directed toward the right side. In this condition, where left visual half-field testing fell in the right half-space, the patient’s left hemianopia improved significantly. In sum, spatial coding of sensory events appears to play an important role for perceptual awareness, possibly indicating a close relationship between detection and localization in space (Gallace & Spence, 2008; Vallar, 2007a).

MULTISENSORY CODING OF SPACE A key aspect of space representation has received much interest from the vantage point of the cognitive neurosciences. In natural conditions, most stimuli in external space, both far and near to the body, present to our senses as a combination of multisensory information (e.g., Calvert, Spence, & Stein, 2004). Often, when we see a dog or a car,

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the visual stimulation is accompanied by a barking or roaring sound. Furthermore, when we want to catch a Frisbee flying toward us, the vision of the approaching object is completed by the somatosensory information that accompanies its contact with our hand, when we finally catch it. Our brain is equipped to integrate multisensory inputs in order to give us a seamless, unitary perception of the outside world. The importance of such an effective multisensory integration is that of increasing our efficiency in orienting toward, detecting, and manipulating external objects. For example, it is faster and easier to visually detect and orient toward a bird hidden among tree branches if we can also hear it chattering. Here, the integration of vision and audition is critical. Similarly, when we want to reach and manipulate an object, tactile and proprioceptive inputs are critically integrated with visual ones. In general, our perceptual system is designed to take into account all the possible information coming from the space around us. When we orient our attention to a particular object of interest, we automatically start to process any information coming from that object, regardless of the sensory modality we attend to. In fact, a good framework to investigate the effects of crossmodal interaction on human behavior is to refer to the typical paradigms studying the orienting of spatial attention (see a collection of review papers in Spence & Driver, 2004). Attention can be deployed toward a certain stimulus, either involuntarily (exogenous attention) or voluntarily (endogenous attention). In both situations, a stimulus in one sensory modality can interfere with, or improve, the response to a stimulus in another modality. A typical exogenous attention situation is illustrated by the experiment by Spence and Driver (1997), who presented auditory pure-tone cues to the left or to the right of central fixation. After an interval, either a visual or an auditory target is presented ipsilaterally or contralaterally to the cue. Ipsilateral cues improve not only the responses to auditory stimuli (intramodal facilitation), but also those to visual targets (crossmodal facilitation). An automatic crossmodal enhancement of perception has been obtained even with subthreshold stimuli: spatially congruent unattended sounds can improve the perception of below-threshold visual targets (Frassinetti, Bolognini, & Làdavas, 2002). An elegant example of crossmodal endogenous orienting of attention is shown by an experiment in which a stream of auditory speech at one spatial location is better decoded if participants actively attend to a video monitor, showing lip movements exactly matching words pronounced in the auditory stream: critically, this facilitation only occurs if the monitor showing the visual stimuli is spatially coincident to the sound (Driver & Spence, 1994). A critical determinant of multisensory integration is the spatial distance between a given sensory event and the

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Multisensory Coding of Space 327

observer. Because our body can reach objects only within a limited space extension, vision and touch are the critical sensory modalities to be integrated when we deal with stimuli close to our body and vision and audition when we deal with stimuli in far space. Several examples of crossmodal integration (between vision and touch in near space and vision and audition in far space) can be illustrated through the crossmodal cuing paradigm (reviews in Spence, Pavani, Maravita, & Holmes, 2004, 2008), largely used to study crossmodal integration. In a typical experimental setting for studying visual-tactile integration, participants have to make a spatial tactile judgment, reporting whether a tactile vibration is delivered to the index or thumb finger of either hand, which is equivalent to an “upper” or “lower” judgment, for the posture that is typically used (Driver & Spence, 1998; Maravita & Driver, 2004). At the same time, visual distracters (LEDs) can be illuminated at any of four positions, one near the index finger (i.e., upper position), and one near the thumb (lower position) for each hand, with all these visual possibilities being equally likely, regardless of where any concurrent tactile target is presented. The tactile judgments are typically slower, and less accurate, if the visual distracter is “incongruent” with the location of the concurrent tactile target (e.g., an upper light near the index finger is combined with a lower vibration at the thumb). Critically, this crossmodal interference effect is reliably larger if the visual distracter appears closer to the tactile target (e.g., in the same hemifield, or, within that hemifield, closer to the current location of the tactually stimulated hand). As discussed next, the hands are highly mobile body parts and the brain must take into account the absolute position of the body in space, in order to allow efficient visual-tactile integration. In particular, when the hands are crossed over in space, then the crossmodal interference effect re-maps accordingly, so that an incongruent visual distracter, closest to the current location of the stimulated hand, brings about the largest interference (see, e.g., Driver & Spence, 1998). This remapping effect may require the activity of the posterior parietal cortex (PPC; Bolognini & Maravita, 2007; Lloyd, Shore, Spence, & Calvert, 2003), and the integrity of the interhemispheric connections via the corpus callosum (Spence, Kingstone, Shore, & Gazzaniga, 2001). Bolognini and Maravita (2007) have shown that interfering with transcranial magnetic stimulation (TMS) over the PPC greatly affects the crossmodal facilitation of TMS-induced visual sensations (phosphenes) by spatially coincident touches with hands crossed: a defective functioning of the PPC makes it difficult for the brain to keep track of the position of the hands when they are crossed over, and thus limits the facilitatory crossmodal effect of touch over visual perception. The PPC may be a critical

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site of multisensory integration (see also the discussion of its neural basis that follows), for orienting unimodal attention through crossmodal cues via feedback mechanisms (Macaluso & Driver, 2003), and for modulating, together with the temporal cortex, the multisensory responses of subcortical structures (Stein, 2005). Furthermore, the tight mutual links of the PPC with the premotor cortex make it a key structure for action planning and execution by keeping personal (somatosensory) and extrapersonal (visual and auditory) sensory maps in a spatial register, in line with modern views of sensory-motor organization (Rizzolatti, Luppino, & Matelli, 1998). The mutual link between vision and proprioception for multisensory spatial representation of the body has been shown using dummy rubber hands (Pavani, Spence, & Driver, 2000). Some dominance of vision was found, with visual distracters near the dummy rubber hands producing the greatest spatial interference with tactile judgments, provided that the rubber hands were in a plausible posture. Moreover, the extent of crossmodal interference from visual distracters near the dummy rubber hands correlated with the extent to which subjects “felt” that they actually experienced touch in the location of those dummy hands. Finally, in the neuropsychological literature, a great deal of attention has been devoted to the phenomenon of crossmodal extinction. This disorder provides a clue into the existence of a multisensory coding of space. In a seminal study, di Pellegrino, Làdavas, and Farnè (1997) showed that a visual stimulus close to the ipsilesional hand can cause the extinction of a contralesional touch. The deficit is critically reduced when the distance between the visual stimulus and the ipsilesional hand increases. This finding emphasizes, once again, the role of spatial proximity to the body for crossmodal interactions to occur (review in Làdavas, 2002). In conclusion, the brain constantly integrates all ongoing sensory stimulation coming from personal and extrapersonal space environments into a seamless reality. This integration takes place regardless of the position of the body, and of its individual parts, in space, and of all of the continuous, intentional, or unexpected, changes of posture or modifications of the position of the sensory inputs in the space around us. Modulation of Multisensory Space Representation: Tools and Mirrors As discussed earlier, space comprises different functional sectors. In particular, there is a sector of space within which we can act directly with our hands. However, in daily life activities, our actions are not always performed directly with bodily effectors, such as the hands, but may

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Personal and extrapersonal Spatial Perception

be mediated by different kinds of tools. These different tools may guide, or extend, the range of our motor performances. One view of such extension of motor skills by tool use is the idea that tools may be somewhat “incorporated” within our body representation, thus becoming a functional extension of it (for early accounts of this theoretical perspective, see Critchley, 1979; Head & Holmes, 1911; Paillard, 1971). Neurophysiological and neuropsychological studies have now given support to this view, showing that the use of different tools can change the way in which we interact with stimuli in far space. The general idea is that a visual stimulus in far space, which, as noted previously, typically undergoes a weak integration with somatosensory inputs delivered to the body, may become more effective for multisensory integration when it is reached by a tool. A far (visual) stimulus, when reached through the tip of a tool, may start to act as a stimulus near to the body (in near peripersonal space), and increase its influence over tactile processing. In the seminal study by Iriki, Tanaka, and Iwamura (1996), macaque monkeys were shown how to use rakes to retrieve pieces of food in the space out of hand reach. After a few weeks of training, the activity of parietal bimodal visual-tactile neurons was recorded. These neurons had tactile receptive fields representing the hand or the shoulder, responded to visual stimuli only if delivered close to the skin of these body parts, and showed scarce or no responses to far visual stimuli. Crucially, after short training sessions with the tool, the responses of these bimodal visual-tactile neurons to those far visual stimuli, reached by the tool, increased substantially, as if they were functionally considered as laying in near space, and therefore suitable for multisensory interactions. In a similar vein, tool use modulates the behavior of patients affected by spatial neglect or extinction. In a single patient study, visuospatial neglect selective for near space extended to far space (as shown by the worsening of line bisection performance in far space), when the far lines were bisected with a stick, instead of a laser pointer (Berti & Frassinetti, 2000; see also Pegna et al., 2001, for a related account). Other relevant evidence comes from patients with crossmodal extinction. The logic is similar to the study by Iriki et al. (1996), discussed earlier. If the patient actively uses a tool with the ipsilesional hand to reach for visual stimuli in far space, far visual stimuli (that are nonetheless close to the tool tip) could become more effective in inducing the loss of contralesional touches. Interesting modulations of crossmodal extinction have recently been obtained with tool use. In these experiments, crossmodal extinction was used as a paradigm to test the effect of prolonged activity with tools in modulating crossmodal body representations (for reviews, see Maravita, 2006; Maravita & Iriki, 2004). The general outcome of these studies is

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that wielding or using a tool in extrapersonal far space can increase the impact of far ipsilesional visual stimuli on contralesional tactile extinction, as if far space were rendered akin to near space, by becoming reachable with the tool (see Farnè & Làdavas, 2000; Maravita, Husain, Clarke, & Driver, 2001). In another single patient study, the related procedure of using a tool with the contralesional hand to operate in the ipsilesional space with a rake may reduce tactile extinction to left touches, presented simultaneously with right-sided visual flashes. This recovery from left crossmodal extinction may be possibly achieved by constructing a common visual-tactile representation of the two sides of space, hence reducing the competition between multiple stimuli (Maravita, Spence, Sergent, & Driver, 2002, see Figure 15.2). Pretraining

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Figure 15.2 Schematic illustration of the experimental setup and results of an experiment by Maravita. Note. A: Crossmodal extinction is tested with invisible touches at the left hand and visual stimuli (starry symbol) close to the right hand. The patient is holding a tool, but no training has been performed. B: The same testing procedure, after 120 minutes of training, consisting of collecting objects in the right side of space, with a tool held with the left hand. C: Extinction rate is higher before the training (leftmost column), significantly decreased one and 30 minutes after the training (two middle columns), going back to the baseline level after 60 minutes (rightmost column). This result is compatible with an extension of the multisensory, visuo-tactile integration from the near, peripersonal left side, before the training (circle in A), to the tip of the tool (circles in B), after the training. From "Active tool use with the contralesional hand can reduce crossmodal extinction of touch on that hand," by A. Maravita, K. Clarke, M. Husain, and J. Driver (2002). Neurocase, 8, 411–416. Based on figures 2 and 3, pp. 413–414.

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Neural Basis in Man and Monkey 329

A further set of studies has dealt with another tool (mirrors) that, with a logic similar to that illustrated earlier, may act by mediating our interactions with external space, through a modulation of multisensory integration. Mirrors have indeed become common tools in everyday life activities, from grooming to driving (evidence of disrupted interactions with mirror reflections are found after parietal lesions, see, e.g., Binkofski, Buccino, Dohle, Seitz, & Freund, 1999). In a series of experiments, Maravita and coworkers (Maravita et al., 2002; Maravita, Spence, Clarke, Husain, & Driver, 2000) have shown that, when people observe visual stimuli close to their hands, but reflected in a mirror, those stimuli are automatically interpreted as being in near peripersonal space, no matter the distance suggested by the reflective properties of the mirror, that would account for a stimulus in far space, as if “through the looking glass” (precisely at double the distance between the stimulus and the mirror). Those visual stimuli produce a strong interference in a crossmodal congruency task (Maravita et al., 2000). Similarly, an ipsilesional flash produces more left crossmodal tactile extinction when observed as the distant mirror-reflection of an LED close to the ipsilesional hand, than a distant LED flash projecting an equivalent visual image directly (Maravita et al., 2002), compatible with the visual stimulus being correctly interpreted as being in near space. These results show that the mere knowledge of the physical characteristics of the environment (such as the properties of reflecting surfaces and their effect on the localization of visual stimuli) is enough to bias the subject’s processing of visual stimuli, as well as crossmodal interactions. More generally, these findings suggest that the coding of stimuli in space occurs with an eye on their functional meaning for both perception and action. Accordingly, no matter how a visual stimulus appears to be in terms of retinal projection (e.g., “far” in the mirror reflection), the subject’s interaction with it occurs according to the knowledge acquired by the brain about its actual position in space.

NEURAL BASIS IN MAN AND MONKEY Different neural structures code space in different perspectives and work in parallel to give us a unitary representation of space. This representation takes into account visual and auditory stimuli that are present in extrapersonal space, as well as their integration with somatosensory stimuli delivered to the body, and information concerning body posture gathered through proprioception. The neural substrate of space representation should be regarded as a network of interconnected structures that manages its different aspects, including the discrete components of personal and extrapersonal space, and their multisensory integration.

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A time-honored view associates the representation of personal and extrapersonal space with the PPC, particularly in the right hemisphere (Critchley, 1953; Jewesbury, 1969). In the past decades, the network concerned with spatial cognition has broadened to include the frontal premotor cortex (PMC; see review in Vallar, 2001), and the white matter connections of these frontal regions with the PPC (see review in Bartolomeo, Thiebaut de Schotten, & Doricchi, 2007). Within the PPC, the supramarginal and angular gyri of the inferior parietal lobule appear to be regions relevant for spatial cognition, as well as the temporoparietal junction, and the superior temporal gyrus (see review in Karnath, 2001), although the role of this area is more controversial (see review in Marshall, Fink, Halligan, & Vallar, 2002). These conclusions are mainly based on studies in which brain-damaged patients are engaged in tasks, such as target cancellation, drawing, or line bisection, performed in near, within hand reach, peripersonal space. Figure 15.3 summarizes the main neural structures concerned with spatial attention and representation as suggested by lesion studies in right-brain-damaged patients with left spatial neglect (A), and in a schematic flow chart, also including subcortical structures (B) (see also Chapters 10 and 18). Reference Frames As far as the representation of visual extrapersonal space is concerned, at the first elementary level, in the primary visual cortex V1 there is the so-called retinotopic representation, whereby any stimulus in the visual field is projected to a given location in the cortex. In this case, the anatomical correspondence of the left and right visual fields is relative to the different hemispheres, with a contralateral representation of each visual field. Critically, the retinotopic representation is completely linked to eye position, since the first map which is formed, and then transferred to V1, is that on the retina. However, other neurons represent space relatively to the head, while totally (or partially) ignoring the absolute spatial projection of the stimulus on the retina. For example, neurons in the PPC and in the PMC may represent left and right space relatively to the animal’s head, with eye position being largely irrelevant (Duhamel, Bremmer, BenHamed, & Graf, 1997; Fogassi et al., 1992; Galletti, Battaglini, & Fattori, 1993; see also Rizzolatti et al., 1997), or only partially modulating the neuronal response (see Andersen, Snyder, Bradley, & Xing, 1997). In this case, the space around us is coded through a more egocentric, nonretinotopic space representation. At a higher level, neuroimaging studies in humans have explored the role of different brain structures for the

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Figure 15.3 A: The neural correlates of left spatial neglect. B: Cortico-subcortical networks for spatial attention and representation. Note. (A) Most anatomoclinical correlation studies show that the responsible lesion involves the right inferior parietal lobule in the PPC (angular gyrus: BA 39; supramarginal gyrus: BA 40, Intermediate gray area), and the temporoparietal junction (white-gray area). Neglect after right frontal damage is less frequent and usually associated with lesions to the PMC, particularly to its more ventral parts (BA 44 and ventral BA 6, dark gray area). Neglect may also be associated with damage to the more dorsal and medial regions of the PMC, and to the superior temporal gyrus (light

representation of space in different reference frames, clarifying the neural networks supporting egocentric versus allocentric coordinate systems. Galati et al. (2000) asked neurologically unimpaired subjects to judge the position (left or right) of a vertical segment drawn over a horizontal line (lines could be overall shifted to the left, or to the right, relatively to the observer ’s midsagittal plane, and segments could be placed to the left or to the right of the line’s objective midpoint). In one condition, subjects had to evaluate the spatial position of the vertical segment relatively to the observer ’s body midline (egocentric frame of reference). In the second task, the position of the vertical segment had to be computed relatively to the objective midpoint of the horizontal line (allocentric frame of reference). The patterns of brain activation showed the existence of partly overlapping, though different, cortical networks. In the egocentric condition, activations included the PPC (superior and inferior parietal lobules) from the medial surface down to the temporoparietal junction, and the lateral PMC (superior and inferior frontal gyri; see also Vallar et al., 1999). Bilateral activations were found, although those in the right hemisphere were much wider. In the allocentric condition, again a frontoparietal network was activated, this time centered on the superior parietal and intraparietal regions, and the superior frontal sulcus of the right hemisphere. A broadly similar activation of a frontoparietal network, more extensive in the right hemisphere, including the PPC around the intraparietal sulcus, the frontal regions around the precentral and superior frontal sulci, and the inferior

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gray areas). From figure 3, p. 129 “Spatial Cognition: Evidence from Visual Neglect,” by P. W. Halligan, G. R. Fink, J. C. Marshall, and G. Vallar, 2003, Trends in Cognitive Sciences, 7, pp. 125–133. Reprinted with permission. (B) The frontal PMC, the PPC/temporoparietal (TP) junction, the subcortical gray nuclei and their connections. From figure 1, p. 34, “Functional Anatomy of Attention and Neglect: from Neurons to Networks” (pp. 33–45), by M.-M. Mesulam, in The cognitive and neural bases of spatial neglect, H.-O. Karnath, A. D. Milner, and G. Vallar (Eds.), 2002 (Oxford: Oxford University Press). Adapted with permission.

and superior frontal gyri, was later found in a tactile task (Galati, Committeri, Sanes, & Pizzamiglio, 2001). Personal versus Extrapersonal Space Individual case reports suggest that personal neglect is associated with right hemispheric damage, involving particularly the PPC, but also the frontal cortex and subcortical structures (Bisiach et al., 1986; Guariglia & Antonucci, 1992), with a lesion pattern broadly overlapping with that found in patients showing extrapersonal neglect. A recent study in 52 right-brain-damaged stroke patients, using lesion density plots and subtraction analysis, has shown an anatomical dissociation between personal and extrapersonal neglect. Personal neglect was assessed by a task requiring the use of familiar objects in the body space, rather than by tasks requiring the exploration of the body, and the reaching of body parts. For extrapersonal neglect, a standard battery, comprising visuo-motor exploratory and perceptual tasks, was used. The suggestion is made that a circuit including the right frontal (ventral premotor cortex and middle frontal gyrus) and superior temporal regions is concerned with the representation of extrapersonal space, while the right inferior parietal regions (supramarginal gyrus, postcentral gyrus, and, particularly, the underlying white matter) would support the representation of personal space (Committeri, Pitzalis, Galati, Patria, Pelle, Sabatini et al., 2007). Data from patients showing selective impairments of pointing to own body parts versus body parts of others, such

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Neural Basis in Man and Monkey 331

as the examiner, suggest a different neurofunctional pattern. In one patient with a neurodegenerative disorder, defective pointing to the patient’s own body parts was associated with dysfunction, as assessed by single photon emission tomography (SPET), of the superior parietal lobule (BA 7) in the left hemisphere. In another patient, the deficit concerned the body parts of the examiner, with the dysfunction involving the left inferior parietal lobule (Felician, Ceccaldi, Didic, ThinusBlanc, & Poncet, 2003). In a successive fMRI experiment performed in neurologically unimpaired subjects, Felician, Romaiguère, et al. (2004) confirmed the role of the superior parietal lobule in the task of pointing to own body parts.

MIP

V6a V6 PIP V2

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Extrapersonal Space: Near, Peripersonal, versus Far Space

Figure 15.4 Representation of a macaque cerebral hemisphere with the arcuate, intraparietal, superior temporal, and lunate sulci (thick lines) opened up.

Not all sectors of the space around us may be represented by the same neural structures. In the past 20 years, the notion of space representation has been progressively integrated with that of action planning and execution. From this perspective, space representation has been considered less and less as the mere construction of a “map” of external space, where we can represent objects of interest, and more and more as the locus of integration between perception, action, and awareness. In this view, the neural coding of an object in external space is closely linked to the neural processing necessary to grasp or manipulate that particular object (Maravita, 2006; Rizzolatti et al., 1998). To this aim, a critical distinction has to be made between a near or peripersonal space, where objects can be reached and manipulated and a far extrapersonal space, which is beyond hand’s grasp.

Note. Boundaries of major functional subdivisions within the frontal and the parietal lobe. AIP = anterior intraparietal area; as ⫽ arcuate sulcus; cs ⫽ central sulcus; DP ⫽ dorsal prelunate area; FEF ⫽ frontal eye fields; FST ⫽ fundus of superior temporal area; ips ⫽ intraparietal sulcus; LIP ⫽ lateral intraparietal area; ls ⫽ lunate sulcus; M1 = motor cortex; MIP = medial intraparietal area; MST = medial superior temporal area; MT ⫽ middle temporal area; PIP ⫽ posterior intraparietal area; PM d/v ⫽ frontal premotor cortex, dorsal/ventral; ps = principal sulcus; S1, S2 ⫽ somatosensory cortex; SEF ⫽ supplementary eye fields; SMA ⫽ supplementary motor area; sts ⫽ superior temporal sulcus; V2, 3, 3a, 4, 6, 6a ⫽ visual areas; VIP ⫽ ventral intraparietal area. See also Colby and Goldberg (1999), and Rizzolatti and Matelli (2003). From figure 1, “Neglect in Monkeys: Effect of Permanent and Reversible Lesions” (pp. 47–58), by C. Wardak, E. Olivier, and J.-R. Duhamel, in The Cognitive and Neural Bases of Spatial Neglect, H. O. Karnath, A. D. Milner, & G. Vallar (Eds.), 2002, Oxford: Oxford University Press. Reprinted with permission.

Monkey Studies Since the 1980s, the difference between these representations of space has been made clear by cortical ablation studies in the monkey (see Figure 15.4). For example, while the ablation of the frontal prearcuate area 8 of the macaque monkey (frontal eye fields, FEF, in Figure 15.4) produces a lack of awareness and reaction to contralateral stimuli in far space, ablation of the postarcuate area 6 (ventral PMC, including area F4, see Figure 15.4) brings about similar deficits, but limited to the space near the animal’s body (Rizzolatti, Matelli, & Pavesi, 1983; Schieber, 2000). These frontal areas represent space, according to the kind of actions (reaching, grasping, eye movements) that can be performed in different sectors of it and are richly interconnected with specific portions of the parietal cortex (see, e.g., Rizzolatti et al., 1998). Similarly, different portions of the PPC support the representation of different sectors of extrapersonal space. Neurons in the ventral intraparietal (VIP) area are visually responsive, but most can also be

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SMA 5

excited by tactile stimuli, with the tactile receptive fields being generally restricted to the head and the face, and the visual and the tactile receptive fields being matched in size and location. Neurons in the medial intraparietal (MIP) area respond to stimuli within reaching distance and their response properties range from purely somatosensory, to bimodal, and to purely visual. Neurons in the anterior intraparietal (AIP) area respond to visual stimuli that the monkey can manipulate, with the represented spatial dimension being the desired shape of the hand, rather than its position in egocentric space. Neurons in the lateral intraparietal (LIP) area respond to the onset of the stimulus, and may maintain activity during the delay, and/ or discharge around the time of the saccade: These neurons may represent the space explored by eye movements—the predominant means by which we explore the world beyond our reach (Colby & Goldberg, 1999). Human Studies In his seminal study, Brain (1941) suggested that different lesion sites in the right hemisphere are associated with

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Personal and extrapersonal Spatial Perception

selective impairments of defective localization of objects within arm reach (case #3: damage to the posterior temporal lobe), and at a farther distance. Neuroimaging activation studies in neurologically unimpaired subjects have confirmed and elucidated in more detail this early suggestion. A task requiring line bisection or pointing to dots in near space activates a number of left hemisphere structures (dorsal occipital, intraparietal, ventral PM cortices, and the thalamus). A similar task performed in far space (eye-to-screen distance 1.7 m) activates the ventral occipital cortex bilaterally, and the right medial temporal cortex (Weiss et al., 2000). A later study by the same group basically confirmed these findings and found that manual bisection activates the extrastriate, superior parietal, and premotor cortex bilaterally, while bisection judgments are associated with activations in the right inferior parietal and dorsolateral prefrontal cortices, the anterior cingulate, and the extra-striate and superior temporal cortices bilaterally, independent of the far versus near condition (Weiss, Marshall, Zilles, & Fink, 2003). An rTMS study has shown that stimulation of the right PPC disrupts the subjects’ performance in a perceptual bisection task (i.e., deciding whether the left or the right side of a line appears longer), in near (50 cm) space. Conversely, stimulation of the right ventral occipital lobe affects such judgments in far (150 cm) space (Bjoertomt, Cowey, & Walsh, 2002). To summarize, the available evidence suggests that different brain areas and neural networks are involved in the representation of near (within hand/arm reach) versus far extrapersonal space. Experiments with neurologically unimpaired subjects being required to bisect lines or to localize dots suggest a segregation of processing in terms of dorsal versus ventral pathways, with the former contributing to the representation of near space, the latter of far space. There is also some indication of a hemispheric asymmetry, with a right hemispheric major role, particularly in perceptual bisection. Multisensory Integration Our experience of the external world is typically gathered through more than one sensory modality at the same time. In this respect, it is critical that relevant neural structures in the brain are capable of integrating multiple, multisensory input into unitary, coherent percepts. Animal Studies This kind of integration starts very early in the brain. Work by Stein and Meredith (1993) has shown that many neurons in the deep layers of the cat’s superior colliculus (SC) respond to multiple sensory modalities (vision, touch, audition). For example, both acoustic and visual stimuli can

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make the same SC neuron discharge (multisensory neuron). One critical feature of some such multisensory neurons is that their discharge can be critically enhanced when visual and auditory stimulation are in spatial register (the so-called spatial rule), or temporally synchronous (temporal rule). This condition is indeed compatible with the typical situations of the daily life, in which a given stimulus provides information through multiple sensory modalities at the same time. Therefore, these cells may critically enhance our responses to the multisensory events that characterize daily life. This enhanced neural discharge observed with spatially coincident multisensory stimuli corresponds to a faster and more accurate orienting of the animal toward the spatial location of a given stimulus. The multisensory responses found in SC neurons are coordinated with the activity of a number of cortical structures. For example, in the cat, the ectosylvian cortex and the lateral suprasylvian sulcus include neurons which discharge in response to both unimodal and multisensory stimuli, and, critically, exert a descending modulation of the activity of multisensory SC neurons (Stein, 2005; Wallace & Stein, 1994). In the monkey, a very intriguing set of studies has addressed the issue of multisensory space coding in near peripersonal space, thus providing a neural basis to the functional relationship between the body and the representation of extrapersonal space. Peripersonal acoustic (Graziano, Reiss, & Gross, 1999), visual and tactile (for a recent review, see Maravita, Spence, & Driver, 2003) stimuli are all specifically coded and integrated in the brain. In particular, in order to control object reaching and manipulation, vision and touch are highly interdependent. In the macaque monkey, around 50% of neurons in the ventral premotor cortex (area F4; Fogassi et al., 1996), 70% of neurons in the ventral intraparietal area (VIP; Duhamel, Colby, & Goldberg, 1998), 20% to 30% of neurons in the PPC (BA area 7b), and 24% of neuronal cells in the putamen (Graziano & Gross, 1994) show receptive fields (RF) in both the visual (vRF), and the somatosensory (sRF) modalities, meaning that they discharge in response to both visual and tactile stimuli. Furthermore, in some neurons, the visual and somatosensory responses are in spatial register. For instance, if a neuron responds to a touch or a joint displacement on the hand region, a visual response will also occur for stimuli nearby that hand. A similar pattern of discharge is found for most neurons in areas F4, 7b, the putamen, and for approximately half of the VIP neurons (Graziano & Gross, 1994; Rizzolatti, Scandolara, Matelli, & Gentilucci, 1981). Critically, each one of these cells codes for a region of peripersonal visual space, which is spatially aligned with the preferred somatosensory receptive field of that cell. For example, VIP neurons with somatosensory receptive fields

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Summary

on the right upper face, respond to stimuli presented to the right upper quadrant of the visual field. A premotor neuron with a tactile receptive field on the arm or hand discharges in response to visual stimuli approaching that body part. These cells constitute a functional network of bimodal neurons, supporting a common representation of the body surface, and the visual space nearby, which may be critical for guiding action. Another relevant finding is that the spatial selectivity of visual responses for some such multisensory neurons in area F4, VIP, and in the putamen is not merely retinotopic. For instance, for many PM neurons, and some neurons in the putamen, with a sRF on the arm, the corresponding vRF may shift along with the arm, if the arm is moved in space (Graziano, Taylor, Moore, & Cooke, 2002), while the effect of gaze shifting may be minimal (Rizzolatti et al., 1997). This neural system may be critical for coding space in egocentric coordinates centered on single body parts, thus putting each body part in strict spatial relationship to any visual event that may occur nearby. In this view, it is important that the brain keeps a constantly updated representation of the body surface and position, together with that of the space immediately around the body (Graziano & Botvinick, 2002; Maravita, 2006; Maravita et al., 2003). The buildup of this representation occurs through different sensory modalities. While vision is surely critical for coding the position of extrapersonal stimuli relative to the body, and of the body as well, proprioception is highly relevant for updating information about the body posture: in particular when the changes of the position of the hand, for example, when it crosses the midline, must be tracked without the aid of vision (Graziano, 1999; Obayashi, Tanaka, & Iriki, 2000). Human Studies Much work has been devoted to multisensory space representation in humans (Calvert et al., 2004; Spence & Driver, 2004). For example, Frassinetti et al. (2002) found that spatially congruent unattended sounds can improve the perception of below-threshold visual targets in a fashion that is reminiscent, though without any neurophysiologic support, of the response enhancement shown by SC audiovisual neurons in response to spatially coincident multisensory stimuli. Functional neuroimaging studies have shown that crossmodal binding of audiovisual stimuli may be critical for higher-order cognitive functions, other than spatial localization. In humans, delivering semantically congruent versus incongruent audiovisual speech stimuli modulates the activity in the superior temporal sulcus, as assessed by fMRI (Calvert, Campbell, & Brammer, 2000; Calvert et al., 2004). Within peripersonal space, the neuroimaging work of Macaluso and Driver (2003) has highlighted

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the functional link between vision and touch for spatial attention. In extrastriate visual cortex, tactile unattended stimuli enhance responses to attended, spatially coincident, visual targets (Macaluso, Frith, & Driver, 2000). This finding suggests that multimodal stimuli may increase the response of unimodal cortical areas, via back projections from multisensory areas, thus enhancing spatial perceptual processing. Finally, a recent body of evidence supports the idea that, in order to maintain the correspondence between the visual and the somatosensory maps for multisensory integration, the PPC plays a crucial role. In addition to the findings from neuroimaging and TMS experiments mentioned earlier (Bolognini & Maravita, 2007; Lloyd et al., 2003), neuropsychological evidence has been provided by Valenza and coworkers (2004), who reported a disruption of visual-tactile spatial interactions (see the crossmodal congruency task illustrated earlier: Spence et al., 2008) in a patient with bilateral parietal damage and Balint’s syndrome [a complex deficit including impairments of reaching (optic ataxia), and of visuo-spatial attention and orientation (Rizzo & Vecera, 2002; Vallar, 2007b)]. In sum, the vast and multidisciplinary literature on multisensory processing suggests that the integration of information coming from multiple senses has a critical role in the building up of a complete representation of the extrapersonal environment, and of the body, and in implementing and controlling the sensorimotor interactions between the body and objects in space.

SUMMARY Evidence from neuropsychological studies in brain-damaged patients with disorders of spatial cognition, from experiments in neurologically unimpaired subjects, and from neurophysiological studies in the animal concur to suggest that the internal representation of space includes a number of independent components. Major divisions are between spatial representations of the body versus extrapersonal space and far versus near extrapersonal space. A main factor that accounts for these multiple spatial representations is the kind of action that is performed and the motor effector used (e.g., space within/outside hand reach). Our experience of space is however highly integrated and, phenomenologically, largely unitary. We can perceive distant objects and, at the same time, attend to the voice of a friend nearby, or walk to a distant target location, while we reach for something in our pocket or scratch our head. In all these examples, multiple frames of references and effectors can be used simultaneously, and many different inputs, located in different space sectors, or on our own body, can be perceived

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as a continuous and uniform perceptual experience. This is achieved largely though the integration of the diverse sensory inputs, which continuously reach the different sensory receptors of our body. Furthermore, in the intact brain, the multiple frames of reference are used simultaneously and with comparable efficiency, to achieve effectively any kind of perceptual/motor goal at any given time. The neurological underpinnings of these spatial representations, which link perception and action, are being progressively discovered and clarified by the modern neurosciences. They include the frontal PMC and the PPC, as well as some subcortical structures, such as the thalamus, the basal ganglia, and the SC. These different structures can exert their control over different aspects of space representation because they hold, on the one hand, a high level of specificity for some given perceptual/motor functions, and, on the other hand, are highly interconnected through cortico-cortical and cortico-subcortical connections. A unitary representation of space and of the body in space can be only produced by the integrity of such complex networks, as shown by the variegate clinical pictures described in the neuropsychological literature.

Bolognini, N., & Maravita, A. (2007). Proprioceptive alignment of visual and somatosensory maps in the posterior parietal cortex. Current Biology, 17, 1890–1895. Bonnier, P. (1905). L’aschématie. Revue Neurologique, 13, 605–609. Brain, W. R. (1941). Visual disorientation with special reference to lesions of the right cerebral hemisphere. Brain, 64, 244–272. Calvert, G. A., Campbell, R., & Brammer, M. J. (2000). Evidence from functional magnetic resonance imaging of crossmodal binding in the human heteromodal cortex. Current Biology, 10, 649–657. Calvert, G. A., Spence, C., &, Stein, B. E. (Eds.). (2004). The handbook of multisensory processes. Cambridge, MA: MIT Press. Colby, C. L., & Goldberg, M. E. (1999). Space and attention in parietal cortex. Annual Review of Neuroscience, 22, 319–349. Committeri, G., Pitzalis, S., Galati, G., Patria, F., Pelle, G., Sabatini, U., et al. (2007). Neural bases of personal and extrapersonal neglect in humans. Brain, 130, 431–441. Cowey, A., Small, M., & Ellis, S. (1994). Left visuo-spatial neglect can be worse in far than in near space. Neuropsychologia, 32, 1059–1066. Critchley, M. (1953). The parietal lobes. New York: Hafner. Critchley, M. (1979). The divine banquet of the brain and other essays. New York: Raven Press. Cutting, J. E., & Vishton, P. M. (1995). Perceiving layout and knowing distances: The integration, relative potency, and contextual use of different information about depth. In W. Epstein & S. Rogers (Eds.), Handbook of perception and cognition: Perception of space and motion (Vol. 5, pp. 69–117). San Diego, CA: Academic Press. di Pellegrino, G., Làdavas, E., & Farnè, A. (1997, August 21). Seeing where your hands are. Nature, 388, 730.

REFERENCES Aglioti, S., Smania, N., & Peru, A. (1999). Frames of reference for mapping tactile stimuli in brain-damaged patients. Journal of Cognitive Neuroscience, 11, 67–79. Andersen, R. A., Snyder, L. H., Bradley, D. C., & Xing, J. (1997). Multimodal representation of space in the posterior parietal cortex and its use in planning movements. Annual Review of Neuroscience, 20, 303–330. Bartolomeo, P., Perri, R., & Gainotti, G. (2004). The influence of limb crossing on left tactile extinction. Journal of Neurology, Neurosurgery, and Psychiatry, 75, 49–55. Bartolomeo, P., Thiebaut de Schotten, M., & Doricchi, F. (2007). Left unilateral neglect as a disconnection syndrome. Cerebral Cortex, 17, 2479–2490. Berti, A., & Frassinetti, F. (2000). When far becomes near: Remapping of space by tool use. Journal of Cognitive Neuroscience, 12, 415–420. Berti, A., & Rizzolatti, G. (2002). Coding near and far space. In H. O. Karnath, A. D. Milner, & G. Vallar (Eds.), The cognitive and neural bases of spatial neglect (pp. 119–129). Oxford: Oxford University Press. Biguer, B., Donaldson, I. M. L., Hein, A., & Jeannerod, M. (1988). Neck muscle vibration modifies the representation of visual motion and direction in man. Brain, 111, 1405–1424. Binkofski, F., Buccino, G., Dohle, C., Seitz, R. J., & Freund, H. J. (1999). Mirror agnosia and mirror ataxia constitute different parietal lobe disorders. Annals of Neurology, 46, 51–61. Bisiach, E., Perani, D., Vallar, G., & Berti, A. (1986). Unilateral neglect: Personal and extrapersonal. Neuropsychologia, 24, 759–767. Bisiach, E., & Vallar, G. (2000). Unilateral neglect in humans. In F. Boller, J. Grafman, & G. Rizzolatti (Eds.), Handbook of neuropsychology (2 ed., Vol. 1, pp. 459–502). Amsterdam: Elsevier Science, B.V. Bjoertomt, O., Cowey, A., & Walsh, V. (2002). Spatial neglect in near and far space investigated by repetitive transcranial magnetic stimulation. Brain, 125, 2012–2022.

c15.indd Sec5:334

Driver, J., & Spence, C. (1994). Spatial synergies between spatial and visual attention. In C. Umiltà & M. Moscovitch (Eds.), Attention and performance: Conscious and nonconscious information processing (Vol. 15, pp. 311–331). Cambridge, MA: MIT Press. Driver, J., & Spence, C. (1998). Attention and the crossmodal construction of space. Trends in Cognitive Sciences, 2, 254–262. Duhamel, J.-R., Bremmer, F., BenHamed, S., & Graf, W. (1997). Spatial invariance of visual receptive fields in parietal cortex neurons. Nature Neuroscience, 389, 845–848. Duhamel, J.-R., Colby, C. L., & Goldberg, M. E. (1998). Ventral intraparietal area of the macaque: Congruent visual and somatic response properties. Journal of Neurophysiology, 79, 126–136. Farnè, A., & Làdavas, E. (2000). Dynamic size-change of hand peripersonal space following tool use. NeuroReport, 11, 1645–1649. Felician, O., Ceccaldi, M., Didic, M., Thinus-Blanc, C., & Poncet, M. (2003). Pointing to body parts: A double dissociation study. Neuropsychologia, 41, 1307–1316. Felician, O., Romaiguère, P., Anton, J. L., Nazarian, B., Roth, M., Poncet, M., et al. (2004). The role of human left superior parietal lobule in body part localization. Annals of Neurology, 55, 749–751. Fogassi, L., Gallese, V., di Pellegrino, G., Fadiga, L., Gentilucci, M., Luppino, G., et al. (1992). Space coding by premotor cortex. Experimental Brain Research, 89, 686–690. Fogassi, L., Gallese, V., Fadiga, L., Luppino, G., Matelli, M., & Rizzolatti, G. (1996). Coding of peripersonal space in inferior premotor cortex (area F4). Journal of Neurophysiology, 76, 141–157. Frassinetti, F., Bolognini, N., & Làdavas, E. (2002). Enhancement of visual perception by crossmodal visuo-auditory interaction. Experimental Brain Research, 147, 332–343. Funnell, M. G., Corballis, P. M., & Gazzaniga, M. S. (2000). Hemispheric interactions and specializations: Insights from the split brain. In F. Boller, J. Grafman, & G. Rizzolatti (Eds.), Handbook of neuropsychology (pp. 103–120). Amsterdam: Elsevier Science, B.V.

8/18/09 5:27:21 PM

References 335 Galati, G., Committeri, G., Sanes, J. N., & Pizzamiglio, L. (2001). Spatial coding of visual and somatic sensory information in body-centred coordinates. European Journal of Neuroscience, 14, 737–746.

Lacquaniti, F. (1997). Frames of reference in sensorimotor coordination. In F. Boller & J. Grafman (Eds.), Handbook of neuropsychology (Vol. 11, pp. 27–64). Amsterdam: Elsevier.

Galati, G., Lobel, E., Vallar, G., Berthoz, A., Pizzamiglio, L., & Le Bihan, D. (2000). The neural basis of egocentric and allocentric coding of space in humans: A functional magnetic resonance study. Experimental Brain Research, 133, 156–164.

Làdavas, E. (2002). Functional and dynamic properties of visual peripersonal space. Trends in Cognitive Sciences, 6, 17–22.

Gallace, A., & Spence, C. (2008). The cognitive and neural correlates of “tactile consciousness”: A multisensory perspective. Consciousness and Cognition, 17, 370–407. Galletti, C., Battaglini, P. P., & Fattori, P. (1993). Parietal neurons encoding spatial locations in craniotopic coordinates. Experimental Brain Research, 96, 221–229. Graziano, M. S. A. (1999). Where is my arm? The relative role of vision and proprioception in the neuronal representation of limb position. Proceedings of the National Academy of Sciences, USA, 96, 10418–10421. Graziano, M. S. A., & Botvinick, M. M. (2002). How the brain represents the body: Insights from neurophysiology and psychology. In W. Prinz & B. Hommel (Eds.), Common mechanisms in perception and action: Attention and performance XIX (pp. 136–157). Oxford: Oxford University Press.

Macaluso, E., & Driver, J. (2003). Multimodal spatial representations in the human parietal cortex: Evidence from functional imaging. Advances in Neurology, 93, 219–233. Macaluso, E., Frith, C. D., & Driver, J. (2000, August 18). Modulation of human visual cortex by crossmodal spatial attention. Science, 289, 1206–1208. Maravita, A. (2006). From “body in the brain” to “body in space.” Sensory and intentional components of body representation. In G. Knoblich, I. Thornton, M. Grosjean, & M. Shiffrar (Eds.), Human body perception from the inside out (pp. 65–88). New York: Oxford University Press. Maravita, A., Clarke, K., Husain, M., & Driver, J. (2002). Active tool use with the contralesional hand can reduce cross-modal extinction of touch on that hand. Neurocase, 8, 411–416.

Graziano, M. S. A., & Gross, C. G. (1994). The representation of extrapersonal space: A possible role for bimodal, visual-tactile neurons. In M. S. Gazzaniga (Ed.), The cognitive neurosciences (pp. 1021–1034). Cambridge, MA: MIT Press.

Maravita, A., & Driver, J. (2004). Crossmodal integration and spatial attention in relation to tool-use and mirror-use: Representing and extending multimodal space near the hand. In G. A. Calvert, C. Spence, & B. E. Stein (Eds.), Handbook of multisensory processes (pp. 819–835). Cambridge, MA: MIT Press.

Graziano, M. S. A., Reiss, L. A., & Gross, C. G. (1999, February 4). A neuronal representation of the location of nearby sounds. Nature, 397, 428–430.

Maravita, A., Husain, M., Clarke, K., & Driver, J. (2001). Reaching with a tool extends visual-tactile interactions into far space: Evidence from cross-modal extinction. Neuropsychologia, 39, 580–585.

Graziano, M. S. A., Taylor, C. S. R., Moore, T., & Cooke, D. F. (2002). The cortical control of movement revisited. Neuron, 36, 349–362.

Maravita, A., & Iriki, A. (2004). Tools for the body (schema). Trends in Cognitive Sciences, 8, 79–86.

Grüsser, O.-J., & Landis, T. (1991). Visual agnosias and other disturbances of visual perception and cognition (Vol. 12). Houndmills, Basingstoke: Macmillan. Guariglia, C., & Antonucci, G. (1992). Personal and extrapersonal space: A case of neglect dissociation. Neuropsychologia, 30, 1001–1009. Halligan, P. W., Fink, G. R., Marshall, J. C., & Vallar, G. (2003). Spatial cognition: Evidence from visual neglect. Trends in Cognitive Sciences, 7, 125–133. Halligan, P. W., & Marshall, J. C. (1989). Is neglect (only) lateral? A quadrant analysis of line cancellation. Journal of Clinical and Experimental Neuropsychology, 11, 793–798. Halligan, P. W., & Marshall, J. C. (1991, April 11). Left neglect for near but not far space in man. Nature, 350, 498–500. Head, H., & Holmes, G. (1911). Sensory disturbances from cerebral lesions. Brain, 34, 102–254. Howard, I. P., & Templeton, W. B. (1966). Human spatial orientation. London: Wiley.

Maravita, A., Spence, C., Clarke, K., Husain, M., & Driver, J. (2000). Vision and touch through the looking glass in a case of crossmodal extinction. NeuroReport, 11, 3521–3526. Maravita, A., Spence, C., & Driver, J. (2003). Multisensory integration and the body schema: Close to hand and within reach. Current Biology, 13, 531–539. Maravita, A., Spence, C., Sergent, C., & Driver, J. (2002). Seeing your own touched hands in a mirror modulates cross-modal interactions. Psychological Science, 13, 350–355. Marshall, J. C., Fink, G. R., Halligan, P. W., & Vallar, G. (2002). Spatial awareness: A function of the posterior parietal lobe? Cortex, 28, 253–257. Merleau-Ponty, M. (1945). Phenomenology of perception. London: Routledge, 2002. Mesulam, M.-M. (2002). Functional anatomy of attention and neglect: From neurons to networks. In H.-O. Karnath, A. D. Milner, & G. Vallar (Eds.), The cognitive and neural bases of spatial neglect (pp. 33–45). Oxford: Oxford University Press.

Iriki, A., Tanaka, M., & Iwamura, Y. (1996). Coding of modified body schema during tool use by macaque postcentral neurones. NeuroReport, 7, 2325–2330.

Milner, B. (1971). Interhemispheric differences in the localization of psychological processes in man. British Medical Bulletin, 27, 272–277.

Jewesbury, E. C. O. (1969). Parietal lobe syndromes. In P. J. Vinken & G. W. Bruyn (Eds.), Handbook of clinical neurology (Vol. 2, pp. 680– 699). Amsterdam: North Holland.

Obayashi, S., Tanaka, M., & Iriki, A. (2000). Subjective image of invisible hand coded by monkey intraparietal neurons. NeuroReport, 11, 3499–3505.

Karnath, H.-O. (2001). New insights into the functions of the superior temporal cortex. Nature Reviews Neuroscience, 2, 568–576.

Ortigue, S., Mégevand, P., Perren, F., Landis, T., & Blanke, O. (2006). Double dissociation between representational personal and extrapersonal neglect. Neurology, 66, 1414–1417.

Kerkhoff, G. (2003). Modulation and rehabilitation of spatial neglect by sensory stimulation. Progress in Brain Research 142, 257–271. Kooistra, C. A., & Heilman, K. M. (1989). Hemispatial visual inattention masquerading as hemianopia. Neurology, 39, 1125–1127. Lackner, J. R. (1988). Some proprioceptive influences on the perceptual representation of body shape and orientation. Brain, 111, 281–297.

c15.indd Sec6:335

Lloyd, D. M., Shore, D. I., Spence, C., & Calvert, G. A. (2003). Multisensory representation of limb position in human premotor cortex. Nature Neuroscience, 6, 17–18.

Paillard, J. (1971). Les determinants moteurs de l’organisation de l’espace. Cahiers de Psycologie, 14, 261–316. Paterson, A., & Zangwill, O. L. (1944). Disorders of visual space perception associated with lesions of the right cerebral hemisphere. Brain, 67, 331–335.

8/18/09 5:27:22 PM

336

Personal and extrapersonal Spatial Perception

Pavani, F., Spence, C., & Driver, J. (2000). Visual capture of touch: Out-ofthe-body experiences with rubber gloves. Psychological Science, 11, 353–359. Pegna, A. J., Petit, L., Caldara-Schnetzer, A. S., Khateb, A., Annoni, J. M., Sztajzel, R., et al. (2001). So near yet so far: Neglect in far or near space depends on tool use. Annals of Neurology, 50, 820–822. Pitzalis, S., & Di Russo, F. (2001). Spatial anisotropy of saccadic latency in normal subjects and brain-damaged patients. Cortex, 37, 475–492. Pitzalis, S., Di Russo, F., Spinelli, D., & Zoccolotti, P. (2001). Influence of the radial and vertical dimensions on lateral neglect. Experimental Brain Research, 136, 281–294. Previc, F. H. (1998). The neuropsychology of 3-D space. Psychological Bulletin, 2, 123–164. Rapcsak, S. Z., Cimino, C. R., & Heilman, K. M. (1988). Altitudinal neglect. Neurology, 38, 277–281. Rizzo, M., & Vecera, S. P. (2002). Psychoanatomical substrates of Balint’s syndrome. Journal of Neurology, Neurosurgery, and Psychiatry, 72, 162–178. Rizzolatti, G., Fadiga, L., Fogassi, L., & Gallese, V. (1997, July 11). The space around us. Science, 277, 190–191. Rizzolatti, G., Luppino, G., & Matelli, M. (1998). The organization of the cortical motor system: New concepts. Electroencephalography and Clinical Neurophysiology, 106, 283–296. Rizzolatti, G., & Matelli, M. (2003). Two different streams form the dorsal visual system: Anatomy and functions. Experimental Brain Research, 153, 146–157. Rizzolatti, G., Matelli, M., & Pavesi, G. (1983). Deficits in attention and movement following the removal of postarcuate (area 6) and prearcuate (area 8) cortex in macaque monkeys. Brain, 106, 655–673. Rizzolatti, G., Scandolara, C., Matelli, M., & Gentilucci, M. (1981). Afferent properties of periarcuate neurons in macaque monkeys: Pt. I. Somatosensory responses. Behavioral Brain Research, 2, 125–146. Rossetti, Y., & Rode, G. (2002). Reducing spatial neglect by visual and other sensory manipulations: Non-cognitive (physiological) routes to the rehabilitation of a cognitive disorder. In H.-O. Karnath, A. D. Milner, & G. Vallar (Eds.), The cognitive and neural bases of spatial neglect (pp. 375–396). Oxford: Oxford University Press. Schieber, M. H. (2000). Inactivation of the ventral premotor cortex biases the laterality of motoric choices. Experimental Brain Research, 130, 497–507. Shelton, P. A., Bowers, D., & Heilman, K. M. (1990). Peripersonal and vertical neglect. Brain, 113, 191–205. Smania, N., & Aglioti, S. (1995). Sensory and spatial components of somaesthetic deficits following right brain damage. Neurology, 45, 1725–1730. Spence, C., & Driver, J. (1997). Audiovisual links in exogenous covert spatial orienting. Perception and Psychophysics, 59, 1–22. Spence, C., & Driver, J. (Eds.). (2004). Crossmodal space and crossmodal attention. Oxford: Oxford University Press. Spence, C., Kingstone, A., Shore, D. I., & Gazzaniga, M. S. (2001). Representation of visuotactile space in the split brain. Psychological Science, 12, 90–93. Spence, C., Pavani, F., Maravita, A., & Holmes, N. (2004). Multisensory contributions to the 3-D representation of visuotactile peripersonal space in humans: Evidence from the crossmodal congruency task. Journal of Physiology Paris, 98, 171–189.

c15.indd Sec6:336

Spence, C., Pavani, F., Maravita, A., & Holmes, G. (2008). Multisensory contributions to the representation of peripersonal space in humans: Evidence from the crossmodal congruency task. In M. Lin & M. Otaduy (Eds.), Haptic rendering: Foundations, algorithms, and applications (pp. 21–52). Wellesley, MA: AK Peters. Stein, B. E. (2005). The development of a dialogue between cortex and midbrain to integrate multisensory information. Experimental Brain Research, 166, 305–315. Stein, B. E., & Meredith, M. A. (1993). The merging of the senses. Cambridge, MA: MIT Press. Valenza, N., Murray, M. M., Ptak, R., & Vuilleumier, P. (2004). The space of senses: Impaired crossmodal interactions in a patient with Balint syndrome after bilateral parietal damage. Neuropsychologia, 42, 1737–1748. Vallar, G. (2000). The methodological foundations of human neuropsychology: Studies in brain-damaged patients. In F. Boller, J. Grafman, & G. Rizzolatti (Eds.), Handbook of neuropsychology (2nd ed., Vol. 1, pp. 305–344). Amsterdam: Elsevier. Vallar, G. (2001). Extrapersonal visual unilateral spatial neglect and its neuroanatomy. Neuroimage, 14, S52–S58. Vallar, G. (2003). Spatial disorders. In L. Nadel (Ed.), Encyclopedia of cognitive science (Vol. 4, pp. 125–131). London: Macmillan Reference. Vallar, G. (2007a). A hemispheric asymmetry in somatosensory processing. Behavioral and Brain Sciences, 30, 223–224. Vallar, G. (2007b). Spatial neglect, Balint-Homes’ and Gerstmann’s syndrome, and other spatial disorders. CNS Spectrum, 12, 527–536. Vallar, G., Guariglia, C., Nico, D., & Bisiach, E. (1995). Spatial hemineglect in back space. Brain, 118, 467–472. Vallar, G., Guariglia, C., & Rusconi, M. L. (1997). Modulation of the neglect syndrome by sensory stimulation. In P. Thier & H.-O. Karnath (Eds.), Parietal lobe contributions to orientation in 3D space (pp. 555578). Heidelberg: Springer-Verlag. Vallar, G., Lobel, E., Galati, G., Berthoz, A., Pizzamiglio, L., & Le Bihan, D. (1999). A fronto-parietal system for computing the egocentric spatial frame of reference in humans. Experimental Brain Research, 124, 281–286. Vallar, G., & Papagno, C. (2003). Pierre Bonnier ’s. (1905). Cases of bodily “aschématie.” In C. Code, C.-W. Wallesch, Y. Joanette, & A. R. Lecours (Eds.), Classic cases in neuropsychology (Vol. 2, pp. 147–170). Hove, East Sussex: Psychology Press. Wallace, M. T., & Stein, B. E. (1994). Cross-modal synthesis in the midbrain depends on input from cortex. Journal of Neurophysiology, 71, 429–432. Wardak, C., Olivier, E., & Duhamel, J.-R. (2002). Neglect in monkeys: Effect of permanent and reversible lesions. In H. O. Karnath, A. D. Milner, & G. Vallar (Eds.), The cognitive and neural bases of spatial neglect (pp. 47–58). Oxford: Oxford University Press. Weiss, P. H., Marshall, J. C., Wunderlich, G., Tellmann, L., Halligan, P. W., Freund, H. J., et al. (2000). Neural consequences of acting in near versus far space: A physiological basis for clinical dissociations. Brain, 123, 2531–2541. Weiss, P. H., Marshall, J. C., Zilles, K., & Fink, G. R. (2003). Are action and perception in near and far space additive or interactive factors? NeuroImage, 18, 837–846. Yamamoto, S., & Kitazawa, S. (2001). Reversal of subjective temporal order due to arm crossing. Nature Neuroscience, 4, 759–765.

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Chapter 16

The Mirror Neuron System GIACOMO RIZZOLATTI AND MADDALENA FABBRI-DESTRO

Rizzolatti, 1992; Gallese, Fadiga, Fogassi, and Rizzolatti, 1996; Rizzolatti, Fadiga, Fogassi, & Gallese, 1996). Mirror neurons show a close relationship between the motor acts they code and the visual motor acts they respond to. Using as classification criterion the congruence between the executed and observed motor acts effective in triggering them, the mirror neurons have been subdivided into two broad classes: strictly congruent and broadly congruent neurons (Gallese et al., 1996). Mirror neurons are defined as strictly congruent when the observed and executed effective motor acts are identical

In the first part of the chapter, we review the functional organization of the mirror neuron system in the monkey; in the second part, we examine the mirror neuron system of humans. The distinction between monkey and human mirror neuron system is of great theoretical importance because, although the basic neural mechanism is the same in the two species, some of the properties of the human mirror neuron system are not present in the monkey. This difference often has not been recognized, with the properties of monkey mirror neuron system being uncritically attributed to humans. This sometimes led to wrong conclusions on the possible explanatory role of the mirror neurons system in some typically human cognitive functions.

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MIRROR NEURON SYSTEM IN MONKEYS Mirror neurons are a distinct class of motor neurons that discharge both when individuals perform a specific motor act and when they observe the same motor act done by another individual (Figure 16.1). Mirror neurons have been originally discovered in the rostral sector of the ventral premotor cortex (area F5). Subsequently, neurons with the same characteristics were also found in the monkey inferior parietal lobule. Figure 16.2 shows the lateral view of the monkey cerebral cortex with the subdivisions of the parietal and premotor areas. The most detailed available description of the properties of mirror neurons is that of area F5. These neurons, as all neurons of this area (Rizzolatti & Luppino, 2001), discharge when the monkey makes specific object-directed motor acts, such as grasping, tearing, holding, and, more rarely, bringing food to the mouth. Unlike another category of visuo-motor neurons of area F5 (“canonical neurons,” Murata et al., 1997), they do not fire in response to a simple presentation of objects including food. The observation of intransitive actions, including mimed actions, is also ineffective (Di Pellegrino, Fadiga, Fogassi, Gallese, &

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Figure 16.1 Visual and motor responses of a grasping mirror neuron recorded from area F5. Note: The neuron fires during the observation of grasping done by the experimenter (A) and during grasping done by the monkey (B). From “Understanding Motor Events: A Neurophysiological Study,” by G. Di Pellegrino, L. Fadiga, L. Fogassi, V. Gallese, and G. Rizzolatti, 1992, Experimental Brain Research, 91, pp. 176–80. Reprinted with permission.

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in terms of goal (e.g., grasping) and in terms of the way in which that goal is achieved (e.g., precision grip). In contrast, mirror neurons are defined as broadly congruent when there is a similarity, but not identity, between the observed and executed effective motor acts. Among the different types of broadly congruent neurons, the most common is constituted of neurons that become active during the execution of a specific motor act made by the monkey (e.g., grasping, holding, or manipulating), but visually respond to more than one motor act (e.g., manipulation and grasping). In the first studies on mirror neurons, it was reported that these neurons do not discharge during the observation of goal-directed actions done using tools (Gallese et al., 1996; Rizzolatti et al., 1996). Subsequently it was shown, however, that, following a relatively long period during which monkeys observed the experimenters perform actions using tools, some mirror neurons respond, although weakly, also to this type of actions (Rizzolatti & Arbib, 1998). More recently, Ferrari, Rozzi, and Fogassi (2005) reported that in a specific lateral sector of F5, there are neurons that discharge very vigorously to the observation of tool use (e.g., a stick or a pair of pliers). It is not clear whether these neurons, as those previously observed, derived this property because of (prolonged action observation) or are a specific sets neurons coding grasping even when done by nonbiological effectors.

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arcuate sulcus; AS ⫽ superior arcuate sulcus; C ⫽ central sulcus; Ca ⫽ calcarine fissure; Cg ⫽ cingulate cortex; FEF ⫽ frontal eye field; IP ⫽ intraparietal sulcus; L ⫽ lateral sulcus; LIP ⫽ lateral intraparietal area; Lu ⫽ lunate sulcus; MIP ⫽ medial intraparietal area; P ⫽ principal sulcus; ST ⫽ superior temporal sulcus.

Anatomical Organization of the Mirror Neuron System in the Monkey The mirror neuron system of the monkey consists of two main nodes: area F5 and the inferior parietal lobule (IPL). Area F5 is not homogeneous. Cytoarchitectonically, it consists of three sectors: a sector lying on the cortical convexity (F5c), a sector located on the posterior bank of the arcuate sulcus dorsally (F5p), and a sector located on the posterior bank of the same sulcus ventrally (F5a) (Figure 16.3; Luppino, Belmalih, Borra, Gerbella, & Rozzi, 2005; Nelissen, Luppino, Vanduffel, Rizzolatti, & Orban, 2005). Mirror neurons appear to be located mostly in F5c, while canonical neurons (those responding to mere object presentation) have been most frequently found in F5p. No neurophysiological data exist on sector F5a (Rizzolatti & Luppino, 2001). An functional magnetic resonance imaging (fMRI) study revealed an interesting different functional difference between F5c, on one side, and F5a on the other. Monkeys, trained to fixate a point of light, were presented with video clips showing hand grasping actions in two main conditions. In one, they saw a full view of the agent performing the action, while in the other only the hand grasping an object. The results showed that the full view of the grasping activated both F5c and F5a, while the view of the isolated grasping hand activated only F5a (Nelissen et al.,

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Figure 16.3 Areas forming monkey ventral premotor cortex (PMv). Note: Area F5 consists of three architectonically subareas: F5c, F5p, and F5a. The large rectangle showing PMv areas is an enlarged projection of the rectangle indicated on the lateral view of the monkey brain. AIP ⫽ anterior intraparietal area; AI ⫽ inferior arcuate sulcus; AS ⫽ superior arcuate sulcus; C ⫽ central sulcus; Ca ⫽ calcarine fissure; Cg ⫽ Cingulate cortex; F5p and F5a are located on the posterior bank of the arcuate sulcus. F5c ⫽ F5 convexity; F5p ⫽ F5 posterior; F5a ⫽ F5 anterior; FEF ⫽ frontal eye field; IP ⫽ intraparietal sulcus; L ⫽ lateral sulcus; LIP ⫽ lateral intraparietal area; Lu ⫽ lunate sulcus; MIP ⫽ medial intraparietal area; P ⫽ principal sulcus; ST ⫽ superior temporal sulcus.

2005). It appears therefore that, while F5c requires an “embodied” situation to be activated, the more rostral F5a codes grasping in a more abstract way, becoming active also when only a part of the elements normally constituting the grasping scene are present. It is well known from the studies of Perrett and coworkers that neurons of STS region code motor acts performed by living individuals (Jellema, Baker, Wicker, & Perrett, 2000; Perrett et al., 1989). According to their data, neurons of the upper bank of STS respond to locomotion, axial movements, and movements of the head and eyes. Hand movements are located essentially in the lower bank of STS (Perrett, Mistlin, Harries, & Chitty, 1990). fMRI data confirmed a representation of hand movements in the lower bank (Nelissen et al., in press). They showed, however, that also STPm, an area located in the upper bank, responds to observation of hand grasping movement. It is very important, in the present context, to note that there is no evidence that the areas forming the STS region respond to active movements. This region cannot be considered, therefore, to be properly part of the mirror neuron system, but rather as a sector of the visual system devoted, besides other functions, to the description of the hand actions. As shown in Figure 16.2, the cortical convexity of inferior parietal lobule (IPL) consists of four areas: PF, PFG, PG, and Opt (Pandya & Seltzer, 1982; see for more recent data Gregoriou, Borra, Matelli, & Luppino, 2006). PF and PFG correspond essentially to area 7b of Vogt and Vogt (1919), while PG and Opt form area 7a. Evidence in

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support of the validity of this parcellation was provided by Rozzi et al. (2006), who showed that each of these four areas has a distinct connectivity pattern. The parcellation of IPL convexity into four areas is also in accord with single neuron studies showing that the rostral most sector, area PF, contains essentially mouth-related neurons, whereas PFG is rich in neurons related to hand actions, although intermixed with others firing in association with arm movements, and PG is mostly related to arm movements (Ferrari, Gregoriou, et al., 2009; Hyvarinen, 1982). It is well known from the studies of Sakata Taira, Murata, and Mine (1995) that area AIP plays a fundamental role in visuo-motor transformation necessary for grasping objects. Mirror neurons were not reported in this area. However, evidence from a recent, detailed study of the mirror properties in IPL indicate that indeed there is a population of AIP neurons endowed of mirror properties (Rozzi, Ferrari, Bonini, Rizzolatti, & Fogassi, 2008). Functions of the Mirror Neurons in the Monkey Before discussing the functions of the mirror neurons, it is important to define three terms at the basis of motor organization: movement, motor act, and action. Movement indicates a displacement of body parts. It does not include the concept of goal. Motor act defines a movement or, most commonly, a series of movements performed to reach a goal (e.g., grasping an object). Finally, motor action is a series of motor acts (e.g., reaching, grasping, bringing to the mouth) that allow individuals to fulfill their intention (e.g., eating). Understanding the Goal of the Motor Acts The most widely accepted hypothesis on the functional role of the mirror neurons is that they play a role in understanding the goal of the observed motor acts (Rizzolatti, Fogassi, & Gallese, 2001). The proposed mechanism is the following: Individuals know the outcome of their motor acts. Thus, when the mirror neurons of an observing individual, which code a given motor act (e.g., grasping), discharge in response to the observation of that motor act (grasping) done by another individual, the observer understands its goal because that discharge corresponds to the one that occurs when the observer wants to achieve the same goal. What is the evidence in favor of such a role of the mirror neurons? Often the most direct way to establish the function of a neural system is to destroy it and look for deficits in the individual’s behavior. However, destroying the entire mirror neuron system could produce such a general cognitive deficit that discovering the specific function of the mirror neuron system would be impossible.

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Figure 16.4 (Figure C.26 in color section) Neuron responding to action observation in full vision and in hidden condition. Note: The lower part of each panel illustrates schematically the experimenter ’s action from the monkey’s vantage point: the experimenter ’s hand starts from a fixed position, moves toward an object and grasps it (A and B), or mimes grasping (C and D). A and C: The monkey sees the whole action; B and D: The monkey sees only the initial part of the action. Note that in hidden conditions, the monkey knows whether the action is directed toward an object. The asterisk indicates the location of a stationary marker. In hidden conditions, the experimenter ’s hand started to disappear from the monkey’s view when crossing the marker. The upper part of each panel shows the neuron responses and relative histograms recorded during the movement of the experimenter ’s hand shown in the lower panel. Rasters and histograms are aligned with the moment when the hand crossed the marker (red markers in the rasters). Green markers in the rasters indicate the movement onset, blue markers indicate the movement end. From “ ‘I Know What You Are Doing’: A Neurophysiological Study,” by M. A. Umiltà et al., 2001, Neuron, 32, 91–101. Reprinted with permission.

So a different strategy was adopted. To assess whether mirror neurons play a role in understanding motor acts, neurons’ responses were investigated when the monkeys could comprehend the meaning of a motor act without actually seeing it. If mirror neurons truly mediate understanding, their activity should reflect the meaning of the motor act rather then its visual features. Two series of experiments were carried out. The first series tested whether mirror neurons could recognize actions merely from their sounds (Kholer et al., 2002). The activity of mirror neurons was recorded while a monkey was observing a motor act, such as ripping a

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piece of paper or breaking a peanut shell, that is normally accompanied by a distinctive sound. Then, the monkey was presented with the sound alone. Many mirror neurons that had responded to visual observation of acts accompanied by sounds also responded to the sound alone. These neurons were named “audio-visual” mirror neurons. In the second series of experiments, it was hypothesized that if mirror neurons are involved in understanding an action, they should also discharge when the monkey does not actually see the action but has sufficient clues to create a mental representation of it. Thus, F5 mirror neurons were tested in two conditions (Umiltà et al., 2001). In one, the monkey was shown a fully visible motor act directed toward an object (“full vision” condition). In the other, the monkey saw the same act but with its final critical part hidden (“hidden” condition). The results showed that more than half of the F5 mirror neurons also discharged in the hidden condition (Figure 16.4). These experiments strongly support the notion that the activity of mirror neurons underpins the understanding of motor acts. Even when the motor act comprehension is possible on a nonvisual basis, such as sound or mental representation, mirror neurons equally discharge signaling the meaning of the motor act. Intention Understanding Voluntary actions are the external manifestations of an internally generated intention to act. The problem of intention has been traditionally considered a philosophical problem. However, some recent neurophysiological experiments appear to be able to provide a neural substrate to some aspects of motor intention. This is true both for the intention of the acting person and for the understanding of the intentions of others. An attempt was made to find out whether the intention behind an action is reflected in the initial motor acts of that action (Fogassi et al., 2005). To this purpose, monkeys were trained to grasp objects for two different goals. In the first case, the monkey had to grasp an object in order to place it into container; in the second, it had to grasp a piece of food to eat it. The initial motor acts, reaching and grasping, were identical in the two conditions, while the final goal of the two actions was different. The tested hypothesis was whether the different intentions underlying the two actions would manifest itself already at the start of the actions when the monkey performed the motor acts common to them. Grasping neurons were therefore recorded from the IPL and their discharge studied in the two conditions mentioned. The results showed that two-thirds of IPL grasping neurons discharged with a different intensity according to the final goal of the action in which grasping

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Figure 16.5 (Figure C.27 in color section) Activity of neurons recorded from the inferior parietal lobule (IPL) during object grasping. Note: (Upper) Apparatus and the paradigm used for the motor task. (Lower) The activity of three IPL neurons during grasping in the conditions “grasp to eat” (2b) and “grasp to place” (2a). Rasters and

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histograms are synchronized with the moment when the monkey touched the object to be grasped. From “Parietal Lobe: From Action Organization to Intention Understanding,” by L. Fogassi et al., 2005, Science, 29, pp. 662–667. Reprinted with permission.

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Organization to Intention Understanding,” by L. Fogassi et al., 2005, Science, 29, pp. 662–667. Reprinted with permission.

The experimenter grasps food either to eat it or to put it into a container. Conventions as in Figure 16.6. From “Parietal Lobe: From Action

was embedded (action-constrained neurons). Grasping in order to bring food to the mouth was the most represented motor act. Examples of action-constrained grasping neurons are shown in Figure 16.5. This action-constrained organization is appropriate for providing fluidity to action execution. Neurons coding a given motor act are functionally tuned for a given action and therefore linked with specific sets of neurons coding the next motor acts. This link determines, therefore, the formation of motor chains that lead to the final goal of the action. These motor chains appear to represent the neural substrate for motor intention of the agent.

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Many of the neurons discharging in relation to grasping tuned for a specific action, also have mirror properties, responding to the observation of actions done by others (Fogassi et al., 2005). To find out whether the visual responses of these neurons were also influenced by the actions in which the motor acts were embedded, the same two conditions that were used for studying their motor properties were used. Monkeys, instead of grasping objects, observed the experimenter performing the two actions. The results showed that the majority of IPL mirror neurons were differently activated when the observed motor act belonged to one action or another (Figure 16.6). What

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could be the explanation of this behavior? If we examine the motor behavior of action-constrained grasping neurons, it is frequently found that the neuron’s discharge continues if grasping is inserted in the appropriate action, although it stops abruptly in the nonappropriate action. This prolonged discharge suggests activation of neurons coding the next step of the executed action. In agreement with this interpretation are data on the receptive field properties of parietal neurons showing that motor acts performed actively, or even passive movements, activate neurons that code the next motor act in an action sequence (Obayashi et al., 2004). Thus, it is very likely that when an actionconstrained grasping neuron is activated by the observation of a grasping motor act inserted into its motor action, it triggers the whole motor chain in the observer, who, in this way, has an internal representation of the action that the agent intends to do. Thanks to this mechanism, the observer understands the intention of the agent. How can action observation activate the appropriate motor chain when the monkey actually sees only the first motor act of it? A systematic study of this problem has not been done. It is clear, however, from the grasping neuron behavior that an important factor in determining the neuron discharge is the type of stimulus that the agent interacts with. Food, for example, tends to activate eating chains as soon as the monkey sees the experimenter grasping it. Another factor is the statistical probability of a given action. Thus, for example, in a block of trials in which grasping is always followed by placing, grasping neurons that are tuned for placing become active. It is interesting to note, that in such a block of trials, if food, rather than an object, is grasped and placed into a container, grasping-to-eat neurons fire initially, then they stop firing while grasping-to-place neurons become active. Inter-Individual Communication Some mirror neurons located in area F5 become active when the monkey observes and execute mouth actions (Ferrari, Gallese, Rizzolatti, & Fogassi, 2003). Most of these mouth mirror neurons respond to the observation of ingestive actions such as biting, tearing with the teeth, sucking, licking, and so on (ingestive mouth mirror neurons). Their characteristics appear to be identical to that of hand mirror neurons. They do not respond to simple object presentation or to mouth-mimed actions and their visual response is often very specific for certain mouth acts. As hand mirror neurons, mouth mirror neurons can also be subdivided into “strictly congruent” and “broadly congruent” neurons. In addition to ingestive mouth mirror neurons, F5 also contains a small set of mouth mirror neurons that discharge when the monkey performs ingestive movements and responds to the obsevation of mouth actions typical of the monkey communicative repertoire, such as

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lips-smacking, lips protrusion, or tongue protrusion and not to, rather than to the observation of ingestive actions. These neurons have been, therefore, named communicative mouth mirror neurons. Examples of ingestive and communicative mouth mirror neurons are shown in Figure 16.7. The presence of neurons that discharge during ingestive movement but prefer, as visual stimulus, a communicative action may seem to be in contrast with the typical visuomotor coupling observed in other mirror neurons. There is, however, an interesting possibility, namely, that this type of neurons represents a transition between ingestive and communicative actions. The view that communicative actions may derive from other, evolutionary, more ancient actions is not new. Van Hoof (1967), for example, in his fundamental work on the origin of monkey communicative gestures, proposed that many the most common communicative monkey gestures, such as lip-smacking or lips protruding, are ritualizations of ingestive actions that monkeys use for affiliative purposes. In a similar vein, McNeilage (1998) suggested that the human vocal communication derived from the cyclic, open-close mandibular alternation originally evolved for food ingestion. The existence of a neurophysiological link between ingestive and communicative actions is in accord with these hypotheses and provide neurophysiological support for them. THE MIRROR NEURON SYSTEM IN HUMANS The mirror neuron mechanism directly matches a sensory description of a motor act ON the motor representation of the same motor act. The mirror mechanism may have different functions that depend, first of all, on the anatomical localization of neurons endowed with mirror properties. On this basis, two mirror neuron systems can be distinguished: one located on the lateral surface of the cortex (parietofrontal mirror system), the other in the insula and rostral cingulate (limbic mirror system). The human parieto-frontal system (Figure 16.8) mediates the same basic functions that the homologous mirror system mediates in the monkey: the understanding of the goal of actions done by others and their intention. It mediates also additional functions unique to humans: imitation and verbal communication. The limbic mirror system has a different functional role. It mediates the comprehension of others’ emotions. Anatomical Organization of the Parieto-Frontal Mirror Neuron System A large number of brain imaging studies showed that parietal and frontal areas that became active during the execution of voluntary actions are also active when an individual

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(Right) Communicative neuron. Same conventions. During observation of communicative actions the rasters and histograms alignment was made with the moment in which the action was fully expressed. From “Mirror Neurons Responding to the Observation of Ingestive and Communicative Mouth Actions in the Monkey Ventral Premotor Cortex,” by P. F. Ferrari, V. Gallese, G. Rizzolatti, and L. Fogassi, 2003, European journal of neuroscience, 17, pp. 1703–1714. Reprinted with permission.

observes similar actions done by others (see Rizzolatti & Craighero, 2004). These regions form the parieto-frontal human mirror neuron system. The two main nodes of this system are the inferior parietal lobule (IPL) and the ventral premotor cortex (PMv) plus the caudal part of the inferior frontal gyrus (IFG), roughly corresponding to the pars opercularis of Broca’s area. The localization of human parieto-frontal mirror neurons nicely corresponds to that of the homologous mirror neuron system in monkey. The human and monkey mirror neuron systems, however, are not identical. The human mirror system is larger and includes cortical sectors that are poorly developed or apparently absent in the monkey. Particularly interesting among them are the angular gyrus and the ventral part of the supramarginal gyrus.

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The observation of object-directed motor acts (transitive motor acts, like grasping an object) activates in humans the sector of the supramarginal gyrus that is located close to and within the intraparietal sulcus. This IPL sector, which in humans is relatively small, could correspond to a large sector of the monkey rostral IPL (areas PF, PFG, and AIP). In contrast, the observation of intransitive hand actions, be they symbolic, mimed or meaningless, activates essentially the angular gyrus (Lui et al., 2008). Note that intransitive hand actions do not belong to the monkeys’ motor repertoire. The observations of actions done with tools activates two parietal sectors: a region around the intraparietal sulcus (the same that also becomes active during the observation of transitive hand actions; Gazzola, Rizzolatti, Wicker, & Keysers, 2007; Orban et al., 2006) and a rostral part of the supramarginal gyrus (Orban et al., 2006). It has been suggested that these two sectors could underlie two different ways in which tool use is understood. The sector around the intraparietal sulcus could mediate an association between a tool and the tool use outcome, without an understanding of tool functioning. This association appears to be at the basis of the monkey’s capacity to learn tool use. In contrast, the rostral supramarginal gyrus could be involved in understanding the tool use in terms of tool mechanism. This capacity appears to be unique to humans (Johnson-Frey, 2004; Povinelli, 2000). As far as the frontal lobe is concerned, in addition to PMv, PMd has frequently been reported to be active in humans during the observation of actions done by others. This activation is especially strong in tasks in which the participants are subsequently required to perform the observed motor acts (Buccino et al., 2004; Grèzes, Armony, Rowe, & Passingham, 2003). Although it is possible that PMd activation is due to a mirror mechanism, it may also be that its activation depends on a mental rehearsal of the impending actions that the observer is required to perform. Functional Organization of the Parieto-Frontal Mirror Neuron System Somatotopy fMRI experiments showed that the human mirror neuron system has a somatotopic, albeit rather coarse, organization. Buccino et al. (2001) presented volunteers with video clips showing motor acts (grasping, biting, kicking) done with different effectors (mouth, arm/hand, leg/foot). The action could be object-directed (transitive actions) or mimed. The observation of transitive mouth movements produced activation of the lower part of PMv and of area 44 bilaterally. Activation foci were also found in the parietal lobe. One of them was located in the rostral part of

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the inferior parietal lobule (most likely area PF), while the other was located in the posterior part of the same lobule. The observation of mimed motor acts determined activation of the same premotor areas, but there was no parietal lobe activation. Observation of hand/arm transitive motor acts determined two areas of activation in the frontal lobe: one in area 44, and the other located in the precentral gyrus, dorsal to that found during the observation of mouth movements. There were again two activation foci in the parietal lobe. The rostral focus was, as in the case of mouth actions, in the rostral part of the inferior parietal lobule, but more caudally located, while the caudal focus was essentially in the same location as that for mouth actions. During the observation of mimed motor acts, the premotor activations were present, but not the parietal ones. Finally, the observation of transitive foot/leg motor acts determined an activation of a dorsal sector of the precentral gyrus and an activation of the posterior parietal lobe, partly overlapping with those seen during mouth and hand actions, partly extending more dorsally. Mimed foot motor acts produced premotor, but not parietal activations. The responses of the premotor cortex (PM) to the presentation of intransitive movements were investigated by Sakreida, Schubotz, Wolfensteller and von Cramon (2005) in an fMRI study. Distal, proximal, and axial motions were studied. The results showed an extended PM activation for each type of movement. Direct contrasts showed that the most significant activations were elicited in the PM ventrally for distal movements and dorsally for proximal movements. Axial movements activated the supplementary motor area. Wheaton, Thompson, Syngeniotis, Abbott, and Puce (2004) also found a somatotopic organization for intransitive movements PM, but was limited to the right hemisphere. Motor Acts Coded by the Mirror Neuron System There is clear evidence that the observation of motor acts that are richly represented in the observer motor repertoire determines a strong activation of the mirror neuron system. In an fMRI study, Calvo-Merino, Glaser, Grèzes, Passingham, and Haggard (2005) demonstrated that the observation of actions performed by others results in different cortical activations depending on the specific motor competencies of the tested individuals. The participants, who included classical dancers, teachers of Capoeira, and people who had never taken a dancing lesson, were shown a video of Capoeira dance steps. The observation of the dance steps caused a greater activation of the mirror neuron system in the teachers than in either the classical dancers or the beginners. Conversely, the observation of classical dance steps resulted in a much stronger activation

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of the classical dancers’ mirror neurons compared to those of the Capoeira teachers and the beginners. In a later experiment, the same researchers tried to understand if the differences in the activation were due to whether the Capoeira teachers had greater visual experience of their dance steps or a knowledge of how to execute them, compared to the classical dancers (Calvo-Merino, Grèzes, Glaser, Passingham, & Haggard, 2006). In Capoeira, some steps are executed by both men and women, while others are different for the two sexes—obviously all the dancers, men and women, must know the steps that their partner will execute. Calvo-Merino and her colleagues showed the Capoiera teachers a video showing dance steps executed by men and women. The results showed that the mirror neuron system was activated more strongly by the sight of the dance steps executed by members of the observer ’s sex, indicating that the activation was regulated by motor practice and not by visual experience (see Figure 16.9). The data by Calvo-Merino were extended by Cross, de Hamilton, and Grafton (2006) in a study in which expert dancers learned and rehearsed novel, complex wholebody dance sequences for 5 weeks. Brain activity was recorded weekly by fMRI as dancers observed and visualized performing different movement sequences. Half these sequences were rehearsed and half were unpracticed control movements. The hypothesis was that the activity in premotor areas would increase as participants observed and simulated movements that they had learned outside the scanner. When dancers observed and simulated another dancer ’s movements, brain regions classically associated with action observation were active, including STS, IPL, and PMv. Critically, IPL and PMv activity was modulated as a function of dancers’ ratings of their own ability to perform the observed movements and their motor experience. These data show that the premotor and parietal mirror neuron system contributes to coding the observed actions by mapping them onto corresponding motor programs of the observer. But how would the mirror neuron system respond to the observation of hand actions if the observer never had hands or arms? Would it not show activations because the observer lacks motor programs for hand action, or would it show them because the observer has motor programs for the foot or mouth that have corresponding goals? Two aplasic individuals, born without arms or hands, were scanned while they observed hand actions. The results showed activations in the parieto-frontal circuit of the aplasic individuals while watching hand actions (Gazzola et al., 2007). This finding demonstrates the brain’s capacity to mirror actions that deviate from the typical motor organization by recruiting brain cortical representations involved in the execution of actions that achieve corresponding goals using different effectors.

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Note: Signal changes in the central voxels of the frontal (a) and parietal (b) mirror neuron system nodes during the observation of classical ballet and Capoeira dancers. Parameter estimates show that the effect of expertise is driven by a crossover interaction between the two groups of expert dancers and the two stimulus types. Black bars reflect parameter estimates for ballet stimulus and white bars reflect Capoeira stimulus. From “Action Observation and Acquired Motor Skills: An fMRI Study with Expert Dancers,” by B. Calvo-Merino, D. E. Glaser, J. Grèzes, R. E. Passingham, & P. Haggard, 2005, Cerebral Cortex, 15, pp. 1243–1249. Reprinted with permission.

An fMRI experiment provided strong evidence of the importance of internal representation of actions to the understanding of actions performed by other individuals. Motor acts done by a human, a monkey, and a dog were presented to normal volunteers. Two types of actions were shown: biting and oral communicative actions (speech reading, lip-smacking, barking). As a control, static images of the same actions were presented (Buccino et al. 2004).

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The results showed that the observation of biting, regardless of who performed the actions, determined bilateral activations in the inferior parietal lobule and in the pars opercularis of the IFG plus the adjacent precentral gyrus. The left activations were virtually identical for all three species, while the right activations were stronger during the observation of actions done by a human being than by an individual of another species. Markedly different results were obtained with communicative actions. Speech reading activated the left pars opercularis of IFG; observation of lip smacking, a monkey communicative gesture, activated a small focus in the right and left pars opercularis of IFG; observation of barking did not produce any frontal lobe activation. Does this mean that we are unable to understand the movements of a dog barking and distinguish them from those it makes when it bites food? The answer is no. This finding depends on two different comprehension modalities; the first is based predominantly on visual information, while the second is visuo-motor in nature. When we observe a dog barking, our comprehension of this act appears to be linked principally to the activation of the areas localized in the superior temporal sulcus (STS). Higher order visual areas also became active when biting is being observed—but in that case, the information sourcing from them activates the potential motor acts codified in the mirror neuron system, which therefore allows immediate comprehension in “first person” of the meaning of the acts that are being observed. This comprehension gives an internal “personal knowledge” (see Merleau-Ponty, 1962) that is lacking in the case of barking. Intention Understanding The sight of motor acts done by others produces, in the observer, the activation of cortical motor areas involved in the organization of the observed motor acts. This activation is at the basis of motor act understanding. Recent experiments showed, besides understanding of motor acts, the mirror neuron system is also involved in understanding the intention behind the observed motor acts. Evidence in this sense has been provided by an fMRI study (Iacoboni et al., 2005). In this study, there were three conditions: In the first one (called “context”), the volunteers saw some objects (a teapot, a mug, a plate with some food on it) arranged as if a person was ready to drink the tea or arranged as if a person had just finished having his or her breakfast; in the second condition (called “action”), the volunteers were shown a hand that grasped a mug without any context; in the third (called “intention”), the volunteers saw the same hand action within the two before and after breakfast contexts. The context and the different grip shapes suggested the intention of the agent, that is, grasping the cup for drinking or grasping it for cleaning the table.

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The results showed that in both action and intention conditions there was an activation of the mirror neuron system. The comparison between intention and action conditions was crucial. This comparison showed that the understanding of the intention of the doer determined a marked increase in activity of the mirror neuron system. The observation of bringing the cup to the mouth in order to drink produced a stronger activation than the observation of grasping done in order to clean the table. This result is somehow similar to monkey findings (see earlier section) showing that the number of neurons coding grasping for bringing to the mouth largely exceeds the number of neurons coding grasping for putting an object into a container. In conclusion, these data show that the intentions behind the actions of others can be recognized by the mirror neuron mechanism. This does not imply that other more cognitive ways of “reading minds” do not exist (see Frith & Frith, 2007). However, the mirror neuron mechanism is most likely the basic neural mechanism from which other aspects of mind reading evolved. More recently, an fMRI study investigated the neural basis of human capacity to differentiate between actions reflecting the intention of the agent (intended actions) and actions that did not reflect it (nonintended actions). Volunteers were presented with video clips showing a large number of actions done with different effectors, each in a double version: One in which the actor achieved the purpose of his or her action (e.g., pour the wine), the other in which the actor performed a similar action but failed to reach the goal of it because of a motor slip or a clumsy movement (e.g., spilled the wine; Buccino et al., 2007). The results showed that both types of actions activated the mirror neuron system. The direct contrast nonintended versus intended actions showed activation in the right temporo-parietal junction, left supramarginal gyrus, and mesial prefrontal cortex. The converse contrast did not show any activation. It was concluded that the capacity to understand when an action is nonintended is based on the activation of attention areas signaling unexpected events in spatial and temporal domains (Corbetta & Shulman, 2002; Coull, 2004). These results indicate that when an individual observes an unexpected action, such as a motor slip, his cortical machinery does not try to simulate it, but rather signals the strangeness of the event. Imitation The term imitation has a number of different and sometimes contrasting meanings, depending on the branch of research examined (developmental psychology, comparative psychology, ethology, etc.; see Hurley & Chater, 2005). Here we narrow the field of possible definitions to two that best

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The Mirror Neuron System in Humans

reflect the mechanisms possibly related to the mirror neuron system (see Rizzolatti, 2005). The first, which is used mainly by experimental psychologists, defines imitation as the capacity of an individual to replicate an act that belongs to his or her motor repertoire after having seen it executed by others; the second, derived from ethology, defines imitation as the capacity to acquire by observation a motor behavior previously not present in the observer ’s motor repertoire and to repeat it using the same movements employed by the teacher (Tomasello & Call, 1997). Repeating Motor Acts Present in the Observer ’s Motor Repertoire Imitation as a replica of the motor act already present in observer ’s motor repertoire has been extensively investigated by Prinz and his coworkers (see Prinz, 2002). They established that the more a motor act resembles one that is present in the observer ’s motor repertoire, the greater the tendency to do it. Perception and execution must therefore possess a “common representational domain.” The discovery of mirror neurons suggested a possible reformulation of this concept by considering the “common representational domain” not as an abstract, amodal domain (Prinz, 1987), but rather as a motor mechanism directly activated by the observed actions. Here there is, however, a problem. Monkey data showed that mirror neurons do not respond to the observation of intransitive gestures. They discharge only when the

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observed motor act has a goal. In contrast, in most human experiments in which imitation was studied, the participants copied simple movements. How can mirror neurons underlie this type of imitation? The answer is rather simple: Unlike monkeys, the human mirror system is able to code, besides goal-directed motor acts, also intransitive meaningless gestures. A large number of experiments prove this point (e.g., Fadiga, Fogassi, Pavesi, & Rizzolatti, 1995; Gangitano, Mottaghy, & Pascual-Leone, 2001; Maeda, Kleiner-Fisman & PascualLeone, 2002; Strafella & Paus, 2000). Fadiga et al. (1995) recorded motor evoked potentials (MEPs) from the right hand/arm muscles elicited by transcranial magnetic stimulation (TMS) of the left motor cortex. Volunteers were required to observe an experimenter grasping objects (transitive hand motor acts) or performing meaningless arm gestures (intransitive arm movements). Detection of the dimming of a small spot of light was used as the control condition. The results showed that the observation of both transitive and intransitive actions determined an increase of the recorded MEPs. The increase concerned selectively those muscles that the volunteers used when producing the observed movements. A study by Gangitano et al. (2001) demonstrated this property of the human mirror neuron system. These authors showed that MEPs recorded from the hand muscles increase during grasping observation, but also that the response facilitation closely reflects the different grasping phases (Figure 16.10).

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refers to the onset of the video clip showing the action); 3,000 ms, hand maximum aperture. From “Phase Specific Modulation of Cortical Motor Output during Movement Observation,” by M. Gangitano, F. M. Mottaghy, A. Pascual-Leone, 2001, NeuroReport, 12, pp. 1489–1492. Reprinted with permission.

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The human mirror neuron system has the potential to imitate intransitive as well as goal-directed motor acts. Direct evidence that the mirror neuron system is involved in imitation was provided by an fMRI study (Iacoboni et al., 1999). These authors studied normal human volunteers in two conditions: “observation-only” and “observation-execution.” In the “observation-only” condition, subjects were shown a moving finger, a cross on a stationary finger, or a cross on an empty background. The instruction was to observe the stimuli. In the “observation-execution” condition, the same stimuli were presented, but, this time, the instruction was to lift the right finger, as fast as possible, in response to them. The crucial contrast was between the trials in which the volunteers made the movement in response to an observed action (“imitation”) and the trials in which the movement was triggered by the cross (a nonimitative behavior). The results showed that the activation of the mirror neuron system was stronger during “imitation.” Similar results were subsequently obtained by Koski, Wohlschlager, Bekkering, Woods, and Dubeau (2002) and Grèzes et al. (2003). The issue of imitation was also addressed by Nishitani and Hari (2000) using magnetoencephalography (MEG), a technique that has a relatively poor spatial resolution, but an excellent temporal resolution. Participants were asked (A) to grasp an object with their right hand, (B) to observe this action being done by the experimenter, and (C) to observe and replicate the seen action. The results showed that in (A) the left inferior frontal cortex (area 44; that is, the frontal node of the mirror neuron system) became active first, the left primary motor cortex activation following it by 100 to 200 ms. During observation and imitation, the sequence of activations was similar, but beginning in the visual areas. The strongest activation was found during action imitation. Similar results were obtained in a subsequent MEG study by the same experimenters, in which volunteers were asked (A) to observe a still picture of lip forms, (B) to imitate them online, or (C) to make similar forms in as self-paced manner. Figure 16.11 illustrates the cortical activations in the three conditions. Further evidence that the mirror neuron system plays a crucial role in imitation was found using repetitive TMS (rTMS). In a group of volunteers, the caudal part of the left frontal gyrus (Broca’s area) was stimulated while they (a) pressed keys on a keyboard; (b) pressed the keys in response to a point of red light which, directed onto the keyboard, indicated which key to press; and (c) imitated a similar movement executed by another individual. The data showed that rTMS lowered the participants’ performance during imitation, but not during the other two tasks (Heiser, Iacoboni, Maeda, Marcus, & Mazziotta, 2003). Summing up, this experiment as well as fMRI and MEG data clearly show that the mirror neuron system plays a

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fundamental role in imitation. It transforms visual information into potential movements and motor act, enabling the observer to repeat immediately the observed motor behavior. Imitation Learning In the preceding section, we discussed imitation as a motor copy of an observed motor act; here we discuss whether mirror neurons are also involved in imitation learning. Byrne (2002) proposed an interesting model based on ethological studies of ape behavior. According to this model, learning by imitation results from the integration of two distinct processes: in the first, the observer segments the action to be imitated into its individual elements, thus converting it into a string of acts belonging to the observer motor repertoire; in the second, the observer organizes these motor acts into a sequence that replicates that of the demonstrator. It is likely that a similar process is also at the basis of learning nonsequential motor patterns, such as notes played on a piano or a guitar. The neural basis of imitation learning was investigated in an fMRI study by Buccino et al. (b) (2004). Naive participants were asked to imitate guitar chords played by an expert guitarist. Cortical activations were mapped during the following events: (a) observation of the chords made by the expert player, (b) pause, (c) execution of the observed chords, and (d) rest. In addition to the imitation condition, there were other conditions to control for observation not followed by imitation and for nonimitative motor activity. The results showed that during observation for imitation there was activation of a cortical network formed by IPL and the dorsal part of PMv, plus the pars opercularis of IFG (Figure 16.12, IMI-1). This circuit was also active during observation in the control conditions in which participants merely observed the chords, or observed them with the instruction to subsequently perform an action not related to guitar chord execution (Figure 16.12, non-IMI-1).

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The Mirror Neuron System in Humans Event 1

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Figure 16.12 Cortical activations during learning by imitation. Note: Upper: Graphic illustration of the events forming the experimental conditions imitation (IMI) and nonimitation (Non-IMI). Both conditions consisted of four events preceded by the presentation of a cue (a square) of different color informing the participants on the task they have to perform. IMI condition: Event 1, observe the teacher ’s hand playing the chord (IMI-1); Event 2, rehearse the observed chord (IMI-2); Event 3, replicate it. Event 4, keep the hand still. Non-IMI condition: Event 1, observe the teacher ’s hand playing the chord (Non-IMI-1); Event 2, do not rehearse the observed chord (Non-IMI-2); Event 3, touch the neck of the guitar, without playing a chord (Non-IMI-3). Event 4, keep the hand still. Lower: Cortical areas activated during Events 1 and 2 in IMI and NonIMI conditions. From “Neural Circuits Underlying Imitation Learning of Hand Actions: An Event-Related fMRI Study,” by G. Buccino, S. Vogt et al., 2004, Neuron, 42, pp. 323–334. Adapted with permission.

During the pause, activation was found in the imitation condition in the same circuit as during observation, but, most interestingly, also in the middle frontal cortex (area 46) and in the anterior mesial cortex (Figure 16.12, IMI-2). Motor activations dominate the picture during chord execution. These data show that during new motor pattern formation there is a strong activation of the mirror neuron system. However, following Byrne (2002), the authors suggested a two-step mechanism for imitation learning. First, the observed actions are decomposed into elementary motor acts that activate, via mirror mechanisms, the corresponding motor representations in the parietal and frontal lobe. Once these motor representations are activated, they are recombined to fit the observed model. For this recombination, a crucial role is played by frontal area 46.

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An fMRI study, based on a similar experimental design, but carried out by expert and naive guitarists confirmed the joint role of the mirror neuron areas and prefrontal lobe in imitation learning, and in particular, the fundamental role that area 46 plays in combining different motor acts in a new specific motor pattern (S. Vogt et al., 2007). Emotions Up to now we discussed the neural mechanisms that enable individuals to understand “cold actions,” that is actions without an obvious emotional content. In social life, however, equally important, and maybe even more so, is the capacity to decipher emotions. Which structures mediate the understanding of emotion of others? Is there a mirror mechanism for emotions similar to that for cold action understanding? It is reasonable to postulate that, as for action understanding, there are two different mechanisms also for emotion understanding. The first consists of cognitive elaboration of sensory aspects of others’ emotional behaviors. The other consists of a direct mapping of sensory aspects of the observed emotional behavior on the motor structures that determine, in the observer, the experience of the observed emotion. These two ways of recognizing emotions are experientially radically different. With the first, the observer understands the emotions expressed by others, but does not feel them. He deduces them. A certain facial or body pattern means fear, another happiness. There is no emotional involvement. Different is the case for the direct matching mechanism. In this case, the recognition occurs because the observed emotion triggers the feeling of the same emotion in the observing person. It is a first-person recognition. It is generally agreed there is a series of emotions, often called primary emotion (e.g., fear, rage), that consist of a collection of responses that have been laid down during the course of evolution due to their original adaptive utility and that occur in the same form in different species and, in the case of humans, in different cultures. In this chapter, we focus on two of these emotions: disgust and pain. For both of them there are data obtained on the same individuals in two conditions: in one where they experienced disgust or pain evoked by appropriate stimulus, in the other when they observed the expression of these emotions in others. Disgust is an emotion whose expression has an important survival value for the conspecifics. In its most basic, primitive form (“core disgust,” Rozin, Haidt, & McCauley, 2000) it indicates that something (e.g., food) that the individual tastes or smells is bad and, most likely, dangerous. Because of its strong communicative value, disgust has been considered an ideal emotion for testing the direct matching hypothesis.

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Brain imaging studies show that when an individual is exposed to disgusting odors or tastes, there is an intense activation of two structures: the amygdala and the insula (Augustine, 1996; Royet, Plailly, Delon-Martin, Kareken, & Segebarth, 2003; Small et al., 2003; Zald & Pardo, 2000). The insula is a complex structure. On the basis of its connections, the insula has been subdivided in the monkey into two main sectors: an anterior visceral sector and a posterior multimodal sector (Mesulam & Mufson, 1982). The anterior sector receives a rich input from olfactory and gustatory centers. In addition, it receives an important input from the inferotemporal lobe, a cortical region where faces are coded (Gross, 1992). The insula is not an exclusively sensory area. In both monkeys and humans, its electrical stimulation produces body movements typically accompanied by autonomic and viscero-motor responses (KrolakSalmon et al., 2003; Penfield & Faulk, 1955; Showers & Lauer, 1961). Brain imaging studies show that, in humans, observation of faces showing disgust activates foci in the anterior insula sector (Phillips et al., 1998; Schienle et al., 2002; Sprengelmeyer, Rausch, Eysel, & Przuntek, 1998). Wicker et al. (2003) carried out an fMRI study to find out whether the insula sites that show activation during the experience of disgust also show activation during the observation of faces expressing disgust. In this study, volunteers were subjected to an fMRI experiment consisting of two sessions. In the first session, the participants were exposed to unpleasant and pleasant odors; in the second session, they watched a video showing the facial expression of people sniffing an unpleasant, a pleasant, or a neutral odor. The two structures that became active during the exposure to smells were the amygdala and the insula. The amygdala was activated by both unpleasant and pleasant odors. Pleasant odorants produced a relatively weak activation located in a posterior part of the right insula, while disgusting odorants activated the anterior sector bilaterally. The results of observation showed activations in various cortical and subcortical centers, but not in the amygdala. The left anterior insula was activated only during the observation of disgust. The most important result of the study was the demonstration that precisely the same foci within the anterior insula that were activated by the exposure to disgusting odorants were also activated by the observation of disgust. These data strongly suggest that the insula contains neural populations that becomes active both when the participants experience disgust and when they see it in others. The finding we have just discussed appear to be valid not only for disgust but also for pain. Singer and coworkers (2004) conducted an fMRI experiment subdivided into two

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(A)

(B) 4 3 2 1 0

Figure 16.13 Activations found when pain was applied to self or to the partner. Note: A and B illustrate the results of a conjunction analysis between the contrasts pain-no pain in the context of self and other. Results are shown on sagittal (A) and coronal (B) sections of the mean structural scan. Coordinates refer to peak activations. Increased pain-related activation was observed in the anterior cingulate (ACC), anterior insula, cerebellum and brainstem (colored area). From “Empathy for pain involves the affective but not sensory components of pain,” by T. Singer et al., 2004, Science, 303, pp. 1157–1162. Reprinted with permission.

parts. First the participants were subjected to a mildly painful electric shock from electrodes placed on their hands, second they were asked to watch while the same electrodes were fastened to the hand of a loved one. They were told that the loved person would receive the same shock they had received earlier. As shown in Figure 16.13, sites of the anterior insula and of the cingulate cortex became active in both conditions, thus showing that both direct pain experience and its evocation are mediated by a mirror mechanism similar to that found for disgust (see also Chapter 48). The hypothesis that we recognize others’ emotion by activating structures mediating the same emotion in ourselves has been advanced by various authors (e.g., Calder, Keane, Facundo Manes, Nagui Antoun, & Young, 2000; Carr, Iacoboni, Dubeau, Mazziotta, & Lenzi, 2003; Damasio, 2003; Gallese, Keysers, & Rizzolatti, 2004; Goldman & Sripada, 2003; Phillips et al., 1997). Particularly influential in this respect has been the work by Damasio and his coworkers. According to their findings, mostly based on brain lesions, the neural basis of emotion understanding is the activation of an “as-if-loop,” the core structure of which is the insula (Adolphs, Tranel, & Damasio, 2003; Damasio, 2003). As already mentioned at the beginning of this section, the direct activation theory denies that we could recognize emotions indirectly, using cognition. However, without the activation of the observers’ emotional centers, emotions would be reduced, as William James (1890) remarked, to “a cold and neutral state of intellectual perception.” Language It might seem surprising that in a chapter devoted to the mirror neuron system there is a section on language. Language is traditionally conceived of as a system of communication based on sound with little or no involvement of the motor

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The Mirror Neuron System in Humans

system, except, of course, for speech production. However, sound-based languages are not the only natural way for communicating. Signed languages represent another complex, fully structured communication system. By using sign language, people express abstract concepts; learn mathematics, physics, philosophy, and even poetry (see Corballis, 2002). Nonetheless, the evidence that signed languages are fully structured communication systems has not modified the traditional view that speech is the only natural way in which humans communicate. If this is true, it logically follows that the evolutionary precursors of speech should be animal calls. A series of facts indicates, however, that this view is highly implausible: First, the brain structures underlying speech and animals’ calls are different. Animals’ calls are mediated primarily by deep, diencephalic, and brain stem structures and by the cingulate cortex (Jürgens, 2002). In contrast, human speech has its anatomical core in the perisylvian areas, including area 44, a basically motor area. Second, speech is not necessarily linked to an emotional behavior, although animals’ calls are. Third, speech is mostly a person-to-person communication. In contrast, animal calls are, typically, directed to “the community,” rather than to a specific individual. Fourth, speech is endowed with combinatorial properties that are absent in animal communication. It is recursive and virtually limitless with respect to its scope of expression. Fifth, but not least, humans do possess a “call” communication system like that of nonhuman primates and its anatomical location is similar. This system mediates the utterances that humans emit when in particular emotional states (cries, yelling, etc.). These utterances, which are preserved in patients with global aphasia, lack the referential character and the combinatorial properties that characterize human speech. The advocates of the sound-based theory of language origin often use as an argument in favor of their theory the presence of referential information in some animal calls. Following the famous study of the alarm calls of vervet monkeys (Cheney & Seyfarth, 1990), the capacity of referential information has been described in a large number of species including diana monkeys, baboons, and suricates (a South Africa mongoose). Evidence also has been provided that baboons are able to acquire sophisticated information from other individual’s vocalizations. However, although animal vocalization may encode semantic as well as emotional information, callers do not intend to provide it. “Listeners acquire information as an inadvertent consequences of signaler behavior” (Seyfarth & Cheney, 2003).

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What then could be the origin of human speech? An alternative hypothesis is that the path leading to speech started with gestural communication. This hypothesis, which was first proposed by the French philosopher Condillac (1947), has several defenders (e.g., Armstrong, Stokoe, & Wilcox, 1995; Corballis, 2002; Gentilucci & Corballis, 2006). According to it, the communication of modern humans ancestors consisted mostly of gesturing. Sounds conveying semantic information were later in evolution and were associated with gestures. The discovery of mirror neurons provided strong support for the gestural theory of speech origin. Mirror mechanism creates a direct link between the sender of a message and its receiver. Thanks to this mechanism, motor acts by an individual activate a similar motor copy in the observers allowing them, in this way, to understand directly the message. Because of this and the finding that the observation of motor acts (e.g., hand grasping) activates the caudal part of IFG (Broca’s area), Rizzolatti and Arbib (1998) proposed that the mirror mechanism is at the basis of language evolution. In fact, the mirror neuron mechanism can solve two fundamental communication problems: parity and direct comprehension. Thanks to the mirror neurons, what counts for the sender of the message also counts for the receiver. No arbitrary symbols are required. The comprehension is inherent in the neural organization of the two individuals. Note also that the activation of Broca’s area during motor act observation is not due to a verbal description of the observed action, but to a real coding of motor act (for evidence on this point, see Fadiga et al., 2006). The mirror neuron system of monkeys is a closed system linked to objects, while, in order to communicate, this system should become an open system, able to describe actions and objects. How did the transition between these two systems occur? It is likely that the great leap from an object-related mirror neuron system to a truly communicative mirror occurred with the evolution of imitative abilities (Arbib, 2005) and the related capacity of mirror neurons to discharge in response to intransitive actions. In fact true imitation (imitation in ethological sense) implies not only the understanding of the purpose of the action to be imitated (a feature already present in the monkey mirror neuron system), but also the capacity to repeat the individual movements that constitute an action in the correct order (Rizzolatti, 2005; Tomasello & Call, 1997). The necessity to keep track of precise movements in order to imitate the motor behavior of others should have sharpened the mirror system, providing it with its capacity, present in modern humans, to convey information on the observed movements per se and on only those leading to a goal. The mirror neuron system as a communication system has a great asset: Its semantics is inherent in the gestures

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used to communicate. This is lacking in speech. In speech, or at least in modern speech, the meaning of the words and the phono-articulatory actions necessary to pronounce them are unrelated. This independence suggests that a necessary step in speech evolution was the transfer of gestural meaning, intrinsic to the gesture itself, to abstract sound meaning. From this follows a clear neurophysiological prediction: Hand/arm and speech gestures must be strictly linked and must, at least in part, share a common neural substrate. A number of studies prove that this is the case. TMS experiments show that the excitability of the motor cortex hand representation increases during both reading and spontaneous speech (Meister et al., 2003; Seyal, Mull, Bhullar, Ahmad, & Gage 1999; Tokimura, Tokimura, Oliviero, Asakura, & Rothwell, 1996). The effect is limited to the left hemisphere. Furthermore, no language-related effect is found in the leg motor area. Note that the increase of hand motor cortex excitability cannot be attributed to word articulation because, while word articulation recruits motor cortex bilaterally, the observed activation is strictly limited to the left hemisphere. The facilitation appears, therefore, to result from a co-activation of the dominant hand motor cortex and language centers (Meister et al., 2003). Gentilucci Benuzzi, Gangitano, and Grimaldi (2001) reached similar conclusions using a different approach. In a series of behavioral experiments, they presented participants with two three-dimensional objects, one large, the other small. Participants were required to grasp the objects and to open their mouth. The kinematics of hand, arm, and mouth movements was recorded. The results showed that lip aperture and the peak velocity of lip aperture increased when the movement was directed to the large object. In another experiment of the same study, Gentilucci et al. (2001) asked participants to pronounce a syllable (e.g., GU, GA) instead of simply opening their mouth. They found that lip aperture was larger when the participants grasped a larger object. Furthermore, the maximal power of the voice spectrum recorded during syllable emission was also higher when the larger object was grasped. In a further study, Gentilucci (2003) asked volunteers to pronounce the syllables BA or GA while observing another individual grasping objects of different size. Kinematics of lip aperture and amplitude spectrum of voice was influenced by the grasping movements of the other individual. Specifically, both lip aperture and voice peak amplitude were greater when the action, done by another individual, was directed to larger objects. Control experiments ruled out that the effect was due to the size of the object or to the velocity of the observed arm movement. Taken together, these experiments show that hand gestures and mouth gestures are strictly linked in humans and

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that this link includes the oro-laryngeal movements used for speech production. Mirror Neuron System and Speech The association between communicative gestures and specific sounds has obvious advantages, such as the possibility of communicating in the dark or when the hands are holding tools or weapons. Such advantages must have exerted a strong evolutionary pressure in favor of a communication system based on sounds. To achieve, however, an efficient communication system, the emitted sounds must be clearly distinguishable by the listeners and, most importantly, maintain constant features. They must be pronounced in a very precise, consistent way. These pronunciation constraints require a sophisticated organization of the motor system dedicated to sound production and rich connections between the cortical motor areas controlling voluntary actions and the centers controlling the oro-laryngeal tract. The large expansion of the posterior part of the inferior frontal gyrus culminating in the appearance of Broca’s area in the human left hemisphere is, most likely, the result of the evolutionary pressure to achieve this voluntarily control. In parallel with these modifications for emitting sounds, a system for understanding them should also have evolved. As already discussed, monkey area F5, the homologue of human area 44, contains neurons—the so called audiovisual neurons (Kohler et al., 2002)—that respond to the observation of actions done by others as well as to the sounds of those actions. This system, however, is tuned for recognition of the sound of physical events and not of sounds made by individuals for communication purposes. To understand the speech sounds, a new mirror neuron system tuned to resonate in response to sounds emitted by the oro-laryngeal tract should have evolved. Is there evidence that humans have such a mirror neuron system? The answer is yes. Fadiga, Craighero, Buccino, and Rizzolatti (2002) recorded MEPs from the tongue muscles in normal volunteers instructed to listen carefully to acoustically presented verbal and nonverbal stimuli. The stimuli were words, regular pseudo-words, and bi-tonal sounds. In the middle of words and pseudo-words, there was either a double “f” or a double “r.” “F” is a labio-dental consonant that, when pronounced, requires virtually no tongue movements, whereas “r” is linguo-palatal fricative consonant that, in contrast, requires, marked tongue muscle involvement to be pronounced. During the stimulus presentation, the left motor cortex of the participants was stimulated with single pulse TMS. The results showed that listening to words and pseudo-words containing the double “r” produced a significant increase of MEPs amplitude recorded from tongue muscles compared to listening

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Summary 1.4

z-score of MEPs’area

1.0 0.6 0.2 ⫺0.2 ⫺0.6 ⫺1.0 ⫺1.4

‘rr’ ‘ff’ Words

‘rr’ ‘ff’ Pseudo-words

Bitonal sounds

Figure 16.14 Modulation of motor cortex excitability during presentation of verbal material. Note. Bars represent motor-evoked potentials (MEPs) total areas recorded from tongue muscles during listening to words, pseudowords, and bitonal sounds. “rr” and “ff” refer to verbal stimuli containing a double lingua-palatal fricative consonant “r,” and a double verbal labio-dental fricative consonant “f,” respectively. From “Speech Listening Specifically Modulates the Excitability of Tongue Muscles: A TMS Study,” by L. Fadiga, L. Craighero, G. Buccino, and Rizzolatti G., 2002, European Journal of Neuroscience, 15, pp. 399–402. Reprinted with permission.

to bi-tonal sounds and words and pseudo-words containing the double “f” (Figure 16.14). Results congruent with those of Fadiga et al. (2002) were obtained by Watkins, Strafella, and Paus (2003). Using the TMS technique, they recorded MEPs from a lip muscle (orbicularis oris) and a hand muscle (first interosseus) in four conditions: listening to continuous prose, viewing speech-related lip movements, listening to nonverbal sounds, and viewing eye and brow movements. Compared to viewing eye and brow movements, listening to and viewing speech enhanced the MEP amplitude recorded from the orbicularis oris muscle. All of these effects were seen only in response to stimulation of the left hemisphere. Speech is not purely a system based on sounds. As shown by Liberman (Liberman, Cooper, Shankweiler, & Studdert-Kennedy, 1967; Liberman & Mattingly, 1985; Liberman & Whalen, 2000), an efficient communication system cannot be built by substituting tones or combinations of tones for speech. There is something special about speech sounds that distinguish them from other auditory material, and this is their capacity to evoke the motor representation of the heard sounds in the listener ’s motor cortex. Note that this capacity, postulated by Liberman on the basis of indirect evidence, now has a precise neural correlate shown by the existence of a mirror neuron system specifically tuned to speech sound. Does this motor resonance provide only a copy of the listened phoneme or does it intervene also in the understanding

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of word meaning? A series of recent studies addressed this issue. In an EEG study, Pulvermueller (2001) assessed cortical activations while volunteers listened to face- and legrelated action verbs (walking versus talking). They found that words describing leg actions evoked stronger in-going current at dorsal sites, close to the cortical leg area, whereas those of the talking type elicited the stronger currents at the inferior site, next to the motor representation of the face and mouth. In an fMRI experiment, Tettamanti et al. (2005) tested whether cortical areas active during action observation were also active during listening to action sentences. Sentences that describe actions performed with mouth, hand/arm, and leg were used. The presentation of abstract sentences of comparable syntactic structure was used as a control condition. The results showed activations in the precentral gyrus and in the posterior part of IFG. The activations in the precentral gyrus, and especially those observed during listening to sentences describing hand actions, spatially corresponded to the activations found during the observation of the same actions. The activation of IFG was strong during listening of mouth actions, but also present during listening of actions done with other effectors. It is likely, therefore, that, in the IFG, in addition to mouth sentences, there is also a more general representation of action verbs. The relation between premotor responses to action observation and to phrases describing actions have recently tested in a more stringent way by Aziz-Zadeh, Wilson, Rizzolatti, and Iacoboni (2006) in an fMRI experiment. Participants observed actions and read phrases relating to foot, hand, or mouth actions. In the analysis, the points of maximal activation of foot, arm, and mouth movements were first determined. Subsequently, they examined which type of sentence activated most of these points. The results showed a strict congruence between effector-specific representations of visually presented actions and of actions described by literal phrases. Taken together, these data clearly indicate that listening to sentences describing actions or reading them activate cortical areas where these actions are coded. It is unsolved; however, the problem remains of determining what is the contribution of these activations for the understanding of verbal material. We do not yet know whether these activations are indispensable for our sentence comprehension and, if they are not, what they add to it.

SUMMARY With the description of mirror neurons in monkey area F5, neuroscientists, and even more so cognitive psychologists, sometimes express surprise that mirror neurons could be

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involved in so many, diverse cognitive functions. In fact, there is no reason for such surprise. As shown in this chapter, mirror neurons of area F5 are only an example of a general neurophysiological mechanism that codifies sensory and motor information in a common format. The mirror mechanism is present not only in the premotor cortex, where they have been originally discovered, but also in other brain centers and areas. The functional role of the mirror mechanism depends, first of all, on the anatomy of the neural circuit where the mirror neurons are located. Mirror neurons of the lateral brain convexity transform the sensory representation of motor acts into a motor representation of the same acts. Their function is to give an immediate understanding of the observed motor behavior. Mirror neurons located in the insula and rostral cingulate transform an observed emotional expression or situation into a viscero-motor activity analogous to that present when an individual actually experiences that emotion. They give the observer a direct feeling of what the others feel. Mirror neurons are obviously not floating in isolation. They are connected with other neurons located in the same area where mirror neurons are located and with neurons of other areas. The first type of relation is at the basis of the activation of the whole neuron chain underlying a motor action following the observation of its first motor act. Thanks to these connections and the mirror mechanism, the observer is able to infer the outcome of an action at its outset. This mechanism appears to be fundamental for understanding the intentions of others. The connections with other areas play a fundamental role in imitation learning. The observed action is decomposed, by the mirror mechanism, into its elementary motor acts and kept in memory. This memory is then used to repeat the observed action. The prefrontal lobe appears to be the structure involved in controlling and orchestrating the activity of the mirror mechanism in this function. Finally, with the evolution of language, the mirror mechanism acquired a new role, that of translating the speech sound into the motor pattern responsible for the emission of the same speech sound. It has been suggested that mirror neuron activation may be also involved in understanding the meaning of verbal material. This fascinating hypothesis however, lacks fully convincing evidence and requires more investigation.

Armstrong, A. C., Stokoe, W. C., & Wilcox, S. E. (1995). Gesture and the nature of language. Cambridge: Cambridge University Press. Augustine, J. R. (1996). Circuitry and functional aspects of the insular lobe in primates including humans. Brain Research: Brain Research Reviews, 22, 229–244. Aziz-Zadeh, L., Wilson, S. M., Rizzolatti, G., & Iacoboni, M. (2006). Congruent embodied representations for visually presented actions and linguistic phrases describing actions. Current Biology, 16, 1818–1823. Borra, E., Belmalih, A., Calzavara, R., Gerbella, M., Murata, A., Rozzi, S., et al. (2008). Cortical connections of the macaque anterior intraparaietal (AIP) area. Cerebral Cortex, 18, 1094–1111. Buccino, G., Baumgaertner, A., Colle, L., Buechel, C., Rizzolatti, G., & Binkofski, F. (2007). The neural basis for understanding non-intended actions. Neuroimage, 2, 119–127. Buccino, G., Binkofski, F., Fink, G. R., Fadiga, L., Fogassi, L., Gallese, V., et al. (2001). Action observation activates premotor and parietal areas in a somatotopic manner: An fMRI study. European Journal of Neuroscience, 13, 400–404. Buccino, G., Lui, F., Canessa, N., Patteri, I., Lagravinese, G., Benuzzi, F., et al. (2004). Neural circuits involved in the recognition of actions performed by non-conspecifics: An fMRI study. Journal of Cognitive Neuroscience, 16, 114–126. Buccino, G., Vogt, S., Ritzl, A., Fink, G., Zilles, K., Freund, H., et al. (2004). Neural circuits underlying imitation learning of hand actions: An event-related fmri study. Neuron, 42, 323–334. Byrne, R. W. (2002). Seeing actions as hierarchically organized structures: Great ape manual skills. In A. N. Meltzoff & W Prinz (Eds.), The imitative mind: Development, evolution and brain bases (pp. 122–140). Cambridge, England: Cambridge University Press. Calder, A. J., Keane, J., Facundo Manes, Nagui Antoun, & Young, A. W. (2000). Impaired recognition and experience of disgust following brain injury. Nature Neuroscience, 3, 1077–10 Calvo-Merino, B., Glaser, D. E., Grèzes, J., Passingham, R. E., & Haggard, P. (2005). Action observation and acquired motor skills: An fMRI study with expert dancers. Cerebral Cortex, 15, 1243–1249. Calvo-Merino, B., Grèzes, J., Glaser, D., Passingham, R., & Haggard, P. (2006). Seeing or doing? Influence of visual and motor familiarity in action observation. Current Biology, 16, 1905–1910. Carr, L., Iacoboni, M., Dubeau, M.-C., Mazziotta, J. C., & Lenzi, G. L. (2003). Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas.Proceedings of the National Academy of Sciences, USA,100, 5497–5502. Cheney, D. L., & Seyfarth, R. M. (1990). How monkeys see the world: Inside The mind of another species. Chicago: University of Chicago Press. Condillac, E. B. (1947). Oevres philosophiqus. In G. Le Roy (Ed.), (Vol. 1). Paris: Presses Universitaires de France. Corballis, M. C. (2002). From hand to mouth: The origins of language. Princeton: Princeton University Press. Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews: Neuroscience, 3, 201–215. Coull, J. T. (2004). fMRI studies of temporal attention: Allocating attention within, or towards, time. Cognitive Brain Research, 21, 216–226.

REFERENCES

Cross, E. S., de Hamilton, A. F., & Grafton, S. T. (2006). Building a motor simulation de novo: Observation of dance by dancers. Neuroimage, 31, 1257–1267.

Adolphs, R., Tranel, D., & Damasio, A. R. (2003). Dissociable neural systems for recognizing emotions. Brain and Cognition, 52, 61–69.

Damasio, A. R. (2003). Looking for Spinoza: Joy, Sorrow and Feeling Brain. Harcourt, New York.

Arbib, M. A. (2005). From monkey-like action recognition to human language: An evolutionary framework for neurolinguistics. Behavioral and Brain Sciences, 28, 105–124.

Di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., & Rizzolatti, G. (1992). Understanding motor events: A neurophysiological study. Experimental Brain Research, 91, 176–180.

c16.indd 354

8/18/09 5:10:18 PM

References 355 Fadiga, L., Craighero, L., Buccino, G., & Rizzolatti, G. (2002). Speech listening specifically modulates the excitability of tongue muscles: A TMS study. European Journal of Neuroscience, 15, 399–402. Fadiga, L., Craighero, L., Fabbri-Destro, M., Finos, L., CotillonWilliams, N., Smith, A. T., et al. (2006). Language in shadow: Social. Neuroscience, 1, 77–89. Fadiga, L., Fogassi, L., Pavesi, G., & Rizzolatti, G. (1995). Motor facilitation during action observation: A magnetic stimulation study. Journal of Neurophysiology, 73, 2608–2611.

Hyvarinen, J. (1982). Posterior parietal lobe of the primate brain. Physiological Reviews, 62, 1060–1129. Iacoboni, M., Molnar-Szakacs, I., Gallese, V., Buccino, G., Mazziotta, J. C., & Rizzolatti, G. (2005). Grasping the intentions of others with one’s own mirror neuron system. PLoS Biology, 3, e79. Iacoboni, M., Woods, R. P., Brass, M., Bekkering, H., Mazziotta, J. C., & Rizzolatti, G. (1999, December 24). Cortical mechanisms of human imitation. Science, 286, 2526–2528.

Ferrari, P. F., Gallese, V., Rizzolatti, G., & Fogassi, L. (2003). Mirror neurons responding to the observation of ingestive and communicative mouth actions in the monkey ventral premotor cortex. European Journal of Neuroscience, 17, 1703–1714.

James, W. (1890). Principles of psychology. London: Macmillian.

Ferrari, P. F., Gregoriou, G., Rozzi, S., Pagliara, S., Rizzolatti, G., & Fogassi, L. (2003). Functional organization of the inferior parietal lobule of the macaque monkey [Program No. 919.7, 2003 Neuroscience Meeting Planner. Washington, DC: Society for Neuroscience 2003. Online Abstract viewer/itinerary planner]. Washington, DC: Society for Neuroscience.

Johnson-Frey, S. H. (2004). The neural bases of complex tool use in humans. Trends in Cognitive Sciences, 8, 71–78.

Ferrari, P. F., Rozzi, S., & Fogassi, L. (2005). Mirror neurons responding to observation of action made with tools in monkey ventral premotor cortex. Journal of Cognitive Neuroscience, 17, 212–226.

Kohler, E., Keysers, C., Umiltà, M. A., Fogassi, L., Gallese, V., & Rizzolatti, G. (2002, August 2). hearing sounds, understanding actions: Action representation in mirror neurons. Science, 297, 846–848.

Fogassi, L., Ferrari, P. F., Gesierich, B., Rozzi, S., Chersi, F., & Rizzolatti, G. (2005). Parietal lobe: From action organization to intention understanding. Science, 29, 662–667.

Koski, L., Wohlschlager, A., Bekkering, H., Woods, R. P., & Dubeau, M. C. (2002). Modulation of motor and premotor activity during imitation of target-directed actions. Cerebral Cortex, 12, 847–855.

Frith, C. D., & Frith, U. (2007). Social cognition in humans. Current Biology, 17, 724–732.

Krolak-Salmon, P., Henaff, M. A., Isnard, J., Tallon-Baudry, C., Guenot, M., Vighetto, A., et al. (2003). An attention modulated response to disgust in human ventral anterior insula. Annals of Neurology, 53, 446–453.

Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119, 593–609. Gallese, V., Keysers, C., & Rizzolatti, G. (2004). A unifying view of the basis of social cognition. Trends in Cognitive Sciences, 8, 396–403. Gangitano, M., Mottaghy, F. M., & Pascual-Leone, A. (2001). Phase specific modulation of cortical motor output during movement observation. NeuroReport, 12, 1489–1492. Gazzola, V., Rizzolatti, G., Wicker, B., & Keysers, C. (2007). The anthropomorphic brain: The mirror neuron system responds to human and robotic actions. NeuroImage, 35, 1674–1684. Gazzola, V., van der Worp, H., Mulder, T., Wicker, B., Rizzolatti, G., & Keysers, C. (2007). Aplasics born without hands mirror the goal of hand actions with their feet. Current Biology, 17, 1235–1240. Gentilucci, M. (2003). Grasp observation influences speech production. European Journal of Medicine, 17, 179–184. Gentilucci, M., Benuzzi, F., Gangitano, M., & Grimaldi, S. (2001). Grasp with hand and mouth: A kinematic study on healthy subjects. Journal of Neurophysiology, 86, 1685–1699.

Jellema, T., Baker, C. I., Wicker, B., & Perrett, D. I. (2000). Neural representation for the perception of the intentionality of actions. Brain and Cognition, 442, 280–302.

Jürgens, U. (2002). Neural pathways underlying vocal control. Neuroscience and Biobehavioral Reviews, 26, 235–258.

Liberman, A. M., Cooper, F. S., Shankweiler, D. P., & Studdert-Kennedy, M. (1967). Perception of the speech code. Psychological Review, 74, 431–461. Liberman, A. M., & Mattingly, I. G. (1985). The motor theory of speech perception revised. Cognition, 21, 1–36. Liberman, A. M., & Whalen, D. H. (2000). On the relation of speech to language. Trends in Cognitive Sciences, 4, 187–196. Lui, F., Buccino, G., Duzzi, D., Benuzzi, F., Crisi, G., Baraldi, P., et al. (2008). Neural substrates for observing and imagining non objectdirected actions. Social Neuroscience, 3, 261–275. Luppino, G., Belmalih, A., Borra, E., Gerbella, M., & Rozzi, S. (2005). Architectonics and cortical connections of the ventral premotor areA F5 of the Macaque [Program No. 194.1, 2005 Neuroscience Meeting Planner]. Washington, DC: Society for Neuroscience 2005. Online. McNeliage, P. F. (1998). The frame/content theory of evolution of speech production. Behavioral and Brain Science, 21, 499–511.

Gentilucci, M., & Corballis, M. C. (2006). From manual gesture to speech: A gradual transition. Neuroscience and Biobehavioral Reviews, 30, 949–960.

Maeda, F., Kleiner-Fisman, G., & Pascual-Leone, A. (2002). Motor facilitation while observing hand actions: Specificity of the effect and role of observer ’s orientation. Journal of Neurophysiology, 87, 1329–1335.

Goldman, A. I., & Sripada, C. S. (2005). Simulationist models of facebased emotion recognition. Cognition, 94, 193–213.

Meister, I. G., Boroojerdi, B., Foltys, H., Sparing, R., Huber, W., & Topper, R. (2003). Motor cortex hand area and speech: Implications for the development of language. Neuropsychologia, 41, 401–406.

Gregoriou, G. G., Borra, E., Matelli, M., & Luppino, G. (2006). Architectonic organization of the inferior parietal convexity of the macaque monkey. Journal of Comparative Neurology, 496, 422–451.

c16.indd 355

Hurley, S., & Chater, N. (2005). Pespective on imitation: From neuroscience to social science (Vol. 1). Cambridge, MA: MIT Press.

Merleau-Ponty, M. (1962). Phenomenology of perception. (Translated from the French by C. Smith). London: Routledge.

Grèzes, J., Armony, J. L., Rowe, J., & Passingham, R. E. (2003). Activations related to “mirror” and “canonical” neurones in the human brain: An fMRI study. NeuroImage, 18, 928–937.

Mesulam, M. M., & Mufson, E. J. (1982). Insula of the old world monkey: Pt. III. Efferent cortical output and comments on function. Journal of Comparative Neurology, 212, 38–52.

Gross, C. G. (1992). Representation of visual stimuli in inferior temporal cortex of the monkey. Philosophical Transactions, Royal Society, London, B, 335, 3–10.

Murata, A., Fadiga, L., Fogassi, L., Gallese, V., Raos, V., & Rizzolatti, G. (1997). Object representation in the ventral premotor cortex (area F5) of the monkey. Journal of Neurophysiology, 78, 2226–2230.

Heiser, M., Iacoboni, M., Maeda, F., Marcus, J., & Mazziotta, J. C. (2003). The essential role of Broca’s area in imitation. European Journal of Neuroscience, 17, 1123–1128.

Nelissen, K., Luppino, G., Vanduffel, W., Rizzolatti, G., & Orban, G. A. (2005, October 14). Observing others: Multiple action representation in the frontal lobe. Science, 310, 332–336.

8/18/09 5:10:18 PM

356 The Mirror Neuron System Nishitani, N., & Hari, R. (2000). Temporal dynamics of cortical representation for action. Proceedings of the National Academy of Sciences, USA, 97, 913–918.

Rozin, R., Haidt, J., & McCauley, C. R. (2000). Disgust. In M. Lewis & J. M. Haviland-Jones (Eds.), Handbook of emotions (2nd ed., pp. 637– 653). New York: Guilford Press.

Nishitani, N., & Hari, R. (2002). Viewing lip forms: Cortical dynamics. Neuron 36, 1211–1220.

Rozzi, S., Calzavara, R., Belmalih, A., Borra, E., Gregoriou, G. G., Matelli, M., et al. (2006). Cortical connections of the inferior parietal cortical convexity of the macaque monkey. Cerebral Cortex, 16, 1389–1417.

Obayashi, S., Suhara, T., Nagai, Y., Okauchi, T., Maeda, J., & Iriki, A. (2004). Monkey brain areas underlying remote-controlled operation. European Journal of Medicine, 19, 1397–1407. Orban G..A, Peeters R, Nellisen K., Buccino, G., Vanduffel, W., Rizzolatti, G. (2006) The use of tools, a unique human feature represented in the left parietal cortex. Program number 114.2. 1006 Neuroscience Meeting Planner. Atlanta, GA: Society for Neuroscience, 2006. Online Pandya, D. N., & Seltzer, B. (1982). Intrinsic connections and architectonics of posterior parietal cortex in the rhesus monkey. Journal of Comparative Neurology, 204, 196–210. Penfield, W., & Faulk, M. E. (1955). The insula: Further observations on its function. Brain, 78, 445–470. Perrett, D. I., Harries, M. H., Bevan, R., Thomas, S., Benson, P. J., Mistlin, A. J., et al. (1989). Frameworks of analysis for the neural representation of animate objects and actions. Journal of Experimental Biology, 146, 87–113. Perrett, D. I., Mistlin, A. J., Harries, M. H., & Chitty, A. J. (1990). Understanding the visual appearance and consequence of hand actions. In M. A. Goodale (Ed.), Vision and action: The control of grasping (pp. 163–342). Norwood, NJ: Ablex. Phillips, M. L., Young, A. W., Scott, S. K., Calder, A. J., Andrew, C., Giampietro, V., et al. (1998). Neural responses to facial and vocal expressions of fear and disgust. Proceedings of the Royal Society of London, B, 265, 1809–1817. Phillips, M. L., Young, A. W., Senior, C., Brammer, M., Andrew, C., Calder, A. J., et al. (1997, October 2). A specific neural substrate for perceiving facial expressions of disgust. Nature, 389, 495–498. Povinelli, D. J. (2000). Folk physics for apes: The chimpanzee’s theory of how the world works. Oxford: Oxford University Press. Prinz, W. (1987). Ideomotor action. In H. Heuer & A. Sanders. (Eds.), Perspective on perception and action (pp. 47–76). Hillsdale, NJ: Erlbaumpp. Prinz, W. (2002). Experimental approaches to imitation. In A. Meltzoff & W. Prinz (Eds.), The imitative mind: Development, evolution, and brain bases (pp. 143–162). Cambridge: Cambridge University Press. Pulvermuller, F. (2001). Brain reflections of words and their meaning. Trends in Cognitive Sciences, 5, 517–524. Rizzolatti, G. (2005). The mirror neuron system and imitation. In S Hurley & Chater N (eds) In Perspective on Imitation. From Neuroscience to Social Science, MIT Press, Cambrige (MA) vol. 1, 55–76. Rizzolatti, G., & Arbib, M. A. (1998). Language within our grasp. Trends in Neurosciences, 21, 188–194. Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169–192. Rizzolatti, G., Fadiga, L., Fogassi, L., & Gallese, V. (1996). Premotor cortex and the recognition of motor actions. Cognitive Brain Research, 3, 131–141. Rizzolatti, G., Fogassi, L., & Gallese, V. (2001). Neurophysiological mechanisms underlying the understanding and imitation of action. Nature Reviews: Neuroscience, 2, 661–670. Rizzolatti, G., & Luppino, G. (2001). The cortical motor system. Neuron, 31, 889–901. Royet, J. P., Plailly, J., Delon-Martin, C., Kareken, D. A., & Segebarth, C. (2003). fMRI of emotional responses to odors: Influence of hedonic valence and judgment, handedness, and gender. Neuroimage, 20, 713–728.

c16.indd 356

Rozzi, S., Ferrari, P.F., Bonini, L., Rizzolatti, G., Fogassi, L. (2008). Functional organization of inferior parietal lobule convexity in the macaque monkey: electrophysiological characterization of motor, sensory and mirror responses and their correlation with cytoarchitectonic areas. Eur J Neurosci, 18, 1569–1588 Sakata, H., Taira, M., Murata, A., & Mine, S. (1995). Neural mechanisms of visual guidance of hand action in the parietal cortex of the monkey. Cerebral Cortex, 5, 429–438. Sakreida, K., Schubotz, R. I., Wolfensteller, U., & von Cramon, D. Y. (2005). Motion class dependency in observers’ motor areas revealed by functional magnetic resonance imaging. Journal of Neuroscience, 25, 1335–1342. Schienle, A., Stark, R., Walter, B., Blecker, C., Ott, U., Kirsch, P., et al. (2002). The insula is not specifically involved in disgust processing: An fMRI study. NeuroReport, 13, 2023–2026. Seyal, M., Mull, B., Bhullar, N., Ahmad, T., & Gage, B. (1999). Anticipation and execution of a simple reading task enhance corticospinal excitability. Clinical Neurophysiology, 110, 424–429. Seyfarth, R. M., & Cheney, D. L. (2003). Signalers and receivers in animal communication. Annual Review of Psychology, 54, 145–173. Showers, M. J. C., & Lauer, E. W. (1961). Somatovisceral motorpatterns in the insula. Journal of Comparative Neurology, 117, 107–115. Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R. J., & Frith, C. D. (2004, February 20). Empathy for pain involves the affective but not sensory components of pain. Science, 303, 1157–1162. Small, D. M., Gregory, M. D., Mak, Y. E., Gitelman, D., Mesulam, M. M., & Parrish, T. (2003). Dissociation of neural representation of intensity and affective valuation in human gustation. Neuron, 39, 701–711. Sprengelmeyer, R., Rausch, M., Eysel, U. T., & Przuntek, H. (1998). Neural structures associated with recognition of facial expressions of basic emotions. Proceedings of the Royal Society of London, B, 265, 1927–1931. Strafella, A. P., & Paus, T. (2000). Modulation of cortical excitability during action observation: A transcranial magnetic stimulation study. NeuroReport, 11, 2289–2292. Tettamanti, M., Buccino, G., Saccuman, M. C., Gallese, V., Danna, M., Scifo, P., et al. (2005). Listening to action-related sentences activates fronto-parietal motor circuits. Journal of Cognitive Neuroscience, 17, 273–281. Tokimura, H., Tokimura, Y., Oliviero, A., Asakura, T., & Rothwell, J. C. (1996). Speech-induced changes in corticospinal excitability. Annals of Neurology, 40, 628–634. Tomasello, M., & Call, J. (1997). Primate cognition. Oxford: Oxford University Press. Umiltà, M. A., Kohler, E., Gallese, V., Fogassi, L., Fadiga, L., Keysers, C., et al. (2001). “I know what you are doing”: A neurophysiological study. Neuron, 32, 91–101. Van Hoof, JARAM (1967). The facial displays of the catarrhine monkeys and apes. In D. Morris (Ed.), Primate ethology (pp. 7–68). London: Weidenfield & Nicolson. Vogt, O., & Vogt, C. (1919). Allgemeinere Ergebnisse unserer Hirnforschung. Journal of Psychology and Neurology (Leipzig), 25, 279–462. Vogt, S., Buccino, G., Wohlschlager, A. M., Canessa, N., Shah, N. J., Zilles, K., et al. (2007). Prefrontal involvement in imitation learning of hand actions: Effects of practice and expertise. Neuroimage, 37, 1371–1383.

8/18/09 5:10:18 PM

References 357

c16.indd 357

Watkins, K. E., Strafella, A. P., & Paus, T. (2003). Seeing and hearing speech excites the motor system involved in speech production. Neuropsychologia, 41, 989–994.

Wicker, B., Keysers, C., Plailly, J., Royet, J.-P., Gallese, V., & Rizzolatti, G. (2003). Both of us disgusted in my insula: The common neural basis of seeing and feeling disgust. Neuron, 3, 655–664.

Wheaton, K. J., Thompson, J. C., Syngeniotis, A., Abbott, D. F., & Puce, A. (2004). Viewing the motion of human body parts activates different regions of premotor, temporal, and parietal cortex. Neuroimage, 22, 277–288.

Zald, D. H., & Pardo, J. V. (2000). Functional neuroimaging of the olfactory system in humans. International Journal of Psychophysiology, 36, 165–181.

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Chapter 17

Varieties of Attention AMIR RAZ

“Everyone knows what attention is…” wrote William James, the American father of modern psychology, in his seminal 1890 volume Principles of Psychology. He described attention as “the taking possession by the mind in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought. . . . It implies withdrawal from some things in order to deal effectively with others, and is a condition which has a real opposite in the confused, dazed, scatterbrained state.” James’ account heavily joins attention with subjective experience. Moreover, James’ effort to deal with both attention to objects and attention to trains of thought is important for understanding current approaches to sensory orienting and executive control. Attention in the sense of orienting to sensory objects, however, can actually be involuntary and occur unconsciously. Furthermore, as any neophyte magician knows, paying attention is not the same as being aware. According to another famous James—James Randi, an accomplished magician, writer-educator, and a vociferous skeptic—magicians are “honest liars,” actors who use an arsenal of techniques, including attentional diversion, to accomplish their entertaining effects. Whereas magicians have been exploiting the vagaries of attention to trick their audiences for thousands of years, scientists have been studying the psychology of attention and unraveling its underlying mechanisms for a little over a mere century. Thus, researchers of attention may benefit from the insight and experience of magicians. The study of attention has turned into one of the oldest and most central issues in psychological science. Investigators have learned a great deal about what attention is, what it does, and how it works. Attention refers to both external and internal information. For example, the preparedness for and selection of certain aspects of our physical environment, such as objects, or some ideas in our mind that are stored in memory. Unlike William James, however, I am less sanguine today that “Everyone knows what attention is . . .” especially as the scientific literature grows exponentially and continues to unravel the neural and psychological substrates of

Attention has many faces—a pivotal theme in psychological science, researchers have unraveled some of the mechanisms underlying the process of attention. Cognitive neuroscientists increasingly construe attention as disparate control networks, which correlate with discrete neural circuitry and respond to focal brain injuries, specific drugs, and mental states. It is possible to tease apart these varieties of attention and elucidate their individual development and function. On the one hand, illuminating the neural correlates of attention exemplifies the links between brain and behavior and binds psychology to the techniques of neuroscience. On the other hand, it shows how it is possible to illuminate different aspects of attention using disparate approaches. In this chapter, we discuss how investigators and magicians interpret attention as an organ system and as a vehicle to an art form, respectively. The way a researcher and a magician approach attention provides complementary perspectives on the varieties of attention and serves to elucidate the correspondence between a psychological phenomenon and its neural underpinnings.

ATTENTION AND MAGIC Attention is one of magic’s main currencies. I have paid attention to magic since childhood. Initially gleaning my information from side panels of cereal boxes, I have gradually amassed specialty books, joined professional societies, and befriended accomplished performers. My interest and proficiency in the art of magic had many redeeming qualities: as a young man, it allowed me to mystify audiences and be the life of social affairs; as an impoverished student, it helped me supplement my income and eke out a better living; and as an eligible bachelor, it was key to many successful excursions. Above all, however, my experience with magic influenced my interest in human behavior, shaped my research program, and paved the road to my academic career as a cognitive neuroscientist with a keen interest in attention. In this chapter, I wear one of two hats intermittently: that of a researcher and that of a magician. 361

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attention. Both before and after William James, great scientists and philosophers have grappled with the study of attention, but James was probably one of the first to broach the concept of attentional varieties, challenging a monolithic conception of attention and recognizing the existence of different shades of attention rather than one unitary form. Several researchers have followed in James’ footsteps and suggested multiple components to describe attention. In this chapter, I show how one of the most influential models in the field of attention illustrates the crosstalk between the science of attention and the art of magic. This approach has been one of my favorite ways of thinking about attention for many years and has gained considerable experimental support in recent time.

A FEW GROSS CHARACTERISTICS OF ATTENTION Before jumping into an in-depth description of any specific model of attention, it is helpful to appreciate a few of its gross characteristics. These qualities may seem intuitive to some, but intuition can be specious, especially when dealing with both attention and magic. Attention can apply to various areas of the visual field and can change the detail with which we look at any given area. For example, you can look at this page and pay attention to its setup as a whole, or you can zoom in on specific words and certain letters therein. If you are paying attention to single characters, you can glean a lot of information about punctuation marks, spelling errors, and even spot minute imperfections on the physical paper. But in that case, you may miss the main idea of a paragraph. We can change the location of attention as well as the size of the attention focus. These “zoom lens” or “attention spotlight” appellations may be clichéd metaphors that only sketch the crux. They are helpful, however, in that they relate to our common experience concerning the kind of attention we use (e.g., for learning versus proofreading). Attention can be either overt or covert. Overt attention involves looking directly at the scene of interest. We usually look straight at what we wish to attend. Sometimes, however, attending to a location different from fixation is advantageous. Covert attention is the ability to select visual information at a cued location, without moving the eyes to study it directly, and to grant such information priority in processing. For example, we often engage in covert attention in social situations when we want to examine a person without being conspicuous. Note, however, that covert attention is not the same as daydreaming—looking at something is not the same as paying attention to it. Attention often involves selection. For example, think of when multiple people are talking simultaneously and

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you try to hone in on one of these streams of conversation in order to follow it in detail (e.g., at a party). You can do it based on the location of the person by visually orienting toward her or locking on her voice frequency—it is easier to separate a female voice from a male voice than it is to separate two male voices—or you may follow by content. When we attend to one input stream, the unattended information goes into the background; although present, it rarely receives the same focal analysis. The brain processes unattended information in subtle and complicated ways. Unattended information can suddenly get interesting because your name or another particularly charged word is present, or because something happens that is related to the conversation you are following, and you find yourself orienting to the new information. Psychologists have studied these phenomena experimentally in great detail and have elucidated some of the mechanisms subserving them as well as the computations that such data receive. Attention can also influence perception and mental processes. For example, an individual reading an engrossing book may fail to identify certain environmental cues. Similarly, attention can aid perception. Improvement in perception, however, is not synonymous with altered thresholds for detection, better performance, or faster reaction times. Cognitive scientists draw a distinction between how attention may be useful for simple detection of events versus how performance can improve at those events. Attention is not a panacea to perception because there is much that attention cannot do. For example, attention can give priority to stimuli appearing at a specific physical location, but it cannot substitute for the acuity provided by the fovea. While the fovea is critical for visual acuity, the costs in latency for an unexpected foveal stimulus are just as great as for an unexpected peripheral event. In other words, visual attention does not compensate for visual acuity. Although performance may improve with increased attentional investment, controversy persists over what orienting attention to a sensory stimulus actually does. General agreement posits that attention provides priority, so that reaction time to the attended stimulus is usually faster. Thus, visual attention influences priority or processing preference. This characteristic of attention applies to other attentional modalities such as hearing.

THREE-NETWORK MODEL OF ATTENTION In line with William James’ early notion of distinct attentional varieties, Michael I. Posner proposed a modern model of attention wherein at least three main functionally and anatomically distinct types of supramodal attentional varieties cooperate and work closely together. Neuromodulators of Attention.

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Pharmacological findings relate each of the three attentional networks with specific chemical neuromodulators: the norepinepherine system, which arises in the locus coeruleus of the midbrain, functions in alerting; the cholinergic system, which arises in the basal forebrain, plays an important role in orienting through its effects in the parietal cortex, where it seems to reduce neural activity and reaction time cost associated with cueing to an invalid target; and the anterior cingulate cortex and lateral prefrontal cortex, involved in executive attention, are target areas of the mesocortical dopamine system (Posner & Rothbart, 2007).

METHODS OF INVESTIGATING ATTENTION. Although attention had already been studied from a neurophysiological view in the 1890s, mental chronometry together with application of the subtraction method provided rich information on psychological processes. In the subtraction method, investigators compared reaction times in two experimental conditions, which allegedly differed only in that one was hypothesized to require an additional cognitive process. Differences in reaction time were then taken to support and index the putative additional process. By systemically varying cognitive processing, researchers developed intricate models of brain function, many of which were subsequently supported by neuroimaging studies. Reaction time assays were later combined with such mathematical formulations as formal information theory. However, because these methods were largely divorced from anatomical and neurobiological data, these approaches were deemed inadequate to elucidate the mechanisms whereby the human brain pays attention. In the 1950s, the advent of microelectrode recordings of single neurons from laboratory animals, at first anesthetized but later awake, afforded examination of neurophysiological processes and supported the notion that the brain processes information in serial stages. Studies using awake monkeys revealed control systems—the terminological precursor to attention networks—where higher brain areas feed back their influence onto earlier processing stages. This top-down effect challenged the then-common view of a completely serial approach to information processing and provided evidence for focal brain areas within the monkey parietal lobe that could be systematically related to processing operations involved in attention (see Chapter 16 for details). These ideas were extended to humans and tested using reactiontime paradigms in neuropsychological patients. The arrival of analog and then digital computers in the 1960s initiated the field of neuroimaging by recording the average electrical event-related potentials (ERPs) from scalp electrodes. Electrophysiology became an ideal tool

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to explore the notion of “attention for action,” which is characterized by millisecond resolution. ERP components were systematically related to sensory and motor stages of information processing. In the late 1980s, neuroimaging experiments made possible the examination of activity in localized brain areas—first through the use of injected radionuclides detected by positron emission tomography (PET) and later through the use of an externally imposed magnetic field in functional magnetic resonance imaging (fMRI). Over the past decade, fMRI has improved in spatial and temporal resolution and can now provide accurate spatial information of focal brain areas that are involved in cognitive tasks such as attention. The inferences obtained from both ERPs and magnetoencephalography (MEG), which probe perceptual processing with fine temporal detail, have been important complements to the millimeteric spatial resolution of fMRI. More recently, neuroimaging technology has been joined by genomics. The Human Genome Project has made great progress in identifying the 30,000 protean genes in the human genome as well as the approximately 1.7 million polymorphic sites scattered across the 6 billion base-pair length of the human genome. These findings hold promising prospects for illuminating how genes can influence disease development and may aid in the association of genes with particular psychopathology. In addition, genomics has the potential to promote the discovery of new treatments and to afford new insights into behavioral genetics, such as the relationship between certain genetic configurations and manifest behavior. Combining neuroimaging with genetics, recent exploratory assays endeavored to noninvasively probe genes that have been shown to result in variation in protein levels or biochemical activity in the context of both typical and atypical attention. Such pooled research efforts promise to elucidate both the neural and genetic correlates of attention. Findings from genetic and neuroimaging studies of attention have provided some converging results. While most neuroimaging studies yield a small number of widely distributed brain areas that must be orchestrated to carry out a cognitive task, it is often unclear what the unique contribution of each area might be. However, in the case of attention, as in the case for language, these mechanisms have been sufficiently elucidated by a careful teasing apart based on chronometry, neuroimaging, and genetics. Attention, therefore, is a primary research domain, which exemplifies the links between brain and behavior and binds psychology to the techniques of neuroscience.

ATYPICAL ATTENTION A number of human practices, such as drug ingestion, meditation, and hypnosis, can dramatically influence attention.

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Cognitive neuroscientists are beginning to unlock the ways these routines influence the human brain and how such effects alter common information processing. It is possible to test the limits of attentional functions by examining healthy individuals under atypical conditions. That more notice should be given to the investigation of healthy individuals driven toward the neuropsychological domain is evident in light of the contributions of social psychology to cognitive science, exploratory assays of evanescent attention deficits, and the impact of reversible lesion research using transcranial magnetic stimulation (TMS). Cognitive neuroscientists generally agree that mental processes come in two varieties: controlled and automatic. Some processes are thought to be innately automatic; others become automatic through practice. General accounts posit that once automatized, these processes are initiated unintentionally, effortlessly, even ballistically, and cannot be easily interrupted or prevented. For example, the Stroop effect suggests that reading words is an automatic process for a proficient reader. The standard account posits that words are processed automatically to the semantic level and that the Stroop effect is the “gold standard” of automated performance. Although cognitive scientists have focused on the processes that lead to automatization with over 4,000 citations to Stroop’s original work alone, the question of whether it is possible to regain control over an automatic process is unanswered, and mostly unasked. However, mounting evidence from assays of atypical attention show that deautomatization is possible. A few meditative practices claim to achieve deautomatization with some sparse evidence of reduced Stroop interference. The most compelling findings addressing this issue

showed that a specific posthypnotic suggestion reduced and even removed Stroop interference in highly hypnotizable individuals. Reduction of the Stroop effect occurred following reduction in anterior cingulated cortex activation and altered processing in an occipito-parietal location that might be related to the chunking of visual letters into words. Independent accounts under typical conditions also challenge the robustness of the Stroop effect. Although critiqued, interpretation of these and other results supports the idea that attention may be employed to derail automatic processes. Clinicians are often interested in deautomatization as a way to unlearn or free one from undesired habits. Such derailment of automaticity may also occur spontaneously in extreme situations (e.g., in combat individuals might not realize that they have been hurt until much later). Other demonstrations of top-down modulation and deautomatization showed that following hypnotic instruction to view a colored picture as grayscale, highly hypnotizable individuals demonstrated reduced activity in color areas of the prestriate cortex. These studies show that atypical attention can influence at least executive attention and possibly some of the other attentional networks. Exploratory assays using other forms of atypical attention may further elucidate the malleability of attentional networks. For example, meditation training may be a way to induce a long-term baseline change in attentional function permitting individuals to achieve better, more effective self-regulation. Alerting refers to the increase and maintenance of response readiness in preparation for an imminent stimulus (Figure 17.1). Roughly equivalent to sustained attention and vigilance, alerting is probably a more foundational

Stay alert! In precisely 250 milliseconds, one of these bombs will go off!

“ALERT?” But, I don’t know where, just when!

Figure 17.1 The alert attention network.

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Atypical Attention

form of attention on which other attentional functions rest. Without putting too fine a point on it, however, alerting is typically task-specific rather than a general cognitive control of arousal. Modern experimentalists have replaced the “older” vigilance tasks by “newer” alerting tasks, although some researchers argue that these two task-types tap different mechanisms. The relationship between alerting and arousal is complex and psychological variables such as stress can further influence alerting, increasing or decreasing it as a function of specific context and task. In contrast to the in-depth studies of the other attention systems (i.e., orienting and executive control), alerting has been relatively understudied and attention research has yet to satisfactorily elucidate its neural substrates. Orienting informs us where an important event is likely to occur (Figure 17.2). It is also the ability to select specific information among multiple sensory stimuli. Sometimes known as scanning or selection, it is the most studied attentional network. Whether overt or covert, orienting has traditionally been measured by reductions in reaction time to a target following a cue that gives information on the location, but not the timing, of an event. Scientists distinguish between exogenous orienting—when the flash of a cue automatically captures attention to a specific location— and endogenous orienting—when a central arrow points to one of two lateralized target presentation locations. Some researchers argue that at least part of the capacity subsumed by alerting is conceptualized as orienting in the temporal domain. The bulk of the evidence, however, supports the notion that orienting and alerting are largely controlled by different brain systems. Although most research in orienting has been conducted in the visual domain, neural activity increases in response to an orienting cue and concomitant performance enhancement have been demonstrated in most sensory systems. Some researchers suggest

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that orienting may encompass not only sensory, but also purely mental events, including memory. Recent work has shown an orienting effect for a variety of internal representations, including items stored in working memory and long-term memory. It is possible to increase the efficiency of a specific attention network by focal training. For example, several rehabilitation programs for patients with specific impairments of the orienting system involve expressly tailored attention exercises. Attention training has also been used in early child education to improve self-regulation. This form of attention operates in close coordination with working memory in many cognitive tasks. (A detailed description of the attention training procedure is available on the web at www.teach-the-brain.org/learn/attention.) Many studies have shown that children between the ages of about 3 and 7 develop a brain network that allows them to regulate their thoughts and emotions. Executive attention typically relates to conflict of the kind you encounter when trying not to scratch a particularly itchy mosquito bite or when confronted with two police officers who demand that you comply with conflicting orders (Figure 17.3). In general, executive functions pertain to planning or decision making, error detection, novel or not well-learned responses, conditions judged to be difficult or dangerous, regulation of thoughts and feelings, and overcoming habitual actions. While some may consider any instance of top-down control as executive attention, others construe it as the monitoring and resolution of conflict between computations in different neural areas. Executive attention is typically measured using experimental tasks where one is faced with an incompatibility between dimensions of the stimulus or response. Whether and to what extent executive attention governs the other attentional networks remains unclear. A more

At some point, the rabbit will come out of the center hat.

Hmm. I know where to ORIENT, but when will things happen?

Figure 17.2 The orient attention network.

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366 Varieties of Attention

Yikes! How do I resolve this CONFLICT? GO

Figure 17.3 The conflict attention network.

successful effort has related concepts such as emotionregulation, self-regulation, effortful control and inhibitory control to executive attention. These findings collectively reveal that attention is a strong modulator of emotion, cognition, thought and action. For example, findings elucidate several aspects of the influence of attention training on executive attention in young children, drawing on measures of brain activity, cognition, and behavior in children as early as 4 years of age. These measures include behavioral assessments of executive attention and intelligence, genotyping of dopamine-related genes, recording electrical activity at the scalp generated by neuronal function, and parental questionnaires relating to the child’s temperament. This training program—adapted to be childfriendly from a method originally used to prepare macaque monkeys for space travel—was given for 5 days over a 2- to 3-week period and resulted in great attention improvements, including increase in IQ and better self-regulation of affect and cognition. This approach potentially opens a new vista for experiments in developmental cognitive neuroscience in which genetics, brain function, and behavior can be related through the study of individual differences and demonstrates that executive attention skills can be trained. In addition, these findings could potentially lead to better intervention strategies for children with attentional and other behavioral problems such as Attention-Deficit Hyperactivity Disorder.

MAGIC, PSYCHOLOGY, AND ATTENTION In the time-honored tradition of a complex, secret, and skillful art, magicians are typically reluctant to share their

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methods with outsiders. The art of deception, however, goes beyond the mechanics of a specific trick and is deeply entrenched in psychology with a special emphasis on cognitive processes and perception. For many years, magicians have successfully used the psychology of deception, including self-deception; psychological science is just beginning to unravel data showing how suggestion and expectation, for example, influence human behavior. Using attention as a vehicle, current research paves the road to realizing this new direction. Magicians and researchers approach attention very differently. To appreciate this difference, consider a person who tries to understand the meaning of time. Consulting with a watchmaker is probably not the best way to go. Speaking to a physicist or a philosopher of science would likely be a better choice. While watchmakers fix timepieces, they are not necessarily experts on time. Similarly, magicians are the watchmakers of attention—they are experts at hoodwinking their audience’s perceptual or cognitive systems, without necessarily having keen insights into the underlying mechanisms. By contrast, investigators typically try to understand the mechanisms and identify the processes that subserve attention. Yet most would not make great magicians. These different approaches to attention may appear disjoint but can actually be complementary, in the same way that social psychology and cognitive neuroscience, two largely separate disciplines, have been increasingly overlapping. Social psychologists have traditionally tried to “push” healthy individuals closer to the pathological spectrum by incorporating into their research arsenal techniques such as suggestion and deception. Cognitive neuroscientists, on the other hand, have shied away from this approach and attempted, instead, to understand brain function by studying patients with specific deficits and focal brain lesions, as well as healthy individuals. The marriage of the methods of social psychology with cognitive neuroscience created an opportunity to test the limits of attentional functions by examining healthy individuals under atypical conditions including hypnosis, meditation, and sleep deprivation (see earlier section on Atypical Attention). Similar to some social psychologists, magicians capitalize on exploring the limits of human processing and triumph in commanding ways to tap these pliable behavioral perimeters. In this way, the cognitive neuroscience of attention can benefit from the contributions of both social psychologists and magicians. At least to some degree, most magic tricks rely on misdirection—or rather on direction—appellations that broadly designate attentional effects. Without knowing much about the science of attention, magicians have devised a vast array of practical and often cunning ways to

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Summary

direct one’s attention. One way is to direct where or when a spectator is looking, granting the performer sufficient, albeit brief, opportunity to accomplish a trick. Attentional misdirection can also create spurious expectations thereby reducing or diverting the spectator ’s suspicion from the modus operandi. In addition, misdirection can influence later recall. Misremembering the exact details often leads to the subsequent reconstruction of a past event, frequently through the spectator ’s own posthoc re-enactments and retrospective ascriptions. This misinformation effect has been thoroughly studied by psychologists. Seasoned magicians, however, especially those who practice mentalism, have long appreciated that while a magical effect can mesmerize the audience in attendance, a true miracle is an event described by those to whom it was told by others who did not even see it. Thus, magicians have known that attention can influence spatial and temporal information as well as meta-cognition and memory. Recent research on visual attention and visual memory confirms what magicians have both known and practiced for a long time. For example, people are surprisingly poor at noticing even large changes to objects, images, and motion pictures from one instant to the next—a phenomenon called change blindness. In addition, inattention blindness—a form of sighted blindness where the inability to perceive involves subjects who are not attending to the stimulus but are attending instead to something else—is a related phenomenon. People usually experience inattentional blindness when they don’t know what they should attend to—exactly what a magician exploits when standing in front of an unsuspecting audience. Simple experiments show that effects such as change and inattentional blindness are no longer esoteric exemplars confined to the psychology research lab but rather compelling demonstrations of plausible perceptual experience (see the videos at http://viscog.beckman.uiuc.edu/djs_lab/demos.html or www.quirkology.com/United States/index.shtml for online demonstrations). Attentional phenomena such as change and inattention blindness raise critical questions about the relationship between attention and perception. For example, how much of our visual world do we perceive when we are not paying attention? Thus, attention or lack thereof—directing attention away from a target object—plays a key role in perception. While magicians can provide many practical demonstrations of these traits, researchers are beginning to unravel the neurocognitive substrates that subserve them. Although practitioners of magic were on the scene way before scientists started to study the limitations of the human ability to deal with multiple concurrent signals in a variety of practical tasks, Posner ’s model of attention nicely illustrates how magic tricks might work. Since

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its inception in the early 1970s, Posner ’s model has been revised and refined, but still retains its original tenor, namely that attention comprises a system of three disparate control networks. Experimental findings suggest that these attentional subsystems can modulate cognition, emotion, thought, and action. Furthermore, these networks can influence early stages of neural processing concerning both the location and time of sensory information as well as relate to meta-cognition and alter certain kinds of memory.

SUMMARY Compelling evidence suggests that different attentional networks exist in the human brain. The exact nature of these networks and the degree to which they are independent is still unclear. Although other important models promote different views concerning the functions and mechanisms of attention, Posner ’s three-network account is an influential model of attention that fits nicely with a magician’s intuition. Individuals outside the magic fraternity often fail to appreciate that although performers may recast their tricks to gel with contemporary culture and employ modern technology, these variations are largely cosmetic and rely on age-old principles, mostly grounded in the psychology of attention and deception. The basic principles of conjuring comprise the subtleties of attentional misdirection, the understanding of human perception, including the understanding of visual and psychological illusion, and good showmanship. Practitioners of magic, like skilled therapists, often use their utterances and gesticulations to create false images and specious expectations in the minds of their spectators. Whereas clinicians may only infrequently resort to trickery, conjurors thrive and constantly seek novel ways to perfect the art of deception, for the audience’s entertainment pleasure as well as for their own fame and fortune. While medical practitioners may play by the rules, for magicians there are no rules—almost everything is allowed, including the unthinkable, in order to win the “Trick the Audience” game. With more research tools becoming progressively available, understanding of attention is likely to yield innovations in education, the treatment of pathological conditions, rehabilitation, cognitive training, and . . . the magical arts. It will also provide insights into cultural and individual differences and further integrate the psychological and brain sciences. While most research has been conducted with normal or pathological individuals in the context of typical, waking attention, carefully designed experimentation in the plane of atypical attention may further accelerate this process in the quest to elucidate human attention.

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368 Varieties of Attention

As researchers begin to pay attention to magic and how it teaches us about human behavior and brain function, practitioners of the world’s second oldest profession may benefit from scientific insights into attention to improve,

polish, and invent new powerful effects. Whether the best magician among the scientists or the best scientist among the magicians, I pay attention to magic and to the magic of attention. Poof!

GLOSSARY Mental chronometry: Reaction time studies, such as those early experiments conducted by Donders in 1868, where researchers try to “time the mind” and attempt to describe the processes going on by fragmenting cognitive processing into separate stages. Change blindness: While changes to a scene typically produce a detectable motion signal, when a change coincides with another event that disrupts the motion signal, observers are often blind to surprisingly large changes. Recent experiments show change blindness during events such as saccades, flashed blank screens, blinks, and real-world occlusions. Emotion regulation: The reduction, increase, or sustaining of an emotional response (e.g., fear, anger or pleasure) based on the actions of the self or others. Self-regulation: The ability to manipulate one’s own emotions, thoughts or actions upon direction from the self or another person. Emotion regulation can be a form of self-regulation but it could also be induced by actions of others. Stroop effect: The Stroop conflict task requires proficient readers to name the ink color of a displayed word. Individuals are usually slower and less accurate indicating the ink color of an incompatible color word (e.g., responding “blue” when the word “RED” is inked in blue) than identifying the ink color of a congruent color name (e.g., “RED” inked in red). This difference in performance constitutes the Stroop conflict and is one of the most robust and wellstudied phenomena in attention research. Effortful control: The ability to inhibit, activate or sustain a response, which includes the capacity to inhibit a dominant response in order to perform a subdominant response. In temperament research, individual differences in effortful control are measured as a factor score that combines scales dealing with attention and the ability to regulate behavior on command. Inhibitory control: The reduction in the probability, speed, or vigor of the normal response to a stimulus based on instruction from the self or others. It is often measured by scale scores on a questionnaire or by a task that requires one to withhold or delay a response. Hypnosis: Attentive receptive concentration whereby certain individuals can change the way they experience themselves and the environment and often display heightened compliance with suggestion. Posthypnotic suggestion: A condition during common wakefulness (after termination of the hypnotic experience) wherein, usually upon a prearranged cue, a subject readily complies with a suggestion made during the hypnotic episode. Functional magnetic resonance imaging (fMRI): A noninvasive technique that permits imaging of the living brain and provides findings that relate neural to cognitive activity by measuring small changes in the magnetic properties of blood. Event-related potentials (ERP): A noninvasive electrophysiological technique based on scalp electrode recordings of evoked-response potentials. Top-down effect: Controlling, regulating, or overriding a stimulus-driven or other bottom-up process by such factors as attention or expectation. Magnetoencephalography (MEG): A technique similar to event-related potentials, which detects the changing magnetic fields associated with brain activity. Vigilance tasks: A set of tasks requiring sustained attention typically requiring participants to monitor displays over extended periods of time for the occasional occurrence of critical events (signals). Signals are low-probability events requiring action, embedded in the context of recurrent nonsignal events which require no overt response. Alerting tasks: A set of tasks requiring participants to prepare for the imminent appearance of a target at a known location. For example, a visual cue may alert the participant that a subsequent target will soon appear at a known location.

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References 369

Positron emission tomography (PET): A technique using positron-emitting radioactive tracers that are attached to molecules that enter biological pathways of interest to study the relationship between energy consumption and neural activity. Transcranial magnetic stimulation (TMS): A technique used to induce transient interruption of normal activity in a relatively restricted area of the brain by rapidly changing a strong magnetic field near the brain area of interest. Mentalism: The simulation of psychic powers for the purpose of entertainment, usually without explicitly claiming to possess such powers. Whereas at least some members of the larger fraternity of magical arts draw a distinction between magic and mentalism, the lay public often views mentalists as anywhere from pseudo-psychics all the way to genuine exemplars of the paranormal. Unfortunately, performers of mentalism typically acquiesce when others attribute paranormal powers to them. Regretfully, only few mentalists judiciously represent their performances for what they really are—entertaining tricks based in deception—and dutifully steer clear of claims of the paranormal.

REFERENCES Hyman, R. (1989). The psychology of deception. Annual Review of Psychology, 50, 133–154. Lamont, P., & Wiseman, R. (1999). Magic in theory. Hertfordshire: University of Hertfordshire Press.

Raz, A., & Buhle, J. (2006). Typologies of attentional networks. Nature Reviews Neuroscience, 7, 367–379. Schiffman, N. (1997). Abracadabra! Secret methods magicians and others use to deceive their audience. Amherst, NY: Prometheus Books. Sorensen, J. (2007). A cognitive theory of magic. Lanham: AltaMira.

Posner, M. I., & Rothbart, M. (2007). Research on attention networks as a model for the integration of psychological science. Annual Review of Psychology, 58, 1–23.

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Chapter 18

Attentional Mechanisms YALCHIN ABDULLAEV AND MICHAEL I. POSNER

Hebb’s contribution). Hebb proposed that cell assemblies link widespread systems of neurons from multiple brain areas. Neuroimaging has revealed brain networks involved in many of the cognitive and emotional tasks (Posner & Raichle, 1994). Some of these are indicated in Table 18.1 with references that discuss them in more detail. In all of these cases, a small number of widely dispersed brain areas are active. It has been argued that each node of these networks carries out its own operation (Posner & Raichle, 1994), but there is still much discussion of the role of each brain area. Hebb also proposed phase sequences that coordinated the activity of cell assemblies in real time. Currently, neuroscientists explore the coordination of remote brain areas by common oscillations (Knight, 2007). Thus, at both the cellular and the network levels, electrical recording within cells (intracellular recording) and from electrodes placed on the head skin surface (electroencephalography or EEG) have revealed the coordination of widespread neural areas in real time. In this chapter, we first trace the modern history of studies of attention. We begin with studies arising shortly after World War II and consider each subsequent decade. We emphasize links between attention and underlying brain mechanisms including studies of patients with brain lesions, recording of electrical activity noninvasively or by use of implanted intracerebral electrodes and efforts to understand the genes related to attention. We then examine studies at the neurosystems level using methods of imaging and lesions to trace critical brain areas that are the sources of attentional networks. Next, we consider studies at the cellular or synaptic level. Evolutionary studies provide needed links between human and nonhuman primates (Chapter 3 by Snowdon & Cronin in this book). Recordings from depth electrodes can penetrate the microstructure involved in computations within neural areas. Finally, we consider the contribution from genetic studies that trace the role of genes and environment in shaping the development of attentional networks.

Neuroscience contributions to the mechanisms of attention can be examined at the systems, cellular, synaptic, or genetic levels. These studies use the methods discussed in the Part I, Foundations, of this Handbook. The integration of appropriate constraints from each of these levels is an important requirement for a full understanding of cognitive processes such as attention. This chapter examines the use of various methods and the attentional typology outlined in Chapter 17. We first summarize the modern history of attention studies and then examine studies at the systems, cellular, synaptic, and genetic levels. Our emphasis is on the executive attention system because it is central to human behavior and it plays a large role in the control or regulation of thoughts and feelings. The field of attention is one of the oldest in psychology. At the turn of the twentieth century, Titchener (1909) called attention “the heart of the psychological enterprise.” Attention is relatively easy to define subjectively. The classical definition of William James, for example, was, “Everyone knows what attention is. It is the taking possession of the mind in clear and vivid form of one or of what seem several simultaneous objects or trains of thought” (1890, p. 403). However, this subjective definition does not provide hints that might lead to an understanding of mechanisms of attention that can illuminate its physical basis in terms of underlying physiological (I) process nor clarify its normal development (II) and pathologies. For these goals, it is useful to think about attention as an organ system with its own anatomy and physiology that develops in early life under the control of genes and experience. This is the focus of this chapter. The modern history of attention as an organ starts with the important studies of Moruzzi and Magoun (1949) on the reticular activating system of the brain. About the same time, Hebb (1949) called attention to the importance of networks of neural areas (cell assemblies) linked in real time (phase sequences) in building conscious representation of stimulus input (see Posner & Rothbart, 2007b, for a review of 370

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Introduction 371 TABLE 18.1

Networks Studied by Neuroimaging and Their References

Arithmetic Dehaene, S. (1997). The number sense. Oxford, UK: Oxford University Press, Figure 8.5). Autobiographical Memory Fink, G. R., Markowitsch, H. J., Reinkemeier, H., Bruckbauer, T., Kessler, J., & Heiss, W. D. (1996). Cerebral representation of one’s own past: Neural networks involved in autobiographical memory. Journal of Neuroscience, 16, 4275–4282. Faces Haxby, J. V. (2004). Analysis of topographically organized patterns of response in fMRI data: Distributed representation of objects in the ventral temporal cortex. In N. Kanwisher and J. Duncan (Eds.), Functional neuroimaging of visual cognition attention and performance XX. Oxford UK: Oxford Universiy Press (pp. 83–97). Fear Ochsner, K. N., Ludlow, D. H., Knierim, K., Hanelin, J., Ramachandran, T., Glover, G. C., & Mackey, S. C. (2006). Neural correlates of individual differences in pain-related fear and anxiety. Pain, 129, 69–77. Reading and Listening Posner, M. I., & Raichle, M. E. (1997). Images of mind (2nd ed.). New York: Scientific American Library. Reward Knutson, B., Fong, G. W., Bennett, S. M., Adams, C. M., & Homme, D. (2003). A region of mesial prefrontal cortex tracks monetarily rewarding outcomes: Characterization with rapid event-related fMRI. NeuroImage, 18, 263–272. Self Reference Johnson, S. C., Schmitz, T. W., Kawahara-Baccus, T. N., Rowley, H. A., Alexander, A. L., Lee, J. H., & Davidson, R. J. (2005). The cerebral response during subjective choice with and without self-reference. Journal of Cognitive Neuroscience, 17, 1897–1906.

INTRODUCTION

1960s

1950s

One of the significant developments of the 1960s involved the ability to average electrical signals from the scalp to create the event-related potential (ERP)—a series of electrical events time locked to the stimulus. The technique was applied to the study of attention. Sutton, Nraren, Zubin, and John (1965) reported that surprising or unexpected cognitive events of the type that might be closely inspected produced a strong positive wave in the scalp potential called the P300 (meaning a positive deflection of electrical activity at about 300 ms after stimulus onset). This component has and continues to play an important role in attention research (Donchin & Cohen, 1967; Rugg & Coles, 1995). At about the same time, Gray Walter reported that the brain produced a marked DC shift during the period following a warning and prior to a target. This was called the contingent negative variation and was viewed as a sign that alerting was taking place (Walter, Cooper, Aldridge, McCallum, & Winter, 1964). Reaction time improved markedly over the first 500 ms following the warning. Often errors increased with warning interval producing a tradeoff between speed and accuracy. This finding suggested that warning effects did not improve the accrual of information but instead made it faster to attend to the input and thus speed the response (Posner, 1978).

D. O. Hebb (1949) argued that each simulus has two effects. One of these involved the reticular activating system and worked to keep the cortex tuned in the waking state, whereas the other used the great sensory pathways and provided information about the identity and location of the stimulating event. In the early 1950s, Colin Cherry (1953) initiated an epic series of experiments designed to examine how subjects selected stimuli that were presented simultaneously to each ear. Rapid presentation of pairs of digits, one to each ear, led people to recall all digits presented to the right ear first, followed by all presented to the left. Broadbent (1958) summarized these and other results by suggesting that a peripheral short-term memory system buffers sensory input prior to a filter, which selects a channel of entry (in this case an ear) and sends information to a limited capacity perceptual system. A second line of attention research that emerged from studies conducted during the World War II involved the study of sustained attention during vigilance tasks (Mackworth & Mackworth, 1956). During continuous tasks, subjects tended to miss more signals as the task continued. Changes in the EEG suggested that there was an increase in a sleep-like state.

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372 Attentional Mechanisms

1970s The work of Hubel and Wiesel (1968) using microelectrodes to probe the structure of the visual system began in the early 1960s. However, before this method could be applied to attention, it was necessary to adapt the microelectrode technique to alert animals. This was accomplished in the early 1970s by Evarts (1968) and applied by Mountcastle (1978) and Wurtz, Goldberg, and Robinson (1980) to examine mechanisms of visual attention in the superior colliculus and parietal lobe. Their findings suggested the importance of both of these areas to a shift of visual attention. It had been known for many years that patients with lesions of the right parietal lobe could suffer from a profound neglect of space opposite the lesion. The findings of attention-related cells in the posterior parietal lobe of alert monkeys suggested that these cells might be responsible for the clinical syndrome. In the 1970s and 1980s, recording neuronal activity during cognitive tasks was accomplished in human neurosurgery patients with diagnostic and/or therapeutic depth intracerebral electrodes (Bechtereva, 1978; Bechtereva & Abdullaev, 2000; Bechtereva, Medvedev, Abdullaev, Melnichuk, & Gurchin, 1989; Engel, Moll, Fried, & Ojemann, 2005; Ojemann, Creutzfeldt, Lettich, & Haglund, 1988). This opened new ways of studying the regional cellular mechanisms of attention and other cognitive functions. An impressive result from the microelectrode work was that the time course of parietal cell activity seemed to follow a visual stimulus by 80 to 100 ms. Beginning in the 1970s, Hillyard (Hink, Van Voorhis, Hillyard, & Smith, 1977) and other investigators explored the use of scalp electrodes to examine time differences in the brain activity between attended and unattended visual locations. They found that the N1 and P2 components of the visual ERP showed changes due to attention starting at about 100 ms after input. These findings showed likely convergence of the latency of psychological processes as measured by ERPs in human subjects and cellular processes measured in alert monkeys. This important development in mental chronometry suggested that scalp recordings could accurately reflect the underlying timing aspects of brain activity.

1980s Posner (1980) studied the use of a cue in an otherwise empty visual field as a way of moving attention to a target. Subject’s task was to press a button as soon as a visual stimulus appeared in either left or right visual field, and a brief presentation of a left or right cue preceded the target stimulus directing attention to the cued location. Electrodes near the eyes were used to ensure there were no eye movements. Because only one response was required, there was

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no way to prepare the response differently depending on the cue, making it clear that whatever changes were induced by the cue were covert and not due to motor adjustment of the eyes or hand. It was found that covert shifts could enhance the speed of responding to the target even in a nearly empty field. Within half a second, one could shift attention to a visual event and, when it indicated a likely target at another location, move attention to enhance processing at the new location. Shulman, Remington, and McClean (1979) showed that response times to probes at intermediate locations were enhanced at intermediate times as though attention actually moved through the space. It was possible to prepare to move the eyes to one location while moving attention covertly in the opposite direction (Posner, 1980). Whether attention moves through the intermediate space and how free covert attention is from the eye movement systems are still disputed matters, suggesting the limitation of purely behavioral studies (LaBerge, 1995; Rizzolatti, Riggio, Dascola, & Umilta, 1987). At the time, it was also hard to understand how a movement of attention could possibly be executed by neurons. Subsequently, it was shown that the population vector of a set of neurons in the motor system of a monkey could carry out what would appear behaviorally, as a mental rotation (Georgopulos, Lurito, Petrides, Schwartz, & Massey, 1989). After that discovery, a covert shift of attention mediated by a population of neurons did not seem too far-fetched. It had been reported that patients with lesions of the parietal lobe could make same-different judgments concerning objects that they were unable to report consciously (Volpe, LeDoux, & Gazzaniga, 1979). It was possible to follow this result in more analytic cognitive studies. What did a right parietal lesion do that made access to material on the left side difficult or impossible for consciousness and yet still left the information available for other judgments? (See also Volume II, Chapter 66 by Taub & Uswatte). This puzzle was partially answered by the systematic study of patients with different lesion locations in the parietal lobe, the pulvinar, and the colliculus. These lesions all tended to show neglect of the side of space opposite the lesion. But in a detailed cognitive analysis, it was clear that they differed in showing deficits in specific mental operations involved in shifting attention (Posner, 1988). These studies supported a limited form of brain localization. The hypothesis was that different brain areas executed individual mental operations or computations such as disengaging from the current focus of attention (parietal lobe), moving or changing the index of attention (colliculus), and engaging the subsequent target (pulvinar). If this hypothesis were correct, it might explain why Lashley (1931) thought the whole brain was involved in mental tasks. While the

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Systems Level 373

neuroimaging studies in the next decade raised serious questions about the details of the localizations of component operations, they tended to support the importance of widespread networks with nodes that carried out computations like these (Posner & Rothbart, 2007b). 1990s to Date In the late 1980s, the Washington University School of Medicine was developing a positron emission tomography (PET) center led by Marc Raichle. These studies helped establish neuroimaging as a means of exploring brain activity during cognitive functions in general and the study of attention in particular (Posner & Raichle, 1994, 1998). In general, these studies have shown that most cognitive tasks, including those that are designed to separate mechanisms of attention, have activated a small number of widely scattered neural areas. Some people have argued that these areas are specific for domains of function like language, face perception, or episodic memory (Kanwisher & Duncan, 2004). In the area of attention, the mental operations or computations carried out by a particular area are more frequently considered (Corbetta & Shulman, 2002). These two ideas are not mutually exclusive. It is certainly possible to talk about the set of areas that are involved in language and at the same time maintain that the areas carry out different computations within that domain. The findings from neuroimaging that cognitive tasks involve a number of different anatomical areas has led to an emphasis on tracing the time dynamics of these areas during tasks involving attention. Because shifts of attention can be so rapid, it is difficult to follow them with hemodynamic imaging. To fill this role, algorithms have been developed (Scherg & Berg, 1993) to relate the scalp distribution of brain electrical activity recorded from high density electrical or magnetic sensors on or near the skull to brain areas active during hemodynamic imaging (see Dale et al., 2000, for a review). In some areas of attention, there has been extensive validation of these algorithms (Heinze et al., 1994) and they allow precise data on the sequence of activations during the selection of visual stimuli (see Hillyard, Di Russo, & Martinez, 2004, for a review). The combination of spatial localization with hemodynamic imaging and temporal precisions from electrical or magnetic skill recordings has provided an approach to the networks underlying attention. To relate imaging to underlying neural systems an important approach is to record extracellular from implanted electrodes in humans or primates. Work in humans was of great importance because neuronal activity usually provides excellent spatial resolution with precise localization of the recorded activity (in the immediate proximity of the tip

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of the recording electrode), and fairly good temporal resolution, usually within a tenth to a hundredth of a second when changes of firing rate are recorded over time. It also provides valuable neurophysiological information about excitation or inhibition of neuronal activity. Disadvantages include its invasiveness, requiring use of patients’ having some chronic brain disorder (epilepsy, Parkinsonism, and others), and providing information from only a few recording sites of the brain that are used to guide therapy. This means that what is happening in many other brain regions is unknown. Subjects for these studies are neurosurgical patients with medically intractable forms of Parkinsonism, epilepsy, some other disorders involving brain tumors, or trauma with stereotactically implanted intracerebral electrodes for diagnostic and treatment purposes. Some institutions use chronically implanted electrodes that stay in the brain from a few weeks to a few months and allow studying neuronal activity after recovery period in a lab close to normal cognitive studies (Bechtereva, 1978; Bechtereva & Abdullaev, 2000; Bechtereva et al., 1989; Abdullaev, Bechtereva, Melnichuk, 1998). Others record neuronal activity in the operating room during open brain surgery (Ojemann et al., 1988). Changes of the cellular firing rate measured during performance of cognitive tasks provide evidence of the participation of these recorded cells in the attention or other measured cognitive function, its time course, and whether this accompanying neuronal activity change is excitatory or inhibitory. For example, cells recorded from the head of the caudate nucleus respond to list of visual words with increasing firing rate when doing high-level semantic tasks (deciding whether each noun is an abstract or concrete word) at about 200 to 300 ms after the stimulus onset (Figure 18.1, upper graph). The same cells respond to the same words with more sustained inhibition when the task is to read the words aloud (Figure 18.1, middle graph), or when making an old/new discrimination of words memorized the day before from new words (Figure 18.1, lower graph).

SYSTEMS LEVEL The late twentieth century methods provide an improved prospect of an integration of psychological science around the ideas introduced by Hebb (1949; see Posner & Rothbart, 2007b, for an extension of this argument). Cell assemblies and phase sequences are names for aspects of neural networks. Now, thanks to work on the computational properties of neural networks (i.e., Rumelhart & McClelland, 1986), we are in a much better position to develop detailed theories integrating information from physiological, cognitive, and behavioral studies.

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374 Attentional Mechanisms c

a

d ML b Left

(A)

a

Right

Lb.Pt

Semantic task

Lb.Fr

1.06 1.00 0.94

Lb.Pc

(B)

Reading aloud task

1.15 12.6

1.00

14.7

15 0 16.5 mm 0.85

CN

(C)

Tk

Memory retrieval task

c GPm GPI

Stk

b da

1.25 AC-PC 1.00

0.75 NCL

Figure 18.1 Anatomical and temporal precision of recording from depth electrodes. Note: The left side of Figure 18.1 shows the anatomical location of four recording sites (a, b, c, and d) in the head of the caudate nucleus (CN) in Parkinsonism patients. The right side of the figure shows recording of neuronal activity from indwelling electrodes in these human patients. The graphs demonstrate responses of neurons recorded from site a in CN during semantic categorization task (upper graph), control task of reading the same words (middle graph), and recognition memory task with similar words discriminating words memorized the day before from new

One of these developments—neuroimaging—allows us to examine neuronal activity in terms of localized changes in blood flow or metabolism by PET or changes in blood oxygenation by functional magnetic resonance imaging (fMRI) (Toga & Mazziotta, 1996). By using tracers that bind to different transmitters, PET can also be used to examine neurotransmitter receptor density in the brain (Fischman & Badgaiyan, 2006). By measuring electrical (EEG) and magnetic (MEG) signals outside the skull, the time course of activation of different brain areas localized by fMRI can be measured (Dale et al., 2000). Use of diffusion tensor imaging, a form of MRI that traces white matter tracts can also image pathways of activation. In addition to the study of naturally occurring lesions, interrupting information flow

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words (lower graph). First vertical line marks onset of word presentation for 200 ms. The second vertical line marks the onset of response cue, allowing subject to say aloud yes or no (in semantic and memory tasks) or the presented word (in reading task). Vertical axis represents a deviation of discharge rate from the mean prestimulus level. Statistical significance under each latency bin, each bin is labeled either by a dot or a bar; long, medium, and short bars correspond to P  .001, P  .01, and P  .05. From “Activity of Human Caudate Nucleus and Prefrontal Cortex in Cognitive Tasks,” by Y. Abdullaev, N. P. Bechtereva, and K. V. Melnichuk, 1998, Behavioural Brain Research, 97, pp. 159–177.

by transcranial magnetic stimulus (TMS) can produce temporary functional lesions of pathways (see Posner, Sheese, Odludas, & Tang, 2006; Toga & Mazziotta, 1996, for a review of these and other methods). These methods provide a tool kit that can be used either alone or together to make human brain networks accessible for detailed physiological study. Sites and Sources Attention can influence processing in most areas of the brain. These areas are sites at which attention has an effect. However, the sources of these influences are much fewer. The sources are the brain networks from which attention

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Systems Level 375 Superior parietal lobe Posterior area

Frontal eye field Anterior cingulate gyrus

Temporoparietal junction

Frontal area

Thalamus Pulvinar

Prefrontal cortex Superior colliculus Alerting Orienting

Executive attention

Figure 18.2 Brain areas involved in various attention networks. Note: The executive network emphasized in this chapter is shown by large triangles and involves the anterior cingulate (frontal midline) and lateral prefrontal brain areas. The alerting network shown in squares involves a thalamic origin in the locus coeruleus and nodes in frontal and parietal areas. Phasic alertness involves mostly the right cerebral hemisphere while tonic alertness involves the left was well. Orienting shown in squares involves superior and inferior parietal areas as well as the frontal eye fields. Subcortical areas also involved in orienting include the superior colliculus and thalamus. Data from Fan, McCandliss, Fossella, Flombaum, and Posner (2005).

effects originate (see Raz Chapter 17). The sources of three common networks underlying attention are shown in Figure 18.2. To distinguish the brain areas that are involved in orienting from the sites at which they operate, it is useful to separate the presentation of a cue indicating where a target will occur from the presentation of the target requiring a response (Corbetta & Shulman, 2002; Posner, 1980). This methodology has been used for behavioral studies with normal people (Posner, 1980); patients (Posner, 1988) and monkeys (Marrocco & Davidson, 1998); and in studies using scalp electrical recording (Hillyard et al., 2004) and event-related neuroimaging (Corbetta & Shulman, 2002). Studies using event-related fMRI have shown that following the presentation of the cue and before the target is presented, a network of brain areas become active (Corbetta & Shulman, 2002; Hillyard et al., 2004; Kastner, Pinsk, De Weerd, Desimone, & Ungerleider, 1999). These include the superior parietal lobe, temporal parietal junction, and frontal eye fields. There is widespread agreement about the identity of these areas (see orienting areas in Figure 18.2) but there remains a considerable amount of work to do in order to understand the function of each area. When a target is presented at the cued location, it is processed more efficiently than if no cue had been presented. The brain sites influenced by orienting are those normally

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used to process the target. For example, in the visual system, orienting can influence sites of processing in the primary visual cortex or in a variety of extrastriate visual areas where the computations related to the target are performed. Orienting to target motion influences area MT (V5) while orienting to target color will influence area V4 (Corbetta, Miezin, Dobmeyer, Shulman, & Petersen, 1991). This principle of activation of brain areas also extends to higher-level visual input as well. For example, attention to faces modifies activity in the face-sensitive area of the fusiform gyrus (Wojciulik, Kanwisher, & Driver, 1998). The finding that attention can modify activity in primary visual areas has been particularly important because the microcircuitry of this brain area has been more extensively studied than others (Posner & Gilbert, 1999). When multiple targets are presented, they tend to suppress the normal level of activity that would have been produced if the targets were presented in isolation (Kastner et al., 1999). This finding has become the cornerstone of one of the most popular views of attention in which emphasis is placed on competition between potential targets within each relevant brain area (Desimone & Duncan, 1995). This view places less stress on top-down control or at least emphasizes that top-down control emerges from bottom-up competition. The biased competition theory is partly based on the work in visual search that has been important in cognitive studies (Treisman & Gelade, 1980). The cognitive studies stress the function of a top-down search of the visual field. The neuroscience data suggests the array of search objects exerts a direct inhibitory effect on each other, which can be counteracted by orienting of attention. Although it is possible to have multiple visual locations when only a single attribute is important (e.g., green), it is not possible to report on multiple attributes from more than one target (Huang & Pashler, 2007). The neuro and cognitive approaches to visual search are being combined and this is an important vehicle for further integration. Localization Some have thought that the influence of imaging has been merely to tell us where in the brain things happened (Utall, 2001). Certainly many, perhaps even most, imaging studies have been concerned with anatomical issues. As Figure 18.2 illustrates several functions of attention that have been shown to involve specific anatomical areas that carry out important functions. However, imaging also probes other neural networks that underlie all aspects of human thought, feelings, and behavior (Posner & Rothbart, 2007b). Networks have been studied in all the topics shown in Table 18.1. The full significance of imaging for (a) viewing brain networks,

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376 Attentional Mechanisms TABLE 18.2 Sites of Attentional Effects for Each Network and the Dominant Neuro-modulator Network

Structures

Modulator

Orient

Superior parietal Temporal parietal junction Frontal eye fields Superior colliculus

Acetylcholine

Alert

Locus coruleus Right frontal Parietal cortex

Norepinephrine

Executive attention

Anterior cingulate Lateral ventral Prefrontal Basal ganglia

Dopamine

(b) examining their computation in real time, (c) exploring how they are assembled in development, and (d) their plasticity following physical damage or training, are common themes in current research that are just beginning to reach their potential. Functional neuroimaging has allowed many cognitive tasks to be analyzed in terms of the brain areas they activate, and studies of attention have been among the most often examined in this way (Corbetta & Shulman, 2002; Driver, Eimer, & Macaluso, 2004; Posner & Fan, 2008. Imaging data have supported the presence of three networks related to different aspects of attention (Fan et al., 2005). These networks carry out the functions of alerting, orienting and executive control (Posner & Fan, 2008). Figure 18.2 and Table 18.2 provide a summary of aspects of these networks. Figure 18.2 shows where key nodes of each network are located. Table 18.2 names these areas and provides information on the dominant neurotransmitter involved in each network. Next we discuss each of these networks briefly (see also Chapter 17 by Raz in this book). Alerting is defined as achieving and maintaining a state of high sensitivity to incoming stimuli; orienting is the selection of information from sensory input; and executive attention involves mechanisms for monitoring and resolving conflict among thoughts, feelings, and responses. The alerting system has been associated with thalamic as well as frontal and parietal regions of the cortex (Fan et al., 2005). A particularly effective way to vary alertness has been to use warning signals prior to targets. The influence of warning signals on the level of alertness is thought to be due to modulation of neural activity by the neurotransmitter norepinepherine (Marrocco & Davidson, 1998). Orienting involves aligning attention with a source of sensory signals. This may be overt, as when eye movements accompany movements of attention, or may occur covertly, without any eye movement. The orienting system for visual

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events has been associated with posterior brain areas, including the superior parietal lobe and temporal parietal junction, and in addition, the frontal eye fields (Corbetta & Shulman, 2002). Orienting can be manipulated by presenting a cue indicating where in space a target is likely to occur, thereby directing attention to the cued location (Posner, 1980). Event-related functional magnetic resonance imaging (fMRI) studies have suggested that the superior parietal lobe is associated with orienting following the presentation of a cue (Corbetta & Shulman, 2002). The superior parietal lobe in humans is closely related to the lateral intraparietal area (LIP) in monkeys, which is involved in the production of eye movements (Andersen, 1989). When a target occurs at an uncued location and attention has to be disengaged and moved to a new location, there is activity in the temporal parietal junction (Corbetta & Shulman, 2002). Lesions of the parietal lobe and superior temporal lobe have been consistently related to difficulties in orienting (Karnath, Ferber, & Himmelbach, 2001). Executive control of attention is often studied by tasks that involve conflict, such as various versions of the Stroop task. In the Stroop task, subjects must respond to the color of ink (e.g., red) while ignoring the color word name (e.g., blue; Bush, Luu, & Posner, 2000). Resolving conflict in the Stroop task activates midline frontal areas (anterior cingulate) and lateral prefrontal cortex (Botvinick, Braver, Barch, Carter, & Cohen, 2001; Fan, Flombaum, McCandliss, Thomas, & Posner, 2003). There is also evidence for the activation of this network in tasks involving conflict between a central target and surrounding flankers that may be congruent or incongruent with the target (Botvinick et al., 2001; Fan, Fossella, Summer, Wu, & Posner, 2003). Experimental tasks may also provide a means of fractionating the contributions of different areas within the executive attention network (MacDonald, Cohen, Stenger, & Carter, 2000). The role of the anterior cingulate cortex (ACC) in modulating sensory input has been demonstrated experimentally by showing enhanced connectivity between ACC and the sensory modality to which the person is asked to attend (Crottaz-Herbette & Menon, 2006). A similar finding showed that the more ventral part of the ACC involved in emotion regulation is coupled to the amygdala during processing of negative information (Etkin, Egner, Peraza, Kandel, & Hirsch, 2006). These findings support the general idea that ACC activity regulates other brain areas and supports the distinction between the more dorsal (the flat portion of cingulate as shown in Figure 18.2) ACC related to cognitive control and the more ventral (where the ACC bends in Figure 18.2) for emotional control (Bush et al., 2000). There is also evidence that lateral prefrontal areas may be involved in the type of regulation involved in inhibiting responses. In many tasks that require the inhibition of responses, right

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Genetic Level 377

frontal activity has been shown to be differentially active on inhibitory (no-go) trials.

CELLULAR AND SYNAPTIC LEVEL Evolutionary Perspectives Studies have revealed a similarity between human and primate studies of alerting and orienting (Corbetta & Shulman, 2002; Marrocco & Davidson, 1998) both in behavior and in the brain areas involved (II-5). However, comparative anatomical studies point to important differences in the evolution of anterior cingulate connectivity between nonhuman primates and people. Anatomical studies show the great expansion of white matter, which has increased more in recent evolution than has the neocortex itself (Zilles, 2005). One type of projection cell called Von Economo neuron is found only in the anterior cingulate and a related area of the anterior insula (Allman, Watson, Tetreault, & Hakeem, 2005). It is thought that this neuron is important in communication between the cingulate and other brain areas. This neuron is not present at all in Macaques and expands greatly in frequency between great apes and humans. The two brain areas in which von Economo neurons are found (cingulate and anterior insula) are also shown to be in close communication even during the resting state when no task is imposed (Dosenbach et al., 2007). Moreover, there is some evidence that the frequency of this type of neuron also increases in development between infancy and later childhood (Allman et al., 2005). These neurons and the rapid and efficient connectivity they provide may be a major reason why self-regulation in adult humans can be so much stronger than in other organisms, and the development of this system may relate to the achievements in self-regulation that occurs in childhood (Posner & Rothbart, 2007a). In addition to functional connectivity, advances in MRI methods now allow noninvasive study of anatomical connections between brain regions in living human subjects that before could only be studied in cadavers. One approach used by MRI is called diffusion tensor imaging (DTI) and allows tracing of white matter tracts that connect neural areas (Conturo et al., 1999; Dougherty, Ben-Shachar, Bammer, Brewer, & Wandell, 2005; Jones, Horsfield, & Simmons, 1999). This form of imaging uses the diffusion of water molecules in particular directions due to the presence of myelinated fibers. This approach has already started providing important insights into differences in anatomical connectivity underlying reading difficulties and other highlevel cognitive disorders. As methods develop to record from many neurons, it has been possible to examine connectivity at the cellular

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level (Buschman & Miller, 2007; Saalmann, Pigarev, & Vidyasagar, 2007). These studies have shown that the cells of remote areas of networks are synchronized during attention demanding tasks (Womelsdorf et al., 2007). They show how networks revealed by MRI can also be studied at the cellular level. Saalmann et al. (2007) showed that parietal activation preceded increased activity in visually selective areas. This idea fit well with fMRI findings suggesting that parietal areas can be a site part of the attention network that influences activity in visual areas (Corbetta & Shulman, 2002). How can the local actions at the various nodes of a network be coordinated in real time? Recent results from surface EEG recordings (Fan et al., 2007) and depth electrodes (Womelsdorf et al., 2007) support the idea that oscillatory neuronal electrical activity within defined frequency ranges support synchronized interactions between anatomically distinct regions during tasks (Lakatos, Karmos, Mehta, Ulbert, & Schroeder, in press). Fan et al. (2007) argues that different attentional networks may use different dominant frequencies. However, many different ranges of frequencies have been reported and there is not as yet a clear framework for predicting what frequencies will be involved. It is likely that precise coordination of neural areas by synchronized activity will be an important topic of concern during the next decade. Knight (2007) writes of the development of cellular studies of network coordination as refutation of phrenology. This appears to be true but the findings also support the network ideas that were articulated by Hebb and by imaging. According to this new view, there is localization of function within nodes of the network, but the network must work together to perform actual tasks. This view supports efforts by neuroscientists to study the microcircuitry in neural areas related to attention in order to determine how their activity produces local calculation and supports interaction among nodes.

GENETIC LEVEL At the turn of the century, the overall sequence of the human genome had been accomplished (Venter et al., 2001). Although humans have a common genome, there are differences among individuals in many genes (polymorphisms). These differences make it possible to examine genes related to individual differences in behavior and in brain activity (Goldberg & Weinberger, 2004; Mattay & Goldberg, 2004). How is it that networks are assembled during the early life of the individual? Developmental psychologists have long been interested in the problem of how children come to

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378 Attentional Mechanisms

be able to regulate their own emotions and behavior. However, this work is often divorced from the study of brain mechanisms (Posner & Rothbart, 2007a). In this section, we address this question by reviewing how genes and experience contribute to the development of the executive attention system. This research has involved genotyping individuals and asking how differences among their genes relate to differences in their executive attention. Individual differences are invariably found in cognitive tasks involving attention. The Attention Network Test (ANT) was developed to examine individual differences in the efficiency of the brain networks of alerting, orienting, and executive attention discussed earlier (Fan, McCandliss, Sommer, Raz, & Posner, 2002; Rueda et al., 2004). The ANT uses differences in reaction time (RT) between conditions to measure the efficiency of each network. Each trial begins with a cue (or a blank interval, in the no-cue condition) that informs the participant either that a target will be occurring soon, or where it will occur, or both. The target always occurs either above or below fixation and consists of a central arrow, surrounded by flanking arrows that can either point in the same direction (congruent) or in the opposite direction (incongruent). Subtracting RTs for congruent from incongruent target trials provides a measure of conflict resolution and assesses the efficiency of the executive attention network. Subtracting RTs obtained in the double-cue condition from RT in the no-cue condition gives a measure of alerting due to the presence of a warning signal. Subtracting RTs to targets at the cued location (spatial cue condition) from trials using a central cue gives a measure of orienting because the spatial cue, but not the central cue, provides valid information on where a target will occur. Individual differences in these networks were shown to be reliable (Fan et al., 2002). Fossella et al. (2002) found that the individual differences between the networks were not correlated across individuals. Although this may not be generally true because the networks are frequently used together, it does provide some support for a degree of independence among individuals in the efficiency of the networks. The ability to measure differences in attention raises the question of the degree to which attention is heritable. To explore this issue, Fan, Wu, Fossella, and Posner (2001) used the ANT to assess attention in monozygotic and dizygotic same-sex twins. Strong heritability of the executive network (.89) was found, some heritability of the alerting network (.18), and no apparent heritability of the orienting network. These results supported a search for genes in executive attention. The association of the executive network with the neuromodulator dopamine (see Table 18.2) was used as a way of searching for candidate genes that might relate to the efficiency of the networks (Fossella et al., 2002). To do this,

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200 persons performed the ANT and were genotyped to examine frequent polymorphisms in genes related to dopamine. Significant association of two genes, the dopamine D4 receptor (DRD4) gene and monoamine oxidase a (MAOA) gene, were found with executive attention. Persons with different alleles of these two genes were compared using neuroimaging while they performed the ANT (Fan, Fossella et al., 2003). Groups with different alleles of these genes showed differences in the ability to resolve conflict as measured by the ANT and also produced significantly different activations in the anterior cingulate, a major node of the executive attention network. These results confirmed the relation between genetic alleles and neural networks related to executive attention. Recent studies have extended these observations. In two different studies employing conflict-related tasks other than the ANT, alleles of the catechol-o-methyl transferase (COMT) gene were related to the ability to resolve conflict (Blasi et al., 2005; Diamond, Briand, Fossella, & Gehlbach, 2004). A study using the child ANT showed a significant relation between the DAT1 and executive attention as measured by the ANT (cholinergic gene, the alpha 4 subunit of the neural nicotinic cholinergic receptor [CHRNA4]), was related to performance differences in the ability to orient attention during tasks involving visual attention (Rueda, Rothbart, McCandliss, Saccamanno, & Posner, 2005). Different alleles of a search (Parasuraman, Greenwood, Kumar, & Fossella, 2005), confirmed the link between orienting and the neuromodulator acetylcholine. There is also increasing evidence that the serotonin system plays a role in executive attention along with the dopamine system (Canli et al., 2005; Reuter, Ott, Vaitl, & Hennig, 2007). The relation of genetic factors to the functioning of the executive attention system does not mean that the system cannot be influenced by experience. Several training-oriented programs have been successful in improving attention in patients suffering from different pathologies. For example, the use of Attention Process Training (APT) has led to specific improvements in executive attention in patients with specific brain injury (Sohlberg, McLaughlin, Pavese, Heidrich, & Posner, 2000) as well as in children with Attention Deficit Hyperactivity Disorder (ADHD; Kerns, Esso, & Thompson, 1999). With normal adults, training with video games produced better performance on a range of visual attention tasks (Green & Bavelier, 2003). Genetic variation allows for additional influence from parenting and other experiences (Sheese, Voelker, Rothbart, & Posner, 2007). It was found that the 7 repeat allele of the DRD4 gene interacted with the quality of parenting to influence such temperamental variables in the child as activity level, sensation seeking, and impulsivity. Other research has shown similar findings for externalizing behavior of the

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References 379

child, as rated by the parents in the Child Behavior Checklist (Bakermans-Kranenburg & van Ijzendoorn, 2006). There is evidence that the 7 repeat allele of the DRD4 gene is under positive selective pressure (Ding et al., 2002). The Ding et al. study used molecular genetics to show the complexity of deriving the 7 repeat allele from the dominant 4 repeat. In addition to mutation, some positive selective pressure would be needed to account for the frequency of the 7 repeat. The 7 repeat allele has been associated with risk taking and also Attention Deficit Disorder (Posner, Rothbart, & Sheese, 2007). Risk taking could well increase the possibility of reproductive success. The interaction between parenting and the 7 repeat might mean that a genetic allele increases the possibility that children will be influenced by their culture, for example, through parenting style. This idea could be important for understanding the principles of why the frequency of genetic alleles changed during human evolution. Human genetic evolution and cultural evolution may be interrelated where certain genetic variations make culture influence more successful. Genes do not directly produce attention. They code for different proteins that influence the efficiency with which modulators such as dopamine are produced and/or bind to their receptors. These modulators are in turn related to individual differences in the efficiency of the attention networks. There is a great deal in common among humans in the anatomy of high-level networks. This must have a basis within the human genome. The same genes that are related to individual differences in attention are also likely to be important in the development of the attentional networks that are common to all humans. Some of these networks are also common to nonhumans. By examining these networks in animals, it should be possible to better understand the role of genes in shaping networks. Can animals perform the same tasks we have developed for humans? The answer is clearly yes (Chapter 3 by Snowdon & Cronin in this book). Monkeys have been trained to shift attention to cues and to carry out conflict tasks like those in the ANT. Rodents have also been trained in attention shifting tasks (Beane & Marrocco, 2004). These tasks make it possible to examine the role that genes play in carrying out the same attentional operations as have been studied in humans. It has also been reported that areas of the frontal midline corresponding to the anterior cingulate are activated in the mouse during trace but not delayed conditioning (Han, O’Tuathaigh, & Koch, 2004). Since trace and delayed conditioning are both very simple tasks and the two are quite similar, they could be used to measure operation of rodent brain areas that may be related to executive attention in humans. We need to develop methods of manipulating relevant genes in specific anatomical locations that are important

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nodes of a particular network. Usually genes are expressed at multiple locations so that changes (e.g., knock out studies) are not specific to one brain area. Subtractive genomics is a method currently being developed to manipulate genes at specific anatomical location (Dumas et al., 2005). This method is now being employed to manipulate the DRD4 gene within the midfrontal cortex of the mouse. It should become possible to determine the specific operations performed by genes at particular locations in attentional networks. In the future, this kind of genetic analysis of network development will create a productive link between genes and the development of the networks involved in self-regulation (Posner & Rothbart, 2007b).

SUMMARY Neuroscience and psychology have been converging on the idea of cognitive tasks including attention that are carried out by networks of neural areas extending over much of the brain, but involving quite localized computation of particular subroutines or mental operations involved in the task (Knight, 2007; Posner & Rothbart, 2007b). Consistent with this view, the functions of alerting, orienting, and executive control have been shown to involve separate but partially overlapping networks. These findings make it important to have strategies for examining such questions as: (a) how are computations performed within each node of a network, (b) how do the nodes of a network communicate the results of their computations in real time, and (c) how do genes and experience combine to shape the efficiency of networks. Progress on each of these fronts is summarized in this chapter and some of the remaining questions are discussed.

REFERENCES Abdullaev, Y., Bechtereva, N. P., & Melnichuk, K. V. (1998). Activity of human caudate nucleus and prefrontal cortex in cognitive tasks. Behavioural Brain Research, 97, 159–177. Allman, J. M., Watson, K. K., Tetreault, N. A., & Hakeem, A. Y. (2005). Intuition and autism: A possible role for Von Economo neurons. Trends in Cognitive Sciences, 9(8), 367–373. Andersen, R. A. (1989). Visual eye movement functions of the posterior parietal cortex. Annual Review of Neuroscience, 12, 377–403. Bakermans-Kranenburg, M. J., & van Ijzendoorn, M. H. (2006). Geneenvironment interaction of the dopamine D4 receptor (DRD4) and observed maternal insensitivity predicting externalizing behavior in preschoolers. Developmental Psychobiology, 48, 406–409. Beane, M., & Marrocco, R. (2004). Cholinergic and noradrenergic inputs to the posterior parietal cortex modulate the components of exogenous attention. In M. I. Posner (Ed.), Cognitive neuroscience of attention (pp. 313–325). New York: Guilford Press.

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380 Attentional Mechanisms Bechtereva, N. P. (1978). The neurophysiological aspects of human mental activity. New York: Oxford University Press. Bechtereva, N. P., & Abdullaev, Y. (2000). Depth electrodes in clinical neurophysiology: Neuronal activity and human cognitive function. International Journal of Psychophysiology, 37, 11–29. Bechtereva, N. P., Medvedev, S. V., Abdullaev, Y., Melnichuk, K. V., & Gurchin, F. A. (1989). Psychophysiological micromapping of the human brain. International Journal of Psychophysiology, 8, 107–135. Blasi, G., Mattay, G. S., Bertolino, A., Elvevåg, B., Callicott, J. H., Das, S., et al. (2005). Effect of cCatechol-O-Methyltransferase val 158 met genotype on attentional control. Journal of Neuroscience, 25(20), 5038–5045. Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Reviews, 108, 624–652. Broadbent, D. E. (1958). Perception and communication. New York: Pergamon Press. Buschman, T. J., & Miller, E. K. (2007, March 30). Top-down versus bottom up control of attention in the prefrontal and parietal cortex. Science, 315, 1860–1862. Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in the anterior cingulate cortex. Trends in Cognitive Sciences, 4, 215–222. Canli, T., Omura, K., Haas, B. W., Fallgatter A., Todd, R., Constable, R. T., et al. (2005). Beyond affect: A role for genetic variation of the serotonin transporter in neural activation during a cognitive attention task. Proceedings of the National Academy of Sciences, USA, 102, 12224–12229. Cherry, E. C. (1953). Some experiments on the recognition of speech with one and two ears. Journal of the Acoustical Society, 25, 975–979. Conturo, T. E., Lori, N. F., Cull, T. S., Akbudak, E., Snyder, A. Z., Shimony, J. S., et al. (1999). Tracking neuronal fiber pathways in the living human brain. Proceedings of the National Academy of Sciences, USA, 96, 10422–10427. Corbetta, M., Miezin, F. M., Dobmeyer, S., Shulman, G. L., & Petersen, S. E. (1991). Selective and divided attention during visual discriminations of shape, color, and speed: Functional anatomy by positron emission tomography. Journal of Neuroscience, 11, 2383–2402. Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Neuroscience Reviews, 3, 201–215. Crottaz-Herbtte, S., & Mennon, V. (2006). Where and when the anterior cingulate cortex modulates attentional response: Combined fMRI and ERP evidence. Journal of Cognitive Neuroscience, 18, 766–780. Dale, A. M., Liu, A. K., Fischi, B. R., Buckner, R., Beliveau, J. W., Lewine, J. D., et al. (2000). Dynamic statistical parameter mapping: Combining fMRI and MEG for high-resolution cortical activity. Neuron, 26, 55–67.

Dougherty, R. F., Ben-Shachar, M., Bammer, R., Brewer, A. A., & Wandell, B. A. (2005). Functional organization of human occipital-callosal fiber tracts. Proceedings of the National Academy of Sciences, USA, 102, 7350–7355. Driver, J., Eimer, M., & Macaluso, E. (2004). Neurobiology of human spatial attention: Modulation, generation, and integration. In N. Kanwisher & J. Duncan (Eds.), Attention and performance XX: Functional brain imaging of visual cognition (pp. 267–300). Oxford: Oxford University Press. Dumas, T., Hostick, U., Wu, H., Spaltenstein, J., Ghatak, C., Nguyen, J., et al. (2005). Maximizing the anatomical specificity of native neuronal promotorers by a subtractive transgenic technique. Society for Neuroscience Abstracts. Engel, A. K., Moll, C. K., Fried, I., & Ojemann, G. A. (2005). Invasive recordings from the human brain: Clinical insights and beyond. Nature Reviews Neuroscience, 6, 35–47. Etkin, A., Egner, T., Peraza, D. M., Kandel, E. R., & Hirsch, J. (2006). Resolving emotional conflict: A role for the rostral anterior cingulate cortex in modulating activity in the amygdala. Neuron, 51, 871–882. Evarts, E. V. (1968). Relation of the pyramidal tract activity to force exerted during voluntary movement. Journal of Neurophysiology, 31, 14–27. Fan, J., Byrne, J., Worden, M. S., Guise, K. G., McCandliss, B. D., Fossella, J., et al. (2007). The relation of brain oscillations to attentional networks. Journal of Neuroscience, 27, 6197–6206. Fan, J., Flombaum, J. I., McCandliss, B. D., Thomas, K. M., & Posner, M. I. (2003). Cognitive and brain consequences of conflict. NeuroImage, 18, 42–57. Fan, J., Fossella, J. A., Summer T., Wu, Y., & Posner, M. I. (2003). Mapping the genetic variation of executive attention onto brain activity. Proceedings of the National Academy of Sciences, USA, 100, 7406–7411. Fan, J., McCandliss, B. D., Fossella, J., Flombaum, J. I., & Posner, M. I. (2005). The activation of attentional networks. NeuroImage, 26, 471–479. Fan, J., McCandliss, B. D., Sommer, T., Raz, M., & Posner, M. I. (2002). Testing the efficiency and independence of attentional networks. Journal of Cognitive Neuroscience, 3(14), 340–347. Fan, J., Wu, Y., Fossella, J., & Posner, M. I. (2001). Assessing the heritability of attentional networks. BioMedCentral Neuroscience, 2, 14. Fischman, A. J., & Badgaiyan, R. D. (2006) Neurotransmitter imaging. In M. Charron (Ed.), Pediatric PET (pp. 385–403). New York: Springer; pp. 385–403. Fossella, J., Sommer, T., Fan, J., Wu, Y., Swanson, J. M., Pfaff, D. W., et al. (2002). Assessing the molecular genetics of attention networks. BMC Neuroscience, 3, 14.

Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222.

Georgopoulos, A. P., Lurito, J. T., Petrides, M., Schwartz, A. B., & Massey, J. T. (1989, January 13). Mental rotation of the neuronal population vector. Science, 243, 234–236.

Diamond, A., Briand, L., Fossella, J., & Gehlbach, L. (2004). Genetic and neurochemical modulation of prefrontal cognitive functions in children. American Journal of Psychiatry, 161, 125–132.

Goldberg, T. E., & Weinberger, D. R. (2004). Genes and the parsing of cognitive processes. Trends in Cognitive Sciences, 8, 325–335.

Ding, Y. C., Chi, H. C., Grady, D. L., Morishima, A., Kidd, J. R., Kidd, K. K., et al. (2002). Evidence of positive selection acting at the human dopamine receptor D4 gene locus. Proceedings of the National Academy of Sciences, USA, 99, 309–314. Donchin, E., & Cohen, L. (1967). Average evoked potentials and intermodal selective attention. Electroencephalography and Clinical Neurophysiology, 22, 537–546. Dosenbach, N. U. F., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A. T., et al. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences, USA, 104, 11073–11078.

c18.indd Sec4:380

Green, C. S., & Bavelier, D. (2003). Action video game modifies visual selective attention. Nature, 423, 434–437. Han, C. J., O’Tuathaigh, C. M., & Koch, C. (2004). A practical assay for attention in mice. In M. I. Posner (Ed.), Cognitive neuroscience of attention (pp. 294–312). New York: Guilford Press. Hebb, D. O. (1949). Organization of behavior. New York: Wiley. Heinze, H. J., Mangun, G. R., Burchert, W., Hinrichs, H., Scholtz, M., Muntel, T. F., et al. (1994, December 8). Combined spatial and temporal imaging of brain. Nature, 372, 543–546. Hillyard, S. A., Di Russo, F., & Martinez, A. (2004). The imaging of visual attention. In N. Kanwisher & J. Duncan (Eds.), Functional neuroimaging

8/17/09 2:11:31 PM

References 381 of visual cognition attention and performance XX (pp. 381–390). Oxford: Oxford Universiy Press.

Posner, M. I. (1980). Orienting of attention: The 7th Sir F.C. Bartlett Lecture. Quarterly Journal of Experimental Psychology, 32, 3–25.

Hink, R. F., Van Voorhis, S. T., Hillyard, S. A. & Smith, T. S. (1977). The division of attention and the human auditory evoked potential. Neuropsychologia, 15, 597–605.

Posner, M. I. (1988). Structures and functions of selective attention. In T. Boll & B. Bryant (Eds.), Master lectures in clinical neuropsychology and brain function: Research, measurement, and practice (pp. 171–202). Washington, DC: American Psychological Association.

Huang, L. Q., & Pashler, H. (2007). A boolean map theory of visual attention. Psychological Review, 114(3), 599–631. Hubel, D., & Wiesel, T. N. (1968). Receptive field and functional architecture of the monkey striate cortex. Journal of Physiology (London), 195, 215–243. James, W. (1890). Principles of psychology. New York: Holt Rinehart and Winston. Jones, D. K., Horsfield, M. A., & Simmons, A. (1999). Optimal strategies for measuring diffusion in anisotropicsystems by magnetic resonance imaging. Magnetic Resonance in Medicine, 42, 515–525. Kanwisher, N., & Duncan, J. (Eds.). (2004). Functional neuroimaging of visual cognition. Attention and performance XX. Oxford: Oxford University Press.

Posner, M. I., & Gilbert, C. D. (1999). Attention and primary visual cortex. Proceedings of the National Academy of Sciences, USA, 96, 2585–2587. Posner, M. I., & Raichle, M. E. (1994). Images of mind. New York: Scientific American. Posner, M. I., & Raichle, M. E. (Eds.). (1998). Overview: The neuroimaging of human brain function. Proceedings of the National Academy of Sciences, USA, 95, 763–764. Posner, M. I., & Rothbart, M. K. (2007a). Educating the human brain. Washington, DC: APA Books.

Karnath, H.-O., Ferber, S., & Himmelbach, M. (2001, May 3). Spatial awareness is a function of the temporal not the posterior parietal lobe. Nature, 411, 950–953.

Posner, M. I., & Rothbart, M. K. (2007b). Research on attention networks as a model for the integration of psychological science. Annual Review of Psychology, 58, 1–23.

Kastner, S., Pinsk, M. A., De Weerd, P., Desimone, R., & Ungerleider, L. G. (1999). Increased activity in human visual cortex during directed attention in the absence of visual stimulation. Neuron, 22, 751–761.

Posner, M. I., Rothbart, M. K., & Sheese, B. E. (2007). Attention genes. Developmental Science, 10, 24–29.

Kerns, K. A., Esso, K., & Thompson, J. (1999). Investigation of a direct intervention for improving attention in young children with ADHD. Developmental Neuropsychology, 16, 273–295. Knight, R. T. (2007, June 15). Neural networks debunk phrenology. Science, 316, 1578–1579.

Posner, M. I., Sheese, B. E., Odludas, Y., & Tang, Y. (2006). Analyzing and shaping human attentional networks. Neural Networks, 19, 1–8. Reuter, M., Ott, U., Vaitl, D., & Hennig, J. (2007). Impaired executive control associated with a variation in the tryp\tophan hydroxylase-2 gene. Journal of Cognitive Neuroscience, 19, 401–408.

LaBerge, D. (1995). Attentional processing. Cambridge, MA: Harvard University Press.

Rizzolatti, G., Riggio, L., Dascola, I., & Umilta, C. (1987). Reorienting attention across the horizontal and vertical meridians: Evidence in favor of the premotor theory of attention. Neuropsychologia, 25, 31–40.

Lakatos, P., Karmos, G., Mehta, A. D., Ulbert, I., Schroeder, C. (in press). Entrainment of neuronal oscillations as a mechanism of attentional selection. Science.

Rueda, M. R., Fan, J., Halparin, J., Gruber, D., Lercari, L. P., McCandliss, B. D., et al. (2004). Development of attention during childhood. Neuropsychologia, 42, 1029–1040.

Lashley, K.S. (1931). Mass action in cerebral function. Science, 73, 245–254.

Rueda, M. R., Rothbart, M. K., McCandliss, B. D., Saccamanno, L., & Posner, M. I. (2005). Training, maturation and genetic influences on the development of executive attention. Proceedings of the National Academy of Sciences, USA, 102, 14931–14936.

MacDonald, A. W., Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000, June 9). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science, 288, 1835–1838. Mackworth, J. F., & Mackworth, N. H. (1956). The overlapping of signals for decisions. American Journal of Psychology, 69, 26–47. Marrocco, R. T., & Davidson, M. C. (1998). Neurochemistry of attention. In R. Parasuraman (Ed.), The attentive brain (pp. 35–50). Cambridge, MA: MIT Press.

Rugg, M. D., & Coles, M. G. H. (1995). Electrophsyiology of mind. Oxford: Oxford University Press. Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing. Cambridge, MA: MIT Press. Saalmann, Y. B., Pigarev, I. N., & Vidyasagar, T. R. (2007, June 15). Neural mechanisms of visual attention: How top-down feedback highlight relevant visual locations. Science, 316, 1612–1615.

Mattay, V. S., & Goldberg, T. E. (2004). Imaging genetic influences in human brain function. Current Opinion in Neurobiology, 14(2), 239–247.

Scherg, M., & Berg, P. (1993). Brain electrical source anlaysis, Version 2.0. Herndon, VA: NeuroScan.

Moruzzi, G., & Magoun, H. W. (1949). Brainstem reticular formation and activation of the EEG. Electroencephalography and Clinical Neurophysiology, 1, 455–473.

Sheese, B. E., Voelker, P. M., Rothbart, M. K., & Posner, M. I. (2007). Parenting quality interacts with genetic variation in Dopamine Receptor DRD4 to influence temperament in early childhood. Developmental and Psychopathology,19, 1039–1046.

Mountcastle, V. M. (1978). The world around us: Neural command functions for selective attention. Neuroscience Research Progress Bulletin, 14, 1–47. Ojemann, G. A., Creutzfeldt, O., Lettich, E., & Haglund, M. M. (1988). Neuronal activity in human lateral temporal cortex related to shortterm verbal memory, naming and reading. Brain, 111, 1383–1403.

c18.indd Sec4:381

Posner, M. I., & Fan, J. (2008). Attention as an organ system. In J. Pomerantz (Ed.), Topics in Integrative Neuroscience (pp. 31–61). New York: Cambridge University Press.

Shulman, G. L., Remington, R. W., & McClean, J. P. (1979). Moving attention through space. Journal of Experimental Psychology: Human Perception and Performance, 5, 522–526. Sohlberg, M. M., McLaughlin, K. A., Pavese, A., Heidrich, A., & Posner, M. I. (2000). Evaluation of attention process therapy training in persons with acquired brain injury. Journal of Clinical and Experimental Neuropsychology, 22, 656–676.

Parasuraman, R., Greenwood, P. M., Kumar, R., & Fossella, J. (2005). Beyond heritability: Neurotransmitter genes differentially modulate visuospatial attention and working memory. Psychological Science, 16, 200–207.

Sutton, S., Nraren, M., Zubin, J., & John, E. R. (1965, November 26). Evoked potential correlates of stimulus uncertainty. Science, 150, 1187–1188.

Posner, M. I. (1978). Chronometric explorations of mind. Hillsdale, NJ: Erlbaum.

Titchener, E. B. (1909). Experimental psychology of the thought processes. New York: Macmillan.

8/17/09 2:11:31 PM

382 Attentional Mechanisms Toga, A. W., & Mazziotta, J. C. (Eds.). (1996). Brain mapping: The methods. San Diego, CA: Academic Press. Treisman, A., & Gelade, G. (1980). A feature integration theory of attention. Cognitive Psychology, 12, 97–136. Uttal, W. R. (2001). The new phrenology: The limits of localizing cognitive processes in the brain. Cambridge, MA: MIT Press. Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., et al. (2001, February 16). The sequence of the human genome. Science, 291, 1304–1335. Volpe, B. T., LeDoux, J. E., & Gazzaniga, M. S. (1979). Information processing of visual stimuli in an extinguished visual field. Nature, 282, 1947–1952. Walter, W. G., Cooper, R., Aldridge, V. J., McCallum, W. C., & Winter, A. L. (1964, July 25). Contingent negative variation: An electrical sigh of

c18.indd Sec4:382

sensorimotor association and expectancy in the human brain. Nature, 203, 380–384. Wojciulik, E., Kanwisher, N., & Driver, J. (1998). Covert visual attention modulates face-specific activity in the human fusiform gyrus: FMRI study. Journal of Neurophysiology, 79, 574–1578. Womelsdorf, T., Schoffelen, J.-M., Oostenveld, R., Singer, W., Desimone, R., Engel, A. K., et al. (2007, June 15). Modulalation of neuronal interactions through neural synchronization. Science, 316, 1609–1612. Wurtz, R. H., Goldberg, E., & Robinson, D. L. (1980). Behavioral modulation of visual responses in monkey: Stimulus selection for attention and movement. Progress in Psychobiology and Physiological Psychology, 9, 43–83. Zilles, K. (2005). Evolution of the human brain and comparative syto and receptor architecture. In S. Dehaene, J.-R. Duhamel, M. D. Hauser, &

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Chapter 19

Mental Imagery STEPHEN M. KOSSLYN, GIORGIO GANIS, AND WILLIAM L. THOMPSON

on the basis of his auditory mental imagery, well after he could no longer hear a sound. Similarly, you can probably visualize what it would be like to sit on the back of an elephant and see over the top of the animal’s head, even though you have never had the experience. Imagery has played a central role in theories of mental function at least since the time of Plato. It has fallen in and out of fashion, in large part because it is inherently a private affair—by definition restricted to the confines of one’s mind. Thus, imagery has been difficult to study. In fact, in 1913 the founder of behaviorism (the school of psychology that focused solely on observable stimuli, responses, and the consequences of responses), John B. Watson, denied that mental images even existed. Instead, he suggested, thinking consists of subtle movements of the vocal apparatus (Watson, 1913). Even after the so-called cognitive revolution of the late 1950s, when the mind was likened to computer software, mental imagery carried a whiff of disrepute. At least in North America, behaviorism has cast a very long shadow. In spite of the fact that Alan Paivio (1971) and his colleagues were able to show that the use of imagery dramatically improves memory, many researchers were not convinced that imagery is a distinct form of thought. Watson’s position was filtered and refracted through the lens of computationally oriented cognitive science 60 years later by Zenon Pylyshyn. This theorist championed the view that mental images are not “images” at all, but rather rely on mental descriptions no different in kind from those that underlie language. According to Pylyshyn (1973, see also, Pylyshyn, 2002, 2003a, 2003b), the pictorial aspects of imagery that are evident to conscious experience are entirely epiphenomenal, like the heat thrown off

Mental imagery has until recently fallen within the purview of philosophy and cognitive psychology. Although both enterprises have raised important questions, they have also encountered significant obstacles when trying to answer these questions. With the advent of cognitive neuroscience, many of these questions are now empirically tractable. Neuroimaging studies, combined with other methods (such as studies of brain-damaged patients and of the effects of transcranial magnetic stimulation) are revealing the ways in which imagery draws on mechanisms used in other activities, such as perception and motor control. Because of its close relation to these basic processes, imagery holds promise of becoming one of the best-understood “higher” cognitive functions.

MENTAL IMAGES IN THE PERCEIVING AND ACTING BRAIN What shape are Mickey Mouse’s ears? Most people report that when they visualize the cartoon rodent’s head, they “see” that his ears are round. Such an experience is a hallmark that certain kinds of representations are being processed, namely those that underlie visual mental imagery. Mental imagery occurs when perceptual information is accessed from memory, giving rise to the experience of “seeing with the mind’s eye,” “hearing with the mind’s ear,” and so on. In contrast, perception occurs when information is registered directly from the senses. Mental images need not be simply the recall of previously perceived objects or events; they also can be created by combining and modifying stored perceptual information in novel ways. For example, Beethoven wrote whole symphonies entirely

Preparation of different parts of this chapter was supported by the National Science Foundation (Grant No. REC-0411725) and the National Institutes of Health (Grant No. 2 R 01 MH60734). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the National Institutes of Health. Parts of this chapter were based on an earlier review, published by Kosslyn, Ganis, and Thompson (2001), adapted with permission of the publisher. 383

Handbook of Neuroscience for the Behavioral Science, edited by Gary G. Berntson and John T. Cacioppo. Copyright # 2009 John Wiley & Sons, Inc. c19.indd 383

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384

Mental Imagery

by a light bulb when you read (which plays no role in the reading process). The emergence of cognitive neuroscience has opened a new chapter in the study of imagery. An enormous amount has been learned about the neural underpinnings of visual perception, memory, emotion, and motor control. Much of this information has come from the study of animal models. Unlike language and reasoning, these more basic functions have many common features among higher mammals—including humans. In addition, neuroimaging technologies, especially positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), offer new ways to test theories of imagery in humans. Researchers have taken advantage of these developments to show that mental imagery uses much of the same neural machinery as perception in the same modality and can engage mechanisms used in memory, emotion, and motor control. In this chapter, we draw on results from a variety of methods, including studies of the effects of selective brain damage on behavior, neuroimaging, and studies examining the effects of transcranial magnetic stimulation (TMS). Each method has its strengths and weaknesses, but they are complementary. Thus, for example, neuroimaging provides correlational (not causal) data (when engaged in a particular task, a particular set of brain areas is activated) but can monitor the entire brain; TMS can be used to establish the causal roles of processes supported by distinct brain areas (e.g., by showing that performance in a task that draws on a specific brain area is impaired when TMS is used to alter neural functioning in that area). TMS must be targeted to a specific location, and often TMS researchers rely on prior findings from fMRI and PET to guide them to stimulate relevant brain loci. Not only are the techniques complementary, but they also provide convergent evidence for specific inferences about the nature of cognition and its neural foundations. That is, if the same conclusion is reached using different methods, then it can be taken more seriously. In this chapter, we briefly review major classes of imagery research. We begin with visual mental imagery, and then turn to auditory imagery, and conclude by considering so-called motor imagery. Along the way, we review convergent evidence from many sources, and emphasize that imagery relies on mechanisms also used in perception, which can affect not only cognition but also action.

VISUAL MENTAL IMAGERY AND PERCEPTION We begin with visual mental imagery, which is the most common and by far the most intensively studied modality.

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In the following sections, we briefly review four main classes of research, which show that visual imagery: engages brain mechanisms also used in perception, is carried out by a system of distinct processes, engages even the earliest visual cortex (Areas 17 and 18) during some forms of imagery, and can activate the autonomic and limbic systems during visualization of emotional material. Shared Mechanisms in Imagery and Perception Well over 100 years ago, researchers described brain-damaged patients who had lost the ability to form visual mental images after they became blind (for review, see Farah, 1984; see also Bartolomeo, 2002; Chatterjee & Southwood, 1995). Methods from cognitive psychology and neuropsychology have allowed researchers to characterize such deficits with increasing precision. For example, some patients have perceptual deficits in only one of the two major cortical visual functions supported by the two major visual pathways. One such pathway runs from the occipital lobe down to the inferior temporal lobes (the so-called “ventral stream,” or the “what” system, or the “object properties processing” pathway; see Chapter 11; Ungerleider & Mishkin, 1982); when damaged, the animal or person cannot easily recognize shapes or, in some cases, color. The other pathway runs from the occipital lobe to the posterior parietal lobes (the so-called “dorsal stream,” or the “where” system, or the “spatial properties processing” pathway); when damaged, the animal or person cannot easily register spatial properties, such as locations. For present purposes, a critical point is that the parallel deficits appear in imagery: damage to the ventral stream disrupts the ability to visualize shapes (as used, for example, to determine from memory whether George Washington had a beard), whereas damage to the dorsal stream disrupts the ability to visualize locations (as used, for example, to indicate the locations of furniture in a room when your eyes are closed; cf. Levine, Warach, & Farah, 1985). Very subtle deficits can occur in imagery that parallel the deficits found in perception. For example, some brain-damaged patients can no longer distinguish colors perceptually or in imagery (De Vreese, 1991) and others can no longer distinguish faces perceptually or in imagery (Young, Humphreys, Riddoch, Hellawell, & de Haan, 1994, for review, see Ganis, Thompson, Mast, & Kosslyn, 2003). However, although the deficits in imagery and perception often parallel each other (Ganis et al., 2003), this is not always the case. In a seminal literature review and analysis, Farah (1984) showed that some patients have selective problems in generating images (i.e., producing them on the basis of information stored in memory) even though they are able to recognize and identify perceptual stimuli.

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Nature of Imagery Representation: Imagery and the Early Visual Cortex 385

In addition, patients have been reported who could visualize but had impaired perception (e.g., Bartolomeo, 2002; Behrmann, Winocur, & Moscovitch, 1992; Jankowiak, Kinsbourne, Shalev, & Bachman, 1992). In short, the results from research with brain-damaged patients suggest that visual mental imagery and visual perception share many common mechanisms, but do not draw on identical processes. Although shape, location, and surface characteristics may be represented and interpreted in comparable ways during both functions, the two differ in key ways: Imagery, unlike perception, does not require low-level organizational processing. And perception, unlike imagery, does not require us to activate information in memory when the stimulus is not present. For reviews of the relationship between imagery and memory, see Behrmann (2000) and Kosslyn, Thompson, and Ganis (2006). The results of neuroimaging studies that compare imagery and perception have dovetailed nicely with those from studies of brain-damaged patients. One study, for example, found that of all the brain areas activated during perception and during imagery, approximately two-thirds were activated in common (Kosslyn, Thompson, & Alpert, 1997). Another study that used more similar imagery and perception stimuli and tasks found that over 90% of the same parts of the brain are activated in common during visual mental imagery and perception (Ganis, Thompson, & Kosslyn, 2004). Presumably, lesions in the areas not activated in common produce the dissociations, when imagery or perception is disrupted independently, whereas lesions in the areas activated in common produce the more frequently reported parallel deficits in imagery and perception. Structure of Visual Mental Imagery Processing The distinction between shape-based imagery (which relies on the ventral visual stream) and spatial imagery (which relies on the dorsal visual stream) is important not simply because it has allowed researchers to document parallels between imagery and perception; this distinction shows that imagery is not a single, undifferentiated ability, but rather relies on sets of distinct processes. Indeed, studies of deficits following brain damage have underscored the fact that imagery—like all other cognitive functions—is accomplished by a collection of abilities, each of which can be disrupted independently. For example, some patients can make imagery judgments about the shape or color of objects but have difficulty imagining an object rotating (for instance, when trying to decide whether the letter p would be another letter when rotated 180 degrees, or whether z would be another letter when rotated 90 degrees clockwise). Other patients have the reverse pattern of deficits. In addition, when participants perform different imagery tasks while

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their brain activity is monitored, different patterns of activation are observed. For example, when participants mentally rotate patterns, their parietal lobes (often bilaterally) and right frontal lobes typically are strongly activated (e.g., Cohen et al., 1996; Jordan, Heinze, Lutz, Kranowski, & Jancke, 2001; Kosslyn, DiGirolamo, Thompson, & Alpert, 1998; Ng et al., 2001; Richter et al., 2000; Wraga, Shephard, Church, Inati, & Kosslyn, 2005). In contrast, if they are asked to visualize previously memorized patterns of stripes and judge which are longer, wider, and so on (all on the basis of their mental images, with eyes closed), these areas are not activated, but other areas in the occipital lobe and left association cortex are activated (Kosslyn et al., 1999; Thompson, Kosslyn, Sukel, & Alpert, 2001). Underscoring the fact that imagery is not a single ability, findings from neuroimaging studies have shown that different sets of areas are activated when different types of imagery tasks are used (Downing, Chan, Peelen, Dodds, & Kanwisher, 2006; Haxby et al., 2001; Kanwisher & Yovel, 2006; O’Craven & Kanwisher, 2000; Thirion et al., 2006). Brain activation during mental imagery may vary according to the type of object that is visualized. Using fMRI, O’Craven and Kanwisher (among others) found activation in the fusiform face area (FFA; see Kanwisher, McDermott, & Chun, 1997) when participants visualized faces; in contrast, when participants visualized indoor or outdoor scenes depicting a spatial layout, these researchers found activation in the parahippocampal place area (PPA). There was no hint of activation of the PPA during face imagery nor of the FFA during place imagery. These results are similar to what was observed when participants actually perceived faces and places. The findings document that imagery and perception share very specific, specialized mechanisms. Hasson, Harel, Levy, and Malach (2003) suggest that seven brain areas represent information in different categories; these areas are in the occipito-temporal cortex, near the early visual areas, and include face-, object-, and building-related areas. Moreover, functional neuroimaging evidence shows that during imagery, some of these category-selective areas are activated predominantly via inputs from prefrontal and parietal cortex, whereas in perception, these regions are activated predominantly bottomup, on the basis of inputs from early visual areas (Mechelli, Price, Friston, & Ishai, 2004).

NATURE OF IMAGERY REPRESENTATION: IMAGERY AND THE EARLY VISUAL CORTEX A large portion of research on the neural bases of imagery focuses on whether the early visual cortex is activated

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during imagery (for a review, see Kosslyn & Thompson, 2003; Kosslyn, Thompson, & Ganis, 2006). The early visual cortex comprises Areas 17 and 18, the first two cortical areas to receive input from the eyes. Researchers have wanted to know whether visual imagery activates these early areas for three main reasons. First, these areas are known to be topographically organized; that is, they preserve (roughly) the local spatial geometry of the retina—and thus patterns of activation in them serve to depict shape. If these areas are activated during imagery, and such activation plays a functional role, this would be evidence that imagery relies on representations that depict information, not describe it. (In other words, this would be evidence that mental imagery relies on actual depictive images represented in the brain.) Such a finding would have implications for more general questions, such as whether there exists more than one language of thought—that is, whether all thought consists of symbolic (propositional) representations or whether at least some of the representations used in cognition are depictive (or spatially analogous to the objects they represent). Second, such findings cannot be explained by appeal to “tacit knowledge,” which Pylyshyn (1981, 2002, 2003a, 2003b) used to explain away the findings from earlier behavioral experiments that attempted to demonstrate that imagery relies on depictive representations. According to this view, participants in imagery experiments may have unconsciously tried to imitate what they thought they would have done in the corresponding perceptual situation (such as by taking more time to scan farther distances across an imaged scene). But such tacit knowledge, stored as descriptions, would not explain why the early visual cortex would be activated when participants had their eyes closed during imagery. Third, if imagery can alter the activation of the early visual cortex, this suggests that one’s knowledge and expectations can (at least under some circumstances) modulate what one actually sees during perception. And this finding would have clear-cut implications for the reliability of eyewitness testimony and the veracity of visual memory more generally. More than 50 neuroimaging studies have examined activation in early visual cortex (for reviews, see Kosslyn & Thompson, 2003; Thompson & Kosslyn, 2000). The studies used, in decreasing order of sensitivity, fMRI, PET, and single photon emission computer tomography (SPECT). According to Kosslyn and Thompson (2003), 19 fMRI, 8 PET, and 2 SPECT studies reported activation in early visual cortex, compared to 8 fMRI, 15 PET, and 7 SPECT studies that reported no such activation. (Note that given the statistical thresholds involved, chance results would not result in half the studies finding this activation and half

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not finding it; rather, the ratio might approximate 1 in 20 studies that would report activation, if chance alone were at work.) The following studies seem to provide strong support for the claim that the early visual cortex is activated during at least some forms of visual mental imagery. Kosslyn, Thompson, Kim, and Alpert (1995) asked participants to visualize line drawings of objects at different sizes (as if they fit into squares of different dimensions that were memorized before the PET scan) and used auditory cues to make specific judgements. Not only was Area 17 activated, compared to a control condition in which identical auditory cues were provided but no imagery was used, but also the specific locus of activation depended on the size of the visualized object. Even though their eyes were closed, the mere fact of visualizing an object at a larger size shifted the activation to more anterior parts of the calcarine sulcus (the major anatomical landmark of Area 17)—just as is found for larger objects in perception proper (e.g., Sereno et al., 1995). This result was replicated by Tootell, Hadjikani, Mendola, Marrett, and Dale (1998) using fMRI and a precise method to localize Area 17. There is no doubt that varying the size of objects in mental images shifts the locus of activation along Area 17 comparably to what occurs in perception. In addition, Klein, Paradis, Poline, Kosslyn, and Le Bihan (2000) used event-related fMRI to chart activation in Area 17 when visual mental images were formed. They found clear activation in every participant, with a clearcut temporal pattern; activation began about 2 seconds after an auditory cue, and peaked around 4 to 6 seconds later, before dropping off during the next 8 seconds or so. Moreover, Klein et al. (2004) found that the activation in visual cortex was related to the orientation of the imaged stimulus; depending on whether the bow-tie-shaped stimulus was vertical or horizontal, the pattern of activation appropriately matched the cortical representation of either the vertical or horizontal meridian. Moreover, also using fMRI, Slotnick, Thompson, and Kosslyn (2005) presented rotating and flickering checkerboard wedges to map the precise retinotopy of each participant. In a separate imagery condition, participants were asked to reproduce the flickering wedges in their mind’s eye. The imagery retinotopic maps were similar to the maps produced in the perception condition and often were more similar to perception than were the maps produced by an attention-based control condition. But is such activation playing a functional role in imagery? In another study, participants memorized four quadrants, each with black-and-white stripes, which varied in length, width, orientation, and separation (Figure 19.1, top panel), and later were asked to visualize them and make subtle shape comparisons, such as which set had longer or

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Nature of Imagery Representation: Imagery and the Early Visual Cortex 387

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wider stripes (Kosslyn et al., 1999). PET scanning revealed that Area 17 was activated during this task. Moreover, in another group of participants, repetitive TMS (rTMS) was applied to Area 17 prior to the same shape comparison tasks; rTMS causes neurons in the cortex beneath the magnetic coil to respond sluggishly to subsequent events (within a brief period of time). Following rTMS to the posterior occipital lobe, every participant required more time to make these judgments than when rTMS was applied so that it did not affect Area 17 (Figure 19.1, bottom panel). The magnitude of the decrement in performance was the same when participants had their eyes closed and visualized the stripes (imagery) as when they had their eyes open and made judgments based on visible stripes (perception). This makes sense if Area 17 was critical in both the imagery and perceptual versions of the task. (For more information on TMS applied to the visual system, see Kammer, Puls, Erb, & Grodd, 2005; Kammer, Puls, Strasburger, Hill, & Wichmann, 2005). Consistent with this finding, Farah, Soso, and Dasheiff (1992) reported that after one occipital lobe was surgically removed from a patient (as part of a medical treatment), the apparent size of the patient's mental images decreased by approximately half—as expected if each occipital lobe represents the contralateral part of space. Finally, in another PET study, participants closed their eyes and visualized named letters of the alphabet, in upper case form (Kosslyn, Thompson, Kim, Rauch, & Alpert, 1996). Four seconds after forming the image, they were asked to judge whether the letter had a specific characteristic (such as any curved lines); the response times and error rates were recorded at the same time that the participants’ brains were scanned. Not only were variations in the level of activation in Area 17 significantly correlated with the time participants required to make the judgments, but this correlation was present even after all other correlations between variations in regional cerebral blood flow and response time were statistically removed. In summary, these results indicate that: (a) Activation in Area 17 is systematically related to spatial properties of the imaged object (specifically size and orientation); (b) if Area 17 is impaired, via TMS or removal of the occipital lobe in one hemisphere, so is the use of visual imagery; and (c) the activation in Area 17 is not likely to be an artifact of activation in other areas, which is merely incidentally sent (via neural connections) to Area 17. Given these positive results, why have so many studies failed to find activation in Area 17? Kosslyn and Thompson (2003) performed a meta-analysis of neuroimaging studies, that pinpointed three factors which account for the variability among findings. First, not surprisingly, the sensitivity of the technique is important (note the proportion of fMRI studies that detected such activation versus those that did

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Figure 19.1 Stimulus display (top) and Results (bottom) Note: (Top) Prior to the imagery condition, the participants memorized the stimulus display. They also learned which quadrants were labeled by the numbers 1, 2, 3, and 4. During the imagery task, the participants visualized the entire display, and then listened to the stimuli. Their task was to decide whether the stripes in the quadrant named first had a pattern that was greater on the named dimension (e.g., longer stripes) than the stripes in the quadrant named second; if so, they were to press the pedal under their left foot, if not, the pedal under their right foot. The participants were told that they should visualize the entire display, and “look” at the image in order to make the discrimination. Repetitive TMS to the medial occipital cortex was performed in a separate group of participants immediately prior to the same task. During real rTMS, the center of the coil targeted the tip of the calcarine fissure. During sham rTMS, the induced magnetic field did not enter the brain, although the touch on the scalp and the sound of the coil's being activated were comparable to those in the real rTMS condition. (Bottom) Response times in the imagery task for each individual participant (N⫽ 5) after the rTMS are illustrated; as evident, performance degraded in the Real TMS condition, in both perception and imagery. From “The Role of Area 17 in Visual Imagery: Convergent Evidence from PET and rTMS,” by S. M. Kosslyn et al., 1999, Science, 284, pp. 167–168. Adapted with permission.

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not, 19:8, compared to the corresponding proportion for the much less sensitive SPECT technique, 2:7). Second, the meta-analysis revealed that if a task requires participants to find a high-resolution detail in an image (such as by evaluating the shape of an animal’s ears or comparing two similar sets of stripes), activation in the early visual cortex is likely. Third, if a task requires a spatial judgment, activation is less likely. Many of the studies that did not report activation in the early visual cortex used spatial tasks (Mellet et al., 1996, 2000; Mellet, Tzourio, Denis, & Mazoyer, 1995). In contrast, spatial imagery tasks activated the parietal lobes, but not the early visual cortex (Thompson & Kosslyn, 2000). A second puzzle is why some brain-damaged patients continue to have some use of imagery in spite of the fact that the early visual cortex has been severely damaged (e.g., Bartolomeo, 2002; Chatterjee & Southwood, 1995). Probably the most straightforward account for this finding is that the early visual cortex is not necessary for all forms of visual imagery. Crick and Koch (1995) make a good case that the experience of visual perception does not arise from the early visual cortex, but rather from later areas that receive input from the earlier ones. The same is probably true in imagery. If so, then when later areas are activated in the absence of the appropriate immediate sensory input, one may experience visual imagery. However, such later areas do not make fine spatial variations accessible to later processes, and hence one apparently needs to reconstruct the local geometry in earlier areas (which have much smaller receptive fields, and hence higher resolution) if one must extract fine-grained details from the imaged object. Visual Imagery and Emotion If visual mental imagery engages many of the mechanisms used in perception, then we should not be surprised that imagery of emotional events activates the autonomic nervous system and (as also evident in single-cell recordings in humans) the amygdala. Thus, we would expect visualizing an object to have many of the same effects on the body as actually seeing the object. And in fact, Lang, Greenwald, Bradley, and Hamm (1993) showed that skin conductance increases, as do heart rate and breathing rate, when participants view pictures of threatening objects. And the same result occurs when they merely visualize the objects. Kosslyn, Shin, et al. (1996) found that mental images of aversive stimuli activate the anterior insula, the major cortical site of feedback from the autonomic nervous system. In addition, Kreiman, Koch, and Fried (2000) recorded from single cells in the human brain (hippocampus, amygdala, enthorinal cortex, and parahippocampal gyrus) while participants were shown pictures or formed mental images of those same pictures. Some of the cells that responded

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selectively when participants viewed specific visual stimuli (e.g., faces) also responded selectively when those same stimuli were visualized. Of particular interest, this pattern was seen in the amygdala, which is known to play a key role in certain emotions, especially fear and anger (LeDoux, 1995, 1996). Thus, visual mental imagery can engage neural structures also engaged in perception, and those neural structures in turn can engage both the autonomic and limbic systems.

AUDITORY IMAGERY Although visual mental imagery has received the most attention from researchers, imagery is not limited to this modality. For instance, try to answer this question: Do the first three notes of the children’s song “Three Blind Mice” ascend or descend? Most people report that they “hear” the song in the process of deciding. Although the literature on the neural bases of auditory imagery is not as rich as that on visual imagery, progress has been made. For example, in one seminal study, Zatorre and Halpern (1993) studied brain-damaged patients to discover whether specific brain areas are critical for auditory imagery. They studied a group of patients who had had the left or right temporal lobe removed (for the treatment of otherwise intractable epilepsy) and compared them to similar control participants. In one condition, the participants heard a familiar song while also reading the lyrics, and judged which of two particular words had the higher pitch. In another condition, the participants saw the lyrics and made the same judgments, but did not actually hear the song—and thus had to rely on their auditory mental imagery. The patients with right-temporal lesions were impaired in both conditions, compared to both other groups. These findings demonstrate that at least some of the neural structures that play a key role in pitch discrimination in perception also play a comparable role in auditory mental imagery. Most research on auditory imagery has focused on imagery for music. For example, Zatorre, Halpern, Perry, Meyer, and Evans (1996) asked whether auditory imagery draws on the same mechanisms used in auditory perception. Their participants either listened to songs and judged the relative pitch of pairs of words, or imagined hearing songs and made the same judgments. No auditory stimulation was present during the baseline condition, which required the participants to judge the relative length of visually presented words. PET revealed that many of the same areas were in fact activated in common in the auditory tasks, including bilateral associative auditory cortex (BA 21/22, in spite of the fact that the left temporal lobe has often been identified with the perception of language and

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Motor Imagery

the right with music or environmental sounds), the bilateral frontal cortex (BA45/9 and 10/47), the left parietal cortex (BA 40/7), and the supplementary motor cortex (BA 6). The bilateral activation in the associative auditory cortex observed in this study, in apparent contrast with the patient studies, may reflect the fact that these researchers used verbal melodies. In a subsequent study, Halpern and Zatorre (1999) asked musically trained participants to listen to the opening notes of familiar (nonverbal) melodies and then continue “hearing the melody with the mind’s ear.” Again using PET, they found activation in two regions of the right temporal lobe (the superior and inferior temporal cortex), which is consistent with their earlier study of brain-damaged patients; both of these areas are involved in storing and interpreting nonverbal sounds. Moreover, auditory imagery of a melody that required retrieval from memory also activated two right-hemisphere regions, in the frontal lobe and superior temporal gyrus (which is critical for auditory perception). Finally, the supplementary motor area (SMA) was also activated by auditory imagery, regardless of whether the melody was retrieved or simply rehearsed online. This is interesting because no overt behavior was required. Halpern and Zatorre infer that stored movements are used in this sort of imagery—which makes sense for verbal melodies, where one can subvocalize the tune as part of the process of retrieving the information. A more recent review finds that “neural activity in auditory cortex can occur in the absence of sound . . . and that this activity likely mediates

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the phenomenological experience of imagining music” (Zatorre & Halpern, 2005, p. 9; Figure 19.2). Finally, Griffiths (2000) reports a novel study of patients who became deaf and then hallucinated hearing music. These patients were neither psychotic nor beset with an obvious neurological problem, such as epilepsy. Griffiths was able to perform PET while the patients had such hallucinations, and reports that the posterior temporal lobes, in the auditory cortex, were activated as well as several other areas (specifically, the right basal ganglia, the cerebellum, and the inferior frontal cortices). In short, auditory imagery appears to draw on most of the neural structures used in auditory perception. However, unlike in visual imagery, there is no discernable evidence that the first auditory cortical area to receive input from the ears, Area A1, is activated during auditory imagery. MOTOR IMAGERY At the outset, we asserted that mental imagery occurs when perceptual information is accessed from memory instead of arising from immediate sensory input. Given this characterization, how can we conceptualize motor imagery, which occurs when people imagine moving in some way? In our view, such imagery does not arise directly from producing the relevant outputs to the motor system, but rather is a result of activating the kinesthetic feedback sensations one would feel if one moved in a specific way. Does this mean that motor imagery and kinesthetic imagery are identical? No, because one can imagine a sensation (e.g., of a feather being used to tickle the back of your neck) without imagining a movement. Motor imagery occurs when a movement is mentally simulated, which leads to the kinesthetic sensations of making that movement. In this section, we review evidence that motor imagery engages neural mechanisms involved in physical movement, that motor imagery is one strategy that people use to transform objects in images, that primary motor cortex is involved in at least some cases of motor imagery, and that mechanisms involved in imitation may also play a role when people use motor imagery to practice an activity. Motor Imagery and Physical Movement

Figure 19.2 A lateral view of the right hemisphere illustrates a hemodynamic increase (darker gray areas, as measured by fMRI), during auditory imagery. Note: Although participants receive no actual auditory input (the task is performed in a silent environment), activation occurs in the posterior superior temporal gyrus, a region of the auditory cortex. From “Mental Concerts: Musical Imagery and Auditory Cortex,” by R. J. Zatorre and A. R. Halpern, 2005, Neuron, 47, pp. 10. Copyright 2005 Elsevier. Reprinted with permission.

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Researchers have produced an impressive body of evidence that motor imagery can in fact simulate the corresponding actual behavior. For instance, when people are asked to imagine walking to a specific goal placed in front of them and to indicate when they would have arrived, their estimates of transit time are remarkably similar to the actual time they subsequently require to walk that distance (Decety & Jeannerod, 1995).

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Many studies have now been carried out to investigate the neural bases of such motor imagery, and to distinguish motor imagery from purely visual imagery (Tomasino, Borroni, Isaja, & Rumiati, 2005; Wraga et al., 2005; Wraga, Thompson, Alpert, & Kosslyn, 2003). Although visual imagery may often accompany motor imagery, researchers have documented that motor imagery relies on distinct mechanisms. Specifically, many researchers have shown that the cortex used in movement control also plays a role in motor imagery. In a classic study, Georgopoulos, Lurito, Petrides, Schwartz, and Massey (1989) recorded activity in individual neurons in the motor strip of monkeys while the animals were planning to move a lever along a specific arc. They found that these neurons fired in a systematic sequence, depending on their orientation tuning. At first, only neurons tuned for orientations near the starting position of the lever fired, followed by those tuned for orientations slightly farther along the trajectory, and so on. All of this occurred before the animal actually began moving. These findings do not, however, show that the processing underlying motor imagery occurs in the motor strip itself; it is possible that the computation takes place elsewhere in the brain (e.g., the posterior parietal lobes), and that the results of such computation are simply being executed in the motor strip. A host of neuroimaging studies of mental rotation— that is, imagining incrementally changing an object’s orientation—have now been reported, all of which have shown that multiple brain areas are activated during mental rotation. For example, Richter et al. (2000) measured brain activation with fMRI while participants mentally rotated the three-dimensional multi-armed angular stimuli invented by Shepard and Metzler (1971; which look as if they had been constructed by gluing small cubes together to form the arms). Participants were shown pairs of such shapes and asked to report whether the figures in each pair were the same or mirror-reversed. Richter et al. (2000) report that the superior parietal lobules (in both hemispheres) were activated during this task, as well as the premotor cortex (in both hemispheres), the supplementary motor cortex, and also the left primary motor cortex. Other neuroimaging studies have provided strong support for the role of motor processes in mental transformations. For example, Parsons et al. (1995) showed participants a sequence of pictures of a hand that could be rotated to various degrees; the pictures were presented in the left visual field (so the image was registered first by the right hemisphere) or in the right visual field (so the image was registered first by the left hemisphere). The participants were to decide whether each picture was a left or right hand. The researchers expected the motor cortices to be activated in this task if participants imagined rotating their own hand into congruence with the stimulus.

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And, in fact, not only was the supplementary motor cortex activated bilaterally, but also prefrontal and insular premotor areas were activated in the hemisphere contralateral to the hand (left or right) used as a stimulus—suggesting that participants did in fact imagine the appropriate movements. Many other areas, including in the frontal and parietal lobes, the basal ganglia and cerebellum, were active, as was Area 17. Some researchers (Decety, 1996; Jeannerod, 1994; Jeannerod & Decety, 1995) have suggested that people often transform images by imagining what they would see if the objects were manipulated in a specific way. One PET study (Kosslyn et al., 1998) directly compared rotation of hands versus inanimate objects, again using the three-dimensional multi-armed angular stimuli invented by Shepard and Metzler (1971). The participants compared pairs of drawings and decided whether they were identical or mirror images (using the task and stimuli from the original Shepard & Metzler study). In the experimental condition, the figures were presented at different relative orientations, and one had to be mentally rotated into congruence with the other; in the baseline condition, the figures were presented at the same orientation, and thus no mental rotation was necessary. The comparison of the two conditions revealed which areas were activated by mental rotation. In another condition, the same design was used for drawings of hands, but now the participants decided whether the two hands in a pair were both left or both right or whether one was a left hand and one a right hand. In this study, several motor areas were activated when participants mentally rotated hands, including the primary motor cortex (Area M1), the premotor cortex, and the posterior parietal lobe. None of the frontal motor areas were activated when objects were mentally rotated. However, Cohen et al. (1996) used fMRI to study mental rotation of exactly the same inanimate objects and found that the premotor cortex was activated in this task, but only in half the participants. Alternative Mechanisms of Mental Transformation The fact that only some participants in the Cohen et al. (1996) study had activation in frontal motor areas during mental rotation of inanimate objects suggests that there may be multiple strategies for performing such rotations. One strategy might involve imagining what you would see if you manipulated an object; another might involve imagining what you would see if someone else (or an external force, such as a motor) manipulated the object. To test this idea, Kosslyn, Thompson, Wraga, and Alpert (2001) asked participants to perform the same mental rotation task used by Cohen et al. (1996), but with a twist: Immediately prior

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Figure 19.3 Internal action minus external action. Note: At the outset, the participants learned to visualize the mental rotation either by external action (EA, which was rotation driven by an electric motor) or internal action (IA, which was rotation driven by manual turning of the figure). An axial PET slice, at 56 mm above the AC-PC line, demonstrates activation in Area M1 when data from the EA condition were subtracted from those in the IA condition. Depending on the strategy used, motor regions of the brain are recruited during mental rotation. The result also shows that the strategy used to accomplish a given task can vary according to previous training and can be adopted voluntarily. From “Imagining Rotation by Endogenous versus Exogenous Forces: Distinct Neural Mechanisms,” by S. M. Kosslyn, W. L. Thompson, M. Wraga, and N. M. Alpert, 2001, NeuroReport, 12, p. 2523. Copyright 2001 Lippincott, Williams & Wilkins. Reprinted with permission.

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in allowing participants to manipulate objects in images. It is possible that the actual computation is taking place in another area that incidentally sends activation to primary motor cortex. To test this hypothesis, Ganis, Keenan, Kosslyn, and Pascual-Leone (2000) disrupted function in the left primary motor cortex by administering TMS while participants mentally rotated pictures of hands and feet (with the to-be-rotated stimulus appearing in the right visual field). The TMS was time-locked so that it disrupted neural processing only a specific amount of time after the stimulus appeared. Participants required more time to perform this task if a single magnetic pulse was delivered to the motor strip (roughly over the “hand area”) 650 ms after the stimuli were presented (but not at the other temporal delays tested); moreover, rotation of hands was impaired more than rotation of feet, as expected if this area is specialized for controlling the hand per se. Within the limits of the spatial resolution afforded by the TMS technique, these results suggest that activation in this area reflects processing used to perform the task. We cannot say, however, whether this area is the site of processing or merely relays information computed elsewhere in the brain. The finding that area M1 is recruited during the mental rotation of hands has been replicated and extended with similar stimuli and procedures (Tomasino et al., 2005). Motor Imagery, Mental Practice, and Mirror Neurons

to the task, the participants saw a wooden model of that type of stimulus (an exemplar not actually used in the task) either being rotated by an electric motor or they themselves physically turned the stimulus. They were told that during the task they should imagine the stimuli being rotated just as they had seen the model rotate at the outset. In this experiment, Area M1 was activated when participants mentally rotated stimuli by imagining themselves physically rotating the stimulus, and not when they imagined the electric motor rotating the stimulus (Figure 19.3). These results show that imagining oneself manipulating an object is one way in which mental transformation of objects in general (not just body parts) can take place and the results also show that humans can voluntarily adopt this strategy or use a strategy in which they imagine what would happen if an external force transformed an object.

Role of the Primary Motor Cortex in Motor Imagery Just as researchers have asked “how low” mental imagery activates the visual system, they have also asked “how low” it activates the motor system. Specifically, they have asked whether the primary motor cortex plays a functional role

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The fact that mental imagery can engage the motor system may help to explain how “mental practice” can improve actual performance in various domains. Mental practice occurs when people rehearse performing an activity in imagery, which often later improves their performance of the actual activity. Researchers have shown that mental practice can improve actual performance in activities ranging from athletics to patient rehabilitation (Butler & Page, 2006; Driskell, Copper, & Moran, 1994; MacIntyre, Moran, & Jennings, 2002; Malouin, Belleville, Richards, Desrosiers, & Doyon, 2004; Maring, 1990; Morganti et al., 2003; Weiss, Hansen, Rost, & Beyer, 1994). In this case, imagining making movements may not only exercise the relevant brain areas, but may build associations among processes implemented in different areas—which in turn facilitates complex performance. One striking aspect of mental practice is that it can be based on observing a model perform the activity. For example, a person can observe a golf coach performing a swing, and then can imagine herself performing that swing. How can observing someone else become translated into a motor program in one’s own head? This process apparently relies on mirror neurons, which originally were discovered in Area F5 of the monkey brain (itself part of premotor

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cortex). Mirror neurons respond selectively not only when the animal performs specific actions with the hand and/or mouth, but also when the animal merely observes the same actions performed by another monkey (or human). Neuroimaging and TMS studies have provided evidence that we humans also have mirror neurons. In many studies, researchers have found that the human premotor cortex is activated when humans observe other people’s actions (e.g., Fadiga, Fogassi, Pavesi, & Rizzolatti, 1995; Gangitano, Mottaghy, & Pascual-Leone, 2001; Grafton, Arbib, Fadiga, & Rizzolatti, 1996; Hari et al., 1998; Rizzolatti et al., 1996; Rizzolatti, Luppino, & Matelli, 1998). The likely homologue of Area F5 in humans is Broca’s area (typically characterized as being involved in speech production), which has prompted some authors to theorize that the mirror neurons in humans may have a crucial role not only in imitation, but also in language acquisition. Mirror neurons may also play a role in motor imagery, consistent with the idea that people often transform images by imagining what they would see if the objects were manipulated in a specific way. Although mirror neurons have not been implicated, researchers have observed a similar cross-modality transfer when people see highly overlearned stimuli. In this case, the mere act of seeing the stimuli can activate the motor system. For example, James and Gauthier (2006) found that interacting with letters engages motor areas of the brain, in addition to a larger network. Seeing and acting apparently are tightly linked in the brain, and using imagery in mental practice can further strengthen or modify that link.

SUMMARY The great behaviorist B. F. Skinner (1977, p. 6) wrote, “There is no evidence of the mental construction of images to be looked at or maps to be followed. The body responds to the world, at the point of contact; making copies would be a waste of time.” We hope that we have convinced the reader that the first part of this claim is incorrect; images are in fact internal representations. With the advent of neuroimaging, imagery may no longer be seen as an awkward holdover from a previous, less rigorous age, a topic unfit for polite company. Rather, researchers agree that most of the neural processes underlying like-modality perception are also used in imagery, and imagery in many ways can “stand in” for (re-present, if you will) a perceptual stimulus or situation. Imagery can not only engage the motor system, but also can engage the autonomic and limbic systems. Nevertheless, many questions remain. For example, under what circumstances is early sensory cortex always recruited during imagery? Why is early sensory cortex often recruited during visual mental imagery, but not during

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auditory imagery? Why do people differ so much in their imagery abilities? Does genetics affect some aspects of imagery more than others? How does semantic content in images engage specific mechanisms? Unlike even 20 years ago, questions such as these can now be answered.

REFERENCES Bartolomeo, P. (2002). The relationship between visual perception and visual mental imagery: A reappraisal of the neuropsychological evidence. Cortex, 38, 357–378. Behrmann, M. (2000). The mind’s eye mapped onto the brain’s matter. Current Directions in Psychological Science, 9, 50–54. Behrmann, M., Winocur, G., & Moscovitch, M. (1992, October 15). Dissociation between mental imagery and object recognition in a braindamaged patient. Nature, 359, 636–637. Butler A.J., & Page S.J. (2006). Mental practice with motor imagery: evidence for motor recovery and cortical reorganization after stroke. Archives of Physical Medicine & Rehabilitation, 87, S2–S1. Chatterjee, A., & Southwood, M. H. (1995). Cortical blindness and visual imagery. Neurology, 45, 2189–2195. Cohen, M. S., Kosslyn, S. M., Breiter, H. C., DiGirolamo, G. J., Thompson, W. L., Bookheimer, S. Y., Belliveau, J. W., & Rosen, B. R. (1996). Changes in cortical activity during mental rotation: A mapping study using functional MRI. Brain, 119, 89–100. Crick, F., & Koch, C. (1995, May 11). Are we aware of neural activity in primary visual cortex? Nature, 375, 121–123. Decety, J. (1996). Neural representation for action. Reviews in the Neurosciences, 7, 285–297. Decety, J., & Jeannerod, M. (1995). Mentally simulated movements in virtual reality: Does Fitts’s law hold in motor imagery? Behavioral Brain Research, 72(1/2), 127–134. De Vreese, L. P. (1991). Two systems for colour-naming defects: Verbal disconnection vs colour imagery disorder. Neuropsychologia, 29, 1–18. Downing, P. E., Chan, A. W., Peelen, M. V., Dodds, C. M., & Kanwisher, N. (2006). Domain specificity in visual cortex. Cerebral Cortex, 16, 1453–1461. Driskell, J., Copper, C., & Moran, A. (1994). Does mental practice enhance performance? Journal of Applied Psychology, 79, 481–492. Fadiga, L., Fogassi, L., Pavesi, G., & Rizzolatti, G. (1995). Motor facilitation during action observation: A magnetic stimulation study. Journal of Neurophysiology, 73, 2608–2611. Farah, M. J. (1984). The neurological basis of mental imagery: A componential analysis. Cognition, 18, 245–272. Farah, M. J., Soso, M. J., & Dasheiff, R. M. (1992). Visual angle of the mind’s eye before and after unilateral occipital lobectomy. Journal of Experimental Psychology: Human Perception and Performance, 18, 241–246. Gangitano, M., Mottaghy, F. M., & Pascual-Leone, A. (2001). Phase-specific modulation of cortical motor output during movement observation. NeuroReport, 12, 1489–1492. Ganis, G., Keenan, J. P., Kosslyn, S. M., & Pascual-Leone, A. (2000). Transcranial magnetic stimulation of primary motor cortex affects mental rotation. Cerebral Cortex, 10, 175–180. Ganis, G., Thompson, W. L., & Kosslyn, S. M. (2004). Brain areas underlying visual mental imagery and visual perception: An fMRI study. Brain Research: Cognitive Brain Research, 20, 226–241.

8/17/09 2:11:56 PM

References 393 Ganis, G., Thompson, W. L., Mast, F. W., & Kosslyn, S. M. (2003). Visual imagery in cerebral visual dysfunction. Neurologic Clinics, 21, 631–646. Georgopoulos, A. P., Lurito, J. T., Petrides, M., Schwartz, A. B., & Massey, J. T. (1989, January 13). Mental rotation of the neuronal population vector. Science, 243, 234–236.

Kosslyn, S. M., Pascual-Leone, A., Felician, O., Camposano, S., Keenan, J. P., Thompson, W. L., Ganis, G., Sukel, K. E., & Alpert, N. M. (1999, April 2). The role of area 17 in visual imagery: Convergent evidence from PET and rTMS. Science, 284, 167–170.

Grafton, S. T., Arbib, M. A., Fadiga, L., & Rizzolatti, G. (1996). Localization of grasp representations in humans by positron emission tomography: Vol. 2. Observation compared with imagination. Experimental Brain Research, 112, 103–111.

Kosslyn, S. M., Shin, L. M., Thompson, W. L., McNally, R. J., Rauch, S. L., Pitman, R. K., & Alpert, N. M. (1996). Neural effects of visualizing and perceiving aversive stimuli: A PET investigation. NeuroReport, 7, 1569–1576.

Griffiths, T. D. (2000). Musical hallucinosis in acquired deafness: Phenomenology and brain substrate. Brain, 123, 2065–2076.

Kosslyn, S. M., & Thompson, W. L. (2003). When is early visual cortex activated during visual mental imagery? Psychological Bulletin, 129, 723–746.

Halpern, A. R., & Zatorre, R. J. (1999). When that tune runs through your head: A PET investigation of auditory imagery for familiar melodies. Cerebral Cortex, 9, 697–704. Hari, R., Forss, N., Avikainen, S., Kirveskari, E., Salenius, S., & Rizzolatti, G. (1998). Activation of human primary motor cortex during action observation: A neuromagnetic study. Proceedings of the National Academy of Sciences, USA, 95, 15061–15065. Hasson, U., Harel, M., Levy, I., & Malach, R. (2003). Large-scale mirrorsymmetry organization of human occipito-temporal object areas. Neuron, 37, 1027–1041. Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001, September 28). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, 2425–2430.

Kosslyn, S. M., Thompson, W. L., & Alpert, N. M. (1997). Neural systems shared by visual imagery and visual perception: A positron emission tomography study. NeuroImage, 6, 320–334. Kosslyn, S. M., Thompson, W. L., & Ganis, G. (2006). The case for mental imagery. New York: Oxford University Press. Kosslyn, S. M., Thompson, W. L., Kim, I. J., & Alpert, N. M. (1995, November 30). Topographical representations of mental images in primary visual cortex. Nature, 378, 496–498. Kosslyn, S. M., Thompson, W. L., Kim, I. J., Rauch, S. L., & Alpert, N. M. (1996). Individual differences in cerebral blood flow in area 17 predict the time to evaluate visualized letters. Journal of Cognitive Neuroscience, 8, 78–82.

James, K. H., & Gauthier, I. (2006). Letter processing automatically recruits a sensory-motor brain network. Neuropsychologia, 44, 2937–2949.

Kosslyn, S. M., Thompson, W. L., Wraga, M., & Alpert, N. M. (2001). Imagining rotation by endogenous versus exogenous forces: Distinct neural mechanisms. NeuroReport, 12, 2519–2525.

Jankowiak, J., Kinsbourne, M., Shalev, R. S., & Bachman, D. L. (1992). Preserved visual imagery and categorization in a case of associative visual agnosia. Journal of Cognitive Neuroscience, 4, 119–131.

Kreiman, G., Koch, C., & Fried, I. (2000, November 16). Imagery neurons in the human brain. Nature, 408, 357–361.

Jeannerod, M. (1994). The representing brain: Neural correlates of motor intention and imagery. Behavioral and Brain Sciences, 17, 187–245.

Lang, P. J., Greenwald, M. K., Bradley, M. M., & Hamm, A. O. (1993). Looking at pictures: Affective, facial, visceral, and behavioral reactions. Psychophysiology, 30, 261–273.

Jeannerod, M., & Decety, J. (1995). Mental motor imagery: A window into the representational stages of action. Current Opinion in Neurobiology, 5, 727–732.

LeDoux, J. E. (1995). Emotion: Clues from the brain. Annual Reviews of Psychology, 46, 209–235.

Jordan, K., Heinze, H. J., Lutz, K., Kanowski, M., & Jancke, L. (2001). Cortical activations during the mental rotation of different visual objects. NeuroImage, 13, 143–152. Kammer, T., Puls, K., Erb, M., & Grodd, W. (2005). Transcranial magnetic stimulation in the visual system: Pt. II. Characterization of induced phosphenes and scotomas. Experimental Brain Research, 160, 129–140. Kammer, T., Puls, K., Strasburger, H., Hill, N. J., & Wichmann, F. A. (2005). Transcranial magnetic stimulation in the visual system: Pt. I. The psychophysics of visual suppression. Experimental Brain Research, 160, 118–128. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 4302–4311.

c19.indd 393

Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2001). Neural foundations of imagery. Nature Reviews Neuroscience, 2, 635–642.

LeDoux, J. E. (1996). The emotional brain: The mysterious underpinnings of emotional life. New York: Simon & Schuster. Levine, D. N., Warach, J., & Farah, M. J. (1985). Two visual systems in mental imagery: Dissociation of ‘what’ and ‘where’ in imagery disorders due to bilateral posterior cerebral lesions. Neurology, 35, 1010–1018. MacIntyre, T., Moran, A., & Jennings, D. J. (2002). Are mental imagery abilities related to canoe-slalom performance? Perceptual & Motor Skills, 94, 1245–1250. Malouin, F., Belleville, S., Richards, C. L., Desrosiers, J., & Doyon, J. (2004). Working memory and mental practice outcomes after stroke. Archives of Physical Medicine and Rehabilitation, 85, 177–183. Maring, J. R. (1990). Effects of mental practice on rate of skill acquisition. Physical Therapy, 70, 165–172.

Kanwisher, N., & Yovel, G. (2006). The fusiform face area: A cortical region specialized for the perception of faces. Philosophical Transactions of the Royal Society of London, Series B, 361, 2109–2128.

Mechelli, A., Price, C. J., Friston, K. J., & Ishai, A. (2004). Where bottomup meets top-down: Neuronal interactions during perception and imagery. Cerebral Cortex, 14, 1256–1265.

Klein, I., Dubois, J., Mangin, J. F., Kherif, F., Flandin, G., Poline, J. B., Denis, M., Kosslyn, S. M., & Le Bihan, D. (2004). Retinotopic organization of visual mental images as revealed by functional magnetic resonance imaging. Brain Research: Cognitive Brain Research, 22, 26–31.

Mellet, E., Briscogne, S., Tzourio-Mazoyer, N., Ghaëm, O., Petit, L., Zago, L., Etard, O., Berthoz, A., Mazoyer, B., & Denis, M. (2000). Neural correlates of topographic mental exploration: The impact of route versus survey perspective learning. NeuroImage, 12, 588–600.

Klein, I., Paradis, A.-L., Poline, J.-B., Kosslyn, S. M., & Le Bihan, D. (2000). Transient activity in human calcarine cortex during visual imagery. Journal of Cognitive Neuroscience, 12, 15–23.

Mellet, E., Tzourio, N., Crivello, F., Joliot, M., Denis, M., & Mazoyer, B. (1996). Functional anatomy of spatial mental imagery generated from verbal instructions. Journal of Neuroscience, 16, 6504–6512.

Kosslyn, S. M., DiGirolamo, G., Thompson, W. L., & Alpert, N. M. (1998). Mental rotation of objects versus hands: Neural mechanisms revealed by positron emission tomography. Psychophysiology, 35, 151–161.

Mellet, E., Tzourio, N., Denis, M., & Mazoyer, B. (1995). A positron emission tomography study of visual and mental spatial exploration. Journal of Cognitive Neuroscience, 4, 433–445.

8/17/09 2:11:57 PM

394

Mental Imagery

Morganti F., Gaggioli A., Castelnuovo G., Bulla D., Vettorello, M., & Riva G. (2003). The use of technology-supported mental imagery in neurological rehabilitation: A research protocol. Cyberpsychology and Behavior, 6, 421–427. Ng, V. W., Bullmore, E. T., de Zubicaray, G. I., Cooper, A., Suckling, J, & Williams, S. C. R. (2001). Identifying rate-limiting nodes in large-scale cortical networks for visuospatial processing: An illustration using fMRI. Journal of Cognitive Neuroscience, 13, 537–545. O’Craven, K. M., & Kanwisher, N. (2000). Mental imagery of faces and places activates corresponding stimulus-specific brain regions. Journal of Cognitive Neuroscience, 12, 1013–1023. Paivio, A. (1971). Imagery and verbal processes. New York: Holt, Rinehart and Winston. Parsons L. M., Fox, P. T., Downs, J. H., Glass, T., Hirsch, T. B., Martin, C. C., Jerabek, P. A., & Lancaster, J. L. (1995, May 4). Use of implicit motor imagery for visual shape discrimination as revealed by PET. Nature, 375, 54–58. Pylyshyn, Z. W. (1973). What the mind’s eye tells the mind’s brain: A critique of mental imagery. Psychological Bulletin, 80, 1–24. Pylyshyn, Z. W. (1981). Psychological explanations and knowledgedependent processes. Cognition, 10(1/3), 267–274.

Slotnick, S. D., Thompson, W. L., & Kosslyn, S. M. (2005). Visual mental imagery induces retinotopically organized activation of early visual areas. Cerebral Cortex, 15, 1570–1583. Thirion, B., Duchesnay, E., Hubbard, E., Dubois, J., Poline, J.-B., Lebihan, D., & Dehaene, S. (2006). Inverse retinotopy: Inferring the visual content of images from brain activation patterns. NeuroImage, 33, 1104–1116. Thompson, W. L., & Kosslyn, S. M. (2000). Neural systems activated during visual mental imagery. A review and meta-analyses. In A. W. Toga & J. C. Mazziotta (Eds.), Brain mapping: Pt. II. The systems (pp. 535–560). San Diego, CA: Academic Press. Thompson, W. L., Kosslyn, S. M., Sukel, K. E., & Alpert, N. M. (2001). Mental imagery of high- and low-resolution gratings activates Area 17. NeuroImage, 14, 454–464. Tomasino, B., Borroni, P., Isaja, A., & Rumiati, R. I. (2005). The role of the primary motor cortex in mental rotation: A TMS study. Cognitive Neuropsychology, 22, 348–363. Tootell, R. B. H., Hadjikani, N. K., Mendola, J. D., Marrett, S., & Dale, A. M. (1998). From retinotopy to recognition: FMRI in human visual cortex. Trends in Cognitive Sciences, 2, 174–183.

Pylyshyn, Z. W. (2002). Mental imagery. In search of a theory. Behavioral and Brain Sciences, 25, 157–237.

Ungerleider, L. G., & Mishkin, M. (1982). Two cortical visual systems. In D. J. Ingle, M. A. Goodale, & R. J. W. Mansfield (Eds.), Analysis of visual behavior (pp. 549–586). Cambridge, MA: MIT Press.

Pylyshyn, Z. W. (2003a). Return of the mental image: Are there really pictures in the head? Trends in Cognitive Sciences, 7, 113–118.

Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological Review, 20, 158–177.

Pylyshyn, Z. W. (2003b). Seeing and visualizing: It’s not what you think. Cambridge, MA: MIT Press.

Weiss, T., Hansen, E., Rost, R., & Beyer, L. (1994). Mental practice of motor skills used in poststroke rehabilitation has own effects on central nervous activation. International Journal of Neuroscience, 78(3–4), 157–166.

Richter, W., Somorjai, R., Summers, R., Jarmasz, M., Tegeler, C., Ugurbil, K., Menon, R., Gati, J. S. Georgopoulos, A. P. & Kim, S.-G. (2000). Motor area activity during mental rotation studied by time-resolved single-trial fMRI. Journal of Cognitive Neuroscience, 12, 310–320. Rizzolatti, G., Fadiga, L., Matelli, M., Bettinardi, V., Paulesu, E., Perani, D., & Fazio, F. (1996). Localization of grasp representations in humans by PET: Pt. 1. Observation versus execution. Experimental Brain Research, 111, 246–252.

Wraga, M., Shephard, J. M., Church, J. A., Inati, S., & Kosslyn, S. M. (2005). Imagined rotations of self versus objects: An fMRI study. Neuropsychologia, 43, 1351–1361. Wraga, M. J., Thompson, W. L., Alpert, N. M., & Kosslyn, S. M. (2003). Implicit transfer of motor strategies in mental rotation. Brain and Cognition, 52, 135–143.

Rizzolatti, G., Luppino, G., & Matelli, M. (1998). The organization of the cortical motor system: New concepts. Electroencephalography and Clinical Neurophysiology, 106, 283–296.

Young, A. W., Humphreys, G. W., Riddoch, M. J., Hellawell, D. J., & de Haan, E. H. (1994). Recognition impairments and face imagery. Neuropsychologia, 32, 693–702.

Sereno, M. I., Dale, A. M., Reppas, J. B., Kwong, K. K., Belliveau, J. W., Brady, T. J., Rosen, B. R, & Tootell, R. B. H. (1995, May 12). Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science, 268, 889–893.

Zatorre, R. J., & Halpern, A. R. (1993). Effect of unilateral temporal-lobe excision on perception and imagery of songs. Neuropsychologia, 31, 221–232.

Shepard, R. N., & Metzler, J. (1971, February 19). Mental rotation of threedimensional objects. Science, 171, 701–703. Skinner, B. F. (1977). Why I am not a cognitive psychologist. Behaviorism, 5, 1–10.

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Zatorre, R. J., & Halpern, A. R. (2005). Mental concerts: Musical imagery and auditory cortex. Neuron, 47, 9–12. Zatorre, R. J., Halpern, A. R., Perry, D. W., Meyer, E., & Evans, A. C. (1996). Hearing in the mind’s ear: A PET investigation of musical imagery and perception. Journal of Cognitive Neuroscience, 8, 29–46.

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Chapter 20

Categorization MICHAEL L. MACK, JENNIFER J. RICHLER, THOMAS J. PALMERI, AND ISABEL GAUTHIER

The survival of most organisms demands that they discriminate predator from prey, edible from inedible, or family from foe. Organisms have to be able to recognize things as kinds of things, not as isolated instances, because what is learned about one thing should generalize to other things of the same kind. We call these kinds of things categories. Recognizing something in the world as a kind of thing is categorization. Organisms may also identify unique objects as individuals, but arguably this identification can be considered a fine-grained form of categorization because matching different views of the same object, or even the same object changing over time, requires labeling different experiences as belonging to the same category. Once a thing is categorized or identified, all of the knowledge we might have about that category can be brought to bear. What’s the most appropriate course of action? Flee? Eat it? Pick up and dial? Humans take categorization to dizzying degrees. First there is the mundane. We easily categorize chairs from tables, trees from shrubs, and birds from dogs. And there is the remarkable. Experts from various domains may easily discriminate subspecies of particular kinds of plants or animals, judge cancerous from noncancerous growths, or distinguish Porsche models just by the shape of the headlight. While this may seem impressive, remember that many everyday categorizations prove remarkable when you consider the processing demands involved. We easily identify the people we know at a glance. Yet structurally, people may be as similar to one another as different chimpanzees. For most people, all chimpanzees look the same but people look much more different. Right now you are engaging in another everyday categorization: With remarkable speed and ease, the letters and words in this sentence are categorized

as just the first step of comprehending (at least we hope) our written language. Face and letter perception are examples of domains in which most people have gained considerable expertise and are very important domains of study. This chapter mainly addresses how people categorize visual objects. People can also categorize things based on their sound, touch, taste, or smell. But outside of speech perception, the majority of categorization research has focused on the visual modality. More complex visual events can also be categorized, such as “a nod” or “a touchdown” or “an armstand back double somersault tuck,” but this has been for the most part studied separately from object categorization (e.g., Zacks, Speer, Swallow, Braver, & Reynolds, 2007). In keeping with the aims of this Handbook, in each section of this chapter we lay out a variety of fundamental behavioral manifestations of object categorization and review some of the key findings from neurophysiology, electrophysiology, neuropsychology, and functional brain imaging that have deepened our understanding of object categorization. We also look to computational cognitive neuroscience models grounded in neuroanatomy and neurophysiology. We begin our discussion with the issue of abstraction. By its very nature, categorization is abstraction. We live in a world of particular experiences. Yet recognizing an object as not simply an isolated perceptual experience but also as an instance of a kind of thing that has been experienced before—as a member of a category—is to abstract from the particular to the general. Does this ability to abstract from particular experience mean that what we know about an object category is itself an abstraction? At first blush, it may seem like the answer is obviously yes. How could we categorize objects abstractly if our knowledge about categories was not itself abstract? But as we will see, decades of behavioral research wrestled with this basic issue and recent neuroscientific evidence has shed important light on this question. We then turn to two parallel issues that have dominated much of the recent research on object categories: (a) The

This work was supported by a grant from the James S. McDonnell Foundation, grant HSD-DHBS05 from the National Science Foundation, and the Temporal Dynamics of Learning Center (NSF Science of Learning Center SBE-0542013). The order of the first two authors was decided by a flip of a coin. 395

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study of when different kinds of knowledge representations, abstract or not, come into play, and whether the variety of categorization behaviors we can observe is best explained by different learning and memory systems in the brain; and (b) whether objects from different domains may be categorized in different ways and by different brain systems. For instance, there may be specialized systems in the brain to process objects from especially important categories such as letters and faces. Whether and how we acquire such specialization through learning, or whether we have evolved systems for some special categories, has been a topic of debate.

ROLE OF ABSTRACTION IN CATEGORIZATION Categorization is abstraction. To begin with, we never see the same object twice, even if it is the very same physical object. When an object is viewed from a different position or under different lighting, the projection of that object onto our retina will vary, often quite dramatically. What is remarkable is that, despite the visual signal being very different, we perceive the same object (Palmeri & Gauthier, 2004; Palmeri & Tarr, 2008). Moreover, physically different objects can be perceived as very different, yet even very young children know that they are the same thing—not the same object, but the same kind of thing (Quinn, 1999). Humans have developed complex systems that permit objects to be categorized at multiple levels of abstraction, from specific (e.g., “Gladys” or “American White Pelican”), to basic level categories (e.g., “chair” or “bird”), to extremely abstract superordinate categories (e.g., “living thing”; Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976). Categorization is a form of abstraction, but does this necessarily imply that the mental representations and processes involved are inherently abstract? Early theories of object categorization took it as nearly an axiom that the goal of visual cognition was to create an abstract representation of the varying world. Early structural description theories of object recognition assumed that the goal of vision was to mentally reconstruct the abstract three-dimensional structure of objects (Marr & Nishihara, 1978). Recognition-by-components (Biederman, 1987) assumes that objects are mentally represented in terms of a small set of qualitative threedimensional primitives known as geons (Figure 20.1). Geons are uniquely recovered by attending to various configurations of view-invariant properties in the twodimensional retinal image. Objects are represented in terms of their geon components and their relative spatial

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Object Representations Image fragments

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Figure 20.1 Illustration of object representations in imagebased versus structural description models. Note: (Top) Image-based models. The object (lamp) is represented in terms of image-based fragments of intermediate complexity. (Bottom) Structural description models. The object (lamp) is represented in terms of geometric primitives (geons) and the spatial relations between them.

configuration. As a consequence of this reconstruction into a geon-defined structure, many sources of variability are eliminated entirely from a mental representation of an object. Different views of the same object and different exemplars within a category such as dog or lamp map onto the same object representation. Early concept models also assumed that our knowledge about object categories is abstract. Semantic network models (e.g., Collins & Quillian, 1969) conceptually organized one kind of thing with another kind of thing through propositional structures. Knowledge is stored efficiently, so that object properties that are true of a superordinate category are only stored at the most general level and only properties unique to subordinate categories or specific individuals are stored at lower levels of the conceptual hierarchy (E. Smith, Shoben, & Rips, 1974). According to this view, what we know about particular object categories is also abstracted away from our experience with objects. By such abstractionist views, categorization of an object requires applying logical rules to object properties (e.g., Bruner, Goodnow, & Austin, 1956; Johansen & Palmeri, 2002) or comparing an object to an abstract category prototype or schema (e.g., Lakoff, 1987). Category abstraction is achieved because our knowledge about categories is abstract. However, later work showed that we do not need viewpoint-invariant and instance-invariant representations in order to achieve categorization that appears invariant across viewing conditions and invariant across instances of a category. Careful experimentation revealed that object categorization can be systematically affected by the particular

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Role of Abstraction in Categorization 397

viewpoints and category instances that have been experienced (see Palmeri & Gauthier, 2004; Palmeri & Tarr, 2008). While there are conditions under which humans readily recognize and categorize objects irrespective of viewpoint (Biederman & Gerhardstein, 1993; Tarr & Bülthoff, 1998), numerous studies have found that if observers learn to recognize novel objects from specific viewpoints, they are both faster and more accurate at recognizing these same objects from familiar viewpoints relative to unfamiliar viewpoints (Bülthoff & Edelman, 1992; Tarr & Bülthoff, 1995; Tarr & Pinker, 1989). Even the recognition of single geons, originally proposed to support view-invariant performance with more complex objects, is sensitive to changes in viewpoint (Tarr, Williams, Hayward, & Gauthier, 1998). Instead, human object recognition was proposed to rely on multiple views, where each view encodes the appearance of an object under specific viewing conditions, including viewpoint, pose, configuration, and lighting (Tarr, 1995; Tarr, Kersten, & Bülthoff, 1998) and a collection of such views constitutes the enduring visual representation of a given object. These ideas are instantiated in image-based models of object recognition (e.g., Edelman, 1997, 1999; Poggio & Edelman, 1990). Rather than assume that the goal of vision is to reconstruct the three-dimensional world, image-based models stress the importance of generalizing from past experiences to the present experience (Shepard, 1987, 1994). This is done by remembering past views of objects and generalizing based on similarity to those stored views. Such models account well for patterns of interpolation and extrapolation to new views. Furthermore, since physically similar objects in the world viewed under similar conditions will be similar to the same set of stored views, generalization to new objects can occur without any explicit representation of three-dimensional shape. For purposes of object recognition and categorization, representation of three-dimensional shape may not be necessary. Instead, such information may be stored in parts of the brain involved in acting on objects (Goodale & Milner, 1992). Similar computational principles are also at work in exemplar-based models of object categorization. The core principle of these models is that object categories are mentally represented in terms of the specific category exemplars that have been previously experienced (Kruschke, 1992; Medin & Schaffer, 1978; Nosofsky, 1986). Categorization is based on the relative similarity of an object to these stored exemplars. In that sense, you judge that a certain object is a cell phone because of its similarity to many other cell phones in memory. While no abstraction occurs, exemplar models can readily account for a range of prototypicality effects that might at first blush appear to demonstrate abstract prototype representations for categories (Busemeyer, Dewey, & Medin, 1984; Hintzman, 1986;

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Figure 20.2 Top: Exemplar-based models of categorization assume that object categories are represented by storing category exemplars that were previously experienced. Middle: Prototype models assume category knowledge is based on a stored prototype abstracted from the experienced category examples. Bottom: Rule-based models represent category knowledge with logical rules along individual features. Note: (Top) Exemplars are represented as points in multidimensional psychological space, with similarity a function of distance in that space, and the generalization gradient around an object indicated by the graded shading around each exemplar. The exemplars on the left (darker circles) represent one category and the exemplars on the right (lighter circles) represent a different category. A probed object (question mark) is categorized based on the relative similarity to stored exemplars in each category. (Middle) A probed object (question mark) is categorized based on the relative similarity to the different category prototypes. (Bottom) These rules partition psychological space into different regions. A probed object (question mark) is categorized according to what region it lies within relative to the category rule.

Nosofsky, 1988; Shin & Nosofsky, 1992; see Figures 20.2 and 20.3). These models also account for a range of category exemplar effects (Nosofsky, Kruschke, & McKinley, 1992) and the time course of categorization (Lamberts, 2000; Nosofsky & Palmeri, 1997; Palmeri, 1997). Neurophysiological evidence supports many important assumptions underlying a host of image-based and exemplarbased models of object categorization (refer to Figure 20.4

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(A)

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Figure 20.3 Exemplar-based models can account for two important phenomena that on the surface seem to challenge exemplar-based models. Note: A: The top panel illustrates prototypicality effects. Category prototypes are usually categorized as well as and sometimes better than category exemplars, even if the category prototypes have never been seen before (far right graph). This result is typically viewed as strong evidence for prototype abstraction. How else could an object that’s never been seen before be classified as well as objects that have been trained on, unless that unseen prototype is in fact abstracted during learning and stored just like an experienced exemplar? But assuming that categorization is based on similarity to stored exemplars only, this prototypicality effect falls out quite naturally. The left and middle figures illustrate how category exemplars and the category prototype might be represented in a psychological space, with the prototype in the middle, the exemplars around the prototype, and distance between objects related to their psychological similarity; the cloud around each point represents the generalization gradients around each stored exemplar. As shown in the left figure, the prototype to be classified (indicated by ?) is similar to many exemplars, yielding a lot of evidence in favor of category membership. By contrast, as shown in the middle figure, an individual category exemplar to be classified (indicated by ?) may only be similar to a subset of exemplars, yielding smaller

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Accuracy (%)

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100 75 50 25 0 Control Amnesic

category evidence compared to that for an unseen prototype. B: The bottom panel illustrates dissociations between categorization and recognition memory. As discussed in the text, whereas amnesic individuals and controls show similar performance on categorization, amnesic individuals are significantly impaired at recognition memory (far right graphs). This behavior dissociation suggests a functional dissociation between categorization and recognition. As in the top panel, individual exemplars are represented as points in a psychological space with the clouds around each point representing the generalization gradients. Following Nosofsky and Zaki (1998) we assume here that amnesic individuals have far poorer exemplar memories than controls, as indicated by the far more diffuse generalization gradients because of impaired memory for amnesic individuals. For categorization, all of the category exemplars are crowded together in the same general region of psychological space. Having finely tuned or diffuse exemplar memories has little impact on categorization because all of the category members are in the same part of the psychological space. However, having finely tuned or diffusion exemplar memories does have significant impact on recognition because the space of old and new patterns is distributed uniformly throughout psychological space; having more diffuse memories makes it far more difficult to discriminate between old objects than have been seen and stored, albeit poorly, from new objects.

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Role of Abstraction in Categorization 399 Response Units Premotor/motor cortex

Decision Units PFC

Category representations

Rule-Based Units AC, PFC

Top-Down Control

Exemplar Units AIT, BG, Hipp.

Viewpoint Units AIT Object representations Object Feature Units V4/LOC/IT

Edge extraction Low-level visual areas V1, V2 Shape/form processing

Figure 20.4 Stages of processing in object recognition and categorization according to a range of models. Note: Low-level features such as edges are processed in early visual areas. Object representations are created by processing object feature units in the V4, lateral occipital cortex (LOC), and/or inferotemporal cortex (IT) and by processing viewpoint in anterior inferotemporal cortex (AIT). Category representations arise from rule-based units in the anterior cingulate (AC) and prefrontal cortex (PFC), or from exemplar units in the anterior inferotemporal cortex (AIT), the basal ganglia (BG), and the hippocampus (Hipp). Information from the category representations is passed to decision units in PFC, which determines category membership and initiates the selection of the appropriate category response in the premotor cortex for execution by the motor cortex. Low-level processing, object representations, and category representations can all be modulated by factors such as attention and various task demands via topdown control.

for brain areas discussed in this section). The responses of inferotemporal (IT) neurons to objects depends on stimulus size and viewpoint (Perrett, Oram, & Ashbridge, 1998; K. Tanaka, 1996). Even accepted notions of retinal position invariance in IT (Tovee, Rolls, & Azzopardi, 1994) have been challenged (DiCarlo & Maunsell, 2003; Op de Beeck & Vogels, 2000). Surprisingly few neural responses in IT are invariant to position, size, or viewpoint (DiCarlo & Maunsell,

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2003; Logothetis & Sheinberg, 1996; but also see Booth & Rolls, 1998). When trained on particular object views, monkeys recognize novel object views according to their similarity to experienced views, and neurons respond in a similarly graded fashion to particular trained views (Logothetis & Pauls, 1995; Logothetis, Pauls, Bülthoff, & Poggio, 1994; Logothetis, Pauls, & Poggio, 1995). Perrett et al. (1998) provided one suggestion for how object recognition could take the form of an accumulation of evidence across all neurons selective for aspects of a given object. By assuming a neural variant of stochastic accumulation of evidence models (Nosofsky & Palmeri, 1997; P. Smith & Ratcliff, 2004) and by assuming that the rate of accumulation depends on the similarity between visible features in the presented viewpoint and those to which individual neurons are tuned, systematic effects of object recognition time and accuracy with changes in viewpoint can be well accounted for. When monkeys are trained to categorize objects, their behavior is consistent with exemplar generalization and not with the abstraction of a prototype (Sigala, Gabbiani, & Logothetis, 2002). IT neurons will respond selectively to specific exemplars that have been studied, not to an average category prototype that was never studied (Freedman, Riesenhuber, Poggio, & Miller, 2003; Op de Beeck, Wagemans, & Vogels, 2001, 2008; Vogels, Biederman, Bar, & Lorincz, 2001). Furthermore, many exemplar models of object categorization assume that similarity between objects is heavily influenced by matches or mismatches along dimensions that are diagnostic of category membership (Gauthier & Palmeri, 2002; Kruschke, 1992; Lamberts, 2000; Nosofsky, 1984, 1986), and neural responses are modulated by dimensional diagnosticity in a similar manner (Sigala & Logothetis, 2002). While responses of IT neurons can be specific to particular exemplars that have been experienced, IT neurons do not seem to respond in a category-specific manner. Instead, category-specific, but not exemplar-specific, neural responses are observed in the prefrontal cortex (Freedman et al., 2003; Jiang et al., 2007; Rotshtein, Henson, Treves, Driver, & Dolan, 2005). These neurophysiological results may seem at odds with the apparent category specificity observed using functional magnetic resonance imaging (fMRI) and in the patterns of deficits in category-specific agnosia due to focal brain injury, which we discuss later in this chapter. One way to reconcile these results is to first consider the vast differences in spatial resolution between single-unit recordings and fMRI or brain lesions. Although individual neurons may respond in a way that highlights exemplar-specific (not category-specific) information, neighboring regions of the cortex may respond to similar objects or objects that are processed in a similar fashion. So objects in the same category may recruit the same area

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of the cortex as measured by fMRI or may be impaired in a category-specific fashion by brain injury, yet the underlying neural activity may respect exemplar-specific and view-specific coding, not category-specific coding per se. Some recent neurally plausible computational models have instantiated this division of labor between learned object representations in IT and learned category representations elsewhere in the brain. For example, the theoretical work of Riesenhuber and Poggio (2000) represents a recent instantiation of a tradition of image-based models of object recognition (Edelman, 1997; Poggio & Edelman, 1990). This model builds on classical models where complex cells are built from simple cells in early visual areas, extending this hierarchy of processing throughout the higher-level visual cortex to view-tuned and exemplartuned units. At each level of the hierarchy, these units have Gaussian-shaped receptive fields (radial-basis functions) that respond preferentially to a particular stimulus property, whether that be edges or junctions at the lowest level, or views or exemplars at the highest level. Categoryspecific units that can represent knowledge of the basiclevel category of an object or the subordinate-level identity of an object are thought to reside in the prefrontal cortex. Other computational models have proposed a similar division of labor between exemplar-like object representations in IT and category representations elsewhere, implicating brain structures such as the basal ganglia as well as the prefrontal cortex in mapping object-specific representations to category-specific representations (Ashby, Ennis, & Spiering, 2007; but see Love & Gureckis, 2007). The hierarchical object representations instantiated in such models make us reflect on one key difference between classic structural description and image-based theories: Under the cartoon view of the world, structural descriptions represent objects in terms of viewpoint-independent threedimensional parts and their spatial relations (Biederman, 1987), and views represent objects in terms of holistic images of the entire object (Edelman, 1997). However, intuition and empirical evidence (e.g., Garner, 1974; Stankiewicz, 2002; Tversky, 1977) suggest that we often represent complex objects in a compositional manner—objects are decomposable into parts. In addition, most exemplar-based and related models of object categorization assume that objects have parts, features, or dimensions that can be selectively attended according to how diagnostic they are for categorization decisions. Is there a way to marry the best qualities of image-based theories with the compositional representations seen in structural-description theories? Some studies attempt to uncover image features that are most informative for classification, based on the mutual information (or mutual dependence) of features and specified categories (Schyns & Rodet, 1997; Ullman, Vidal-Naquet,

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& Sali, 2002). Some of this work has found that features of “intermediate complexity” are best for basic-level classification (see Figure 20.1). For faces, what features emerge from this analysis are those we would generally call the “parts of a face” such as the eyes or the nose, even though the features are not selected a priori to correspond to meaningful parts per se; and for cars, parts such as the wheels or the driver ’s side window emerge. In this context, we mean “emerge” in the sense that these features are uncovered by a computational analysis of hundreds of images as they relate to categories of objects without any kind of intervention from a human teacher. It is tempting to speculate about the relationship between such “ad hoc” image-based features to the observed feature selectivity of neurons in IT (K. Tanaka, 1996, 2003). The best responses for individual IT neurons are elicited by somewhat odd patterns that do not correspond to what we might typically think of as distinct object parts. These appear to be ad hoc. And they appear to be of intermediate complexity. So representations of object parts, as well as objects themselves, seem to be tuned by specific experience with objects in the world; object parts are not general-purpose parts such as those instantiated in models like recognition-by-components.

MEMORY AND LEARNING SYSTEMS THAT SUPPORT CATEGORIZATION The role of abstraction in categorization defined much of the early research and debates about categorization (Murphy, 2002). Initial accounts assumed that categories are represented by abstracting logical rules (Figure 20.2) that define the necessary and sufficient conditions for category membership (Bourne, 1970; Bruner et al., 1956; Levine, 1975; Trabasso & Bower, 1968). While rule-based accounts described well how people learned categories defined by explicit rules, natural categories were found to have a graded structure that suggested instead notions like “family resemblance” and “similarity” as core constructs (Barsalou, 1985; Rosch, 1973; Wittgenstein, 1953). It is easier to categorize a robin as a bird than a penguin as a bird, the argument goes, because a robin is more similar to the prototypical bird (Rosch & Mervis, 1975). Such results suggested that prototypes (Figure 20.2), not rules, define natural categories and that prototypes are learned by abstracting core properties of the category from experience with category members (Homa, Cross, Cornell, Goldman, & Schwartz, 1973; Posner & Keele, 1968; J. D. Smith & Minda, 1998). But as discussed earlier, later work showed that models assuming specific exemplar representations (Figure 20.2), instead of abstract prototype representations, can account well for prototype effects,

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a whole host of other behavioral effects, and are consistent with a significant amount of the neurophysiological data (Figure 20.3). Arguably, most successful models of categorization have an exemplar model as a critical component (Erickson & Kruschke, 1998; Palmeri, 1997) or fall on a continuum between prototype abstraction models and pure exemplar models (Ashby & Waldron, 1999; Love, Medin, & Gureckis, 2004; Rosseel, 2002). Much of this early work was grounded in an assumption—a perfectly reasonable parsimonious assumption—that all kinds of categories are represented the same way at all stages of learning. Categories are represented by rules or prototypes or exemplars. More recent work has instead asked whether different kinds of category representations are used for learning different kinds of categories, under different kinds of conditions, and at different stages of learning. Some kinds of categories can be learned using rules, but others cannot. Perhaps people try to use rules when they first learn a category, but make use of other less explicit kinds of category knowledge with experience. The burgeoning interest in cognitive neuroscience over the past decade has led researchers quite naturally to ask how categories are represented in the brain. If categories can be represented in different ways at different points in learning under different conditions, it is likely that there are multiple memory and learning systems in the brain that support categorization. We should note that in this context we use the term system in the broadest possible sense: A system could reflect functionally independent kinds of representations and processes, or interacting systems, or different critical subcomponents of a single processing architecture (e.g., Palmeri & Flanery, 2002; Roediger, Buckner, & McDermott, 1999). Categorization and Rules Despite the success of exemplar models of categorization, there have always been some lingering concerns about the processing and storage requirements that come with theories that demand individual memory traces of each and every experience with an object (e.g., Logan, 1988). One response to this criticism has been to view pure exemplar models as a sort of theoretical ideal point, whereas in reality categories may be represented by a subset of the space of experienced exemplars that produces a sufficient level of performance (e.g., Ashby & Waldron, 1999; Kruschke, 1992; Rosseel, 2002). But an alternative response has been to reconsider whether people might use simple rules to categorize objects. What possessed researchers to reconsider an idea that was largely abandoned decades earlier? To begin with, many subjects asked to learn novel categories will say they are forming rules, even if the rules they verbalize

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do not account all that well for their own categorization behavior. In addition, it is clear that novices are often taught categories using rules. For example, field guides for identifying birds, butterflies, or mushrooms certainly include many pictures but they also include lists of critical features for distinguishing different species. In the case of mushrooms, these explicit rules can be particularly important because edible and poisonous mushrooms often look quite similar. One important factor driving this theoretical shift was the finding that when subjects were told to use a particular categorization rule, exemplar-based models could not account for the observed categorization behavior (e.g., Nosofsky, Clark, & Shin, 1989). The RULEX model (Nosofsky, Palmeri, & McKinley, 1994) posits that even when people are not given a rule or are not told to create a rule they form simple rules anyway when learning a category. What distinguishes RULEX from earlier rule-based models is that it is a rule-plus-exception model, hence the name RULEX: People form simple rules that may work pretty well and then store in memory any exceptions to those rules (see also Nosofsky & Palmeri, 1998; Palmeri & Nosofsky, 1995; Sakamoto & Love, 2004). RULEX accounts extremely well for a wide array of phenomena that are also consistent with prototype and exemplar models; and under some conditions individual subject behavior is more consistent with RULEX than exemplar or prototype models (Johansen & Palmeri, 2002; Nosofsky et al., 1994). RULEX was perhaps the first of a class of hybrid categorization models combining rules with other nonanalytic forms of category representations (Ashby, AlfonsoReese, Turken, & Waldron, 1998; Erickson & Kruschke, 1998; Goodman, Tenebaum, Feldman, & Griffiths, 2008; Nosofsky & Palmeri, 1998; Palmeri, 1997). The success of a model like RULEX provides just one illustration of how difficult it can be to distinguish abstract rule-based from exemplar-based (or more generally similarity-based) models of categorization (see also Johansen & Palmeri, 2002; Nosofsky & Johansen, 2000). What are arguably polar extremes of the representational continuum can produce remarkably similar behavioral predictions. Researchers have more recently looked to cognitive neuroscience data for evidence for a rule-based mode of categorization. Motivated by hypotheses about the underlying neural systems supporting different kinds of categorization, Ashby, Maddox, and colleagues have conducted a series of behavioral experiments that attempt to selectively influence rule-based versus similarity-based categorization. For example, introducing certain kinds of secondary distractor tasks during category learning can selectively interfere with rule-based but not similarity-based categorization (Waldron & Ashby, 2001, but see Nosofsky & Kruschke, 2001), whereas delaying corrective feedback

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can selectively interfere with similarity-based but not rulebased categorization (Maddox, Ashby, & Bohil, 2003). Neuropsychological evidence also suggests a role for rule-based categorization and provides clues as to the specific brain structures involved. For example, patients with prefrontal cortex lesions are impaired at the Wisconsin Card Sorting Test (WCST), a task that requires sorting cards according to logically defined rules (Milner, 1963; Robinson, Heaton, Lehan, & Stilson, 1980). Parkinson’s disease patients also seem to show selective impairment in rule-based but not similarity-based categorization (Ashby, Noble, Filoteo, Waldron, & Ell, 2003; Brown & Stubbs, 1988; Cools, van den Bercken, van Spaendonck, & Berger, 1984; Downes et al., 1989). Parkinson’s disease has been linked to basal ganglia damage, specifically in the head of the caudate nucleus, which has reciprocal connections to the prefrontal cortex. Additional evidence for a rulebased system comes from neuroimaging data in healthy adults. One early study contrasted similarity-based versus rule-based categorization strategies (Allen & Brooks, 1991) that seemed to recruit different networks of brain areas as revealed by PET (E. E. Smith, Patalano, & Jonides, 1998). fMRI during rule-based categorization reveals activation in the right dorsal-lateral prefrontal cortex (Konishi et al., 1998; Seger & Cincotta, 2005) and the head of the right caudate nucleus (Konishi et al., 1998; see also Lombardi et al., 1999; Monchi, Petrides, Petre, Worsley, & Dagher, 2001; Seger & Cincotta, 2005). A variety of computational cognitive neuroscience models have implicated an interactive role for the prefrontal cortex and the basal ganglia (specifically the caudate nucleus of the striatum) in important aspects of various cognitive tasks (Ashby et al., 1998; Frank & Claus, 2006; Houk & Wise, 1995), but these models differ in important details regarding whether the basal ganglia is the core locus of learning or plays a more modulatory role. Overall, the converging results from behavioral, neuropsychological, neuroimaging, and computational studies suggest the existence of a network of brain areas, including the prefrontal cortex and the caudate, that are critically involved in rule-based categorization (Ashby & O’Brien, 2005). Categorization as a Skill While some categorizations require explicit rules—and sometimes complex rules at that—other categorizations are made quickly and effortlessly, and perhaps without conscious intention. Such categorization has a qualitatively different flavor from rule use and can be considered something more like a habit or a skill that can be executed automatically. Palmeri (1997) explored how categorizations as skills can become automatized through an elaboration of Logan’s

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(1988) instance theory of automaticity. Instance theory is a general theory of automaticity of cognitive skills that posits a shift from more algorithmic or rule-based processing early in learning to memory retrieval of specific experienced instances later in learning (for some fMRI evidence consistent with instance theory, see Dobbins, Schnyer, Verfaellie, & Schacter, 2004; see also Logan, 1990, 2002; Palmeri, Wong, & Gauthier, 2004). Palmeri (1997) conceptualized the development of automaticity as a race between a rule-based categorization process and an exemplar-based categorization process (Nosofsky & Palmeri, 1997). Early in learning, rules are executed faster than category exemplars can be retrieved. But as more and more exemplars are experienced and are stored as part of the category representation, the exemplar-based categorization process eventually wins the race. Categorization is automatic when it’s based on exemplar retrieval instead of rule use. Ashby et al. (2007) proposed a computational cognitive neuroscience model called Subcortical Pathways Enable Expertise Development (SPEED) that shares some important computational principles with instance theory and exemplar-based models of categorization (Nosofsky & Palmeri, 1997; Palmeri, 1997). Like exemplar models, SPEED is a member of a family of computational theories called “nonparametric classifiers” (Ashby & AlfonsoReese, 1995). These models are nonparametric in the sense of a contrast with so-called “parametric classifiers” like prototype theories that assume a specific (often normal) distribution of category members (Ashby, 1992). But SPEED specifically assumes a shift from category representations mediated by cortico-striatal loops to category representations mediated by direct cortico-cortico connections. Cortico-striatal loops appear to play an important role in category learning (Ashby et al., 1998), even if more permanent long-term category knowledge may ultimately rely on direct cortical representations. Significant evidence suggests an important role for the basal ganglia, specifically the striatum, in categorization— at least for certain kinds of categorization and at certain points in learning (Shohamy, Myers, Kalanithi, & Gluck, 2008). Huntington’s disease (HD) and Parkinson’s disease (PD) are characterized by damage to the basal ganglia (for HD there is direct damage to the striatum whereas for PD there is damage to the substantia nigra that interacts critically with the striatum). HD and PD are classically characterized by their severe motor impairments, but it has long been known that these diseases also more generally impair motor skill learning and other procedural learning tasks (e.g., Mishkin, Malamut, & Bachevalier, 1984; Saint-Cyr, Taylor, & Lang, 1988). HD and PD also impair certain kinds of category learning as well, such as those involving a probabilistic association of cues to categories

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(e.g., Knowlton, Mangels, & Squire, 1996; Knowlton, Squire, et al., 1996) and those involving an integration of information across multiple stimulus dimensions (e.g., Ashby et al., 2003; Filoteo, Maddox, & Davis, 2001; Maddox, Aparicio, Marchant, & Ivry, 2005; Maddox & Filoteo, 2001). These patterns of deficits in HD and PD implicate an important role of the striatum in novel category learning (Ashby & O’Brien, 2005). In addition to such neuropychological studies, a body of fMRI data also implicates the basal ganglia, specifically the striatum, in these kinds of novel category learning tasks (Poldrack et al., 2001; Poldack, Prabhakaran, Seger, & Gabrieli, 1999; Poldrack & Rodriguez, 2004; Seger & Cincotta, 2005). Categorization and Episodic Memory Having an episodic memory allows us to recognize when we have seen particular objects in particular situations. For example, in order to recognize that you have previously seen a yawning, orange cat sitting on a green bench in a grassy park, you must be able to access a coherent memory trace that includes all of the characteristics of this scene. The relationship, both computational and neuroanatomical, between the memories used to support explicit recognition of objects and the representations used to support object categorization has been vigorously debated. On the one hand, exemplar-based models propose that the same exemplar memories used to support categorization are used to support explicit recognition as well (e.g., Nosofsky, 1991, 1992). On the other hand, some have argued that while exemplar memories may be used to support some relatively ad hoc categories (Ashby & O’Brien, 2005), they play little or no role in most kinds of categorization (e.g., Ashby et al., 2007). The primary source of evidence against any close relationship between episodic memory and categorization and their underlying neural underpinnings comes from studies testing individuals with anterograde amnesia, a condition characterized by profound explicit memory deficits caused by damage to the hippocampus and neighboring medial temporal brain areas. Specifically, Knowlton and Squire (1993; Squire & Knowlton, 1995; see also Reed, Squire, Patalano, E. E. Smith, & Jonides, 1999) observed a behavioral dissociation between recognition and categorization, whereby individuals with anterograde amnesia who are significantly impaired at explicit recognition memory perform normally at categorization. According to Knowlton and Squire, this behavioral dissociation between categorization and recognition provided a direct falsification of exemplar-based models. But dissociations, and even double dissociations, are only weak evidence in favor of modular theories (Plaut, 1995; Shallice, 1988). A direct instantiation of an exemplar

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model, whereby simulated individuals with amnesia have significantly degraded exemplar memories compared to simulated controls, predicts the very dissociation Knowlton and Squire claimed as a falsification of exemplar models (Nosofsky & Zaki, 1998; Palmeri & Flanery, 2002). Other research supporting a functional dissociation between categorization and recognition (Filoteo et al., 2001; Reed et al., 1999; J. D. Smith & Minda, 2001) suffers from a variety of theoretical, statistical, and methodological problems (Kinder & Shanks, 2001; Palmeri & Flanery, 1999, 2002; Zaki, 2005; Zaki & Nosofsky, 2001, 2004). Moreover, there is research showing that individuals with explicit memory deficits show impairments in categorization as well (Graham et al., 2006; Hopkins, Myers, Shohamy, Grossman, & Gluck, 2004; Zaki, Nosofsky, Ramercad, & Unverzagt, 2003; see also Meeter, Myers, Shohamy, Hopkins, & Gluck, 2006). The most widely studied cases of anterograde amnesia are caused by damage to the hippocampus and associated medial temporal lobe (MTL) structures (e.g., Squire, 2004). So debates about the relationship between categorization and episodic memory engender debates about the role of the hippocampus in categorization. According to some multiple memory systems theories, explicit episodic memory is supported by the hippocampus whereas categorization involves implicit procedural memory that is supported by the basal ganglia and cortex (Squire & Zola, 1996). Some computational cognitive neuroscience models eschew entirely any role for the hippocampus in categorization (e.g., Ashby et al., 1998, 2007; Ashby & O’Brien, 2005) or do not discuss whether the hippocampus has any role (e.g., Riesenhuber & Poggio, 1999, 2002). But evidence is building for a role of the hippocampus in categorization. As discussed previously, hippocampal damage in individuals with anterograde amnesia does lead to significant categorization deficits. These results mirror other neuropsychological findings that suggest the hippocampus is involved in purportedly implicit forms of memory (e.g., Chun & Phelps, 1999). In addition, functional brain imaging provides evidence that the hippocampus is recruited during categorization. Reber, Gitelman, Parrish, and Mesulam (2003) found greater MTL activation when healthy adults learned categories intentionally compared to when they learn them implicitly. Poldrack et al. (1999, 2001; see also Foerde, Knowlton, & Poldrack, 2006; Seger & Cincotta, 2005) observed a trade off between hippocampal and basal ganglia activation during novel category learning, suggesting an interaction in the computations performed by these two important neural systems (Poldrack & Rodriguez, 2004). The recruitment of the hippocampus appears to be more pronounced early in learning novel categories. One hypothesis is that the MTL helps set up the representations

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of novel stimuli that are then used by other brain areas (such as the basal ganglia or prefrontal cortex) to assign those stimuli to categories. This role as a novel representational engine has been proposed in computational models (e.g., Gluck & Myers, 1993; Meeter, Myers, & Gluck, 2005). Specifically, SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network; Love et al., 2004) is a cognitive model of categorization that shares properties with various exemplar, prototype, and rule-based models, and has accounted for an array of fundamental categorization phenomena. More recently, the computational mechanisms within SUSTAIN have been grounded in a network of brain areas, with the hippocampus playing a critical role in encoding novel stimuli that cannot be accommodated by the current category representations (Love & Gureckis, 2007). Instead of linking specific brain areas with particular kinds of cognitive tasks, whether episodic memory or categorization or priming, it seems more fruitful to consider the computations performed by those brain areas in the service of complex tasks (Palmeri & Flanery, 2002; Turke-Browne, Yi, & Chun, 2006).

CATEGORY-SPECIFIC SYSTEMS FOR CATEGORIZATION Some arguments for multiple systems for categorization are based on structural aspects of the categories to be learned (e.g., whether they permit single rules or not), aspects of the task (e.g., the timing and quality of feedback), and the amount of learning. In the following section, we introduce work from a different tradition that studies the organization of the neural substrates responsible for the perception of different object categories in the brain. In this work, claims of multiple categorization systems have also been made. Specifically, that some categories are special in that they engage specialized brain areas. Specialized systems dedicated to perception of specialized categories have been claimed for faces (Kanwisher, McDermott, & Chun, 1996, 1997), places (Epstein, Harris, Stanley, & Kanwisher, 1999), body parts (Downing, Jiang, Shuman, Kanwisher, 2001), words (Cohen et al., 2000; Nobre, Allison, & McCarthy, 1994), letterstrings (Polk et al., 2002), and even single letters (K. H. James, James, Jobard, Wong, & Gauthier, 2005). We provide an overview of the evidence that has led researchers to postulate category-specific perceptual systems and then discuss some alternative interpretations of these results. To the extent that categorization studies are performed with visual stimuli such as faces (e.g., Goldstone & Styvers, 2001) or novel items that may be animal-like (Allen & Brooks, 1991; Reed et al., 1999) or not (Knowlton & Squire, 1993; Posner & Keele, 1968),

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understanding the systems involved in their perception may be crucial. We often use face processing as the main example domain in what follows because it has been studied the most extensively. Studies of patients with brain damage resulting in deficits in the visual recognition of objects suggest that the visual system, at least on a fairly coarse scale, may be organized around categories. While most cases of brain damage to the visual cortex result in deficits with virtually any category tested, in relatively rare cases, category-specific deficits are observed. These patients have difficulty identifying visually presented objects from certain categories, despite good basic visual skills. For example, when shown a picture of a banana, a patient may be unable to say “banana” or retrieve semantic information about bananas, but they may be able to describe its shape and identify that the object is yellow. Category-specific agnosias have been found for biological objects (e.g., Hillis & Caramazza, 1991; McCarthy & Warrington, 1988; Warrington & Shallice, 1984) artifacts (e.g., Hillis & Caramazza, 1991; Warrington & McCarthy, 1983, 1987), faces (e.g., Farah, 1996; Farah, Levinson, & Klein, 1995; Henke, Schweinberger, Grigo, Klos, & Sommer, 1998), and words (e.g., Warrington & Shallice, 1980). One patient presented with deficits in recognizing any object or word, except for extremely well-preserved face recognition skills (Moscovitch, Winocur, & Behrmann, 1997). At the other end of the spatial scale, neurophysiology in the monkey reveals selectivity of single cells for particular objects, such as faces, in several regions of the temporal lobe (e.g., Baylis & Rolls, 1987; Desimone, Albright, Gross, & Bruce, 1984; Gross, Bender, & Rocha-Miranda, 1969) and elsewhere in the brain such as the amygdala (e.g., Rolls, 1992) and the frontal cortex (e.g., Wilson, Scalaidhe, & GoldmanRakic, 1993) although the cells selective for any category are only a fraction, typically about 20%, of the population of neurons recorded from. Recent work, however, suggests that when using single cell recording within the face-selective patches localized with fMRI in the monkey brain, virtually all neurons are selective for faces (Tsao, Freiwald, Tootell, & Livingstone, 2006). Thus, neuropsychology and neurophysiology together suggest categoryselective responses that are distributed over the ventral cortex, with at least some categories showing a high degree of spatial clustering. Much of our knowledge about the organization of the visual recognition system in the human brain comes from much less invasive work using brain imaging in normal subjects. For instance, scalp recordings reveal faceselective (e.g., Bentin, Allison, Puce, Perez, & McCarthy, 1996; Rossion et al., 2000) and letter-selective (e.g., Wong, Gauthier, Woroch, DeBuse, & Curran, 2005) potentials that peak about 170 ms after the presentation of the image.

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Category-Specific Systems for Categorization

But the evidence that has perhaps received the most attention comes from studies using fMRI, a technique with better spatial resolution than event-related potentials (ERP), and which reveals brain regions selectively engaged by faces (Gauthier, Tarr, Moylan, Anderson, & Gore, 2000; fusiform gyri, lateral occipital gyri, superior temporal sulcus; e.g., Kanwisher et al., 1997; Puce, Allison, Bentin, Gore, & McCarthy, 1998; see also Sergent, Ohta, MacDonald, & Zuck, 1994), animals (lateral fusiform, e.g., Chao, Haxby, & Martin, 1999; Martin, Wiggs, Ungerleider, & Haxby, 1996), tools (left premotor area, medial fusiform gyrus; e.g., Chao et al., 1999; Martin et al., 1996), words, letter strings, and single letters (left fusiform, left occipito-temporal junction, e.g., Cohen et al., 2000; Flowers et al., 2004; K. H. James et al., 2005; Polk et al., 2002; Puce, Allison, Asgari, Gore, & McCarthy, 1996). Categories that are even more rarely selectively impaired in brain damage also reveal similar specialization. For instance, a “place area” was discovered in the parahippocampal gyrus that responds strongly to scenes, buildings, and other spatial landmarks (Aguirre, Zarahn, & D’Esposito, 1998; Epstein et al., 1999; Epstein & Kanwisher, 1998). Regions of the lateral occipitotemporal cortex (Downing et al., 2001) and fusiform gyrus (Peelen, Wiggett, & Downing, 2006) were found to selectively respond to body parts and areas of the superior temporal sulcus respond selectively to biological motion (Grossman & Blake, 2002). The typical locus for some of these areas is shown in Figure 20.5. There are several possible explanations for the apparent category specialization in the brain. One option is to take the compartmentalization observed in fMRI maps at face value and conclude that there may be separate modules responsible for processing different object categories. In this context, modularity does not simply refer to an anatomically distinct neural area, but instead invokes a Fodorian (Fodor, 1983) sense of modules as specialized, encapsulated mental subsystems that handle specific information—they are domain-specific entities that function independently of one another and of background beliefs of the subject. Modular claims are found throughout psychology and cognitive neuroscience and it is rare that they do not lead to heated debates. We briefly summarize some of the evidence that has led researchers to question the idea that category specialization in the ventral visual system represents modular organization. Modular Accounts Modular accounts of category specialization often suggest that evolutionary pressures caused the creation of specific modules for processing categories that are relevant to survival, like animals, plants, and conspecifics, more quickly

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Figure 20.5 Typical location of some category-selective peak activations shown on a ventral view of brain. Note: An individual brain was segmented and then inflated so as to make the sulci (dark grey) as well as the gyri (light grey) visible. 1 ⫽ Right fusiform face area (Gauthier, Skudlarski, et al., 2000); 2 ⫽ Left fusiform face area (Gauthier, Skudlarski, et al., 2000); 3 ⫽ Right occipital face area (Gauthier, Skudlarski, et al., 2000); 4 ⫽ Visual word form area (K. H. James et al., 2005); 5 ⫽ Single letters (K. H. James et al., 2005); 6 ⫽ Letterstrings (K. H. James et al., 2005); 7, 8, and 9 ⫽ Animals (Chao et al., 1999); 10⫽tools (Chao et al., 1999); 11 ⫽ Left parahippocampal place area (Epstein et al., 1999); 12 ⫽ Right parahippocampal area (Epstein et al., 1999); 13 ⫽ Fusiform body area (Peelen et al., 2006); 14 ⫽ Left extrastriate body area (Peelen et al., 2006); 15 ⫽ Right extrastriate body area (Peelen et al., 2006); 16 ⫽ Left biological motion area (Grossman & Blake, 2002); 17 ⫽ Right biological motion area (Grossman & Blake, 2002).

(Caramazza & Shelton, 1998): Is that animal a potential predator, a potential food source, or a potential mate? Is this plant poisonous, edible, or medicinal? Similarly, if you are walking alone at night, recognizing the face of the person coming toward you as either a friend or an enemy is a decision you would want to make rapidly and accurately. A specialized processing module for important categories of objects would confer survival advantage. It is clear, however, that for some domains of apparent modularity, such as reading, it begs reason to suggest that such specialization would be innate. Therefore, modules, if they exist, can be either innate or learned. Generally, modular accounts do not predict that there is a module in the brain for every object category we interact with. Instead, a few categories are thought to have a special status either because of evolutionary pressures or experience. For instance, there is a double dissociation between living and nonliving things, with some patients showing an impairment for living but not nonliving things (e.g., Farah, McMullen, & Meyer, 1991; Hillis & Caramazza, 1991; McCarthy & Warrington, 1988; Sheridan & Humphreys,

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1993) and other patients show the opposite deficit (e.g., Hillis & Caramazza, 1991; Sacchett & Humphreys, 1992; Warrington & McCarthy, 1983, 1987). However, the cases of deficits recognizing living things far outnumber the reported cases of deficits recognizing nonliving things. This suggests that it is the processing of living things that is specialized, or at least more localized (Caramazza & Shelton, 1998). A similar double dissociation has been observed with faces and objects, where patients with either acquired or congenital deficits with a condition known as prosopagnosia are impaired at recognizing faces, although recognition of other objects is relatively unimpaired (e.g., Duchaine, 2000; Farah, 1996; Farah et al., 1995). In very rare cases, when object recognition is impaired, face recognition can be spared (Moscovitch et al., 1997; Rumiati & Humphreys, 1997). Though rare, the existence of patients who show a selective impairment in a domain that is more frequently preserved is crucial to the modularity argument: Their existence refutes the idea that one domain (e.g., face perception) may simply be more difficult than another domain (e.g., object perception). Distributed Representations Modular explanations of the mind and the brain capture the imagination and capture the attention of the press. The apparent discovery of brain modules responsible for recognizing body parts (Downing et al., 2001), intelligence (Duncan et al., 2000), and moral reasoning (Greene, Sommerville, Nystrom, Darley, & Cohen, 2001) are covered by the press in much the same way as the discovery of a new dinosaur skeleton, a new planet, or a new bird species. Yet, neuropsychologists have long recognized significant challenges for inferring modularity from patterns of behavioral deficits caused by brain damage: Deficits result from large lesions that vary considerably between patients and the behavioral dissociations are rarely all that “clean.” For example, in the case of the living/nonliving dissociation, the majority of patients present deficits that cross the living/nonliving boundaries (Bukach, Bub, Masson, & Lindsay, 2004; Warrington & McCarthy, 1987; Warrington & Shallice, 1984). Similarly, prosopagnosic patients, whether acquired by brain damage or through congenital defect, often present with problems in nonface perception (Behrmann, Avidan, Marotta, & Kimchi, 2005; Gauthier, Behrmann, & Tarr, 1999). A common interpretation of this pattern of results is that the lesions in most patients extend beyond the boundaries of a single module (e.g., Farah, 1990). And even if this is correct, it is clear that dissociations may be caused by a different modular organization from what might be apparent at first blush. For example, the living/nonliving dissociation

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may actually represent modular organization along visual features versus functional features (Farah & McClelland, 1991; Warrington & Shallice, 1984). But another interpretation of double dissociations based on rare patients is that these rare patients are simply outliers who are not representative of the underlying population of brain structures. Unfortunately, brain insults happen on a daily basis. Yet, category-specific deficits occur in just a tiny fraction of cases. Simulated brain damage in neural networks that have no modular organization whatsoever can yield a small number of cases that appear to suggest modularity (Plaut, 1995). If modules exist, then we should expect double dissociations. But double dissociations are not sufficient to prove the existence of modules (Shallice, 1988). This makes it necessary to use converging evidence from many techniques to help interpret patterns of deficits. Category representations can be fairly distributed and overlapping in the brain yet brain damage can produce, in some rare cases, quite selective deficits that suggest modularity. There is now considerable evidence that the representations of different categories are distributed and overlapping. In a classic study, Haxby et al. (2001) found that objects from different categories elicit replicable (and partly overlapping) patterns of activation across the entire ventral temporal cortex, rather than selective activation in a localized region. Subjects in the scanner were shown images of objects from various categories such as faces, houses, bottles, cats, and shoes. The pattern of activity for these categories over thousands of voxels was found to replicate between two halves of the data set, demonstrating how one could decode what a subject is seeing from the brain activity alone. This demonstration led many scientists to consider the importance of more distributed patterns of cortical activity. Some of the most exciting methods for analyzing fMRI data were inspired by that work (Kamitani & Tong, 2005; Norman, Polyn, Detre, & Haxby, 2006). Nonetheless, other researchers still emphasize the significance of the maximal response elicited in a specific brain area rather than the distributed pattern (Op de Beeck, 2008; Spiridon & Kanwisher, 2002). While the finding of distributed and partly overlapping maps for different categories is generally accepted, what remains vigorously debated is whether all categories are represented in this manner or if some special categories, such as faces, are much more localized (Hanson & Halchenko, 2008; Spiridon & Kanwisher, 2002). That category representations are distributed within the visual system may seem even less surprising when considering evidence that categories are in fact distributed over the whole brain. For instance, according to Barsalou’s (1999) perceptual symbol systems theory (Barsalou, 2008; Martin, 2007), concepts are represented in the collection

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of modal systems for perception and action, rather than amodal symbols. Concepts, even abstract concepts, are thought to recruit a distributed representation across the brain because information from different sensory modalities is stored in modality-specific systems. When participants engage in a verbal conceptual task with words from different categories (e.g., animals and tools), the resulting activation is highly similar to the patterns evoked by the presence of physical objects from different categories (Chao et al., 1999). Modality-specific information associated with a concept appears to be automatically engaged, regardless of the task. Such findings are relevant to the interpretation of studies where objects from different categories are contrasted. Not only do objects from the same category look alike, but they are likely associated with similar semantic knowledge. These associations influence the pattern of brain activity observed in response to the presentation of the object. This was demonstrated in a study where arbitrary semantic information was associated with novel objects through a short training task, and where these features appeared to be engaged automatically upon object perception (T. W. James & Gauthier, 2003). Outside of the scanner, objects were first associated with verbal labels describing auditory features (e.g., “whistles,” “hisses”) or motion features (e.g., “hops,” “crawls”). Later in the scanner, subjects performed visual matching judgments on pairs of objects. Strikingly, modality-specific cortices (the auditory cortex and an area that responds to biological motion) were engaged automatically based on prior associations that were completely irrelevant to the visual matching task. If these effects can emerge after a short training procedure, there could be a challenge in interpreting patterns of selectivity to visually presented familiar objects that subjects have acquired a lifetime of associations. Cats and faces and bottles have different shapes and they are also associated with different semantic information, making it difficult to know whether the distributed object maps in the visual system are maps of shape per se or maps of other dimensions (Op de Beeck, 2008). Experience and Expertise Another alternative to a modular account for how different categories are represented in the brain is that the observed cortical representation of categories represents the interaction between processing biases in the cortex and the varied task demands associated with the objects. One specific account, the process map hypothesis (Gauthier, 2000), argues that category-selectivity reflects the automatization of strategies that are learned during experience with a category. Automatic strategies associated with category membership could produce patterns of category-selectivity

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in the brain even if there were no maps of object shape or of object categories. This could happen if the ventral temporal cortex shows organization that reflects intersecting gradients in processing. For example, a gradient of eccentricity exists over the topographic extent of the visual cortex in the temporal lobe, and a continuum from local parts to holistic representations has been proposed (Hasson, Levy, Behrmann, Hendler, & Malach, 2001; Lerner, Hendler, Ben-Bashat, Harel, & Malach, 2001). Whatever the nature of the underlying dimensions relevant to processing (and they are largely unknown), the general idea is that any point in such a map would be unique and best suited to learn a specific visual categorization task. For instance, faces have to be identified at the subordinate level, and for that purpose, metric relations between parts (also called configural information) appear to be particularly useful (Tanaka & Sengco, 1997; Young, Hellawell, & Hay, 1987). Training at the subordinate level encourages participants to use a more “holistic” strategy (Diamond & Carey, 1986), in which participants find it more difficult to ignore task-irrelevant parts of the object (Young et al., 1987). The process-map hypothesis suggests that faces come to engage the fusiform face area (FFA) because it is best suited for holistic processing, the default mode of processing for faces, and predicts that other objects recognized using the same strategy, regardless of their shape, should also engage the same area. This prediction was first tested in a perceptual expertise training study with a set of artificial stimuli called “Greebles.” Greebles were designed to replicate some critical aspect of faces, such as the fact that they share a small number of parts in a common configuration (Figure 20.6). The training was modeled after the constraints of face recognition and other types of real-world expertise. That is, subjects learned to categorize Greebles in families and to name individual Greebles and to discriminate them from other visually similar Greebles, as we do every day with faces. Training continued until subjects were as quick to categorize Greebles at the individual level as they were at categorizing them at a more abstract “family” level. Fast individuation is a hallmark of expertise in real-world domains (e.g., Tanaka & Taylor, 1991). Behavioral studies of Greeble training showed that these objects were processed more like faces following training. In particular, Greeble experts processed Greebles more holistically, finding it difficult to selectively attend to part of these objects (Gauthier & Tarr, 1997; Gauthier, Williams, Tarr, & Tanaka, 1998). A comparison of brain activity before and after Greeble training revealed an increase of activity for upright Greebles in faceselective areas in the occipital lobe (what is now called occipital face area or OFA), the mid-fusiform face area (FFA; Figure 20.6) and a face-selective region of the anterior

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Figure 20.6 (Figure C.28 in color section) A: Examples of the Greeble objects used in the Gauthier and Tarr (1997), Gauthier et al. (1998), Gauthier and Tarr (2002), and Gauthier, Behrmann, et al., (1999) expertise studies. B: Average fMRI results before and after Greeble expertise training. Note: (A) Greeble objects share a general configuration of parts, and the set is organized hierarchically with two genders (defined by all parts pointing up versus down) and several families (defined by body shapes). Training required subjects to learn to discriminate Greebles of the same gender and family (red arrow) as fast as they could discriminate two objects from different families (yellow arrow). (B) The highlighted region is centered on the FFA. Red and yellow areas responded more to upright than upside-down stimuli, while blue to purple areas responded more to upside-down images. Upright faces elicit more activity in this area than upside-down faces. However, the same effect is only observed for Greebles after expertise training with upright Greebles. From Gauthier, Tarr, Anderson, Skudlarski, & Gore (1999). Adapted with permission.

temporal lobe (Gauthier, Tarr, Moylan, Anderson, & Gore, 2000). Later work showed that behavioral increases in configural processing were correlated with changes of activity in the FFA across subjects (Gauthier & Tarr, 2002). The Greeble work suggests that changes in the way that a category is processed with the acquisition of perceptual expertise are critical in recruiting specific areas of the ventral temporal cortex for its processing. The recruitment of the FFA in expert perception has been confirmed in studies of real-world expertise with cars or birds, where the degree of FFA activity in response to images of cars, for example, shows a very strong correlation with a behavioral measure of expertise over several independent experiments (Gauthier, Skudlarski, Gore, & Anderson, 2000; Gauthier, Curby, Skudlarski, & Epstein,

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2005; Xu, 2005). As might be predicted based on such results, individuals with Autism, who show abnormalities in face processing that can be apparent early in development (e.g., Klin & Jones, 2008), show reduced selectivity to faces in the fusiform gyrus (e.g., Hubl et al., 2003; Pierce, Muller, Ambrose, Allen, & Courshenes, 2001; Schultz et al., 2000). Consistent with the idea that this hypoactivity is due to a lack of expertise, a boy with Autism who acquired perceptual expertise with Digimon cartoon characters showed specialization for Digimon but not faces in the fusiform gyrus (Grelotti et al., 2005). Finally, consistent with an expertise account of face-selective effects, the N170 face-selective ERP component is larger in amplitude for various nonface homogenous objects in expert observers (Busey & Vanderkolk, 2005; Gauthier, Curran, Curby, & Collins, 2003; Rossion, Gauthier, Goffaux, Tarr, & Crommelinck, 2002; J. W. Tanaka & Curran, 2001). However, extensive practice with a category does not always recruit face-selective areas. A handful of fMRI training studies with object categories have been conducted and have led to inconsistent results in terms of the specific regions engaged. With close examination of the particulars of these studies, this inconsistency may not be surprising given that the studies varied greatly on several dimensions, including object geometry, amount of training, and the specific training task practiced by subjects (Jiang et al., 2007; Moore, Cohen, & Ranganath, 2006; Op de Beeck, Baker, DiCarlo, & Kanwisher, 2006; Xue & Poldrack, 2007; Yue, Tjan, & Biederman, 2006). Despite these differences, one region, the lateral occipital complex, is a more consistent locus of change across studies, suggesting that it may be more sensitive to exposure to a category than to the specific constraints of the training. Human ERPs and recordings in monkeys reveal that responses to objects can change in the ventral occipital cortex due to mere exposure (Peissig, Singer, Kawasaki, & Sheinberg, 2007; Scott, Tanaka, Sheinberg, & Curran, 2006). In contrast, the FFA may be more important when experts process objects holistically, a strategy that was only assessed directly in the Greeble training study. The adoption of a holistic strategy by subjects was suggested in one study (Moore et al., 2006) where training led to an inversion effect (inversion disrupts holistic processing with faces; Tanaka & Sengco, 1997; Young et al., 1987) and in that study, a small training effect was obtained in the FFA. Clearly, there are domains of expertise with visual categories, such as print, that do not rely on configural perception and lead to specialization outside of the face-selective system (McCandliss, Cohen, & Dehaene, 2003). Thus, exposure with objects may be enough to produce some changes in the visual system (Freedman, Riesenhuber, Poggio, &

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Miller, 2006) but there may also be a record of the manner in which experience with a category is acquired, in terms of the perceptual strategy and neural substrates that come to be automatically engaged by category members. Our ability to interpret patterns of differences across training studies is seriously limited by the fact that fMRI training studies almost never compare two types of trainings with the same object category. Wong (2007) trained two groups of subjects with the same set of objects. One group learned to individuate objects as in Greeble training, while the other group was given equal exposure to objects but learned to classify them rapidly at the basic level. Only the individuation group demonstrated a switch to configural processing and an increase of activity near the FFA, with the behavioral and neural changes correlated across subjects. In contrast, rapid basic-level processing led to changes in more lateral areas of the occipito-temporal cortex, near the standard visual word form area. This work is unique in contrasting different types of experiences for the same category, as the majority of fMRI studies contrast different object categories, leading to effects that can be interpreted as indicating that the pattern of selectivity in ventral temporal cortex codes for variations in the shape of objects. Although there is no question that objects with similar shapes tend to recruit similar neural substrates in the same subject, which part of the neural network is recruited for objects with a given geometry in a given individual may be to some extent determined by experience processing objects from that category. Computational modeling supports the claim that the FFA is a subordinate-level, fine-grained visual discrimination area, whose main feature is performing transformations that magnify differences between highly similar visual items (Joyce & Cottrell, 2004). Tong, Joyce, and Cottrell (2007) first trained neural networks to discriminate several basic-level categories (e.g., cups, Greebles, and cans). “Expert” networks were additionally trained to discriminate items within one of these categories at the subordinate level. In the second phase, the learned weights from the first phase of training were saved, and both the basic-level and expert-level networks were trained on new subordinate-level discriminations. Results showed that although in the first phase basic-level discriminations were learned more quickly than subordinate-level discriminations, once the “expert” network was trained, learning new subordinate-level discriminations occurred more rapidly for the expert network than the basic-level network. This suggests that a neural network trained to perform subordinate-level discriminations on one class of objects shows an advantage in learning a new class at the subordinate level—because of extensive early experience with faces,

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the FFA becomes a skilled subordinate-level classifier for faces that is later recruited by other domains of visual expertise. So far we have only considered the case of expertise for objects in homogeneous categories such as faces, cars, and birds, where the goal is rapid individuation. Recent work has also explored expertise for letters and words. In contrast to faces, birds, and cars, which are typically individuated at the subordinate level by experts, for letters the goal of experts is basic-level categorization (an A is an A regardless of changes in font or style; Wong & Gauthier, in press). However, to facilitate reading, one wants to rapidly perceive a sequence of items to make a word. This is made easier by regularity in font style—it is easier to READ THIS than it is to rEaD tHiS (Sanocki, 1987, 1988). Furthermore, this effect is not limited to Roman characters: Chinese readers are faster to serially scan a matrix of Chinese characters for targets when the characters are all in the same font, whereas subjects who do not read Chinese do not show this sensitivity to style (Gauthier, Wong, Hayward, & Cheung, 2006). Such sensitivity to font is one example of a perceptual strategy that is more useful for letter perception than for the processing of most other categories. Neurally, several brain regions have been implicated in letter and word expertise: The visual word form area (VWFA; Cohen et al., 2000) responds more to words and pseudowords than nonpronounceable consonant strings. Surprisingly, this area does not show visual selectivity for letters or letter strings, for instance it is equally recruited by strings of Chinese characters in non-Chinese readers (K. H. James et al., 2005). In contrast, visual selectivity for letter strings and single letters is obtained in other parts of the left fusiform gyrus (Flowers et al., 2004; K. H. James et al., 2005; Polk et al., 2002). These findings are not restricted to one particular character set because Chinese-character and Roman-character selective areas overlapped in Chinese-English bilinguals (Baker et al., 2007; Wong, Jobard, James, James, & Gauthier, submitted). The N170 ERP potential is also obtained for words or letter strings (Bentin, Deouell, & Soroker, 1999) and for letters or other characters of expertise (Wong et al., 2005). Because of its selectivity for two very different types of expertise, the N170 may be a general marker of expert processes that can be localized in different brain areas. Scott et al. (2006) compared different trainings with bird categories revealing that both basic- and subordinate-level training enhanced the early N170 component, but only subordinate-level training amplified a later N250 component. Further comparisons of trainings in both ERP and fMRI could lead to a better understanding of the dynamics of perceptual expertise.

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High-Resolution Imaging and Competition Studies In recent years, two different lines of research offer new data for interpreting category selectivity in the FFA. The first uses high-resolution imaging in an attempt to separate patterns of responses to faces and objects, while the second attempts to measure neural (and behavioral) competition that could result from functional overlap. Standard fMRI has a resolution around 3 mm3. At that resolution, each voxel (3D pixel) in the FFA yields a maximal response to faces and a nonzero response to nonface objects. Recent work using higher resolution imaging looked “inside the voxel” to reveal the functional organization of the FFA at a finer spatial scale (1-mm3; GrillSpector, Sayres, & Ress, 2006); this represents a 27-fold increase in resolution. The results revealed that all voxels were maximally selective to faces, but highly face-selective voxels are intermingled with voxels that also showed comparable responses for at least some nonface category, such as animals or cars. The reproducibility of face-selectivity at a finer scale in the FFA is consistent with single-cell recordings in macaque monkeys, within face-selective regions identified by fMRI where 97% of cells are found to be face-selective (Tsao et al., 2006). Analyses in a prior expertise study with car and bird experts had revealed that the single most face-selective FFA voxel at standard resolution showed a clear expertise effect (Gauthier, Skudlarski, et al., 2000), which suggests that expert object responses in the FFA would overlap with face-selectivity at highresolution, and perhaps even at the single-cell level. If a considerable number of neurons in the fusiform gyrus are selective for both faces and objects of expertise, interference between these two domains may be expected in some situations. There could also be interference between face and object perception even if there were no shared neurons, as long as the two populations were strongly interconnected. In other words, instead of focusing on spatial overlap, one can address functional overlap: Is face perception functionally independent from the perception of nonface objects, especially for cases of expertise where a face-like configural strategy is recruited? In one study (Gauthier et al., 2003), subjects with a range of car expertise saw a sequence of faces alternating with cars. Each car or face was made out of two parts (top and bottom) and subjects selectively attended to the bottom of these images and made 1-back judgments for both categories; in this way, the degree of holistic processing could be measured for both categories. In this dual task situation, car experts processed cars more holistically then car novices and processed faces less holistically in the context of cars: Simultaneous processing of faces and cars by car experts

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appears to create a competition for common resources. This behavioral interference was correlated with the magnitude of the N170 face-selective ERP potential (see also Rossion, Kung, & Tarr, 2004; Rossion, Collins, Goffaux, & Curran, 2007). In more recent work, competition between car and face perception was also obtained in tasks where the cars were completely task-irrelevant (McKeeff, Tong, & Gauthier, 2007; Williams, 2007). Competition between face perception and objects of expertise suggests one or more functional bottlenecks in the brain for configural processing, and because the FFA responds to both faces and objects of expertise, it is tempting to assume that the FFA is one such bottleneck. This is difficult to verify with fMRI at standard resolution because the response to cars and faces cannot be separated, but this could be addressed in future work using high-resolution imaging.

SUMMARY Understanding how objects are categorized is a complex challenge that requires bridging the study of visual perception and visual cognition and cannot be studied without also considering how objects are perceived, identified, and remembered (Palmeri & Tarr, 2008). To date, different aspects of this problem, such as the format of visual object representations and the principles that govern decisions about the categories to which these objects belong, have been explored in separate fields. But more than once, such as on the issue of abstraction or modularity, these independent lines of research have faced similar debates or reached similar conclusions (Palmeri & Gauthier, 2004). The advent of cognitive neuroscience, which provides evidence and constraints from techniques as diverse as psychophysics, brain imaging, neuropsychology, and neurophysiology, may help blur old boundaries between approaches to produce more complete models of object categorization.

REFERENCES Aguirre, G. K., Zarahn, E., & D’Esposito, M. (1998). An area within human ventral cortex sensitive to building stimuli: Evidence and implications. Neuron, 21, 373–383. Allen, S., & Brooks, L. (1991). Specializing the operation of an explicit rule. Journal of Experimental Psychology: General, 120, 3–19. Ashby, F. G. (1992). Multidimensional models of perception and cognition. Hillsdale, NJ: Erlbaum. Ashby, F. G., & Alfonso-Reese, L. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39, 216–233. Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A formal neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442–481.

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References 411 Ashby, F. G., Ennis, J. M., & Spiering, B. J. (2007). A neurobiological theory of automaticity in perceptual categorization. Psychological Review, 114, 632–656.

Caramazza, A., & Shelton, J. (1998). Domain-specific knowledge systems in the brain the animate-inanimate distinction. Journal of Cognitive Neuroscience, 10, 1–34.

Ashby, F. G., Noble, S., Filoteo, J., Waldron, E., & Ell, S. (2003). Category learning deficits in parkinson’s disease. Neuropsychology, 17, 115–124.

Chao, L. L., Haxby, J. V., & Martin, A. (1999). Attribute-based neural substrates in temporal cortex for perceiving and knowing about objects. Nature Neuroscience, 2, 913–919.

Ashby, F. G., & O’Brien, J. B. (2005). Category learning and multiple memory systems. Trends in Cognitive Sciences, 9, 83–89.

Chun, M. M., & Phelps, E. A. (1999). Memory deficits for implicit contextual information in amnesic subjects with hippocampal damage. Nature Neuroscience, 2, 844–847.

Ashby, F. G., & Waldron, E. M. (1999). On the nature of implicit categorization. Psychonomic Bulletin and Review, 6, 363–378. Baker, C. I., Liu, J., Wald, L. L., Kwong, K. K., Benner, T., & Kanwisher, N. (2007). Visual word processing and experiential origins of functional selectivity in human extrastriate cortex. Proceedings of the National Academy of Sciences, USA, 104, 9087–9092. Barsalou, L. W. (1985). Ideals, central tendency, and frequency of instantiation as determinants of graded structure in categorie. Journal of Experimental Psychology: Learning, Memory, and Cognitive, 11, 629–654. Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577–660. Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645. Baylis, G. C., & Rolls, E. T. (1987). Responses of neurons in the inferior temporal cortex in short term and serial recognition memory tasks. Experimental Brain Research, 65, 614–622. Behrmann, M., Avidan, G., Marotta, J. J., & Kimchi, R. (2005). Detailed exploration of face-related processing in congenital prosopagnosia: Pt. 1. Behavioral findings. Journal of Cognitive Neuroscience, 17, 1130–1149. Bentin, S., Allison, T., Puce, A., Perez, E., & McCarthy. (1996). Electrophysiological studies of face perception in humans. Journal of Cognitive Neuroscience, 8, 551–565. Bentin, S., Deouell, L. Y., & Soroker, N. (1999). Selective visual streaming in face recognition: Evidence from developmental prosopagnosia. NeuroReport, 10, 823–827. Biederman, I. (1987). Recognition-by-components: A theory of human image understanding. Psychological Review, 94, 115–147. Biederman, I., & Gerhardstein, P. C. (1993). Recognizing depth-rotated objects: Evidence and conditions for three-dimensional viewpoint invariance. Journal of Experimental Psychology: Human Perception and Performance, 19, 1162–1182.

Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8, 240–248. Cools, A., van den Bercken, J., van Spaendonck, K., & Berger, H. (1984). Cognitive and motor shifting aptitude disorder in parkinson’s disease. Journal of Neurology, Neurosurgery, and Psychiatry, 47, 443–453. Desimone, R., Albright, T., Gross, C., & Bruce, C. (1984). Stimulusselective properties of inferior temporal neurons in the macaque. Journal of Neurosciences, 4, 2051–2062. Diamond, R., & Carey, S. (1986). Why faces are and are not special: An effect of expertise. Journal of Experimental Psychology: General, 115, 107–117. DiCarlo, J. J., & Maunsell, J.-H. R. (2003). Anterior inferotemporal neurons of monkeys engaged in object recognition can be highly sensitive to object retinal position. Journal of Neurophysiology, 89, 3264–3278. Dobbins, I. G., Schnyer, D. M., Verfaellie, M., & Schacter, D. L. (2004, March 18). Cortical activity reductions during repetition priming can result from rapid response learning. Nature, 428, 316–319. Downes, J. J., Roberts, A. C., Sahakian, B. J., Evenden, J. L., Morris, R. G., & Robbins, T. W. (1989). Impaired extra-dimensional shift performance in medicated and unmedicated parkinson’s disease: Evidence for a specific attentional dysfunction. Neuropsychologia, 27, 1329–1343. Downing, P. E., Jiang, Y., Shuman, M., & Kanwisher, N. (2001, September 28). A cortical area selective for visual processing of the human body. Science, 293, 2470–2473. Duchaine, B. C. (2000). Developmental prosopagnosia with normal configural processing. Cognitive Neuroscience and Neuropsychology, 11, 79–83.

Booth, M. C. A., & Rolls, E. T. (1998). View-invariant representations of familiar objects by neurons in the inferior temporal visual cortex. Cerebral Cortex, 8, 510–523.

Duncan, J., Seitz, R. J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A., et al. (2000, July 21). A neural basis for general intelligence [see comment]. Science, 289, 457–460.

Bourne, L. (1970). Knowing and using concepts. Psychological Review, 77, 546–556.

Edelman, S. (1997). Computational theories of object recognition. Trends in Cognitive Sciences, 1, 296–304.

Brown, S. W., & Stubbs, D. A. (1988). The psychophysics of retrospective and prospective timing. Perception, 17, 297–310.

Edelman, S. (1999). Representation and recognition in vision. Cambridge, MA: MIT Press.

Bruner, J., Goodnow, J., & Austin, A. (1956). A study of thinking. New York: Wiley.

Epstein, R., Harris, A., Stanley, D., & Kanwisher, N. (1999). The parahippocampal place area: Recognition, navigation, or encoding? Neuron, 23, 115–125.

Bukach, C. M., Bub, D. N., Masson, M. E., & Lindsay, D. S. (2004). Category specificity in normal episodic learning: Applications to object recognition and category-specific agnosia. Cognitive Psychology, 48, 1–46.

c20.indd 411

Cohen, L., Dehaene, S., Naccache, L., Lehericy, S., Dehaene-Lambertz, G., Henaff, M. A., et al. (2000). The visual word form area: Spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. Brain, 123(Pt. 2), 291–307.

Epstein, R., & Kanwisher, N. (1998, April 9). A cortical representation of the local visual environment. Nature, 392, 598–601.

Bülthoff, H. H., & Edelman, S. (1992). Psychophysical support for a two-dimensional view interpolation theory of object recognition. Proceedings of the National Academy of Sciences, USA, 89, 60–64.

Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107–140.

Busemeyer, J., Dewey, G., & Medin, D. (1984). Evaluation of exemplarbased generalization and the abstraction of categorical information. Journal of Experimental Psychology: Learning Memory and Cognition, 10, 638–648.

Farah, M. J. (1990). Visual agnosia: Disorders of object recognition and what they tell us about normal vision. Cambridge, MA: MIT Press.

Busey, T. A., & Vanderkolk, J. R. (2005). Behavioral and electrophysiological evidence for configural processing in fingerprint experts. Vision Research, 45, 431–448.

Farah, M. J., Levinson, K. L., & Klein, K. L. (1995). Face perception and within-category discrimination in prosopagnosia. Neuropsychologia, 33, 661–674.

Farah, M. J. (1996). Is face recognition special? Evidence from neuropsychology, Behavioural Brain Research, 76(1–2), 181–189.

8/17/09 2:12:52 PM

412

Categorization

Farah, M. J., & McClelland, J. L. (1991). A computational model of semantic memory impairment: Modality-specificity and emergent category-specificity. Journal of Experimental Psychology: General, 120, 339–357. Farah, M. J., McMullen, P. A., & Meyer, M. M. (1991). Can recognition of living things be selectively impaired? Neuropsychologia, 29, 185–193. Filoteo, J. V., Maddox, T. W., & Davis, J. D. (2001). Quantitative modeling of category learning in amnesic patients. Journal of the International Neuropsychological Society, 7, 1–19. Flowers, D. L., Jones, K., Noble, K., VanMeter, J., Zeffiro, T. A., Wood, F. B., et al. (2004). Extrastriate representation of letter recognition. Neuroimage, 21, 829–839. Fodor, J. A. (1983). Modularity of mind. Cambridge, MA: MIT Press. Foerde, K., Knowlton, B. J., & Poldrack, R. A. (2006). Modulation of competing memory systems by distraction. Proceedings of the National Academy of Sciences, USA, 103, 11778–11783. Frank, M., & Claus, E. (2006). Anatomy of a decision: Striato-orbitofrontal interactions in reinforcement learning, decision making and reversal. Psychological Review, 113, 300–326. Freedman, D. J., Riesenhuber, M., Poggio, T., & Miller, E. K. (2003). A comparison of primate prefrontal and temporal cortices during visual categorization. Journal of Neuroscience, 23, 5235–5246. Freedman, D. J., Riesenhuber, M., Poggio, T., & Miller, E. K. (2006). Experience-dependent sharpening of visual shape selectivity in inferior temporal cortex. Cerebral Cortex, 16, 1631–1644. Garner, W. R. (1974). The processing of information and structure. Potomac, MD: Erlbaum.

Gauthier, I., Wong, A. C., Hayward, W. G., & Cheung, O. S. (2006). Font tuning associated with expertise in letter perception. Perception, 35, 541–559. Gluck, M. A., & Myers, C. E. (1993). Hippocampal mediation of stimulus representation: A computational theory. Hippocampus, 3, 491–516. Goldstone, R. L., & Styvers, M. (2001). The sensitization and differentiation of dimensions during category learning. Journal of Experimental Psychology: General, 130, 116–139. Goodale, M. A., & Milner, D. A. (1992). Separate visual pathways for perception and action. Trends in Neuroscience, 15, 20–25. Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008). A rational analysis of rule-based concept learning. Cognitive Science, 32, 108–154. Graham, K. S., Scahill, V. L., Hornberger, M., Barense, M. D., Lee, A. C. H., Bussey, T. J., et al. (2006). Abnormal categorization and perceptual learning in patients with hippocampal damage. Journal of Neuroscience, 26, 7547–7554. Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M., & Cohen, J. D. (2001, September 14). An fMRI investigation of emotional engagement in moral judgment. Science, 293, 2105–2108. Grelotti, D. J., Klin, A. J., Gauthier, I., Skudlarski, P., Cohen, D. J., Gore, J. C., et al. (2005). fMRI activation of the fusiform gyrus and amygdala to cartoon characters but not to faces in a boy with autism. Neuropsychologia, 43, 373–385. Grill-Spector, K., Sayres, R., & Ress, D. (2006). High-resolution imaging reveals highly selective nonface clusters in the fusiform face area. Nature Neuroscience, 9, 1177–1185.

Gauthier, I. (2000). What constrains the organization of the ventral temporal cortex? Trends in Cognitive Science, 4, 1–2.

Gross, C., Bender, D., & Rocha-Miranda, C. (1969, December 5). Visual receptive fields of neurons in inferotemporal cortex of the monkey. Science, 166, 1303–1306.

Gauthier, I., Behrmann, M., & Tarr, M. J. (1999). Can face recognition really be dissociated from object recognition? Journal of Cognitive Neuroscience, 11, 349–370.

Grossman, E. D., & Blake, R. (2002). Brain areas active during visual perception of biological motion. Neuron, 36, 1167–1175.

Gauthier, I., Curby, K. M., Skudlarski, P., & Epstein, R. A. (2005). Individual differences in ffa activity suggest independent processing at different spatial scales. Cognitive and Affective Behavioral Neuroscience, 5, 222–234. Gauthier, I., Curran, T., Curby, K. M., & Collins, D. (2003). Perceptual interference supports a non-modular account of face processing. Journal of Neuroscience, 6, 428–432. Gauthier, I., & Palmeri, T. J. (2002). Visual neurons: Categorization-based selectivity. Current Biology, 12, R282–R284. Gauthier, I., Skudlarski, P., Gore, J. C., & Anderson, A. W. (2000). Expertise for cars and birds recruits brain areas involved in face recognition. Nature Neuroscience, 3, 191–197. Gauthier, I., & Tarr, M. J. (1997). Becoming a “greeble” expert: Exploring mechanisms for face recognition. Vision Research, 37, 1673–1682. Gauthier, I., & Tarr, M. J. (2002). Unraveling mechanisms for expert object recognition: Bridging brain activity and behavior. Journal of Experimental Psychology: Human Perception and Performance, 28, 431–446. Gauthier, I., Tarr, M. J., Anderson, A. W., Skudlarski, P., & Gore, J. C. (1999). Activation of the middle fusiform ‘face area’ increases with expertise in recognizing novel objects. Nature Neuroscience, 2, 568–573. Gauthier, I., Tarr, M. J., Moylan, J., Anderson, A. W., & Gore, J. C. (2000). Does subordinate-level categorization engage the functionallydefined fusiform face area? Cognitive Neuropsychology, 17(1/2/3), 143–163. Gauthier, I., Williams, P., Tarr, M. J., & Tanaka, J. (1998). Training “greeble” experts: A framework for studying expert object recognition processes. Vision Research, 38(15/16), 2401–2428.

c20.indd 412

Hanson, J., & Halchenko, Y. (2008). Brain reading using full brain support vector machines for object recognition: There is no face identification area. Neural Computation, 20, 486–503. Hasson, U., Levy, I., Behrmann, M., Hendler, T., & Malach, M. (2001). Eccentricity bias as an organizing principle for human high order object areas. Neuron, 34(3), 479–490. Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001, September 28). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, 2425–2430. Henke, K., Schweinberger, S. R., Grigo, A., Klos, T., & Sommer, W. (1998). Specificity of face recognition: Recognition of exemplars of non-face objects in prosopagnosia. Cortex, 34, 289–296. Hillis, A., & Caramazza, A. (1991). Category-specific naming and comprehension impairment: A double dissociation. Brain, 114, 2081–2094. Hintzman, D. L. (1986). “Schema abstraction” in a mutiple-trace memory model. Psychology Review, 93, 411–428. Homa, D., Cross, J., Cornell, D., Goldman, D., & Schwartz, S. (1973). Prototype abstraction and classification of new instances as a function of number of instances defining the prototype (concept formation and learning). Journal of Experimental Psychology: Learning Memory and Cognition, 101, 116–122. Hopkins, R. O., Myers, C. E., Shohamy, D., Grossman, S., & Gluck, M. A. (2004). Impaired probabilistic category learning in hypoxic subjects with hippocampal damage. Neuropsychologia, 42, 524–535. Houk, J., & Wise, S. (1995). Distributed modular architectures linking basal ganglia, cerebellum, and cerebral cortex: Their role in planning and controlling action. Cerebral Cortex, 5, 95–110. Hubl, D., Bolte, S., Feineis-Matthews, S., Lanfermann, H., Federspiel, A., Strik, W., et al. (2003). Functional imbalance of visual pathways indicates alternative face processing strategies in autism. Neurology, 61, 1232–1237.

8/17/09 2:12:53 PM

References 413 James, K. H., James, T. W., Jobard, G., Wong, A. C.-N., & Gauthier, I. (2005). Letter processing in the visual system: A different activation pattern for single letters and strings. Cognitive and Affective Behavioral Neuroscience, 5, 452–466.

Logothetis, N. K., Pauls, J., & Poggio, T. (1995). Shape representation in the inferior temporal cortex of monkeys. Current Biology, 5, 552–563.

James, T. W., & Gauthier, I. (2003). Auditory and action semantic features activate sensory-specific perceptual brain regions. Current Biology, 13, 1792–1796.

Lombardi, W. J., Andreason, P. J., Sirocco, K. Y., Rio, D. E., Gross, R. E., Umhau, J. C., et al. (1999). Wisconsin card sorting test performance following head injury: Dorsolateral fronto-striatal circuit activity predicts perseveration. Journal of Clinical and Experimental Neuropsychology, 21, 2–16.

Jiang, X., Bradley, E., Rini, R. A., Zeffiro, T., Vanmeter, J., & Riesenhuber, M. (2007). Categorization training results in shape- and category-selective human neural plasticity. Neuron, 53, 891–903. Johansen, M. K., & Palmeri, T. J. (2002). Are there representational shifts during category learning? Cognitive Psychology, 45, 482–553. Joyce, C., & Cottrell, G. W. (2004). Solving the visual expertise mystery in connectionist models of cognition and perception II. Paper presented at the proceedings of the eighth neural computation and psychology workshop. Kamitani, Y., & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8, 679–685. Kanwisher, N., McDermott, J., & Chun, M. M. (1996). A module for the visual representation of faces. NeuroImage, 3(Suppl. 3), S361. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 4302–4311.

Love, B., & Gureckis, T. (2007). Models in search of a brain. Cognitive, Affective, and Behavioral Neuroscience, 7(2), 90–108. Love, B., Medin, D. L., & Gureckis, T. M. (2004). Sustain: A network model of category learning. Psychological Review, 111, 309–332. Maddox, W. T., Aparicio, P., Marchant, N. L., & Ivry, R. B. (2005). Rulebased category learning is impaired in patients with parkinson’s disease but not in patients with cerebellar disorders. Journal of Cognitive Neuroscience, 17, 707–723. Maddox, W. T., Ashby, F. G., & Bohil, C. (2003). Delayed feedback effects on rule-based and information-integration category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 650–662.

Kinder, A., & Shanks, D. (2001). Amnesia and the declarative/nondeclarative distinction: A recurrent network model of classification, recognition, and repetition priming. Journal of Cognitive Neuroscience, 13, 1–22.

Maddox, W. T., & Filoteo, J. V. (2001). Striatal contributions to category learning: Quantitative modeling of simple linear and complex nonlinear rule learning in patients with parkinson’s disease. Journal of the International Neuropsychological Society, 7, 710–727.

Klin, A., & Jones, W. (2008). Altered face scanning and impaired recognition of biological motion in a 15-month-old infant with autism. Developmental Science, 11, 40–46.

Marr, D., & Nishihara, H. K. (1978). Representation and recognition of the spatial organization of three-dimensional shapes. Proceedings of the Royal Society of London, B, 200, 269–294.

Knowlton, B. J., Mangels, J. A., & Squire, L. R. (1996, September 6). A neostriatal habit learning system in humans. Science, 273, 1399–1402.

Martin, A. (2007). The representation of object concepts in the brain. Annual Review of Psychology, 58, 25–45.

Knowlton, B. J., & Squire, L. R. (1993, December 10). The learning of categories: Parallel brain systems for item memory and category knowledge. Science, 262, 1747–1749.

Martin, A., Wiggs, C. L., Ungerleider, L. G., & Haxby, J. V. (1996, February 15). Neural correlates of category-specific knowledge. Nature, 379, 649–652.

Knowlton, B. J., Squire, L. R., Paulsen, J. S., Swerdlow, N. R., Swenson, M., & Butters, N. (1996). Dissociations within nondeclarative memory in huntington’s disease. Neuropsychology, 10, 538–548.

McCandliss, B. D., Cohen, L., & Dehaene, S. (2003). The visual word form area: Expertise for reading in the fusiform gyrus. Trends in Cognitive Sciences, 7, 293–299.

Konishi, S., Nakajima, K., Uchida, I., Kameyama, M., Nakahara, K., Sekihara, K., et al. (1998). Transient activation of inferior prefrontal cortex during cognitive set shifting. Nature Neuroscience, 1, 80–84.

McCarthy, R., & Warrington, E. (1988, August 4). Evidence for modalityspecific meaning systems in the brain. Nature, 334, 428–430.

Kruschke, J. K. (1992). Alcove: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.

McKeeff, T., Tong, F., & Gauthier, I. (2007). Perceptual expertise with cars leads to greater perceptual interference with faces but not objects [abstract]. Journal of Vision, 7, 1032.

Lakoff, G. (1987). Women, fire, and dangerous things: What categories reveal about the mind. Chicago: University of Chicago Press.

Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.

Lamberts, K. (2000). Information-accumulation theory of speeded categorization. Psychological Review, 107, 227–260.

Meeter, M., Myers, C. E., & Gluck, M. A. (2005). Integrating incremental learning and episodic memory models of the hippocampal region. Psychological Review, 112, 560–585.

Lerner, Y., Hendler, T., Ben-Bashat, D., Harel, M., & Malach, R. (2001). A hierarchical axis of object processing stages in the human visual cortex. Cerebral Cortex, 11, 287–297. Levine, M. (1975). A cognitive theory of learning: Research on hypothesis testing. Hillsdale, NJ: Erlbaum. Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95, 492–527. Logan, G. D. (1990). Repetition priming and automaticity: Common underlying mechanisms? Cognitive Psychology, 22, 1–35. Logan, G. D. (2002). An instance theory of attention and memory. Psychological Review, 109, 376–400. Logothetis, N. K., & Pauls, J. (1995). Psychophysical and physiological evidence for viewer-centered object representations in the primate. Cerebral Cortex, 5, 270–288. Logothetis, N. K., Pauls, J., Bülthoff, H. H., & Poggio, T. (1994). Viewdependent object recognition in monkeys. Current Biology, 4, 401–414.

c20.indd 413

Logothetis, N. K., & Sheinberg, D. L. (1996). Visual object recognition. Annual Review of Neuroscience, 19, 577–621.

Meeter, M., Myers, C. E., Shohamy, D., Hopkins, R. O., & Gluck, M. A. (2006). Strategies in probabilistic categorization: Results from a new way of analyzing performance. Learning and Memory, 13, 230–239. Milner, B. (1963). Effects of different brain lesions on card sorting. Archives of Neurology, 9, 90–100. Mishkin, M., Malamut, B., & Bachevalier, J. (1984). Memories and habits: Two neural systems. In Lynch G., McGaugh J. L., Weinberger M. (Eds.), Neurobiology of learning and memory (pp. 65–77). New York: Guilford Press. Monchi, O., Petrides, M., Petre, V., Worsley, K., & Dagher, A. (2001). Wisconsin card sorting revisited: Distinct neural circuits participating in different stages of the task identified by event-related functional magnetic resonance imaging. Journal of Neuroscience, 21, 7733–7741. Moore, C. D., Cohen, M. X., & Ranganath, C. (2006). Neural mechanisms of expert skills in visual working memory. Journal of Neuroscience, 26, 11187–11196.

8/17/09 2:12:53 PM

414

Categorization

Moscovitch, M., Winocur, G., & Behrmann, M. (1997). What is special about face recognition? Nineteen experiments on a person with visual object agnosia and dyslexia but normal face recognition. Journal of Cognitive Neuroscience, 9, 555–604. Murphy, G. (2002). The big book of concepts. Cambridge, MA: MIT Press. Nobre, A. C., Allison, T., & McCarthy, G. (1994, November 17). Word recognition in the human inferior temporal lobe. Nature, 372, 260–263. Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data [see comment]. Trends in Cognitive Sciences, 10, 424–430. Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 104–114. Nosofsky, R. M. (1986). Attention, similarity, and the identificationcategorization relationship. Journal of Experimental Psychology: General, 115, 39–61. Nosofsky, R. M. (1988). Exemplar-based accounts of relations between classification, recognition, and typicality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 700–708. Nosofsky, R. M. (1991). Tests of an exemplar model for relating perceptual classification and recognition memory. Journal of Experimental Psychology: Human Perception and Performance, 17, 3–27. Nosofsky, R. M. (1992). Exemplar-based approach to relating categorization, identification, and recognition. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition. Hillsdale, NJ: Erlbaum.

Op de Beeck, H. P., Wagemans, J., & Vogels, R. (2008). The representation of perceived shape similarity and its role for category learning in monkeys: A modeling study. Vision Research, 48, 598–610. Palmeri, T. J. (1997). Exemplar similarity and the development of automaticity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 324–354. Palmeri, T. J., & Flanery, M. A. (1999). Learning about categories in the absence of training: Profound amnesia and the relationship between perceptual categorization and recognition memory. Psychological Science, 10, 526–530. Palmeri, T. J., & Flanery, M. A. (2002). Memory systems and perceptual categorization. In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 41, pp. 141–187). Elsevier, San Diego. Palmeri, T. J., & Gauthier, I. (2004). Visual object understanding. Nature Reviews: Neuroscience, 5, 291–303. Palmeri, T. J., & Nosofsky, R. M. (1995). Recognition memory for exceptions to the category rule. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 548–568. Palmeri, T. J., & Tarr, M. (2008). Visual object perception and longterm memory. In S. Luck & A. Hollingworth (Eds.) Visual Memory (pp. 163–207). Visual Memory. Oxford University Press. Palmeri, T. J., Wong, A. C., & Gauthier, I. (2004). Computational approaches to the development of perceptual expertise. Trends in Cognitive Sciences, 8, 378–386. Peelen, M. V., Wiggett, A., & Downing, P. (2006). Patterns of fMRI activity dissociate overlapping functional brain areas that respond to biological motion. Neuron, 16, 815–822.

Nosofsky, R. M., Clark, S. E., & Shin, H. J. (1989). Rules and exemplars in categorization, identification, and recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 282–304.

Peissig, J. J., Singer, J., Kawasaki, K., & Sheinberg, D. L. (2007). Effects of long-term object familiarity on event-related potentials in the monkey. Cerebral Cortex, 17, 1323–1334.

Nosofsky, R. M., & Johansen, M. K. (2000). Exemplar-based accounts of “multiple-system” phenomena in perceptual categorization. Psychonomic Bulletin and Review, 7, 375–402.

Perrett, D. I., Oram, M. W., & Ashbridge, E. (1998). Evidence accumulation in cell populations responsive to faces: An account of generalisation of recognition without mental transformations. Cognition, 67(1, 2), 111–145.

Nosofsky, R. M., & Kruschke, J. (2001). Single-system models and interference in category learning: Commentary on waldron and ashby. Psychonomic Bulletin and Review, 9, 175–180.

Pierce, K., Muller, R. A., Ambrose, J., Allen, G., & Courshenes, E. (2001). Face processing occurs outside the fusiform face area. In autism: Evidence form functional MRI. Brian, 124, 2059–2073.

Nosofsky, R. M., Kruschke, J. K., & McKinley, S. C. (1992). Combining exemplar-based category representations and connectionist learning rules. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18, 211–233.

Plaut, D. C. (1995). Double dissociation without modularity: Evidence from connectionist neuropsychology. Journal of Clinical and Experimental Neuropsychology, 17, 291–321.

Nosofsky, R. M., & Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104, 266–300. Nosofsky, R. M., & Palmeri, T. J. (1998). A rule-plus-exception model for classifying objects in continuous-dimension spaces. Psychonomic Bulletin and Review, 5, 345–369. Nosofsky, R. M., Palmeri, T. J., & McKinley, S. C. (1994). Rule-plusexception model of classification learning. Psychological Review, 101, 53–79. Nosofsky, R. M., & Zaki, S. R. (1998). Dissociations between categorization and recognion in amnesic and normal individuals: An exemplarbased interpretation. Psychological Science, 9, 247–255. Op de Beeck, H. P. (2008). Interpreting fMRI data: Maps, modules and dimensions. Nature Review Neuroscience, 9, 123–135. Op de Beeck, H. P., Baker, C. I., DiCarlo, J. J., & Kanwisher, N. G. (2006). Discrimination training alters object representations in human extrastriate cortex. Journal of Neuroscience, 26, 13025–13036.

Poggio, T., & Edelman, S. (1990, January 18). A network that learns to recognize three-dimensional objects. Nature, 343, 263–266. Poldrack, R. A., Clark, J., Pare-Blagoev, E. J., Shohamy, D., Creso Moyano, J., Myers, C., et al. (2001). Interactive memory systems in the human brain. Nature, 29, 546–550. Poldrack, R. A., Prabhakaran, V., Seger, C. A., & Gabrieli, J. D. (1999). Striatal activation during acquisition of a cognitive skill. Neuropsychology, 13, 564–574. Poldrack, R. A., & Rodriguez, P. (2004). How do memory systems interact? Evidence from human classification learning. Neurobiology of Learning and Memory, 82, 324–332. Polk, T. A., Stallcup, M., Aguirre, G. K., Alsop, D. C., D’Esposito, M., Detre, J. A., et al. (2002). Neural specialization for letter recognition. Journal of Cognitive Neuroscience, 14, 145–159. Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.

Op de Beeck, H. P., & Vogels, R. (2000). Spatial sensitivity of macaque inferior temporal neurons. Journal of Comparative Neurology, 426, 505–518.

Puce, A., Allison, T., Asgari, M., Gore, J. C., & McCarthy, G. (1996). Differential sensitivity of human visual cortex to faces, letterstrings, and textures: A functional magnetic resonance imaging study. Journal of Neuroscience, 16, 5205–5215.

Op de Beeck, H. P., Wagemans, J., & Vogels, R. (2001). Inferotemporal neurons represent low-dimensional configurations of parameterized shapes. Journal of Neuroscience, 4, 1244–1252.

Puce, A., Allison, T., Bentin, S., Gore, J. C., & McCarthy, G. (1998). Temporal cortex activation in humans viewing eye and mouth movements. Journal of Neuroscience, 18, 2188–2199.

c20.indd 414

8/17/09 2:12:54 PM

References 415 Quinn, P. C. (1999). Development of recognition and categorization of objects and their spatial relations in young infants. In L. Balter & C. S. Tamis-LeMonda (Eds.), Child psychology: A handbook of contemporary issues. Philadelphia: Psychology Press/Taylor & Francis. Reber, P., Gitelman, D., Parrish, T., & Mesulam, M. (2003). Dissociating explicit and implicit category knowledge with fMRI. Journal of Cognitive Neuroscience, 15, 574–583. Reed, J. M., Squire, L. R., Patalano, A. L., Smith, E. E., & Jonides, J. (1999). Learning about categories that are defined by object-like stimuli despite impaired declarative memory. Behavioral Neuroscience, 113, 411–419. Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2, 1019–1025. Riesenhuber, M., & Poggio, T. (2000). Models of object recognition. Journal of Neuroscience, 3(Suppl), 1199–1204. Riesenhuber, M., & Poggio, T. (2002). Neural mechanisms of object recognition. Current Opinion in Neurobiology, 12, 162–168. Robinson, A., Heaton, R., Lehan, R., & Stilson, D. (1980). The utility of the wisconsin card sorting test in detecting and localizing frontal lobe lesions. Journal of Consulting and Clinical Psychology, 48, 605–614. Roediger, H., Buckner, R., & McDermott, K. (1999). Components of processing. In J. K. Foster & M. Jelicic (Eds.), Memory: Systems, process, or function? (pp. 31–65). Oxford: Oxford University Press. Rolls, E. (1992). Neurophysiology and functions of the primate amygdala. In J. Aggleton (Ed.), The amygdala (pp. 143–165). New York: Wiley-Liss.

Sakamoto, Y., & Love, B. (2004). Schematic influences on category learning and recognition memory. Journal of Experimental Psychology: General, 133, 534–553. Sanocki, T. (1987). Visual knowledge underlying letter perception: Fontspecific, schematic tuning. Journal of Experimental Psychology: Human Perception and Performance, 13, 267–278. Sanocki, T. (1988). Font regularity constraints on the process of letter recognition. Journal of Experimental Psychology: Human Perception and Performance, 14, 472–480. Schultz, R. T., Gauthier, I., Klin, A., Fulbright, R. K., Anderson, A. W., Volkmar, F., et al. (2000). Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and asperger syndrome. Archives of General Psychiatry, 37, 331–340. Schyns, P. G., & Rodet, L. (1997). Categorization creates functional features. Journal of Experimental Psychology: Learning, Memory and Cognition, 23, 681–696. Scott, L. S., Tanaka, J. W., Sheinberg, D. L., & Curran, T. (2006). A reevaluation of the electrophysiological correlates of expert object processing. Journal of Cognitive Neuroscience, 18, 1453–1465. Seger, C. A., & Cincotta, C. M. (2005). Dynamics of frontal, striatal, and hippocampal systems during rule learning. Cerebral Cortex, 16, 1546–1555. Sergent, J., Ohta, S., MacDonald, B., & Zuck, E. (1994). Segregated processing of facial identity and emotion in the human brain: A pet study. Visual Cognition, 1(2/3), 349–369.

Rosch, E. (1973). On the internal structure of perceptual and semantic categories. In T. E. Moore (Ed.), Cognitive development and the acquisition of language (pp. 111–144). San Diego, CA: Academic Press.

Shallice, T. (1988). From neuropsychology to mental structure. Cambridge, England: Cambride University Press.

Rosch, E., & Mervis, C. (1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 573–605.

Shepard, R. N. (1987, September 11). Toward a universal law of generalization for psychological science. Science, 237, 1317–1323.

Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382–439.

Shepard, R. N. (1994). Perceptual-cognitive universals as reflections of the world. Psychonomic Bulletin and Review, 1, 2–28.

Rosseel, Y. (2002). Mixture models of categorization. Journal of Mathematical Psychology, 46, 178–210. Rossion, B., Collins, D., Goffaux, V., & Curran, T. (2007). Long-term expertise with artificial objects increases visual competition with early face categorization processes. Journal of Cognitive Neuroscience, 19, 543–555. Rossion, B., Gauthier, I., Goffaux, V., Tarr, M. J., & Crommelinck, M. (2002). Expertise training with novel objects leads to left lateralized face-like electrophysiological responses. Psychological Science, 13, 250–257. Rossion, B., Gauthier, I., Tarr, M. J., Despland, P., Bruyer, R., Linotte, S., et al. (2000). The n170 occipito-temporal component is delayed and enhanced to inverted faces but not to inverted objects: An electrophysiological account of face-specific processes in the human brain. NeuroReport, 11, 69–74.

Sheridan, J., & Humphreys, G. (1993). A verbal-semantic category-specific recognition impairment. Cognitive Neuropsychology, 10, 143–184. Shin, H. J., & Nosofsky, R. (1992). Similarity-scaling studies of dot-pattern classification and recognition. Journal of Experimental Psychology: General, 121, 278–304. Shohamy, D., Myers, C., Kalanithi, J., & Gluck, M. (2008). Basal ganglia and dopamine contributions to probabilistic category learning. Neuroscience and Biobehavioral Reviews, 32(2), 219–236. Sigala, N., Gabbiani, F., & Logothetis, N. K. (2002). Visual categorization and object representation in monkeys and humans. Journal of Cognitive Neuroscience, 14, 1–12. Sigala, N., & Logothetis, N. K. (2002, January 17). Visual categorization shapes feature selectivity in the primate temporal cortex. Science, 415, 318–320. Smith, E. E., Patalano, A. L., & Jonides, J. (1998). Alternative strategies of categorization. Cognition, 65, 167–196.

Rossion, B., Kung, C. C., & Tarr, M. J. (2004). Visual expertise with nonface objects leads to competition with the early perceptual processing of faces in the human occipitotemporal cortex. Proceedings of the National Academy of Sciences, USA, 101, 14521–14526.

Smith, E., Shoben, E., & Rips, L. (1974). Structure and process in semantic memory: A featural model for semantic decisions. Psychological Review, 81, 214–241.

Rotshtein, P., Henson, R., Treves, A., Driver, J., & Dolan, R. (2005). Morphing marilyn into maggie dissociates physical and identity face representations in the brain. Nature Neuroscience, 8, 107–113.

Smith, J. D., & Minda, J. P. (1998). Prototypes in the mist: The early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 1411–1436.

Rumiati, R. I., & Humphreys, G. W. (1997). Visual object agnosia without alexia or prosopagnosia: Arguments for separate knowledge stores. Visual Cognition, 4, 207–217.

Smith, J. D., & Minda, J. P. (2001). Journey to the center of the category: The dissociation in amnesia between categorization and recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 984–1002.

Sacchett, C., & Humphreys, G. (1992). Calling a squirrel a squirrel but a canoe a wigwam: A category specific deficit for artefactual objects and body parts. Cognitive Neuropsychology, 9, 73–86.

c20.indd 415

Saint-Cyr, J. A., Taylor, A. E., & Lang, A. E. (1988). Procedural learning and neostyrial dysfunction in man. Brain, 111, 941–960.

Smith, P., & Ratcliff, R. (2004). Psychology and neurobiology of simple decisions. Trends in Neuroscience, 27, 161–168.

8/17/09 2:12:54 PM

416

Categorization

Spiridon, M., & Kanwisher, N. (2002). How distributed is visual category information in human occipito-temporal cortex? An fMRI study. Neuron, 35, 1157–1165.

Ullman, S., Vidal-Naquet, M., & Sali, E. (2002). Visual features of intermediate complexity and their use in classification. Journal of Neuroscience, 5, 682–687.

Squire, L. R. (2004). Memory systems of the brain: A brief history and current perspective. Neurobiology of Learning and Memory, 82, 171–177.

Vogels, R., Biederman, I., Bar, M., & Lorincz, A. (2001). Inferior temporal neurons show greater sensitivity to nonaccidental than to metric shape differences. Journal of Cognitive Neuroscience, 13, 444–453.

Squire, L. R., & Knowlton, B. (1995). Learning about categories in the absence of memory. Proceedings of the National Academy of Sciences, USA, 92, 12470–12474. Squire, L. R., & Zola, S. M. (1996). Structure and function of declarative and nondeclarative memory systems. Proceedings of the National Academy of Sciences, USA, 93, 13515–13522. Stankiewicz, B. J. (2002). Empirical evidence for independent dimensions in the visual representation of three-dimensional shape. Journal of Experimental Psychology: Human Perception and Performance, 28, 913–932. Tanaka, J. W., & Curran, T. (2001). A neural basis for expert object recognition. Psychological Science, 12, 43–47. Tanaka, J. W., & Sengco, J. A. (1997). Features and their configuration in face recognition. Memory and Cognition, 25, 583–592. Tanaka, J. W., & Taylor, M. (1991). Object categories and expertise: Is the basic level in the eye of the beholder? Cognitive Psychology, 23, 457–482. Tanaka, K. (1996). Inferotemporal cortex and object vision. Annual Review of Neuroscience, 19, 109–139. Tanaka, K. (2003). Columns for complex visual object features in the inferotemporal cortex: Clustering of cells with similar but slightly different stimulus selectivities. Cerebral Cortex, 13, 90–99. Tarr, M. J. (1995). Rotating objects to recognize them: A case study of the role of viewpoint dependency in the recognition of three-dimensional objects. Psychonomic Bulletin and Review, 2, 55–82. Tarr, M. J., & Bülthoff, H. H. (1998). Image-based object recognition in man, monkey and machine. Cognition, 67(1–2), 1–20. Tarr, M. J., & Bülthoff, H. H. (1995). Is human object recognition better described by geon-structural-descriptions or by multiple-views? Journal of Experimental Psychology: Human Perception and Performance, 21, 1494–1505. Tarr, M. J., Kersten, D., & Bülthoff, H. H. (1998). Why the visual system might encode the effects of illumination. Vision Research, 38(15/16), 2259–2275. Tarr, M. J., & Pinker, S. (1989). Mental rotation and orientation-dependence in shape recognition. Cognitive Psychology, 21, 233–282. Tarr, M. J., Williams, P., Hayward, W. G., & Gauthier, I. (1998). Threedimensional object recognition is viewpoint dependent. Journal of Neuroscience, 1, 275–277.

Waldron, E. M., & Ashby, F. G. (2001). The effects of concurrent task interference on category learning: Evidence for multiple category learning systems. Psychonomic Bulletin and Review, 8, 168–176. Warrington, E., & McCarthy, R. (1983). Category specific access dysphasia. Brain, 106, 859–878. Warrington, E., & McCarthy, R. (1987). Categories of knowledge: Further fractionations and an attempted integration. Brain, 110, 1273–1296. Warrington, E., & Shallice, T. (1980). Word-form dyslexia. Brain, 103, 99–112. Warrington, E., & Shallice, T. (1984). Category specific semantic impairments. Brain, 107, 829–854. Williams, N. R. (2007). Competition between domains of expertise in a visual search task [abstract]. Journal of Vision, 7, 335. Wilson, F., O., Scalaidhe, S., & Goldman-Rakic, P. (1993, June 25). Dissociation of object and spatial processing domains in primate prefrontal cortex. Science, 260, 1955–1958. Wittgenstein, L. (1953). Philosophical investigations. Oxford: Blackwell. Wong, A. C.-N. (2007). The effect of different training experiences on object recognition in the visual system. Nashville, TN: Vanderbilt University. Wong, A. C.-N., & Gauthier, I. (in press). An analysis of letter expertise in a levels-of-categorization framework. Visual Cognition. Wong, A. C.-N., Gauthier, I., Woroch, B., DeBuse, C., & Curran, T. (2005). An early electrophysiological response associated with expertise in letter perception. Cognitive and Affective Behavioral Neuroscience, 5, 306–318. Wong, A. C.-N., Jobard, G., James, K. H., James, T. W., & Gauthier, I. (submitted). Expertise with characters in alphabetic and non-alphabetic writing systems engage the same occipito-temporal area. Xu, Y. (2005). Revisiting the role of the fusiform face area in visual expertise. Cerebral Cortex, 15, 1234–1242. Xue, G., & Poldrack, R. (2007). The neural substrates of orthographic learning: Implication for the vwfa hypothesis. Journal of Cognitive Neuroscience, 19, 1643–1655. Yue, X., Tjan, B. S., & Biederman, I. (2006). What makes faces special? Vision Research, 46, 3802–3811. Young, A. W., Hellawell, D., & Hay, D. (1987). Configural information in face perception. Perception, 10, 747–759.

Tong, M., Joyce, C., & Cottrell, G. (2007). Why is the fusiform face area recruited for novel categories of expertise? A neurocomputational investigation. Brain Research, 1202, 14–24.

Zacks, J. M., Speer, N. K., Swallow, K. M., Braver, T. S., & Reynolds, J. R. (2007). Event perception: A mind-brain perspective. Psychological Bulletin, 133, 273–293.

Tovee, M. J., Rolls, E. T., & Azzopardi, P. (1994). Translation invariance in the responses to faces of single neurons in the temporal visual cortical areas of the alert macaque. Journal of Neurophysiology, 72, 1049–1060.

Zaki, S. R. (2005). Is categorization really intact in amnesia? A metaanalysis. Psychonomic Bulletin and Review, 11, 1048–1054.

Trabasso, T., & Bower, G. (1968). Attention in learning: Theory and research. New York: Wiley. Tsao, D. Y., Freiwald, W. A., Tootell, R., & Livingstone, M. (2006, February 3). A cortical region consisting entirely of face-selective cells. Science, 311, 670–674. Turke-Browne, N., Yi, D.-J., & Chun, M. M. (2006). Linking implicit and explicit memory: Common encoding factors and shared representations. Neuron, 49, 917–927. Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327–352.

c20.indd 416

Zaki, S. R., & Nosofsky, R. M. (2001). A single-system interpretation of dissociations between recognition and categorization in a task involving object-like stimuli. Cognitive, Affective and Behavioral Neuroscience, 1, 344–359. Zaki, S. R., & Nosofsky, R. M. (2004). False prototype enhancement effects in dot pattern categorization. Memory and Cognition, 32, 390–398. Zaki, S. R., Nosofsky, R. M., Ramercad, R., & Unverzagt, F. (2003). Categorization and recognition performance in probable alzheimer ’s disease: Evidence for single-system models. Journal of the International Neuropsychological Society, 9, 394–406.

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Chapter 21

Cognitive Neuroscience of Thinking VINOD GOEL

have been designed to evaluate processes that may modulate complex cognitive processes. For example, Stroop-type tasks have been used to measure selective attention (Perret, 1974), while boring/monotonous tasks have been used to measure sustained attention (Wilkins, Shallice, & McCarthy, 1987). Maze tracing has been used to measure instruction following (Corkin, 1965). Drawing tasks have been used to measure perseveration (Goldberg & Bilder, 1987). The A-not-B (Diamond, 1990) and the Antisaccade tasks (Roberts, Hager, & Heron, 1994) have been used to study inhibitory mechanisms. Traditionally, there has not been a large overlap between the neuropsychology instruments and the tasks used in cognitive psychology. For example, logical reasoning tasks and judgment and decision-making tasks have been largely absent from the lesion literature. In terms of problemsolving tasks, only the Towers of London and Hanoi tasks are extensively used. However, over the past 15 to 20 years, crossover fertilization between neuropsychology and cognitive psychology is beginning to alter the landscape. Neuroimaging and patient studies of reasoning (Acuna, Eliassen, Donoghue, & Sanes, 2002; Christoff et al., 2001; Goel, 2005; Goel, Buchel, Frith, & Dolan, 2000; Goel & Dolan, 2003; Goel, Gold, Kapur, & Houle, 1997; Goel, Shuren, Sheesley, & Grafman, 2004; Houde et al., 2000; Knauff, Fangmeier, Ruff, & Johnson-Laird, 2003; Monti, Osherson, Martinez, & Parsons, 2007; Noveck, Goel, & Smith, 2004; Osherson et al., 1998; Parsons & Osherson, 2001; Prado & Noveck, 2007), problem solving (Cardoso & Parks, 1998; Carlin et al., 2000; Colvin, Dunbar, & Grafman, 2001; Fincham, Carter, van Veen, Stenger, &Anderson, 2002; Goel, 2002; Goel & Grafman, 1995, 2000; Goel, Grafman, Tajik, Gana, & Danto, 1997; Goel, Pullara, & Grafman, 2001; Morris, Miotto, Feigenbaum, Bullock, & Polkey, 1997; Newman, Carpenter, Varma, & Just, 2003; Owen, Doyon, Petrides, & Evans, 1996; Rowe, Owen, Johnsrude, & Passingham, 2001), and decision making (Bechara, Damasio, Damasio, & Anderson, 1994; Bechara, Damasio, Tranel, & Damasio, 2005; De Neys & Goel, in press;

The study of thinking in psychology is distributed over three largely independent branches: problem solving, reasoning, and judgment and decision making. These domains are delineated by the type of tasks they study and the underlying formal apparatus they appeal to in their explanatory framework. The problem-solving literature (Newell & Simon, 1972) studies tasks such as cryptarithmetic, theorem proving, Tower of Hanoi, and also more open-ended, real-world problems such as planning, design, and even scientific induction, among others. The basic theoretical framework is one of search through a problem space using the formal apparatus of production rules (and more generally, recursive function theory). The reasoning literature (Evans, Newstead, & Byrne, 1993) is largely focused on deductive inference tasks and draws on the formal apparatus of deductive inference. The judgment and decision-making literature (Kahneman, Slovic, & Tversky, 1988) uses such tasks as the base rate fallacy, conjunction fallacy, and so on, and draws on the formal apparatus of probability theory. The goal of these psychological enterprises is to articulate the underlying cognitive mechanisms of thinking. Unfortunately, there is little or no communication across the subdomains. Neuropsychology has focused on measuring the impact of brain injury and disease on various aspects of thinking, using IQ and memory tests, along with numerous specifically developed tasks (Lezak, 1995). Some of these tasks have directly targeted complex cognitive processes. For example, card sorting (Milner, 1963), word similarity, proverbs (Rylander, 1939), and word definition tasks have been used to measure abstraction and generalization ability. Nonsense drawing (Smith & Milner, 1988) and word generation tasks have been used to measure nonverbal and verbal fluency, respectively. Shell games have been used to measure rule/pattern induction (McCarthy & Warrington, 1990). Choice reaction time studies have been used to measure the use of advance information (Alivisatos & Milner, 1989). The Tower of London has been used to measure look ahead/anticipatory abilities (Shallice, 1982) and cognitive estimation has been used to measure judgment (Shallice & Evans, 1978). Other studies 417

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De Neys, Vartanian, & Goel, 2008; Fellows & Farah, 2003; Glimcher, 2002; Manes et al., 2002; Paulus et al., 2001; Sanfey, Rilling, Aronson, Nystrom, & Cohen, 2003; Tranel, Bechara, & Denburg, 2002; Wolford, Miller, & Gazzaniga, 2000) have generated considerable data on the neural systems underlying these processes. A direct review of this literature would consist of lists of neural activations associated with the different tasks in the various studies. While this may be of some pedagogical value, it is not clear that it would deepen or enrich our understanding of the underlying cognitive systems. Goel, (2007) provides such a summary for the deductive reasoning literature. My goals, in this review, are somewhat different. I want to (a) discuss neuropsychology’s contributions to the three domains within a common framework and (b) do so in a way that is relevant and informative to the development of cognitive theories of thinking. In terms of the former goal, I propose to organize the data generated from recent neuroimaging and lesion studies into the following three themes derived from the behavioral literature on reasoning, decision making, and problem solving: (a) heuristics versus formal/universal methods; (b) conflict detection and inhibition; and (c) ill-structured versus well-structured problems. These themes crop up in each of the three areas to varying degrees. In terms of the latter goal, I have previously argued (Goel, 2005) that the most immediate and valuable contribution that cognitive neuroscience can make to the understanding of cognitive processes is not in terms of listing a series of neural activations but in terms of identifying double dissociations (see Appendix A). Double dissociations are indicative of causal joints in the neural machinery (Shallice, 1988) that cognitive theories will ultimately need to respect. Putting these two aspects together, I argue that the neuropsychological evidence indicates dissociations respecting the three themes identified from the cognitive literature and suggests that these themes/ issues pick out real causal joints in the neural machinery that cognitive theories will need to respect. As such, this review is selective and nonexhaustive.

HEURISTICS VERSUS FORMAL/ UNIVERSAL PROCESSES The distinction between heuristic and formal/universal processes is an important, common theme in the problemsolving, decision-making, and reasoning literatures. Within the modern cognitive literature, a useful place to begin tracing the distinction is with Simon’s notion of bounded rationality (Simon, 1981, 1983) and the incorporation of this idea into Newell & Simon’s (1972) models of human problem solving. The key idea was the introduction of the

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notion of the problem space, a computational modeling space shaped by the constraints imposed by the structures of a time- and memory-bound serial information processing system and the task environment. The built-in strategies for searching this problem space include such content-free universal methods as Means Ends Analysis, Breadth First Search, Depth First Search, and so on. But the universal applicability of these methods comes at the cost of enormous computational resources. Given that the cognitive agent is a time-and-memory bound serial processor, it would often not be able to respond in real time, if it had to rely on formal, context independent processes. So the first line of defense for such a system is the deployment of task-specific knowledge to circumvent formal search procedures. Consider the following example: I arrive at the airport in Paris and need to make a telephone call before catching my connecting flight in an hour. I notice that the public telephones require a special calling card. The airport is a multistory building with shops on several floors. If I know nothing about France, I could start on the top floor at one end of the building, enter a store and ask for a telephone card. If I find one, I can terminate my search and make my phone call. If I don’t, I can proceed to the next store, continuing until I have visited every store on the floor (or found a telephone card). I could then go down to the next floor and continue in the same fashion. Following this breadth first (British Museum) search strategy, I will systematically visit each store and find a telephone card if one of them sells it. The search will terminate when I have found the telephone card or visited the last store. This may take several hours, and I may miss my connecting flight. However, I may have a specific piece of knowledge about France that may help me circumvent this search: Telephone cards are sold by Tabac Shops. If I know this, I merely have to search the directory of shops, find the Tabac shop and go directly there, circumventing the search procedure. Notice this knowledge is very powerful, but very situation specific. It will not help me find a pair of socks in Paris or make a telephone call in Delhi. On this account, heuristics are situation specific, learned, and consciously applied procedures, not the type of things one makes a science out of. So not surprisingly, much of the subsequent research effort of this program was devoted to developing formal search algorithms and computational constraints on cognitive architecture (Newell, 1990) rather than heuristic procedures. The work of Tversky and Kahneman (1974) in the judgment and decision-making literature can be viewed as a development of the heuristic branch of the cognitive system. Perhaps their most important contribution was the identification of a number of content general “biases” or fallacies that we are all subject to, such as the conjunction fallacy and base rate fallacy (Gilovich & Kahneman, 2002;

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Heuristics versus Formal/Universal Processes

Kahneman et al., 1988; Kahneman & Tversky, 1996). Here is an example of the latter (Kahneman & Tversky, 1973): A psychologist wrote thumbnail descriptions of a sample of 100 participants consisting of 15 engineers and 85 lawyers. The description that follows was chosen at random from the 100 available descriptions: Jack is a 45-year-old man. He is married and has four children. He is generally conservative, careful, and ambitious. He shows no interest in political and social issues and spends most of his free time on his many hobbies that include home carpentry, sailing, and mathematical puzzles.

Which one of the following statements is most likely? 1. Jack is an engineer. 2. Jack is a lawyer. 3. Equally likely that Jack is an engineer or lawyer. In such problems, the correct normative answers are given by the base rates, so the response should be 2 in this example. However, the accompanying description engages heuristic processes that often override the normative response, so many subjects will respond 1 based on the description. Kahneman and Tversky identified several heuristics including representativeness, availability, and anchoring and adjustment to explain such fallacies. The one at work in this example is “representativeness.” The description is more representative of a typical engineer than a lawyer and this often overrides the base rate information. Similar phenomenon have been identified in the logical reasoning literature under the guise of the belief-bias effect. Logical arguments with believable conclusions are accepted much more readily than arguments with unbelievable conclusions (Wilkins, 1928). For example, the following valid argument with a believable conclusion is accepted as valid 96% of the time: No cigarettes are inexpensive. Some addictive things are inexpensive. ⬖ Some addictive things are not cigarettes. By contrast, a logically identical argument, but with an unbelievable conclusion, is accepted as valid only 46% of the time (Evans, Barston, & Pollard, 1983): No addictive things are inexpensive. Some cigarettes are inexpensive. ⬖ Some cigarettes are not addictive. The explanation is that instead of engaging in formal logical analysis, subjects are falling victim to a believability

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heuristic that leads them astray in this particular case (Evans, 2003; Evans & Over, 1996; Sloman, 1996). Although there are some interesting and important differences—in terms of ontological commitments—in the development of these ideas in the three literatures, they are beyond the scope of this review. The important point for our purposes is the distinction between using knowledge and beliefs to solve a problem versus using more general or “universal,” content-free procedures. This is an important theme in all three areas under consideration. The neuropsychological data provides compelling evidence for these two systems in terms of double dissociations respecting the distinction. In the balance of this section, I review studies where the experimental design involves the manipulation between heuristic and formal strategies. In the next section, studies involving explicit conflict between the two strategies are considered. Perhaps the most extensive evidence for anatomical dissociation along the heuristic and formal dimension comes from neuroimaging work on deductive reasoning. Goel, Buchel, et al. (2000) presented subjects with logical arguments containing familiar content (i.e., propositions that they would have beliefs about) such as: All dogs are pets. All poodles are dogs. ⬖ All poodles are pets. and logically identical arguments lacking any meaningful content (i.e., subjects can have no beliefs about the truth or falsity of these propositions) such as: All P are B. All C are P. ⬖ All C are B. These studies indicate that two distinct systems are involved in reasoning about familiar and unfamiliar material. More specifically, a left lateralized frontal-temporal conceptual/language system processes familiar, conceptually coherent material, corresponding to the heuristic system, while a bilateral parietal visuospatial system processes unfamiliar, nonconceptual material, corresponding to the formal/universal system (see Figure 21.1). The involvement of the left frontal-temporal system in reasoning about familiar or meaningful content has also been demonstrated in neurological patients with focal unilateral lesions to the prefrontal cortex (parietal lobes intact) using the Wason card selection task (see Figure 21.2; Goel, Shuren, et al., 2004). These patients performed as well as normal controls on the arbitrary version of the task, but unlike the normal controls they failed to benefit from the

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(B) Reasoning with familiar material

Figure 21.1 Dissociation of systems involved in heuristic and formal reasoning processes. Note: A: Reasoning about familiar material (All apples are red fruit. All red fruit are nutritious. All apples are nutritious) activates a left frontal (BA 47) temporal (BA 21/22) system. B: Reasoning about familiar material (All A

Arbitrary condition M

A

8

3

“if a card has a vowel on one side, then it has an even number on the other side” Familiar content condition Beer

7-Up

12

are B. All B are C. All A are C) activates bilateral parietal lobes (BA 7, 40) and the dorsal prefrontal cortex (BA 6). From “Dissociation of Mechanisms Underlying Syllogistic Reasoning,” by V. Goel, C. Buchel, C. Frith, and R. J. Dolan, 2000, NeuroImage, 12, p. 510. Reprinted with permission.

There is even some evidence to suggest that the response of the frontal-temporal system to familiar situations may be content-specific to some degree (in keeping with some content specificity in the organization of temporal lobes; McCarthy & Warrington, 1990). One source of evidence comes from a study involving landmarks in familiar and unfamiliar environments (Goel, Makale, & Grafman, 2004). Arguments such as:

25

“if someone is drinking beer, then they’re at least 21 years old”

Figure 21.2 Wason card selection task. Note: The Wason card selection task is perhaps the most widely used task in the cognitive literature to assess content effects in reasoning. In the original (or arbitrary) version of the task, four cards are placed on a table, each having a letter on one side and a number on the other side. Two letters and two numbers are visible. The task is to determine which cards need to be turned over in order to verify the rule “if a card has a vowel on one side, then it has an even number on the other side.” (The correct answer is to turn over the first and fourth cards. The first card provides confirmation for the statement while the fourth card provides evidence of disconfirmation. Information on the other two cards is irrelevant.) The basic finding is that if the task involves arbitrary material, as in the first example, performance is relatively poor (25% to 30%). However, the presence of meaningful content (e.g., “if someone is drinking beer, then they are at least 21 years old”), as in the second example, dramatically improves performance (90% to 95%).

presence of familiar content in the meaningful version of the task. In fact, consistent with the neuroimaging data, the latter result was driven by the exceptionally poor performance of patients with left frontal lobe lesions. Patients with lesions to the right prefrontal cortex performed as well as normal controls.

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Reasoning with unfamiliar material

Paris is south of London. London is south of Edinburgh. ⬖ Paris is south of Edinburgh. involving propositions that subjects would have beliefs about (as confirmed by a postscan questionnaire), were compared with arguments such as: The AI lab is south of the Roth Center. Roth Center is south of Cedar Hall. ⬖ The AI lab is south of Cedar Hall. containing propositions that subjects could not have beliefs about because they describe a fictional, unknown environment. These stimuli not only resulted in a dissociation between a frontal temporal system for the familiar environments and a parietal system for the unfamiliar environments, the temporal lobe activation in the former case included posterior hippocampus and parahippocampal gyrus, regions implicated in spatial memory and navigation tasks. These data provide support for the generalization of the previous results to transitive reasoning and indicate variability in temporal lobe activation as a function of content. Perhaps the most studied example of content specificity in the organization of

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Conflict Detection/Inhibition

the heuristic system is the Theory of Mind reasoning system identified by a number of studies (Fletcher et al., 1995; Goel, Grafman, Sadato, & Hallet, 1995). There is at least one study (De Neys & Goel, in press) that suggests a similar breakdown for problems from the decision-making literature, in particular the lawyer-engineer type base rate problems described previously. Participants were scanned while they solved lawyer-engineer type problems. Results showed that, just as during deductive reasoning, belief-mediated decisions based on stereotypical descriptions activate a left temporal lobe system whereas a bilateral parietal system is activated when the response is in line with the base rates. While the hypothesis for dissociation of heuristic and universal/formal processes at the neural level has not been tested directly with tasks from the problem-solving literature, there is at least some suggestive data, and at least one theory of frontal lobe functioning predicts that this should be the case. Grafman (2002; Wood & Grafman, 2003) has long maintained that the prefrontal cortex is critical for the storage and retrieval of large-scale knowledge structures generally called “scripts.” Scripts guide our behavior in daily, routine situations like going to work, ordering a meal at the restaurant, going shopping, and so on. There are many studies showing patients with lesions to the prefrontal cortex often show greater impairment in problem-solving tasks involving real world knowledge than in the more abstract neuropsychological test batteries. One such task tapping simple real world knowledge is the scripts task (Sirigu, Zalla, Pillon, Grafman, Agid, et al., 1995; Sirigu, Zalla, Pillon, Grafman, Dubois, et al., 1995). Subjects are given familiar situations and asked to list what they would do. For example, “you’re going on a date tonight, list all the things that you would do.” The responses of patients are compared with the normed response of controls. Patients with lesions to the prefrontal cortex have greater difficulty with this type of task than would be anticipated by their neuropsychological testing profile. However, the interpretation of these data is complicated by the fact that real world problems and neuropsychological test batteries differ on many dimensions (including task structure, which is discussed later), not just the presence of real world knowledge. Ideally, what is required is a task that can be undertaken in a condition where the subject’s real world knowledge is very relevant and another condition where it is less relevant. I’m unaware of such manipulation in the neuropsychology literature.

CONFLICT DETECTION/INHIBITION A natural consequence of dual systems/processes is the potential for conflict and facilitation between the two

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systems. For example, in the case of logical reasoning, given arguments such as: All apples are fruit. All fruit are poisonous. ⬖ All apples are poisonous. There is a conflict that arises between the validity of the argument and the truth of the conclusion (valid argument but false conclusion). A robust consequence of the content effect is that subjects perform better on reasoning tasks when the logical conclusion is consistent with their beliefs about the world than when it is inconsistent with their beliefs (Evans et al., 1983; Wilkins, 1928). A very similar situation arises in many decision-making tasks. Consider the base rate fallacy task. The base rates point to one response (95% chance that Jack is a lawyer) even though the description of Jack is more prototypical of an engineer. This generates a conflict that the subject must recognize and resolve. Is the description sufficiently poignant/salient to overcome the odds in this particular instance? The rectangle and polygon task (Stavy, Goel, Critchley, & Dolan, 2006) provides an example from the problem-solving literature. Subjects are shown a rectangle followed by a polygon derived from the rectangle by a minor modification (see Figure 21.3). They are asked to compare the Original Rectangle

Lesser area ⫽ Same perimeter (Incongruent trial)

Greater area ⫽ Greater perimeter (Congruent trial)

Figure 21.3 The rectangle and polygon task. Note: In this task, subjects are shown a rectangle followed by a polygon, generated by a modification to the rectangle. They are asked to compare the perimeters of the two figures and determine whether the second is larger than the first. The area of the second figure also changes with the perimeter. This change in area seems to be a salient que for most subjects. When the perimeter changes in the same direction as the area (congruent trials), subjects perform very well on the task. However, when the perimeter does not change in the same direction as the area (incongruent trials), task performance suffers.

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perimeters of the two figures and determine whether the second is larger than the first. In some trials (congruent condition), the perimeter and area change in the same direction (i.e., both increase or decrease as a result of the modification). In other trials (incongruent condition), the area changes but the perimeter stays the same (e.g., when a small square is removed from the upper right hand corner of the triangle). Young adults accurately respond to the congruent trials but many (46%) claim that the perimeter of the derived polygon in the incongruent condition is smaller than that of the original rectangle (Stavy et al., 2006). They explain this response in terms such as “a corner has been taken away,” suggesting they’re using a strategy that might be referred to as “more A (area) ⫽ more B (perimeter).” The data suggest that they do the task by attending to both the area and perimeter of the rectangle. But for most subjects, the area seems to be the more salient feature. In the congruent condition, both processing streams result in the same response. In the incongruent condition, a conflict arises between the responses generated by processing the area and the perimeter. To generate a correct response in this condition, the conflict must be detected and the salient response based on the area must be inhibited. The neural basis of conflict detection issue has been extensively explored within the reasoning domain. Within inhibitory belief trials, the prepotent response is the incorrect response associated with belief-bias (e.g., all children are nasty; all nasty people should be punished; therefore all children should be punished). Incorrect responses in such trials indicate that subjects failed to detect the conflict between their beliefs and the logical inference and/or inhibit the prepotent response associated with the belief-bias. These beliefbiased responses activate the ventral medial prefrontal cortex (BA 11, 32), highlighting its role in nonlogical, belief-based responses (Goel & Dolan, 2003). The correct response indicates that subjects detected the conflict between their beliefs and the logical inference, inhibited the prepotent response associated with the belief-bias, and engaged the formal reasoning mechanism. The detection of this conflict requires engagement of the right lateral/dorsal lateral prefrontal cortex (BA 45, 46; see Figure 21.4; Goel, Buchel, et al., 2000; Goel & Dolan, 2003; Prado & Noveck, 2007). This conflict detection role of the right lateral/dorsal prefrontal cortex is a generalized phenomenon that has been documented in a wide range of paradigms in the cognitive neuroscience literature (Fink et al., 1999; Picton, Stuss, Shallice, Alexander, & Gillingham, 2006; Vallesi, Mussoni, et al., 2007; Vallesi, Shallice, & Walsh, 2007). One demonstration of this system with lesion data was carried out by Caramazza, Gordon, Zurif, and DeLuca (1976) using simple two-term reasoning problems such as the following: “Mike is taller than George”; who is taller? They reported that left hemisphere patients were impaired in

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Figure 21.4 Conflict detection system. Note: The right lateral/dorsal lateral prefrontal cortex (BA 45, 46) is activated during conflict detection. For example, in the following argument “All apples are red fruit; all red fruit are poisonous; all apples are poisonous” the correct logical answer is “valid”/“true” but the conclusion is inconsistent with our world knowledge, resulting in a belief-logic conflict. From “Explaining Modulation of Reasoning by Belief,” by V. Goel and R. J. Dolan, 2003, Cognition, 87, p. B18. Reprinted with permission.

all forms of the problem but—consistent with imaging data (Goel, Buchel, et al., 2000; Goel & Dolan, 2003)—right hemisphere patients were only impaired when the form of the question was incongruent with the premise (e.g., who is shorter?). The involvement of the right lateral/dorsolateral prefrontal cortex in conflict detection in decision-making tasks is illustrated by De Neys, Vartanian, et al. (2008). They scanned normal healthy volunteers with fMRI while participants engaged in the lawyer-engineer type base rate problems (introduced above). As in the reasoning paradigm, activation of right lateral prefrontal cortex was evident when participants inhibited the stereotypical heuristic responses and correctly completed the decision making task. In terms of problem-solving tasks, there are several relevant examples one can choose from, though the results are more mixed. Above we introduced the rectangle and polygon task. Neuroimaging studies of this task (Stavy et al., 2006) found activation in bilateral prefrontal cortex in the incongruent condition compared to the congruent condition, where the conflict between two strategies needs to be detected and overcome. Reverberi, Lavaroni, Gigli, Skrap, and Shallice (2005) provided a second example of the role of the right prefrontal cortex and content detection. They carried out a revised version of the Brixton Task with neurological patients with focal lesions. In the first half of this task subjects are presented with a series of cards, one at a time. Each card contains a 2 ⫻ 5 matrix of numbered circles. One circle on each card is colored blue, the others are white. The position of the blue circle moves from card to card following one of seven rules. The rule is switched every five to seven cards without warning. Upon being presented with a card the subject’s task is to indicate the position of the blue circle on the next card,

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III-Structured and Well-Structured Situations

thus indicating their ability to induce the current rule. The second half of the task is similar to the first, except for the following important differences: (a) rules stay active for 6 to 10 trials and (b) before the end of the particular series of rules an interfering rule is introduced. This consists of the sequence of four cards from the first part (only they contain red-filled circles rather than blue ones). These four cards follow a previously presented rule, but differ from the current rule thus introducing a conflict between the interfering rule and the previously active rule. This conflict must be detected, the interfering rule inhibited, and the response generated based on the active rule. They report that while patients with lesions to the left prefrontal cortex show an impairment in rule induction, patients with lesions to the right prefrontal cortex are impaired specifically in the ruleconflict condition.

III-STRUCTURED AND WELL-STRUCTURED SITUATIONS The issue of ill-structured and well-structured task environments or situations has been a crucial point of debate and contention in the problem-solving literature for 40 years. The distinction originates with Reitman (1964) who classified problems based on the distribution of information within the three components (start state, goal state, and the transformation function) of a problem vector. Problems where the information content of each of the vector components is absent or incomplete are said to be ill-structured. To the extent the information is completely specified, the problem is well-structured. A mundane example of an ill-structured problem is provided by the task of planning a meal for a guest. The start state is the current state of affairs. While some of the salient facts are apparent, it is not clear that all the relevant aspects can be immediately specified or determined (e.g., How hungry will they be? How much time and effort do I want to expend?). The goal state, while clear in the broadest sense (i.e., have a successful meal), cannot be fully articulated (e.g., How much do I care about impressing the guest? Should there be 3 or 4 courses? Would salmon be appropriate? Would they prefer a barbecue or an indoor meal?). And finally, the transformation function is also incompletely specified (e.g., Should I have the meal catered, prepare it myself, or ask everyone to bring a dish? If I prepare it, should I use fresh or frozen salmon?). Well-structured problems are characterized by the presence of information in each of the components of the problem vector. The Tower of Hanoi (see Figure 21.5) provides a relevant example (Goel & Grafman, 1995). The start state is completely specified (e.g., the disks are stacked in

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2

1 A B

3

1

2

3 A BC

C Start

423

Goal

Figure 21.5 Tower of Hanoi Task. Note: The Tower of Hanoi puzzle consists of three pegs and several disks of varying size. Given a start state, in which the disks are stacked on one or more pegs, the task is to reach a goal state in which the disks are stacked in descending order on a specified peg. There are three constraints on the transformation of the start state into the goal state. (1) Only one disk may be moved at a time. (2) Any disk not being currently moved must remain on the pegs. (3) A larger disk may not be placed on a smaller disk.

descending order on peg 1). There is a clearly defined test for the goal state (e.g., stack the disks in descending order on peg 3). The transformation function is restricted to moving disks within the following constraints: (1) Only one disk may be moved at a time. (2) Any disk not being currently moved must remain on a peg. (3) A larger disk may not be placed on a smaller disk. Goel (1995) has extended Reitman’s original characterization along the number of dimensions and articulated the cognitive consequences of these differences. In particular, it has been argued that qualitatively different cognitive and computational machinery is required to deal with illstructured and well-structured situations/problems (Goel, 1995). Contrary to this position, others have argued that there are no qualitative differences between ill- and wellstructured problem situations and that the information processing theory machinery developed to deal with wellstructured problems can also account for ill-structured problems (Simon, 1973). The neuropsychological data, however, support a distinction. These issues can also arise in the reasoning and decisionmaking literature, though they have not garnered equivalent attention in these domains. The most natural place for illstructured situations in the reasoning literature is in induction tasks, where, by definition, the information provided in the premises always underdetermines the conclusion. However, subjects often make assumptions that eliminate uncertainty from the conclusion. For example, consider the following argument: “Sand can be red; the planet Mars is red; the sand on Mars is red.” In pilot studies, some subjects confidently responded “no” to this argument. When asked to explain, they made responses such as “there is no sand on Mars.” Deductive reasoning (when undertaken by nonexperts) is a prototypical example of a well-structured task. However, there are certain logical forms that result in indeterminate conclusions. For example, given A ⬎ B and A ⬎ C what is the relationship between B and C? Technically, this is not an ill-structured inference. Any proposed relationship between B and C is undetermined and therefore

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any proposed conclusion is invalid. However, it may not be construed as such by subjects. Cognitive theories of reasoning do not make much of this and treat these indeterminate forms in the same way as determinate forms (A ⬎ B; B ⬎ C; A ⬎ C). The neuropsychological data discussed below suggest otherwise. In terms of tasks from the decision-making literature, elements of ill-structured situations will arise where the information is inconclusive. For example, a base rate problem with the base rate of 50:50 and ambiguous or neutral descriptions would result in an ill-structured problem. To my knowledge, these types of conditions have not been explored in the decision-making literature. There is an interesting puzzle in the neuropsychology literature that can be explained in terms of the different cognitive resources required to deal with ill-structured and well-structured problems (Goel, 1995). A subset of patients with frontal lobe lesions perform very well on neuropsychological test batteries (including IQ and memory measures) but encounter serious problems in coping with real life situations (see Goel & Grafman, 2000; Eslinger & Damasio, 1985; Shallice & Burgess, 1991; among others). Different explanations have been offered for the phenomenon. Damasio (1994) argues that the cause of this difficulty is the patient’s inability to inform cognitive processes by visceral, noncognitive factors. Grafman’s (1989) underlying intuition, already mentioned previously, is that the crucial issue is patients’ inability to perform in routine, over-learned situations. His structured-event complex (SEC) theory proposes that much of our world knowledge is stored in scriptlike data structures and frontal lobe patients have difficulty in accessing/retrieving these structures. Shallice (1988) suggests that the key deficit in frontal lobe lesion patients is dealing with task novelty. The idea is that there is a builtin contention scheduler that determines responses in overlearned, routine situations. However, when the organism is confronted with a novel situation, the contention scheduler is unable to cope. At this point, control passes to the more sophisticated supervisory attentional system (SAS), which is damaged in frontal lobe patients, thus rendering them incapable of coping with novel situations. Goel (2002; Goel, Grafman, et al., 1997) has argued that neuropsychological test batteries contain largely wellstructured problems while problems encountered in real life situations contain both ill-structured and well-structured components. Given that different cognitive mechanisms are required to deal with the two situations, there may be an anatomical dissociation corresponding to the cognitive and computational dissociations. In particular, I am suggesting that when the task environment contains either facilitative patterns (real or imaginary) that can be locked onto and extrapolated for successful

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solution, or at least does not contain built-in hindrances to pattern extraction, the left prefrontal cortex may be necessary and sufficient for task solution. However, in cases where the start state pattern obstructs/hinders or totally underspecifies a solution path through the problem space, the left hemisphere interpreter may prematurely lock on to erroneous solutions. In such situations, the right prefrontal cortex plays a necessary role in generating possibilities that can aid in navigating through the problem space. It does so by supporting the encoding and processing of illstructured representations that facilitate lateral transformations (Goel, 1995). An apt example of the patient profile under discussion is provided by Goel and Grafman’s (2000) patient PF. PF was an accomplished professional architect with a right prefrontal cortex lesion. This patient scored 128 on the WAIS-R, but was simply unable to cope in the world. At age 56, he found himself unemployable and living at home with his mother. Because the patient was an architect, a task that required him to develop a new design for a lab space was administered. His performance was compared to two age- and educationmatched controls (an architect and a lawyer). The patient had superior memory and IQ and understood the task, and even observed that “this is a very simple problem.” His sophisticated architectural knowledge base was still intact and he used it quite skillfully during the problem-structuring phase. However, the patient’s problem-solving behavior differed from the controls’ behavior in the following ways: (a) he had difficulty in making the transition from problem structuring to problem solving; (b) as a result the preliminary planning phase did not start until two-thirds of the way into the session; (c) when it did occur it was minimal and erratic, consisting of three independently generated fragments; (d) there was no progression or lateral development of these fragments; (e) there was no carryover of abstract information into the preliminary planning or later phases; and (f) the patient did not make it to the detailing phase. This suggests that the key to understanding this patient’s deficit is to understand the cognitive processes and mechanisms involved in the preliminary (ill-structured) planning phase of the task. Another relevant example comes from the predicaments task (Channon & Crawford, 1999). Channon and Crawford presented subjects (patients with anterior lesions, posterior lesions, and normal controls) with stories of everyday awkward situations or predicaments such as the following: Anne is in her office when Tony comes in. She asks how he is, and he says he is all right, but tired. She agrees that he looks tired, and asks what is the matter. He has new neighbors who moved into the flat above his a couple weeks ago. They are nice people, but they own dogs and keep them in their kitchen

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III-Structured and Well-Structured Situations

at night, which is directly above Tony’s bedroom. All night, and every night since they moved in, the dogs jump around and bark. He finds it impossible to get to sleep. He says he has had a word with the neighbors, and although they were very reasonable, they said they had nowhere else to put the dogs as it is a block of flats.

Subjects were required to generate solutions to these scenarios. Even though this may be an “everyday” situation, it is very clearly an ill-structured situation. Subjects also carried out more abstract neuropsychological tests that would satisfy the definition of well-structured problems. Patients as a group were impaired relative to the normal controls in both the everyday predicaments task and the more abstract neuropsychological tests. Patients with anterior lesions were impaired in more aspects of the predicaments task than the posterior patients. Ill-structured problems do not easily lend themselves to the technical constraints of brain imaging studies. To get around this difficulty, Goel and colleagues tried to simulate specific aspects of ill-structured problems within wellstructured problems. In one such attempt, Vartanian and Goel (2005) manipulated the constraints on the search space of an anagram task. On unconstrained trials, subjects were required to rearrange letters to generate solutions (e.g., generate a word from IKFEN). On semantically constrained trials, they were required to rearrange letters to generate solutions within particular semantic categories (e.g., generate a word for a kitchen utensil from IKFEN). On baseline trials, they rearranged letters to make specific words (e.g., generate the word KNIFE from IKFEN). The critical comparison of unconstrained versus semantically constrained trials revealed significant activation in areas

1.5 1 0.5 0 ⫺0.5 ⫺1 ⫺1.5

Figure 21.6 Right ventral lateral prefrontal cortex is activated by underconstrained situations. Note: A: Hypothesis generation in unconstrained anagram trials is associated with significant activation in the right ventral lateral prefrontal cortex (BA 47). B: Furthermore, activation in the right ventral lateral prefrontal

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including the right ventral lateral prefrontal cortex (see Figure 21.6), left superior frontal gyrus, frontal pole, right superior parietal lobe, right post central gyrus, and the occipital-parietal sulcus. They argued that the activation in the right ventral lateral prefrontal cortex is related to hypothesis generation in unconstrained settings, whereas activation in other structures is related to additional semantic retrieval, semantic categorization, and cognitive monitoring processes. These results extend the lesion data by demonstrating that an absence of constrains on the solution space is sufficient to engage the right ventral lateral prefrontal cortex in hypothesis generation, even in a linguistic task. As noted, while deductive reasoning is probably the prototypical example of a well-structured task for most people, indeterminate trials do allow for situations of incomplete information. Goel, Tierney, et al. (2007) tested neurological patients with focal unilateral frontal lobe lesions on a transitive inference task while systematically manipulating completeness of information regarding the status of the conclusion (i.e., determinate and indeterminate trials). The results demonstrated a double dissociation such that patients with left prefrontal cortex lesions were selectively impaired in trials with complete information (i.e., determinate trials such as A ⬎ B, B ⬎ C, A ⬎ C; and A ⬎ B, B ⬎ C, C ⬎ A), while patients with right prefrontal cortex lesions were selectively impaired in trials with incomplete information (i.e., indeterminate trials; A ⬎ B, A ⬎ C, B ⬎ C) (see Figure 21.7). These findings are very similar to those of the problem-solving tasks. While it is possible to have ill-structured situations within decision-making task paradigms (see above), I am unaware of any studies that have addressed this issue.

(B) Contrast Estimate at (30, 34, ⫺20)

(A)

425

Unconstrained

Semantically Constrained

Baseline

cortex increases as a function of decreasing constraints on the problem space in the anagram task. From “Task Constraints Modulate Activation in Right Ventral Lateral Prefrontal Cortex,” by O. Vartanian and V. Goel, 2005, Neuroimage, 27, p. 931. Reprinted with permission.

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(B) 8

Performance of left and right PFC patients on determinate and indeterminate trials

7 6

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Figure 21.7 Double dissociation between systems for processing certain and uncertain information. Note: A: Lesion overlay maps (transverse slices, R ⫽ L), displaying left and right prefrontal cortex lesions. B: Accuracy scores on three-term transitive reasoning. A Lesion (right prefrontal cortex, left prefrontal cortex, normal controls) by Determinacy (determinate, indeterminate)

SUMMARY In this chapter, I provide a selective, conceptually motivated review of cognitive neuroscience’s contributions to the understanding of thought processes (i.e., reasoning, problem solving, and decision making). The strategy has been to select three issues (heuristic versus formal processes, conflict detection/inhibition, and ill-structured and well-structured task situations) that have played important roles in the development of cognitive theories of thinking processes and suggest that these behavioral/functional distinctions correspond to distinctions in the underlying neural machinery. The exercise is valuable for at least three reasons: 1. The identification of dissociations corresponding to functional/behavioral distinctions reinforces those distinctions and provides support for cognitive theories that respect them. 2. The fact that the dissociations involved similar anatomical structures in reasoning, problem-solving, and decision-making tasks (see Table 21.1) suggests a degree of underlying similarity or unity in these task domains at the anatomical level that is often ignored at the cognitive level. 3. The strategy of identifying dissociations may help to provide much needed mid-level constructs for our theories of thinking.

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20

Determinate trials Normal controls

Left PFC lesions

Indeterminate trials Right PFC lesions

interaction shows a crossover double dissociation in the performance of left and right prefrontal cortex patients in determinate and indeterminate trials. From “Hemispheric Specialization in Human Prefrontal Cortex for Resolving Certain and Uncertain Inferences,” by V. Goel, Tierney, et al., 2007, Cerebral Cortex, 17, p. 2246. Reprinted with permission.

Current theories of thinking operate at the level of either phenomenological descriptions or computational descriptions. A well-known example of the latter was introduced above in terms of the problem space construct (Newell & Simon, 1972). While this provides some critical constraints in terms of theoretical vocabulary, short-term memory limitations, and sequential processing, it is essentially a Turing machine-level description. What is missing from our theories are mid-level constructs that connect the phenomenological description to the Turing Machine-level description. The dissociations that have been identified by lesion and neuroimaging studies—namely a formal pattern matcher, a content sensitive pattern matcher, the conflict detection system, and a system for maintaining uncertain information—are good candidates for these mid-level systems or constructs. The first two systems may be part of Gazzaniga’s “left hemisphere interpreter” (Gazzaniga, 1985, 2000). The function of the “interpreter” is to make sense of the world by completing patterns (i.e., filling in the gaps in the available information). I suspect that this system does not care whether the pattern is logical, causal, social, statistical, and so on. It simply abhors uncertainty and will complete any pattern, often prematurely, to the detriment of the organism. The roles of the conflict detection and uncertainty maintenance systems are, respectively, to detect conflicts in patterns and actively maintain representations of indeterminate/ambiguous situations and bring them to the attention of the “interpreter.” While there

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Appendix A: Role of Neuropsychological Data in Informing Cognitive Theories

427

TABLE 21.1 Summary of studies and areas of brain activation discussed in the chapter organized by task domain (reasoning, decision-making, and problem solving) and issues of interest (familiarity, conflict detection, and task structure). Familiarity Domain & Studies

Method

Heuristic

Formal

PL

Task Structure Conflict Detection

Complete Information

Incomplete Information

Left PFC

Right PFC

Reasoning Goel et al. (2000)

fMRI

Left F-TL

Goel, Shuren, et al. (2004)

lesion

Left PFC

Goel, Mikale & Grafman (2004)

fMRI

Left F-TL

Goel et al. (2000)

fMRI

PL Right PFC

Goel and Dolan (2003)

fMRI

Right PFC

Caramazza et al. (1976)

lesion

Right TL

Goel et al. (2006)

lesion

Decision-Making De Neys & Goel (in press)

fMRI

De Neys, Vartanian & Goel (2008)

fMRI

Left F-TL Right PFC

N/A Problem Solving Sirigu et al. (1995)

lesion

PFC

Stavy et al. (2006)

fMRI

Right PFC

Reverberi et al. (2005)

lesion

Right PFC

Goel & Grafman (2000)

lesion

Channon & Crawford (1999)

lesion

Vartanian & Goel (2005)

fMRI

Right PFC PFC Left PFC

Right VLPFC

Abbreviations: F-TL ⫽ frontal-temporal lobes, PFC ⫽ prefrontal cortex; TL ⫽ temporal lobes; VLPFC ⫽ ventral lateral prefrontal cortex; PL ⫽ parietal lobes

is considerable evidence for the existence of such systems, their time course of processing and interactions are largely unknown. One account of how these systems may interact in the case of logical reasoning appears in Goel (2008).

APPENDIX A: ROLE OF NEUROPSYCHOLOGICAL DATA IN INFORMING COGNITIVE THEORIES Although few cognitive psychologists today question the value of neuroimaging and lesion data, there is still a lack of consensus as to their role in informing cognitive theory. They have at least two immediate roles: localization of functions and of the dissociation of functions. Arguably the latter is much more important than the former. Localization of Brain Functions It is now generally accepted that there is a degree of modularity in aspects of brain organization. Over the years, neuropsychologists and neuroscientists have accumulated

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some knowledge of this organization. For example, we know some brain regions are involved in processing language while other regions process visual spatial information. Finding selective involvement of these regions in complex cognitive tasks—like reasoning—can help us differentiate between competing cognitive theories that make different claims about linguistic and visuo-spatial processes (as do mental logic and mental model theories in the case of reasoning). However, we also know that for much of the brain there is at least a one to many mapping from brain structures to cognitive processes (and probably a many to many mapping) which undercuts much of the utility of localization. Despite this caveat, localization seems to loom large in the literature. Dissociation of Brain Functions Brain lesions result in selective impairment of behavior. Such selective impairments are called dissociations. A single dissociation occurs when we find a case of a lesion in region x resulting in a deficit of function a but not function b. If we find another case, in which a lesion in region y results in a

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deficit in function b but not in function a, then we have a double dissociation. The most famous example of a double dissociation comes from the domain of language. In the 1860s, Paul Broca described patients with lesions to the left posterior inferior frontal lobe who had difficulties in the production of speech but were quite capable of speech comprehension. This is a case of a single dissociation. In the 1870s, Carl Wernicke described two patients (with lesions to the posterior regions of the superior temporal gyrus) who had difficulty in speech comprehension, but were quite fluent in speech production. Jointly the two observations indicate a double dissociation and tell us something important about the causal independence of language production and comprehension systems. If this characterization is accurate (and there are now some questions about its accuracy), it tells us that any cognitive theory of speech production and comprehension needs to postulate two distinct functions/mechanisms. Recurrent patterns of double dissociation provide indication of causal joints in the cognitive system invisible in uninterrupted normal behavioral measures (Shallice, 1988). Double dissociations manifest themselves as crossover interactions in neuroimaging studies. Thus, if in the case of reasoning, decision making, and problem solving, we find double dissociations along the lines of familiarity/ unfamiliarity, conflict/agreement, and certainty/uncertainty, cognitive theories will need to take these dissociations into consideration. Indeed, some neuropsychologists have argued that it really does not matter where the lesions are in patients (or where the activations are in neuroimaging studies), but only that there are double dissociations. While this is an extreme position, it is not without some merit.

neural network modeling: Neuropsychology and cognitive neuropsychology and cognitive neuroscience. Cambridge, MA: MIT Press. Carlin, D., Bonerba, J., Phipps, M., Alexander, G., Shapiro, M., & Grafman, J. (2000). Planning impairments in frontal lobe dementia and frontal lobe lesion patients. Neuropsychologia, 38, 655–665. Channon, S., & Crawford, S. (1999). Problem-solving in real-life-type situations: The effects of anterior and posterior lesions on performance. Neuropsychologia, 37, 757–770. Christoff, K., Prabhakaran, V., Dorfman, J., Zhao, Z., Kroger, J. K., Holyoak, K. J., et al. (2001). Rostrolateral prefrontal cortex involvement in relational integration during reasoning. Neuroimage, 14, 1136–1149. Colvin, M. K., Dunbar, K., & Grafman, J. (2001). The effects of frontal lobe lesions on goal achievement in the water jug task. Journal of Cognitive Neuroscience, 13, 1129–1147. Corkin, S. (1965). Tactually-guided maze learning in man: Effects of unilateral cortical excisions and bilateral hippocampal lesions. Neuropsychologia, 3, 339–351. Damasio, A. R. (1994). Descartes’ error. New York: Avon Books. De Neys, W., & Goel, V. (in press). Heuristics and biases in the brain: Dual neural pathways for decision making. In O. Vartanian & D. R. Mandel (Eds.), Neuroscience of decision making. New York: Psychology Press. De Neys, W., Vartanian, O., & Goel, V. (2008). Smarter than we think: When our brain detects we’re wrong. Psychological Science, 19, 483–489. Diamond, A. (1990). Inhibition and executive control: Developmental time course in human infants and infant monkeys, and the neural bases of, inhibitory control in reaching. In A. Diamond (Ed.), Annals of the New York Academy of Sciences: Vol. 608. The Development and Neural Bases of Higher Cognitive Functions (Part 7, pp. 637–676). New York: New York Academy of Sciences. Eslinger, P. J., & Damasio, A. R. (1985). Severe disturbance of higher cognition after frontal lobe ablation: Patient EVR. Neurology, 35, 1731–1741. Evans, J. S. B. T. (2003). In two minds: Dual-process accounts of reasoning. Trends in Cognitive Sciences, 7, 454–459. Evans, J. S. B. T., Barston, J., & Pollard, P. (1983). On the conflict between logic and belief in syllogistic reasoning. Memory and Cognition, 11, 295–306. Evans, J. S. B. T., Newstead, S. E., & Byrne, R. M. J. (1993). Human reasoning: The psychology of deduction. Hillsdale, NJ: Erlbaum. Evans, J. S. B. T., & Over, D. E. (1996). Rationality and reasoning. New York: Psychology Press.

REFERENCES Acuna, B. D., Eliassen, J. C., Donoghue, J. P., & Sanes, J. N. (2002). Frontal and parietal lobe activation during transitive inference in humans. Cerebral Cortex, 12, 1312–1321. Alivisatos, B., & Milner, B. (1989). Effects of frontal or temporal lobectomy on the use of advance information in a choice reaction time task. Neuropsychologia, 27, 495–504. Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1–3), 7–15.

Fellows, L. K., & Farah, M. J. (2003). Ventromedial frontal cortex mediates affective shifting in humans: Evidence from a reversal learning paradigm. Brain, 126, 1830–1837. Fincham, J. M., Carter, C. S., van Veen, V., Stenger, V. A., & Anderson, J. R. (2002). Neural mechanisms of planning: A computational analysis using event-related fMRI. Proceedings of the National Academy of Sciences, USA, 99, 3346–3351. Fink, G. R., Marshall, J. C., Halligan, P. W., Frith, C. D., Driver, J., Frackowiak, R. S., et al. (1999). The neural consequences of conflict between intention and the senses. Brain, 122(Pt. 3), 497–512. Fletcher, P. C., Happe, F., Frith, U., Baker, S. C., Dolan, R. J., Frackowiak, R. S. J., et al. (1995). Other minds. In the brain: A functional imaging study of theory of mind: In story comprehension. Cognition, 57, 109–128.

Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (2005). The Iowa gambling task and the somatic marker hypothesis: Some questions and answers. Trends in Cognitive Sciences, 9, 159–162.

Gazzaniga, M. S. (1985). The social brain. New York: Basic Books.

Caramazza, A., Gordon, J., Zurif, E. B., & DeLuca, D. (1976). Righthemispheric damage and verbal problem solving behavior. Brain and Language, 3, 41–46.

Gazzaniga, M. S. (2000). Cerebral specialization and interhemispheric communication: Does the corpus callosum enable the human condition? Brain, 123(Pt. 7), 1293–1326.

Cardoso, J., & Parks, R. W. (1998). Neural network modeling of executive functioning with the tower of Hanoi in frontal lobe: Lesioned patients. In R. W. Parks, D. S. Levine, & D. L. Long (Eds.), Fundamentals of

Gilovich, T., & Kahneman, D. (Eds.). (2002). Heuristics and biases: The psychology of intuitive judgment. New York: Cambridge University Press.

c21.indd 428

8/17/09 2:20:02 PM

References 429 Glimcher, P. (2002). Decisions, decisions, decisions: Choosing a biological science of choice. Neuron, 36, 323–332.

Kahneman, D., & Tversky, A. (1996). On the reality of cognitive illusions. Psychological Review, 103, 582–591.

Goel, V. (1995). Sketches of thought. Cambridge, MA: MIT Press.

Knauff, M., Fangmeier, T., Ruff, C. C., & Johnson-Laird, P. N. (2003). Reasoning, models, and images: Behavioral measures and cortical activity. Journal of Cognitive Neuroscience, 15, 559–573.

Goel, V. (2002). Planning: Neural and psychological. In L. Nadel (Ed.), Encyclopedia of cognitive science (pp. 697–703). New York: Macmillan. Goel, V. (2005). Cognitive neuroscience of deductive reasoning. In K. Holyoak & R. Morrison (Eds.), Cambridge handbook of thinking and reasoning (pp. 475–492). Cambridge: Cambridge University Press. Goel, V. (2007). Anatomy of deductive reasoning. Trends in Cognitive Sciences, 11, 435–441. Goel, V. (2008). Fractionating the system of deductive reasoning. In E. Pppel, B. Gulyas, & E. Kraft (Eds.), The neural correlates of thinking. New York: Springer Science. Goel, V., Buchel, C., Frith, C., & Dolan, R. J. (2000). Dissociation of mechanisms underlying syllogistic reasoning. NeuroImage, 12, 504–514. Goel, V., & Dolan, R. J. (2003). Explaining modulation of reasoning by belief. Cognition, 87, B11–B22. Goel, V., Gold, B., Kapur, S., & Houle, S. (1997). The seats of reason: A localization study of deductive and inductive reasoning using PET (O15) blood flow technique. NeuroReport, 8, 1305–1310. Goel, V., & Grafman, J. (1995). Are frontal lobes implicated in “planning” functions: Interpreting data from the tower of Hanoi. Neuropsychologia, 33, 623–642. Goel, V., & Grafman, J. (2000). The role of the right prefrontal cortex: Pt. Ill. Structured problem solving. Cognitive Neuropsychology, 17, 415–436. Goel, V., Grafman, J., Sadato, N., & Hallet, M. (1995). Modelling other minds. NeuroReport, 6, 1741–1746. Goel, V., Grafman, J., Tajik, J., Gana, S., & Danto, D. (1997). A study of the performance of patients with frontal lobe lesions in a financial planning task. Brain, 120, 1805–1822.

Manes, F., Sahakian, B., Clark, L., Rogers, R., Antoun, N., Aitken, M., et al. (2002). Decision-making processes following damage to the prefrontal cortex. Brain, 125(Pt. 3), 624–639. McCarthy, R. A., & Warrington, E. K. (1990). Cognitive neuropsychology: A clinical introduction. New York: Academic Press. Milner, B. (1963). Effects of different brain lesions on card sorting: The role of the frontal lobes. Archives of Neurology, 9, 100–110. Monti, M. M., Osherson, D. N., Martinez, M. J., & Parsons, L. M. (2007). Functional neuroanatomy of deductive inference: A language-independent distributed network. NeuroImage, 37, 1005–1016. Morris, R. G., Miotto, E. C., Feigenbaum, J. D., Bullock, P., & Polkey, C. E. (1997). The effect of goal-subgoal conflict on planning ability after frontal- and temporal-lobe lesions in humans. Neuropsychologia, 35, 1147–1157. Newell, A. (1990). Unified theories of cognition. Cambridge: MA: Harvard University Press. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall. Newman, S. D., Carpenter, P. A., Varma, S., & Just, M. A. (2003). Frontal and parietal participation in problem solving in the Tower of London: FMRI and computational modeling of planning and high-level perception. Neuropsychologia, 41, 1668–1682. Noveck, I. A., Goel, V., & Smith, K. W. (2004). The neural basis of conditional reasoning with arbitrary content. Cortex, 40(4–5), 613–622.

Goel, V., Makale, M., & Grafman, J. (2004). The hippocampal system mediates logical reasoning about familiar spatial environments. Journal of Cognitive Neuroscience, 16, 654–664.

Osherson, D., Perani, D., Cappa, S., Schnur, T., Grassi, F., & Fazio, F. (1998). Distinct brain loci in deductive versus probabilistic reasoning. Neuropsychologia, 36, 369–376.

Goel, V., Pullara, S. D., & Grafman, J. (2001). A computational model of frontal lobe dysfunction: Working memory and the tower of Hanoi. Cognitive Science, 25, 287–313.

Owen, A. M., Doyon, J., Petrides, M., & Evans, A. C. (1996). Planning and spatial working memory: A positron emission tomography study in humans. European Journal of Medicine, 8, 353–364.

Goel, V., Shuren, J., Sheesley, L., & Grafman, J. (2004). Asymmetrical involvement of frontal lobes in social reasoning. Brain, 127(Pt. 4), 783–790.

Parsons, L. M., & Osherson, D. (2001). New evidence for distinct right and left brain systems for deductive versus probabilistic reasoning. Cerebral Cortex, 11, 954–965.

Goel, V., Tierney, M., Sheesley, L., Bartolo, A., Vartanian, O., & Grafman, J. (2007). Hemispheric specialization in human prefrontal cortex for resolving certain and uncertain inferences. Cerebral Cortex, 17, 2245–2250.

Paulus, M. P., Hozack, N., Zauscher, B., McDowell, J. E., Frank, L., Brown, G. G., et al. (2001). Prefrontal, parietal, and temporal cortex networks underlie decision-making in the presence of uncertainty. NeuroImage, 13, 91–100.

Goldberg, E., & Bilder, R. M. (1987). The frontal lobes and hierarchical organization of cognitive control. In E. Perecman (Ed.), The frontal lobes revisited. New York: The Psychology Press.

Perret, E. (1974). The left frontal lobe of man and the suppression of habitual responses in verbal categorical behaviour. Neuropsychologia, 12, 323–374.

Grafman, J. (1989). Plans, actions, and mental sets: Managerial knowledge units in the frontal lobes. In E. Perecman (Ed.), Integrating theory and practice in clinical neuropsychology. Hillsdale, NJ: Erlbaum.

Picton, T. W., Stuss, D. T., Shallice, T., Alexander, M. P., & Gillingham, S. (2006). Keeping time: Effects of focal frontal lesions. Neuropsychologia, 44, 1195–1209.

Grafman, J. (2002). The structured event complex and the human prefrontal cortex. In D. T. Stuss & R. T. Knight (Eds.), The frontal lobes (pp. 292–310). New York: Oxford University Press.

Prado, J., & Noveck, I. A. (2007). Overcoming perceptual features in logical reasoning: A parametric functional magnetic resonance imaging study. Journal of Cognitive Neuroscience, 19, 642–657.

Houde, O., Zago, L., Mellet, E., Moutier, S., Pineau, A., Mazoyer, B., et al. (2000). Shifting from the perceptual brain to the logical brain: The neural impact of cognitive inhibition training. Journal of Cognitive Neuroscience, 12, 721–728.

Reitman, W. R. (1964). Heuristic decision procedures, open constraints, and the structure of ill-defined problems. In M. W. Shelly & G. L. Bryan (Eds.), Human judgments and optimality (pp. 282–315). New York: Wiley.

Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1988). Judgment under uncertainty: Heuristics and biases. New York: Cambridge University Press. Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80, 237–251.

c21.indd 429

Lezak, M. D. (1995). Neuropsychological assessment (3rd ed.). New York: Oxford University Press.

Reverberi, C., Lavaroni, A., Gigli, G. L., Skrap, M., & Shallice, T. (2005). Specific impairments of rule induction in different frontal lobe subgroups. Neuropsychologia, 43, 460–472. Roberts, R. J., Hager, L. D., & Heron, C. (1994). Prefrontal cognitive processes: Working memory and inhibition in the antisaccade task. Journal of Experimental Psychology: General, 123, 374–393.

8/17/09 2:20:02 PM

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Cognitive Neuroscience of Thinking

Rowe, J. B., Owen, A. M., Johnsrude, I. S., & Passingham, R. E. (2001). Imaging the mental components of a planning task. Neuropsychologia, 39, 315–327. Rylander, G. (1939). Personality changes after operations on the frontal lobes. Acta Psychiatrica et Neurologica Scandinavica, Supplementum(20). Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003, June 13). The neural basis of economic decision-making in the ultimatum game. Science, 300, 1755–1758. Shallice, T. (1982). Specific impairments of planning. Philosophical Transactions of the Royal Society of London. Series B, 298, 199–209. Shallice, T. (1988). From neuropsychology to mental structure. Cambridge, MA: Cambridge University Press. Shallice, T., & Burgess, P. W. (1991). Deficits in strategy application following frontal lobe damage in man. Brain, 114, 727–741. Shallice, T., & Evans, M. E. (1978). The involvement of the frontal lobes in cognitive estimation. Cortex, 14, 294–303. Simon, H. A. (1973). The structure of ill-structured problems. Artificial Intelligence, 4, 181–201. Simon, H. A. (1981). The sciences of the artificial (2nd ed.). Cambridge, MA: MIT Press. Simon, H. A. (1983). Reason in human affairs. Stanford, CA: Stanford University Press.

Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3–22. Smith, M. L., & Milner, B. (1988). Estimation of frequency of occurrence of abstract designs after frontal or temporal lobectomy. Neuropsychologia, 26, 297–306. Stavy, R., Goel, V., Critchley, H., & Dolan, R. (2006). Intuitive interference in quantitative reasoning. Brain Research, 1073, 383–388. Tranel, D., Bechara, A., & Denburg, N. L. (2002). Asymmetric functional roles of right and left ventromedial prefrontal cortices in social conduct, decision-making, and emotional processing. Cortex, 38, 589–612. Tversky, A., & Kahneman, D. (1974, September 27). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131. Vallesi, A., Mussoni, A., Mondani, M., Budai, R., Skrap, M., & Shallice, T. (2007). The neural basis of temporal preparation: Insights from brain tumor patients. Neuropsychologia, 45, 2755–2763. Vallesi, A., Shallice, T., & Walsh, V. (2007). Role of the prefrontal cortex in the foreperiod effect: TMS evidence for dual mechanisms in temporal preparation. Cerebral Cortex, 17, 466–474. Vartanian, O., & Goel, V. (2005). Task constraints modulate activation in right ventral lateral prefrontal cortex. NeuroImage, 27, 927–933. Wilkins, A. J., Shallice, T., & McCarthy, R. A. (1987). Frontal lesions and sustained attention. Neuropsychologia, 25, 359–365. Wilkins, M. C. (1928). The effect of changed material on the ability to do formal syllogistic reasoning. Archives of Psychology, 16, 5–83.

Sirigu, A., Zalla, T., Pillon, B., Grafman, J., Agid, Y., & Dubois, B. (1995). Selective impairments in managerial knowledge following pre-frontal cortex damage. Cortex, 31, 301–316.

Wolford, G., Miller, M. B., & Gazzaniga, M. (2000). The left hemisphere’s role in hypothesis formation. Journal of Neuroscience, 20, 1–4.

Sirigu, A., Zalla, T., Pillon, B., Grafman, J., Dubois, B., & Agid, Y. (1995). Planning and script analysis following prefrontal lobe lesions. Annals of the New York Academy of Sciences, 769, 277–288.

Wood, J. N., & Grafman, J. (2003). Human prefrontal cortex: Processing and representational perspectives. Nature Reviews: Neuroscience, 4, 139–147.

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Chapter 22

Motor Control: Pyramidal, Extrapyramidal, and Limbic Motor Control KRISTA MCFARLAND

on organizational features that influence the production of goal-directed behavior. An extensive circuitry including subcortical and cortical structures (alternately termed the basal ganglia or motive circuit) has been implicated in motivational control of goal-directed behavior, and central to this circuitry are the dopaminergic neurons of the ventral midbrain. Thus, following a discussion of the anatomical and functional organization of the basal ganglia, we address the importance of dopaminergic signaling in the production of adaptive motor responses. Finally, we conclude with a brief examination of neuropsychiatric disorders that arise from disruptions of basal ganglia circuitry, including schizophrenia, Parkinson’s disease, and Huntington’s disease. The discussion emphasizes how understanding the organization of motivational circuitry can provide a framework for understanding these neuropsychiatric conditions.

One of the cardinal features of behavior is that it is goal directed. Animals seek food or water; they avoid predators and explore novel environments. Humans work overtime to buy a new home, get up early to go to the gym, or put their health and happiness in jeopardy to get drugs. One of the primary goals of behavioral scientists is to understand how goal-directed behaviors are produced. The present chapter reviews the anatomical, neurobiological, and behavioral evidence regarding the neural substrates of goal-directed behavior. A necessary first step involves understanding the organization and function of motor pathways. Perhaps more importantly, it involves understanding sensory and limbic control over motor pathways. Thus, an individual’s memories, expectations, past learning, and history of reward or punishment influences how information is processed and which behaviors are generated as a result of incoming information. For this reason, this chapter describes motor pathways, but primarily focuses on the integrative aspects of motor control, that is, how cognition and motivation influence behavior. This involves examination of a motive circuit and basal ganglia within the basal forebrain. The motive circuit and basal ganglia are comprised of multiple parallel circuits, some more directly connected with limbic functions and some more directly connected with motor functions. It is hypothesized that limbic circuits are important for processing environmental stimuli, relative to an individual’s past experience and current motivational state and transmitting this information to portions of the motor circuit, thus instigating novel and practiced adaptive motor responses. Within this framework, the neuroanatomical and neurochemical organization of the motive circuit provides the neural substrates of motivation and reinforcement and functions to elicit adaptive motor responses in the presence of motivationally significant stimuli. With these goals in mind, we begin with a description of the pyramidal motor system and then progress to a description of motivational circuitry with an emphasis

PYRAMIDAL MOTOR SYSTEM Although brain-stem activity is sufficient to regulate a number of very basic aspects of motor function, including respiration, maintenance of equilibrium, eye movements, and cardiovascular and gastrointestinal function, production of precise behaviors requires control from higher brain centers (Grillner, 1990). The pyramidal motor system provides the primary mechanism for descending cortical control of movement and is comprised of two pathways: the corticospinal and corticobulbar pathways. Corticospinal projections originate from multiple cortical areas, including primary motor cortex, premotor cortex, and sensorymotor cortex (Dum & Strick, 1991). Most axons in the corticospinal tract decussate (cross the midline) in the brain stem and descend in the lateral corticospinal tract to the contralateral spinal cord where they synapse (directly or indirectly) on neurons that control movement of the arms, 431

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hands, fingers, feet, and toes. Neurons that do not decussate descend ipsilaterally in the ventral corticospinal tract where they synapse on neurons that control movement of the upper legs and trunk. Corticobulbar neurons project to the medulla and synapse on motor nuclei of cranial nerves that are critical for control of the face, neck, lips, and tongue. Thus, the cortex modulates activity of virtually all muscle groups via descending corticospinal inputs. Identification and mapping of motor cortex began in the second half of the nineteenth century. Experiments demonstrated that stimulation of the precentral gyrus in dogs and primates resulted in limb movements (Fritsch & Hitzig, 1870; Leyton & Sherrington, 1917). Later, Penfield and colleagues stimulated the precentral gyrus in human surgical patients and showed a disproportionate somatotopic map of the body (i.e., homunculus) in the cortex (Penfield & Boldrey, 1937; Penfield & Rasmussen, 1952), where specific areas of the cortex were responsible for muscle movements within corresponding body parts. This area was identified as primary motor cortex and subsequent work has demonstrated that there are many cortical areas that interact with primary motor cortex and the spinal cord, including premotor cortex and supplementary motor cortex (Dum & Strick, 1991; Picard & Strick, 1996, 2001). These cortical areas are topographically organized (He, Dum, & Strick, 1993, 1995), and each contains a separate somatotopic representation of the body (Dum & Strick, 2002; Gentilucci et al., 1989; Godschalk, Mitz, van Duin, & van der Burg, 1995; Mitz & Wise, 1987). Despite similarities in organization and spinal connectivity, different cortical motor areas have dissociable effects on behavior. In general, electrical stimulation of primary motor cortex causes individual muscles to contract, whereas stimulation of premotor or supplementary motor areas causes coordinated groups of muscles to contract, suggesting that these areas might be critical for coordination of muscle groups in the performance of behavior. The results from lesion studies suggest that a significant function of primary motor cortex is independent movement of the fingers that are critical for grasping objects (Baker, Spinks, Jackson, & Lemon, 2001; Brinkman & Kuypers, 1973; Lemon, Mantel, & Muir, 1986; Passingham, Perry, & Wilkinson, 1978, 1983). This idea is consistent with the observations from comparative anatomy studies that show a relationship between the development of precision grip and the size of the corticospinal tract (Heffner & Masterton, 1983; Nudo & Masterton, 1990). As previously mentioned, nonprimary motor areas seem to be important for coordinating movements of groups of muscles. As such, they participate in numerous functions, including preparation for specific tasks (e.g., positioning arms so the hands are oriented properly for a specific

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task), postural adjustments, positional movements of the head and eyes, and coordinating bilateral movements. Notably, part of the premotor cortex has been implicated in learning associations between stimuli and movements. Functional disturbance of this area (e.g., lesions, pharmacological manipulations, or transcranial magnetic stimulation) disrupt the ability to learn or use previously learned relationships between environmental cues and motor responses (Chouinard, Leonard, & Paus, 2005; Halsband & Passingham, 1982, 1985; Kurata & Hoffman, 1994). The different roles of various areas of motor cortex in modulating behavioral output are presumably related to their differential connectivity. Different cortical motor areas receive a unique set of inputs from parietal (sensory and association) and prefrontal cortices, as well as participating in different loops with the basal ganglia (Alexander & Crutcher, 1990; Dum & Strick, 1991, 1993; Graybiel, 1991; Hoover & Strick, 1993), a topic that will be explored in greater detail later in this chapter within the context of the basal ganglia.

LIMBIC MOTOR CONTROL: THE MOTIVE CIRCUIT AND BASAL GANGLIA The previous section described the motor pathways important for controlling behavior. Activation of motor cortex and supplementary motor areas causes muscles to contract and consequently produce movement. However, of critical importance for understanding behavior is appreciating that behavior occurs within a particular context. Why does a particular behavior occur at a particular time? Out of all possible behaviors that could occur, how does a hungry individual successfully obtain food? How does an animal successfully avoid a predator, even in a novel environment? The answer to these questions lies in the ability of an animal to integrate information regarding its internal environment (e.g., hunger or thirst) with its expectations about the outcome of a particular behavior in the presence of available contextual cues. Thus, when entering a dark room, a person might flip the light switch, even if the light bulb burned out the day before, based on the expectation that flipping a light switch produces light. The integration of sensory and motivational information with previous learning and memory is the function of an extensive circuitry including subcortical and cortical structures. Examination of these structures has arisen from two different scientific perspectives: one focused on the role of the dorsal striatum (and its associated structures) in the control of movement, and another focused on the ventral striatum (and its associated structures) in reinforcement and reward. The dorsal striatum has been studied as part of basal ganglia circuitry,

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Limbic Motor Control: The Motive Circuit and Basal Ganglia

which also includes the substantia nigra, globus pallidus, and subthalamic nucleus. The ventral striatum has been studied as part of the “motive circuit,” which is comprised of corresponding ventral components of many of the same nuclei, including the ventral striatum (nucleus accumbens), ventral tegmental area, ventral pallidum, and medial dorsal nucleus of the thalamus. Although these circuits have historically been studied independently, what follows is an attempt to integrate what is known about their structure and function, focusing on the parallels between the two, in order to ascribe function to the circuitry, as a whole.

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(A) DS Inputs (Cortex/Amyg/Hipp)

GP VS

VP

Output (Thalamus/Cortex)

VTA SN (B) NAs

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Anatomy of the Motive Circuit VTA

Early biological studies of motivated behavior focused on the importance of a few individual nuclei in the production of adaptive motor responses. The amygdala and nucleus accumbens (ventral striatum) garnered particular attention. Early work demonstrated that lesions of the amygdala produced changes in emotionality (Kluver & Bucy, 1939) and deficits in the ability to identify and respond appropriately to biologically significant stimuli. For example, animals with amygdala lesions were shown to have impaired avoidance responding to stimuli that signaled shock (Weiskrantz, 1956), as well as impaired visual discrimination for food reward (Jones & Mishkin, 1972). These studies, when combined with anatomical studies demonstrating connectivity of the amygdala with sensory, autonomic, and motor structures (Aggleton, Burton, & Passingham, 1980; Herzog & Van Hoesen, 1976; Nauta, 1961) suggested a critical modulatory function in regulating motivated behavior. Olds and Milner (1954) discovered that animals would work for electrical simulation of the medial forebrain bundle. Later demonstrations that this effect was largely due to stimulation of dopaminergic afferents to the nucleus accumbens (NA; Corbett & Wise, 1980; Fibiger & Phillips, 1986), coupled with emerging evidence that the neurochemical mode of action of many drugs of abuse depended on the accumbens (Kornetsky & Esposito, 1979; Wise, 1982), indicated a central role in goal-directed behavior. Behavioral evidence, considered in conjunction with the connectivity of the NA with limbic and cortical structures, suggested a probable role in behavior elicited by at least positive motivational stimuli. In his now classic formulation, Mogenson moved beyond the single nucleus approach to suggest that the amygdala and NA form part of a circuit that functions to integrate limbic information and elicit appropriate behavioral responses (Mogenson, Jones, & Yim, 1980). Within this “motive circuit,” the NA was proposed to serve as a functional interface between the limbic system (including the amygdala) and motor output structures. Heimer

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SN

Figure 22.1 Topographical organization of subcortical projections in motivational circuitry.

formalized the anatomical interrelationships between the amygdala and NA in motivation as the “extended amygdala” (Heimer et al., 1997; Heimer, Alheid, & Zahm, 1993). This interconnected series of nuclei, including the central nucleus of the amygdala, bed nucleus of the stria terminalis, medial ventral pallidum (VP), and ventromedial NA, is hypothesized to be a prime contributor of emotional context. Subcortical Circuitry Central to the organization of motive circuit is a trio of interconnected nuclei that form parallel loops through the midbrain, striatum, and pallidum (see Figure 22.1A). An important organizational feature of these nuclei is the precise topographical projections they display. Thus, a group of neurons in the striatum that receives a projection from neurons of the mesencephalon sends a reciprocal projection to these same neurons. Further, these striatal neurons share a reciprocal projection with neurons in the pallidum that share a reciprocal projection with the same region of the mesencepholon to which the striatal neurons project. In this manner, parallel circuits for processing of motivational information are formed. The motive circuit contains the ventral portions of these nuclei and can be further subdivided into two subcircuits that are parallel (see Figure 22.1B), but have different input and output structures. Figure 22.2 depicts connectivity of the motive circuit. The mesolimbic dopamine system projects from the ventral tegmental area (VTA) to the NA in the ventral striatum (Beckstead, 1979; Beckstead, Domesick, & Nauta, 1979;

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Motor Control: Pyramidal, Extrapyramidal, and Limbic Motor Control

Cortical and Allocortical Input PFCd PFCv

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Glutamate

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Transition

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Figure 22.2 The connectivity of the motive circuit.

Fallon & Moore, 1978), although, up to 20% of the pathway contains ␥-aminobutyric acid (GABA; Carr & Sesack, 2000b). The VTA innervates and receives innervation from ventromedial portions of the NA, termed the shell (NAs; Heimer, Zahm, Churchill, Kalivas, & Wohltmann, 1991; Swanson, 1982). Accumbal projections to the VTA contain GABA, dynorphin and substance P (Kalivas, Churchill, & Klitenick, 1993; Zahm & Heimer, 1990). Although the VTA also innervates the medial portions of the accumbens, termed the core (NAc), reciprocal projections with the NAc arise from the substantia nigra (SN, Heimer et al., 1991), which is a component of the basal ganglia and will be discussed in further detail later. The parallel topography of these subcortical loops is maintained in efferent projections from the NA to the VP. Thus, the NAs projects to ventromedial portions of the VP (VPm), while the NAc projects primarily to the dorsolateral, subcomissural VP (VPl; Zahm, 1989; Zahm & Heimer, 1990). The striatopallidal projections contain GABA, enkephalin, substance P, and neurotensin (Churchill, Dilts, & Kalivas, 1990; Napier et al., 1995; Olive, Bertolucci, Evans, & Maidment, 1995; Zahm & Heimer, 1988), while reciprocal projections are primarily GABAergic (Churchill & Kalivas, 1994). Like the NA, the VP exhibits mediolateral topography in its innervation of the mesencephalon, with the VPm providing GABAergic innervation of the VTA and the VPl innervating the SN (Kalivas et al., 1993; Zahm & Heimer, 1990). However, reciprocal innervation of the VP is not as discrete. The VTA projects to both the VPm and the VPl, while the SN shows little, if any, innervation of the VP (Klitenick, Deutch, Churchill, & Kalivas, 1992).

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Afferent innervation of the subcortical circuit arises from a number of sources. Primary among these is the medial prefrontal cortex that maintains topographic connectivity with both the VTA and the NA, and thus forms an extension of the parallel subcortical circuitry. Both the dorsal (mPFCd) and ventral (mPFCv) prefrontal cortices receive mesocortical dopamine projections from the VTA (Fuxe, Hokfelt, Johansson, Lidbrink, & Ljungdahl, 1974), which like the mesolimbic system has a significant (up to 40%) GABAergic component (Carr & Sesack, 2000a). Additionally, both the mPFCd and the mPFCv send reciprocal projections back to the VTA, and the mPFCd also sends a projection to SN (Beckstead, 1979; Sesack, Deutch, Roth, & Bunney, 1989). Whereas the mPFCv and mPFCd show some degree of mediolateral topography in corticofugal innervation of the VTA, the mPFC displays very discrete target specificity in its innervation of the NA. The dorsal mPFCd projects selectively to the NAc and the mPFCv projects to the NAs (Berendse, Galis-de Graaf, & Groenewegen, 1992; Sesack et al., 1989). The main transmitter of the efferent mPFC projection is glutamate (Christie, Summers, Stephenson, Cook, & Beart, 1987; Fonnum, Storm-Mathisen, & Divac, 1981; Ray, Russchen, Fuller, & Price, 1992; Spencer, 1976). In addition to excitatory glutamatergic afferents arising from the mPFC, the ventral striatum also receives prominent afferents from allocortical regions, including the basolateral amygdala and hippocampus (Groenewegen, Vermeulen-Van der Zee, te Kortschot, & Witter, 1987; Kelley & Domesick, 1982; Kelley, Domesick, & Nauta, 1982; Wright, Beijer, & Groenewegen, 1996), as well as nonisocortical limbic regions, including entorhinal cortex, anterior cingulate cortex, orbitofrontal cortex, and insula (Chikama, McFarland, Amaral, & Haber, 1997; Ferry, Ongur, An, & Price, 2000; Haber, Kunishio, Mizobuchi, & Lynd-Balta, 1995), all of which have been termed the limbic lobe (Heimer & Van Hoesen, 2006). In addition to cortical innervation, the VTA and substantia nigra receive innervation from the central nucleus of the amygdala (Fudge & Haber, 2000). This is thought to be a primary source of enkephalin and neurotensin innervation to the VTA. Likewise, the VTA has a substantive dopaminergic and GABAergic projection to the central nucleus. As part of the extended amygdala, the central nucleus also provides innervation to the VPm and NAs (Heimer et al., 1993). Thus, the ventral striatum is closely related to areas of the brain that are critical for emotion, memory, and contextual relevance. This connectivity is hypothesized to confer the ability to choose and mobilize adaptive motor responses in the context of cues in the environment that are predictive of important outcomes.

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Limbic Motor Control: The Motive Circuit and Basal Ganglia

Motor and Limbic Subcircuits

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The basal ganglia consist of a several nuclei, including the substantia nigra, dorsal striatum, globus pallidus, and subthalamic nucleus. Notably, these include the more dorsal aspects of the nuclei involved in the motive circuit (see Figure 22.1A). In fact, the motive circuit described previously can be viewed as the “limbic” loop of an extensive circuitry that progresses into “association” and “sensory motor” loops in these more dorsal regions of the basal ganglia. The current review does not cover in detail all aspects of basal ganglia organization and function (a topic to which many books have been devoted), but instead focuses on parallels between the basal ganglia and motive circuit, in an attempt to provide an integrated framework for thinking about the production of adaptive motor responses. Figure 22.3 depicts basal ganglia circuitry implicated in the control of motivated behavior, integrating aspects of the motive circuit described previously.

Limbic

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Anatomy of Basal Ganglia Circuitry

Pyramidal pathway Cortex

Indirect pathway

An inspection of Figure 22.2 and the preceding description reveals that there are two subcircuits that comprise the larger motive circuit, one comprised of predominantly limbic-related structures and one of primarily of motorrelated structures. Thus, the VTA, NAs, and VPm are associated with limbic structures like the mPFCv, basolateral amygdala, hippocampus, and bed nucleus of the stria terminalis. In fact, recent conceptualizations of the ventral forebrain suggest that the centromedial amygdala, sublenticular VP, bed nucleus of the stria terminalis, and NAs form a continuous network termed the extended amygdala (Alheid, 2003; De Olmos & Heimer, 1999). Conversely, the mPFCd, NAc, and VPl form contacts with motor structures, like the SN, motor cortex, pedunculopontine nucleus, and subthalamic nucleus. Consistent with the idea that there are two distinct circuits that run through the ventral striatum, the NAc and NAs accumbal subregions can be distinguished on the basis of connectivity, histochemistry, and behavior (Heimer et al., 1997; Kelley, 2004; Meredith, Baldo, Andrezjewski, & Kelley, 2008; Voorn, Vanderschuren, Groenewegen, Robbins, & Pennartz, 2004; Zahm, 1999; Zahm & Brog, 1992). Thus, converging evidence suggests that two relatively closed-loop systems within the larger motive circuit separately integrate motor and limbic information (Kalivas, Churchill, & Romanides, 1999). For an individual to effectively integrate incoming motivational stimuli and emit appropriate behavioral responses, there must be interplay between the motor and limbic systems. Transfer of information among various loops within motivational circuitry is discussed next.

435

GPe

SNr/GPi

STN

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Figure 22.3 The major projections within the basal ganglia that contribute to the control motivated behavior.

Subcortical Circuitry As with the motive circuit, topographical, reciprocal connections among three nuclei are central to basal ganglia circuitry: the substantia nigra, dorsal striatum, and globus pallidus. The nigrostriatal dopamine system arises in the substantia nigra pars compacta (SNc) and terminates in the dorsal striatum (including both the caudate and putamen) and the globus pallidus (Anaya-Martinez, Martinez-Marcos, Martinez-Fong, Aceves, & Erlij, 2006; Fallon & Moore, 1978; Hedreen & DeLong, 1991; Lavoie, Smith, & Parent, 1989; Lynd-Balta & Haber, 1994a; Szabo, 1980). The dorsal striatum, in turn, sends highly topographically organized projections back to both the SNc and the substantia nigra pars reticulata (SNr), as well as to both the internal and external divisions of the globus pallidus (GPi and GPe, respectively; Haber, Groenewegen, Grove, & Nauta, 1985; Hazrati & Parent, 1992; Hedreen & DeLong, 1991; Parent, Bouchard, & Smith, 1984). Finally, the SNr, SNc, and dorsal striatum all receive GABAergic projections from the globus pallidus (Beckstead, 1983; Bevan, Smith, & Bolam, 1996; Kita, Tokuno, & Nambu, 1999; Rajakumar, Elisevich, & Flumerfelt, 1994; Smith & Bolam, 1990). Cortical Inputs The striatum is the major input structure of the basal ganglia. It receives dense, topographically organized projections

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Motor Control: Pyramidal, Extrapyramidal, and Limbic Motor Control

from extensive parts of the cerebral cortex. The corticostriatal projection imposes a functional organization on the striatum that is largely maintained in striatal projections to the pallidum. Thus, the striatum has been subdivided based on the cortical area from which it receives a projection into at least three distinct areas: limbic, associative, and sensorimotor (Alexander & Crutcher, 1990; Alexander, DeLong, & Strick, 1986; Haber, 2003; Joel & Weiner, 1997; Parent & Hazrati, 1995; Tisch, Silberstein, LimousinDowsey, & Jahanshai, 2004). The limbic striatum is largely synonymous with the nucleus accumbens, although the most ventral parts of the caudate and putamen and the olfactory tubercle can also be deemed limbic striatum. As previously described, limbic striatum receives projections from limbic cortical and allocortical areas including the medial prefrontal cortex, amygdala, and hippocampus, as well as nonisocortical areas like the anterior cingulate and orbitofrontal cortex. These areas have been implicated in numerous functions including reward-based learning, stimulus-guided behavior, expression of emotion, and control of impulsive behavior (Apicella, 2002; Badre & Wagner, 2004; Botvinick, Nystrom, Fissell, Carter, & Cohen, 1999; Floresco, Magyar, Ghods-Sharifi, Vexelman, & Tse, 2006; Holland & Gallagher, 2004; Hollerman, Tremblay, & Schultz, 2000; Laurens, Kiehl, & Liddle, 2005; Rolls & Baylis, 1994; Schultz, Tremblay, & Hollerman, 2000; Vertes, 2006; Woodward, Chang, Janak, Azarov, & Anstrom, 1999). The associative striatum includes the putamen rostral to the anterior commissure and most of the caudate, except a small portion near the internal capsule. Associative striatum receives projections primarily from the dorsolateral prefrontal cortex that has been implicated in executive functions like attentional control in working memory, set shifting, and strategic planning (Arikuni & Kubota, 1986; Fuster, 2000a, 2000b; Goldman & Nauta, 1977; GoldmanRakic, 1996; Mitchell, Rhodes, Pine, & Blair, 2008; Novais-Santos et al., 2007). Sensorimotor striatum includes the dorsolateral putamen (caudal to the anterior commissure) and the dorsolateral rim of the head of the caudate. These areas receive projections from motor cortices, including primary motor cortex, supplementary motor cortex, and premotor cortices, which have been functionally implicated in the control of motor behavior, and from sensory cortices (Flaherty & Graybiel, 1994, 1995; Kunzle, 1975; Liles & Updyke, 1985; N. R. McFarland & Haber, 2000). Data suggest involvement of these projections in sensorimotor control and motor planning (Alexander & DeLong, 1985; Bailey & Mair, 2006; Boecker et al., 1998; DeLong, Alexander, Mitchell, & Richardson, 1986; Hikosaka, 2007; Lehericy et al., 2006). Thus, projections from the cortex to the striatum form a functional gradient from limbic through associative to sensory and motor

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(i.e., from emotional to motor control) along the ventromedial to dorsolateral axis of the striatum. These projections are largely topographically segregated, thus parallel streams of information seem to enter the striatum. Cortical and allocortical projections to the striatum are excitatory, glutamatergic projections. Thus, it seems that the cortex is situated to stimulate behavior, while striatal circuitry is positioned to modulate cortical output. Notably, striatal circuitry also projects back to the cortex. This arrangement allows for environmentally important stimuli to guide ongoing behavior and provide a feedback mechanism for learning about the outcome of goal-directed behavior. Another important organizational feature of the circuitry is extensive cortico-cortical interactions, allowing integration to occur among various functional circuits. For example, the dorsolateral PFC is interconnected with orbital and medial prefrontal cortices (Barbas, 2000; Passingham, Stephan, & Kotter, 2002; Petrides & Pandya, 1999), as are somatosensory and premotor cortices (Carmichael & Price, 1995; Conde, Maire-Lepoivre, Audinat, & Crepel, 1995). Similarly, somatosensory and motor cortices are highly interconnected (Cipolloni & Pandya, 1999; Morecraft, Cipolloni, Stilwell-Morecraft, Gedney, & Pandya, 2004; Simonyan & Jurgens, 2002). These connections are probably crucial for smooth behavior because they would allow integration of information about behavioral outcomes and emotional and environmental context and memory.

FLOW OF INFORMATION THROUGH MOTIVATIONAL CIRCUITRY Striatal Efferents: Direct versus Indirect Pathways GABAergic medium spiny neurons make up approximately 95% of striatal neurons and are the main recipient of cortical and allocortical input to the striatum. Striatal projections are topographically organized, thus largely maintaining the functional organization of the striatum in the output nuclei (Hedreen & DeLong, 1991; Lynd-Balta & Haber, 1994b; Parent et al., 1984; Parent & Hazrati, 1994). These projections form two efferent pathways from the striatum to the substantia nigra and pallidum. These are sometimes called the striatonigral and striatopallidal projections, but more frequently are referred to as the “direct” and “indirect” pathways (see Figure 22.3; Albin, Young, & Penney, 1989; Bolam, Hanley, Booth, & Bevan, 2000; DeLong, 1990). The pathways differ in projection targets, expression of dopamine (DA) receptors, and peptide content. The direct pathway projects monosynaptically to the SNr/GPi, while the indirect pathway from the dorsal striatum projects through

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Flow of Information through Motivational Circuitry

the GPe to the subthalamic nucleus before projecting on to the SNr/GPi (Gerfen, 1992; Kawaguchi, Wilson, & Emson, 1990; Parent & Hazrati, 1995). The indirect pathway from the ventral striatum projects to the ventral pallidum (Zahm, 1989; Zahm & Heimer, 1990). Neurons of the direct pathway contain dynorphin and substance P, and mainly express DA D1 receptors that are positively coupled to adenylate cyclase; Neurons of the indirect pathway contain enkephalin and neurotensin and express DA D2 receptors that are negatively coupled to adenylate cycles activity (Beckstead & Kersey, 1985; Besson, Graybiel, & Quim, 1990; Fallon & Leslie, 1986; Gerfen et al., 1990; Hersch et al., 1995; Le Moine & Bloch, 1995; Stoof & Kebabian, 1981). The SNr/GPi and VP send prominent GABAergic efferent projections to the thalamus, with the VP (primarily the VPm with only minor involvement of the VPl) projecting to the mediodorsal nucleus, and the SNr/GPi projecting to the ventral anterior and ventral lateral nuclei. The thalamic nuclei, in turn, provide glutamatergic innervation of the cortex. Projections to the thalamus and cortex are functionally and topographically organized, forming the last link in cortico-striato-cortico re-entrant circuits (see Figure 22.4), where a particular region of the cortex receives projections from the same portions of the basal ganglia to which it sends projections (DeVito & Anderson, 1982; Groenewegen, 1988; Haber, Lynd-Balta, & Mitchell, 1993; Kim, Nakano, Jayaraman, & Carpenter, 1976; Kuo & Carpenter, 1973; Kuroda, Murakami, Kishi, & Price, 1995; McFarland & Haber, 2002; Mogenson, Ciriello, Garland, & Wu, 1987; Zahm, Williams, & Wohltmann, 1996). The direct and indirect pathways have opposing effects on the activity of efferent neurons in the thalamus and cortex. Striatal neurons have the electrophysiological property of being tonically active, and thus exhibit a tonic inhibitory influence on their pallidal and nigral targets. Neurons in the direct pathway inhibit SNr/GPi targets, thus disinhibiting

thalamic and cortical structures (Chevalier & Deniau, 1990). By contrast, neurons in the indirect pathway inhibit neurons of the GPe, which disinhibits the subthalamic nucleus and consequently activates the GABAergic neurons of the SNr/GPi, resulting in inhibition of thalamic and cortical targets. Thus, the balance of activity between direct and indirect pathways is a critical component for determining basal ganglia output and consequently cortical motor or cognitive activity. We return to this topic in our discussion of Parkinson’s disease, as an imbalance of activity in direct and indirect pathways is central to the disorder. Basal Ganglia Loops: Parallel versus Integrative The striatum, pallidum, and thalamus are connected with the frontal cortex in a series of parallel modules that maintain parallel anatomical and functional organization (see Figure 22.4), leading to the suggestion that information is processed by this circuitry via a series of parallel loops, and it is the function of these parallel circuits to regulate motivated behavior (Alexander et al., 1986; Groenewegen, Berendse, Wolters, & Lohman, 1990; Heimer, Switzer, & Van Hoesen, 1982; Middleton & Strick, 2001). However, for an individual to effectively integrate incoming motivational stimuli and emit appropriate behavioral responses there must be interplay between the motor and limbic systems. Thus, there has been an increasing appreciation of potential integrative aspects of motivational circuitry (Haber, 2003; Haber, Fudge, & McFarland, 2000; Joel & Weiner, 1994; Percheron & Filion, 1991; Zahm & Brog, 1992). Midbrain-Striatal Interactions Several pathways have been proposed that could subserve integrative functions within motivational circuitry, including striato-nigro-striatal projections. Midbrain dopamine neurons project to striatum and, in turn, receive striatal

The cortico-basal ganglia re-entrant circuits (B) (C)

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Figure 22.4 The cortico-basal ganglia re-entrant circuits.

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Motor Control: Pyramidal, Extrapyramidal, and Limbic Motor Control

efferents. The midbrain-striatal projection is organized with inverse dorsal-ventral topography, such that the ventral midbrain projects to dorsal striatum and dorsal midbrain projects to ventral striatum. The NAs receives the most limited DA input, with this coming primarily from the VTA. The NAc receives dopaminergic input from the VTA and dorsomedial portions of the SNc. Associative striatum receives input from the densocellular group of the SNc and sensorimotor (dorsolateral) striatum receives input from the entire SNc, including both the densocellular group and the cell columns. Thus, along the gradient from limbic to motor striatum, there is also a gradient in the density of DA projections, with the densest projections going to motor striatum. Limbic striatum projects to the VTA and SNc; associative striatum projects to the SNc, primarily the ventral densocellular group; and sensorimotor striatum projects to the cell columns of the ventrolateral SNc (Beckstead et al., 1979; Carmona, Catalina-Herrera, & Jimenez-Castellanos, 1991; Haber, Lynd, Klein, & Groenewegen, 1990; Hedreen & DeLong, 1991; Parent & Hazrati, 1994; Selemon & Goldman-Rakic, 1990; Szabo, 1980). Loosely there is reciprocal, topographic organization to striatonigro-striatal projections. However, the ventral striatum receives limited dopaminergic input, but projects to a large region. In contrast, sensorimotor striatum has limited influence on midbrain DA cells, but receives a dense dopaminergic projection. In addition, for each striatal region, there are nonreciprocal connections with midbrain. Dorsal to the reciprocal projection lies a group of cells that that project to the same striatal region, but does not receive a projection from it. Ventral to the reciprocal projection lies efferent terminals without a reciprocal projection. With this arrangement, information from limbic striatum can reach motor striatum (Haber, 2003; Haber et al., 2000). More concretely, limbic striatum sends a projection to the VTA that extends beyond the portion that projects back to limbic striatum. This terminal region projects to associative striatum. Associative striatum participates in reciprocal projections with the densocellular region, but also send a projection to more ventral regions that share reciprocal projections with motor striatum. Thus, the connections “spiral” between midbrain and striatum in a manner that allows information to move in a rectified manner from limbic to motor loops. The nonreciprocal interactions of the VTA are evident in its connections to the PFC and VP, as well as the striatum. Thus, the VTA forms reciprocal connections with both the mPFCd and the mPFCv. Additionally, although it receives a projection only from the VPm, it sends projections to both the VPm and VPl. The permissive topography of VTA efferent projections within the motive circuit positions it to

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influence the activity of both the motor and limbic subcircuits of the motive circuit. Thalamo-Cortical Interactions Although within the framework of basal ganglia loops, the thalamus is frequently described as a relay to the cortex, in reality, the thalamus participates in nonreciprocal interactions with the cortex that regulate the activity of cortical ensembles (Deschenes, Veinante, & Zhang, 1998; Jones, 1985; McFarland & Haber, 2002; Sherman & Guillery, 1996). These nonreciprocal projections are in position to integrate information across functional circuits. Although the thalamus displays reciprocal topography with cortex, closing cortico-basal ganglia-cortical segregated functional circuits, cortico-thalamic projections to the ventroanterior, ventrolateral, and mediodorsal thalamus are more extensive than thalamo-cortical projections. These extra projections are derived from areas of the cortex not innervated by that area of thalamus. For example, the ventroanterior thalamus has reciprocal projections with dorsolateral PFC (associative) and also nonreciprocal inputs from medial PFC (limbic); ventrolateral thalamus has reciprocal projections with caudal motor regions and also nonreciprocal inputs from more rostral motor regions (Haber, 2003). Thus, like striato-nigro-striatal projections, these projections mediate the flow of information from higher “association” areas to “motor” areas, allowing rectified integration of information across basal ganglia circuits. Another means of moving information from limbic to motor-related circuitry via the thalamus occurs within the motive circuit via the MD. The VP sends a prominent GABAergic efferent to the MD, with the primary contribution coming from the VPm and only minor involvement of the VPl (Mogenson et al., 1987; Vives & Mogenson, 1985; Zahm et al., 1996). The MD does not send a reciprocal projection to the VP, but there is reciprocal glutamatergic innervation of the mPFCd (Groenewegen, 1988; Kuroda, Murakami, Shinkai, Ojima, & Kishi, 1995). Thus, the MD receives information from the limbic circuit via the VPm but sends a projection to the motor-associated mPFCd, consequently forming a bridge between limbic and motor subcircuits (see Figure 22.2). Communication between the limbic and motor basal ganglia loops is rectified to bias information flow from limbic to motor while flow in the reverse direction requires multisynaptic communication. The location of rectified information flow is strategic for the movement of information from limbic to motor, and it has been suggested that information may “spiral” outward from the more medial limbic nuclei to the more lateral and dorsal motor nuclei (Haber, 2003; Haber & Fudge, 1997; Zahm & Brog, 1992). Motivationally relevant limbic information can exit the

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Dopamine and Motivated Behavior 439

motive circuit to the motor system via a number of different pathways. Hence, the VPl projects to the pedunculopontine nucleus, subthalamic nucleus, and SN, and subsequently to all parts of the extrapyramidal motor system (Haber et al., 1985; Zahm, 1989). There is also a projection to motor cortex that arises from the mPFCd (Zahm & Brog, 1992). Finally, the SN receives a projection directly from the NAc. Thus, the limbic motive circuit has several conduits by which it can influence motor behavior following presentation of motivationally relevant stimuli.

DOPAMINE AND MOTIVATED BEHAVIOR Although the function of dopamine in motivated behavior has been extensively reviewed by myself and others (Ettenberg, 1989; Fibiger & Phillips, 1988; McFarland & Kalivas, 2003; Robbins, 2003; Robinson & Berridge, 2000; Salamone & Correa, 2002; Schultz, 2002; Wise, 1982, 2004), its critical importance in motivational circuitry warrants discussion. There are three prominent pathways arising from the dopaminergic cell bodies of the ventral midbrain. The nigrostriatal pathway projects from the SN to the dorsal striatum; the mesolimbic pathway projects from the VTA to the NA; and the mesocortical projects from the VTA to the mPFC. For several reasons, extensive efforts to understand motivated behavior have focused on the role of dopamine. Primary among these is the demonstration that animals will work to deliver stimulation to brain regions containing dopaminergic neurons (for reviews, see Fibiger & Phillips, 1988; Redgrave & Dean, 1981; Wise, 1978). This finding led to the suggestion the dopamine system served as a brain substrate for reinforcement or reward that would serve to strengthen adaptive behaviors (i.e., those followed by a positive outcome). This notion was consistent with evidence that was emerging to indicate disrupting dopamine function altered responding of animals working for natural reinforcers, including food and water (Ettenberg & Horvitz, 1990; Gerber, Sing, & Wise, 1981; Mason, Beninger, Fibiger, & Phillips, 1980; Tombaugh, Tombaugh, & Anisman, 1979; Wise, Spindler, deWit, & Gerberg, 1978; Wise, Spindler, & Legault, 1978), electrical brain stimulation (Fouriezos, Hansson, & Wise, 1978; Fouriezos & Wise, 1976; Gallistel, Boytim, Gomita, & Klebanoff, 1982; Stellar & Corbett, 1989; Stellar, Kelley, & Corbett, 1983), or drugs of abuse (Bozarth & Wise, 1981; De Wit & Wise, 1977; Lyness, Friedle, & Moore, 1979; Roberts, Corcoran, & Fibiger, 1977; Yokel & Wise, 1976). Despite the well-documented role of DA in regulating goal-directed behavior, its specific function remains a matter for debate. Theories suggest a role for DA in everything from reward (Schultz, 1998, 2006; Wise & Rompre, 1989) to response

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initiation or selection (Kelley, Baldo, Pratt, & Will, 2005; Salamone & Correa, 2002) to motivation/wanting (Robinson & Berridge, 2000). The following is an attempt to integrate what is known about dopaminergic function in order to frame its role in the production of motivated behavior. Many postulates suggest that midbrain DA neurons function in reward, indicating that they govern behavior directed toward appetitive, rather than aversive, stimuli. Such suggestions seem, at best, incomplete since DA neurons have been shown to respond to presentation of aversive, as well as appetitive, stimuli (Abercrombie, Keefe, DiFrischia, & Zigmond, 1989; Doherty & Gratton, 1992; Louilot, Le Moal, & Simon, 1986; Young, Joseph, & Gray, 1993), and DA has been shown to increase in the NA during associative learning of neutral stimuli (Young, Ahier, Upton, Joseph, & Gray, 1998). Additionally, DA receptor antagonism has been shown to disrupt learning about aversive stimuli (Salamone, 1994). Furthermore, DA neurons do not fire in a temporal pattern consistent with a role in pleasure or hedonics. Thus, once reward is expected, DA neurons have been shown to respond not to presentation of the reward itself, but instead to presentation of a stimulus that is most predictive of the reward, even before it is presented (Schultz, Apicella, & Ljungberg, 1993). For these reasons, theories of DA function that depend purely on notions of hedonics and reward have largely been dismissed. Dopamine Mediates the Learning of Motivational Responding, but Not the Emission of Motivated Behavior Purported roles for DA in wanting or craving, as well as in response initiation or response selection are motivational theories of DAergic function, and are very influential in contemporary thinking about motivated behavior. They suggest that DA is involved in the energizing or directing of behavior toward the appropriate goal. However, behavioral evidence suggests that DA receptor antagonism leaves motivational processes very much intact. For example, animals can be trained to run a straight alley when presented with an olfactory cue (S⫹) predictive of either food or drug reinforcement in the goal box. Following DA receptor antagonist treatment, such animals still traverse the alley normally when presented with the reinforcement-predictive cue (McFarland & Ettenberg, 1995, 1998). Furthermore, in subjects having undergone training to run an alley for heroin reinforcement and a subsequent period of extinction (with no cues or reinforcement available), haloperidol does not block the ability of the S⫹ to reinstate drug-seeking behavior (McFarland & Ettenberg, 1997). Additionally, the ability of an S⫹ conditioned

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in this fashion to elicit conditioned locomotor activation, conditioned place preference and conditioned autonomic activation remain intact during dopamine receptor antagonist treatment (Ettenberg & McFarland, 2003; McFarland & Ettenberg, 1999). Together these data strongly suggest that the motivational capacity of the S⫹ stimulus (i.e., its ability to activate and direct behavior) remains intact despite DA receptor blockade. Studies examining the role of conditioned stimuli in behavioral activation have produced comparable results. Horvitz and Ettenberg (Horvitz & Ettenberg, 1991) showed that administration of pimozide, a nonselective DA receptor antagonist, did not reduce locomotor activity in the presence of a stimulus previously paired with food delivery. This suggests that the motivational properties of food-paired stimuli are left intact. Such data are also consistent with demonstrations that environments or stimuli previously paired with amphetamine reinforcement retained their conditioned behavior-activating effects under dopamine receptor antagonist challenge (Beninger & Herz, 1986; Robbins, Cador, Taylor, & Everitt, 1989). Additionally, preferential responding on a lever associated with conditioned reinforcement is preserved following dopaminergic dennervation of the ventral striatum (Everitt & Robbins, 1992; Robbins et al., 1989). Thus, it seems that the motivating capacity of reinforcement-associated cues remains intact following disruption of DA function. When subjects are actively engaged in operant responding, administration of a DA receptor antagonist produces one of two behavioral patterns. Low doses produce increases in responding similar to that seen when the reinforcer is diminished (Ettenberg, Pettit, Bloom, & Koob, 1982; Geary & Smith, 1985; Rolls et al., 1974; Schneider, Davis, Watson, & Smith, 1990). High doses produce withinsession declines in operant behavior, similar to “extinction curves” that result from removal of the reinforcer (Franklin, 1978; Franklin & McCoy, 1979; Gallistel et al., 1982; Gerber et al., 1981; Lovibond, 1980; Wise et al., 1978). The fact that, in both situations, animals will initiate responding, and do so with normal (or near normal) response latencies suggests that the motivation of these subjects to engage in goal-oriented behavior remains intact. Franklin and McCoy (1979) trained animals to press a lever in order to receive electrical brain stimulation. They demonstrated that when pretreated with pimozide, animals showed an extinction-like pattern of responding. However, presentation of a conditioned stimulus (CS) that was previously paired with brain stimulation reward, successfully reinstated operant responding. Thus, subjects maintained motivational responding to a reward-paired stimulus despite the reinforcement decrement that presumably led to the progressive decline in responding through the initial course

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of the session. Similarly, Gallistel and colleagues (1982) showed that while dopamine antagonists elevated brain reward thresholds for intracranial stimulation in a runway paradigm, they did not prevent the motivational effects of “priming” stimulation that incited animals to run the alley in the first place. Taken together, these data suggest that DA receptor antagonism, while capable of blocking the ability of reinforcing stimuli to maintain responding, does not alter the motivation to seek reinforcement. Further evidence that motivational processes remain intact during DA receptor antagonism comes from choice experiments. In such experiments, subjects are allowed to choose between two alternative responses: one that leads to reinforcer delivery and one that does not. Doses of dopamine receptor antagonist drugs that are sufficient to disrupt operant response rates have little effect on response choices in lever-press (Bowers, Hamilton, Zacharko, & Anisman, 1985; Evenden & Robbins, 1983) or T-maze (Tombaugh, Szostak, & Mills, 1983) tasks. Rats still prefer to make a response that has previously led to reinforcement over one that has not, even following challenge with DA receptor antagonists. Taken together, the data described suggest that the fundamental aspects of motivation remain intact despite disruption of DA transmission. Although the midbrain DA system does not appear to signal either reward or motivation, it is clear that intact dopaminergic function is important for both the acquisition and maintenance of operant responding (for reviews see Beninger & Miller, 1998; Di Chiara, Acquas, Tanda, & Cadoni, 1993; Kiyatkin, 1995). Thus, DA must serve a function related to the learning and maintenance of motivated responding, while the emission of previously learned behavior progresses independent of dopamine receptor activation. Dopamine Stimulates Plasticity within Motivational Circuitry An examination of the firing pattern of DA neurons reveals that most DA neurons display phasic activation after novel stimuli and after delivery of primary reinforcers (e.g., food). Additionally, when a biologically significant stimulus is predicted by an environmental cue, with experience DA neurons come to respond to the predictive cue, rather than to the reinforcer itself. Such changes in firing rate produce a pattern of responding whereby DA neurons increase firing to better than expected outcomes, remain unaffected by predictable outcomes and decrease firing in response to worse than expected outcomes (for a review, see Schultz, 1998). Thus, DA neurons respond to the difference between actual reward and expected reward, not the presence of reward itself. This suggests that the function of DA within

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Dopamine and Motivated Behavior 441

the production of goal-directed behavior is to signal the need to create an adaptive behavioral response, that is, promote neuronal plasticity. Such a suggestion is consistent with evidence regarding the anatomical location of DA synapses. Anatomical studies indicate that DA afferents are well situated to modulate or gate the probability of cells being activated (O’Donnell & Grace, 1995). Thus, DA synapses in both the mPFC and striatum tend to be located proximal to excitatory contact, with excitatory inputs forming on the head of the spine and dopamine terminals synapsing on the neck (Arbuthnott, Ingham, & Wickens, 1998; Carr & Sesack, 1999; Sesack & Pickel, 1990; Smiley & Goldman-Rakic, 1993; Yang, Seamans, & Gorelova, 1999). From a purely anatomical perspective, DA synapses seem to be poised to modulate incoming excitatory information. Ample electrophysiological data also suggest that DA is capable of modulating the efficiency of neuronal responses to other inputs, particularly to glutamate, either supporting or diminishing neuronal activity, depending on the quality of excitatory inputs received by target cells (O’Donnell & Grace, 1995). Both pyramidal cells in the mPFC and spiny cells of the accumbens have been shown to exist in a bistable state (Bazhenov, Timofeev, Steriade, & Sejnowski, 1998; O’Donnell & Grace, 1995; Timofeev, Grenier, & Steriade, 1998; Yim & Mogenson, 1988). Thus, cells fluctuate between a “down state” where membrane potential is relatively hyperpolarized and an “up state” where membrane potential is relatively depolarized. Dopamine tends to inhibit cells in the down state but excite cells in the up state (Hernandez-Lopez, Bargas, Surmeier, Reyes, & Galarraga, 1997; Kiyatkin & Rebec, 1999; O’Donnell, Greene, Pabello, Lewis, & Grace, 1999; Yang & Seamans, 1996). If there is more depolarizing (i.e., glutamatergic) input to a cell, DA D1 receptor activation increases the duration of depolarization via increasing a calcium (Hernandez-Lopez et al., 1997). In the absence of depolarizing input, DA will support the inactive state via D2 receptor activation of potassium conductances (O’Donnell & Grace, 1996). Dopamine appears to serve a similar role within the basolateral amygdala, where there are two types of neurons, inhibitory interneurons and pyramidal-like projection neurons. Stimulation of DA receptors in the basolateral amygdala increases the firing rate of interneurons thereby decreasing the firing rate of projection neurons. Further, DA attenuates activation of pyramidal cells in the basolateral amygdala that is elicited by electrical stimulation of the mPFC and mediodorsal nucleus of the thalamus, while potentiating the responses evoked by electrical stimulation of sensory association cortex (Rosenkranz & Grace, 1999, 2001). This organization is suggested to produce a

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global filtration of inputs such that, upon presentation of an affective stimulus, there is potentiation of the strongest sensory input and concomitant dampening of cortical inhibition; thereby augmenting the response to affective stimuli. When considered as a whole, DA neurotransmission seems to increase the signal to noise ratio and consequently gate the flow of information within the motive circuit (Le Moal & Simon, 1991; Rosenkranz & Grace, 1999, 2001). The pattern of DA innervation of its target structures is also consistent with a general filtration and modulatory function. Dopaminergic projections to target structures are very divergent, with each axon being highly ramified (Anden, Hfuxe, Hamberger, & Hokfelt, 1966; Percheron, Francois, Yelnik, & Fenelon, 1989). Nearly every striatal neuron and many cortical neurons receive dopaminergic innervation. Additionally, these neurons display homogeneous and synchronous responsivity following presentation of motivationally significant stimuli that activate DA cells. Thus, DA neurons broadcast a global wave of activity to the NA and mPFC, rather than a stimulus or response specific signal (Schultz, 1998). Such a pattern of responding is suited to simultaneous modulation of ongoing activity in these forebrain and allocortical structures.

Role for Dopamine-Induced Plasticity in the Acquisition of Adaptive Behavior The data outlined previously suggest that behavioral responding to motivationally relevant stimuli proceeds in at least two phases; the acquisition of a response and the maintenance of a response. During the acquisition phase, synaptic DA is increased by presentation of primary reinforcers or novel stimuli. This DA signal can specifically strengthen those synapses receiving simultaneous excitatory glutamatergic input (e.g., corticostriatal or amygdalostriatal). In this fashion, DA would serve to facilitate the learning of adaptive behavioral responses, as well as increase access of limbic and cortical structures to the motor system. With repeated presentations of motivationally relevant stimuli (either primary or conditioned), these same excitatory inputs would be recruited and strengthened such that they no longer require dopaminergic influence to elicit motor output. Thus, the primary function of DA is to facilitate synaptic (and behavioral) plasticity, rather than to direct elicitation of motor responses. This helps explain why behavioral data show that animals do not acquire behavioral responses when DA transmission is disrupted, however, they will exhibit previously learned behaviors (see earlier discussion). This explanation is also consistent with the observation that the activity of DA neurons fails to discriminate among different salient stimuli, regardless of valence, or among different sensory modalities. Thus, DA

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facilitates the learning goal-directed responses, in general, rather than specific motor responses to specific stimuli. The involvement of DA in both the acquisition and maintenance of operant responding has been difficult to explain with a single theory of DA function. Theories emphasizing the modulatory effects of DA in learning can explain acquisition effects, but typically fail to explain effects on maintenance. Thus, if the inhibition of DA neurotransmission blocks plasticity, it should cause a kind of behavioral and neuronal inflexibility that leads to a decrease in responding for reinforcers and perseverance in previously learned behavioral patterns. However, if one remembers that both increases and decreases in firing rates of dopaminergic neurons have functional implications, then a possible explanation presents itself. As discussed earlier, increases in DA firing rates seem able to support behavioral and neuronal plasticity leading to the learning of new adaptive responses. Similarly, depressed DA transmission (like that resulting from DA receptor antagonism) provides a signal indicating a less than expected outcome. From a functional perspective, such an error signal could lead to compensatory adaptations that would weaken the strength and persistence of the preceding behavior. It seems possible that both an augmentation and a diminution in DA cell firing rates would elicit behavioral plasticity resulting in a change in behavioral output.

NEUROPSYCHIATRIC INDICATIONS In human patients, disruption of motivational circuitry results in a number of behavioral disorders. Primary lesions of the “motor” aspects of the circuitry have been shown to lead to disorders associated with kinetic disturbances (e.g., Parkinson’s disease), while disturbances in more “limbic” aspects of the circuitry are associated with schizophrenia, which is characterized by emotional and sensory gating difficulties. Notably, however, neuropsychiatric disorders relating to disruption of motivational circuitry are characterized by a broad range of symptoms from motor to cognitive and affective. This suggests that integrative aspects of motivational circuitry are a fundamental feature of motivated behavior and that understanding behavior (in both healthy and disease states) depends on an appreciation of both parallel and integrative processing. Schizophrenia Schizophrenia is a complex disorder characterized by hallucinations and delusions (positive symptoms), as well as flattened affect, apathy, and anhedonia (negative symptoms). It is now appreciated that a major determinant

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of functional impairment in schizophrenic patients is their cognitive deficits, which include impairments in executive function, working memory, and attention (Breier, Schreiber, Dyer, & Pickar, 1991; Keefe et al., 1987; Revheim et al., 2006). Based on the fact that amphetamines exacerbate psychotic symptoms, while blockade of dopamine receptors alleviates schizophrenic symptoms, early theories of schizophrenia were centered around the “dopamine hypothesis” (Carlsson, 1988; Joseph, Frith, & Waddington, 1979; Meltzer & Stahl, 1976; Snyder, 1976), which suggested that schizophrenia was the result of excessive DA, triggering sensory, cognitive, and affective abnormalities. Too much dopamine was hypothesized to inhibit striatal efferents, thereby disinhibiting the thalamus (Swerdlow, Braff, Geyer, & Koob, 1986), producing an impaired ability to gate incoming sensory information and respond appropriately. There is evidence that dopamine turnover is increased is drug-naïve schizophrenic patients (DaoCastellana et al., 1997; Hietala et al., 1994; Kumakura et al., 2007), and dopamine D2 receptor blockade remains the most clinically useful mechanism to treat psychosis (Hietala & Syvalahti, 1996). However, recent evidence converges to suggest that a purely dopaminergic explanation of schizophrenia is overly simplistic. Current theories of schizophrenia continue to identify dysfunction of frontostriatal circuitry as a core component. For example, studies suggest that schizophrenia is associated with functional and structural changes within the thalamus, striatum, and prefrontal cortex (Andrews, Wang, Csernansky, Gado, & Barch, 2006; Cullen et al., 2006; Juckel et al., 2006; Kemether et al., 2003; Lawrie, McIntosh, Hall, Owens, & Johnstone, 2008; Mitelman, Byne, Kemether, Hazlett, & Buchsbaum, 2005; Rubin et al., 1994; Schlosser et al., 2007). However, transmitters other than DA, such as serotonin, glutamate, and GABA have also been implicated, particularly in the PFC (Aghajanian & Marek, 2000; Carlsson, Hansson, Waters, & Carlsson, 1999; Carlsson, Waters, & Carlsson, 1999; Gray & Roth, 2007; Jentsch & Roth, 1999; Lewis, Hashimoto, & Volk, 2005; Lewis & Moghaddam, 2006; Lewis, Pierri, Volk, Melchitzky, & Woo, 1999; Weiner et al., 2001). Consistent with the importance of PFCstriatal processing in schizophrenia, animal studies have shown that prepulse inhibition of the startle response (a model for studying sensorimotor gating) depends on intact nucleus accumbens and prefrontal cortical circuitry (Bubser & Koch, 1994; Kodsi & Swerdlow, 1994; Reijmers, Vanderheyden, & Peeters, 1995; Swerdlow, Braff, Masten, & Geyer, 1990; Swerdlow et al., 1995). Taken together, the data suggest that altered processing within motivational cotrico-basal-ganglia-cortical circuitry is central to schizophrenia. Within this circuitry,

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Neuropsychiatric Indications

numerous changes have taken place, and identifying the primary deficit seems to be something of a chickenand-egg question. However, the balance of all these neurochemical and neuroanatomical changes presumably explains the complex features of schizophrenia. Parkinson’s Disease Parkinson’s disease (PD) is a neurodegenerative disorder that is characterized by motor symptoms, such as resting tremor, rigidity, and bradykinesia (Carpenter, Allum, Honegger, Adkin, & Bloem, 2004; Gelb, Oliver, & Gilman, 1999; Mardsen, 1994). In addition to motor disturbances, it is now recognized that PD is associated with cognitive impairment, and in extreme cases dementia (Brown & Marsden, 1984; Owen, 2004). The primary pathology in PD is degeneration of the dopamine-containing cells of the SNc that project to the striatum (Bernheimer, Birkmayer, Hornykiewicz, Jellinger, & Seitelberger, 1973; Damier, Hirsch, Agid, & Graybiel, 1999; Graybiel, Hirsch, & Agid, 1990; Hirsch, Graybiel, & Agid, 1988; Hornykiewicz & Kish, 1987), with less involvement of VTA dopamine neurons (Fearnley & Lees, 1991; Gibb & Lees, 1989). Dopaminergic degeneration is earliest and most severe in neurons projecting to the dorsolateral (motor) striatum, but progresses ventromedially through the striatum to associative and limbic areas (Damier et al., 1999; Fearnley & Lees, 1990; Graybiel et al., 1990). The principal loss of DA within the motor loop of the basal ganglia is consistent with the primary pathology consisting of motor deficits, and the degree of DA loss in the striatum has been correlated with the severity of motor symptoms. However, PD is associated with several neuroadaptive changes throughout motivational circuitry, including supersensitivity of postsynaptic D2 DA receptors in the striatum, degeneration of serotonin and acetylcholine neurons projecting to the striatum, and loss of DA in the PFC (Birkmayer, Danielczyk, Neumayer, & Riederer, 1975; Calabresi, Mercuri, Sancesario, & Bernardi, 1993; Jellinger, 1991; Jellinger, 1998; Kostrzewa, Kostrzewa, Nowak, Kostrzewa, & Brus, 2004; Scatton, Javoy-Agid, Rouquier, Dubois, & Agid, 1983; Scatton, Rouquier, Javoy-Agid, & Agid, 1982). Thus, the complete constellation of symptoms associated with PD presumably results from both alterations in the normal flow of information through cortico-striato-cortical circuitry (resulting from striatal dopamine depletion) and additionally from the concomitant neuradapataions within the distributed motivational network. Loss of DA within the striatum has differential effects on different efferent striatal pathways, and it is the competing balance of these pathways that regulates thalamic and cortical

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excitatory activity, and consequently behavior. We previously discussed two efferent pathways from the striatum: the direct and indirect pathways (see earlier discussion). A third “hyperdirect” pathway has also been suggested (Nambu, Tokuno, & Takada, 2002). This pathway bypasses the striatum and runs from the cortex, through the subthalamic nucleus to the GPi to thalamus and back to cortex. While activity of the direct pathway results in thalamic and cortical activation, activity of the indirect or hyperdirect pathways has the opposite effect—increased inhibition of the thalamus and less activation of cortex. Circuit-based explanations have suggested that PD results from decreased activity of the direct pathway relative to either the indirect or hyperdirect pathway, resulting in a reduction in thalamic and cortical activity (Albin et al., 1989; DeLong, 1990; Leblois, Boraud, Meissner, Bergman, & Hansel, 2006; Nambu, 2005; Nambu et al., 2002). DA, via activation of D1 receptors, activates the direct pathway, but via activation of D2 receptors inhibits activity of the indirect pathway (Gerfen, 2000; Mallet, Ballion, Le Moine, & Gonon, 2006; O’Connor, 1998). Following DA denervation, decreased dopamine stimulation of the direct pathway results in reduced GABAergic inhibition of SNr/GPi, thereby disinhibiting the GABA projection to the thalamus and resulting in decreased cortical activation by basal ganglia circuitry. DA denervation is hypothesized to have less influence on the indirect pathway and no effect on the hyperdirect pathway because it bypasses the striatum. Thus, the symptoms of PD arise from a relative increase in the influence of indirect or hyperdirect pathways on thalamic and cortical excitation. Cortical dysfunction within different functional loops is presumed to underlie the various functional deficits of PD, like akinesia and executive function deficits. Because the loops are integrated (see earlier discussion), dysfunction of one corticobasal ganglia-cortical loop has the potential to additionally disrupt information processing within other loops, as well. This integrative aspect of motivational circuitry explains how cognitive deficits arise, even early in the course of PD when DA denervation is restricted predominantly to motor striatum. Huntington’s Disease Huntington’s disease (HD) is a genetic neurodegenerative disorder that, like PD, is characterized clinically by prominent motor dysfunction. In this case, however, movements are described as jerky, random, and uncontrollable. HD is also associated with cognitive deficits including deficiencies in executive function (planning, cognitive flexibility, abstract thinking, rule acquisition, initiating appropriate actions, and inhibiting inappropriate

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actions) and psychiatric symptoms, which may include blunted affect, aggression, or compulsivity (Albin et al., 1989; Folstein, Folstein, & McHugh, 1979; Martin, 1984; Martin & Gusella, 1986; Wilson & Garron, 1979). Huntington’s disease is caused by a trinucleotide (GAC) repeat expansion in the gene coding for Huntingtin protein. The exact mechanism by which the mutated form of the protein leads to Huntington’s disease is not known, but it is clear that it leads to cell death in medium spiny neurons of the striatum—although some degeneration other brain regions, including the cortex, have also been reported (Ferrante, Kowall, Richardson, Bird, & Martin, 1986; Hedreen & Folstein, 1995; Kowall, Ferrante, & Martin, 1987; Li, 1999; Penney & Young, 1986). Striatal degeneration is progressive with respect to topography with the earliest and most pronounced degeneration occurring in associative striatum and damage to motor and finally limbic striatum appearing in more advanced stages of the disease (Augood, Faull, Love, & Emson, 1996; Ferrante et al., 1986; Kowall et al., 1987; Vonsattel & DiFiglia, 1998). Striatal degeneration is also progressive with respect to target. Early stages of HD are characterized by loss of GABAergic projections to the GPe and SN, with GPi projections only affected in later stages of the disease (Albin et al., 1992; Albin, Reiner, Anderson, Penney, & Young, 1990; Albin, Young, et al., 1990; Pearson, Heathfield, & Reynolds, 1990; Reiner et al., 1988; Richfield & Herkenham, 1994; Sapp et al., 1995). Symptoms of HD are generally attributed to frontostriatal dysfunction resulting from striatal degeneration. Most frequently implicated is degeneration of indirect striatal pathway to the GPe (Albin et al., 1989; Hallett, 1993; Kipps et al., 2005; Penney & Young, 1986; Wakai, Takahashi, & Hashizume, 1993). Degeneration of the indirect pathway leads to underactivity of the subthalamic nucleus, and subsequent underactivity of the GPi/SNr, thus removing inhibitory control over the thalamus. Overactivity of the thalamus is hypothesized to result in chorea, which is the hallmark motor abnormality of HD. Chorea is described as intrusion of undesirable motor programs into the normal flow of behavior. Thus, the primary motor dysfunction in HD seems to result from a failure of striatal circuitry to appropriately inhibit undesired behaviors, resulting in jerky and undesired movements. Consistent with the finding that the initial neurodegeneration in HD occurs in associative striatum, cognitive symptoms frequently predate the appearance of motor symptoms in HD (Butters, Albert, & Sax, 1979; Kirkwood et al., 2000; Lemiere, Decruyenaere, Evers-Kiebooms, Vandenbussche, & Dom, 2004; Stout et al., 2007; Wilson & Garron, 1979). Motor symptoms in early stages (prior to degeneration of motor striatum) presumably occur via the integrative

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aspects of basal ganglia circuitry, with the associative loop positioned to influence the activity of the motor loop (as discussed previously, see also Joel, 2001). In this respect, given the hypothesized overactivity of the thalamus in HD, thalamo-cortical interactions that integrate associative information into motor circuitry might be particularly important.

SUMMARY The fact that there are separate, but interactive, subcircuits within the motivational circuitry suggests that they have separable functions in the production of goal-directed behaviors. Limbic structures, like the VTA, basolateral amygdala, hippocampus, and medial prefrontal cortex are more intimately connected with the NA and VP, while motor structures like the primary motor cortex, SN, and the pedunculopontine nucleus are more intimately connected with the dorsolateral striatum and globus pallidus. In between (functionally and anatomically) lies associative circuitry. Such segregation leads to the suggestion that the limbic loop is involved in learning about motivationally relevant stimuli and subsequently integrating incoming information about such stimuli when they are presented; the associative loop is involved in executive functions like strategic planning initiating appropriate actions, and inhibiting inappropriate actions; the motor loop is involved in sending information about the welllearned responses to motor systems. The production of behavior requires activation of the motor cortex and pyramidal motor systems, which are most intimately connected with the motor loop. While the existence of parallel circuit processing within motivational circuitry is an important feature, an equally important attribute is the integrative nature of the circuitry. It is difficult to imagine that behavior could be smoothly produced in the face of changing environmental cues (either interoceptive or exteroceptive) without motivational circuitry being able to integrate environmental stimuli, relative to an individual’s past experience and current motivational state, plan, and then execute appropriate behavioral responses. It is hypothesized that this integration occurs via striatal-midbrain and cortico-thalamic interactions. These interactions are rectified such that limbic and associative information can be transferred through the circuitry and affect ongoing motor processing and goal-directed behavior. The integrative nature of processing within motivational circuitry is evidenced by the disturbance at multiple functional levels that occurs in neuropsychiatric disorders where processing within motivational circuitry is disrupted.

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REFERENCES Abercrombie, E. D., Keefe, K. A., DiFrischia, D. S., & Zigmond, M. J. (1989). Differential effect of stress on in vivo dopamine release in striatum, nucleus accumbens, and medial frontal cortex. Journal of Neurochemistry, 52, 1655–1658.

Bailey, K. R., & Mair, R. G. (2006). The role of striatum in initiation and execution of learned action sequences in rats. Journal of Neuroscience, 26, 1016–1025.

Aggleton, J. P., Burton, M. J., & Passingham, R. E. (1980). Cortical and subcortical afferents to the amygdala of the rhesus monkey (macaca mulatta). Brain Research, 190, 347–368.

Baker, S. N., Spinks, R., Jackson, A., & Lemon, R. N. (2001). Synchronization in monkey motor cortex during a precision grip task: Pt. I. Task-dependent modulation in single-unit synchrony. Journal of Neurophysiology, 85, 869–885.

Aghajanian, G. K., & Marek, G. J. (2000). Serotonin model of schizophrenia: Emerging role of glutamate mechanisms. Brain Research: Brain Research Reviews, 31(2/3), 302–312. Albin, R. L., Reiner, A., Anderson, K. D., Dure, L. S. T., Handelin, B., Balfour, R., et al. (1992). Preferential loss of striato-external pallidal projection neurons in presymptomatic Huntington’s disease. Annals of Neurology, 31, 425–430. Albin, R. L., Reiner, A., Anderson, K. D., Penney, J. B., & Young, A. B. (1990). Striatal and nigral neuron subpopulations in rigid Huntington’s disease: Implications for the functional anatomy of chorea and rigidityakinesia. Annals of Neurology, 27, 357–365. Albin, R. L., Young, A. B., & Penney, J. B. (1989). The functional anatomy of basal ganglia disorders. Trends in Neurosciences, 12, 366–375. Albin, R. L., Young, A. B., Penney, J. B., Handelin, B., Balfour, R., Anderson, K. D., et al. (1990). Abnormalities of striatal projection neurons and n-methyl-d-aspartate receptors in presymptomatic Huntington’s disease. New England Journal of Medicine, 322, 1293–1298. Alexander, G. E., & Crutcher, M. D. (1990). Functional architecture of basal ganglia circuits: Neural substrates of parallel processing. Trends in Neurosciences, 13, 266–271. Alexander, G. E., & DeLong, M. R. (1985). Microstimulation of the primate neostriatum: Pt. II. Somatotopic organization of striatal microexcitable zones and their relation to neuronal response properties. Journal of Neurophysiology, 53, 1417–1430. Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357–381. Alheid, G. F. (2003). Extended amygdala and basal forebrain. Annals of the New York Academy of Sciences, 985, 185–205. Anaya-Martinez, V., Martinez-Marcos, A., Martinez-Fong, D., Aceves, J., & Erlij, D. (2006). Substantia nigra compacta neurons that innervate the reticular thalamic nucleus in the rat also project to striatum or globus pallidus: Implications for abnormal motor behavior. Neuroscience, 143, 477–486. Anden, N. E., Hfuxe, K., Hamberger, B., & Hokfelt, T. (1966). A quantitative study on the nigro-neostriatal dopamine neuron system in the rat. Acta Physiologica Scandinavica, 67, 306–312. Andrews, J., Wang, L., Csernansky, J. G., Gado, M. H., & Barch, D. M. (2006). Abnormalities of thalamic activation and cognition in schizophrenia. American Journal of Psychiatry, 163, 463–469. Apicella, P. (2002). Tonically active neurons in the primate striatum and their role in the processing of information about motivationally relevant events. European Journal of Neuroscience, 16, 2017–2026. Arbuthnott, G. W., Ingham, C. A., & Wickens, J. R. (1998). Modulation by dopamine of rat corticostriatal input. Advances in Pharmacology, 42, 733–736. Arikuni, T., & Kubota, K. (1986). The organization of prefrontocaudate projections and their laminar origin in the macaque monkey: A retrograde study using hrp-gel. Journal of Comparative Neurology, 244, 492–510. Augood, S. J., Faull, R. L., Love, D. R., & Emson, P. C. (1996). Reduction in enkephalin and substance p messenger rna in the striatum of early grade Huntington’s disease: A detailed cellular in situ hybridization study. Neuroscience, 72, 1023–1036.

c22.indd 445

Badre, D., & Wagner, A. D. (2004). Selection, integration, and conflict monitoring: Assessing the nature and generality of prefrontal cognitive control mechanisms. Neuron, 41, 473–487.

Barbas, H. (2000). Connections underlying the synthesis of cognition, memory, and emotion in primate prefrontal cortices. Brain Research Bulletin, 52, 319–330. Bazhenov, M., Timofeev, I., Steriade, M., & Sejnowski, T. J. (1998). Cellular and network models for intrathalamic augmenting responses during 10-hz stimulation. Journal of Neurophysiology, 79, 2730–2748. Beckstead, R. M. (1979). An autoradiographic examination of corticocortical and subcortical projections of the mediodorsal-projection (prefrontal) cortex in the rat. Journal of Comparative Neurology, 184, 43–62. Beckstead, R. M. (1983). A pallidostriatal projection in the cat and monkey. Brain Research Bulletin, 11, 629–632. Beckstead, R. M., Domesick, V. B., & Nauta, W. J. (1979). Efferent connections of the substantia nigra and ventral tegmental area in the rat. Brain Research, 175, 191–217. Beckstead, R. M., & Kersey, K. S. (1985). Immunohistochemical demonstration of differential substance p-, met-enkephalin-, and glutamic-aciddecarboxylase-containing cell body and axon distributions in the corpus striatum of the cat. Journal of Comparative Neurology, 232, 481–498. Beninger, R. J., & Herz, R. S. (1986). Pimozide blocks establishment but not expression of cocaine-produced environment-specific conditioning. Life Sciences, 38, 1425–1431. Beninger, R. J., & Miller, R. (1998). Dopamine d1-like receptors and reward-related incentive learning. Neuroscience and Biobehavioral Reviews, 22, 335–345. Berendse, H. W., Galis-de Graaf, Y., & Groenewegen, H. J. (1992). Topographical organization and relationship with ventral striatal compartments of prefrontal corticostriatal projections in the rat. Journal of Comparative Neurology, 316, 314–347. Bernheimer, H., Birkmayer, W., Hornykiewicz, O., Jellinger, K., & Seitelberger, F. (1973). Brain dopamine and the syndromes of Parkinson and Huntington: Clinical, morphological and neurochemical correlations. Journal of the Neurological Sciences, 20, 415–455. Besson, M. J., Graybiel, A. M., & Quinn, B. (1990). Co-expression of neuropeptides in the cat’s striatum: An immunohistochemical study of substance p, dynorphin b and enkephalin. Neuroscience, 39, 33–58. Bevan, M. D., Smith, A. D., & Bolam, J. P. (1996). The substantia nigra as a site of synaptic integration of functionally diverse information arising from the ventral pallidum and the globus pallidus in the rat. Neuroscience, 75, 5–12. Birkmayer, W., Danielczyk, W., Neumayer, E., & Riederer, P. (1975). Dopaminergic supersensitivity in Parkinsonism. Advances in Neurology, 9, 121–129. Boecker, H., Dagher, A., Ceballos-Baumann, A. O., Passingham, R. E., Samuel, M., Friston, K. J., et al. (1998). Role of the human rostral supplementary motor area and the basal ganglia in motor sequence control: Investigations with h2 15o pet. Journal of Neurophysiology, 79, 1070–1080. Bolam, J. P., Hanley, J. J., Booth, P. A., & Bevan, M. D. (2000). Synaptic organisation of the basal ganglia. Journal of Anatomy, 196(Pt. 4), 527–542. Botvinick, M., Nystrom, L. E., Fissell, K., Carter, C. S., & Cohen, J. D. (1999, November 11). Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature, 402, 179–181.

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446

Motor Control: Pyramidal, Extrapyramidal, and Limbic Motor Control

Bowers, W., Hamilton, M., Zacharko, R. M., & Anisman, H. (1985). Differential effects of pimozide on response-rate and choice accuracy in a self-stimulation paradigm in mice. Pharmacology, Biochemistry, and Behavior, 22, 521–526.

Christie, M. J., Summers, R. J., Stephenson, J. A., Cook, C. J., & Beart, P. M. (1987). Excitatory amino acid projections to the nucleus accumbens septi in the rat: A retrograde transport study utilizing d[3h]aspartate and [3h]gaba. Neuroscience, 22, 425–439.

Bozarth, M. A., & Wise, R. A. (1981). Heroin reward is dependent on a dopaminergic substrate. Life Sciences, 29, 1881–1886.

Churchill, L., Dilts, R. P., & Kalivas, P. W. (1990). Changes in gammaaminobutyric acid, mu-opioid and neurotensin receptors in the accumbens-pallidal projection after discrete quinolinic acid lesions in the nucleus accumbens. Brain Research, 511, 41–54.

Breier, A., Schreiber, J. L., Dyer, J., & Pickar, D. (1991). National institute of mental health longitudinal study of chronic schizophrenia: Prognosis and predictors of outcome. Archives of General Psychiatry, 48, 239–246. Brinkman, J., & Kuypers, H. G. (1973). Cerebral control of contralateral and ipsilateral arm, hand and finger movements in the split-brain rhesus monkey. Brain, 96, 653–674. Brown, R. G., & Marsden, C. D. (1984). How common is dementia in Parkinson’s disease? Lancet, 2, 1262–1265. Bubser, M., & Koch, M. (1994). Prepulse inhibition of the acoustic startle response of rats is reduced by 6-hydroxydopamine lesions of the medial prefrontal cortex. Psychopharmacology, 113(3–4), 487–492. Butters, N., Albert, M. S., & Sax, D. (1979). Investigations of the memory disorders of patients with Huntington’s disease. Advances in Neurology, 23, 203–213. Calabresi, P., Mercuri, N. B., Sancesario, G., & Bernardi, G. (1993). Electrophysiology of dopamine-denervated striatal neurons: Implications for Parkinson’s disease. Brain, 116(Pt. 2), 433–452. Carlsson, A. (1988). The current status of the dopamine hypothesis of schizophrenia. Neuropsychopharmacology, 1, 179–186. Carlsson, A., Hansson, L. O., Waters, N., & Carlsson, M. L. (1999). A glutamatergic deficiency model of schizophrenia. British Journal of Psychiatry: Supplement(37), 2–6.

Churchill, L., & Kalivas, P. W. (1994). A topographically organized gamma-aminobutyric acid projection from the ventral pallidum to the nucleus accumbens in the rat. Journal of Comparative Neurology, 345, 579–595. Cipolloni, P. B., & Pandya, D. N. (1999). Cortical connections of the frontoparietal opercular areas in the rhesus monkey. Journal of Comparative Neurology, 403, 431–458. Conde, F., Maire-Lepoivre, E., Audinat, E., & Crepel, F. (1995). Afferent connections of the medial frontal cortex of the rat: Pt. II. Cortical and subcortical afferents. Journal of Comparative Neurology, 352, 567–593. Corbett, D., & Wise, R. A. (1980). Intracranial self-stimulation in relation to the ascending dopaminergic systems of the midbrain: A moveable electrode mapping study. Brain Research, 185, 1–15. Cullen, T. J., Walker, M. A., Eastwood, S. L., Esiri, M. M., Harrison, P. J., & Crow, T. J. (2006). Anomalies of asymmetry of pyramidal cell density and structure in dorsolateral prefrontal cortex in schizophrenia. British Journal of Psychiatry, 188, 26–31. Damier, P., Hirsch, E. C., Agid, Y., & Graybiel, A. M. (1999). The substantia nigra of the human brain: Pt. II. Patterns of loss of dopamine-containing neurons in Parkinson’s disease. Brain, 122(Pt. 8), 1437–1448.

Carlsson, A., Waters, N., & Carlsson, M. L. (1999). Neurotransmitter interactions in schizophrenia-therapeutic implications. European Archives of Psychiatry and Clinical Neuroscience, 249(Suppl. 4), 37–43.

Dao-Castellana, M. H., Paillere-Martinot, M. L., Hantraye, P., Attar-Levy, D., Remy, P., Crouzel, C., et al. (1997). Presynaptic dopaminergic function in the striatum of schizophrenic patients. Schizophrenia Research, 23, 167–174.

Carmichael, S. T., & Price, J. L. (1995). Sensory and premotor connections of the orbital and medial prefrontal cortex of macaque monkeys. Journal of Comparative Neurology, 363, 642–664.

DeLong, M. R. (1990). Primate models of movement disorders of basal ganglia origin. Trends in Neurosciences, 13, 281–285.

Carmona, A., Catalina-Herrera, C. J., & Jimenez-Castellanos, J. (1991). Nigrocaudate and nigroputaminal projections in the monkey. Acta Anatomica, 141, 145–150. Carpenter, M. G., Allum, J. H., Honegger, F., Adkin, A. L., & Bloem, B. R. (2004). Postural abnormalities to multidirectional stance perturbations in Parkinson’s disease. Journal of Neurology, Neurosurgery, and Psychiatry, 75, 1245–1254. Carr, D. B., & Sesack, S. R. (1999). Terminals from the rat prefrontal cortex synapse on mesoaccumbens vta neurons. Annals of the New York Academy of Sciences, 877, 676–678. Carr, D. B., & Sesack, S. R. (2000a). Gaba-containing neurons in the rat ventral tegmental area project to the prefrontal cortex. Synapse, 38, 114–123. Carr, D. B., & Sesack, S. R. (2000b). Projections from the rat prefrontal cortex to the ventral tegmental area: Target specificity in the synaptic associations with mesoaccumbens and mesocortical neurons. Journal of Neuroscience, 20, 3864–3873. Chevalier, G., & Deniau, J. M. (1990). Disinhibition as a basic process in the expression of striatal functions. Trends in Neurosciences, 13, 277–280.

DeLong, M. R., Alexander, G. E., Mitchell, S. J., & Richardson, R. T. (1986). The contribution of basal ganglia to limb control. Progress in Brain Research, 64, 161–174. De Olmos, J. S., & Heimer, L. (1999). The concepts of the ventral striatopallidal system and extended amygdala. Annals of the New York Academy of Sciences, 877, 1–32. Deschenes, M., Veinante, P., & Zhang, Z. W. (1998). The organization of corticothalamic projections: Reciprocity versus parity. Brain Research: Brain Research Reviews, 28, 286–308. DeVito, J. L., & Anderson, M. E. (1982). An autoradiographic study of efferent connections of the globus pallidus in macaca mulatta. Experimental Brain Research, 46, 107–117. De Wit, H., & Wise, R. A. (1977). Blockade of cocaine reinforcement in rats with the dopamine receptor blocker pimozide, but not with the noradrenergic blockers phentolamine or phenoxybenzamine. Canadian Journal of Psychology, 31, 195–203. Di Chiara, G., Acquas, E., Tanda, G., & Cadoni, C. (1993). Drugs of abuse: Biochemical surrogates of specific aspects of natural reward? Biochemical Society Symposium, 59, 65–81. Doherty, M. D., & Gratton, A. (1992). High-speed chronoamperometric measurements of mesolimbic and nigrostriatal dopamine release associated with repeated daily stress. Brain Research, 586, 295–302.

Chikama, M., McFarland, N. R., Amaral, D. G., & Haber, S. N. (1997). Insular cortical projections to functional regions of the striatum correlate with cortical cytoarchitectonic organization in the primate. Journal of Neuroscience, 17, 9686–9705.

Dum, R. P., & Strick, P. L. (1991). The origin of corticospinal projections from the premotor areas in the frontal lobe. Journal of Neuroscience, 11, 667–689.

Chouinard, P. A., Leonard, G., & Paus, T. (2005). Role of the primary motor and dorsal premotor cortices in the anticipation of forces during object lifting. Journal of Neuroscience, 25, 2277–2284.

Dum, R. P., & Strick, P. L. (1993). Cingulate motor areas. In B. A. Vogt & M. Gabriel (Eds.), Neurobiology of cingulate cortex and limbic thalamus (pp. 415–441). Boston: Birkhäuser.

c22.indd 446

8/17/09 2:21:05 PM

References Dum, R. P., & Strick, P. L. (2002). Motor areas in the frontal lobe of the primate. Physiology and Behavior, 77(4–5), 677–682. Ettenberg, A. (1989). Dopamine, neuroleptics and reinforced behavior. Neuroscience and Biobehavioral Reviews, 13(2–3), 105–111. Ettenberg, A., & Horvitz, J. C. (1990). Pimozide prevents the responsereinstating effects of water reinforcement in rats. Pharmacology, Biochemistry, and Behavior, 37, 465–469. Ettenberg, A., & McFarland, K. (2003). Effects of haloperidol on cueinduced autonomic and behavioral indices of heroin reward and motivation. Psychopharmacology, 168(1–2), 139–145. Ettenberg, A., Pettit, H. O., Bloom, F. E., & Koob, G. F. (1982). Heroin and cocaine intravenous self-administration in rats: Mediation by separate neural systems. Psychopharmacology, 78, 204–209. Evenden, J. L., & Robbins, T. W. (1983). Dissociable effects of d-amphetamine, chlordiazepoxide and alpha-flupenthixol on choice and rate measures of reinforcement in the rat. Psychopharmacology, 79(2–3), 180–186. Everitt, B. J., & Robbins, T. W. (1992). Amygdala-ventral striatal interactions and reward-related processes. In J. P. Aggleton (Ed.), The amygdala: Neurobiological aspects of emotion, memory, and mental dysfunction (pp. 401–429). New York: Wiley-Liss.

Fouriezos, G., & Wise, R. A. (1976). Pimozide-induced extinction of intracranial self-stimulation: Response patterns rule out motor or performance deficits. Brain Research, 103, 377–380. Franklin, K. B. (1978). Catecholamines and self-stimulation: Reward and performances effects dissociated. Pharmacology, Biochemistry, and Behavior, 9, 813–820. Franklin, K. B., & McCoy, S. N. (1979). Pimozide-induced extinction in rats: Stimulus control of responding rules out motor deficit. Pharmacology, Biochemistry, and Behavior, 11, 71–75. Fritsch, G., & Hitzig, E. (1870). Uber die electrische erregbarkeit des grosshirns. Archiv für Anatomie, Physiologie, und Wissenschaftliche Medicin, 37, 300–332. Fudge, J. L., & Haber, S. N. (2000). The central nucleus of the amygdala projection to dopamine subpopulations in primates. Neuroscience, 97, 479–494. Fuster, J. M. (2000a). Executive frontal functions. Experimental Brain Research, 133, 66–70. Fuster, J. M. (2000b). Prefrontal neurons in networks of executive memory. Brain Research Bulletin, 52, 331–336.

Fallon, J. H., & Leslie, F. M. (1986). Distribution of dynorphin and enkephalin peptides in the rat brain. Journal of Comparative Neurology, 249, 293–336.

Fuxe, K., Hokfelt, T., Johansson, O., Lidbrink, P., & Ljungdahl, A. (1974). The origin of the dopamine nerve terminals in limbic and frontal cortex: Evidence for meso-cortico dopamine neurons. Brain Research, 82, 349–355.

Fallon, J. H., & Moore, R. Y. (1978). Catecholamine innervation of the basal forebrain: Pt. IV. Topography of the dopamine projection to the basal forebrain and neostriatum. Journal of Comparative Neurology, 180, 545–580.

Gallistel, C. R., Boytim, M., Gomita, Y., & Klebanoff, L. (1982). Does pimozide block the reinforcing effect of brain stimulation? Pharmacology, Biochemistry, and Behavior, 17, 769–781.

Fearnley, J. M., & Lees, A. J. (1990). Striatonigral degeneration. A clinicopathological study. Brain, 113(Pt. 6), 1823–1842.

Geary, N., & Smith, G. P. (1985). Pimozide decreases the positive reinforcing effect of sham fed sucrose in the rat. Pharmacology, Biochemistry, and Behavior, 22, 787–790.

Fearnley, J. M., & Lees, A. J. (1991). Ageing and Parkinson’s disease: Substantia nigra regional selectivity. Brain, 114(Pt. 5), 2283–2301. Ferrante, R. J., Kowall, N. W., Richardson, E. P., Jr., Bird, E. D., & Martin, J. B. (1986). Topography of enkephalin, substance p and acetylcholinesterase staining in Huntington’s disease striatum. Neuroscience Letters, 71, 283–288. Ferry, A. T., Ongur, D., An, X., & Price, J. L. (2000). Prefrontal cortical projections to the striatum in macaque monkeys: Evidence for an organization related to prefrontal networks. Journal of Comparative Neurology, 425, 447–470. Fibiger, H. C., & Phillips, A. G. (1986). Reward, motivation, cognition: Psychobiology of mesotelencephalic dopamine systems. Handbook of Physiology, 4, 647–675. Fibiger, H. C., & Phillips, A. G. (1988). Mesocorticolimbic dopamine systems and reward. Annals of the New York Academy of Sciences, 537, 206–215. Flaherty, A. W., & Graybiel, A. M. (1994). Input-output organization of the sensorimotor striatum in the squirrel monkey. Journal of Neuroscience, 14, 599–610. Flaherty, A. W., & Graybiel, A. M. (1995). Motor and somatosensory corticostriatal projection magnifications in the squirrel monkey. Journal of Neurophysiology, 74, 2638–2648. Floresco, S. B., Magyar, O., Ghods-Sharifi, S., Vexelman, C., & Tse, M. T. (2006). Multiple dopamine receptor subtypes in the medial prefrontal cortex of the rat regulate set-shifting. Neuropsychopharmacology, 31, 297–309. Folstein, S. E., Folstein, M. F., & McHugh, P. R. (1979). Psychiatric syndromes in Huntington’s disease. Advances in Neurology, 23, 281–289. Fonnum, F., Storm-Mathisen, J., & Divac, I. (1981). Biochemical evidence for glutamate as neurotransmitter in corticostriatal and corticothalamic fibres in rat brain. Neuroscience, 6, 863–873. Fouriezos, G., Hansson, P., & Wise, R. A. (1978). Neuroleptic-induced attenuation of brain stimulation reward in rats. Journal of Comparative and Physiological Psychology, 92, 661–671.

c22.indd 447

447

Gelb, D. J., Oliver, E., & Gilman, S. (1999). Diagnostic criteria for Parkinson disease. Archives of Neurology, 56, 33–39. Gentilucci, M., Fogassi, L., Luppino, G., Matelli, M., Camarda, R., & Rizzolatti, G. (1989). Somatotopic representation in inferior area 6 of the macaque monkey. Brain, Behavior and Evolution, 33(2/3), 118–121. Gerber, G. J., Sing, J., & Wise, R. A. (1981). Pimozide attenuates lever pressing for water reinforcement in rats. Pharmacology, Biochemistry, and Behavior, 14, 201–205. Gerfen, C. R. (1992). The neostriatal mosaic: Multiple levels of compartmental organization. Journal of Neural Transmission (Suppl. 36), 43–59. Gerfen, C. R. (2000). Molecular effects of dopamine on striatal-projection pathways. Trends in Neurosciences, 23(Suppl. 10), S64–S70. Gerfen, C. R., Engber, T. M., Mahan, L. C., Susel, Z., Chase, T. N., Monsma, F. J., Jr., et al. (1990, December 7). D1 and d2 dopamine receptor-regulated gene expression of striatonigral and striatopallidal neurons. Science, 250, 1429–1432. Gibb, W. R., & Lees, A. J. (1989). The significance of the lewy body in the diagnosis of idiopathic Parkinson’s disease. Neuropathology and Applied Neurobiology, 15, 27–44. Godschalk, M., Mitz, A. R., van Duin, B., & van der Burg, H. (1995). Somatotopy of monkey premotor cortex examined with microstimulation. Neuroscience Research, 23, 269–279. Goldman, P. S., & Nauta, W. J. (1977). An intricately patterned prefrontocaudate projection in the rhesus monkey. Journal of Comparative Neurology, 72, 369–386. Goldman-Rakic, P. S. (1996). The prefrontal landscape: Implications of functional architecture for understanding human mentation and the central executive. Philosophical Transactions of the Royal Society of London, B: Biological Sciences, 351, 1445–1453. Gray, J. A., & Roth, B. L. (2007). Molecular targets for treating cognitive dysfunction in schizophrenia. Schizophrenia Bulletin, 33, 1100–1119. Graybiel, A. M. (1991). Basal ganglia: Input, neural activity, and relation to the cortex. Current Opinion in Neurobiology, 1, 644–651.

8/17/09 2:21:05 PM

448

Motor Control: Pyramidal, Extrapyramidal, and Limbic Motor Control

Graybiel, A. M., Hirsch, E. C., & Agid, Y. (1990). The nigrostriatal system in Parkinson’s disease. Advances in Neurology, 53, 17–29. Grillner, S. (1990). Neurobiology of vertebrate motor behavior: From flexion reflexes to manipulative movements. In G. M. Edelman, W. E. Gall, & W. M. Cowan (Eds.), Signal and sense (pp. 187–208). New York: Wiley-Liss. Groenewegen, H. J. (1988). Organization of the afferent connections of the mediodorsal thalamic nucleus in the rat, related to the mediodorsalprefrontal topography. Neuroscience, 24, 379–431. Groenewegen, H. J., Berendse, H. W., Wolters, J. G., & Lohman, A. H. (1990). The anatomical relationship of the prefrontal cortex with the striatopallidal system, the thalamus and the amygdala: Evidence for a parallel organization. Progress in Brain Research, 85, 95–116; discussion 116–118. Groenewegen, H. J., Vermeulen-Vander Zee, E., te Kortschot, A., & Witter, M. P. (1987). Organization of the projections from the subiculum to the ventral striatum in the rat: A study using anterograde transport of phaseolus vulgaris leucoagglutinin. Neuroscience, 23, 103–120. Haber, S. N. (2003). The primate basal ganglia: Parallel and integrative networks. Journal of Chemical Neuroanatomy, 26, 317–330. Haber, S. N., & Fudge, J. L. (1997). The primate substantia nigra and vta: Integrative circuitry and function. Critical Reviews in Neurobiology, 11, 323–342. Haber, S. N., Fudge, J. L., & McFarland, N. R. (2000). Striatonigrostriatal pathways in primates form an ascending spiral from the shell to the dorsolateral striatum. Journal of Neuroscience, 20, 2369–2382. Haber, S. N., Groenewegen, H. J., Grove, E. A., & Nauta, W. J. (1985). Efferent connections of the ventral pallidum: Evidence of a dual striato pallidofugal pathway. Journal of Comparative Neurology, 235, 322–335. Haber, S. N., Kunishio, K., Mizobuchi, M., & Lynd-Balta, E. (1995). The orbital and medial prefrontal circuit through the primate basal ganglia. Journal of Neuroscience, 15(7, Pt. 1), 4851–4867.

Heffner, R. S., & Masterton, R. B. (1983). The role of the corticospinal tract in the evolution of human digital dexterity. Brain, Behavior and Evolution, 23(3–4), 165–183. Heimer, L., Alheid, G. F., de Olmos, J. S., Groenewegen, H. J., Haber, S. N., Harlan, R. E., et al. (1997). The accumbens: Beyond the core-shell dichotomy. Journal of Neuropsychiatry and Clinical Neurosciences, 9, 354–381. Heimer, L., Alheid, G. F., & Zahm, D. S. (1993). Basal forebrain organization: An anatomical framework for motor aspects of drive and motivation. In P. W. Kalivas & C. D. Barnes (Eds.), Limbic motor circuits and neuropsychiatry (pp. 1–32). Boca Raton, FL: CRC Press. Heimer, L., Switzer, R. D., & Van Hoesen, G. W. (1982). Ventral striatumand ventral pallidum: Components of the motor system? TINS, 5, 83–87. Heimer, L., & Van Hoesen, G. W. (2006). The limbic lobe and its output channels: Implications for emotional functions and adaptive behavior. Neuroscience and Biobehavioral Reviews, 30, 126–147. Heimer, L., Zahm, D. S., Churchill, L., Kalivas, P. W., & Wohltmann, C. (1991). Specificity in the projection patterns of accumbal core and shell in the rat. Neuroscience, 41, 89–125. Hernandez-Lopez, S., Bargas, J., Surmeier, D. J., Reyes, A., & Galarraga, E. (1997). D1 receptor activation enhances evoked discharge in neostriatal medium spiny neurons by modulating an l-type ca2+ conductance. Journal of Neuroscience, 17, 3334–3342. Hersch, S. M., Ciliax, B. J., Gutekunst, C. A., Rees, H. D., Heilman, C. J., Yung, K. K., et al. (1995). Electron microscopic analysis of d1 and d2 dopamine receptor proteins in the dorsal striatum and their synaptic relationships with motor corticostriatal afferents. Journal of Neuroscience, 15(7, Pt. 2), 5222–5237. Herzog, A. G., & Van Hoesen, G. W. (1976). Temporal neocortical afferent connections to the amygdala in the rhesus monkey. Brain Research, 115, 57–69. Hietala, J., & Syvalahti, E. (1996). Dopamine in schizophrenia. Annals of Medicine, 28, 557–561.

Haber, S. N., Lynd, E., Klein, C., & Groenewegen, H. J. (1990). Topographic organization of the ventral striatal efferent projections in the rhesus monkey: An anterograde tracing study. Journal of Comparative Neurology, 293, 282–298.

Hietala, J., Syvalahti, E., Vuorio, K., Nagren, K., Lehikoinen, P., Ruotsalainen, U., et al. (1994). Striatal d2 dopamine receptor characteristics in neuroleptic-naive schizophrenic patients studied with positron emission tomography. Archives of General Psychiatry, 51, 116–123.

Haber, S. N., Lynd-Balta, E., & Mitchell, S. J. (1993). The organization of the descending ventral pallidal projections in the monkey. Journal of Comparative Neurology, 329, 111–128.

Hikosaka, O. (2007). Basal ganglia mechanisms of reward-oriented eye movement. Annals of the New York Academy of Sciences, 1104, 229–249.

Hallett, M. (1993). Physiology of basal ganglia disorders: An overview. Canadian Journal of Neurological Sciences, 20, 177–183.

Hirsch, E., Graybiel, A. M., & Agid, Y. A. (1988, July 28). Melanized dopaminergic neurons are differentially susceptible to degeneration in Parkinson’s disease. Nature, 334, 345–348.

Halsband, U., & Passingham, R. E. (1982). The role of premotor and parietal cortex in the direction of action. Brain Research, 240, 368–372.

Holland, P. C., & Gallagher, M. (2004). Amygdala-frontal interactions and reward expectancy. Current Opinion in Neurobiology, 14, 148–155.

Halsband, U., & Passingham, R. E. (1985). Premotor cortex and the conditions for movement in monkeys (macaca fascicularis). Behavioral Brain Research, 18, 269–277.

Hollerman, J. R., Tremblay, L., & Schultz, W. (2000). Involvement of basal ganglia and orbitofrontal cortex in goal-directed behavior. Progress in Brain Research, 126, 193–215.

Hazrati, L. N., & Parent, A. (1992). The striatopallidal projection displays a high degree of anatomical specificity in the primate. Brain Research, 592(1–2), 213–227.

Hoover, J. E., & Strick, P. L. (1993, February 5). Multiple output channels in the basal ganglia. Science, 259, 819–821.

He, S. Q., Dum, R. P., & Strick, P. L. (1993). Topographic organization of corticospinal projections from the frontal lobe: Motor areas on the lateral surface of the hemisphere. Journal of Neuroscience, 13, 952–980. He, S. Q., Dum, R. P., & Strick, P. L. (1995). Topographic organization of corticospinal projections from the frontal lobe: Motor areas on the medial surface of the hemisphere. Journal of Neuroscience, 15(5, Pt. 1), 3284–3306. Hedreen, J. C., & DeLong, M. R. (1991). Organization of striatopallidal, striatonigral, and nigrostriatal projections in the macaque. Journal of Comparative Neurology, 304, 569–595. Hedreen, J. C., & Folstein, S. E. (1995). Early loss of neostriatal striosome neurons in Huntington’s disease. Journal of Neuropathology and Experimental Neurology, 54, 105–120.

c22.indd 448

Hornykiewicz, O., & Kish, S. J. (1987). Biochemical pathophysiology of Parkinson’s disease. Advances in Neurology, 45, 19–34. Horvitz, J. C., & Ettenberg, A. (1991). Conditioned incentive properties of a food-paired conditioned stimulus remain intact during dopamine receptor blockade. Behavioral Neuroscience, 105, 536–541. Jellinger, K. A. (1998). Neuropathology of movement disorders. Neurosurgery Clinics of North America, 9, 237–262. Jellinger, K. A. (1991). Pathology of Parkinson’s disease: Changes other than the nigrostriatal pathway. Molecular and Chemical Neuropathology, 14, 153–197. Jentsch, J. D., & Roth, R. H. (1999). The neuropsychopharmacology of phencyclidine: From nmda receptor hypofunction to the dopamine hypothesis of schizophrenia. Neuropsychopharmacology, 20, 201–225.

8/17/09 2:21:05 PM

References Joel, D. (2001). Open interconnected model of basal ganglia-thalamocortical circuitry and its relevance to the clinical syndrome of Huntington’s disease. Movement Disorders, 16, 407–423.

Kiyatkin, E. A. (1995). Functional significance of mesolimbic dopamine. Neuroscience and Biobehavioral Reviews, 19, 573–598.

Joel, D., & Weiner, I. (1994). The organization of the basal gangliathalamocortical circuits: Open interconnected rather than closed segregated. Neuroscience, 63, 363–379.

Kiyatkin, E. A., & Rebec, G. V. (1999). Striatal neuronal activity and responsiveness to dopamine and glutamate after selective blockade of d1 and d2 dopamine receptors in freely moving rats. Journal of Neuroscience, 19, 3594–3609.

Joel, D., & Weiner, I. (1997). The connections of the primate subthalamic nucleus: Indirect pathways and the open-interconnected scheme of basal ganglia-thalamocortical circuitry. Brain Research: Brain Research Reviews, 23(1–2), 62–78.

Klitenick, M. A., Deutch, A. Y., Churchill, L., & Kalivas, P. W. (1992). Topography and functional role of dopaminergic projections from the ventral mesencephalic tegmentum to the ventral pallidum. Neuroscience, 50, 371–386.

Jones, B., & Mishkin, M. (1972). Limbic lesions and the problem of stimulus: Reinforcement associations. Experimental Neurology, 36, 362–377. Jones, E. B. (1985). The thalamus. New York: Plenum Press.

Kluver, H., & Bucy, P. C. (1939). Preliminary analysis of the functions of temporal lobe in monkeys. Archives of Neurology and Psychiatry, 42, 979–1000.

Joseph, M. H., Frith, C. D., & Waddington, J. L. (1979). Dopaminergic mechanisms and cognitive deficit in schizophrenia. A neurobiological model. Psychopharmacology, 63, 273–280.

Kodsi, M. H., & Swerdlow, N. R. (1994). Quinolinic acid lesions of the ventral striatum reduce sensorimotor gating of acoustic startle in rats. Brain Research, 643(1–2), 59–65.

Juckel, G., Schlagenhauf, F., Koslowski, M., Filonov, D., Wustenberg, T., Villringer, A., et al. (2006). Dysfunction of ventral striatal reward prediction in schizophrenic patients treated with typical, not atypical, neuroleptics. Psychopharmacology, 187, 222–228.

Kornetsky, C., & Esposito, R. U. (1979). Euphorigenic drugs: Effects on the reward pathways of the brain. Federation Proceedings, 38, 2473–2476.

Kalivas, P. W., Churchill, L., & Klitenick, M. A. (1993). Gaba and enkephalin projection from the nucleus accumbens and ventral pallidum to the ventral tegmental area. Neuroscience, 57, 1047–1060. Kalivas, P. W., Churchill, L., & Romanides, A. (1999). Involvement of the pallidal-thalamocortical circuit in adaptive behavior. Annals of the New York Adademy of Sciences, 877, 64–70. Kawaguchi, Y., Wilson, C. J., & Emson, P. C. (1990). Projection subtypes of rat neostriatal matrix cells revealed by intracellular injection of biocytin. Journal of Neuroscience, 10, 3421–3438. Keefe, R. S., Mohs, R. C., Losonczy, M. F., Davidson, M., Silverman, J. M., Kendler, K. S., et al. (1987). Characteristics of very poor outcome schizophrenia. American Journal of Psychiatry, 144, 889–895. Kelley, A. E. (2004). Ventral striatal control of appetitive motivation: Role in ingestive behavior and reward-related learning. Neuroscience and Biobehavioral Reviews, 27, 765–776. Kelley, A. E., Baldo, B. A., Pratt, W. E., & Will, M. J. (2005). Corticostriatalhypothalamic circuitry and food motivation: Integration of energy, action and reward. Physiology and Behavior, 86, 773–795. Kelley, A. E., & Domesick, V. B. (1982). The distribution of the projection from the hippocampal formation to the nucleus accumbens in the rat: An anterograde- and retrograde-horseradish peroxidase study. Neuroscience, 7, 2321–2335. Kelley, A. E., Domesick, V. B., & Nauta, W. J. (1982). The amygdalostriatal projection in the rat: An anatomical study by anterograde and retrograde tracing methods. Neuroscience, 7, 615–630. Kemether, E. M., Buchsbaum, M. S., Byne, W., Hazlett, E. A., Haznedar, M., Brickman, A. M., et al. (2003). Magnetic resonance imaging of mediodorsal, pulvinar, and centromedian nuclei of the thalamus in patients with schizophrenia. Archives of General Psychiatry, 60, 983–991.

c22.indd 449

449

Kostrzewa, R. M., Kostrzewa, J. P., Nowak, P., Kostrzewa, R. A., & Brus, R. (2004). Dopamine d2 agonist priming in intact and dopamine-lesioned rats. Neurotoxicity Research, 6, 457–462. Kowall, N. W., Ferrante, R. J., & Martin, A. B. (1987). Patterns of cell loss in Huntington’s disease. Trends in Neurosciences, 10, 24–29. Kumakura, Y., Cumming, P., Vernaleken, I., Buchholz, H. G., Siessmeier, T., Heinz, A., et al. (2007). Elevated [18f]fluorodopamine turnover in brain of patients with schizophrenia: An [18f]fluorodopa/positron emission tomography study. Journal of Neuroscience, 27, 8080–8087. Kunzle, H. (1975). Bilateral projections from precentral motor cortex to the putamen and other parts of the basal ganglia: An autoradiographic study in macaca fascicularis. Brain Research, 88, 195–209. Kuo, J. S., & Carpenter, M. B. (1973). Organization of pallidothalamic projections in the rhesus monkey. Journal of Comparative Neurology, 151, 201–236. Kurata, K., & Hoffman, D. S. (1994). Differential effects of muscimol microinjection into dorsal and ventral aspects of the premotor cortex of monkeys. Journal of Neurophysiology, 71, 1151–1164. Kuroda, M., Murakami, K., Kishi, K., & Price, J. L. (1995). Thalamocortical synapses between axons from the mediodorsal thalamic nucleus and pyramidal cells in the prelimbic cortex of the rat. Journal of Comparative Neurology, 356, 143–151. Kuroda, M., Murakami, K., Shinkai, M., Ojima, H., & Kishi, K. (1995). Electron microscopic evidence that axon terminals from the mediodorsal thalamic nucleus make direct synaptic contacts with callosal cells in the prelimbic cortex of the rat. Brain Research, 677, 348–353. Laurens, K. R., Kiehl, K. A., & Liddle, P. F. (2005). A supramodal limbicparalimbic-neocortical network supports goal-directed stimulus processing. Human Brain Mapping, 24, 35–49.

Kim, R., Nakano, K., Jayaraman, A., & Carpenter, M. B. (1976). Projections of the globus pallidus and adjacent structures: An autoradiographic study in the monkey. Journal of Comparative Neurology, 169, 263–290.

Lavoie, B., Smith, Y., & Parent, A. (1989). Dopaminergic innervation of the basal ganglia in the squirrel monkey as revealed by tyrosine hydroxylase immunohistochemistry. Journal of Comparative Neurology, 289, 36–52.

Kipps, C. M., Duggins, A. J., Mahant, N., Gomes, L., Ashburner, J., & McCusker, E. A. (2005). Progression of structural neuropathology in preclinical Huntington’s disease: A tensor based morphometry study. Journal of Neurology, Neurosurgery, and Psychiatry, 76, 650–655.

Lawrie, S. M., McIntosh, A. M., Hall, J., Owens, D. G., & Johnstone, E. C. (2008). Brain structure and function changes during the development of schizophrenia: The evidence from studies of subjects at increased genetic risk. Schizophrenia Bulletin, 34, 330–340.

Kirkwood, S. C., Siemers, E., Hodes, M. E., Conneally, P. M., Christian, J. C., & Foroud, T. (2000). Subtle changes among presymptomatic carriers of the Huntington’s disease gene. Journal of Neurology, Neurosurgery, and Psychiatry, 69, 773–779.

Leblois, A., Boraud, T., Meissner, W., Bergman, H., & Hansel, D. (2006). Competition between feedback loops underlies normal and pathological dynamics in the basal ganglia. Journal of Neuroscience, 26, 3567–3583.

Kita, H., Tokuno, H., & Nambu, A. (1999). Monkey globus pallidus external segment neurons projecting to the neostriatum. NeuroReport, 10, 1467–1472.

Lehericy, S., Bardinet, E., Tremblay, L., Van de Moortele, P. F., Pochon, J. B., Dormont, D., et al. (2006). Motor control in basal ganglia circuits using fmri and brain atlas approaches. Cerebral Cortex, 16, 149–161.

8/17/09 2:21:06 PM

450

Motor Control: Pyramidal, Extrapyramidal, and Limbic Motor Control

Lemiere, J., Decruyenaere, M., Evers-Kiebooms, G., Vandenbussche, E., & Dom, R. (2004). Cognitive changes in patients with Huntington’s disease (hd) and asymptomatic carriers of the hd mutation: A longitudinal follow-up study. Journal of Neurology, 251, 935–942. Le Moal, M., & Simon, H. (1991). Mesocorticolimbic dopaminergic network: Functional and regulatory roles. Physiological Reviews, 71, 155–234. Le Moine, C., & Bloch, B. (1995). D1 and d2 dopamine receptor gene expression in the rat striatum: Sensitive crna probes demonstrate prominent segregation of d1 and d2 mrnas in distinct neuronal populations of the dorsal and ventral striatum. Journal of Comparative Neurology, 355, 418–426. Lemon, R. N., Mantel, G. W. H., & Muir, R. B. (1986). Corticospinal facilitation of hand muscles during voluntary movement in the conscious monkey. Journal of Physiology, 381, 497–527. Lewis, D. A., Hashimoto, T., & Volk, D. W. (2005). Cortical inhibitory neurons and schizophrenia. Nature Reviews: Neuroscience, 6, 312–324. Lewis, D. A., & Moghaddam, B. (2006). Cognitive dysfunction in schizophrenia: Convergence of gamma-aminobutyric acid and glutamate alterations. Archives of Neurology, 63, 1372–1376. Lewis, D. A., Pierri, J. N., Volk, D. W., Melchitzky, D. S., & Woo, T. U. (1999). Altered gaba neurotransmission and prefrontal cortical dysfunction in schizophrenia. Biological Psychiatry, 46, 616–626. Leyton, A. S. F., & Sherrington, C. S. (1917). Observations on the excitbale cortex of the chimpanzee, orang-utan and gorilla. Quarterly Journal of Experimental Physiology, 11, 135–222.

McFarland, K., & Ettenberg, A. (1995). Haloperidol differentially affects reinforcement and motivational processes in rats running an alley for intravenous heroin. Psychopharmacology, 122, 346–350. McFarland, K., & Ettenberg, A. (1997). Reinstatement of drug-seeking behavior produced by heroin-predictive environmental stimuli. Psychopharmacology, 131, 86–92. McFarland, K., & Ettenberg, A. (1998). Haloperidol does not affect motivational processes in an operant runway model of food-seeking behavior. Behavioral Neuroscience, 112, 630–635. McFarland, K., & Ettenberg, A. (1999). Haloperidol does not attenuate conditioned place preferences or locomotor activation produced by foodor heroin-predictive discriminative cues. Pharmacology, Biochemistry, and Behavior, 62, 631–641. McFarland, K., & Kalivas, P. W. (2003). Motivational sytems. In M. Gallagher & R. J. Nelson (Eds.), Handbook of psychology (Vol. 3, pp. 379–404). Hoboken, NJ: Wiley. McFarland, N. R., & Haber, S. N. (2000). Convergent inputs from thalamic motor nuclei and frontal cortical areas to the dorsal striatum in the primate. Journal of Neuroscience, 20, 3798–3813. McFarland, N. R., & Haber, S. N. (2002). Thalamic relay nuclei of the basal ganglia form both reciprocal and nonreciprocal cortical connections, linking multiple frontal cortical areas. Journal of Neuroscience, 22, 8117–8132. Meltzer, H. Y., & Stahl, S. M. (1976). The dopamine hypothesis of schizophrenia: A review. Schizophrenia Bulletin, 2, 19–76.

Li, X. J. (1999). The early cellular pathology of Huntington’s disease. Molecular Neurobiology, 20(2–3), 111–124.

Meredith, G. E., Baldo, B. A., Andrezjewski, M. E., & Kelley, A. E. (2008). The structural basis for mapping behavior onto the ventral striatum and its subdivisions. Brain Structure and Function, 213, 17–27.

Liles, S. L., & Updyke, B. V. (1985). Projection of the digit and wrist area of precentral gyrus to the putamen: Relation between topography and physiological properties of neurons in the putamen. Brain Research, 339, 245–255.

Middleton, F. A., & Strick, P. L. (2001). A revised neuroanatomy of frontal-subcortical circuits. In D. G. Lichter & J. Cummings (Eds.), Frontal-subcortical circuits in psychiatric and neurological disorders (pp. 44–58). New York: Guilford Press.

Louilot, A., Le Moal, M., & Simon, H. (1986). Differential reactivity of dopaminergic neurons in the nucleus accumbens in response to different behavioral situations: An in vivo voltammetric study in free moving rats. Brain Research, 397, 395–400.

Mitchell, D. G., Rhodes, R. A., Pine, D. S., & Blair, R. J. (2008). The contribution of ventrolateral and dorsolateral prefrontal cortex to response reversal. Behavioural and Brain Research, 187, 80–87.

Lovibond, P. F. (1980). Effects of long- and variable-duration signals for food on activity, instrumental responding, and eating. Learning and Motivation, 11, 164.

Mitelman, S. A., Byne, W., Kemether, E. M., Hazlett, E. A., & Buchsbaum, M. S. (2005). Metabolic disconnection between the mediodorsal nucleus of the thalamus and cortical brodmann’s areas of the left hemisphere in schizophrenia. American Journal of Psychiatry, 162, 1733–1735.

Lynd-Balta, E., & Haber, S. N. (1994a). The organization of midbrain projections to the striatum in the primate: Sensorimotor-related striatum versus ventral striatum. Neuroscience, 59, 625–640.

Mitz, A. R., & Wise, S. P. (1987). The somatotopic organization of the supplementary motor area: Intracortical microstimulation mapping. Journal of Neuroscience, 7, 1010–1021.

Lynd-Balta, E., & Haber, S. N. (1994b). Primate striatonigral projections: A comparison of the sensorimotor-related striatum and the ventral striatum. Journal of Comparative Neurology, 345, 562–578.

Mogenson, G. J., Ciriello, J., Garland, J., & Wu, M. (1987). Ventral pallidum projections to mediodorsal nucleus of the thalamus: An anatomical and electrophysiological investigation in the rat. Brain Research, 404(1–2), 221–230.

Lyness, W. H., Friedle, N. M., & Moore, K. E. (1979). Destruction of dopaminergic nerve terminals in nucleus accumbens: Effect on d-amphetamine self-administration. Pharmacology, Biochemistry, and Behavior, 11, 553–556. Mallet, N., Ballion, B., Le Moine, C., & Gonon, F. (2006). Cortical inputs and gaba interneurons imbalance projection neurons in the striatum of Parkinsonian rats. Journal of Neuroscience, 26, 3875–3884. Mardsen, C. D. (1994). Parkinson’s disease. Journal of Neurology, Neurosurgery, and Psychiatry, 57, 672–681. Martin, J. B. (1984). Huntington’s disease: New approaches to an old problem. The Robert Wartenberg lecture. Neurology, 34, 1059–1072.

Mogenson, G. J., Jones, D. L., & Yim, C. Y. (1980). From motivation to action: Functional interface between the limbic system and the motor system. Progress in Neurobiology, 14(2–3), 69–97. Morecraft, R. J., Cipolloni, P. B., Stilwell-Morecraft, K. S., Gedney, M. T., & Pandya, D. N. (2004). Cytoarchitecture and cortical connections of the posterior cingulate and adjacent somatosensory fields in the rhesus monkey. Journal of Comparative Neurology, 469, 37–69. Nambu, A. (2005). A new approach to understand the pathophysiology of Parkinson’s disease. Journal of Neurology, 252 (Suppl. 4), IV1–IV4.

Martin, J. B., & Gusella, J. F. (1986). Huntington’s disease: Pathogenesis and management. New England Journal of Medicine, 315, 1267–1276.

Nambu, A., Tokuno, H., & Takada, M. (2002). Functional significance of the cortico-subthalamo-pallidal ‘hyperdirect’ pathway. Neuroscience Research, 43, 111–117.

Mason, S. T., Beninger, R. J., Fibiger, H. C., & Phillips, A. G. (1980). Pimozide-induced suppression of responding: Evidence against a block of food reward. Pharmacology, Biochemistry, and Behavior, 12, 917–923.

Napier, T. C., Mitrovic, I., Churchill, L., Klitenick, M. A., Lu, X. Y., & Kalivas, P. W. (1995). Substance p in the ventral pallidum: Projection from the ventral striatum, and electrophysiological and behavioral consequences of pallidal substance p. Neuroscience, 69, 59–70.

c22.indd 450

8/17/09 2:21:06 PM

References Nauta, W. J. (1961). Fiber degeneration following lesions of the amygdaloid complex in the monkey. Journal of Anatomy, 95, 515–531. Novais-Santos, S., Gee, J., Shah, M., Troiani, V., Work, M., & Grossman, M. (2007). Resolving sentence ambiguity with planning and working memory resources: Evidence from fMRI. Neuroimage, 37, 361–378. Nudo, R. J., & Masterton, R. B. (1990). Descending pathways to the spinal cord: Pt. IV. Some factors related to the amount of cortex devoted to the corticospinal tract. Journal of Comparative Neurology, 296, 584–597. O’Connor, W. T. (1998). Functional neuroanatomy of the basal ganglia as studied by dual-probe microdialysis. Nuclear Medicine and Biology, 25, 743–746. O’Donnell, P., & Grace, A. A. (1995). Synaptic interactions among excitatory afferents to nucleus accumbens neurons: Hippocampal gating of prefrontal cortical input. Journal of Neuroscience, 15(5, Pt. 1), 3622–3639. O’Donnell, P., & Grace, A. A. (1996). Dopaminergic reduction of excitability in nucleus accumbens neurons recorded in vitro. Neuropsychopharmacology, 15, 87–97. O’Donnell, P., Greene, J., Pabello, N., Lewis, B. L., & Grace, A. A. (1999). Modulation of cell firing in the nucleus accumbens. Annals of the New York Academy of Sciences, 877, 157–175. Olds, J., & Milner, P. (1954). Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain. Journal of Comparative and Physiological Psychology, 47, 419–427. Olive, M. F., Bertolucci, M., Evans, C. J., & Maidment, N. T. (1995). Microdialysis reveals a morphine-induced increase in pallidal opioid peptide release. NeuroReport, 6, 1093–1096. Owen, A. M. (2004). Cognitive dysfunction in Parkinson’s disease: The role of frontostriatal circuitry. Neuroscientist, 10, 525–537. Parent, A., Bouchard, C., & Smith, Y. (1984). The striatopallidal and striatonigral projections: Two distinct fiber systems in primate. Brain Research, 303, 385–390.

Petrides, M., & Pandya, D. N. (1999). Dorsolateral prefrontal cortex: Comparative cytoarchitectonic analysis in the human and the macaque brain and corticocortical connection patterns. European Journal of Neuroscience, 11, 1011–1036. Picard, N., & Strick, P. L. (1996). Motor areas of the medial wall: A review of their location and functional activation. Cerebral Cortex, 6, 342–353. Picard, N., & Strick, P. L. (2001). Imaging the premotor areas. Current Opinion in Neurobiology, 11, 663–672. Rajakumar, N., Elisevich, K., & Flumerfelt, B. A. (1994). The pallidostriatal projection in the rat: A recurrent inhibitory loop? Brain Research, 651(1/2), 332–336. Ray, J. P., Russchen, F. T., Fuller, T. A., & Price, J. L. (1992). Sources of presumptive glutamatergic/aspartatergic afferents to the mediodorsal nucleus of the thalamus in the rat. Journal of Comparative Neurology, 320, 435–456. Redgrave, P., & Dean, P. (1981). Intracranial self-stimulation. British Medical Bulletin, 37, 141–146. Reijmers, L. G., Vanderheyden, P. M., & Peeters, B. W. (1995). Changes in prepulse inhibition after local administration of nmda receptor ligands in the core region of the rat nucleus accumbens. European Journal of Pharmacology, 272(2–3), 131–138. Reiner, A., Albin, R. L., Anderson, K. D., D’Amato, C. J., Penney, J. B., & Young, A. B. (1988). Differential loss of striatal projection neurons in Huntington disease. Proceedings of the National Academy of Sciences, USA, 85, 5733–5737. Revheim, N., Schechter, I., Kim, D., Silipo, G., Allingham, B., Butler, P., et al. (2006). Neurocognitive and symptom correlates of daily problemsolving skills in schizophrenia. Schizophrenia Research, 83, 237–245. Richfield, E. K., & Herkenham, M. (1994). Selective vulnerability in Huntington’s disease: Preferential loss of cannabinoid receptors in lateral globus pallidus. Annals of Neurology, 36, 577–584.

Parent, A., & Hazrati, L. N. (1994). Multiple striatal representation in primate substantia nigra. Journal of Comparative Neurology, 344, 305–320.

Robbins, T. W. (2003). Dopamine and cognition. Current Opinion in Neurology, 16(Suppl. 2), S1–S2.

Parent, A., & Hazrati, L. N. (1995). Functional anatomy of the basal ganglia: Pt. I. The cortico-basal ganglia-thalamo-cortical loop. Brain Research: Brain Research Reviews, 20, 91–127.

Robbins, T. W., Cador, M., Taylor, J. R., & Everitt, B. J. (1989). Limbicstriatal interactions in reward-related processes. Neuroscience and Biobehavioral Reviews, 13(2–3), 155–162.

Passingham, R. E., Perry, H., & Wilkinson, F. (1978). Failure to develop a precision grip in monkeys with unilateral neocortical lesions made in infancy. Brain Research, 145, 410–414.

Roberts, D. C., Corcoran, M. E., & Fibiger, H. C. (1977). On the role of ascending catecholaminergic systems in intravenous self-administration of cocaine. Pharmacology, Biochemistry, and Behavior, 6, 615–620.

Passingham, R. E., Perry, V. H., & Wilkinson, F. (1983). The long-term effects of removal of sensorimotor cortex in infant and adult rhesus monkeys. Brain, 106(Pt. 3), 675–705. Passingham, R. E., Stephan, K. E., & Kotter, R. (2002). The anatomical basis of functional localization in the cortex. Nature Reviews: Neuroscience, 3, 606–616. Pearson, S. J., Heathfield, K. W., & Reynolds, G. P. (1990). Pallidal gaba and chorea in Huntington’s disease. Journal of Neural Transmission: General Section, 81, 241–246. Penfield, W., & Boldrey, E. (1937). Somatic motor and sensory representation in the cerebral cortex of man as a studies by electrical brain stimulation. Brain, 60, 389–443. Penfield, W., & Rasmussen, T. (1952). The cerebral cortex of man. New York: Macmillan. Penney, J. B., Jr., & Young, A. B. (1986). Striatal inhomogeneities and basal ganglia function. Movement Disorders, 1, 3–15. Percheron, G., & Filion, M. (1991). Parallel processing in the basal ganglia: Up to a point. Trends in Neurosciences, 14, 55–59. Percheron, G., Francois, C., Yelnik, J., & Fenelon, G. (1989). The primate nigro-striato-pallido-nigral system: Not a mere loop. In A. R. Crossman & M. A. Sambrook (Eds.), Neural mechanisms in disorders of movements (pp. 103–109). London: John Libbey.

c22.indd 451

451

Robinson, T. E., & Berridge, K. C. (2000). The psychology and neurobiology of addiction: An incentive-sensitization view. Addiction, 95(Suppl. 2), S91–S117. Rolls, E. T., & Baylis, L. L. (1994). Gustatory, olfactory, and visual convergence within the primate orbitofrontal cortex. Journal of Neuroscience, 14, 5437–5452. Rolls, E. T., Rolls, B. J., Kelly, P. H., Shaw, S. G., Wood, R. J., & Dale, R. (1974). The relative attenuation of self-stimulation, eating and drinking produced by dopamine-receptor blockade. Psychopharmacologia, 38, 219–230. Rosenkranz, J. A., & Grace, A. A. (1999). Modulation of basolateral amygdala neuronal firing and afferent drive by dopamine receptor activation in vivo. Journal of Neuroscience, 19, 11027–11039. Rosenkranz, J. A., & Grace, A. A. (2001). Dopamine attenuates prefrontal cortical suppression of sensory inputs to the basolateral amygdala of rats. Journal of Neuroscience, 21, 4090–4103. Rubin, P., Hemmingsen, R., Holm, S., Moller-Madsen, S., Hertel, C., Povlsen, U. J., et al. (1994). Relationship between brain structure and function in disorders of the schizophrenic spectrum: Single positron emission computerized tomography, computerized tomography and psychopathology of first episodes. Acta Psychiatrica Scandinavica, 90, 281–289.

8/17/09 2:21:07 PM

452

Motor Control: Pyramidal, Extrapyramidal, and Limbic Motor Control

Salamone, J. D. (1994). The involvement of nucleus accumbens dopamine in appetitive and aversive motivation. Behavioural Brain Research, 61, 117–133.

Snyder, S. H. (1976). The dopamine hypothesis of schizophrenia: Focus on the dopamine receptor. American Journal of Psychiatry, 133, 197–202.

Salamone, J. D., & Correa, M. (2002). Motivational views of reinforcement: Implications for understanding the behavioral functions of nucleus accumbens dopamine. Behavioural Brain Research, 137(1–2), 3–25.

Spencer, H. J. (1976). Antagonism of cortical excitation of striatal neurons by glutamic acid diethyl ester: Evidence for glutamic acid as an excitatory transmitter in the rat striatum. Brain Research, 102, 91–101.

Sapp, E., Ge, P., Aizawa, H., Bird, E., Penney, J., Young, A. B., et al. (1995). Evidence for a preferential loss of enkephalin immunoreactivity in the external globus pallidus in low grade Huntington’s disease using high resolution image analysis. Neuroscience, 64, 397–404.

Stellar, J. R., & Corbett, D. (1989). Regional neuroleptic microinjections indicate a role for nucleus accumbens in lateral hypothalamic selfstimulation reward. Brain Research, 477(1–2), 126–143.

Scatton, B., Javoy-Agid, F., Rouquier, L., Dubois, B., & Agid, Y. (1983). Reduction of cortical dopamine, noradrenaline, serotonin and their metabolites in Parkinson’s disease. Brain Research, 275, 321–328.

Stellar, J. R., Kelley, A. E., & Corbett, D. (1983). Effects of peripheral and central dopamine blockade on lateral hypothalamic self-stimulation: Evidence for both reward and motor deficits. Pharmacology, Biochemistry, and Behavior, 18, 433–442.

Scatton, B., Rouquier, L., Javoy-Agid, F., & Agid, Y. (1982). Dopamine deficiency in the cerebral cortex in Parkinson disease. Neurology, 32, 1039–1040.

Stoof, J. C., & Kebabian, J. W. (1981, November 26). Opposing roles for d-1 and d-2 dopamine receptors in efflux of cyclic amp from rat neostriatum. Nature, 294, 366–368.

Schlosser, R. G., Nenadic, I., Wagner, G., Gullmar, D., von Consbruch, K., Kohler, S., et al. (2007). White matter abnormalities and brain activation in schizophrenia: A combined dti and fMRI study. Schizophrenia Research, 89(1–3), 1–11.

Stout, J. C., Weaver, M., Solomon, A. C., Queller, S., Hui, S., Johnson, S. A., et al. (2007). Are cognitive changes progressive in prediagnostic hd? Cognitive and Behavioral Neurology, 20, 212–218.

Schneider, L. H., Davis, J. D., Watson, C. A., & Smith, G. P. (1990). Similar effect of raclopride and reduced sucrose concentration on the microstructure of sucrose sham feeding. European Journal of Pharmacology, 186, 61–70. Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80, 1–27. Schultz, W. (2002). Getting formal with dopamine and reward. Neuron, 36, 241–263. Schultz, W. (2006). Behavioral theories and the neurophysiology of reward. Annual Review of Psychology, 57, 87–115. Schultz, W., Apicella, P., & Ljungberg, T. (1993). Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task. Journal of Neuroscience, 13, 900–913. Schultz, W., Tremblay, L., & Hollerman, J. R. (2000). Reward processing in primate orbitofrontal cortex and basal ganglia. Cerebral Cortex, 10, 272–284. Selemon, L. D., & Goldman-Rakic, P. S. (1990). Topographic intermingling of striatonigral and striatopallidal neurons in the rhesus monkey. Journal of Comparative Neurology, 297, 359–376. Sesack, S. R., Deutch, A. Y., Roth, R. H., & Bunney, B. S. (1989). Topographical organization of the efferent projections of the medial prefrontal cortex in the rat: An anterograde tract-tracing study with phaseolus vulgaris leucoagglutinin. Journal of Comparative Neurology, 290, 213–242. Sesack, S. R., & Pickel, V. M. (1990). In the rat medial nucleus accumbens, hippocampal and catecholaminergic terminals converge on spiny neurons and are in apposition to each other. Brain Research, 527, 266–279.

Swanson, L. W. (1982). The projections of the ventral tegmental area and adjacent regions: A combined fluorescent retrograde tracer and immunofluorescence study in the rat. Brain Research Bulletin, 9(1–6), 321–353. Swerdlow, N. R., Braff, D. L., Geyer, M. A., & Koob, G. F. (1986). Central dopamine hyperactivity in rats mimics abnormal acoustic startle response in schizophrenics. Biological Psychiatry, 21, 23–33. Swerdlow, N. R., Braff, D. L., Masten, V. L., & Geyer, M. A. (1990). Schizophrenic-like sensorimotor gating abnormalities in rats following dopamine infusion into the nucleus accumbens. Psychopharmacology, 101, 414–420. Swerdlow, N. R., Lipska, B. K., Weinberger, D. R., Braff, D. L., Jaskiw, G. E., & Geyer, M. A. (1995). Increased sensitivity to the sensorimotor gating-disruptive effects of apomorphine after lesions of medial prefrontal cortex or ventral hippocampus in adult rats. Psychopharmacology, 122, 27–34. Szabo, J. (1980). Organization of the ascending striatal afferents in monkeys. Journal of Comparative Neurology, 189, 307–321. Timofeev, I., Grenier, F., & Steriade, M. (1998). Spike-wave complexes and fast components of cortically generated seizures: Pt. IV. Paroxysmal fast runs in cortical and thalamic neurons. Journal of Neurophysiology, 80, 1495–1513. Tisch, S., Silberstein, P., Limousin-Dowsey, P., & Jahanshahi, M. (2004). The basal ganglia: Anatomy, physiology, and pharmacology. Psychiatric Clinics of North America, 27, 757–799. Tombaugh, T. N., Szostak, C., & Mills, P. (1983). Failure of pimozide to disrupt the acquisition of light-dark and spatial discrimination problems. Psychopharmacology, 79(2/3), 161–168.

Sherman, S. M., & Guillery, R. W. (1996). Functional organization of thalamocortical relays. Journal of Neurophysiology, 76, 1367–1395.

Tombaugh, T. N., Tombaugh, J., & Anisman, H. (1979). Effects of dopamine receptor blockade on alimentary behaviors: Home cage food consumption, magazine training, operant acquisition, and performance. Psychopharmacology, 66, 219–225.

Simonyan, K., & Jurgens, U. (2002). Cortico-cortical projections of the motorcortical larynx area in the rhesus monkey. Brain Research, 949(1/2), 23–31.

Vertes, R. P. (2006). Interactions among the medial prefrontal cortex, hippocampus and midline thalamus in emotional and cognitive processing in the rat. Neuroscience, 142, 1–20.

Smiley, J. F., & Goldman-Rakic, P. S. (1993). Heterogeneous targets of dopamine synapses in monkey prefrontal cortex demonstrated by serial section electron microscopy: A laminar analysis using the silverenhanced diaminobenzidine sulfide (seds) immunolabeling technique. Cerebral Cortex, 3, 223–238.

Vives, F., & Mogenson, G. J. (1985). Electrophysiological evidence that the mediodorsal nucleus of the thalamus is a relay between the ventral pallidum and the medial prefrontal cortex in the rat. Brain Research, 344, 329–337.

Smith, Y., & Bolam, J. P. (1990). The output neurones and the dopaminergic neurones of the substantia nigra receive a gaba-containing input from the globus pallidus in the rat. Journal of Comparative Neurology, 296, 47–64.

c22.indd 452

Vonsattel, J. P., & DiFiglia, M. (1998). Huntington disease. Journal of Neuropathology and Experimental Neurology, 57, 369–384. Voorn, P., Vanderschuren, L. J., Groenewegen, H. J., Robbins, T. W., & Pennartz, C. M. (2004). Putting a spin on the dorsal-ventral divide of the striatum. Trends in Neurosciences, 27, 468–474.

8/17/09 2:21:07 PM

References Wakai, M., Takahashi, A., & Hashizume, Y. (1993). A histometrical study on the globus pallidus in Huntington’s disease. Journal of the Neurological Sciences, 119, 18–27.

Yang, C. R., Seamans, J. K., & Gorelova, N. (1999). Developing a neuronal model for the pathophysiology of schizophrenia based on the nature of electrophysiological actions of dopamine in the prefrontal cortex. Neuropsychopharmacology, 21, 161–194.

Weiner, D. M., Burstein, E. S., Nash, N., Croston, G. E., Currier, E. A., Vanover, K. E., et al. (2001). 5-hydroxytryptamine2a receptor inverse agonists as antipsychotics. Journal of Pharmacology and Experimental Theraupeutics, 299, 268–276.

Yim, C. Y., & Mogenson, G. J. (1988). Neuromodulatory action of dopamine in the nucleus accumbens: An in vivo intracellular study. Neuroscience, 26, 403–415.

Weiskrantz, L. (1956). Behavioral changes associated with the ablation of the amygdaloid complex in monkeys. Journal of Comparative and Physiological Psychology, 49, 381–391.

Yokel, R. A., & Wise, R. A. (1976). Attenuation of intravenous amphetamine reinforcement by central dopamine blockade in rats. Psychopharmacology, 48, 311–318.

Wilson, R. S., & Garron, D. C. (1979). Cognitive and affective aspects of Huntington’s disease. Advances in Neurology, 23, 193–201.

Young, A. M., Ahier, R. G., Upton, R. L., Joseph, M. H., & Gray, J. A. (1998). Increased extracellular dopamine in the nucleus accumbens of the rat during associative learning of neutral stimuli. Neuroscience, 83, 1175–1183.

Wise, R. A. (1978). Neuroleptic attenuation of intracranial self-stimulation: Reward or performance deficits? Life Sciences, 22, 535–542. Wise, R. A. (1982). Neuroleptics and operant behavior: The anhedonia hypothesis. Behavior Brain Science, 39–88. Wise, R. A. (2004). Dopamine, learning and motivation. Nature Reviews: Neuroscience, 5, 483–494. Wise, R. A., & Rompre, P. P. (1989). Brain dopamine and reward. Annual Review of Psychology, 40, 191–225. Wise, R. A., Spindler, J., deWit, H., & Gerberg, G. J. (1978, July 21). Neuroleptic-induced “anhedonia” in rats: Pimozide blocks reward quality of food. Science, 201, 262–264. Wise, R. A., Spindler, J., & Legault, L. (1978). Major attenuation of food reward with performance-sparing doses of pimozide in the rat. Canadian Journal of Psychology, 32, 77–85. Woodward, D. J., Chang, J. Y., Janak, P., Azarov, A., & Anstrom, K. (1999). Mesolimbic neuronal activity across behavioral states. Annals of the New York Academy of Sciences, 877, 91–112. Wright, C. I., Beijer, A. V., & Groenewegen, H. J. (1996). Basal amygdaloid complex afferents to the rat nucleus accumbens are compartmentally organized. Journal of Neuroscience, 16, 1877–1893. Yang, C. R., & Seamans, J. K. (1996). Dopamine d1 receptor actions in layers v-vi rat prefrontal cortex neurons in vitro: Modulation of dendriticsomatic signal integration. Journal of Neuroscience, 16, 1922–1935.

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Young, A. M., Joseph, M. H., & Gray, J. A. (1993). Latent inhibition of conditioned dopamine release in rat nucleus accumbens. Neuroscience, 54, 5–9. Zahm, D. S. (1989). The ventral striatopallidal parts of the basal ganglia in the rat: Pt. II. Compartmentation of ventral pallidal efferents. Neuroscience, 30, 33–50. Zahm, D. S. (1999). Functional-anatomical implications of the nucleus accumbens core and shell subterritories. Annals of the New York Adademy of Sciences, 877, 113–128. Zahm, D. S., & Brog, J. S. (1992). On the significance of subterritories in the “accumbens” part of the rat ventral striatum. Neuroscience, 50, 751–767. Zahm, D. S., & Heimer, L. (1988). Ventral striatopallidal parts of the basal ganglia in the rat: Pt. I. Neurochemical compartmentation as reflected by the distributions of neurotensin and substance p immunoreactivity. Journal of Comparative Neurology, 272, 516–535. Zahm, D. S., & Heimer, L. (1990). Two transpallidal pathways originating in the rat nucleus accumbens. Journal of Comparative Neurology, 302, 437–446. Zahm, D. S., Williams, E., & Wohltmann, C. (1996). Ventral striatopallidothalamic projection: Pt. IV. Relative involvements of neurochemically distinct subterritories in the ventral pallidum and adjacent parts of the rostroventral forebrain. Journal of Comparative Neurology, 364, 340–362.

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Chapter 23

Neural Perspectives on Activation and Arousal NICHOLAS D. SCHIFF AND DONALD W. PFAFF

in terms of the extent of usage of metabolic energy by the individual activity during behavioral activity or behavioral response (pp. 17–18). Pfaff (2006) hypothesized that underlying the activation of behavior is a set of CNS mechanisms that support generalized arousal. An animal or human with higher generalized arousal (a) is more responsive to sensory stimuli in all modalities, (b) emits more voluntary motor activity, and (c) is more reactive emotionally. This definition is precise, complete, and yields quantitative, physical measures. In a meta-analysis of five experiments directed toward measurements of mouse behaviors reflecting arousal, principal components analysis indicated that about one-third of the variance was due to a generalized arousal factor (Garey et al., 2003). Our generalized arousal concept maps onto established neurology of consciousness and its disorders (Plum & Posner, 1982; Posner, Saper, Schiff, & Plum, 2007). Some of the modern approaches to disorders of consciousness are reviewed briefly next. Likewise, circadian rhythms of behavioral activity, including but not limited to the sleep/ wake cycle, must depend on fluctuations of generalized CNS arousal. The relation of our arousal concept to stress is more complex. Arousal is a valence-free force that supplies the energy of stress responses. However, there is an asymmetry between these two neurobiological concepts. Whereas all stressful stimuli must be arousing, not all arousing stimuli are stressful (Pfaff, Martin, & Ribeiro, 2007). We note that the slopes of avoidance functions are sometimes higher than the slopes of approach functions, so that greater arousal would yield greater negative affective activation, but this inequality in behavioral result does not gainsay the concept that generalized arousal itself is without valence. Finally, it is clear that generalized arousal is necessary to provide the strength and persistence of emotional response related to the primitive, physiological side of libido, the aspect of Freud’s libido that has been conserved from the animal into the human brain (Pfaff, 1999).

Most of the work on biologically or psychologically motivated behaviors in animals and humans has been done using specific forms of motivational conditions such as hunger, thirst, sex, or fear. A different line of thought and experimentation, however, has concentrated on the fundamental ability and, indeed, the need of animals and humans to initiate general activity (reviewed in Cofer & Appley, 1964). Apparently, spontaneous activity is a universal phenomenon. In addition, animals in various states of need often show greater activity, perhaps in the service of finding the corresponding incentive object to reduce drive. Whether there is an independent motivational system demanding activity has been debated without a clear conclusion. However, it is notable that biologists can breed animals to achieve substrains that have high levels of activity versus low. Cofer and Appley recognize that in some cases high levels of activity, for example during food deprivation, are not achieved in vacuo, but instead they are achieved by virtue of increased sensitivity to sensory stimulation. A closely related question is whether there is a separate need for exploration, in other words, a need for environmental stimulation. Early theorists such as Donald Hebb (1955) worked on the assumption that many exploratory movements by animals would have the effect of increasing one kind of stimulation and decreasing another kind, or, perhaps, simply achieving more stimulation in general. We recognize the feeling of restlessness as a result of boredom, often perceived as unpleasant and even stressful. Thus, the need for stimulation and activity makes intuitive sense. Elizabeth Duffy, prominent in this field by virtue of her empirical work as well as her theoretical writing (1962) stated that “for performances of many kinds, there appears to be an optimal level of activation at which the performance reaches its greatest excellence” (p. 158), and we note that this optimum point varies according to the task and according to the individual. Duffy (1962) defines the activation of behavior not in terms of cortical arousal or autonomic arousal but instead 454

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Neuroanatomical, Physiological, Genomic Mechanisms 455

This chapter on arousal and the activation of behavior seems timely because of what we perceive as a sea change in neuroscience. During the twentieth century almost exclusive emphasis was placed on the specificity of response to sensory stimuli and on the regulation of specific, simple reflex responses. Thus, Hubel and Wiesel studied the visual system, while Mountcastle studied cortical responses to somatosensory stimulation. Meanwhile, neurophysiologists such as John Eccles and David P. C. Llyor charted the circuitry and synaptology underlying specific spinal reflex responses. However, now, there is a much greater emphasis on changes of CNS state—sleep/ wake, mood, motivational states, and so on—that, in fact, tend to be of much greater medical importance. First, we refer briefly to a literature that describes some of the mechanisms underlying generalized CNS arousal, a literature that depends heavily on animal research. Second, we cover modern neurological experience with efforts to bring patients who lack normal arousal and self-awareness to a point where they can initiate purposeful behaviors. Finally, we identify some of the key issues for new research. NEUROANATOMICAL, PHYSIOLOGICAL, GENOMIC MECHANISMS Decades of work have gone into the elucidation of the neuroanatomical pathways and neurophysiological mechanisms related to arousal and stress. They have been well reviewed (e.g., Pfaff, 2006) and will be treated here only briefly. Mechanisms for generalized arousal of the CNS are fairly well known at the neuroanatomical level. Their most important features emphasize multiplicity and redundancy of ascending arousal pathways in such a way as to prevent failure. Five major neurochemically distinct systems all work together to increase arousal. They use norepinephrine, dopamine, serotonin, acetylcholine, and histamine as transmitters. They all begin in the brain stem and converge in the thalamus or in the basal forebrain. They overlap and cooperate. Their very multiplicity ensures against failure. Four sensory systems feed ascending arousal pathways in a straightforward fashion. These clearly show how vestibular stimuli, somatosensory, auditory, and taste stimuli on the tongue could arouse an animal or human being. Pain mechanisms further dramatize how a vastly amplified somatosensory signal from the skin or the viscera can wake up and alert an individual. Moreover, pain pathways and sexual cutaneous signals overlap and share the ability to cause states of high arousal. In contrast, electrical impulses triggered by odor stimuli enter the brain through tracts in the basal forebrain, and project to a primary receiving zone which itself is connected with high degrees of arousal,

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during both sex and fear—the amygdala. Visual stimuli impact CNS arousal pathways both through the outer layers of the superior colliculus and through the reticular and medial cell groups of the thalamus. An important point, to reiterate, is that these various arousal-related transmitter systems and sensory signals converge. Whether in the basal forebrain or in the medial thalamus, a strong signal for cortical arousal is generated and must be distributed broadly in the cerebral cortex to command the attention of a wide variety of higher-level perceptual processers and motor control cell groups. In terms of the general principles illustrated by CNS arousal pathways, it is eminently clear that arousal mechanisms are bilateral (B). Unilateral damage in the animal brain or human brain has little effect on generalized arousal or consciousness. Second, they are bidirectional (B). In addition to the classical aminergic ascending pathways just mentioned, there are crucial descending pathways (e.g., vasopressin, histamine, and orexin). Third, these pathways have been conserved across a variety of species, including humans; they are universal (U). Finally, these pathways always potentiate an animal’s or human’s behavioral responsivity (response potentiation, RP). One of us (DP) has informally described this formulation as BBURP theory, schematically illustrated in Figure 23.1. These responses may be active, approach responses but in the case of fearful or stressful inputs, these are avoidance responses.

POA

OLF

OLF

POA

PVN

PVN

T-C T-C

BF

BF

Figure 23.1 Bilaterally symmetric, bidirectional (descending as well as ascending) pathways regulating CNS arousal. Note: We propose that these systems are universal among mammalian brains and that they are always necessary for response potentiation, whether approach responses or avoidance responses. More phylogenetically ancient systems work through the basal forebrain (BF), whereas thalamo-cortical (T-C) systems evolved later. Descending systems sketched here as examples include those consequent to olfactory (OLF) stimulation, and those from the preoptic area (POA) and the paraventricular nucleus (PVN) of the hypothalamus.

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A current hypothesis states that the most elementary, primitive, and universal set of cells that initiate the activation of behavior have their cell bodies in the medullary reticular formation and influence the generalized arousal of the CNS through bifurcating axons that ascend the brain stem (for cortical arousal) and descend to the cord (for autonomic arousal). Without adequate levels of this elementary, primitive arousal, it is impossible to be alert. Without adequate alertness, it is impossible to pay attention. Older concepts of autonomic arousal pictured two extreme states: total dominance by sympathetic autonomic mechanisms versus total dominance by parasympathetic mechanisms. The two autonomic systems were thought always to oppose each other. We now understand that the situation is much more complicated than that. In several examples, for example, erection and ejaculation by the male, and control over the anal sphincter in both sexes, temporally patterned coordination between the two systems achieve the desired physiological result. Berntson, Cacioppo, and Quigley (1991) reviewed the relevant literature and concluded that various autonomic states are best described in a two-dimensional space (rather than laying along a single continuum). This concept allowed them to account for a larger percentage of the variance in psychophysiological studies than had previously been achieved. Correspondingly, it suggests a greater sophistication and complexity of medullary neuronal integration than had been supposed because nerve cell groups in, for example, the dorsomotor nucleus of the vagus and rostral ventrolateral cells of the medulla would have to be well coordinated. We note that in several experimental situations individual mechanisms and measures of arousal are not highly correlated with each other. On the one hand, this fact emphasizes that global CNS arousal mechanisms are not simply monolithic. Different ascending monoaminergic systems, for example, contribute differently to arousal states. Noradrenergic pathways certainly support increased attention; dopaminergic pathways support directed motor acts toward salient stimuli; and serotonergic systems are intimately related to emotional regulation. On the other hand, we have elsewhere (Pfaff, 2006) envisioned this lack of correlation in the following way: By analogy to the movements of different members of an athletic team, different components of CNS arousal mechanisms may be coordinated in an adaptive fashion even when they are not regulated in a manner identical to each other. In neurophysiological terms, cells involved in generalized arousal of the CNS would be expected to respond to a variety of stimuli in several sensory modalities. During electrophysiological recordings from reticular and raphe neurons in the medulla, such neurons have been found (Hubscher & Johnson, 2002; Leung & Mason, 1998, 1999;

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Martin, Pavlides, & Pfaff, unpublished data). Moving anterior in the brain stem, certain pontomedullary reticular neurons (Peterson, Anderson, & Filion, 1974) as well as the omnipause neurons (Phillips, Ling, & Fuchs, 1999) recorded in the pons, also fit the requirement that cells be multimodal in their range of sensitivities and have firing rates correlated with arousal and visual attention. In the midbrain, Horvitz, Stewart, and Jacobs (1997), recording from dopaminergic neurons, revealed responses that were correlated with the activation of behavior, that is, the initiation of motor responses directed toward salient stimuli. These and many other reports supply the neurophysiological basis of generalized arousal responses. Functional Genomics Data are accumulating rapidly with respect to neurochemical and genomic mechanisms for both arousal and stress. A large number of genes, more than 120, participate in regulating generalized CNS arousal. The large number is due to the inclusion of genes encoding synthetic enzymes, receptors (for serotonin, alone, there are 14), transporters, and catabolic enzymes for both the relevant neurotransmitters and neuropeptides; both those increasing and those decreasing arousal (Pfaff, 2006). As might be expected, sex hormones are involved in CNS arousal. Disruption of the gene encoding estrogen receptor alpha severely reduced arousal measures in female mice, compared to their wildtype littermate controls. Disruption of the gene for estrogen receptor beta, a likely gene duplication product, had no significant effect (Garey et al., 2003). There are additional implications of having so many genes controlling arousal mechanisms. The heterogeneity among the genes involved presumably provides for great flexibility of response. The very multiplicity yields the possibility of large numbers of meaningful patterns of gene expression. In a neuroendocrine context, we have shown that one never could understand gene/behavior relations on a one-by-one basis. Moving beyond Beadle and Tatum’s concept from their work with the fungus Neurospora— their classical one gene/one enzyme concept—we reached the conclusion that different patterns of gene expression yield different patterns of sociosexual behaviors (Pfaff et al., 2002).

EVOLUTION OF CNS AROUSAL PATHWAYS Certain aspects of the literature on ascending CNS arousal pathways have highlighted distinctions among them. In particular, some workers would protest that pathways activating the basal forebrain cholinergic neurons are the only

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Clinical Study: Forebrain Arousal Regulation Mechanisms and Neurological Disorders of Consciousness 457

important ones, while others would argue that thalamocortical systems are the most important. We propose an evolutionary approach to these questions, emphasizing two main principles. First, that as brain stem reticular mechanisms evolved, both in their internal structure and in their connectivities with the forebrain, new layers of capacities for aroused, alert, and even conscious behaviors likewise emerged. More primitive capacities governing autonomic arousal and the most rudimentary form of attention to simple stimuli are required for later developed, higher capacities, but not vice versa. This neuroanatomical and functional idea can also be expressed in the form of an equation:

Cerebral cortex

Thalamus

Cerebellum Basal forebrain Hypothalamus

Spinal cord

A  Fg(Ag)  (F1(As )  F2(As ). . .  . . . Fsn(Asn)) 1

2

Figure 23.2 Human brain from left side.

(23.1)

where the overall state of CNS arousal, A, is a function of the most primitive arousal force, generalized arousal (Ag), multiplied by the effects of several specific states or arousal As. This equation represents a mathematical statement of a hypothesis. We must determine experimentally how different sources of arousal interact in order to produce an overall CNS arousal state. However, one thing is clear: In this equation, if generalized arousal goes to zero, no behavioral response will take place. Second, there is no need to set basal forebrain and thalamocortical systems against one another in a false competition for the designation of “most important.” Instead, we propose that the older ascending CNS arousal pathway uses the basal forebrain route. For example, Jones (2003) carefully charted axons ascending from the lower brain stem reticular formation that followed a “low road” into the medial forebrain bundle, continuing into the basal forebrain “where fibers were visible in the lateral preoptic area, substantia innominata, and the nuclei of the horizontal and vertical limbs of the diagonal band.” Clearly, such a system is in place to affect the activity of basal forebrain cholinergic neurons, whose influences on cerebral cortical activity are powerful and subtle (Xiang, Huguenard, & Prince, 1998). In addition, ascending aminergic systems have projections through the medial forebrain bundle, some axons of which reach the cerebral cortex. Of course, the more recently evolved ascending CNS arousal pathway would involve the thalamus. For example, in Jones’ and Yang’s (1985) neuroanatomical work, axons from neurons in the hindbrain reticular formation took a “high road.” They “passed into the internal medullary lamina of the thalamus and left collaterals in the intralaminar nuclei (parafascicular, paracentral, centrolateral, and centromedial), and midline nuclei (rhomboid and reunions).” In mice with reduced

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Note: Looking at a sketch of the human brain from the left side, one can see that the more ancient sets of arousal-facilitating axons that travel the medial forebrain bundle of the hypothalamus to innervate the basal forebrain, have been preserved. Especially striking, however, is the elaboration of projections from the medial and intralaminar thalamus. Stimulation of these thalamo-cortical systems can increase arousal in mice and in human patients.

arousal responses consequent to anoxia, electrical stimulation of the midline thalamus can activate greater responses (Arietta-Cruz and Pfaff, unpublished data). The distinction between the more ancient (‘low road’) and the more recently evolved (high road) pathways are easily schematized on a drawing of the human brain (Figure 23.2).

CLINICAL STUDY: FOREBRAIN AROUSAL REGULATION MECHANISMS AND NEUROLOGICAL DISORDERS OF CONSCIOUSNESS Arousal Regulation Mechanisms In the human brain, arousal regulation appears to be strongly dependent on the integrity of prefrontal and frontal lobe systems that have descending projections to brain stem and basal forebrain neurons identified as the primary arousal systems. Even mild brain trauma to the frontal lobes can be associated with decreased vigilance and fatigue, while cognitive control of behaviors that tax attentional and working memory resources require engagement of distributed neuronal systems within the frontal lobe (Knight & Stuss, 2003). The key intermediary structure mediating effects across the cortex and basal ganglia of adjustments in arousal level is the central thalamus (Steriade & Glenn, 1982; reviewed in Schiff & Purpura, 2002). Neurons within the central thalamus (thalamic intralaminar nuclei, and related paralaminar

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association nuclei including median dorsalis, ventral anterior, and ventral lateral) are uniquely specialized in terms of their wide point-to-point connections across cortical areas, innervation of the upper (supergranular layers) of the cerebral cortex contacting dendritic elements of both input and output neurons within the cortical column, and providing strong activating inputs to striatal neurons of the basal ganglia (Lacey, Bolam, & Magill, 2007; Purpura & Schiff, 1997). As illustrated in Figure 23.2, in the human brain the brain stem and basal forebrain arousal systems project strongly widely to the cerebral cortical and most heavily to these central thalamic neurons that play an essential role in arousal and activation of the forebrain per se. The intrinsic organization of these arousal regulation pathways in the human brain is also suggested by the effect of selective injuries that may produce global disorders of consciousness (Schiff & Plum, 2000). Subcortical injuries associated with global disorders of consciousness prominently involve the central thalamus (the intralaminar nuclei and paralaminar regions of the thalamus), the caudate nuclei of the striatum, basal forebrain, and the mesencephalic reticular formation and upper pontine brain stem reticular regions. The pioneering work of Morison and Dempsey (1942) and Moruzzi and Magoun (1949) assigned the mesencephalic reticular formation and the thalamic intralaminar nuclei the role of mediating arousal and setting the stage for sensory processing in higher integrative brain functions. Moruzzi and Magoun’s experiments showed that electrical stimulation of these mesodiencephalic structures produced electroencephalographic (EEG) desynchronization and behavioral arousal in anesthetized animals. This classical view of forebrain arousal has given way to an understanding that overall shift of shift of spectral content reflected in the EEG and increased behavior activity level associated with higher level of arousal is interdependent on the output of cholinergic, serotoninergic, adrenergic, and histaminergic nuclei located predominantly in the brain stem, basal forebrain, and posterior hypothalamus (Marracco, Witte, & Davidson, 1994; McCormick, 1994; Steriade, 1997). Forebrain arousal is now viewed in terms of global modulations of the thalamocortical system that define specific functional states (McCormick, 1994; Steriade & Llinas, 1988). Although several studies have sought to determine how necessary or sufficient for arousal different neuronal groups may be no study has provided compelling evidence that any single group is indispensable (see Steriade, 1997). Berntson, Shafi, and Sarter (2002) used an immunotoxin to lesion corticopetal cholinergic neurons while sparing septo-hippocampal neurons and found that, compared to control animals, there was a significant reduction in the spectral power of high-frequency EEG activity that is

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typical of the aroused alert state. Rather than affecting the portion of the day occupied by sleeping or waking behaviors, these lesions reduced high frequency activity across all stages, sleeping and waking. Working with both alert and anaesthetized cats, Skinner and Yingling (1977) pioneered a series of experiments that examined the integrative physiology of the mesencephalic reticular formation, the reticular thalamic nucleus, and the medial thalamic-mesial frontal cortical systems (including the thalamic intralaminar nuclei and related thalamic association nuclei). These investigators proposed that gating of attention was achieved by medial thalamo-frontal cortical and mesencephalic reticular formation modulation of strong inhibition of thalamic relay nuclei by the gabaergic neurons of the reticular thalamic nucleus. Human functional neuroimaging studies demonstrate coactivation of the upper midbrain and thalamic regions consistent with anatomical model during attentional processing (Kinomura, Larssen, Gulyas, & Roland, 1996) and transitions to wakefulness (Balkin et al., 2002). In these studies, increased regional blood flow in the pontine/mesencephalic reticular formation and central thalamus is correlated with increased activations of prefrontal, frontal, and parietal and primary sensory cortices during both periods of increased vigilance and awakening. Neurological Disorders of Consciousness Perhaps unsurprising in light of the previous discussion, only relatively large bilateral injuries to the dorsal regions of the upper pons, midbrain, or central regions of the thalamus can produce unconsciousness in humans on the basis of a small structural brain injury. Coma (an unresponsive brain state with no cyclical variation in arousal as judged behaviorally and by EEG content) arising from focal injuries is usually quite brief lasting only hours or a few days, reflecting the multiplicity and pleuripotentiality of the arousal pathways. Brain stem injuries producing coma are concentrated bilaterally in the rostral pons and dorsal midbrain, regions containing the cholinergic neurons and other monoaminergic afferents from the brain stem arousal projecting to the basal forebrain, central thalamus, and wide territories of the cerebral cortex. Bilateral focal injury to the central thalamus alone can induce coma when the injuries involve both sides of the brain often including damage to the upper midbrain. Most of the time, however, coma is the result of widespread damage to the brain from trauma or anoxic/hypoxic/ ischemic injury (Posner et al., 2007). What is not well known is that the same central thalamic structures index the recovery from such multifocal brain injuries. Recovery following coma may or may not be complete, with some

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References 459

patients remaining in vegetative state, minimally conscious state, and other related clinical syndromes. These different conditions are often confused with coma, but each is distinct in clinical presentation and natural history following coma and may reflect both the functional integrity of the central thalamus and related frontal systems. Patterns of structural injuries producing vegetative state—a condition with cyclic eye opening and closure but no behavioral responsiveness—overlap with those producing coma. Autopsy studies of patients remaining permanently unconscious show overwhelming loss of thalamic neurons, with extensive and specific loss of central thalamic neurons, particularly the neurons of the thalamic intralaminar nuclei and closely adjacent components of thalamic association nuclei (Adams, Graham, & Jennett, 2000). Vegetative state is differentiated from minimally conscious state by evidence of unequivocal but inconsistent evidence of awareness of self or the environment that may range from isolated tracking of objects in the visual field to high-level responses such as intermittent verbalization and communication (Giacino & Whyte, 2005). Pathological studies reveal many different underlying structural pathologies with a consistent feature of loss of central thalamic neurons particularly the rostral intralaminar nuclei (Maxwell, MacKinnon, Smith, McIntosh, & Graham, 2006). Patients may remain in a minimally conscious state yet retain recruitable large-scale cerebral networks that appear to be underactivated (Schiff et al., 2005). In a single-subject study of a patient who remained in a minimally conscious state for 6 years, electrical brain stimulation of the central thalamus restored a variety of integrative behaviors including spoken language, attentive behavior, motor control, and eating (Schiff et al., 2007). Localization of electrical activity in the EEG suggested modulation of midline frontal systems during stimulation consistent with reactivation of the frontalcentral thalamic arousal regulation circuit.

SUMMARY Among a large number of outstanding questions about CNS arousal and the activation of behavior, we list just a few here as a brief summary of where we are and where we need to be. First, is our evolutionary theory about more ancient basal forebrain pathways and more recently developed thalamocortical pathways really correct, or are some medial thalamic mechanisms equally primitive? In any case, how do these two types of pathways interact? Do their effects on cortical arousal add? Multiply? Or do they interfere with each other?

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Second, large numbers of genes are seen as participating in the regulation of arousal (Pfaff, 2006). How do they interact with environmental determinants of purposeful, motivated behaviors? Do their separate identities portend adjunct neurochemical therapies that could accompany and enhance the effects of deep brain stimulation as demonstrated in the Schiff et al. (2007) paper? Third, regarding such deep brain stimulation, can we improve on standard stimulation parameters (e.g., fixed numbers of pulses per second) by discovering and exploiting the nonlinear dynamics of CNS arousal systems? For example, it has been proposed that arousal-related neurons in the brain stem are susceptible to chaotic dynamics, but that they operate close to a phase transition that they cross when coming under the control of well-organized motor control mechanisms (Pfaff & Banavar, 2007). Fourth, as data are collected in coming years, how will Equation 23.1 have to be modified? The mathematical structure of CNS arousal is ready for investigation. Clinical Treatments Among the many problems of diagnosing and treating patients at various levels of vegetative states, three could be highlighted here in a logical sequence. First, in minimally conscious state patients who will recover, are there neurophysiological signatures that can be detected and that are early harbingers of recovery? Second, why do some patients have those neurophysiological signatures and not others? Third, will knowledge of such signatures be able to be used in clinical trials with deep brain stimulation with the purpose of facilitating recovery?

REFERENCES Adams, J. H., Graham, D. I., & Jennett, B. (2000). The neuropathology of the vegetative state after acute insult. Brain, 123, 1327–1338. Balkin, T. J., Braun, A. R., Wesensten, N. J., Jeffries, K., Varga, M., Baldwin, P., et al. (2002). The process of awakening: A PET study of regional brain activity patterns mediating the re-establishment of alertness and consciousness. Brain, 125(Pt. 10), 2308–2319. Berntson, G. G., Cacioppo, J., & Quigley, K. (1991). Autonomic determinism: The modes of autonomic control, the doctrine of autonomic space and the laws of autonomic constraint. Psychological Review, 98, 459–487. Berntson, G. G., Shafi, R., & Sarter, M. (2002). Specific contributions of the basal forebrain corticopetal cholinergic system to electroencephalographic activity and sleep/waking behaviour. European Journal of Neuroscience, 16, 2453–2461. Cofer, C., & Appley, M. (1964). Motivation: Theory and research. New York: Wiley. Duffy, E. (1962). Activation and behavior. New York: Wiley. Garey, J., Goodwillie, A., Frohlich, J., Morgan, M., Gustafsson, J.-A., Smithies, O., et al. (2003). Genetic contributions to generalized

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arousal of brain and behavior. Proceedings of the National Academy of Sciences, USA, 100, 11019–11022.

Pfaff, D., & Banavar, J. (2007). A theoretical framework for CNS arousal. BioEssays, 29, 803–810.

Giacino, J. T., & Whyte, J. (2005). The vegetative state and minimally conscious state: Current knowledge and remaining questions. Journal of Head Trauma Rehabilitation, 20(1), 30–50.

Pfaff, D., Martin, E., & Ribeiro, A. (2007). Relations between mechanisms of CNS arousal and mechanisms of stress. Stress, 10, 316–325.

Hebb, D. (1955). Drives and the CNS (conceptual nervous system). Psychiatric Review, 62, 243–254. Horvitz, J., Stewart, T., & Jacobs, B. (1997). Burst activity of ventral tegmental dopamine neurons is elicited by sensory stimuli in the awake cat. Brain Research, 759, 251–258. Hubscher, C., & Johnson, R. (2002). Inputs from spinal and vagal sources converge on individual medullary reticular neurons. Society for Neuroscience Abstracts, 28. Jones, B. E. (2003). Arousal systems. Frontiers in Biosciences 8,438–451. Kinomura, S., Larssen, J., Gulyas, B., & Roland, P. E. (1996, January 26). Activation by attention of the human reticular formation and thalamic intralaminar nuclei. Science, 271, 512–515. Knight, R., & Stuss, D. (2003). Principle of frontal lobe function. Oxford: Oxford University Press. Lacey, C. J., Bolam, J. P., & Magill, P. J. (2007). Novel and distinct operational principles of intralaminar thalamic neurons and their striatal projections. Journal of Neuroscience, 27, 4374–4384. Leung, C., & Mason, P. (1998). Physiological survey of medullary raphe and magnocellular reticular neurons in the anesthetized rat. Journal of Neurophysiology, 80, 1630–1646. Leung, C., & Mason, P. (1999). Physiological properties of raphe magnus neurons during sleep and waking. Journal of Neurophysiology, 81, 584–595. Marrocco, R. T., Witte, E., & Davidson, M. C. (1994). Arousal systems. Current Opinion in Neurobiology, 4, 166–170. Maxwell, W. L., MacKinnon, M. A., Smith, D. H., McIntosh, T. K., & Graham, D. I. (2006). Thalamic nuclei after human blunt head injury. Journal of Neuropathology and Experimental Neurology, 65, 478–488. McCormick, D. A. (1994). Neurotransmitter actions in the thalamus and cerebral cortex and their role in neuromodulation of thalamocortical activity. Progress in Neurobiology, 39, 337–388. Morison, R. S., & Dempsey, E. W. (1942). A study of thalamo-cortical relationships. American Journal of Physiology, 135, 281–292. Moruzzi, G., & Magoun, H. W. (1949). Brainstem reticular formation and activation of the EEG. Electroencephalography Clinical Neurophysiology, 1, 455–473. Peterson, B., Anderson, M., & Filion M. (1974). Responses of pontomedullary reticular neurons to cortical, tectal and cutaneous stimuli. Experimental Brain Research, 21, 19–44. Pfaff, D. (1999). Drive. Cambridge, MA: MIT Press. Pfaff, D. (2006). Brain arousal and information theory. Cambridge, MA: Harvard University Press.

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Pfaff, D., Ogawa, S., Kia, K., N. Vasudevan, C. Krebs, J. Frolich, et al. (2002). Genetic mechanisms in controls over female reproductive behaviors. In D. W. Pfaff, A. P. Arnold, A. M. Etgen, S. E. Fahrbach, & R. T. Rubin (Eds.), Hormones, brain and behavior (pp. 441–510). San Diego, CA: Academic Press/Elsevier. Phillips, J., Ling, L., & Fuchs, A. (1999). Action of the brainstem saccade generator during horizontal gaze shifts: Pt. I. Discharge patterns of omnidirectional pause neurons. Journal of Neurophysiology, 81, 1284–1295. Plum, F., & Posner, J. B. (1982). The diagnosis of stupor and coma (3rd ed.). Philadelphia: Davis. Posner, J., Saper, C., Schiff, N., & Plum, F. (2007). Plum and Posner ’s Diagnosis of stupor and coma (4th ed.). Oxford: Oxford University Press. Purpura, K. P., & Schiff, N. D. (1997). The thalamic intralaminar nuclei: Role in visual awareness. Neuroscientist, 3, 8–14. Schiff, N. D., Giacino, J. T., Kalmar, K., Victor, J. D., Baker, K., Gerber, M., et al. (2007, August 2). Behavioral improvements with thalamic stimulation after severe traumatic brain injury. Nature, 448, 600–603. Schiff, N. D., & Plum, F. (2000). The role of arousal and ‘gating’ systems in the neurology of impaired consciousness. Journal of Clinical Neurophysiology, 17, 438–452. Schiff, N. D., & Purpura, K. P. (2002). Towards a neurophysiological basis for cognitive neuromodulation. Thalamus and Related Systems, 2(1), 55–69. Schiff, N. D., Rodriguez-Moreno, D., Kamal, A., Kim, K. H., Giacino, J., Plum, F., et al. (2005). FMRI reveals large-scale network activation in minimally conscious patients. Neurology, 64, 514–523. Skinner, J. E., & Yingling, C. D. (1977). Central gating mechanisms that regulate event-related potentials and behavior. In J. E. Desmedt (Ed.), Progress in clinical neurophysiology: Attention, voluntary contraction and event-related cerebral potentials (Vol. 1, pp. 30–69). Basel: Karger. Steriade, M. (1997). Thalamic substrates of disturbances in states of vigilance and consciousness in humans. In M. Steriade, E. Jones, & D. McCormick (Eds.), Thalamus (pp. 721–742). Elsevier, Amsterdam. Steriade, M., & Glenn, L. L. (1982). Neocortical and caudate projections of intralaminar thalamic neurons and their synaptic excitation from midbrain reticular core. Journal of Neurophysiology, 48, 352–371. Steriade, M., & Llinas, R. R. (1988). The functional states of the thalamus and the associated neuronal interplay. Physiological Reviews, 68, 649–742. Xiang, Z., Huguenard, R., & Prince, D. (1998, August 14). Cholinergic switching within neocortical inhibitory networks. Science, 281, 985–989.

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Chapter 24

Sleep and Waking Across the Life Span RETO HUBER AND GIULIO TONONI

A PRIMER ON SLEEP PHYSIOLOGY

skin near the eyes, detects small electrical fields generated by eye movements. The electromyogram (EMG), which is generally recorded from electrodes attached to the chin, is used to detect sustained (tonic) and episodic (phasic) changes in muscle activity that correlate with changes in behavioral state. In the course of the night, the EEG, EOG, and EMG patterns undergo coordinated changes that are used to distinguish among different sleep stages. A brief description of the major vigilance states follows:

Sleep can be defined behaviorally as a state of reduced responsiveness to the environment that is readily reversible. By this definition, sleep appears to be a rather universal phenomenon, being present in most if not all species investigated, from Drosophila melanogaster to humans (Borbély & Achermann, 2000; Shaw, Cirelli, Greenspan, & Tononi, 2000; Tobler, 2000). The introduction of continuous recordings of brain electrical activity (electroencephalogram, EEG) during sleep and wakefulness (Berger, 1929) has greatly enriched the study of sleep. Thus, rapid eye movement (REM) sleep was recognized as a specific state different from non-REM (NREM) sleep (Aserinsky & Kleitman, 1953). These two kinds of sleep are present in both mammals and birds (Dement & Kleitman, 1957). In humans, sleep is studied for clinical and research purposes by combining behavioral observations with electrophysiological recordings. The EEG records synchronous synaptic activity from millions of neurons underlying electrodes applied to the scalp (Figure 24.1). The electrooculogram (EOG), which is recorded from electrodes attached to the

Wakefulness

• Wakefulness: Wakefulness is reflected in the EEG by low-voltage, fast-frequency activities—also called a desynchronized or activated EEG. When eyes close in preparation for sleep, EEG alpha activity (8 to 13 Hz) becomes prominent, particularly in occipital regions. Such alpha activity is thought to correspond to an “idling” rhythm in visual areas. The waking EOG reveals frequent voluntary eye movements and eyeblinks. The EMG reveals tonic muscle activity with additional phasic activity related to voluntary movements. In wakefulness, most cortical neurons are steadily depolarized close to their firing threshold and

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into four substates of which stage 2 and 3 are illustrated. Stage 3 is also called slow-wave sleep. The typical EEG features of human stage 2 sleep, spindles and K-complexes, are highlighted by arrows. EEG calibration marks correspond to 100 ␮V2/0.25Hz.

Figure 24.1 Vigilance states. Note. 12-s Electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) traces are plotted for the three vigilance states wakefulness, NREM sleep and REM sleep. NREM sleep is subdivided

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thus are ready to respond to stimuli. The steady depolarization is enabled by the release of acetylcholine and other neuromodulators, which close leakage potassium channels on the membrane of cortical neurons and force positively charged potassium ions to stay inside the cell. Neurons show irregular spiking patterns (Steriade, McCormick, & Sejnowski, 1993). The activity of other neuromodulatory systems, such as the noradrenergic system, enables the occurrence of plastic changes (Cirelli & Tononi, 2000, 2004). • NREM Sleep: Falling asleep is a gradual phenomenon of progressive disconnection from the environment: we stop responding to stimuli and, to the extent that we remain conscious, our experiences become largely independent of the current environment. This disconnection appears to be important because we make several behavioral adjustments to bring it about: we seek a quiet environment, find a comfortable position, and close our eyes. However, people (especially small children) can fall asleep in noisy environments and in uncomfortable positions—in the laboratory people have even slept with their eyes taped open. Everything else being equal, the threshold for responding to peripheral stimuli gradually increases with the succession of NREM sleep stages and remains high during REM sleep (Rechtschaffen, Hauri, & Zeitlin, 1966; Williams, Hammack, Daly, Dement, & Lubin, 1964). Sleep is usually entered through a transitional state, stage 1 (N1), characterized by loss of alpha activity and the appearance of a lowvoltage mixed-frequency EEG pattern with prominent theta activity (3 to 7 Hz). Eye movements become slow and rolling, and muscle tone relaxes. Although there is decreased awareness of sensory stimuli, a subject in N1 may deny that he was asleep. Motor activity may persist for a number of seconds during N1. Occasionally, individuals experience sudden muscle contractions (hypnic jerks), sometimes accompanied by a sense of falling and dreamlike imagery. Individuals deprived of sleep often have “microsleep” episodes that consist of brief (5 to 10 seconds) bouts of stage 1 sleep; these episodes can have serious consequences in situations that demand constant attention, such as driving a car. After a few minutes in N1, people usually progress to stage 2 (N2), followed, especially at the beginning of the night, by a period comprised of stage 3 (N3). N2 qualifies fully as sleep because people are partially disconnected from the environment, meaning that they do not respond to the events around them—their arousal threshold is increased. If stimuli are strong enough to wake them, people in N2 will confirm that they were asleep. During N2, the EEG shows prominent sleep spindles, brief sequences of waves at around 12 to 15 Hz.

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During N3, the process of awakening is drawn out, and subjects often remain confused for some time. This change in arousal threshold is accompanied by a dramatic change in the EEG, which shows high-voltage, slowfrequency waves at around 1 to 2 Hz, which is why this stage is also known as slow wave sleep. During NREM sleep, the transition from the low-voltage, fast-activity EEG observed during wakefulness to the characteristic EEG of NREM sleep is due to the occurrence of brief periods of hyperpolarization, also called down states, in thalamocortical and cortical neurons. Down states are due to reduced activating input from ascending cholinergic and other neuromodualtory pathways (for reviews, see Llinas & Steriade, 2006; McCormick & Bal, 1997; Steriade et al., 1993), which is primarily due to and increase in leakage potassium conductances (McCormick & Pape, 1990). The resulting slow oscillation is found in virtually every cortical neuron and is synchronized across much of the cortical mantle by cortico-cortical connections, which is why the EEG records high-voltage, low frequency waves. Human EEG recordings using 256 channels have revealed that the slow oscillation behaves as a traveling wave that sweeps across a large portion of the cerebral cortex (Massimini, Huber, Ferrarelli, Hill, & Tononi, 2004). Sleep spindles occur during the depolarized phase of the slow oscillation and are generated in thalamic circuits involving the reticular thalamic nucleus as a consequence of cortical firing (Steriade et al., 1993). Imaging studies show that brain metabolism and blood flow are diffusely reduced during NREM sleep as compared to wakefulness (Braun et al., 1997), probably due to the repeated occurrence of down states characterized by synaptic silence. • REM Sleep: The eyes move little during NREM sleep, whereas REM sleep is characterized by bursts of typical rapid eye movements (Aserinsky & Kleitman, 1953). During REM sleep, the EEG shows low-voltage, fast activity similar to wakefulness, which is it is also referred to as paradoxical sleep (Jouvet, 1962, 1965, 1998). Also during this phase, muscles are paralyzed with occasional brief jerks. REM sleep is almost invariably accompanied by dreaming, though some mental activity also occurs during NREM sleep, especially during lighter stages and the morning hours (Hobson, Pace-Schott, & Stickgold, 2000). Neuroimaging studies show increased activity compared to NREM sleep, however, similarly to NREM sleep, activity in frontoparietal association cortices is reduced compared to wakefulness. This reduced activity in association cortices may explain why, during NREM and REM sleep, our thoughts are less logical.

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A Primer on Sleep Physiology

Sleep Architecture During adulthood, roughly 75% of total sleep time is spent in NREM sleep and 25% in REM sleep. We alternate between NREM and REM sleep throughout the night with each sleep cycle lasting between 90 and 120 minutes (Figure 24.2). Slow wave sleep is prominent early in the night, especially during the first sleep cycle, and diminishes as the night progresses. As slow-wave sleep wanes, periods of REM sleep lengthen. The proportion of time spent in each stage and the pattern of stages across the night is fairly consistent in normal adults. A healthy young adult will typically spend about 5% of the sleep period in N1 sleep, about 50% in N2 sleep, 20% to 25% in slow-wave sleep (N3), and 20% to 25% in REM sleep. These sleep cycles are an example of ultradian rhythms. Brain Centers Regulating Wakefulness and Sleep Though in the 1940s it was generally believed that sleep was a passive process, whereby a brain deprived of sensory input would fall asleep, it is clear from the well-regulated alternation of vigilance states that this is not the case. This point was nicely illustrated by an experiment in which the sensory afferents to an animal’s brain were blocked, nevertheless, the animal continued to exhibit cycles of sleep and waking (Moruzzi & Magoun, 1949). But what are the neural mechanisms controlling the alternation between sleep and waking? Two antagonistic sets of brain structures are responsible for orchestrating the regular alternation between wakefulness and sleep. The neuronal groups that promote wakefulness are located in the basal forebrain, posterior hypothalamus, and in the upper brain stem, whereas those promoting NREM sleep are located in the anterior hypothalamus and basal forebrain (Jones, 2003, 2005; McGinty et al., 2004; McGinty & Szymusiak, 2003; Saper, Scammell, & Lu, 2005; Szymusiak, Steininger, Alam, & McGinty, 2001). Other cellular groups in the dorsal part of the pons and in the medulla comprise the so-called REM

sleep generator (Figure 24.3; Basheer, Strecker, Thakkar, & McCarley, 2004; Chase & Morales, 1990; Jouvet, 1962, 1965, 1994; Kripke, Garfinkel, Wingard, Klauber, & Marler 2002; Siegel, 2005). The circadian clock, centered on the suprachiasmatic nucleus of the hypothalamus (SCN), exerts an overall control on many of these brain areas, to ensure that sleep occurs at the appropriate time of the 24-hour light-dark cycle (Aston-Jones, 2005; Mistlberger, 2005; Saper, Lu, Chou, & Gooley, 2005; Zee & Manthena, 2007). Maintenance of Wakefulness Maintenance of wakefulness is dependent on several heterogeneous cell groups extending from the upper pons and midbrain (the so-called reticular activating system, RAS; Lindsley, Bowden, & Magoun, 1949; Moruzzi & Magoun, 1949), to the posterior hypothalamus and basal forebrain. These cell groups are strategically placed so that they can release, over wide regions of the brain, neuromodulators and neurotransmitters that produce EEG activation, such as acetylcholine, hypocretin, histamine, norepinephrine, and glutamate. The main mechanism by which these neuromodulators and neurotransmitters produce cortical activation is by closing leakage potassium channels on the cell membrane of cortical and thalamic neurons, thus keeping cells depolarized and ready to fire. Falling Asleep As we seek a quiet, dark, and silent place to fall asleep, and close our eyes, the activity of the waking promoting neuronal groups is decreased due to reduced sensory input.

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Figure 24.3 Sleep centers. Note. The major brain areas involved in initiating and maintaining wakefulness (dark gray), NREM sleep (hatched), and REM sleep (light gray circle). Ach ⫽ acetylcholine; BF ⫽ basal forebrain; Cb ⫽ cerebellum; Cx ⫽ cortex; glu ⫽ glutamate; H ⫽ histamine; Hy ⫽ Hypothalamus; Me ⫽ medulla; Mi ⫽ midbrain; NA ⫽ noradrenaline; OB ⫽ orbitofrontal cortex; ore ⫽ orexin/hypecretin; P ⫽ pons; T = thalamus.

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In addition, several of these brain areas are actively inhibited by antagonistic neuronal populations located in the hypothalamus and basal forebrain, which become active at sleep onset. When the waking promoting neuronal groups become nearly silent, the decreasing levels of acetylcholine and other waking promoting neuromodulators and neurotransmitters lead to the opening of leak potassium channels in cortical and thalamic neurons, which become bistable and start exhibiting brief, recurring periods of hyperpolarization (down states). The importance of hypothalamic structures for sleep induction was recognized at the beginning of the twentieth century during an epidemic of a viral infection of the brain called encephalitis lethargica. von Economo concluded that if the infection destroyed the posterior hypothalamus, patients became lethargic, but if the anterior hypothalamus was lesioned, patients became severely insomniac (von Economo, 1930). Indeed, subsequent studies confirmed that cell groups within the anterior hypothalamus are involved in the initiation and maintenance of sleep. The ventrolateral preoptic area (VLPO) has been suggested as a possible sleep switch (Fuller, Gooley, & Saper, 2006; Sherin, Shiromani, McCarley, & Saper, 1996; Szymusiak, Alam, Steininger, & McGinty, 1998). However, many other neurons scattered through the anterior hypothalamus, for instance, in the median preoptic nucleus (Suntsova, Szymusiak, Alam, Guzman-Marin, & McGinty, 2002) and in the basal forebrain, also play a major role in initiating and maintaining sleep. These neurons tend to fire during sleep and stop firing during wakefulness. When they are active, many of them release GABA and the peptide galanin, and inhibit most waking-promoting areas, including cholinergic, noradrenergic, histaminergic, hypocretinergic, and serotonergic cells. In turn, the latter inhibit several sleep promoting neuronal groups (McGinty et al., 2004; McGinty & Szymusiak, 2003; Saper, Scammell, et al., 2005; Szymusiak et al., 2001). This reciprocal inhibition provides state stability, in that each state reinforces itself as well as inhibits the opponent state. Generation of REM Sleep The REM sleep generator consists of pontine cholinergic cell groups (LDT and PPT) that we have already encountered as waking-promoting areas, and of nearby cell groups in the medial pontine reticular formation and in the medulla (Basheer et al., 2004; Chase & Morales, 1990; Jouvet, 1962, 1965, 1994; Kripke et al., 2002; Siegel, 2005). Lesions in these areas eliminate REM sleep without significantly disrupting NREM sleep. REM sleep can also be eliminated by certain antidepressants, especially monoamine oxidase inhibitors. As we have seen, pontine cholinergic neurons produce EEG activation by releasing acetylcholine to the

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thalamus and to cholinergic and glutamatergic basal forebrain neurons that in turn activate the limbic system and cortex. However, while during wakefulness other waking promoting neuronal groups, such as noradrenergic, histaminergic, hypocretinergic, and serotonergic neurons, are also active, they are inhibited during REM sleep. Other REM active neurons in the dorsal pons are responsible for the tonic inhibition of muscle tone during REM sleep. Finally, neurons in the medial pontine reticular formation fire in bursts and produce phasic events of REM sleep, such as rapid eye movements and muscle twitches. Molecular Correlates of Sleep and Wakefulness It might seem unlikely that this mere change from wakefulness to sleep should lead to changes in the expression of genes in the brain, but this is actually what happens, and on a massive scale. Hundreds of gene transcripts (messenger RNAs) are expressed at higher levels in the waking brain, and a different set of transcripts are expressed at higher levels in sleep (Cirelli, Gutierrez, & Tononi, 2004). Many of these molecular changes are specific to the brain, since they do not occur in other tissues such as liver and muscle. Transcripts upregulated during wakefulness code for proteins that help the brain to face high energy demand, high synaptic excitatory transmission, high transcriptional activity, as well as the cellular stress that may derive from one or more of these processes. Moreover, wakefulness is associated with the increased expression of several genes that are involved in long-term potentiation of synaptic strength, such as P-CREB, Arc, NGFI-A and BDNF (Cirelli, Pompeiano, & Tononi, 1996; Cirelli & Tononi, 2000). As has been seen, one reason these genes are expressed in wakefulness and not in sleep has to do with the release of norepinephrine, which is high during wakefulness, when animals make decisions and learn about the environment, but is low during sleep. By contrast, the genes that increase their expression during sleep include several that may be involved in long term depression of synaptic strength and possibly in synaptic consolidation (Cirelli et al., 2004). Other sleep-related genes favor protein synthesis, which is also increased in sleep. Finally, many sleep-related genes play a significant role in membrane trafficking and maintenance. Thus, these findings suggest that although sleep is a state of behavioral inactivity, it is associated not only with intense neural activity, but also with the increased expression of many genes that may favor specific cellular functions. Sleep Quality and Sleep Homeostasis It was discovered early on that arousal thresholds—measured, for example, as the duration of an acoustic stimulus required

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A Primer on Sleep Physiology

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Sleep - Waking Cycle

Figure 24.4 The two-process model of sleep regulation.

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Note. A homeostatic process, process S, and a circadian process, process C, interact to generate the sleepwake cycle. From “Timing of Human Sleep: Recovery Process Gated by a Circadian Pacemaker,” by S. Daan, G. M. B. Domien, and A. A. Borbély, 1984, American Journal of Physiology, 246, p. R163. Reprinted with permission.

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to awaken a sleeping subject—is positively correlated with the amount of slow waves in the EEG of NREM sleep. It was also noticed that high amplitude slow waves predominate in the first two hours of sleep and decreases thereafter (Blake & Gerard, 1937). It was later shown that the amount of slow-wave sleep is positively correlated with the duration of prior waking (Webb & Agnew, 1971), suggesting that this aspect of sleep is homeostatically regulated. The positive relationship between slow waves and the duration of wakefulness is best seen under the influence of sleep deprivation. If we are not allowed to sleep and are forced to stay awake longer than usual, sleep pressure mounts and soon becomes overwhelming. Thus, sleep is homeostatically regulated: The more we stay awake, the longer and more intensely we sleep afterwards: arousal thresholds increase, there are fewer awakenings, and during NREM sleep the amplitude and prevalence of slow waves becomes much higher (see below). Two-Process Model of Sleep Regulation The two-process model of sleep regulation provides a conceptual framework that is frequently used in the interpretation of sleep studies. This model postulates that sleep propensity is determined by the interaction of a homeostatic process S and a circadian process C (Borbély, 1982; Figure 24.4). Process S increases during waking and decreases during sleep. An important advance has been the demonstration that Process S is reflected accurately by the amount of slow-wave activity (SWA, electroencephalographic [EEG] power in the low frequency range between 0.5 and 4.5 Hz) during NREM sleep (Borbély, 1982; Borbély & Achermann, 2000). As repeatedly shown in both humans and mammals, SWA increases exponentially with the duration of prior wakefulness and decreases exponentially during sleep, thus reflecting the accumulation of sleep pressure during wakefulness and its release during sleep (Figure 24.5). Therefore, the immediate history of sleep and waking determines the level of Process S.

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Figure 24.5 Slow-wave activity. Note. Time course of EEG slow-wave activity (power density in the 0.75 to 4.5 Hz frequency range) during an 8-hour nocturnal sleep episode of a 23-year-old, healthy man. The solid line indicates the exponential decline of SWA during the night.

In contrast, process C does not depend on the prior history of sleep but is generated by an intrinsic pacemaker located in the suprachiasmatic nuclei (SCN) of the hypothalamus. Process C is thought to modulate the timing of sleep episodes by enforcing an upper and a lower threshold so that whenever one of these thresholds is reached by process S a sleep episode is terminated or initiated. One important concept of the model is that NREM sleep loss can be recovered by an intensification of NREM sleep, reflected in an SWA increase, and not necessarily by an increase in duration. A second important concept is that the homeostatic and the circadian processes operate independently. This has been confirmed by sleep deprivation studies in SCN-lesioned rats. These animals no longer exhibit circadian modulation of sleep and wakefulness. Nevertheless sleep deprivation still results in an increase of SWA (Mistlberger, Bergmann, Waldenar, & Rechtschaffen, 1983; Tobler, Borbély, & Groos, 1983; Trachsel, Edgar, Seidel, Heller, & Dement, 1992). The two process model of sleep regulation has been tested under numerous experimental designs (Achermann, Dijk, Brunner, & Borbély, 1993; Daan et al., 1984) and in several mammalian species including: rats (Franken, Tobler, & Borbély, 1991), guinea pigs (Tobler, Franken, Trachsel, & Borbély, 1992), and mice (Huber, Deboer, & Tobler 2000). In these studies predictions of the time course of

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Process S are based on a mathematical model of its dynamics (Achermann & Borbély, 2003). Such an approach allows a precise quantification of the dynamics of Process S and has been used to search for genes underlying the homeostatic regulation of sleep (Franken, Chollet, & Tafti, 2001). Functions of Sleep Why we sleep—wasting in mindless slumber a third of our life—is one of the most mysterious questions in biology— one that still eludes a satisfactory scientific answer. The simplest possibility would be that sleep is just a time filler, a way to avoid trouble at times of day (or night) during which it is not safe to look for food or mates. Depending on the species, both the amount and the quality of sleep might be adjusted so as to fit the ecological niche. However, such ecological hypothesis seems at odds with some key observations. First, sleep appears to be universal. All animal species studied so far sleep, from invertebrates such as fruit flies and bees to birds and mammals. Even animals who need continuous vigilance while swimming or flying, for example, certain dolphins and migrating birds, have developed alternating unihemispheric sleep rather than eliminating sleep altogether. If sleep were dispensable, one would think that in such cases it would have disappeared. Second, sleep is carefully regulated. As we have seen, the longer we stay awake, the more and the more intensely do we need to sleep. This homeostatic regulation of sleep appears too to be universal, not just in mammals and birds, but even in fruit flies. Usually, if something is regulated, it serves some important function. Third, lack of sleep has deleterious consequences, especially for the brain. In humans, for example, the most prominent effect of total sleep deprivation, and even of sleep restriction (for several nights), is cognitive impairment, with striking practical consequences (Bonnet & Arand, 2003; Dinges, 2006). Just consider that each year drowsy driving is responsible for at least 100,000 automobile crashes, 71,000 injuries, and 1,550 fatalities (Iber Ancoli-Israel, Chesson, & Quan, 2007); Radun & Summala, 2004). A sleep-deprived person tends to take longer to respond to stimuli, particularly when tasks are monotonous and low in cognitive demands. However, sleep deprivation produces more than just decreased alertness. Tasks emphasizing higher cognitive functions, such as logical reasoning, encoding, decoding, and parsing complex sentences; complex subtraction tasks and tasks involving a flexible thinking style, and the ability to focus on a large number of goals simultaneously, are all significantly affected even after one night of sleep deprivation. Tasks requiring sustained attention, such as those including goal-directed activities, can be impaired

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by even a few hours of sleep loss. For example, Barger et al. (2006) showed that medical interns make more frequent serious diagnostic errors when they worked frequent shifts of 24 hours or more than when they worked shorter shifts. There are also indications that sleep plays a role in metabolic and endocrine regulation (Spiegel, Leproult, & Van Cauter, 1999). For example, a recent study showed a close relationship between insulin sensitivity and the amount of slow-wave sleep (Tasali, Leproult, Ehrmann, & Van Cauter, 2008). And finally, unless sleep serves an important function, why should we engage every night in prolonged periods of immobility during which we are dangerously out of touch with the environment? Sleep and Memory In the past decade, numerous studies appeared which seem to support a role for sleep in learning and memory. Specifically, a growing number of studies have demonstrated that sleep can enhance performance of tasks learned during prior wakefulness. This enhancement is not merely timedependent, but specifically requires sleep, and is independent of circadian factors (Walker & Stickgold, 2004). Using a variety of behavioral paradigms, evidence of sleepdependent memory enhancement has been found in humans and nonhuman primates such as cats, rats, mice, and zebra finches (Peigneux, Laureys, Delbeuck, & Maquet, 2001; Walker & Stickgold, 2004). Initial studies focused on a role for REM sleep (Karni, Tanne, Rubenstein, Askenasy, & Sagi, 1994), but more recent studies have emphasized the importance of NREM sleep (Gais & Born, 2004; Peigneux et al., 2004), of specific components within NREM sleep such as spindles (Gais, Molle, Helms, & Born, 2002; Rosanova & Ulrich, 2005) and slow waves (Czarnecki, Birtoli, & Ulrich, 2007; Huber, Hill, Ghilardi, Massimini, & Tonomi, 2004; Schmidt et al., 2006), and of a combination of NREM and REM sleep (Mednick, Nakayama, & Stickgold, 2003; Stickgold, James, & Hobson, 2000). Behavioral studies in humans and other species leave little doubt that sleep plays a critical role in learning and memory. How sleep might promote performance enhancement is not yet understood. An intriguing possibility is that the offline reactivation during sleep of circuits involved in learning during wakefulness, and perhaps the involvement of other, connected circuits, might promote memory consolidation. Several studies in animals have shown that, during NREM sleep after learning, there is an increased correlation in the firing of cells coactivated during learning tasks in prior waking, primarily in the hippocampus (Skaggs & McNaughton, 1996; Wilson & McNaughton, 1994). In humans, neuroimaging studies have shown that hippocampal areas that are activated during route learning in a virtual

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A Primer on Sleep Physiology

town are likewise activated during subsequent NREM sleep (Peigneux et al., 2004). EEG studies have shown an increase in NREM spindle density after learning pairs of unrelated words as compared to a nonlearning task (Gais et al., 2002). Similar findings have been reported after learning a maze task (Meier-Koll, Bussmann, Schmidt, & Neuschwander, 1999). Finally, high-density EEG recordings show that a visuomotor learning task, compared to a control nonlearning task, produces an increase in SWA that is localized to the brain region (right parietal cortex) that is known to be involved in learning the task (Huber, Ghilardi, Massimini, & Tononi, 2004). Many unknowns remain, however. Whether sleep may favor the consolidation of newly established memories or the maintenance of older ones is not clear. The molecular correlates of such processes are still unclear. Molecular markers of memory acquisition are turned off during sleep, which may be advantageous given that the intense neural activity of sleep occurs while the animal is disconnected from the environment. Nevertheless, evidence exists that neural activity during NREM sleep may promote brain plasticity (Jha et al., 2005; Steriade, 1999), especially in developing animals (Frank, Issa, & Stryker, 2001). Sleep and Brain Restitution When we have been awake too long, we say we are tired, and after sleep we feel refreshed. Not surprisingly, the most intuitively compelling idea about the function of sleep is that sleep may restore some precious fuel or energy charge that was depleted during wakefulness. It is likely that sleep may reduce energy waste by enforcing body rest in animals with high metabolic rates (this is certainly what hibernation does by drastically reducing body and brain metabolism, shutting off brain activity, and reducing temperature). However, in humans the metabolic savings of spending the night asleep rather than quietly awake are no more than a slice of bread (Horne, 1980). Moreover, we also say we are tired after muscle exertions, yet most bodily organs can recover through quiet wakefulness and do not need sleep. The notable exception is the brain: If we do not sleep, even though we may remain immobile, we rapidly suffer cognitive impairment. Therefore, most researchers agree that sleep may be especially important for restoring the brain and provide something not afforded by quiet waking. However, there is great uncertainty when it comes to what might actually accumulate (or deplete) during waking and be restored during sleep. A long search for humoral factors that might accumulate in the brain during wakefulness has not been successful (Borbély & Tononi, 1998). One of the best-studied substances is adenosine, not surprising given the well-known anti-sleep

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effect of the A1 antagonist caffeine (Basheer et al., 2004; Porkka-Heiskanen, Alanko, Kalinchuk, & Stenberg, 2002). Extracellular adenosine accumulates in the basal forebrain area during wakefulness, inhibiting cholinergic neurons and promoting sleep (Porkka-Heiskanen et al., 1997), although the importance of this feedback mechanism has recently been disputed (Blanco-Centurion et al., 2006). Also, in humans, extracellular adenosine does not seem to accumulate in several brain areas as a function of previous wakefulness (Zeitzer et al., 2006). Prostaglandin D2, another sleep promoting substance, acting on the prostaglandin D (PGD) receptor, indirectly activates adenosine A2A-dependent pathways in the basal forebrain (Huang, Urade, & Hayaishi, 2007). However, neither A1 nor PGD receptor knockout mice have abnormal baseline sleep. Similarly, a number of lymphokines, such as interleukin1 (IL-1) and tumor necrosis factor (TNF) alpha, modulate sleep. These effects are often species-specific and could be most relevant in the context of acute inflammation or infection. However, the TNF and IL-1 type I receptor knockouts have abnormal sleep, suggesting also a role in baseline sleep regulation (Krueger, Obal, Fang, Kubota, & Taishi, 2001). As an alternative, it has been suggested that sleep may favor not so much the elimination of some toxic factors accumulated during wakefulness, but rather the replenishment of some important resource, for instance glycogen in glial stores (Benington & Heller, 1995). However, studies show that glycogen depletion may only occur in a few brain regions and only in certain strains of animals (Franken, Gip, Hagiwara, Ruby, & Heller, 2003, 2006; Gip, Hagiwara, Ruby, & Heller, 2002). The molecular changes that take place between wakefulness and sleep suggest other possibilities as well (Cirelli et al., 2004): Sleep could counteract synaptic fatigue by favoring the replenishment of calcium in presynaptic stores, the replenishment of glutamate vesicles, the resting of mitochondria, the synthesis of proteins, or the trafficking and recycling of membranes. Unfortunately, most of these possibilities remain unexplored. Sleep and Synaptic Homeostasis Memory consolidation and brain restitution are important perspectives on the function of sleep that are not mutually exclusive. Recently, a comprehensive hypothesis concerning the function of NREM sleep has been advanced, the synaptic homeostasis hypothesis (Tononi & Cirelli, 2003, 2006; Figure 24.6). The hypothesis, which is broadly consistent with a large body of evidence, also makes specific suggestions concerning the mechanisms leading to the increase of SWA as a function of prior wakefulness.

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Figure 24.6 Synaptic homeostasis hypothesis. Note. Synapitc strength increases during wakefulness (W) and is downscaled during sleep (S). The hypothesis proposes a close relationship between synaptic strength and sleep slow-wave activity. From “Sleep Function and Synaptic Homeostasis,” by G. Tononi and C. Cirelli, 2006, Sleep Medicine Reviews, 10, p. 50. Reprinted with permission.

The synaptic homeostasis hypothesis proposes that plastic processes occurring during wakefulness result in a net increase in synaptic strength in many brain circuits. The main function performed by sleep is to downscale synaptic strength to a baseline level that is energetically sustainable and beneficial for memory and performance. In other words, according to the synaptic homeostasis hypothesis, sleep is the price we have to pay for plasticity, and its goal is the homeostatic regulation of the total synaptic weight impinging on neurons. An appealing feature of the synaptic homeostasis hypothesis is that it reconciles the restorative, homeostatic function of sleep with its beneficial effects on learning and memory. The main points of the hypothesis are as follows. During wakefulness, we interact with the environment and acquire information about it. The EEG is activated, neurons are tonically depolarized and spontaneously active (Steriade et al., 1993), and the neuromodulatory milieu (e.g., a high level of noradrenaline [NA]; Cirelli & Tononi, 2004) favors the storage of information, which occurs largely through synaptic potentiation (Trachtenberg et al., 2002). This potentiation occurs when the firing of a presynaptic neuron is followed by the depolarization or firing of a postsynaptic neuron, and the neuromodulatory milieu signals the occurrence of salient events (Bliss & Collingridge, 1993; Bliss & Lomo, 1973). A key functional corollary of the hypothesis is that, due to the net increase in synaptic strength, waking plasticity has a cost in terms of energy requirements, space requirements, supplies of key cellular constituents, and progressively saturates our capacity to learn. When we go to sleep, we become virtually disconnected from the environment (Steriade et al., 1993). Changes in neuromodulatory milieu trigger slow oscillations, comprising depolarized

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and hyperpolarized phases, which affect every neuron in the cortex, and that are reflected in the EEG as SWA (Steriade & Timofeev, 2003). The changed neuromodulatory milieu (e.g., low NA; Aston-Jones & Bloom, 1981; Cirelli & Tononi, 2004) also ensures that synaptic activity is not followed by synaptic potentiation, which makes adaptive sense given that synaptic activity during sleep is not driven by interactions with the environment. Since the average strength of synaptic interactions at the end of the wake period is high, neurons synchronize their firing better and the slow oscillations of early sleep are of high amplitude (Esser, Hill, & Tononi, 2007). The slow oscillations, however, are not just an epiphenomenon of increased synaptic strength, but would have a role to play. Specifically, the repeated sequences of depolarization—hyperpolarization would lead to the downscaling of the synapses impinging on each neuron (Turrigiano & Nelson, 2000, 2004), meaning that they all would decrease in strength proportionally. The reduced synaptic strength reduces the amplitude and synchronization of the slow oscillations, which is reflected in a reduced SWA in the sleep EEG. Because of the dampening of the slow oscillation, downscaling is progressively reduced, making the process self-limiting when synaptic strength reaches a baseline level. By returning total synaptic weight to an appropriate baseline level, sleep enforces synaptic homeostasis. Again, the key functional corollary is that synaptic homeostasis has benefits in terms of energy and space requirements, of the supply of key cellular constituents and, due to increased signal-tonoise ratios, in terms of learning and memory. Thus, when we wake up, neural circuits do preserve a trace of previous experiences, but are kept efficient at a recalibrated level of synaptic strength, and the cycle can begin again. The synaptic homeostasis hypothesis is based on a large number of observations at many different levels, from molecular and cellular biology to systems neurophysiology and neuroimaging (for more details, see Tononi & Cirelli, 2003, 2006). The best electrophysiological and molecular evidence comes from a study in rats showing that wakefulness is associated with markers of cortical synaptic potentiation (e.g., increased number of synaptic AMPARs containing GluR1 subunits), whereas sleep is associated with markers of synaptic depression (e.g., dephosphorylation of synaptic GluR1; Vyazovskiy, Cirelli, Pfister-Genskow, Faraguna, & Tononi, 2008). Moreover, the slopes of cortical evoked potentials, reflecting cortical excitability, increased after wakefulness and decreased after sleep. Other electrophysiological and behavioral evidence support the hypothesis (Hairston et al., 2004; Huber et al., 2006; Huber, Ghilardi, et al., 2004; Huber, Tononi, & Cirelli, 2007), but there are alternative explanations, and critical tests still need to be performed.

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Figure 24.7 Sleep duration.

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Note. Histogram of the reported sleep duration of more than 1 million adult Americans from the Cancer Prevention Study I. From “Short and Long Sleep and Sleeping Pills: Is Increased Mortality Associated?” by D. F. Kripke, R. N. Simons, L. Garfinkel, E. C. Hammond, 1979, Archives of General Psychiatry, 36, pp. 103–16. Adapted with permission.

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Large surveys show that the average sleep duration for adult humans is about 7 to 8 hours (Figure 24.7; Kripke, Simons, Garfinkel, & Hammond, 1979). However, sleep duration is widely distributed: some people get along perfectly with 5 hours while others need more than 10 hours. There are indications that true short sleepers live under higher sleep pressure than long sleepers (Aeschbach, Cajochen, Landolt, & Borbély, 1996; Aeschbach et al., 2001). Such studies point to a genetic determination of sleep duration (Linkowski, Kerkhofs, Hauspie, Susanne, & Mendlewicz 1989; Partinen, Kaprio, Koskenvuo, Putkonen, & Langinvainio, 1983). In general, only a small percentage of sleep (e.g., ⫾1h) can be changed by training without an individual suffering the detrimental effects of chronic sleep deprivation (Banks & Dinges, 2007). Intriguingly, sleep duration is related to mortality. Epidemiologic studies have consistently shown that sleeping more than 8 hours per night is associated with increased mortality (Kripke et al., 2002; Youngstedt & Kripke, 2004). Youngstedt and Kripke state that “were long sleep the cause of all deaths with which it has been associated, it would be the fourth leading cause of death in the U.S.” (p. 160).

Real-time ultrasonography has made systematic and prolonged behavioral observations of the undisturbed human fetus possible. At 8 to 12 weeks, the human fetus displays episodic spontaneous movements, which are cycles of activity interspersed with periods of quiescence. These cycles increase in duration and become more regular from the mid-trimester onward (Dierker, Rosen, Pillay, & Sorokin, 1982). More specifically, the increasing synchronization of cyclic motor activity with periodic changes in heart rate and eye movements as gestation advances are considered a milestone in central nervous system development (Visser, Poelmannweesjes, Cohen, & Bekedam, 1987). At a gestational age of 16 to 20 weeks, a healthy fetus shows pronounced daily rhythms of the heartbeat and locomotor activity (Kintraia, Zarnadze, Kintraia, & Kashakashvili, 2005). The part of the brain that orchestrates such circadian rhythmicity is the suprachiasmatic nucleus (SCN) in the anterior hypothalamus. The SCN cells receive information about the presence and intensity of light in the environment via specialized retinal ganglion cells. The ganglion cells project to the SCN directly via the optic tract as well as via the intergeniculate leaflet and raphe nuclei. Because these connections are made late in human gestation, the developing fetus relies on maternal rhythms. Although information about humans is lacking, the fetal SCN of rodents is rhythmic in the absence of a functional input pathway (Reppert & Schwartz, 1984). The fetal SCN cells are exposed to maternal hormone rhythms (e.g., melatonin; Zemdegs, McMillen, Walker, Thorburn, & Nowak, 1988) and possibly to fetal pituitary hormone

DEVELOPMENT OF THE SLEEP-WAKING CYCLE AND CHANGES DURING THE LIFE SPAN Many aspects of sleep change during the life span. Next, we consider how sleep develops in utero, during childhood and adolescence, and how it changes with old age.

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rhythms, again in response to the mother ’s response to the environment. Newborn and Infants: From Arrhythmicity to the Appearance of Rhythms At birth, humans are essentially arrhythmic showing hardly any circadian organization of sleep and wakefulness (Figure 24.8; Swaab, Hofman, & Honnebier, 1990). This arrhythmicity is present in spite of the fact that cells in the SCN are likely already rhythmic (see Reppert & Schwartz, 1984), probably due to underdeveloped input/ output pathways. The lack of rhythmicity is evidenced by the equal distribution of sleep across day and night. However, some evidence for day/night differences in sleep and waking might be found as early as in the first days of life in some babies (Jenni, Deboer, & Achermann, 2006). In general, the lack of consolidated sleep is accompanied by the absence of hormonal and body temperature rhythmicity (Rivkees & Hao, 2000). Longitudinal studies have shown that these rhythms appear around 9 to 12 weeks of age in humans (Kennaway, Stamp, & Goble, 1992), and that the appearance of these rhythms is accompanied by the ability to sustain longer episodes of sleep and wakefulness (Kleitmann & Engelmann, 1953; Parmelee, 1961). This sleep consolidation is accompanied by an increase in sleep during the night and decrease during the day (Iglowstein, Jenni, Molinari, & Largo, 2003). Besides the influence of the circadian system, the homeostatic regulation of sleep and daily parental activities such as feeding, can exert an influence on the development of the 24-hour sleep-wake cycle.

Circadian Rhythmicity in Childhood and Adolescence The circadian timing system remains stable in the course of childhood. However, during puberty, distinct changes occur influencing the phase of the circadian timing system. Teenagers commonly show a prominent phase delay (Carskadon & Acebo, 2002). The mechanism of this phase delay is not entirely clear but may include a phase delay of the intrinsic circadian rhythm (Carskadon, Acebo, Richardson, Tate, & Seifer, 1997), a lengthening of the intrinsic period of the circadian clock (Carskadon, Acebo, & Jenni, 2004), and a heightened sensitivity to evening light or decreased sensitivity to morning light (Jenni & Carskadon, 2007). This change in the circadian rhythm in adolescents is often accompanied by difficulties falling asleep and/or daytime sleepiness (Carskadon et al., 1980) and might be best explained by the interaction of the circadian and homeostatic process of sleep regulation (see two process model; Borbély, 1982). Although it is generally accepted that the circadian and the homeostatic process of sleep regulation are independent processes, they interact in a complex way to control vigilance states and sleep timing. Thus, the increase of the homeostatic sleep pressure during wakefulness is opposed by the increasing circadian alertness in the course of the day. This interaction allows adults to maintain a constant level of vigilance throughout the waking period. In contrast, during sleep, the increasing circadian sleep tendency counteracts the declining homeostatic sleep pressure, thereby ensuring sleep maintenance. During adolescence, a delay in the circadian phase reorganizes the alignment of the two processes such that the

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Note. Diurnal sleep-wake pattern during the first 425 days after birth of a healthy male infant. Black and white areas represent sleep and waking, respectively, as recorded by daily sleep logs. Gray dots indicate feeding episodes. From “Sleep Behavior and Sleep Regulation from Infancy through Adolescence: Normative Aspects,” by O. G. Jenni and M. A. Carskadon, 2007, Sleep Medicine Clinics. (Jenni & Carskadon, 2007), p. 322.

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Total Sleep Duration (hours)

increasing circadian sleep tendency as well as the circadian evening rise in alertness occur later, thus rendering even a well-slept adolescent sleepy in the morning hours and reluctant to fall asleep at night. In other words, adolescents assume the chronotype characteristic of night people (owls). End of adolescence is associated with a change in chronotype whereby young adults assume an early–rising profile (larks; Roenneberg et al., 2004). The Delayed Sleep Phase Syndrome (DSPS) may be a distinct clinical entity or an extreme manifestation of phase delaying in adolescents. DSPS is typically diagnosed during the second decade of life or earlier (Thorpy, Korman, Spielman, & Glovinsky, 1988). Weitzman and colleagues 20

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Figure 24.9 Sleep duration. Note. Percentiles for total sleep duration per 24 hours from infancy to adolescence. Sleep log data was obtained from 493 subjects of the Zurich Longitudinal Studies (Largo et al., 1996). From “Sleep Duration from Infancy to Adolescence: Reference Values and Generational Trends,” by I. Iglowstein, O. G. Jenni, L. Molinari, and R. H. Largo, 2003, Pediatrics, 111, p. 303 Reprinted with permission.

(1981) first characterized DSPS as a cluster of features including: a chronic inability to fall asleep and awaken at a desired clock time, consistency in reporting sleep times at later hours than other individuals, and otherwise normal sleep when measured by all-night polysomnography, if the delayed schedule is allowed. This disorder, with a prevalence of 0.1% to 3.1% (Wyatt, 2004), is significant because it has the highest prevalence during adolescence and school performance is often compromised. Changes in Sleep Duration On average, infants spend more than 50% of the time asleep (Figure 24.9). During the first year of life, total sleep duration remains relatively constant (Figure 24.9). However, the large variability of total sleep in infants decreases over time; during the first 1 to 2 years, the variability of sleep duration goes from 9 to 19 hours at 1 month to 11 to 16 hours at 2 years (Iglowstein et al., 2003). Early childhood is typically characterized by a decrease in sleep length, which at first is mainly the consequence of a reduction in daytime naps (Iglowstein et al., 2003). Most children stop napping between the ages of 3 and 5, though large cultural differences exist (Jenni & Carskadon, 2007). However, even after these ages, sleep duration continues to decrease, from an average of 14 hours in the first month to an average of 8 hours in 16-year-olds (Iglowstein et al., 2003; Klackenberg, 1982). The subsequent decline in sleep duration in the years from early adulthood into old age is moderate, 1 to 2 hours, and data collection methods may influence our knowledge of this period (Roffwarg, Muzio, & Dement, 1966). Several studies indicate that there are no significant gender differences in sleep duration during childhood (e.g., Klackenberg, 1982; Iglowstein et al., 2003).

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Figure 24.10 Distribution of vigilance states across life.

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Changes in Sleep Architecture Differences in total sleep duration across the life span are not the only changes that occur; there are also alterations in the proportion of sleep stages. In the first few months of life, infant sleep is divided evenly between NREM and REM sleep (Figure 24.10). From early childhood until adolescence, the proportion of REM sleep decreases, reaching an adult level of about 20% to 25% of nocturnal sleep (Jenni & Carskadon, 2007; Roffwarg et al., 1966). Furthermore, the composition of NREM sleep changes, in that SWS (NREM sleep stages N3) increases until puberty and subsequently shows an exponential decline during adolescence (Feinberg, 1982). The sequence of sleep stages when falling asleep also changes during infancy: When young infants fall asleep, the initial sleep stage is typically REM sleep. After 3 months, sleep onset REM periods are replaced by NREM periods, as is the case in adults (Jenni & Carskadon, 2007). In the first 6 months after birth, due to frequent muscle twitches and body jerks that break through muscle inhibition, REM sleep is also called active sleep. NREM sleep is referred to as quiet sleep. In newborns, quiet and active sleep are often disorganized and immature and thus called indeterminate or transitional sleep. However, by using fetal ultrasound, behavioral observations, and fetal heart rate monitoring, quiet and active sleep can be differentiated as early as 32 weeks gestation (Mulder, Visser, Bekedam, & Prechtl, 1987; Visser et al., 1987). Between 32 and 40 weeks of gestation, quiet sleep increases and indeterminate sleep decreases (Mulder et al., 1987). The alternation between NREM and REM sleep cycles, the ultradian sleep rhythm, is already present in newborns. The period of this ultradian rhythm gradually lengthens during childhood, from about 50 minutes in infancy, to 90 to 110 minutes around school age (Jenni & Carskadon, 2007). Large amplitude slow-wave sleep dominates the sleep cycles early at night and REM sleep the last part of the night. In early infancy, subsequent NREM sleep episodes may alternate between having high and low amounts of slow waves (Bes, Schulz, Navelet, & Salzarulo, 1991; Jenni, Borbély, & Achermann, 2004). The mechanism underlying this alternating pattern is unknown.

whereas sleep duration remains constant. Similar modifications in the homeostatic regulation of sleep are found during the human neonatal period. For example, selective or total sleep deprivation in human neonates leads to compensatory increases in NREM sleep duration only (Anders & Roffwarg, 1973; Thomas et al., 1996). Exactly when sleep deprivation produces increases in EEG slow-wave activity in human neonates is still unknown. A decline in SWA in the course of the night is first visible during the second postnatal month or even later (Bes et al., 1991). A decline in the theta frequency range (4.5 to 7 Hz) during the night may represent the first indication of homeostatic regulation (Jenni et al., 2004). Tolerance to Increased Sleep Pressure In general, neonates are unable to maintain consolidated bouts of waking comparable to those typically observed in adults. In humans, short periods of sleep deprivation that have negligible effects in adults produce compensatory increases in sleep time and/or intensity during recovery (Anders & Roffwarg, 1973; Thomas et al., 1996). Young rats (P23) show an increase of SWA after only 2 hours of sleep deprivation that is just as large as that observed after 6 hours in postpubertal rats (P40) (Alfoldi, Tobler, & Borbély, 1990). These findings suggest either that the saturation level of sleep pressure is lower during infancy or that sleep pressure accumulates at a greater rate in infancy compared with adulthood. Indeed, modeling of the dynamics of sleep homeostasis according to the twoprocess model of sleep regulation suggests that the build up of homeostatic sleep pressure during wakefulness is faster in prepubertal children compared with young adolescents (Jenni, Achermann, & Carskadon, 2005). In contrast, the decline of the homeostatic process is similar in both groups. The faster increase of sleep pressure in young children may reduce the ability for young children to stay awake at the end of the day, suggesting that they live under higher sleep pressure. Sleep Slow Waves and Brain Plasticity during Development

Homeostatic Response to Sleep Deprivation In both animals and humans, the initial response to sleep deprivation is an increase in sleep duration; changes in sleep intensity are observed only at a later stage. For example, when very young rats (P12) are sleep deprived, they mainly compensate the sleep depth by increasing sleep duration (Frank, Morrissette, & Heller, 1998). Twelve days later (P24), however, sleep deprivation results in an increase in sleep SWA, as is the case in adult animals,

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The amplitude of slow waves in the sleep EEG increases during childhood. Then, during puberty, there is a dramatic decline in the amplitude of sleep slow waves and SWA (Campbell, Darchia, Khaw, Higgins, & Feinberg, 2005; Feinberg, Higgins, Khaw, & Campbell, 2006; Jenni & Carskadon, 2004). While the factors underlying such changes are not clear, increasing evidence points to the importance of changes in neural plasticity. During early childhood, neurons grow bushier and establish more numerous

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Moreover, the development of sleep homeostasis may be directly related to the appearance of synaptic mechanisms leading to long-term potentiation in association with waking exploratory activities. A developmental study found that sleep deprivation in rats is followed by an induction of cortical BDNF at postnatal day 24, the age when SWA showed a compensatory response, but not at postnatal day 16 or 20, the age when no SWA rebound occurred (Hairston et al., 2004). This finding is important because BDNF is necessary for the induction of synaptic potentiation (Aicardi et al., 2004; Barco et al., 2005), and the level of BDNF induction during waking activities is positively correlated with the amount of SWA during subsequent

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connections to other cells (De Felipe, Marco, Fairen, & Jones, 1997). Moreover, axons initially explore areas much wider than their final targets (Gao, Yue, Cerretti, Dreyfus, & Zhou, 1999). Then, in the course of adolescence, more synapses are eliminated than formed (Zuo, Lin, Chang, & Gan, 2005), in part through activity-dependent processes (Hua & Smith, 2004). Synaptic pruning during adolescence is accompanied by a reorganization of neuronal connections whereby mistargeted axons and unused synapses are eliminated, and connectivity becomes more specific. The decrease of synaptic density during adolescence, which is reflected in changes in grey matter, proceeds asynchronously in different brain areas (Paus, 2005), in line with the maturation of specific cognitive functions (Shaw et al., 2006). As illustrated in Figure 24.11, changes in slow-wave amplitude are paralleled by changes in synaptic density (Feinberg, 1982; Huttenlocher & Dabholkar, 1997). This observation has been confirmed both in humans and in rats (Glantz, Gilmore, Hamer, Lieberman, & Jarskog, 2007; Nakamura, Kobayashi, Ohashi, & Ando, 1999). Moreover, glucose consumption shows a similar profile (Figure 24.12; Chugani, 1998), presumably due to the increased energy requirements associated with increased synaptic activity. As suggested by the synaptic homeostasis hypothesis (Tononi & Cirelli, 2006) and confirmed by computer simulations and experimental studies in both humans and rats, changes in synaptic efficacy can account for the observed changes in sleep slow waves (Esser et al., 2007; Riedner et al., 2007; Vyazovskiy, Riedner, Cirelli, & Tononi, 2007). Thus, sleep slow-wave activity could be taken as a reliable indicator of net changes in average synaptic density/ strength both in the course of the night (sleep homeostasis) and in the course of development.

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Note. The development of the amplitude of slow waves is reproduced from (Feinberg, 1982), the changes in synapse density is reproduced from (Huttenlocher & Dabholkar, 1997). For the slow-wave amplitude, each point represents the mean of 50 waves for one subject selected as described in (Feinberg et al., 1967). Synapse density was obtained from a postmortem specimen of normal human brains.

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sleep (Huber et al., 2007). In humans, the increase of BDNF mRNA levels in the dorsolateral prefrontal cortex during adolescence coincides with the time when the frontal cortex matures both structurally and functionally (Webster, Weickert, Herman, & Kleinman, 2002). Changes in sleep parameters may not only reflect plastic processes during development, but may play an active role in shaping such processes. Frank et al. (2001) showed that sleep greatly enhanced the synaptic changes induced by a preceding period of monocular deprivation, while wakefulness in complete darkness did not. In another set of experiments, the same group showed that ocular dominance plasticity was significantly reduced in cats whose visual cortices were reversibly silenced during sleep (Jha et al., 2005). These findings demonstrate that the mechanisms governing this form of plasticity requires specific cortical activity during sleep. Dark-rearing of cats and mice produced a robust and reversible decrement of slow-wave electrical activity during sleep that was restricted to the visual cortex and impaired by gene-targeted reduction of NMDA receptor function (Miyamoto, Katagiri, & Hensch, 2003). A role for sleep in brain plasticity during development is not necessarily limited to slow-wave sleep. REM sleep is more abundant during periods of rapid brain development and synaptic plasticity than at any other time of life (Frank & Heller, 1997; Jouvet-Mounier, Astic, & Lacote, 1970; Roffwarg et al., 1966; Shaw et al., 2000). Indeed, several experiments employing REM sleep deprivation in animals have suggested that REM sleep may play a key role in maturational processes (Blumberg & Lucas, 1996; Mirmiran et al., 1983; Shaffery et al., 1998; Shaffery, Sinton, Bissette, Roffwarg, & Marks, 2002). For example, 1 week of REM sleep deprivation in immature rats prolonged the critical period for plasticity of the visual system and can alter the balance between inhibitory and excitatory mechanisms in the visual cortex (Shaffery, Lopez, Bissette, & Roffwarg, 2006; Shaffery et al., 2002). Furthermore, besides slow waves during NREM sleep, sleep spindles also seem to be involved in plastic processes (Sirota, Csicsvari, Buhl, & Buzsaki, 2003) and were suggested as an indicator of the severity of developmental disorders in mental retardation and abnormal maturational processes (De Gennaro & Ferrara, 2003). For example, mentally retarded children show a decreased spindle density as compared to normal full-term children (Shibagaki, Kiyono, & Watanabe, 1982).

evidence in favor of such a relationship is becoming available. For example, a positive correlation between increased sleep/earlier bedtimes and higher school grades was found in a representative population of high school students (Wolfson & Carskadon, 1998). Moreover, actigraphy, an objective measure for evaluating sleep patterns, revealed that sleep fragmentation correlates significantly with daytime sleepiness, attentional deficits, and learning impairments (Sadeh, Raviv, & Gruber, 2000). Such effects seem to be more evident in younger children (Sadeh, Gruber, & Raviv, 2002). Finally, evidence for a link between sleep and cognition during childhood comes from the study of sleep-disorder breathing (SDB; O’Brien et al., 2004). Several studies have shown an association between SDB, attention deficits, excessive daytime sleepiness, cognitive impairment, and poor learning in children (Halbower & Mahone, 2006; Urschitz et al., 2003). In addition, habitual snoring is found in 1 in 10 primary school children (Urschitz et al., 2003) and is associated with sleep fragmentation (Halbower & Mahone, 2006). Children who snored habitually had at least twice the risk of performing poorly at school. Notably, this association became stronger with increased snoring frequency (Urschitz et al., 2003). The relationship between habitual snoring and poor academic performance did not appear to be mediated via intermittent hypoxia because it was not diminished after excluding children with intermittent hypoxia in an overnight study (Urschitz et al., 2003). Finally, there is evidence for earlyonset sleep disturbances in children with several neurological and psychiatric disorders. For example, based on parent reports, autistic children show a prevalence of 44% to 83% for sleep disorders (Gail Williams, Sears, & Allard, 2004). Another example is the Fragile X Syndrome (FraX), the most common inherited cause of mental retardation. FraX patients show abnormal dendritic spine morphology, with more long and thin spines. Fragile X boys have difficulty in sleep maintenance compared to control subjects (Gould et al., 2000), suggesting again a possible link between sleep, synapses, and cognitive function. In conclusion, the evidence for an association between sleep and cognitive development is intriguing, but it remains largely correlative, and the long-term effects of primary disturbances of sleep is unknown. Moreover, we still ignore whether there are critical time windows of cortical development that are particularly sensitive to sleep disturbances.

Sleep and Cognitive Functioning during Development

Old Age and Sleep

If sleep does play a critical role in brain development and learning, then sleep disorders, sleep restriction, and sleep loss early in life should impair cognitive functioning. Some

It is well known that memory function in general, and declarative memory in particular, progressively declines after the age of 30 (Prull, Gabrieli, & Bunge, 2000). This age-related

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Development of the Sleep-Waking Cycle and Changes during the Life Span 475

Sleep Disorders It is estimated that 15% to 35% of people suffer from sleep disturbances (Weyerer & Dilling, 1991), ranging from breathing disorders such as sleep apnea (Banno & Kryger, 2007), abnormal motor behaviors including restless legs syndrome (Montplaisir, 2004), insomnia due to hyperarousal (Roth, Roehrs, & Pies, 2007), and narcolepsy (Dauvilliers, Arnulf, & Mignot, 2007). The prevalence of sleep disorders changes across life. For example, the onset of insomnia seems to take place around puberty (Johnson, Roth, Schultz, & Breslau, 2006), and the percentage of people suffering from insomnia increases with age, from about 2% in adolescents to about 27% in persons 70⫹ years of age (Figure 24.13; Weyerer & Dilling, 1991). There are also clear gender differences, with females showing a much stronger increase in insomnia prevalence than males. An increase in insomnia prevalence is also observable in psychiatric

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Figure 24.13 Insomnia prevalence across life span. Note. Moderate to severe insomnia within last 7 days. For both sexes, the prevalence of insomnia increases with age. From “Prevalence and Treatment of Insomnia in the Community: Results from the Upper Bavarian Field Study,” by S. Weyerer and H. Dilling, 1991, Sleep, 14, 392–398. Adapted with permission.

14

14 Girls Boys

12 Cumulative Incidence

decline in memory performance is accompanied by structural and functional changes in cortical and subcortical areas (Hedden & Gabrieli, 2004; Hof & Morrison, 2004). Once again, changes in brain structure reflecting a decrease in synaptic density are associated with a continuous decrease in sleep slow-wave activity after 30 years of age (Carrier, Land, Buysse, Kupfer, & Monk, 2001; Feinberg, 1982; Landolt, Dijk, Achermann, & Borbely, 1996). Moreover, the sleep-dependent enhancement of declarative memory that occurs in young subjects decreases during midlife, in line with a decrease in early nocturnal SWS (Backhaus et al., 2007). However, sleep homeostasis seems to be intact in old age: relative SWA changes in response to high sleep pressure (sleep deprivation) and low sleep pressure (naps) were similar in old and young subjects (Cajochen, Munch, Knoblauch, Blatter, & Wirz-Justice, 2006). Unlike SWS, REM sleep does not change much after age 30 (Van Cauter, Leproult, & Plat, 2000). Since the near-complete loss of REM sleep due to brain stem lesions (Vertes & Eastman, 2000) or to antidepressant treatment does not seem to have significant consequence on memory performance, it is possible that the connection between REM sleep and plasticity may be limited to early development. Finally, old age is associated with a weakening of circadian regulation, as suggested by the diminished secretion of melatonin—the major circadian signal in old subjects (Cajochen et al., 2006)—and a degeneration of the SCN observed in human postmortem studies (Hofman & Swaab, 2006). A dysregulation of the circadian timing system is especially pernicious in Alzheimer ’s disease because it impairs the maintenance of a normal sleep-wake cycle (Mishima et al., 1999), with further negative consequences on memory (Cole & Richards, 2005).

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Figure 24.14 Age at onset of insomnia. Note. Cumulative incidence of insomnia by gender. Females reported a significantly older median age at onset of insomnia (age 12) than did males (age 10). From “Epidemiology of DSM-IV Insomnia in Adolescence: Lifetime Prevalence, Chronicity, and an Emergent Gender Difference,” by E. O. Johnson, T. Roth, L. Schultz, and N. Breslau, 2006, Pediatrics, 117, e247–e256. Adapted with permission.

disorders—about 50% of patients suffering from depressive disorders also suffer from insomnia (Weyerer & Dilling, 1991), although it is not yet clear whether chronic insomnia is causally related to the development of depression. Other sleep disturbances show an age-specific incidence. Thus, parasomnias like sleepwalking are common during childhood (Kales, Soldatos, & Kales, 1987; Mahowald & Rosen, 1990), adolescence is associated with the occurrence of narcolepsy and the delayed sleep phase syndrome (Crowley, Acebo, & Carskadon, 2007; Dauvilliers et al., 2007),

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obstructive sleep apnea (Tabba & Johnson, 2006) normally occurs in adults, and REM sleep behavior disorders appear more commonly in older adults, often anticipating by many years the development of Parkinson’s disease (Mahowald, Schenck, & Bornemann, 2007). A large percentage of sleep disorders, especially related to old age can be attributed to bad sleep hygiene. Among the measures for improving sleep hygiene are: maintaining a regular daily schedule of activities, utilizing the bedroom for rest and sleep rather than conflict and worry, and improving the sleep environment by minimizing noise and disruptions. Also, regular exercise, not close to bedtime, has been shown to increase early night slow-wave sleep in normal sleepers. In the elderly, special instructions include education regarding effects of age on sleep patterns; discouraging multiple naps; and suggesting daytime activities such as hobbies and special interests (Kamel & Gammack, 2006; Vgontzas & Kales, 1999).

SUMMARY Sleep is an active process that is tightly regulated. Thus, every night we cycle through a seemingly predefined series of discrete states (NREM and REM sleep) each with its characteristic activity pattern. Sleep need is also regulated and depends to a large extent on how long we stay awake. Moreover, the longer we stay awake the more intense sleep becomes, which is reflected by the homeostatic regulation of slow-wave activity (SWA), a quantification of the low-frequency (⬍4 Hz) EEG components. A recent hypothesis about the function of sleep now considers that there is a cellular need for sleep that is triggered by the induction of plastic changes during wakefulness. Our brain undergoes prominent plastic changes across a life span. A major change is the increase in connectivity until puberty and its decrease thereafter. Interestingly, the change in connectivity is paralleled by changes in sleep intensity, i.e. the amount of SWA, during childhood and adolescence. Thus, the question arises whether there exists a relationship between sleep and brain maturation.

Aeschbach, D., Postolache, T. T., Sher, L., Matthews, J. R., Jackson, M. A., & Wehr, T. A. (2001). Evidence from the waking electroencephalogram that short sleepers live under higher homeostatic sleep pressure than long sleepers. Neuroscience, 102, 493–502. Aicardi, G., Argilli, E., Cappello, S., Santi, S., Riccio, M., Thoenen, H., et al. (2004). Induction of long-term potentiation and depression is reflected by corresponding changes in secretion of endogenous brainderived neurotrophic factor. Proceedings of the National Academy of Sciences, USA, 101, 15788–15792. Alfoldi, P., Tobler, I., & Borbély, A. A. (1990). Sleep regulation in rats during early development. Journal of General Physiology, 258, R634–R644. Anders, T. F., & Roffwarg, H. P. (1973). The effects of selective interruption and deprivation of sleep in the human newborn. Developmental Psychobiology, 6, 77–89. Aserinsky, E., & Kleitman, N. (1953, September 4). Regularly occurring periods of eye motility, and concomitant phenomena, during sleep. Science, 118, 273–274. Aston-Jones, G. (2005). Brain structures and receptors involved in alertness. Sleep Medicine, 6(Suppl, 1), S3–S7. Aston-Jones, G., & Bloom, F. E. (1981). Activity of norepinephrine-containing locus coeruleus neurons in behaving rats anticipates fluctuations in the sleep-waking cycle. Journal of Neuroscience, 1, 876–886. Backhaus, J., Born, J., Hoeckesfeld, R., Fokuhl, S., Hohagen, F., & Junghanns, K. (2007). Midlife decline in declarative memory consolidation is correlated with a decline in slow wave sleep. Learning and Memory, 14, 336–341. Banks, S., & Dinges, D. F. (2007). Behavioral and physiological consequences of sleep restriction. Journal of Clinical Sleep Medicine, 3, 519–528. Banno, K., & Kryger, M. H. (2007). Sleep apnea: Clinical investigations in humans. Sleep Medicine, 8, 400–426. Barco, A., Patterson, S., Alarcon, J. M., Gromova, P., Mata-Roig, M., Morozov, A., et al. (2005). Gene expression profiling of facilitated L-LTP in VP16-CREB mice reveals that BDNF is critical for the maintenance of LTP and its synaptic capture. Neuron, 48, 123–137. Barger, L. K., Ayas, N. T., Cade, B. E., Cronin, J. W., Rosner, B., Speizer, F. E., et al. (2006). Impact of extended-duration shifts on medical errors, adverse events, and attentional failures. PLoS Medicine, 3, e487. Basheer, R., Strecker, R. E., Thakkar, M. M., & McCarley, R. W. (2004). Adenosine and sleep-wake regulation. Progress in Neurobiology, 73, 379–396. Benington, J. H., & Heller, H. C. (1995). Restoration of brain energy metabolism as the function of sleep. Progress in Neurobiology, 45, 347–360. Berger, H. (1929). On the electroencephalogram of man. Archives of Psychiatrie, 87, 527–570. Bes, F., Schulz, H., Navelet, Y., & Salzarulo, P. (1991). The distribution of slow-wave sleep across the night: A comparison for infants, children, and adults. Sleep, 14, 5–12. Blake, H., & Gerard, R. W. (1937). Brain potentials during sleep. American Journal of Physiology, 119, 692–703.

REFERENCES Achermann, P., & Borbély, A. A. (2003). Mathematical models of sleep regulation. Frontiers in Bioscience, 8, S683–S693. Achermann, P., Dijk, D. J., Brunner, D. P., & Borbély, A. A. (1993). A model of human sleep homeostasis based on EEG slow-wave activity: Quantitative comparison of data and simulations. Brain Research Bulletin, 31, 97–113. Aeschbach, D., Cajochen, C., Landolt, H., & Borbély, A. A. (1996). Homeostatic sleep regulation in habitual short sleepers and long sleepers. Journal of General Physiology, 270, R41–R53.

c24.indd 476

Blanco-Centurion, C., Xu, M., Murillo-Rodriguez, E., Gerashchenko, D., Shiromani, A. M., Salin-Pascual, R. J., et al. (2006). Adenosine and sleep homeostasis in the Basal forebrain. Journal of Neuroscience, 26, 8092–8100. Bliss, T. V., & Collingridge, G. L. (1993, January 7). A synaptic model of memory: Long-term potentiation in the hippocampus. Nature, 361, 31–39. Bliss, T. V., & Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. Journal of Physiology, 232, 331–356. Blumberg, M. S., & Lucas, D. E. (1996). A developmental and component analysis of active sleep. Developmental Psychobiology, 29, 1–22.

8/18/09 5:51:45 PM

References 477 Bonnet, M. H., & Arand, D. L. (2003). Clinical effects of sleep fragmentation versus sleep deprivation. Sleep Medicine Reviews, 7, 297–310.

De Gennaro, L. & Ferrara, M. (2003) Sleep spindles: an overview. Sleep Med Rev, 7, 423–440.

Borbély, A. A. (1982). A two process model of sleep regulation. Human Neurobiology, 1, 195–204.

Dement, W., & Kleitman, N. (1957). Cyclic variations in EEG during sleep and their relation to eye movements, body motility, and dreaming. Electroencephalography and Clinical Neurophysiology, 9, 673–690.

Borbély, A. A., & Achermann, P. (2000). Homeostasis of human sleep and models of sleep regulation. In M. H. Kryger, T. Roth, & W. C. Dement (Eds.), Principles and practice of sleep medicine (pp. 377–390). Philadelphia: Saunders. Borbély, A. A., & Tononi, G. (1998). The quest for the essence of sleep. Daedalus, 127, 167–196. Braun, A. R., Balkin, T. J., Wesenten, N. J., Carson, R. E., Varga, M., Baldwin, P., et al. (1997). Regional cerebral blood flow throughout the sleep-wake cycle. An H2(15)O PET study. Brain, 120(Pt. 7), 1173–1197. Cajochen, C., Munch, M., Knoblauch, V., Blatter, K., & Wirz-Justice, A. (2006). Age-related changes in the circadian and homeostatic regulation of human sleep. Chronobiology International, 23, 461–474. Campbell, I. G., Darchia, N., Khaw, W. Y., Higgins, L. M., & Feinberg, I. (2005). Sleep EEG evidence of sex differences in adolescent brain maturation. Sleep, 28, 637–643. Carrier, J., Land, S., Buysse, D. J., Kupfer, D. J., & Monk, T. H. (2001). The effects of age and gender on sleep EEG power spectral density in the middle years of life (ages 20–60 years old). Psychophysiology, 38, 232–242. Carskadon, M. A., & Acebo, C. (2002). Regulation of sleepiness in adolescents: Update, insights, and speculation. Sleep, 25, 606–614. Carskadon, M. A., Acebo, C., & Jenni, O. G. (2004). Regulation of adolescent sleep: Implications for behavior. Annals of the New York Academy of Sciences, 1021, 276–291. Carskadon, M. A., Acebo, C., Richardson, G. S., Tate, B. A., & Seifer, R. (1997). An approach to studying circadian rhythms of adolescent humans. Journal of Biological Rhythms, 12, 278–289. Carskadon, M. A., Harvey, K., Duke, P., Anders, T. F., Litt, I. F., & Dement, W. C. (1980). Pubertal changes in daytime sleepiness. Sleep, 2, 453–460. Chase, M. H., & Morales, F. R. (1990). The atonia and myoclonia of active (REM) sleep. Annual Review of Psychology, 41, 557–584. Chugani, H. T. (1998). A critical period of brain development: Studies of cerebral glucose utilization with PET. Preventive Medicine, 27, 184–188. Cirelli, C., Gutierrez, C. M., & Tononi, G. (2004). Extensive and divergent effects of sleep and wakefulness on brain gene expression. Neuron, 41, 35–43. Cirelli, C., Pompeiano, M., & Tononi, G. (1996, November 15). Neuronal gene expression in the waking state: A role for the locus coeruleus. Science, 274, 1211–1215. Cirelli, C., & Tononi, G. (2000). Differential expression of plasticityrelated genes in waking and sleep and their regulation by the noradrenergic system. Journal of Neuroscience, 20, 9187–9194.

Dinges, D. F. (2006). The state of sleep deprivation: From functional biology to functional consequences. Sleep Medicine Reviews, 10, 303–305. Esser, S. K., Hill, S., & Tononi, G. (2007). Sleep homeostasis and cortical synchronization: Pt. I. Modeling the effects of synaptic strength on sleep slow waves. Sleep, 30, 1617–1630. Feinberg, I. (1982). Schizophrenia: Caused by a fault in programmed synaptic elimination during adolescence? Journal of Psychiatric Research, 17, 319–334. Feinberg, I., Higgins, L. M., Khaw, W. Y., & Campbell, I. G. (2006). The adolescent decline of NREM delta, an indicator of brain maturation, is linked to age and sex but not to pubertal stage. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 291, R1724–R1729. Feinberg, I., Koresko, R.L. & Heller, N. (1967) EEG sleep patterns as a function of normal and pathological aging in man. J Psychiatr Res, 5, 107–144. Frank, M. G., & Heller, H. C. (1997). Development of REM and slow wave sleep in the rat. Journal of General Physiology, 272, R1792–R1799. Frank, M. G., Issa, N. P., & Stryker, M. P. (2001). Sleep enhances plasticity in the developing visual cortex. Neuron, 30, 275–287. Frank, M. G., Morrissette, R., & Heller, H. C. (1998). Effects of sleep deprivation in neonatal rats. Journal of General Physiology, 275, R148–R157. Franken, P., Chollet, D., & Tafti, M. (2001). The homeostatic regulation of sleep need is under genetic control. Journal of Neuroscience, 21, 2610–2621. Franken, P., Gip, P., Hagiwara, G., Ruby, N. F., & Heller, H. C. (2003). Changes in brain glycogen after sleep deprivation vary with genotype. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 285, R413–R419. Franken, P., Gip, P., Hagiwara, G., Ruby, N. F., & Heller, H. C. (2006). Glycogen content in the cerebral cortex increases with sleep loss in C57BL/6J mice. Neuroscience Letters, 402, 176–179. Franken, P., Tobler, I., & Borbély, A. A. (1991). Sleep homeostasis in the rat: Simulation of the time course of eeg slow-wave activity. Neuroscience Letters, 130, 141–144.

Cirelli, C., & Tononi, G. (2004). Locus ceruleus control of state-dependent gene expression. Journal of Neuroscience, 24, 5410–5419.

Fuller, P. M., Gooley, J. J., & Saper, C. B. (2006). Neurobiology of the sleep-wake cycle: Sleep architecture, circadian regulation, and regulatory feedback. Journal of Biological Rhythms, 21, 482–493.

Cole, C. S., & Richards, K. C. (2005). Sleep and cognition in people with Alzheimer ’s disease. Issues in Mental Health Nursing, 26, 687–698.

Gail Williams, P., Sears, L. L., & Allard, A. (2004). Sleep problems in children with autism. Journal of Sleep Research, 13, 265–268.

Crowley, S. J., Acebo, C., & Carskadon, M. A. (2007). Sleep, circadian rhythms, and delayed phase in adolescence. Sleep Medicine, 8, 602–612.

Gais, S., & Born, J. (2004). Declarative memory consolidation: Mechanisms acting during human sleep. Learning and Memory, 11, 679–685.

Czarnecki, A., Birtoli, B., & Ulrich, D. (2007). Cellular mechanisms of burst firing-mediated long-term depression in rat neocortical pyramidal cells. Journal of Physiology, 578, 471–479.

Gais, S., Molle, M., Helms, K., & Born, J. (2002). Learning-dependent increases in sleep spindle density, Journal of Neuroscience, 22, 6830–6834.

Daan, S., Domien, G. M. B., & Borbély, A. A. (1984). Timing of human sleep: Recovery process gated by a circadian pacemaker. American Journal of Physiology, 246, R161–R178.

Gao, P. P., Yue, Y., Cerretti, D. P., Dreyfus, C., & Zhou, R. (1999). Ephrindependent growth and pruning of hippocampal axons. Proceedings of the National Academy of Sciences, USA, 96, 4073–4077.

Dauvilliers, Y., Arnulf, I., & Mignot, E. (2007). Narcolepsy with cataplexy. Lancet, 369, 499–511.

Gip, P., Hagiwara, G., Ruby, N. F., & Heller, H. C. (2002). Sleep deprivation decreases glycogen in the cerebellum but not in the cortex of young rats. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 283, R54–R59.

De Felipe, J., Marco, P., Fairen, A., & Jones, E. G. (1997). Inhibitory synaptogenesis in mouse somatosensory cortex. Cerebral Cortex, 7, 619–634.

c24.indd 477

Dierker, L. J., Rosen, M. G., Pillay, S., & Sorokin, Y. (1982). Correlation between gestational-age and fetal activity periods. Biology of the Neonate, 42, 66–72.

8/18/09 5:51:46 PM

478

Sleep and Waking Across the Life Span

Glantz, L. A., Gilmore, J. H., Hamer, R. M., Lieberman, J. A., & Jarskog, L. F. (2007). Synaptophysin and postsynaptic density protein 95 in the human prefrontal cortex from mid-gestation into early adulthood. Neuroscience, 149, 582–591. Gould, E. L., Loesch, D. Z., Martin, M. J., Hagerman, R. J., Armstrong, S. M., & Huggins, R. M. (2000). Melatonin profiles and sleep characteristics in boys with fragile X syndrome: A preliminary study. American Journal of Medical Genetics, 95, 307–315. Hairston, I. S., Peyron, C., Denning, D. P., Ruby, N. F., Flores, J., Sapolsky, R. M., et al. (2004). Sleep deprivation effects on growth factor expression in neonatal rats: A potential role for BDNF in the mediation of delta power. Journal of Neurophysiology, 91, 1586–1595. Halbower, A. C., & Mahone, E. M. (2006). Neuropsychological morbidity linked to childhood sleep-disordered breathing. Sleep Medicine Reviews, 10, 97–107. Hedden, T., & Gabrieli, J. D. (2004). Insights into the ageing mind: A view from cognitive neuroscience. Nature Reviews: Neuroscience, 5, 87–96. Hobson, J. A., Pace-Schott, E. F., & Stickgold, R. (2000). Dreaming and the brain: Toward a cognitive neuroscience of conscious states. Behavioral and Brain Sciences, 23, 793–842; discussion 904–1121. Hof, P. R., & Morrison, J. H. (2004). The aging brain: Morphomolecular senescence of cortical circuits. Trends in Neurosciences, 27, 607–613. Hofman, M. A., & Swaab, D. F. (2006). Living by the clock: The circadian pacemaker in older people. Ageing Research Reviews, 5, 33–51. Horne, J. A. (1980). Sleep and body restitution. Experientia, 36, 11–13.

Jenni, O. G., Deboer, T., & Achermann, P. (2006). Development of the 24-h rest-activity pattern in human infants. Infant Behavior and Development, 29, 143–152. Jha, S. K., Jones, B. E., Coleman, T., Steinmetz, N., Law, C. T., Griffin, G., et al. (2005). Sleep-dependent plasticity requires cortical activity. Journal of Neuroscience, 25, 9266–9274. Johnson, E. O., Roth, T., Schultz, L., & Breslau, N. (2006). Epidemiology of DSM-IV insomnia in adolescence: Lifetime prevalence, chronicity, and an emergent gender difference. Pediatrics, 117, e247–e256. Jones, B. E. (2003). Arousal systems. Frontiers in Bioscience, 8, s438–s451. Jones, B. E. (2005). From waking to sleeping: Neuronal and chemical substrates. Trends in Pharmacological Sciences, 26, 578–586. Jouvet, M. (1962). [Research on the neural structures and responsible mechanisms in different phases of physiological sleep.]. Archives Italiennes de Biologie, 100, 125–206. Jouvet, M. (1965). Paradoxical sleep: A study of its nature and mechanisms. Progress in Brain Research, 18, 20–62. Jouvet, M. (1994). Paradoxical sleep mechanisms. Sleep, 17, S77–S83. Jouvet, M. (1998). Paradoxical sleep as a programming system. Journal of Sleep Research, 7(Suppl, 1), 1–5. Jouvet-Mounier, D., Astic, L., & Lacote, D. (1970). Ontogenesis of the states of sleep in rat, cat, and guinea pig during the first postnatal month. Developmental Psychobiology, 2, 216–239.

Hua, J. Y., & Smith, S. J. (2004). Neural activity and the dynamics of central nervous system development. Journal of Neuroscience, 7, 327–332.

Kales, A., Soldatos, C. R., & Kales, J. D. (1987). Sleep disorders: Insomnia, sleepwalking, night terrors, nightmares, and enuresis. Annals of Internal Medicine, 106, 582–592.

Huang, Z. L., Urade, Y., & Hayaishi, O. (2007). Prostaglandins and adenosine in the regulation of sleep and wakefulness. Current Opinion in Pharmacology, 7, 33–38.

Kamel, N. S., & Gammack, J. K. (2006). Insomnia in the elderly: Cause, approach, and treatment. American Journal of Medicine, 119, 463–469.

Huber, R., Deboer, T., & Tobler, I. (2000). Effects of sleep deprivation on sleep and sleep EEG in three mouse strains: Empirical data and simulations. Brain Research, 857, 8–19. Huber, R., Ghilardi, M. F., Massimini, M., Ferrarelli, F., Riedner, B. A., Peterson, M. J., et al. (2006). Arm immobilization causes cortical plastic changes and locally decreases sleep slow wave activity. Journal of Neuroscience, 9, 1169–1176. Huber, R., Ghilardi, M. F., Massimini, M., & Tononi, G. (2004, July 1). Local sleep and learning. Nature, 430, 78–81. Huber, R., Tononi, G., & Cirelli, C. (2007). Exploratory behavior, cortical BDNF expression, and sleep homeostasis. Sleep, 30, 129–139. Huttenlocher, P. R., & Dabholkar, A. S. (1997). Regional differences in synaptogenesis in human cerebral cortex. Journal of Comparative Neurology, 387, 167–178. Iber, C., Ancoli-Israel, S., Chesson, A. & Quan, S.F. (2007) The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. In Medicine, A.A.o.S. (ed), Westchester, Illinois. Iglowstein, I., Jenni, O. G., Molinari, L., & Largo, R. H. (2003). Sleep duration from infancy to adolescence: Reference values and generational trends. Pediatrics, 111, 302–307. Jenni, O. G., Achermann, P., & Carskadon, M. A. (2005). Homeostatic sleep regulation in adolescents. Sleep, 28, 1446–1454. Jenni, O. G., Borbély, A. A., & Achermann, P. (2004). Development of the nocturnal sleep electroencephalogram in human infants. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 286, R528–R538.

Karni, A., Tanne, D., Rubenstein, B. S., Askenasy, J. J., & Sagi, D. (1994, July 29). Dependence on REM sleep of overnight improvement of a perceptual skill. Science, 265, 679–682. Kennaway, D. J., Stamp, G. E., & Goble, F. C. (1992). Development of melatonin production in infants and the impact of prematurity. Journal of Clinical Endocrinology and Metabolism, 75, 367–369. Kintraia, P. I., Zarnadze, M. G., Kintraia, N. P., & Kashakashvili, I. G. (2005). Development of daily rhythmicity in heart rate and locomotor activity in the human fetus. Journal of Circadian Rhythms, 3, 5. Klackenberg, G. (1982). Sleep behavior studied longitudinally: Data from 4–16 years on duration, night-awakening and bed-sharing. Acta Paediatrica Scandinavica, 71, 501–506. Kleitmann, N., & Engelmann, T. G. (1953). Sleep characteristics of infants. Journal of Applied Physiology, 6, 269–282. Kripke, D. F., Garfinkel, L., Wingard, D. L., Klauber, M. R., & Marler, M. R. (2002). Mortality associated with sleep duration and insomnia. Archives of General Psychiatry, 59, 131–136. Kripke, D. F., Simons, R. N., Garfinkel, L., & Hammond, E. C. (1979). Short and long sleep and sleeping pills. Is increased mortality associated? Archives of General Psychiatry, 36, 103–116. Krueger, J. M., Obal, F. J., Fang, J., Kubota, T., & Taishi, P. (2001). The role of cytokines in physiological sleep regulation. Annals of the New York Academy of Sciences, 933, 211–221. Landolt, H. P., Dijk, D. J., Achermann, P., & Borbély, A. A. (1996). Effect of age on the sleep EEG: Slow-wave activity and spindle frequency activity in young and middle-aged men. Brain Research, 738, 205–212.

Jenni, O. G., & Carskadon, M. A. (2004). Spectral analysis of the sleep electroencephalogram during adolescence. Sleep, 27, 774–783.

Largo, R.H., Molinari, L., von Siebenthal, K. & Wolfensberger, U. (1996) Does a profound change in toilet-training affect development of bowel and bladder control? Dev Med Child Neurol, 38, 1106–1116.

Jenni, O.G. & Carskadon, M.A. (2007) Sleep behavior and sleep regulation from infancy through adolescence: Normative aspects. Sleep Medicine Clinics, 2, 321–329.

Lindsley, D. B., Bowden, J. W., & Magoun, H. W. (1949). Effect upon the EEG of acute injury to the brainstem activating system. EEG Clinical Neurophysiology, 1, 475–486.

c24.indd 478

8/18/09 5:51:46 PM

References Linkowski, P., Kerkhofs, M., Hauspie, R., Susanne, C., & Mendlewicz, J. (1989). EEG sleep patterns in man: A twin study. Electroencephalography and Clinical Neurophysiology, 73, 279–284.

Partinen, M., Kaprio, J., Koskenvuo, M., Putkonen, P., & Langinvainio, H. (1983). Genetic and environmental determination of human sleep. Sleep, 6, 179–185.

Llinas, R. R., & Steriade, M. (2006). Bursting of thalamic neurons and states of vigilance. Journal of Neurophysiology, 95, 3297–3308.

Paus, T. (2005). Mapping brain maturation and cognitive development during adolescence. Trends in Cognitive Sciences, 9, 60–68.

Mahowald, M. W., & Rosen, G. M. (1990). Parasomnias in children. Pediatrician, 17, 21–31.

Peigneux, P., Laureys, S., Delbeuck, X., & Maquet, P. (2001). Sleeping brain, learning brain: The role of sleep for memory systems. NeuroReport, 12, A111–A124.

Mahowald, M. W., Schenck, C. H., & Bornemann, M. A. (2007). Pathophysiologic mechanisms in REM sleep behavior disorder. Current Neurology and Neuroscience Reports, 7, 167–172. Massimini, M., Huber, R., Ferrarelli, F., Hill, S., & Tononi, G. (2004). The sleep slow oscillation as a traveling wave. Journal of Neuroscience, 24, 6862–6870. McCormick, D. A., & Bal, T. (1997). Sleep and arousal: Thalamocortical mechanisms. Annual Review of Neuroscience, 20, 185–215. McCormick, D. A., & Pape, H. C. (1990). Properties of a hyperpolarization-activated cation current and its role in rhythmic oscillation in thalamic relay neurones. Journal of Physiology, 431, 291–318. McGinty, D., Gong, H., Suntsova, N., Alam, M. N., Methippara, M., Guzman-Marin, R., et al. (2004). Sleep-promoting functions of the hypothalamic median preoptic nucleus: Inhibition of arousal systems. Archives Italiennes de Biologie, 142, 501–509.

Peigneux, P., Laureys, S., Fuchs, S., Collette, F., Perrin, F., Reggers, J., et al. (2004). Are spatial memories strengthened in the human hippocampus during slow wave sleep? Neuron, 44, 535–545. Porkka-Heiskanen, T., Alanko, L., Kalinchuk, A., & Stenberg, D. (2002). Adenosine and sleep. Sleep Medicine Reviews, 6, 321–332. Porkka-Heiskanen, T., Strecker, R. E., Thakkar, M., Bjorkum, A. A., Greene, R. W., & McCarley, R. W. (1997, May 23). Adenosine: A mediator of the sleep-inducing effects of prolonged wakefulness. Science, 276, 1265–1268. Prull, M. W., Gabrieli, J. D. E., & Bunge, S. A. (2000). Age-related changes in memory: A cognitive neuroscience perspective. In F. I. M. Craik & T. A. Salthouse (Eds.), The handbook of aging and cognition (2nd ed., pp. 91–153). Mahawah, NJ: Lawrence Earlbaum.

McGinty, D., & Szymusiak, R. (2003). Hypothalamic regulation of sleep and arousal. Frontiers in Bioscience, 8, s1074–s1083.

Radun, I., & Summala, H. (2004). Sleep-related fatal vehicle accidents: Characteristics of decisions made by multidisciplinary investigation teams. Sleep, 27, 224–227.

Mednick, S., Nakayama, K., & Stickgold, R. (2003). Sleep-dependent learning: A nap is as good as a night. Journal of Neuroscience, 6, 697–698.

Rechtschaffen, A., Hauri, P., & Zeitlin, M. (1966). Auditory awakening thresholds in REM and NREM sleep stages. Perceptual and Motor Skills, 22, 927–942.

Meier-Koll, A., Bussmann, B., Schmidt, C., & Neuschwander, D. (1999). Walking through a maze alters the architecture of sleep. Perceptual and Motor Skills, 88, 1141–1159.

Reppert, S. M., & Schwartz, W. J. (1984). The suprachiasmatic nuclei of the fetal-rat: Characterization of a functional circadian clock using C14-labeled deoxyglucose. Journal of Neuroscience, 4, 1677–1682.

Mirmiran, M., Scholtens, J., van de Poll, N. E., Uylings, H. B., van der Gugten, J., & Boer, G. J. (1983). Effects of experimental suppression of active (REM) sleep during early development upon adult brain and behavior in the rat. Brain Research, 283, 277–286.

Riedner, B. A., Vyazovskiy, V. V., Huber, R., Massimini, M., Esser, S. K., Murphy, M., et al. (2007). Sleep homeostasis and cortical synchronization: Pt. III. A high-density EEG study of sleep slow waves in humans. Sleep, 30, 1643–1657.

Mishima, K., Tozawa, T., Satoh, K., Matsumoto, Y., Hishikawa, Y., & Okawa, M. (1999). Melatonin secretion rhythm disorders in patients with senile dementia of Alzheimer ’s type with disturbed sleep-waking. Biological Psychiatry, 45, 417–421.

Rivkees, S. A., & Hao, H. P. (2000). Developing circadian rhythmicity. Seminars in Perinatology, 24, 232–242.

Mistlberger, R. E. (2005). Circadian regulation of sleep in mammals: Role of the suprachiasmatic nucleus. Brain Research: Brain Research Reviews, 49, 429–454. Mistlberger, R. E., Bergmann, B. M., Waldenar, W., & Rechtschaffen, A. (1983). Recovery sleep following sleep deprivation in intact and suprachiasmatic nuclei-lesioned rats. Sleep, 6, 217–233. Miyamoto, H., Katagiri, H., & Hensch, T. (2003). Experience-dependent slow-wave sleep development. Nature Neuroscience, 6, 553–554. Montplaisir, J. (2004). Abnormal motor behavior during sleep. Sleep Medicine, 5(Suppl. 1), S31–S34. Moruzzi, G., & Magoun, H. W. (1949). Brain stem reticular formation and activation of the EEG. Electroencephalography and Clinical Neurophysiology, 1, 455–473. Mulder, E. J., Visser, G. H., Bekedam, D. J., & Prechtl, H. F. (1987). Emergence of behavioural states in fetuses of type-1-diabetic women. Early Human Development, 15, 231–251. Nakamura, H., Kobayashi, S., Ohashi, Y., & Ando, S. (1999). Age-changes of brain synapses and synaptic plasticity in response to an enriched environment. Journal of Neuroscience Research, 56, 307–315.

c24.indd 479

479

Roenneberg, T., Kuehnle, T., Pramstaller, P. P., Ricken, J., Havel, M., Guth, A., et al. (2004). A marker for the end of adolescence. Current Biology, 14, R1038–R1039. Roffwarg, H. P., Muzio, J. N., & Dement, W. C. (1966, April 29). Ontogenetic development of the human sleep-dream cycle. Science, 152, 604–619. Rosanova, M., & Ulrich, D. (2005). Pattern-specific associative long-term potentiation induced by a sleep spindle-related spike train. Journal of Neuroscience, 25, 9398–9405. Roth, T., Roehrs, T., & Pies, R. (2007). Insomnia: Pathophysiology and implications for treatment. Sleep Medicine Reviews, 11, 71–79. Sadeh, A., Gruber, R., & Raviv, A. (2002). Sleep, neurobehavioral functioning, and behavior problems in school-age children. Child Development, 73, 405–417. Sadeh, A., Raviv, A., & Gruber, R. (2000). Sleep patterns and sleep disruptions in school-age children. Developmental Psychology, 36, 291–301. Saper, C. B., Lu, J., Chou, T. C., & Gooley, J. (2005). The hypothalamic integrator for circadian rhythms. Trends in Neurosciences, 28, 152–157. Saper, C. B., Scammell, T. E., & Lu, J. (2005, October 27). Hypothalamic regulation of sleep and circadian rhythms. Nature, 437, 1257–1263.

O’Brien, L. M., Mervis, C. B., Holbrook, C. R., Bruner, J. L., Smith, N. H., McNally, N., et al. (2004). Neurobehavioral correlates of sleep-disordered breathing in children. Journal of Sleep Research, 13, 165–172.

Schmidt, C., Peigneux, P., Muto, V., Schenkel, M., Knoblauch, V., Munch, M., et al. (2006). Encoding difficulty promotes postlearning changes in sleep spindle activity during napping. Journal of Neuroscience, 26, 8976–8982.

Parmelee, A. H. (1961). Sleep patterns in infancy: A study of one infant from birth to 8 months of age. Acta Paediatrica, 50, 160.

Shaffery, J. P., Lopez, J., Bissette, G., & Roffwarg, H. P. (2006). Rapid eye movement sleep deprivation in post-critical period, adolescent rats

8/18/09 5:51:47 PM

480

Sleep and Waking Across the Life Span

alters the balance between inhibitory and excitatory mechanisms in visual cortex. Neuroscience Letters, 393, 131–135. Shaffery, J. P., Oksenberg, A., Marks, G. A., Speciale, S. G., Mihailoff, G., & Roffwarg, H. P. (1998). REM sleep deprivation in monocularly occluded kittens reduces the size of cells in LGN monocular segment. Sleep, 21, 837–845. Shaffery, J. P., Sinton, C. M., Bissette, G., Roffwarg, H. P., & Marks, G. A. (2002). Rapid eye movement sleep deprivation modifies expression of long-term potentiation in visual cortex of immature rats. Neuroscience, 110, 431–443. Shaw, P. J., Cirelli, C., Greenspan, R. J., & Tononi, G. (2000, March 10). Correlates of sleep and waking in Drosophila melanogaster. Science, 287, 1834–1837. Shaw, P.J., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., et al. (2006, March 30). Intellectual ability and cortical development in children and adolescents. Nature, 440, 676–679. Sherin, J. E., Shiromani, P. J., McCarley, R. W., & Saper, C. B. (1996, January 12). Activation of ventrolateral preoptic neurons during sleep. Science, 271, 216–219. Shibagaki, M., Kiyono, S., & Watanabe, K. (1982). Spindle evolution in normal and mentally retarded children: A review. Sleep, 5, 47–57. Siegel, J. M. (2005, October 27). Clues to the functions of mammalian sleep. Nature, 437, 1264–1271. Sirota, A., Csicsvari, J., Buhl, D., & Buzsaki, G. (2003). Communication between neocortex and hippocampus during sleep in rodents. Proceedings of the National Academy of Sciences, USA, 100, 2065–2069. Skaggs, W. E., & McNaughton, B. L. (1996, March 29). Replay of neuronal firing sequences in rat hippocampus during sleep following spatial experience. Science, 271, 1870–1873. Spiegel, K., Leproult, R., & Van Cauter, E. (1999). Impact of sleep debt on metabolic and endocrine function. Lancet, 354, 1435–1439. Steriade, M. (1999). Coherent oscillations and short-term plasticity in corticothalamic networks. Trends in Neurosciences, 22, 337–345. Steriade, M., McCormick, D. A., & Sejnowski, T. J. (1993, October, 29). Thalamocortical oscillations in the sleeping and aroused brain. Science, 262, 679–685.

states, breathing events, peripheral chemoresponsiveness and arousal propensity in healthy 3 month old infants. European Respiratory Journal, 9, 932–938. Thorpy, M. J., Korman, E., Spielman, A. J., & Glovinsky, P. B. (1988). Delayed sleep phase syndrome in adolescents. Journal of Adolescent Health Care, 9, 22–27. Tobler, I. I. (2000). Phylogeny of sleep regulation. In M. H. Kryger, T. Roth, & W. C. Dement (Eds.), Principles and practice of sleep medicine (pp. 72–81). Philadelphia: Saunders. Tobler, I. I., Borbély, A. A., & Groos, G. (1983). The effect of sleep deprivation on sleep in rats with suprachiasmatic lesions. Neuroscience Letters, 42, 49–54. Tobler, I. I., Franken, P., Trachsel, L., & Borbély, A. A. (1992). Models of sleep regulation in mammals. Journal of Sleep Research, 1, 125–127. Tononi, G., & Cirelli, C. (2003). Sleep and synaptic homeostasis: A hypothesis. Brain Research Bulletin, 62, 143–150. Tononi, G., & Cirelli C. (2006). Sleep function and synaptic homeostasis. Sleep Medicine Reviews, 10, 49–62. Trachsel, L., Edgar, D. M., Seidel, W. F., Heller, H. C., & Dement, W. C. (1992). Sleep homeostasis in suprachiasmatic nuclei-lesioned rats: Effects of sleep deprivation and triazolam administration. Brain Research, 589, 253–261. Trachtenberg, J. T., Chen, B. E., Knott, G. W., Feng, G., Sanes, J. R., Welker, E., et al. (2002, December 19). Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex. Nature, 420, 788–794. Turrigiano, G. G., & Nelson, S. B. (2000). Hebb and homeostasis in neuronal plasticity. Current Opinion in Neurobiology, 10, 358–364. Turrigiano, G. G., & Nelson, S. B. (2004). Homeostatic plasticity in the developing nervous system. Nature Reviews: Neuroscience, 5, 97–107. Urschitz, M. S., Guenther, A., Eggebrecht, E., Wolff, J., UrschitzDuprat, P. M., Schlaud, M., et al. (2003). Snoring, intermittent hypoxia and academic performance in primary school children. American Journal of Respiratory and Critical Care Medicine, 168, 464–468.

Steriade, M., & Timofeev, I. (2003). Neuronal plasticity in thalamocortical networks during sleep and waking oscillations. Neuron, 37, 563–576.

Van Cauter, E., Leproult, R., & Plat, L. (2000). Age-related changes in slow wave sleep and REM sleep and relationship with growth hormone and cortisol levels in healthy men. Journal of the American Medical Association, 284, 861–868.

Stickgold, R., James, L., & Hobson, J. A. (2000). Visual discrimination learning requires sleep after training. Nature Neuroscience, 3, 1237–1238.

Vertes, R. P., & Eastman, K. E. (2000). The case against memory consolidation in REM sleep. Behavioral and Brain Sciences, 23, 867–876; discussion 904–1121.

Suntsova, N., Szymusiak, R., Alam, M. N., Guzman-Marin, R., & McGinty, D. (2002). Sleep-waking discharge patterns of median preoptic nucleus neurons in rats. Journal of Physiology, 543, 665–677.

Vgontzas, A. N., & Kales, A. (1999). Sleep and its disorders. Annual Review of Medicine, 50, 387–400.

Swaab, D. F., Hofman, M. A., & Honnebier, M. B. O. M. (1990). Development of vasopressin neurons in the human suprachiasmatic nucleus in relation to birth. Developmental Brain Research, 52, 289–293. Szymusiak, R., Alam, N., Steininger, T. L., & McGinty, D. (1998). Sleepwaking discharge patterns of ventrolateral preoptic/anterior hypothalamic neurons in rats. Brain Research, 803, 178–188. Szymusiak, R., Steininger, T., Alam, N., & McGinty, D. (2001). Preoptic area sleep-regulating mechanisms. Archives Italiennes de Biologie, 139, 77–92. Tabba, M. K., & Johnson, J. C. (2006). Obstructive sleep apnea: A practical review. Missouri Medicine, 103, 509–513. Tasali, E., Leproult, R., Ehrmann, D. A., & Van Cauter, E. (2008). Slowwave sleep and the risk of type 2 diabetes in humans. Proceedings of the National Academy of Sciences, USA, 105, 1044–1049. Thomas, D. A., Poole, K., McArdle, E. K., Goodenough, P. C., Thompson, J., Beardsmore, C. S., et al. (1996). The effect of sleep deprivation on sleep

c24.indd 480

Visser, G. H. A., Poelmannweesjes, G., Cohen, T. M. N., & Bekedam, D. J. (1987). Fetal behavior at 30 to 32 weeks of gestation. Pediatric Research, 22, 655–658. von Economo, C. (1930). Sleep as a problem of localization. Journal of Nervous and Mental Diseases, 71. Vyazovskiy, V. V., Cirelli, C., Pfister-Genskow, M., Faraguna, U., & Tononi, G. (2008). Molecular and electrophysiological evidence for net synaptic potentiation in wake and depression in sleep. Journal of Neuroscience, 11, 200–208. Vyazovskiy, V. V., Riedner, B. A., Cirelli, C., & Tononi, G. (2007). Sleep homeostasis and cortical synchronization: Pt. II. A local field potential study of sleep slow waves in the rat. Sleep, 30, 1631–1642. Walker, M. P., & Stickgold, R. (2004). Sleep-dependent learning and memory consolidation. Neuron, 44, 121–133. Webb, W. B., & Agnew, H. W., Jr. (1971, December 24). Stage 4 sleep: Influence of time course variables. Science, 174, 1354–1356. Webster, M. J., Weickert, C. S., Herman, M. M., & Kleinman, J. E. (2002). BDNF mRNA expression during postnatal development, maturation and

8/18/09 5:51:47 PM

References aging of the human prefrontal cortex. Brain Research: Developmental Brain Research, 139, 139–150.

Wyatt, J. K. (2004). Delayed sleep phase syndrome: Pathophysiology and treatment options. Sleep, 27, 1195–1203.

Weitzman, E. D., Czeisler, C. A., Coleman, R. M., Spielman, A. J., Zimmerman, J. C., Dement, W., et al. (1981). Delayed sleep phase syndrome: A chronobiological disorder with sleep-onset insomnia. Archives of General Psychiatry, 38, 737–746.

Youngstedt, S. D., & Kripke, D. F. (2004). Long sleep and mortality: Rationale for sleep restriction. Sleep Medicine Reviews, 8, 159–174.

Weyerer, S., & Dilling, H. (1991). Prevalence and treatment of insomnia in the community: Results from the upper Bavarian field study. Sleep, 14, 392–398. Williams, H. L., Hammack, J. T., Daly, R. L., Dement, W. C., & Lubin, A. (1964). Responses to auditory stimulation, sleep loss and the eeg stages of sleep. Electroencephalography and Clinical Neurophysiology, 16, 269–279. Wilson, M. A., & McNaughton, B. L. (1994, July 29). Reactivation of hippocampal ensemble memories during sleep. Science, 265, 676–679. Wolfson, A. R., & Carskadon, M. A. (1998). Sleep schedules and daytime functioning in adolescents. Child Development, 69, 875–887.

c24.indd 481

481

Zee, P. C., & Manthena, P. (2007). The brain’s master circadian clock: Implications and opportunities for therapy of sleep disorders. Sleep Medicine Reviews, 11, 59–70. Zeitzer, J. M., Morales-Villagran, A., Maidment, N. T., Behnke, E. J., Ackerson, L. C., Lopez-Rodriguez, F., et al. (2006). Extracellular adenosine in the human brain during sleep and sleep deprivation: An in vivo microdialysis study. Sleep, 29, 455–461. Zemdegs, I. Z., McMillen, I. C., Walker, D. W., Thorburn, G. D., & Nowak, R. (1988). Diurnal rhythms in plasma melatonin concentrations in the fetal sheep and pregnant ewe during late gestation. Endocrinology, 123, 284–289. Zuo, Y., Lin, A., Chang, P., & Gan, W. B. (2005). Development of longterm dendritic spine stability in diverse regions of cerebral cortex. Neuron, 46, 181–189.

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Chapter 25

Consciousness CHADD M. FUNK, MARY COLVIN PUTNAM, AND MICHAEL S. GAZZANIGA

experience, maintaining that phenomenal conscious experience is associated with activity in specific modules rather than activity in a distributed system connecting these modules and suggesting that an interpretive process in the left hemisphere ensures that conscious experience is unified. We conclude by attempting to reconcile these opposing perspectives.

While the topic of consciousness has puzzled thinkers for millennia, the scientific endeavor to understand consciousness in terms of brain function is relatively new. Empirical investigation of the mechanisms underlying or related to conscious experience is flourishing, and the results are providing astounding insight into the very essence of human behavior. But even as neuroscientists and modern philosophers move toward an understanding of the complex neural interactions that are both necessary and sufficient for conscious experience, we remain keenly aware that solving what Chalmers (1995) referred to as the “hard problem” of consciousness, that is, achieving an understanding of how an individual’s specific patterns of neuronal firing create rich, textured, and unique conscious experience, is but a distant goal. Presently, the foci of scientific investigation are the more tractable, but nonetheless daunting, “soft problems,” that address how brain activity related to various cognitive functions, such as attention, perception, and action, gives rise to conscious experience.

DEFINING CONSCIOUSNESS The term consciousness is typically used in one of two ways. One meaning refers to the general state of being conscious, as opposed to being unconscious. The second definition refers to being conscious of specific content. Though an intuitive and useful distinction, it can be misleading in two ways when used as a framework to guide investigation of the neuroanatomical basis of conscious experience. First, it may veil an important characteristic of general conscious states, specifically, that the state of being conscious inherently provides a certain amount of information to an organism. Second, the content of consciousness may vary along a continuum of complexity. This may not be immediately apparent because it is inescapable human nature to associate conscious content with the fullest realization of the content spectrum, the healthy human end, in which vivid subjective representations stir rich and varied cognitive associations. Nonetheless, the content spectrum also dwindles down to basic conscious representations, stripped of cognitive associations, if for no other reason than their being processed in more primitive neural apparatus. One may argue that refining the definition of consciousness in order to guard against neglect of these characteristics is a mere technicality, unlikely to affect theoretical or empirical inquiries of the neural basis of conscious experience. However, overlooking these simple facts may have real consequences because it may lead one to oversimplify the role of neural structures that provide the foundation for

In this chapter, we first evaluate the uses of the term consciousness and address the neural foundations of conscious states. We then consider theoretical accounts of the mechanisms by which distributed modules are integrated to generate unified conscious experience and assess the present understanding of the neural underpinnings of separate functions central to conscious experience, each organized in discrete modules that process highly specific information and produce distinct conscious content. Next, we address a peculiar observation from parallel investigations of split-brain patients, namely, that the isolated left hemisphere reports no disruption to its conscious experience following callosal transection. Theories that posit long-distance, reentrant connectivity as the basis of all conscious experience appear unable to explain the striking fact that severing interhemispheric fibers produces little subjectively noticeable effect on the scope or unity of conscious experience. Accordingly, we promote a different perspective on the neural basis of conscious 482

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conscious experience. In turn, potential inconsistencies within theoretical explanations are masked. We return to this possibility in subsequent sections. We favor Damasio’s (1999) proposed distinction between core consciousness and extended consciousness, which better accounts for the properties of general conscious states and the gradient of possible content. Core consciousness provides an organism with a sense of “here and now.” An organism with core consciousness is awake and aware at a very basic level. Furthermore, they are aware of specific content at the simple end of the content continuum, or representations of objects as they exist in a given moment. Notably absent from this sort of content are explicit associations with past encounters or plans for future ways of interacting with the represented object. For instance, core consciousness may include a crude perception of a piece of chocolate, but it would not evoke memories of a favorite chocolate shop in Santa Barbara, nor would it conjure plans to pick up that chocolate and give it to a lover to regain their favor. Edelman (1989) calls this cognitively isolated moment the “remembered present” (incidentally, in many ways, his conceptualization of “primary” and “high-order consciousness” is in the same spirit as Damasio’s proposal). Importantly, core consciousness is necessary for extended consciousness, the lavish form of consciousness that allows us to perceive, to form associations with an object, to explicitly recall past events, to think about and plan for the future, and to perform countless other mental operations. A critical attribute of extended consciousness is self-consciousness, the understanding that “we,” agents who perceive and act, are indeed conscious. As these examples suggest, extended consciousness encompasses the rich and complex end of the content continuum; it includes the subjective flavor associated with our many special capabilities. Whereas core consciousness is defined as a set of neural interactions that provides the foundation for all conscious experience and thus only supports basic awareness of specific content, extended consciousness purely enriches and expands the scope of conscious experience.

FOUNDATIONS OF CONSCIOUSNESS We now address the critical neural nodes that compromise the neural basis of consciousness, beginning with core consciousness. Consistent with the previous theoretical depiction, core consciousness depends upon brain areas that are necessary for any form of conscious experience (Damasio, 1999). Studies of patients who have sustained neurological damage and are subsequently left unconscious, or

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completely lacking core consciousness, have provided the most direct means of identifying the brain regions necessary for core consciousness. This research suggests that even core consciousness exists along a continuum. The loss of core consciousness can be absolute, resulting in coma, or it can be slightly less severe, ranging from vegetative states to minimally conscious states. The lines between these states are often vague at best, and it is yet unclear whether these states vary with respect to providing a platform to support extended consciousness. Nonetheless, the patterns of neurological damage distinguishing these states has revealed much about the brain regions involved in enabling core consciousness, and thus, these states will be briefly described here. The most profound of the unconscious states is coma. Comatose patients cannot be aroused and are not aware of either self or environment. They do not make purposeful movements in response to stimulation and may retain only very basic motor reflexes (Giacino et al., 2002; Laureys, Perrin, & Brédart, 2007). The comatose state arises from a significant disruption of the ascending arousal system located throughout the pons and midbrain, or of this system’s two primary targets, the thalamus or hypothalamus. It may also arise from widespread dysfunction within both cerebral hemispheres (e.g., following a metabolic or toxic condition impacting the entire brain; Saper, 2000). Thus, in the comatose state, cortical neurons are unable to receive ascending signals, and incoming sensory stimuli are not represented at the levels of core or extended consciousness. The vegetative state (VS) is differentiated from coma by a greater level of arousal, specifically the presence of sleep and wake cycles. Vegetative patients exhibit periods of wakefulness, accompanied by eye opening and spontaneous eye movements. Like comatose patients, basic reflexes may be intact. However, vegetative patients do not exhibit purposeful responses to sensory stimuli that would indicate an awareness of either self or environment (Laureys et al., 2007; Schiff & Plum, 2000). Thus, this condition demonstrates a dissociation between arousal and awareness. While arousal is necessary to support awareness, it is not sufficient for a conscious state to emerge. Finally, there is the minimally conscious state (MCS). Patients in this state demonstrate some level of awareness of either self or environment but are unable to communicate consistently. According to the most recently defined criteria for MCS, the patient must demonstrate at least one of the following behaviors, either more than once or on a sustained basis: (a) follow simple commands, (b) gestural or verbal yes/no response (regardless of accuracy), (c) intelligible verbalization, or (d) purposeful behavior not due to reflexive activity (Laureys et al., 2007). In other words, the MCS patient is able to either inconsistently

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communicate with others or inconsistently respond appropriately to environmental stimuli. The border between the VS and the MCS is anything but precise. Such ambiguity may easily lead to misdiagnosis, which in these cases, often carries significant legal and ethical weight. Traditionally, such classifications relied purely on behavioral responses and occasionally, the patterns of neuronal firing noted on electroencephalogram (EEG). More recently, neuroimaging technology has provided a unique opportunity to look at the function of brains in the various unconscious or minimally conscious states. The results have been both illuminating and controversial. Multiple studies have shown that the amount of neural processing that can occur in the VS is far greater than expected (Coleman et al., 2007; Owen et al., 2006; see Owen & Coleman, 2007, for a review). It is important to emphasize that evidence of neural activity does not indicate that all patients in a VS are conscious; instead, it exposes limitations in current diagnostic protocols. As a clearer understanding of the neural areas and interactions necessary and sufficient for conscious experience emerges, it could immediately impact and dramatically improve diagnosis of the various global disorders of consciousness. As previously discussed, coma can result from disruption along the ascending arousal system and/or widespread cortical dysfunction. Coma and VS may also result from small lesions to particular areas of the brain, including the brain stem reticular formation and the intralaminar nuclei of the thalamus (Baars, 1995; Bogen, 1995), while MCS may arise from lesions to these areas or the anterior cingulate cortex (Damasio, 1999; Laureys et al., 2007). The brain stem, which includes the medulla, pons, and midbrain, contains various nuclei responsible for basic autonomic functions that are necessary for an organism’s survival and is involved in relaying an enormous amount of information about the state of the organism to higher brain areas (Parvizi & Damasio, 2001). These efferent pathways include serotonergic, noradrenergic, dopaminergic, cholinergic, and glutamergic projections. As observed in the case of coma, total disruption of these projections to the thalamus or hypothalamus results in loss of arousal and therefore, core consciousness. Similar states, ranging from coma to MCS, can arise from bilateral damage to the intralaminar nuclei (ILN) of the thalamus (Bogen, 1995), which receives dense glutamergic projections from the ascending reticular formation. Schiff and colleagues (2007) reported that stimulation of the ILN in a minimally conscious patient dramatically improved his level of awareness and enabled him to communicate with family members after 6 years of silence. Though one must exercise caution when drawing conclusions from a single patient, this report nonetheless

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provides highly suggestive evidence of the important role that the ILN play in supporting core consciousness. The cellular composition of the ILN is consistent with this conclusion. In fact, the ILN are distinguishable from other thalamic nuclei based on their cellular composition. The ILN are composed mainly of matrix cells, which project diffusely throughout the cortex, rather than to specific cortical targets (Jones, 1998a, 1998b). These diffuse projections are believed to orchestrate long-distance interactions between various cortical areas, which are necessary for extended consciousness (Jones, 2002a, 2002b). In contrast, other thalamic nuclei are comprised mainly of core cells, which project in a highly specific pattern to distinct cortical areas (Jones, 1998a, 1998b). These cells presumably relay specific content and may provide the nonconscious foundation for modality-specific perception (Jones, 2002a, 2002b). Though the ILN projects in a fairly diffuse manner, it sends its densest projections to the ACC (Van der Werf, Witter, & Groenewegen, 2002). Dysfunction of this connection, as evidenced by altered functional connectivity between the ILN and ACC, has been noted in a patient in a VS (Laureys et al., 2000). Furthermore, though selective damage that encompasses the entirety of the ACC is rare, when it occurs, it results in a condition usually referred to as akinetic mutism (Damasio, 1999; Devinsky, Morrell, & Vogt, 1995). Akinetic mutism is a type of minimally conscious state, characterized by an inability to generate or internally guide action as well as abolishment or severe impairment of thought, speech, and emotion (Laureys et al., 2007). Though awake and occasionally able to track a visual stimulus with their eyes, these patients demonstrate few signs of core consciousness (Damasio, 1999). Taken together, these studies indicate that the ACC is a critical node in the neural circuitry of core consciousness. However, relative to the brain stem and the ILN, the ACC interacts to a far greater extent with cortical areas associated with sensory, motor, cognitive, and emotional cortical areas. Thus, the ACC is uniquely poised at the interface between areas that support core consciousness and those that contribute to the complex content of extended consciousness. In the next section, we describe the neural processes involved in supporting extended consciousness in greater depth.

EXTENDED CONSCIOUSNESS: INTEGRATION ACROSS CONTENT MODULES Though necessary for core consciousness, neural activity in the brain stem, ILN, and possibly also some types of activity in the ACC, is not modulated in a highly specific

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fashion by the content of conscious experience (Koch, 2004). Thus, core consciousness encompasses the basic end of the content continuum; it is unlikely (but perhaps impossible to empirically demonstrate, see Nagel, 1974) that the conscious representations emerging solely from these basic processes is comparable to the conscious experience with which we are familiar. Rather, areas throughout the rest of the thalamus and cortex are responsible for the integration of an enormous amount of information, related to perception, cognition, and action, that eventually leads to the content of extended consciousness. According to most current theories of consciousness (Baars, 1988; Crick & Koch, 2003; Dehaene, Kerszberg, & Changeux, 1998; Dehaene & Naccache, 2001; Tononi & Edelman, 1998) in any given moment, there is a privileged, dynamic subset of this cortical activity, likely summated over innumerable simultaneous neural interactions yet excluding countless others, that represents the current content of the conscious experience of an individual. Groups of neurons gain, briefly maintain, and finally surrender this transient “cerebral celebrity” (Dennett, 1993) to the next subset of neurons whose content then becomes conscious. These theories can be characterized as an attempt to explain how a group of neurons distinguishes itself from other groups of neurons in order to consciously “broadcast” its content (Baars, 1988), generally through some form of competition. If correct, these theories could potentially identify the leading coalition of neurons in a given instant and thereby determine the particular content of one’s conscious experience. However, an actual understanding of how these neurons (and the mechanisms they employ to out-compete their rivals) generate specific qualia, or the phenomenal constituents of conscious experience, is currently beyond comprehension. This is the unrelenting “hard problem.” We contend that many of these theories overemphasize the importance of long-distance cortical connectivity in sustaining phenomenal conscious experience. It may be a necessary condition, in concert with activity in certain modules, for some kinds of phenomenal conscious content but it is not necessary for all conscious content. This is subtly predicted by the possibility that core consciousness, which does not depend on the distributed cortical activity postulated by the global workspace theory to underlie conscious experience (Baars, 1988; Dehaene & Naccache, 2001), may generate some basic form of phenomenal conscious experience. But before more thoroughly addressing these conflicting accounts, we review a breadth of studies in order to identify cortical areas that contribute specific content to extended consciousness, to understand their patterns of neuronal activity, and to characterize how these areas interact.

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The core assumption involved is that the brain is organized in a highly modular fashion, with unique modules processing highly specific information (Fodor, 1983; Gazzaniga, 1989). When neuronal activity within an individual module is disrupted or eliminated, the ability to process that category of information is lost and the content of consciousness is altered, such that disorders of extended consciousness emerge (Cooney & Gazzaniga, 2003). For example, among the cortical areas involved in processing visual stimuli, there are functionally specific areas that process specific classes of stimuli. One such area is the fusiform face area (Kanwisher, 2001; Kanwisher, McDermott, & Chun, 1997); selective damage to this region can lead to impaired recognition of known faces, while other aspects of visual perception are preserved. Thus, these cortical modules are not necessary for core or extended consciousness. They contribute only one element to extended consciousness, an indication that the content of extended consciousness must be the product of integration across multiple brain areas that operate in parallel. Consequently, understanding extended consciousness broadly requires investigation of both the individual modules and the mechanisms by which they interact. The diversity of potential conscious content indicates that the tedious nonconscious/conscious divide must be inspected for a great variety of neuronal processing. For the purpose of this chapter, we choose to define modules as cortical areas with distinct functions and focus on those modules that are best understood and arguably most relevant to conscious experience. Specifically, we discuss the cortical areas involved in the diverse processes of attention, visual perception, emotion, memory, and motor function. Because it is not enough to examine each of these modules in isolation, at the end of the chapter we discuss how the products of these various content-laden processes are assimilated into our undeniably united conscious experience. We initially consider the concept of a “global workspace,” proposed by Dehaene and Naccache (2001), after Baars (1988). Briefly, the workspace consists of the top levels of hierarchical processors that are connected by long-distance, reciprocal projections. The various modular processors compete to “broadcast” their content into the workspace, and the content of the workspace in a given instant is tantamount to the content of consciousness. Importantly, the reciprocal connections serve to sustain the firing in the victorious processor and are thus considered to be necessary for conscious experience. While active in the workspace, content of one processor is available to other processors, enabling the great many possible operations that can be consciously performed. Though we believe that the workspace ultimately fails to explain certain observations gleaned from split-brain patients,

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we nonetheless refer to it throughout the following sections and emphasize the apparent importance of connectivity between various modules and areas thought to be integral components of the workspace. We employ this unconventional approach for two reasons: First, the workspace theory is representative of the prevalent notion that extensive reciprocal connectivity between cortical modules is a necessary substrate of conscious experience and thus merits equitable review. Second, it may be possible to delineate specific kinds of conscious content that require a neural architecture similar to the workspace, which would ultimately enable beneficial integration of the two conflicting theories.

MODULES OF EXTENDED CONSCIOUSNESS Attention and Consciousness Attention, as we discuss it here, refers to the ability to orient to an external stimulus and to briefly sustain the representation of that stimulus in mind. This process involves two different mechanisms: stimulus-driven, bottom-up attention and selective, top-down attention (see Chapters 17 and 18). In both cases, neuronal activity is amplified within cortical modules processing especially pertinent content, thereby facilitating conscious awareness of that content (Dehaene & Naccache, 2001; Posner, 1994; Posner & Dehaene, 1994). It is easy to see how the terms attention and consciousness are often confused; as attention can be viewed as responsible for providing access to consciousness (Baars, 1997; Dehaene & Changeux, 2004). Yet consciousness is not strictly reliant on attention, nor does attention assure access into consciousness (Koch & Tsuchiya, 2007). In what follows, we discuss recent research into brain activity at the boundary between attention and consciousness, which has provided support for this double dissociation. Manipulating Attention Prevents Stimuli from Reaching Consciousness In many situations, attention is necessary for conscious experience of specific content, and manipulating attention can alter perception. The relationship between attention and the content of consciousness has been most rigorously explored in the sensory modality of vision. We address higher-order visual functions more thoroughly in the next section, focusing now on insights gained from the phenomena of the attentional blink and change blindness. In each of these situations, varying one’s level of attention leads to important omissions in the content of consciousness. The attentional blink paradigm probes the temporal limits of visual processing by presenting two visual targets

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in rapid succession. Repeated studies have demonstrated that if the second target follows the first by less than 500 ms, subjects do not consciously report the presence of the second target (Broadbent & Broadbent, 1987; Raymond, Shapiro, & Arnell, 1992). Reporting the first target engages a limited capacity processing stage, or bottleneck, impeding similar processing of the second target (Marois & Ivanoff, 2005). However, while the content of the second stimulus escapes conscious awareness, it is still processed on a nonconscious level, and may therefore impact subsequent behavior. For instance, this phenomenon was first demonstrated by a patient who could correctly identify two stimuli as identical or different despite only being consciously aware of one stimulus (Volpe, Ledoux, & Gazzaniga, 1979). Thus, by manipulating the extent of processing for two sequentially presented stimuli, the attentional blink paradigm provides a tactic for understanding the fine line separating nonconscious and conscious processing. Using the attentional blink paradigm, Marois, Yi, and Chun (2004) performed a clever experiment to compare patterns of cortical activity involved in processing the perceived stimulus and the masked stimulus. They took advantage of the specificity of the inferotemporal (IT) area, by using faces (processed in the fusiform face area, or FFA) and places (processed in the parahippocampal place area, or PPA). When the first target was a face and the second target was a picture of a scene, they found that both IT regions (i.e., the FFA and PPA) were active, but that specific frontal lobe regions were only active in concert with the activity in the PPA (related to processing of the scene) when subjects consciously reported detecting the scene. Importantly, while PPA activity was observed regardless of whether the scene was consciously detected, PPA activity was modulated by conscious awareness, such that activity was further increased by detection. Thus, it appears that a certain level of cortical activity involved in processing a stimulus, in concert with frontal activity, may be important for conscious experience. Electroencephalography (EEG) studies investigating the temporal dynamics of cortical activity in the attentional blink paradigm have suggested that the modulation of activity in lower-level visual areas associated with conscious awareness occurs after the initial perception of the stimulus. When Sergent, Baillet, and Dehaene (2005) compared EEG response patterns to seen and unseen targets in an attentional blink paradigm, they found that early processing (up to 170 ms) of the second target did not differ in seen or unseen trials. However, processing of seen versus unseen targets had different EEG profiles from 170 ms on. For seen targets only, late activations lasting 200 to 300 ms were observed in the dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC), and inferior

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parietal lobe. Seen targets also evoked a late P3b waveform, indicative of top-down processing, likely initiated and sustained by frontal activity. Furthermore, as the peak of the P3b waveform resolved, the second target became more likely to be reported, suggesting that the top-down activity underlying the P3b waveform may compete with bottom-up activity of the second stimulus, preventing the coherence necessary for further processing. Altogether, this research suggests that early visual processing is not sufficient for visual consciousness and that coherence with frontal and parietal areas, which likely provide top-down feedback modulating activity in visual areas, may be necessary for consciousness (Sergent et al., 2005). Using the related paradigm of change blindness, Beck, Rees, Frith, and Lavie (2001) replicated the findings described previously. Participants were presented with two identical or slightly different pictures in rapid sequence and were asked to judge whether the pictures were the same or different while performing a distracter task. Cortical activity in the fusiform area varied depending on whether a change occurred, regardless of whether the subject actually detected the change. When subjects did detect a change, cortical activity in the DLPFC, parietal lobe, and fusiform gyrus was greater relative to when they did not detect the change. Taken together, these results suggest that activity in cortical areas involved in lower-level visual processing (i.e., the fusiform area) is independent from conscious awareness, and that cortical networks involving frontal and parietal areas must interact with visual areas to facilitate the conscious representation of a stimulus (Rees, Kreiman, & Koch, 2002). The different levels of cortical activity in the inferotemporal and the subsequent interactions with frontal/parietal areas reflects the roles of bottom-up and top-down attentional mechanisms in promoting conscious representation of a stimulus. In both the attentional blink and change blindness paradigms, bottom-up mechanisms are involved in processing the masked stimuli and the changed picture, respectively, but whether this information enters conscious awareness is determined by top-down mechanisms. There is a complex relationship between these two mechanisms such that bottom-up processing engages top-down processing and top-down processing modulates bottom-up processing. A key question then, relates to the neural processes at the intersection of bottom-up and top-down attentional systems. To explore this question, Crottaz-Herbette and Menon (2006) used an oddball paradigm, in which one stimulus occurs 80% of the time and the oddball stimulus occurs 20%. Subjects come to expect the usual stimulus, so the oddball stimulus automatically captures the attention of the participant. Cortical activity in modality-specific

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regions (primary auditory or primary visual cortex) and ACC increased after presentation of an oddball stimulus. The investigators then assessed the effective connectivity between the ACC and the modality-specific regions. They found that visual oddballs induced increased effective connectivity between the ACC and striate cortex, while auditory oddballs induced increased effective connectivity between the ACC and Heschl’s gyrus. These results indicated that the ACC plays an important role in directing top-down attentional mechanisms in order to enhance stimulus processing. Using EEG, Crottaz-Herbette and Menon (2006) further explored the temporal dynamics underlying top-down enhancement of stimulus processing. Dipole modeling revealed that the ACC was the source of the N2b wave, which occurs between 200 to 300 ms poststimulus and is thought to be associated with controlled orientation to salient environmental stimuli. About 50 ms before the N2b wave appears, a negative deflection was recorded above the primary sensory cortex after presentation of oddball stimuli, in keeping with bottom-up enhancement of cortical activity. This bottom-up activity appears to activate the ACC, which results in the N2b wave. A second waveform, the P3a, then arises from various frontal areas and may be indicative of the reallocation of attention and the various cognitive resources that accompany attention. Thus, these results illustrate an interaction between bottom-up and topdown attention mechanisms and further indicate that attention often facilitates access to consciousness (Dehaene & Changeux, 2004). Separation of Consciousness and Attention Having cited evidence demonstrating how attentional mechanisms contribute to conscious awareness, we now consider evidence supporting the other half of the double dissociation between attention and consciousness, specifically evidence for consciousness without attention. Such evidence is commonly generated by studies that have employed divided attention paradigms, during which participants perform simultaneous tasks. In these studies, the primary task is designed to consume all available attentional resources, while a second task is used to assess perception of a peripheral stimulus. In general, subjects cannot successfully perform the primary task without an associated performance cost on the second task. Similarly, when attention to the primary task wavers, or subjects attempt to perform both tasks simultaneously, a performance cost is seen for the primary task. Using a dual-task paradigm, Li, VanRullen, Koch, and Perona (2002) demonstrated that particular kinds of stimuli may reach conscious awareness even when attentional mechanisms are not directed toward their processing.

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Subjects performed a demanding primary task, requiring that they indicate whether five letters, presented in various directions and in nine possible locations around a fixation point, were identical. When their performance on this task under dual-task conditions was equivalent to their performance on the same task when presented alone, subjects were unable to simultaneously perform a secondary task requiring the discrimination of letters or colored shapes. Yet when the secondary task involved determining whether an animal or vehicle was present in more visually complex pictures of a natural scene, subjects were able to accurately perform the task. These surprising results suggest that particular kinds of stimuli may be perceived without attention. In other words, for particular kinds of stimuli, their “gist” may be extracted and consciously represented without direct attentional resources (Koch, 2004). As Koch (2004) has pointed out, gist is a powerful concept that could explain how stimuli beyond the focus of attention are consciously perceived. This idea is similar to Hochstein and Ahissar ’s (2002) reverse hierarchy theory, which maintains that conscious visual perception does not necessarily follow the traditional, bottom-up visual processing hierarchy. “Vision at a glance” quickly provides us with a semantic representation of objects void of details (gist), while “vision under scrutiny” provides detailed perception by employing attentional resources and increasing activity in the various early visual areas. Given the research discussed earlier, one might expect that top-down projections from frontal and parietal areas to high-level visual modules facilitate “vision at a glance” (Koch, 2004), while bottom-up cortical activity in primary sensory areas may be amplified by top-down feedback and thereby support the more detailed “vision under scrutiny” (Hochstein & Ahissar, 2002). This theory of perception emphasizes the important, but easy to overlook, fact that conscious perception is not a direct representation of the physical world. Instead, it is a reconstruction that depends on the integrity of the various processing modules, including bottom-up modules processing the broad spectrum of perceptual content, and top-down modules supporting extraction of the gist of external stimuli. This general description of neural processing is aligned with the spectrum of conscious content we described in a previous section. On one end, stimuli receiving enhanced processing through allocation of attentional modules (i.e., “vision under scrutiny”) are consciously constructed with great accuracy and precision. On the opposite end, entire aspects of consciousness can be missing due to damage to specific modules or attention modules that serve to enhance activity in other modules. The hypothesis of specialized modules aided by attentional amplification neatly accounts for absent content

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and accurate content, the two extremes of the content continuum. But between these two extremes exists activity in intact perceptual modules that, at a given instant, is not enhanced by attentional modules. Even if the concept of gist is able to explain how we perceive the content in unattended regions of space or entire unattended modules, the mechanism underlying the seamless, dynamic integration of unattended content with attended content remains a mystery. Presumably, this integration is responsible for creating the wonderful illusion that we perceive the vivid panorama before us as an equal whole rather than a highly specific area surrounded by only crude conscious representations. Thus, investigation of the unique role that attention often, but not always, plays in facilitating perception provides important clues about the nature of the neural areas underlying consciousness. For instance, we have repeatedly emphasized the important role that assembling information from various modules appears to play in generating conscious experience. Empirical investigation of attention suggests that the parietal lobe is a critical component of modules responsible for attentional amplification of information in other modules. Furthermore, there is evidence that suggests frontal areas, such as the DLPFC and ACC, also may need to properly orchestrate their activity with parietal and more posterior temporal and occipital areas in order for conscious experience to emerge. These observations lend credibility to the global workspace theory (Dehaene & Naccache, 2001). Yet, frontal and parietal areas that function to amplify activity elsewhere in the brain require a substrate on which to act. Accordingly, we proceed by considering various types of highly specialized cortical processors for functions such as vision, emotion, memory, and action. Visual Awareness Arguably the best characterized class of modules are those responsible for processing visual stimuli. Not surprisingly, then, the search for the neural correlates of consciousness has been most intense in the field of vision (Koch, 2004). Yet, despite all we know about vision, relating neural activity to conscious visual content poses a major problem to investigators because the latter is directly unobservable. Though this impasse may be a central reason why neuroscientists long avoided the problem of consciousness (Searle, 2000), it is not an insurmountable obstacle. Selfreport provides a window into the content of consciousness, and participants (human and nonhuman alike) can also be trained to perform behavioral responses that inform the investigators of the current conscious content. In this way, investigators can associate objective measures of

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neural activity with the reported subjective experience, though there is no absolute way of verifying the veracity of the report. Much insight has been drawn from the subjective reports and neuropsychological testing of patients with damage to visual areas. Deficits arising after such damage provide excellent evidence of the modular organization of the brain and also reveal critical neural regions for visual perception. Blindsight In the cortex, visual processing begins in the primary visual cortex (V1). Damage to V1 may result in blindsight, a condition characterized by a profound loss of subjective visual experience, with preserved abilities to act on nonconscious visual information (Weiskrantz, Warrington, Sanders, & Marshall, 1974). Humphrey and Weiskrantz (1967) first described this phenomenon in a rhesus monkey with damage to V1 that nonetheless retained certain unexpected visual abilities, such as the ability to accurately reach for a piece of food that should be in the blind field. Weiskrantz and colleagues (1974) then studied patient D. B., who had part of his occipital cortex surgically removed to treat an arteriovenous malformation (AVM). As expected, he lost the ability to perceive stimuli presented in one half of his visual field (hemianopsia). However, on investigation, he demonstrated an intact ability to reach for an object presented in his blind field. He also demonstrated some intact visual discrimination functions, including the ability to distinguish between vertical and horizontal lines, as well as Xs and Os. The neural basis of these preserved visual abilities is debated. Some hypothesize that they arise from projections that bypass primary visual cortex, specifically projections from the superior colliculi and the pulvinar nuclei of the thalamus to extrastriate areas (Weiskrantz, 1996). However, other research indicates that these abilities depend on remnants of the geniculostriate projections (Fendrich, Wessinger, & Gazzaniga, 1992, 2001). By this view, the circuitry necessary for visual conscious experience could be disrupted despite somewhat intact output from V1 that facilitates certain other functions. It is possible to induce the experience of blindsight in healthy individuals using metacontrast masking. Lau and Passingham (2006) presented subjects with a diamond or a square, followed by a metacontrast mask either 33 or 100 ms after the stimulus. Subjects were forced to choose whether they had seen the diamond or square, then immediately were asked if they had actually seen it or if they had guessed. The performance at the two different time points was not significantly different; however, subjects reported seeing the stimulus rather than guessing significantly more often when the mask followed the stimulus by 100 ms.

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The increased level of subjective awareness of the stimulus with the later mask onset was associated with increased activity in the left mid-dorsolateral prefrontal cortex (BA 46). Whereas self-reports or behavioral responses usually used to assess the content of consciousness are associated with performance (i.e., the response used on a task is the response used to assess conscious contents), this study is unique in that the self-report is directly about the conscious experience rather than about the stimulus itself, eliminating potential performance-related confounds. These results indicate that performance, specifically in forced-judgment tasks, may not be the best indicator of subjective experience. Furthermore, they indicate that this dlPFC region may be associated with subjective visual experience. Ventral Visual Stream Disorders Higher-order visual processing has been broadly divided into two functionally and anatomically dissociable streams that emerge from V1. The ventral stream extends to the IT, while the dorsal stream leads to the posterior parietal cortex (PPC) (Ungerleider & Mishkin, 1982). Neurons in the ventral stream are tuned for specific stimulus features that allow for object identification, such as color, shape, and texture (Desimone & Gross, 1979), thus the ventral visual stream is commonly referred to as the “what” pathway (Ungerleider & Mishkin, 1982). In contrast, neurons in the dorsal stream are sensitive to stimulus features that allow for object localization, such as speed and direction of stimulus motion (Andersen, Snyder, Bradley, & Xing, 1997; Newsom, Britten, Salzman, & Movshon, 1990) thus the dorsal visual pathway is commonly referred to as the “where” pathway (Ungerleider & Mishkin, 1982). Damage to either stream eliminates certain visual functions while leaving others unscathed. Damage to the ventral stream may result in agnosia, which is broadly defined as “impairment of object recognition not caused by sensory deficits or generalized intellectual loss” (Farah, 1992, p. 162). Traditionally, a distinction has been made between associative agnosias, stemming from loss of access to semantic knowledge associated with an object, and apperceptive agnosias, which are due to higher-level perceptual disturbances (Goldberg, 1990). Visual agnosias can be highly specific and dissociable, reflecting the specific tunings of neurons in different areas of the occipital and IT areas. For instance, patients with damage to occipital or inferior temporal areas may suffer from prosopagnosia, or an inability to recognize familiar or unfamiliar faces (Damasio, 1985; Warrington & James, 1967). Patients may also exhibit color agnosia, which may include the inability to appreciate differences between colors and/or the ability to associate colors with objects in the presence of intact color vision (Gloning, Gloning, & Hoff, 1968; Lennie, 2001).

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Dorsal Visual Stream Disorders In contrast, damage to the dorsal stream typically results in deficits involving the integration of attention, vision, motor information, and spatial information. The two hemispheres play different roles in these capacities; damage to the left dorsal stream areas typically results in greater disruption of visuomotor functions, while damage to the right dorsal stream areas typically results in greater disruption of visuospatial functions (Colvin, Handy, & Gazzaniga, 2003). This dissociation reflects differences between the two hemispheres in terms of their respective contributions to conscious experience, which will be discussed further later in the chapter. Damage to the left inferior parietal region may result in apraxia, defined as a deficit in performing learned movements, often impacting both hands (Goodale, 1990; Kimura, 1982). There are two common forms of apraxia. Ideomotor apraxia, characterized by an inability to execute movements in response to a verbal command (Weintraub, 2000), has been associated with damage to the supramarginal guyrs (Leiguarda & Marsden, 2000). Conceptual apraxia, characterized by an inability to sequence a series of actions necessary to execute a complex, goal-directed action involving the use of tools, has been associated with damage to the left parieto-occipital or temporoparietal regions. Importantly, while apraxic patients are unable to execute these complex movements in response to a verbal command, they are able to spontaneously perform them (Weintraub, 2000). This suggests that the left parietal region is critical for the conscious awareness and execution of motor programs, but is not responsible for representing the motor program itself. Optic ataxia, defined as an inability to make accurate movements to objects with the contralesional hand, is a related condition, and may result from left or right superior parietal damage (Milner & Goodale, 1995). Yet reflecting the differential roles of the two hemispheres in attention, optic ataxia patients with left parietal damage make errors with their right hand throughout the entire visual field. In contrast, optic ataxia patients with right parietal damage only make errors with their left hands in the left visual field (Perenin & Vighetto, 1988), suggesting that the left parietal lobe plays a unique role in attending to motor acts throughout the entire visual field. From the perspective of understanding extended consciousness, what is important about this condition is that the actual perception of objects is unaffected in optic ataxia; it is the patient’s ability to interact with those objects through conscious visuomotor acts that is solely impacted. The right superior parietal lobe plays a complementary role in directing attention to space throughout the entire

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visual field. Following unilateral damage to the right inferior parietal lobe, patients may experience unilateral neglect, or selective inattention to stimuli presented in the contralateral (i.e., left) hemifield (Driver & Vuilleumier, 2001). Patients with unilateral neglect may often fail to eat from the left side of a plate, or fail to complete dressing on the left side of their body. Clinically, they also demonstrate the phenomenon of extinction. When presented with a stimulus in the field contralateral to the lesion, the patient readily perceives it. However, if stimuli are simultaneously presented in the contralateral and ipsilateral visual fields, the patient is likely to only report the ipsilateral stimulus. Using the extinction paradigm, Vuilleumier, Armony, Driver, and Dolan (2001) compared patterns of cortical activity associated with perceived and extinguished faces in a patient with unilateral neglect. As might be expected based on the literature reviewed in a previous section, perceived and extinguished faces induced activity in the primary visual cortex and IT but only perceived faces were associated with intact parietal lobe activity (Vuilleumier et al., 2001). Once again, this study suggests that modular processing within the basic visual areas may rely on input from frontoparietal networks in order to generate conscious awareness. Finally, simultaneous agnosia is characterized as an inability to perceive more than one object or point in space at a time, and is seen following bilateral damage to the parietal lobes (Luria, 1959). Clinically, this condition comes to attention when the patient is unable to perceive either of two simultaneously presented stimuli (simultanagnosia). These patients often have difficulty directing their gaze and shifting from a fixation point (Rizzo & Robin, 1990). For these patients, the world is perceived as a series of fragmented still frames; both time and space are impacted, such that the patient no longer perceives a continuous flow of visual information. However, as is the case in the other types of agnosia discussed previously, the basic elements of perception are undisturbed, such that the representations emerging from intact brain areas are integrated into extended consciousness. Thus, patients with specific patterns of neurological damage have revealed much about the modular organization of visual processing, and that the elimination of these modules may result in the loss of particular types of content in extended consciousness, but do not eliminate extended consciousness itself. Bistable Perception In this section, we return to studies of visual processing in healthy brains, focusing on how simultaneously presented stimuli are integrated into the content of consciousness. Bistable perception makes use of stimuli that do not physically change throughout experimental trials but nonetheless

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result in alternating unique percepts. The underlying assumption is that there should not be any difference in activity in areas that unconsciously process the stable physical stimulus (or stimuli) but there must be changes at higher levels that induce or correlate with the alternating percepts. In other words, an area responding purely to the physical stimulus should not demonstrate any changes in activity during different perceptions, while areas necessary for creating the percept should vary in activity as perception changes over time. By far the most widely used inducer of bistable perception is binocular rivalry. During binocular rivalry, corresponding areas of each eye are shown separate stimuli of equal saliency. Rather than perceiving both stimuli simultaneously or perceiving a composite of the stimuli, subjects report only seeing one stimulus at a time. Over time, the percept alternates between from one stimulus to the other. This phenomenon has been investigated using single cell recording in monkeys and using fMRI in humans. Nikos Logothetis and colleagues (Logothetis & Sheinberg, 1996; Logothetis, Leopold, & Sheinberg, 1996; Leopold & Logothetis, 1996; Logothetis, 1998; Sheinberg & Logothethis, 1997) have used single-unit recordings to search for neurons in various visual areas whose activity is modulated by perception rather than physical stimulus. They found only a small fraction in early visual areas such as V1 and V2 (18%), but an increasing amount in V4 (38%), MT (43%), and inferior temporal cortex IT (90%). The variance of these firing patterns is consistent with the increase in complexity of preferred stimuli ascending through these visual areas. These areas constitute the previously discussed ventral pathway for visual processing (Ungerleider & Mishkin, 1982), with the IT being the final purely visual area in this object-recognition pathway (Logothetis & Sheinberg, 1996). The IT also projects to and receives input from the prefrontal cortex, indicating that it could be a transition zone, processing and classifying highlevel visual stimuli and passing condensed but informationrich input to executive frontal areas. Koch (2004) calls this the “executive summary,” which he speculates may be one of the reasons that consciousness evolved. Human brains are bombarded with a vast amount of sensory input, and to survive, they must figure out which information is relevant and devote their cognitive resources to processing that particular information. An unanswered question central to this research and the theoretical speculation that it inspires is this: To what extent is the activity of the IT a neural correlate of consciousness? Is it responsible for the actual percept, or is it responsible for the experience of recognizing and semantically labeling that percept? Evidence of covert object recognition in visual aphasias suggests that recognition may

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still occur after damage to the IT (Farah, 1992). Awareness may be lost due to an inability for that module to interact with frontal areas, or to “broadcast” its content in the global workspace (Deheane & Naccache, 2001). As clinical evidence and imaging studies identify specific modules necessary for awareness of certain visual features, it is necessary to understand the interactions of these modules and other cortical areas. We have already encountered evidence that implicates frontal and parietal activity with awareness of the content in various processors, though the specific dynamics of these interactions demand future investigation. A commendable attempt to better characterize these dynamics is found in a study that assessed the visual abilities of patients with focal lesions to the dorsolateral prefrontal cortex (Barceló, Suwazono, & Knight, 2000). Patients and controls had to detect inverted triangles in sequences that contained the target, as well as upright triangles and novel stimuli (pictures of fish or flowers). One stimulus at a time was presented either to the contralesional or ipsilesional visual field. Dorsolateral prefrontal patients demonstrated a deficit in detecting the target when it appeared in the contralesional field. Furthermore, EEG data revealed reduced neural activity in the ipsilesional extrastriate cortex of dlPFC patients at early (125 ms) and late (200 to 650 ms) time points. Neural activity at these time points is thought to indicate a tonic maintenance of an “attention template” and phasic reentrant feedback from the frontal lobes, respectively. The observed deficits in both of these systems after dlPFC damage further implicate this frontal area in visual awareness. Koch (2004) has made a strong case that visual awareness is the most tractable aspect of conscious experience to empirically approach, as evidenced by the impressive progress in identifying potential neural correlates of visual consciousness described previously. This is surely a valid point, but it does not follow (nor does Koch imply) that investigation of the neural correlates of other aspects of conscious experience is entirely futile. On the contrary, as the topic of consciousness has returned to scientific discussion, investigators have begun exploring with great vigor the curious split between conscious and nonconscious across a breadth of neural functions. Emotion and Consciousness Some of the earliest work on emotional processing in the brain identified critical roles for neural structures comprising the limbic system (MacLean, 1949; Papez, 1937). This interconnected network of subcortical nuclei and cortical areas includes the amygdala, thalamus, hypothalamus, cingulate cortex, insula, and orbitofrontal cortex. Particular

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regions within this system are involved in nonconscious emotional processing. For example, it has long been recognized that the amygdala plays a critical role in fear conditioning, binding the physical sensations of fear mediated by the autonomic nervous system and relayed through the hypothalamus, to the perception of a particular environmental cue. As such, the amygdala and hypothalamus, together with neighboring medial temporal structures (such as the hippocampus) are involved in implicit learning of appropriate fight-or-flight responses to dangerous cues, and these structures have become a focus of research into understanding the neurobiological mechanisms underlying anxiety disorders involving a fear-conditioned response, such as posttraumatic stress disorder and social phobia (Etkin & Wager, 2007; Rauch, Shin, & Phelps, 2006). At this basic level, the emotional binding process is nonconscious. Multiple neuroimaging studies have demonstrated that the amygdala is active when fearful faces or fear-relevant stimuli are presented, regardless of whether those stimuli are consciously perceived (Jiang & He, 2006; Morris, Ohman, & Dolan, 1998; Vuilleumier et al., 2001). Similarly, in unilateral neglect patients, the amygdala responds to fearful faces that are extinguished from conscious awareness by the simultaneous presentation of a second stimulus (Vuilleumier et al., 2002). This is not surprising given our subjective sense that the physical sensations associated with emotions often precede our conscious understanding of their origin. It is the activity within these basic modules that is then broadcast via connections with higher-order cortical areas, resulting in the emergence of an emotional experience. Conscious emotional processing involves two related phenomena—the awareness of one’s own feelings and the awareness of another ’s feelings. One can be conscious of his or her feelings, and one can also be conscious of the feelings of another. The extent to which these two functions can be dissociated is yet unclear. This is likely in part because while one’s accuracy in discriminating and perceiving the emotions of others is quantifiable, it is more difficult to capture the validity of one’s own emotional experience. However, it also likely stems from the fact that the conscious experience of one’s own emotions is a more automatic process than empathizing with another (de Vignemont & Singer, 2006). One possibility is that the topdown processes that engage the limbic system drive cognitive processes involved with empathy (Carr, Iacoboni, Dubeau, Mazziotta, & Lenzi, 2003), while bottom-up processes drive the cognitive processes involved in one’s own conscious experience. This would allow for dissociation between the two types of conscious emotional experience. For example, one could be acutely attuned to one’s personal emotional experience, but have particular difficulty

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empathizing with another ’s experience. Such dissociation could exist even if the two types of conscious emotional processing share neural substrates. Indeed, both the awareness of one’s own emotional experiences and the emotions of others depend critically on two cortical areas at the interface between conscious and nonconscious processing, the insula and the ACC. Neuroimaging studies have demonstrated that both regions are active when one is experiencing pain or empathizing with someone else who is experiencing pain (Singer et al., 2004, 2006). These regions likely make unique contributions to the conscious experience of emotions. The insula has been repeatedly linked to the experience and perception of disgust (Adolphs, Tranel, & Damasio, 2003; Calder et al., 2000), and may play a unique role in processing emotional responses elicited by physical contact between individuals (Olausson et al., 2002). Similarly, activity in the ACC is modulated by the extent to which one is aware of his or her emotions (Lane et al., 1998), and also relates to the experience of basic interoceptive stimuli (Liotti, Brannan, Egan, Shade, Madden, Abplanalp et al. 2001). This function may reflect a broader role in mediating conscious awareness across perceptual and cognitive domains; as we discussed earlier, the ACC is also critically involved in visual awareness. There is considerable convergent evidence that modules within the right hemisphere play a preferential role in emotional processing. Some of the earliest observations of behavioral changes following unilateral damage to the right hemisphere noted reduced emotional expression and inappropriate affect (e.g., Mills, 1912a, 1912b). Since then, subsequent studies have revealed that right hemisphere damage impacts nearly all aspects of emotional expression and interpretation. For example, following right hemisphere damage, individuals may have difficulty conveying affective information through the tone of their voice (i.e., prosody) and may also have difficulty interpreting the tone of another ’s voice to better understand their emotional experience (Blonder, Bowers, & Heilman, 1991). More recent studies have pointed to a specialized role of the right hemisphere in nonconscious processing of facial expressions (Morris et al., 1998), suggesting that the specialized role of the right hemisphere in emotional processing extends to both nonconscious and conscious processing. The etiology of the right hemisphere’s specialization for emotional processing has been widely debated, but one leading hypothesis is that the differential role of the two hemispheres in emotional processing stems from an asymmetry in the autonomic nervous system. Specifically, the left hemisphere has been associated with parasympathetic activity, while the right hemisphere has been associated with sympathetic activity. Based on this proposal, the left hemisphere is involved in approach or “group-oriented”

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emotions, while the right hemisphere is involved in withdrawal or “individual-oriented” emotions (e.g., Craig, 2005). This model neatly accounts for the fact that right hemisphere damage, from a variety of neurological conditions, may result in secondary mania (for review, see Cummings, 1997), which can be conceptualized as a lack of withdrawal behavior. Similarly, abnormal patterns of right hemisphere activity have been observed in patients with bipolar affective disorder who are currently in the manic state (Caligiuri et al., 2004) and visuospatial weaknesses are often observed in patients with chronic bipolar affective disorder (Osuji & Cullum, 2005). However, the model would also predict that social deficits in relating to others would be associated with left hemisphere dysfunction. The preponderance of evidence accumulated to date suggests that these abilities are also related to right hemisphere function. Degeneration of the frontotemporal regions of the right hemisphere may be associated with acquired sociopathy, or diminished regard for the impact of one’s behaviors on others (Mendez, Chen, Shapira, & Miller, 2005), perhaps due to downstream disruption of ACC functioning (Rudebeck, Buckley, Walton, & Rushworth, 2006). Similarly, impaired social communication skills, including empathy for others’ experiences, are the hallmark of several developmental disorders associated with right hemisphere dysfunction. Patients with certain autism-spectrum disorders, specifically nonverbal learning disability (NLD) and Asperger ’s syndrome often exhibit excellent verbal skills, yet struggle to understand the complexities of everyday language, including the implications of prosody and pragmatics, and exhibit concomitant cognitive weaknesses suggestive of right hemisphere dysfunction (Rourke et al., 2002). It is yet unclear whether the autonomic nervous system asymmetry model can account for these findings, but regardless of etiology, it is clear that the right hemisphere has a specialized role in emotional processing. Altogether, current research strongly suggests that the limbic system is comprised of the neural modules involved in emotional processing, and that some of those modules within the right hemisphere play a specialized role in emotional processing. At the most fundamental level, the hypothalamus and amygdala are involved in nonconscious processing of affective stimuli, particularly those related to the basic emotion of fear. The interaction of these modules with higher-order cortical areas, particularly the insula and the ACC, gives rise to the conscious experience of feelings, likely combining input from widespread cortical modules involved in other cognitive functions. Ultimately, this system enables not only an understanding of one’s own emotional experiences, but also an understanding of the emotional experiences of others.

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Memory and Consciousness Recall that in defining the components of consciousness, core consciousness refers to an awareness of a stimulus that is limited to the present moment, while extended consciousness refers to the integration of past experiences to elaborate on this representation (Damasio, 1999). As such, extended consciousness is inextricably linked to memory, the latter of which inserts novel subjective qualia into the present experience and thereby allows us to forge a temporal link between the present moment, the past, and an imagined future (Tulving, 2002). One may have some limited conscious awareness of this binding process by explicitly recollecting a past experience. However, the fusion between one’s current experience and one’s past may also occur on an implicit level. These parallel processes reflect the structure of networks involved in the consolidation of new memories. A distinction is often drawn between long-term memories that are procedural and those that are declarative (Tulving, 1983). Procedural memory encompasses learned perceptual, motor, and cognitive skills and is largely nonconscious. For example, learning to ride a bicycle requires rote practice until the integration of the sensory and motor components is seamless. Once learned, this skill can be called on automatically, so that you can ride almost any bicycle, in almost any situation, with relative ease and with relatively little explicit thought to the process involved. If you imagine your own experience of learning to ride a bicycle, and your most recent experience of riding a bicycle, it quickly becomes clear that procedural learning, and the retrieval of procedural memories, is largely unavailable to conscious awareness. Procedural learning is critically dependent on basal ganglia functioning, particularly that of the striatum. Patients with neurodegenerative diseases that disrupt striatal functioning, such as Parkinson’s or Huntington’s disease, have difficulty learning new skilled procedures (Squire & Zola, 1996). Yet, representations of procedural skills are likely diffusely distributed throughout the brain, involving particular regions of frontal cortex, the basal ganglia, and the cerebellum (e.g., Ullman, 2001), thus the sudden loss of a wide variety of well-learned skills is relatively rare. While the procedural memory system operates automatically and implicitly, the experiences spanning acquisition and application of these skills are stored in a declarative memory system that has two components—semantic and episodic memory. Semantic memory refers to a particular class of learned information that is impersonal and not necessarily tied to a specific moment in the past. Thus, semantic memory can be conceptualized as a collection of facts. Recollection of these facts is associated with noetic conscious experience, or the feeling of knowing (Tulving, 1985).

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The second component of declarative memory, episodic memory, is thought to have evolved from semantic memory because it relates to a special type fact—memory of the actual experience of events from the first-person point of view (Squire & Zola, 1996). For instance, recalling the capitol city of one’s home state, an objective fact, requires semantic memory. However, the memory of a particular visit to that city, a specific and personal past experience, requires episodic memory. Both semantic and episodic knowledge require explicit learning and retrieval, and the consolidation of this information into permanent representations is critically dependent on medial temporal structures. This is demonstrated most clearly by anterograde amnesia, a disorder characterized by an inability to form new declarative memories that results from bilateral damage to medial temporal structures, including the hippocampus (Scoville & Milner, 1957). Episodic memory is critically linked to extended consciousness. In the words of Endel Tulving, “The essence of episodic memory lies in the conjunction of three concepts—self, autonoetic awareness, and subjectively sensed time” (2002, p. 5). Each of these three concepts plays a central role in extended consciousness, an indication of why episodic memory itself is critical for the fullest realization of extended consciousness. First, episodic memory provides the basis for a much more elaborate form of self, which Damasio (1999) has called the “autobiographical self.” This construction of the self is not merely a distinction between the neural representation of the physical self and representations of external objects. Instead, it is a self with explicit access to its past. Second, just as recalling a fact has a specific subjective flavor, remembering an experience is associated with a unique feeling, which Tulving refers to as “autonoetic awareness.” Investigation of this experience provides another angle from which to investigate the neural correlates of consciousness. Finally, episodic memory liberates humans from the present moment, enabling mental time-travel forward and backward. In addition to producing anterograde amnesia, bilateral damage to the medial temporal lobe structures can disrupt autonoetic conscious experience and one’s ability to think about the future. Tulving (1985) describes patient K. C., who sustained damage to the hippocampus and parahippocampal gyrus, and who is unable to conceptualize what he may be doing in the near future, describing his state of mind when asked to do so as “blank.” A similar pattern of deficit has been noted in other case studies (Kitchener, Hodges, & McCarthy, 1998; Klein & Loftus, 2002), and a study of five patients with retrograde episodic amnesia caused by bilateral hippocampus damage revealed a diminished ability to image future events (Hassabis, Kumaran, Vann, & Maguire, 2007). Additionally, age-related deterioration of

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episodic memory has recently been demonstrated to correlate with a decreased ability to mentally simulate rich, detailed future events (Addis, Wong, & Schacter, 2008). Thus, at a general level, extended consciousness without episodic memory resembles in many ways primitive core consciousness. In both cases, the organism is trapped in the present moment. However, this version of impaired extended consciousness still provides a greater breadth of conscious experience and conscious cognitive function. For instance, anterograde amnesia patients exhibit remarkable preservation of functions in other cognitive domains, including attention, perception, and motor abilities (Tulving, 2002). Similarly, aspects of one’s core self, including values, beliefs, and morals, may be preserved, even in the absence of the knowledge base that may have guided their formation (Corkin, 2002). This is further indication of the modular nature of extended consciousness. Because consciousness emerges from concerted activity across distributed modules, even damage that eliminates the subjective past and future is unable to decimate the entirety of extended consciousness. Instead, it dramatically reduces the repertoire of modules available to be integrated into conscious experience. Importantly, associating activity in episodic memory modules with autonoetic conscious experience provides another approach for investigating the neural basis of extended consciousness. Based on the location of K. C.’s damage, the medial temporal lobe is presumably involved. Additionally, early positron emission tomography (PET) studies found that frontal lobe activity was associated with autonoetic consciousness (Tulving et al., 1994), leading to a theory that tied episodic memory to frontal lobe function (Wheeler, Stuss, & Tulving, 1997). Subsequent EEG evidence supported the distinction between noetic and autonoetic conscious experience because each was associated with a distinct EEG pattern that indicated orchestrated activity in temporal, parietal, and frontal regions (Düzel, Yonelinas, Mangun, Heinze, & Tulving, 1997). More recently, Addis and colleagues (2008) performed a study in which they used an event-related design to distinguish between the correlates of constructing an episodic memory or imagined future event and the correlates of elaborating (remembering or imagining specific details) that event. Construction of events, both past and future, was associated with activity in the left hippocampus, bilateral inferior temporal and fusiform cortices, and superior and middle occipital gyrus. Construction of future events also engaged frontal and prefrontal regions associated with prospective thinking and semantic generation. Elaboration of past or imagined events commonly involved engagement of a network associated with autobiographical memory retrieval (Maguire, 2001), including the parahippocampal

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cortex, retrosplenial cortex, posterior cingulate, and precuneus. Additional activity was seen in the left medial PFC. These results, as well as other similar findings (see Schacter, Addis, & Buckner, 2007, for a review), are remarkable for at least two reasons. First, they have greatly enhanced our understanding of memory and are the basis of new theories about how the past contributes to construction of mental images of the future (Hassabis & Maguire, 2007; Schacter et al., 2007). Second, though it has rarely been stated, these results also inform our understanding of how the brain generates conscious experience. Indeed, modules that have classically been associated with memory, such as medial temporal lobe structures, consistently interact with frontal and parietal areas, as well as posterior modules involved in visual perception when visual imagery is involved, in order to produce autonoetic conscious experience. Remembering and imagining specific future events involve similar, distributed neural correlates that generate distinct conscious content. Furthermore, we begin to see how integration, the topic of the final section of this chapter, may occur. In this case, modules that initially processed an experience (e.g., visual modules) were recruited during elaboration of episodic memory of that experience. An elegant study by Daselaar and colleagues (2008) expanded on this observation. They found that vividness of recall, which they call “reliving,” was associated with increased activity in extrastriate visual cortex. They also demonstrated that emotional intensity ratings correlated with frontopolar activity during elaboration of the memory (other areas, such as the amygdala, temporoparietal regions, and inferior frontal gyrus were modulated by emotional intensity during the retrieval process only). Thus, while all memories invoked an autonoetic experience, each experience was associated with unique perceptual and emotional flavors that could be traced to activity in discrete modules. This distributed activity was integrated to form subjectively rich conscious content. Conscious Awareness of Action There are some patients who possess functioning modules responsible for attention, perception, and memory but are nonetheless nearly diagnosed as being in a vegetative state. It seems unthinkable that a patient possessing much of the expanse of extended consciousness could be mistaken for someone without even core consciousness, but this is an unavoidable consequence of trying to diagnose an intrinsically first-person experience from a third-person point of view. As we have repeatedly conceded, scientists are utterly reliant on self-report to assess consciousness in other individuals. If all motor function, including that required for speech, is lost, it is very difficult to ascribe

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consciousness to another individual. Thus, patients suffering from locked-in syndrome are essentially conscious beings trapped in a paralyzed body. Their only means of communication is often a preserved ability to execute vertical or lateral eye movements (Laureys et al., 2007). This simple, subtle means of communication reveals a fully functional mind. In fact, one locked-in patient wrote a stunning memoir by using left eyeblinks to select letters from a recited stream of letters, crafting his masterpiece literally letter-by-letter (Bauby, 1997). It is important to realize that locked-in syndrome further contributes to the modular hypothesis of extended consciousness. Abolishing motor modules does not eliminate extended consciousness altogether; it merely limits its scope. Yet, there is something peculiar about addressing this set of modules. It has nothing to do with actual neural mechanisms; indeed, the principles that govern these modules, their interactions, and their role in generating conscious experience are presumably no different from those of the various other modules discussed thus far. Rather, it is a philosophical point. Understanding the neural basis of the conscious experience of intending and executing an act inevitably mounts an attack on free will, one of the most fundamental and deeply cherished human doctrines. Objective reality clashes with intuition, cruelly leveling the edifice of free will. However, we view this conclusion in a far more optimistic light. Just as understanding the neural basis of consciousness will not make our conscious experience any less rich and wondrous, understanding that the brain determines behavior while simultaneously producing the useful illusion of free will does not detract from our day-to-day feeling of controlling our thoughts and actions (see Aharoni, Funk, Sinnott-Armstrong, & Gazzaniga, 2008; Gazzaniga, 2000, 2005). Despite the weighty issues surrounding this subject, we shall continue to adhere to empirical data and consider the various modules involved in intention and motor awareness. Because we are purely interested in examining the neural bases of conscious experience and not the intricacies of the various neural causes of action initiation and execution, we chose here to focus on the two most tractable types of conscious content associated with this class of modules: conscious intention and motor awareness. The former is associated with voluntary actions and subjectively precedes awareness of action initiation. The latter is not specific to a class of actions and is a more general experience that is associated with movement, regardless of cause. Conscious Intentions The work of the late Benjamin Libet was responsible for igniting years of intense research on conscious intentions (see Libet, 2004, for a summary of his work). In their

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classic experiment, Libet, Gleason, Wright, and Pearl (1983) recorded event-related potentials (ERPs) while subjects voluntarily lifted their right hand at a time of their choosing. Subjects were instructed to observe the location of a dot rotating around a screen in front of them and to remember the location of the dot when they first felt the onset of the conscious decision to move their hand, or the onset of the intention. EEG recordings revealed a distinct wave of activity that preceded each hand movement by around 500 ms, called the “readiness potential.” Libet found that the onset of the readiness potential also preceded the conscious experience of intention by about 300 ms. The brain appeared to be preparing for the action before the subject had any subjective notion of intending to move his or her hand. This observation prompted the speculation that such activity might be the neural precursor or perhaps correlate of conscious intention. Though some have questioned Libet’s methods and conclusions (Kilner, Vargas, Duval, Blakemore, & Sirigu, 2004; Mele, 2006), the spirit of his work indisputably pervades present investigations of the neural correlates of conscious intention. While the readiness potential (RP), a rise in negative potential (i.e., change in electrical activity) preceding voluntary initiation of a movement, was initially associated with the conscious experience of intending and perhaps motor initiation itself, more recent research has revealed that the readiness potential is also present when an individual watches another person perform an act (Kilner et al., 2004). Thus, this activity may reflect a more general motor processes engaged by planning, acting, or observing action. So while the RP is, in principle, quite interesting, it is necessary to go beyond the poor spatial resolution of EEG and more closely scrutinize the neural events underlying intention formation. Toward this end, Lau, Rogers, Haggard, and Passingham (2004) found that attending to the experience of intention is marked by increased activity in the presupplementary motor area (pre-SMA) relative to activity observed when one attends to the actual action. This increased activity was accompanied by increased activity in the dorsal prefrontal cortex (dPFC; BA 46) and the intraparietal sulcus. Furthermore, connectivity between the pre-SMA and dPFC was enhanced in the attention to intention condition. The authors suggest that “attention to intention may be one mechanism by which effective conscious control of actions becomes possible” (p. 1210). Thus, these results are crucial for two reasons: Pre-SMA activity is associated with awareness of intentions and connectivity between the pre-SMA and dPFC may represent a pathway by which cognitive control systems could influence intentions and thus behavior. Additional studies have provided further evidence that the pre-SMA is a key node for intention formation (Hoshi & Tanji, 2004; Nachev, Rees, Parton, Kennard, & Husain,

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2005). However, a study of patients with damage to the angular gyrus of the parietal lobe (Sirigu et al., 2004) suggests that these frontal areas alone may not be sufficient for proper conscious experience of intentions. Sirigu and colleagues repeated Libet’s experiments with three groups of subjects: patients with lesions to the angular gyrus, patients with cerebellar damage, and healthy controls. Parietal patients reported the experience of intention to be nearly at the initiation of action (20 ms after action initiation), whereas the other two groups properly judged the intention as occurring about 200 ms before action initiation. None of the groups had any difficulty initiating voluntary movements, and all three groups properly judged the time of action initiation on separate trials. Importantly, EEG data indicated a lack of an RP when the parietal patients judged the time of the onset of intention and a greatly reduced RP when they judged the time of action onset. Though the RP is not uniquely associated with the conscious experience of intention, the reduction of the RP in patients with parietal damage is nonetheless significant because it is likely indicative of a loss of reentrant feedback from the parietal lobe to the frontal lobe (Sirigu et al., 2004). Because this disruption to the normal connectivity is associated with a deficit in conscious intention formation, these results emphasize the importance of proper connectivity, particularly between specific modules in the frontal lobe and parietal lobe, for normal conscious experience. In summary, study of conscious intention indicates that attending to the experience of intention requires proper connectivity between the dPFC and the pre-SMA, while the experience of intention relies on proper connectivity between the frontal motor areas and the angular gyrus of the parietal lobe. Motor Awareness In the experiments described in the preceding section, judgments of the onset of action rely on conscious awareness of the movement. Yet, awareness of limb movement is a difficult problem to experimentally approach due to the unchanging nature of bodily awareness. The constant input received by areas that represent limbs is more difficult to manipulate than the content of visual or emotional experience. Nonetheless, Tsakiris, Hesse, Boy, Haggard, and Fink (2007) devised a clever solution to this problem. They found that simultaneously stroking a visible rubber hand and the subject’s hand, which was hidden from view, led to the subjective experience of ownership over that limb. Expressed ownership of the limb was associated with activity in the right posterior insula and right frontal operculum. Thus, by artificially engaging the area of the brain that represents bodily awareness, they were able to lead individuals to believe that a rubber arm belonged to them.

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Additional evidence for the role of the posterior insula in bodily awareness is found in the patient literature. Damage to the posterior insula results in an inability to detect one’s paralysis. A study of two groups of patients, one with anosognosia for hemiplegia and one with hemiplegia without anosognosia, found that the group with anosognosia (or denial of their deficit) had differential damage to the posterior insula (Karnath, Baier, & Nägele, 2005). Thus, patients with hemiplegia/hemiparesis who nonetheless have intact posterior insula are aware of their paralysis, while patients with damage to this area are oblivious. These results suggest that the posterior insula is critical for bodily awareness and consequently, for proper awareness of action execution (Farrer et al., 2008).

UNITY OF CONSCIOUSNESS Until now, we have focused extensively on the various modules that process different classes of conscious content. We have repeatedly encountered empirical evidence of the importance of reciprocal connectivity between these modules and frontal and parietal areas for normal conscious experience. Such evidence supports theories, like the global workspace, that posit long-range connectivity as a mechanism for the sharing of information across processors. Indeed, the integration of activity across interconnected modules throughout the cortex is a hallmark of most leading theories of the neural basis of consciousness (Crick & Koch, 2003; Dehaene & Naccache, 2001; Tononi & Edelman, 1998). But despite the empirical momentum afforded to these theories, their requirement of extensive, long-distance reciprocal connectivity as a basis for normal conscious experience seems fundamentally incongruent with the undisturbed conscious unity experienced by the left hemisphere of the split-brain. The Split-Brain and the Interpreter Over the past 5 decades, investigation of patients who have had their corpus callosum transected in order to control the spread of epileptic seizures has led to great insight into the functional specialization of the hemispheres of the human cerebral cortex (see Gazzaniga, 2000, for a review). Disconnection of the hemispheres leads to specific deficits in each hemisphere, revealing a substantial amount of specialization in each hemisphere. Yet, despite these deficiencies, each hemisphere retains a surprising amount of functionality. Furthermore, though it is difficult to directly characterize the experience of the mute right hemisphere, there is considerable evidence that each hemisphere generates a unique conscious experience (Gazzaniga, Ledoux, &

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Wilson, 1977). Even more striking, the left brain does not miss the right brain and the conscious experience that it generates (Gazzaniga & Miller, 2009). Conscious unity is somehow preserved in the left hemisphere of the splitbrain patient, an observation that dramatically undermines explanations of the neural basis of consciousness that rely upon extensive reentrant connectivity in a global network that spans both hemispheres. A second observation derived from split-brain research that we maintain is of singular importance to coherent, integrated conscious experience is the remarkable tendency of the left hemisphere to tirelessly generate, and wholeheartedly believe, hypotheses that explain the actions initiated by the disconnected right hemisphere. This interpretive process was first discovered while testing split-brain patient P.S. on a simultaneous concept task (Gazzaniga & LeDoux, 1978). In this task, each hemisphere is simultaneously shown a different picture and is asked to select a related picture out of a lineup of eight pictures. The left hemisphere was shown a chicken claw, and the right hemisphere was shown a snow scene. Next, P.S. was asked to choose a related picture with both hands. His right hand (controlled by his left hemisphere) selected a picture of a chicken, and his left hand selected a shovel. The hemispheres could not exchange visual information but could, however, see the picture selected by the other hemisphere. Because language is almost always localized in the left hemisphere (Gazzaniga et al., 1977; Lenneberg, 1967), asking a patient to describe something verbally assesses the knowledge of only the left hemisphere in callosectomy patients. Interestingly, when P. S. was asked why he chose the shovel, rather than replying “I don’t know. I had a surgery and now transfer of information between my left and right hemispheres is hindered, so my left hand does things for reasons I cannot describe,” he said “Easy. The chicken goes with the claw, and you need the shovel to clean out the chicken house.” This shocking response led one of us (MSG) to propose the existence of an interpretative process, located in the left hemisphere, which is responsible for constantly generating explanations that make sense of our interactions in the physical and social environment. In support of this theory, further investigation revealed that the left hemisphere automatically interprets even more complicated actions initiated by the right hemisphere. For instance, when a command such as “laugh” is tachistoscopically presented to the right hemisphere, the patient begins laughing. When asked why, the patient responds, “You guys come up and test us every month. What a way to make a living!” Or, if the command is “walk,” the patient typically stands up and begins to leave the testing van. When asked where he or she is going, the interpreter responds, “I’m going into the house to get a Coke” (Gazzaniga, 1983).

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The existence of the interpreter explains previous experimental results obtained by Schacter and Singer (1962). They injected subjects with epinephrine and subsequently allowed them to interact with either a euphoric or an angry confederate. Epinephrine activates the sympathetic nervous system, causing an increased heart rate, hand tremors, and facial flushing. Some participants were told that they had been injected with epinephrine and were informed about its effects. When asked why they were aroused and displayed the symptoms they did during their interactions with the confederate, they attributed the response to the drug. However, other subjects were not informed of the drug’s properties, and this group attributed the autonomic arousal to the interaction with the confederate. They felt the interaction resulted in an experience of elation or anger, and that this was the source of the autonomic arousal. This finding illustrates the human tendency to generate causal explanations for events, which the foregoing split-brain research indicates is a function of the left hemisphere. Accordingly, we assert that the brain continuously constructs our conscious experience bit by bit via activity in parallel, specialized modules, and it quickly interprets this experience after it occurs (Roser & Gazzaniga, 2004). This theory of conscious experience, derived from split-brain research, explains other extraordinary deficits and bizarre patterns of deficit denial arising after various types of cortical damage (Cooney & Gazzaniga, 2003; Gazzaniga, 2000). For instance, patients suffering from anosognosia for hemiplegia strangely deny that their limbs are paralyzed or, in even more extreme forms, deny ownership of the limb and attribute it to another agent (Karnath et al., 2005). Cereda Ghika, Maeder, and Bogousslausky, (2002) report a 75-yearold woman who “woke up suddenly in the night with a sensation of being touched by a stranger hand and alarmed by a foreign body in her bed, not recognizing her own left upper limb” (p. 1953). Though unusual and by most accounts difficult to comprehend, we posit that such responses are a natural consequence of the modular brain and the ever-active interpreter. The interpreter analyzes activity in modules that are accustomed to receiving input about the state of the body. When the input to these modules is unusual or absent, the module conveys the problem and, for instance, the subjective experience of being paralyzed emerges. However, when these bodily awareness modules are damaged, the conscious representation of the limb simply disappears. Meanwhile, the interpreter does the best it can with whatever information is present. Without input from the apparatus that usually indicates that “my hand is doing what I want it to” or even “I can’t feel or control my hand,” the interpreter concludes the hand must belong to someone else. Another telling example is that of Capgras’ syndrome. Patients are fully able to perceive that the person in front of

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them looks identical to a loved one, but they are adamant that it is really an imposter. The present understanding of these delusions is that the emotional response normally elicited by a familiar individual (an example of nonconscious emotional processing because one does not feel this every time one sees one’s significant other) becomes disconnected from the representation of the person (Ramachandran, 1998). Normally, a simultaneous visual and emotional response leads to positive identification. When the emotional response is absent, the interpreter is faced with paradoxical information, so it hypothesizes that the person must be an imposter or alien. Perhaps one of the most unusual syndromes is reduplicative paramnesia, a deficit that involves believing multiple copies of a place exist (Gazzaniga, 2000). One patient mistakenly believed that she was in her home in Freeport, Maine, while she was examined at New York Hospital. She was otherwise quite intelligent and fully understood that her opinion was at odds with that of her doctors. But she held steadfast to her strange belief. Attempts to convince her that she could not possibly be at her house were quickly processed and refuted by the interpreter. For instance, when the doctors pointed out elevators in the hallway, she quickly responded, “Doctor, do you know how much it cost me to have those put in?” (Gazzaniga, 2000). The activity of the interpreter may also explain other, more subtle observations. Recall that patients with damage to the angular gyrus have difficulty making temporal judgments of the onset of intention (Sirigu et al., 2004). They judge the onset of intention at the same time that they judge the onset of action (on separate trials). It is possible that they know intentions are meant to precede voluntary actions and when forced to identify the onset of intention, they report it as soon as they note movement because they realize it should have occurred by that time. If this is the case, conscious intention may be absent but the interpreter may infer the occurrence of intention in order to account for the action. The concept of an interpreter compellingly explains how the feeling of mental unity can arise from the modular brain. Conscious experience emerges from activity in discrete modules, leading to specific subjective content. The interpreter makes sense of this content by making causal inferences immediately after the experiences occur, constantly interpreting and seamlessly linking the serial conscious moments into a united whole. Insults to the nervous system corrupt the input to modules specialized for processing and representing certain information. When this occurs, the conscious content constructed by these processors conveys this malfunction and the deficit is experienced as such. However, if the processor itself is

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corrupted, the bit of conscious experience it usually contributed disappears without any higher cortical remnant to note its absence. The interpreter, not at all disconcerted by such destruction, continues interpreting the remaining conscious experiences as they occur. Thus, as one of us has previously written: “The ‘interpreter ’ is a specialized system that makes sense of all the information bombarding the brain, interpreting our responses—cognitive or emotional—to what we encounter in our environment, asking how one thing relates to another, making hypotheses, bringing order out of chaos, creating a running narrative of our actions, emotions, thoughts, and dreams. The interpreter is the glue that keeps our story unified and creates our sense of being into a coherent, rational agent. It is the insertion of the interpreter into an otherwise functioning brain that enables such a rich experience” (Gazzaniga, 2008). The interpreter fills a substantial, and often ignored, void in other accounts of conscious unity. Other theories focus on a separate aspect of conscious integration, the socalled “binding problem,” or the problem of how various features and objects that are processed in distributed brain regions end up correctly integrated in a percept (see Engel, Fries, König, Brecht, & Singer, 1999, for a review). Though the binding problem is most often discussed in regard to visual perception, some have suggested that the underlying mechanism of synchronized activity across modular processors must be more robustly applicable to other aspects of conscious experience (Engel et al., 1999; W. Singer, 2001). For example, precisely synchronized activity may explain how emotional and visual content is integrated into autonoetic experience (Daselaar et al., 2008). Certainly, understanding the set of neural mechanisms that operate in tandem to generate simultaneous, properly bound qualia and thus a united conscious landscape is essential to a complete characterization of the neural basis of conscious experience. But such mechanisms are purely constructive and thus require a complementary, interpretive mechanism for truly coherent, united conscious experience to emerge (Roser & Gazzaniga, 2004). Integrating Empirical Evidence for Frontal and Parietal Connectivity Evidence from a spectrum of patient populations, most notably split-brain patients, indicates that discrete aspects of conscious experience emerge from localized modules rather than a global network. Nonetheless, much of this chapter highlighted contrary evidence that associated long-distance connectivity between modules with conscious experience. Rather than reject or discredit this evidence, we instead embrace it and speculate that it can be

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incorporated into the proposed framework when cast under a different light. Philosopher Ned Block (1995) argued for two different types of consciousness, each supported by unique neural correlates. He maintains that phenomenal consciousness is the “what it’s like” to be in a conscious state, and access consciousness is the information processing component of conscious experience. Importantly, one hallmark of access consciousness is that it is reportable, which requires maintenance of the content to be reported and likely involves working memory and attention (Block, 1995, 2005; Sergent et al., 2005; Courtney, Petit, Haxby, & Ungerleider, 1998). Though this perspective is quite useful in the present context, we do not see the need to define two separate types of consciousness. Instead, we view the processes that make up access consciousness as a subset of modules that are associated with unique phenomenal content. The global workspace is fit to describe the function of processes that are associated with access consciousness, such as working memory, episodic memory, attention, and even explicit emotion (Block, 2005). These processes require coordination by a central executive, or areas involved with cognitive control (Dehaene & Naccache, 2001), and likely involve mechanisms of competition between modules in a way that construction of conscious experience more generally does not. Tasks aimed at elucidating the mechanisms of pure conscious construction may be confounded by the fact that assessment of conscious content requires self-report, which in turn engages attention and working memory systems. Some studies that have associated frontoparietal activity with conscious visual perception have acknowledged that this activity could be responsible for the ability to report the experience rather than construction of the conscious percept itself (Lau & Passingham, 2006; Sergent et al., 2005). This view of consciousness explains a key characteristic of conscious experience that was alluded to in the earlier discussion of attention. While cognitive processes like attention and working memory are known to be limited (Marois & Ivanoff, 2005), subjective conscious experience operates under different constraints. To demonstrate this, consider the act of reading this page. It is impossible to attend to all of the words simultaneously, and we can only maintain a limited number of words in working memory if we close our eyes. Nonetheless, there is a phenomenal representation of the rest of the page, the book, and its surroundings. Rather than thinking of the neural basis of this experience as an enormous, interacting coalition, we maintain it is constructed piecewise by parallel activity. Though many qualia may be produced in parallel, conscious experience feels serial in nature because it is constrained by reportability, a process that relies on limited processes

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like attention and working memory. Furthermore, limited resources produce competition, so the foundational notion of competition within the workspace need not be abandoned when considering the facets of conscious experience that pertain to Block’s access consciousness (1995). By this view, the workspace does not underlie conscious experience in a general sense. Consequently, splitting the workspace in half by severing the corpus callosum is not predicted to have a drastic effect on conscious experience. One hemisphere of the split-brain patient cannot report stimuli presented to the opposite hemisphere, but this is of little concern because the ignorant hemisphere does not realize that the other side of the world exists. Unaffected connections between frontal and parietal areas with more posterior regions, a hemiworkspace, facilitate the existing ability to generate content related to access consciousness, such as attending to and reporting stimuli presented to that hemisphere. This is consistent with Tononi’s (2004) claim that the internal architecture of each hemisphere is sufficient to generate private conscious experience based on its ability to process and integrate a broad range of information. Hence, through the activity of both localized modules and distributed modules that depend on the hemiworkspace, conscious experience of one side of the world and of the remaining capabilities of that hemisphere emerges. The disappearance of a whole other side of the world and a more complete set of capabilities, a seemingly epic event, passes quietly. And in the left hemisphere, the resilient interpreter continues to narrate.

REFERENCES Addis, D., Wong, A., & Schacter, D. (2008). Age-related changes in the episodic simulation of future events. Psychological Science, 19, 33–41. Adolphs, R., Tranel, D., & Damasio, A. (2003). Dissociable neural systems for recognizing emotions. Brain and Cognition, 52, 61–69. Aharoni, E., Funk, C., Sinnott-Armstrong, W., & Gazzaniga, M. (2008). Can neurological evidence help courts assess criminal responsibility? Lessons from law and neuroscience. Annals of the New York Academy of Sciences, 1124, 145–160. Andersen, R., Snyder, L., Bradley, D., & Xing, J. (1997). Multimodal representation of space in the posterior parietal cortex and its use in planning movements. Annual Review of Neuroscience, 20, 303–330. Baars, B. J. (1995). Tutorial commentary: Surprisingly small subcortical structures are needed for the state of waking consciousness, while cortical projection areas seem to provide perceptual contents of consciousness. Consciousness and Cognition, 4, 159–162. Baars, B. J. (1997). Some essential differences between consciousness and attention, perception, and working memory. Consciousness and Cognition, 6(2–3), 363–371. Baars, B. J. (1988). A cognitive theory of consciousness. New York: Cambridge University Press. Barceló, F., Suwazono, S., & Knight, R. (2000). Prefrontal modulation of visual processing in humans. Journal of Neuroscience, 3, 399–403. Bauby, J.-D. (1997). The diving bell and the butterfly. New York: Knopf/ Random House. Beck, D., Rees, G., Frith, C., & Lavie, N. (2001). Neural correlates of change detection and change blindness. Journal of Neuroscience, 4, 645–650. Block, N. (1995). On a confusion about a function of consciousness. Behavioral and Brain Sciences, 18, 227–247. Block, N. (2005). Two neural correlates of consciousness. Trends in Cognitive Sciences, 9, 46–52. Blonder, L., Bowers, D., & Heilman, K. (1991). The role of the right hemisphere in emotional communication. Brain, 114, 1115–1127. Bogen, J. (1995). On the neurophysiology of consciousness: Pt. I. An overview. Consciousness and Cognition, 4, 52–62.

SUMMARY Analysis of a considerably diverse spectrum of conscious experience, from the basic representations facilitated by core consciousness to emotional episodic memories and stirring visual panoramas, has led to the conclusion that conscious experience is an emergent property that is associated with activity in specific modules. Localized activity in these modules is both necessary and sufficient for a range of phenomenal conscious experience to emerge, though some conscious content, such as visual images sustained by working memory or the experience of reporting conscious content, inherently requires long-distance connectivity between modules. These latter connections and the mechanisms by which they operate contribute serial conscious content associated with specific functions, which is assimilated with the rest of the conscious content that is constructed in parallel by other local modules. Meanwhile, the interpreter busily explains this experience immediately after it occurs. The overall product is an endlessly rich and amazingly coherent conscious experience.

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Broadbent, D., & Broadbent, M. (1987). From detection to identification: Response to multiple targets in rapid serial visual presentation. Perception and Psychophysics, 42, 105–113. Calder, A. J., Keane, J., Manes, F., Antoun, N., & Young, A.W. (2000). Impaired recognition and experience of disgust following brain injury. Nature Neuroscience, 3, 1077–1078. Caligiuri, M., Brown, G., Meloy, M., Eyler, L., Kindermann, S., Eberson, S., et al. (2004). A functional magnetic resonance imaging study of cortical asymmetry in bipolar disorder. Bipolar Disorders, 6, 183–196. Carr, L., Iacoboni, M., Dubeau, M., Mazziotta, J., & Lenzi, G. (2003). Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas. Proceedings of the National Academy of Sciences, USA, 100, 5497–5502. Cereda, C., Ghika, J., Maeder, P., & Bogousslavsky, J. (2002). Strokes restricted to the insular cortex. Neurology, 59, 1950–1955. Chalmers, D. (1995). Facing up to the problem of consciousness. Journal of Consciousness Studies, 2, 200–219. Coleman, M., Rodd, J., Davis, M., Johnsrude, I., Menon, D., Pickard, J., et al. (2007). Do vegetative patients retain aspects of language comprehension? Evidence from fMRI. Brain, 130, 2494–2507. Colvin, M., Handy, T., & Gazzaniga, M. S. (2003). Hemispheric asymmetries in the parietal lobes. Advances in Neurology, 93, 321–334. Cooney, J., & Gazzaniga, M. S. (2003). Neurological disorders and the structure of human consciousness. Trends in Cognitive Sciences, 7, 161–165.

8/17/09 2:24:15 PM

References 501 Corkin, S. (2002). What’s new with the amnesic patient, H. M.? Nature Reviews: Neuroscience, 3, 153–160. Courtney, S., Petit, L., Haxby, J., & Ungerleider, L. (1998). The role of prefrontal cortex in working memory: Examining the contents of consciousness. Philosophical Transactions of the Royal Society of London, B, 353, 1819–1828. Craig, A. (2005). Forebrain emotional asymmetry: A neuroanatomical basis? Trends in Cognitive Sciences, 9, 566–571. Crick, F., & Koch, C. (2003). A framework for consciousness. Journal of Neuroscience, 6, 119–126.

Fodor, J. A. (1983). Modularity of mind: An essay on faculty psychology. Cambridge, MA: MIT Press. Gazzaniga, M. S. (1983). Right hemisphere language following brain bisection. A 20-year perspective. American Psychologist, 38, 525–537. Gazzaniga, M. S. (1989). Organization of the human brain. Science, 245, 947–952. Gazzaniga, M. S. (2000). Cerebral specialization and interhemispheric communication: Does the corpus callosum enable the human condition? Brain, 123(Pt. 7), 1293–1326.

Crottaz-Herbette, S., & Menon, V. (2006). Where and when the anterior cingulate cortex modulates attentional response: Combined fMRI and ERP evidence. Journal of Cognitive Neuroscience, 18, 766–780.

Gazzaniga, M. S. (2005). The ethical brain. New York: Dana Press.

Cummings, J. (1997). Neuropsychiatric manifestations of right hemisphere lesions. Brain and Language, 57, 22–37.

Gazzaniga, M. S. (2008). Human: The science behind what makes us unique. New York: HarperCollins.

Damasio, A. (1985). Prosopagnosia. Trends in Neurosciences, 8, 132–135.

Gazzaniga, M. S., & LeDoux, J. E. (1978). The integrated mind. New York: Plenum Press.

Damasio, A. (1999). The feeling of what happens: Body and emotion in the making of consciousness (1st ed.). New York: Harcourt Brace. Daselaar, S., Rice, H., Greenberg, D., Cabeza, R., LaBar, K., & Rubin, D. (2008). The spatiotemporal dynamics of autobiographical memory: Neural correlates of recall, emotional intensity, and reliving. Cerebral Cortex, 18, 217–229. Dehaene, S., & Changeux, J.-P. (2004). Neural mechanisms for access to consciousness. In M. Gazzaniga (Ed.), The Cognitive Neurosciences III. Cambridge, MA: MIT Press. Dehaene, S., Kerszberg, M., & Changeux, J. P. (1998). A neuronal model of a global workspace in effortful cognitive tasks. Proceedings of the National Academy of Sciences, USA, 95,14529–14534. Dehaene, S., & Naccache, L. (2001). Towards a cognitive neuroscience of consciousness: Basic evidence and a workspace framework. Cognition, 79(1–2), 1–37. Dennett, D. (1993). The message is: There is no medium. Philosophy and Phenomenological Research, 53, 889–931. Desimone, R., & Gross, C. (1979). Visual areas in the temporal cortex of the macaque. Brain Research, 178(2–3), 363–380. de Vignemont, F., & Singer, T. (2006). The empathic brain: How, when and why? Trends in Cognitive Sciences, 10, 435–441. Devinsky, O., Morrell, M., & Vogt, B. (1995). Contributions of anterior cingulate cortex to behaviour. Brain, 118, 279–306. Driver, J., & Vuilleumier, P. (2001). Perceptual awareness and its loss in unilateral neglect and extinction. Cognition, 79(1–2), 39–88. Düzel, E., Yonelinas, A., Mangun, G., Heinze, H., & Tulving, E. (1997). Event-related brain potential correlates of two states of conscious awareness in memory. Proceedings of the National Academy of Sciences, USA, 94, 5973–5978. Edelman, G. M. (1989). The remembered present: A biological theory of consciousness. New York: Basic Books. Engel, A., Fries, P., König, P., Brecht, M., & Singer, W. (1999). Temporal binding, binocular rivalry, and consciousness. Consciousness and Cognition, 8, 128–151. Etkin, A., & Wager, T. (2007). Functional neuroimaging of anxiety: A meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. American Journal of Psychiatry, 164, 1476–1488.

c25.indd 501

Fendrich, R., Wessinger, C., & Gazzaniga, M. (2001). Speculations on the neural basis of islands of blindsight. Progress in Brain Research, 134, 353–366.

Gazzaniga, M. S., LeDoux, J., & Wilson, D. (1977). Language, praxis, and the right hemisphere: Clues to some mechanisms of consciousness. Neurology, 27, 1144–1147. Gazzaniga, M. S., & Miller, M. B. (2009). The left hemisphere does not miss the right hemisphere. In S. Laureys & G. Tononi (Eds). The Neurology of Consciousness (pp. 261–70). London: Elsevier. Giacino, J., Ashwal, S., Childs, N., Cranford, R., Jennett, B., Katz, D., et al. (2002). The minimally conscious state: Definition and diagnostic criteria. Neurology, 58, 349–353. Gloning, I., Gloning, K., & Hoff, H. (1968). Neuropsychological symptoms and syndromes in lesions of the occipital lobe and adjacent areas. Paris: Gauthier-Villars. Goldberg, E. (1990). Higher cortical functions in humans: The gradiental approach. In E. Goldberg (Ed.), Contemporary neuropsychology and the legacy of Luria (pp. 229–276). Hillsdale, NJ: Erlbaum. Goodale, M. A. (1990). Brain asymmetries in the control of reaching. In M. A. Goodale (Ed.) Vision and action: The control of grasping (pp.14–32). Norwood NJ: Ablex. Hassabis, D., Kumaran, D., Vann, S., & Maguire, E. (2007). Patients with hippocampal amnesia cannot imagine new experiences. Proceedings of the National Academy of Sciences, USA, 104, 1726–1731. Hassabis, D., & Maguire, E. (2007). Deconstructing episodic memory with construction. Trends in Cognitive Sciences, 11, 299–306. Hochstein, S., & Ahissar, M. (2002). View from the top: Hierarchies and reverse hierarchies in the visual system. Neuron, 36, 791–804. Hoshi, E., & Tanji, J. (2004). Differential roles of neuronal activity in the supplementary and presupplementary motor areas: From information retrieval to motor planning and execution. Journal of Neurophysiology, 92, 3482–3499. Humphrey, N., & Weiskrantz, L. (1967). Vision in monkeys after removal of the striate cortex. Nature, 215, 595–597. Jiang, Y., & He, S. (2006). Cortical responses to invisible faces: Dissociating subsystems for facial-information processing. Current Biology, 16, 2023–2029. Jones, E. (1998a). A new view of specific and nonspecific thalamocortical connections. Advances in Neurology, 77, 49–71; discussion 72–43.

Farah, M. (1992). Agnosia. Current Opinion in Neurobiology, 2, 162–164.

Jones, E. (1998b). Viewpoint: The core and matrix of thalamic organization. Neuroscience, 85, 331–345.

Farrer, C., Frey, S., Van Horn, J., Tunik, E., Turk, D., Inati, S., et al. (2007). The angular gyrus computes action awareness representations. Cerebral Cortex, 18, 254–261.

Jones, E. (2002a). Thalamic circuitry and thalamocortical synchrony. Philosophical Transactions of the Royal Society of London, B, 357, 1659–1673.

Fendrich, R., Wessinger, C., & Gazzaniga, M. (1992). Residual vision in a scotoma: Implications for blindsight. Science, 258, 1489–1491.

Jones, E. (2002b). Thalamic organization and function after cajal. Progress in Brain Research, 136, 333–357.

8/17/09 2:24:15 PM

502

Consciousness

Kanwisher, N. (2001). Neural events and perceptual awareness. Cognition, 79(1–2), 89–113.

Logothetis, N. (1998). Single units and conscious vision. Philosophical Transactions of the Royal Society of London, B, 353, 1801–1818.

Kanwisher, N., McDermott, J., & Chun, M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 4302–4311.

Logothetis, N., Leopold, D., & Sheinberg, D. (1996). What is rivalling during binocular rivalry? Nature, 380, 621–624.

Karnath, H., Baier, B., & Nägele, T. (2005). Awareness of the functioning of one’s own limbs mediated by the insular cortex? Journal of Neuroscience, 25, 7134–7138.

Logothetis, N., & Sheinberg, D. (1996). Visual object recognition. Annual Review of Neuroscience, 19, 577–621. Luria, A. (1959). Disorders of “simultaneous perception” in a case of bilateral occipito-parietal brain injury. Brain, 82, 437–449.

Kilner, J., Vargas, C., Duval, S., Blakemore, S., & Sirigu, A. (2004). Motor activation prior to observation of a predicted movement. Journal of Neuroscience, 7, 1299–1301.

MacLean, P. (1949). Psychosomatic disease and the ‘visceral brain’: Recent developments bearing on the Papez theory of emotion. Psychosomatic Medicine, 11, 338–353.

Kimura, D. (1982). Left-hemisphere control of oral and brachial movements and their relation to communication. Philosophical Transactions of the Royal Society of London, B, 298, 135–149.

Maguire, E. (2001). Neuroimaging studies of autobiographical event memory. Philosophical Transactions of the Royal Society of London, B, 356, 1441–1451.

Kitchener, E., Hodges, J., & McCarthy, R. (1998). Acquisition of post-morbid vocabulary and semantic facts in the absence of episodic memory. Brain, 121, 1313–1327.

Marois, R., & Ivanoff, J. (2005). Capacity limits of information processing in the brain. Trends in Cognitive Sciences, 9, 296–305.

Klein, S., & Loftus, J. (2002). Memory and temporal experience: The effects of episodic memory loss on an amnesic patient’s ability to remember the past and imagine the future. Social Cognition, 20, 353–379. Koch, C. (2004). The quest for consciousness: A neurobiological approach. Denver, CO: Roberts and Co. Koch, C., & Tsuchiya, N. (2007). Attention and consciousness: Two distinct brain processes. Trends in Cognitive Sciences, 11, 16–22. Lane, R., Reiman, E., Axelrod, B., Yun, L., Holmes, A., & Schwartz, G. (1998). Neural correlates of levels of emotional awareness: Evidence of an interaction between emotion and attention in the anterior cingulate cortex. Journal of Cognitive Neuroscience, 10, 525–535.

Marois, R., Yi, D., & Chun, M. (2004). The neural fate of consciously perceived and missed events in the attentional blink. Neuron, 41, 465–472. Mele, A. R. (2006). Free will and luck. Oxford, New York: Oxford University Press. Mendez, M., Chen, A., Shapira, J., & Miller, B. (2005). Acquired sociopathy and frontotemporal dementia. Dementia and Geriatric Cognitive Disorders, 20(2–3), 99–104. Mills, C. (1912a). The cerebral mechanisms of emotional expression. Transactions of the College of Physicians of Philadelphia, 34, 381–390.

Lau, H., & Passingham, R. (2006). Relative blindsight in normal observers and the neural correlate of visual consciousness. Proceedings of the National Academy of Sciences, USA, 103, 18763–18768.

Mills, C. (1912b). The cortical representation of emotion, with a discussion of some points in the general nervous mechanism of expression in its relation to organic nervous mental disease. Proceedings of the American Medico-Psychological Association, 19, 297–300.

Lau, H., Rogers, R., Haggard, P., & Passingham, R. (2004). Attention to intention. Science, 303, 1208–1210.

Milner, A. D., & Goodale, M. A. (1995). The visual brain in action. Oxford: Oxford University Press.

Laureys, S., Faymonville, M., Luxen, A., Lamy, M., Franck, G., & Maquet, P. (2000). Restoration of thalamocortical connectivity after recovery from persistent vegetative state. Lancet, 355, 1790–1791.

Morris, J., Ohman, A., & Dolan, R. (1998). Conscious and unconscious emotional learning in the human amygdala. Nature, 393, 467–470.

Laureys, S., Perrin, F., & Brédart, S. (2007). Self-consciousness in noncommunicative patients. Consciousness and Cognition, 16, 722–741; discussion 742–725. Leiguarda, R., & Marsden, C. (2000). Limb apraxias: Higher-order disorders of sensorimotor integration. Brain, 123, 860–879. Lenneberg, E. (1967). Biological foundations of language. London: Wiley. Lennie, P. (2001). Color coding in the cortex. In K. Gegenfurtner & L. Sharpe (Eds.), Color vision: From genes to perception (pp. 235–248). New York: Cambridge University Press.

Nachev, P., Rees, G., Parton, A., Kennard, C., & Husain, M. (2005). Volition and conflict in human medial frontal cortex. Current Biology, 15, 122–128. Nagel, T. (1974). What is it like to be a bat? Philosophical Review, 83, 435–450. Newsome, W., Britten, K., Salzman, C., & Movshon, J. (1990). Neuronal mechanisms of motion perception. Cold Spring Harbor Symposia on Quantitative Biology, 55, 697–705. Olausson, H., Lamarre, Y., Backlund, H., Morin, C., Wallin, B., Starck, G., et al. (2002). Unmyelinated tactile afferents signal touch and project to insular cortex. Journal of Neuroscience, 5, 900–904.

Leopold, D., & Logothetis, N. (1996). Activity changes in early visual cortex reflect monkeys’ percepts during binocular rivalry. Nature, 379, 549–553.

Osuji, I., & Cullum, C. (2005). Cognition in bipolar disorder. Psychiatric Clinics of North America, 28, 427–441.

Li, F., VanRullen, R., Koch, C., & Perona, P. (2002). Rapid natural scene categorization in the near absence of attention. Proceedings of the National Academy of Sciences, USA, 99, 9596–9601.

Owen, A., & Coleman, M. (2007). Functional MRI in disorders of consciousness: Advantages and limitations. Current Opinion in Neurology, 20, 632–637.

Libet, B. (2004). Mind time: The temporal factor in consciousness. Cambridge, MA: Harvard University Press.

Owen, A., Coleman, M., Boly, M., Davis, M., Laureys, S., & Pickard, J. (2006). Detecting awareness in the vegetative state. Science, 313, 1402.

Libet, B., Gleason, C., Wright, E., & Pearl, D. (1983). Time of conscious intention to act in relation to onset of cerebral activity (readinesspotential). The unconscious initiation of a freely voluntary act. Brain, 106, 623–642. Liotti, M., Brannan, S., Egan, G., Shade, R., Madden, L., Abplanalp, B., et al. (2001). Brain responses associated with consciousness of breathlessness (air hunger). Proceedings of the National Academy of Sciences, USA, 98, 2035–2040.

c25.indd 502

Papez, J. (1937). A proposed mechanism of emotion. Archives of Neurology and Psychiatry, 38, 725–743. Parvizi, J., & Damasio, A. (2001). Consciousness and the brainstem. Cognition, 79(1–2), 135–160. Perenin, M., & Vighetto, A. (1988). Optic ataxia: A specific disruption in visuomotor mechanisms: Pt. I. Different aspects of the deficit in reaching for objects. Brain, 111, 643–674.

8/17/09 2:24:16 PM

References 503 Posner, M. (1994). Attention: The mechanisms of consciousness. Proceedings of the National Academy of Sciences, USA, 91, 7398–7403.

Singer, W. (2001). Consciousness and the binding problem. Annals of the New York Academy of Sciences, 929, 123–146.

Posner, M., & Dehaene, S. (1994). Attentional networks. Trends in Neurosciences, 17, 75–79.

Sirigu, A., Daprati, E., Ciancia, S., Giraux, P., Nighoghossian, N., Posada, A., et al. (2004). Altered awareness of voluntary action after damage to the parietal cortex. Journal of Neuroscience, 7, 80–84.

Ramachandran, V. (1998). Consciousness and body image: Lessons from phantom limbs, Capgras syndrome and pain asymbolia. Philosophical Transactions of the Royal Society of London, B, 353, 1851–1859. Rauch, S., Shin, L., & Phelps, E. (2006). Neurocircuitry models of posttraumatic stress disorder and extinction: Human neuroimaging research: Past, present, and future. Biological Psychiatry, 60, 376–382. Raymond, J., Shapiro, K., & Arnell, K. (1992). Temporary suppression of visual processing in an RSVP task: An attentional blink? Journal of Experimental Psychology: Human Perception and Performance, 18, 849–860. Rees, G., Kreiman, G., & Koch, C. (2002). Neural correlates of consciousness in humans. Nature Reviews: Neuroscience, 3, 261–270. Rizzo, M., & Robin, D. (1990). Simultagnosia: A defect of sustained attention yields insights on visual information processing. Neurology, 40, 447–455.

Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5, 42. Tononi, G., & Edelman, G. (1998). Consciousness and complexity. Science, 282, 1846–1851. Tsakiris, M., Hesse, M., Boy, C., Haggard, P., & Fink, G. (2007). Neural signatures of body ownership: A sensory network for bodily self-consciousness. Cerebral Cortex, 17, 2235–2244. Tulving, E. (1983). Elements of episodic memory. Oxford: Oxford University Press. Tulving, E. (1985). Memory and consciousness. Canadian Psychology, 26, 1–12.

Roser, M., & Gazzaniga, M. (2004). Automatic brains- interpretive minds. Current Directions in Psychological Science, 13, 56–59.

Tulving, E. (2002). Episodic memory: From mind to brain. Annual Review of Psychology, 53, 1–25.

Rourke, B., Ahmad, S., Collins, D., Hayman-Abello, B., Hayman-Abello, S., & Warriner, E. (2002). Child clinical/pediatric neuropsychology: Some recent advances. Annual Review of Psychology, 53, 309–339.

Tulving, E., Kapur, S., Markowitsch, H., Craik, F., Habib, R., & Houle, S. (1994). Neuroanatomical correlates of retrieval in episodic memory: Auditory sentence recognition. Proceedings of the National Academy of Sciences, USA, 91, 2012–2015.

Rudebeck, P., Buckley, M., Walton, M., & Rushworth, M. (2006). A role for the macaque anterior cingulate gyrus in social valuation. Science, 313, 1310–1312. Saper, C. (2000). Brain stem modulation of sensation, movement, and consciousness. In E. Kandel, J. Schwartz, & T. Jessell (Eds.), Principles of neural science (4th ed., pp. 889–909). New York: McGraw-Hill. Schacter, D., Addis, D., & Buckner, R. (2007). Remembering the past to imagine the future: The prospective brain. Nature Reviews: Neuroscience, 8, 657–661. Schacter, S., & Singer, J. (1962). Cognitive, social, and physiological determinants of emotional state. Psychological Review, 69, 379–399. Schiff, N., Giacino, J., Kalmar, K., Victor, J., Baker, K., Gerber, M., et al. (2007). Behavioural improvements with thalamic stimulation after severe traumatic brain injury. Nature, 448, 600–603.

Ullman, M. (2001). A neurocognitive perspective on language: The declarative/procedural model. Nature Reviews: Neuroscience, 2, 717–726. Ungerleider, L., & Mishkin, M. (1982). Two cortical visual systems. In D. Ingle, M. Goodale, & R. Mansfield (Eds.), Analysis of visual behavior (pp. 549–586). Cambridge, MA: MIT Press. Van der Werf, Y., Witter, M., & Groenewegen, H. (2002). The intralaminar and midline nuclei of the thalamus: Anatomical and functional evidence for participation in processes of arousal and awareness. Brain Research: Brain Research Reviews, 39(2–3), 107–140. Volpe, B., Ledoux, J., & Gazzaniga, M. (1979). Information processing of visual stimuli in an “extinguished” field. Nature, 282, 722–724.

Schiff, N., & Plum, F. (2000). The role of arousal and “gating” systems in the neurology of impaired consciousness. Journal of Clinical Neurophysiology, 17, 438–452.

Vuilleumier, P., Armony, J., Clarke, K., Husain, M., Driver, J., & Dolan, R. (2002). Neural response to emotional faces with and without awareness: Event-related fMRI in a parietal patient with visual extinction and spatial neglect. Neuropsychologia, 40, 2156–2166.

Scoville, W., & Milner, B. (1957). Loss of recent memory after bilateral hippocampal lesions. Journal of Neurology, Neurosurgery, and Psychiatry, 20, 11–21.

Vuilleumier, P., Armony, J., Driver, J., & Dolan, R. (2001). Effects of attention and emotion on face processing in the human brain: An eventrelated fMRI study. Neuron, 30, 829–841.

Searle, J. (2000). Consciousness. Annual Review of Neuroscience, 23, 557–578.

Warrington, E., & James, M. (1967). An experimental investigation of facial recognition in patients with unilateral cerebral lesions. Cortex, 3, 317–326.

Sergent, C., Baillet, S., & Dehaene, S. (2005). Timing of the brain events underlying access to consciousness during the attentional blink. Journal of Neuroscience, 8, 1391–1400.

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Squire, L., & Zola, S. (1996). Structure and function of declarative and nondeclarative memory systems. Proceedings of the National Academy of Sciences, USA, 93, 13515–13522.

Weintraub, S. (2000). Neuropsychological Assessment of Mental State. In M-M. Mesulam (Ed.) Principles of Behavioral and Cognitive Neurology (pp. 135–136). Oxford: Oxford University Press.

Sheinberg, D., & Logothetis, N. (1997). The role of temporal cortical areas in perceptual organization. Proceedings of the National Academy of Sciences, USA, 94, 3408–3413.

Weiskrantz, L. (1996). Blindsight revisited. Current Opinion in Neurobiology, 6, 215–220.

Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R. J., & Frith, C. D. (2004). Empathy for pain involves the affective but not sensory components of pain. Science, 303, 1157–62.

Weiskrantz, L., Warrington, E., Sanders, M., & Marshall, J. (1974). Visual capacity in the hemianopic field following a restricted occipital ablation. Brain, 97, 709–728.

Singer, T., Seymour, B., O’Doherty, J., Stephan, K., Dolan, R., & Frith, C. (2006). Empathic neural responses are modulated by the perceived fairness of others. Nature, 439, 466–469.

Wheeler, M., Stuss, D., & Tulving, E. (1997). Toward a theory of episodic memory: The frontal lobes and autonoetic consciousness. Psychological Bulletin, 121, 331–354.

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Chapter 26

Neuronal Basis of Learning JOSEPH E. STEINMETZ AND DERICK H. LINDQUIST

the past 25 years—eyeblink classical conditioning and fear conditioning. Both are Pavlovian associative learning procedures that involve the pairing of discrete stimuli in a relatively short temporal window. Both procedures appear to be conserved across mammalian species, behaviorally and neurally, meaning that acquisition and performance of the learned response are similar across species as are the neural structures and systems involved in the acquisition and performance of the responses. The use of both procedures has produced a wealth of data about the neuronal basis of learning. We also summarize data gathered through the use of other associative learning procedures, including instrumental aversive and appetitive conditioning paradigms.

For well over 100 years, experimental psychologists and brain scientists have explored the neuronal basis of learning—that is, the relationship between changes in behavior or cognition and changes in activity in the nervous system that produce or are affected by the behavioral or cognitive change. The early work of Sherrington, Pavlov, Lashley, Hebb, and other giants of neuroscience on learning, memory, and behavioral plasticity provided the groundwork for the past 30 years of discoveries (e.g., Hebb, 1949; Lashley, 1930; Pavlov, 1927; Sherrington, 1906). The field has come a long way in describing and defining the neuronal correlates of learning. The research has been at virtually all levels of analysis including descriptions of nervous system structures and interacting systems involved in learning and behavior change (Thompson, 1976; Thompson & Spencer, 1966), analyses of the activity of individual neurons in areas known to be involved in encoding learning (GoldmanRakic, 1995; Olds, Disterhoft, Segal, Kornblith, & Hirsh, 1972), description of events at neuronal receptors that promote short-term and long-term learning-related neuronal change (Morris, Anderson, Lynch, & Baudry, 1986; Wise, 2004), and explorations of cellular, molecular, or genetic processes that are related to behavioral plasticity (Abel & Lattal, 2001; Tsien, Huerta, & Tonegawa, 1996). A very productive approach to the study of the neuronal basis of learning and memory has been the development and use of simple behavioral and neural model systems, which have allowed detailed analysis of neuronal activity associated with learning. Examples of this approach include the vertebrate models developed by Thompson and associates (e.g., Patterson, Cegavske, & Thompson, 1973; Thompson, 1976), the invertebrate models developed by Kandel and colleagues (e.g., Carew & Sahley, 1986; Hawkins, Kandel, & Bailey, 2006), and brain slice approaches used by many researchers (e.g., Alger & Teyler, 1976; Schreurs, Oh, & Alkon, 1996). In this chapter, we provide a summary of our current understanding of the neuronal basis of learning as revealed through the use of model systems. We feature two model systems that have been used extensively over

EYEBLINK CLASSICAL CONDITIONING Behavioral Paradigm In a typical eyeblink classical conditioning experiment, a neutral stimulus, such as a tone or light, is presented 150 ms to 1,500 ms before an aversive stimulus, which is usually a periorbital eye shock or an air puff delivered to the cornea of the eye. The neutral stimulus is called the conditional stimulus (CS). Initially the presentation of the CS produces no visible response or, at most, a slight orienting response that declines with repeated presentation. The second stimulus is called the unconditional stimulus (US) and it produces a vigorous eyeblink when presented. The reflexive US-elicited eyeblink is called the unconditioned response (UR). With several pairings of the CS and US, an eyeblink response to the CS can be observed and it is called the conditioned response (CR). With enough training, the eyeblink CR becomes well-timed such that the peak of the response occurs at the time of the US onset. If the interval of time between the CS and US (called the interstimulus interval, ISI) is changed, with additional training the response topography will change such that the peak of the response moves to the new time when US onset occurs. 507

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CS US Trace conditioning

Figure 26.1 Schematic drawing of the delay eyeblink conditioning and trace eyeblink conditioning behavioral procedures. Note: During delay conditioning, the CS is presented (indicated by upward deflection in the drawing) and overlaps in time either fully or partially by presentation of the US. During trace conditioning, the CS is presented, a length of time elapses (the trace interval), and then the US is presented. The CS and the US do not overlap during trace conditioning and the procedure is commonly considered to be more complex because a memory trace of the CS must be created for learning to occur.

Most eyeblink conditioning studies have involved either delay or trace conditioning procedures (see Figure 26.1). In the delay conditioning procedure, the CS overlaps with presentation of the US. In the trace conditioning procedure, the CS is presented and a gap of time is allowed before the US is presented. In this fashion, a “memory” component is added such that the two stimuli do not overlap in time. Trace conditioning is often used as a more complex variation of the Pavlovian conditioning procedure. By far, the majority of eyeblink conditioning experiments that have been conducted have involved rabbits, animals that adapt to restraint easily and have proven to be ideal subjects for studies involving neural recording, stimulation, or pharmacology (see Romano & Patterson, 1987, for review). More recently, however, rats, mice, and humans have been increasingly used in eyeblink conditioning experiments, especially those designed to explore developmental, genetic, or clinical/cognitive questions (see Lavond & Steinmetz, 2003, for a comprehensive review of eyeblink classical conditioning). Early Studies of the Neuronal Bases of Eyeblink Conditioning For the first three-quarters of the past century, most of the efforts to understand the neural substrates of learning and memory concentrated on the involvement of cerebral cortex and associated higher structures in the brain. In part, this was due to the fact that processes such as attention, perception, decision making, and learning and memory were thought to be higher functions than the vegetative and reflex functions typically associated with brain stem and lower structures. This is exemplified by the work

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of Lashley (1950), who spent many years systematically removing portions of the cerebral cortex in hopes of identifying the memory or “engram” for simple maze learning and memory. Given the historical view that higher brain functions were likely involved in learning and memory it is not surprising that prior to 1980 most eyeblink conditioning experiments concentrated on the involvement of the forebrain. For example, Oakley and Steele-Russell published a series of studies that revealed that large lesions of cerebral neocortex did not abolish eyeblink CRs (Oakley & Russell, 1972, 1974, 1976, 1977). Mauk and Thompson (1987) used a decerebrate preparation to show that eyeblink conditioning was not critically dependent on neocortical function— that is, when decerebrations followed acquisition training, eyeblink CRs were not affected. Although these studies showed that neocortex was not necessary for the expression of eyeblink conditioning, there are other studies that suggest that neocortex may be engaged during the learning of this task. In an extensive series of studies involving the cat, Woody and his colleagues studied the involvement of cerebral cortex in an eye-blink conditioning task that involved pairing an auditory CS with a glabellar tap US (e.g., Woody & Black-Cleworth, 1973; Woody & Engel, 1972). Using intracellular and extracellular recording methods, these researchers showed that learning-related neuronal spiking patterns could be observed in motor neocortical areas and that persistent, learning-related changes in neuronal excitability could be recorded (Woody, 1986). Although these data demonstrate that neurons in the cat motor neocortex can encode the conditioning process, extensive lesions of the rabbit motor cortex failed to affect conditioning in the rabbit (Ivkovich & Thompson, 1997). The studies that involved complete removal of neocortex argue strongly that neocortical neurons are not essential for the learning. Many studies have been conducted over the years to assess the involvement of the hippocampus in eyeblink conditioning. The major historic reason for these studies is straightforward—the involvement of hippocampus and related limbic structures have been implicated in a wide range of learning, memory, and plasticity functions that range from anterograde human amnesia to spatial learning to basic associative learning (see Squire, 1992, for review). In the mid-1970s, Richard Thompson and his colleagues began to use the rabbit eyeblink conditioning preparation to study the involvement of hippocampal neurons in associative learning. They recorded the activity of single hippocampal pyramidal cells and reported that before behavioral CRs could be seen, the hippocampal units began to show a spiking pattern that was closely related to the topography of the behavioral response. That is, their

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summed spiking formed an amplitude-time course model of the CR (Berger, Alger, & Thompson, 1976; Berger, Rinaldi, Weisz, & Thompson, 1983; Berger & Thompson, 1978a). The pattern was not seen during unpaired presentations of the CS and US and disappeared with extinction. Other limbic system structures were also studied including the medial septum, subiculum, and retrosplenial cortex. Berger and Thompson (1978b) showed that medial septal neurons were vigorously activated by presentation of the CS and/or US but that this activity declined with overtraining. Because medial septal neurons provide the lion-share of cholinergic input to the hippocampus, many studies have used pharmacological manipulation, medial septal lesions, or stimulation to examine the role of the cholinergic input in eyeblink conditioning. Solomon and associates (Solomon, Solomon, Van der Schaaf, & Perry, 1983) systemically administered the anticholinergic drug scopolamine and showed the disruption of eyeblink conditioning and associated learning-related activity. While the recording and pharmacological studies provided strong support that neurons in the hippocampus were recruited during eyeblink conditioning, other data argue, that at least for delay conditioning, the hippocampus is not required for eyeblink conditioning. Schmaltz and Theios (1972) showed that lesions of the hippocampus had little or no effect on delay eyeblink conditioning. What then is the role of the hippocampus in conditioning? It has been suggested that neurons in the hippocampus only transiently encode the learning (Sears & Steinmetz, 1990) and that hippocampal activation may be related to aspects of training such as shifts in the CS, encoding of training stimuli or context, or perhaps awareness of contingency (e.g., Clark & Squire, 1998; Miller & Steinmetz, 1997; Schmajuk & DiCarlo, 1992; Solomon & Moore, 1975). A long line of research has demonstrated that the hippocampus is involved in trace conditioning procedures, where CS offset occurs before US onset, therefore requiring the subject to form a memory trace of the CS. Lesions of the hippocampus have been shown to significantly impair or abolish trace conditioned responding without affecting delay conditioning (Moyer, Deyo, & Disterhoft, 1990; Port, Romano, Steinmetz, Mikhail, & Patterson, 1986; Solomon, Van der Schaaf, Thompson, & Weisz, 1986). Trace conditioning is known to significantly increase hippocampal activity (Disterhoft & McEchron, 2000) and also produce cellularlevel changes in pyramidal neurons, such as a long-term reduction in calcium-dependent after-hyperpolarization potentials (Coulter et al., 1989; Disterhoft, Golden, Read, Coulter, & Alkon, 1988). Together these data suggest that the hippocampus may be essentially engaged during trace conditioning to somehow encode the trace period. If this is the case, what has yet to be resolved is how hippocampal

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activity influences brain stem and cerebellar areas know to be critical for both delay and trace learning (see the discussion that follows). Some recent experiments suggest that hippocampal connections to regions of the prefrontal cortex may be involved in the processing of this important information. The medial prefrontal cortex has been implicated in executive function and other higher cognitive processes including learning and memory (e.g., Asaad, Rainer, & Miller, 1998; Browning, Easton, Buckley, & Gaffan, 2005; Goldman-Rakic, 1995). Although a number of studies have shown little or no effect of prefrontal lesions on simple delay learning (e.g., McLaughlin, Skaggs, Churchwell, & Powell, 2002; Powell, Churchwell, & Burriss, 2005) other studies have shown that prefrontal cortex lesions impair trace conditioned responding (Kronforst-Collins & Disterhoft, 1998; McLaughlin et al., 2002; Powell et|nb|al., 2005; Weible, McEchron, & Disterhoft, 2000). Powell and his colleagues used trace conditioning procedures to train rabbits to a conditioning criterion (Simon, Knuckley, Churchwell, & Powell, 2005). They then lesioned the prelimbic area of the medial prefrontal cortex immediately, 24 hours, 1 week, 2 weeks, or 1 month after training. They saw temporary deficits when training was resumed after the lesions. Lesions given before training had no effect, thus indicating a possible posttraining lesion effect on a retrieval process. The lesions did not support a role for the medial prefrontal cortex in the acquisition process or as a storage site for memory. The authors speculated that perhaps hippocampal input to extrastriatal structures is necessary for the persistence of the memory trace for the CS during the trace period (hypothesized to take place through a brain circuit involving the subiculum, prefrontal cortex, and neostriatum) and also that the hippocampus to medial prefrontal cortex connections may critically inform essential eyeblink conditioning circuitry located in noncortical structures during the trace procedure. Later Studies of the Neuronal Basis of Eyeblink Conditioning: The Cerebellum and Brain Stem The data collected in the 1970s clearly indicated that an area of the brain below the level of the forebrain was important for the acquisition and performance of the classically conditioned eyeblink response. These data suggested that neurons in the forebrain were engaged during conditioning process but they were not necessary for conditioning to be expressed. These findings raised two possibilities concerning the nature of the neuronal system involved in this simple form of associative learning. First, it was possible that there were one or more populations of neurons in lower brain areas that were essential for acquisition and

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Neuronal Basis of Learning

performance of eyeblink conditioning. Alternatively, it was possible that parallel conditioning pathways existed in higher and lower brain systems for eyeblink conditioning and only one of these were necessary for conditioning to occur. It was known in the early 1970s that a portion of the essential circuitry for eyeblink conditioning resided in lower areas of the brain. A number of experiments showed that motoneurons involved in generating the CR and UR were contained in the abducens and accessory abducens nuclei as well as the facial nucleus (Cegavske, Thompson, Patterson, & Gormezano, 1976). Neuronal recordings taken from these nuclei revealed activations when a CR or an UR was executed (Cegavske, Patterson, & Thompson, 1979) and lesions of these nuclei abolished portions of the CR and UR that were activated by the nuclei that were removed (Disterhoft, Quinn, Weiss, & Shipley, 1985; Steinmetz, Lavond, Ivkovich, Logan, & Thompson, 1992). Armed with strong evidence suggesting that a region or regions of the lower brain was part of the necessary system involved in classical eyeblink conditioning, beginning around 1980, Richard Thompson and his colleagues began a systematic study of potential brain stem areas that might be involved in conditioning. A variety of techniques were used including brain lesion, multiple- and single-unit recording, and pharmacological activation and inactivation methods. These studies and 25 years of subsequent work have established critical roles for the cerebellum and discrete regions of the brain stem in eyeblink conditioning. The earliest lesion studies involved large aspiration lesions of the cerebellum that encompassed both cerebellar cortex and deep cerebellar nuclei. These large lesions were found to abolish previously established CRs and also prevent the formation of new CRs if training was given after the lesion (Lincoln, McCormick, & Thompson, 1982; McCormick et al., 1981). The lesion effect was complete and affected CRs only on the eye ipsilateral to the lesion. This was an important finding. Since CRs could be established on the contralateral side, nonspecific lesion effects, such as loss of motivation for learning, could be ruled out. Also, URs were largely not affected by the lesions thus indicating that the observed loss of CRs was not due to a generalized performance deficit. Subsequent lesion studies more precisely defined the location of the populations of neurons involved in conditioning. Small electrolytic lesions confined to the dorsolateral region of the interpositus nucleus produced a complete abolition of CRs (McCormick & Thompson, 1984a; Steinmetz et al., 1992). Kainic acid lesions that destroyed as little as a cubic millimeter of the dorsolateral interpositus nucleus were found effective in abolishing CRs (Lavond, Hembree, & Thompson, 1985). And the effect was demonstrated to be permanent—CRs do not reemerge with as

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much as 12 months of daily postlesion, paired, CS-US training (Steinmetz, Logue, & Steinmetz, 1992). Studies using the GABA agonist muscimol to temporarily inactivate the dorsolateral interpositus nucleus have perhaps produced the most compelling case for the necessary involvement of this brain area in eyeblink conditioning. Krupa, Thompson, and Thompson (1993) infused muscimol into this region of the interpositus nucleus during several days of acquisition training. As expected, no CRs were seen during those initial training days. The most important result came during the next several days of training with saline infusion: Rabbits showed acquisition of CRs at a rate that was identical to animals trained in the initial phase with saline injections. It was as if no training trials were delivered during the muscimol infusion stage (i.e., in learning terms, no savings of training were seen as would be expected if critical plasticity processes occurred during the muscimol infusion). Other infusion studies have demonstrated the critical nature of interpositus nucleus involvement. For example, infusions of the NMDA antagonist AP5 into the interpositus nucleus severely retarded conditioning (G. Chen & Steinmetz, 2000a) as did infusions of the general purpose protein kinase inhibitor, H7 (G. Chen & Steinmetz, 2000b) or anisomycin, a general protein synthesis inhibitor (Bracha et al., 1998). In another study, Gomi and associates (1999) showed that interpositus infusions of the transcription inhibitory astinomycin D blocked learning of the CR and that training increased the expression of KKIAMRE, a cdc2related kinase. Finally, there are data available that show that structural changes occur in the interpositus nucleus as a result of eyeblink classical conditioning. Kleim and colleagues (2002) showed that rats given eyeblink conditioning had significantly more excitatory synapses per neuron in the interpositus nucleus relative to rats given either no training or explicitly unpaired presentations of the CS and the US. This study demonstrated that CR development was related to synaptogenesis in the interpositus nucleus—a solid demonstration of a neuronal structural change related to learning and memory formation. Together, these data provided very strong evidence that the interpositus nucleus of the cerebellum was essential for acquisition and performance of the classically conditioned eyeblink response and that parallel activation of higher brain systems was not necessary for basic delay eyeblink conditioning. It appears that the learning-related increase in activity in the interpositus nucleus engages neurons in the red nucleus, which in turn activates motor neurons responsible for the CR (Chapman, Steinmetz, Sears, & Thompson, 1990; Haley, Thompson, & Madden, 1988). Information about the CR is projected to higher brain areas, however, via ascending input that arises from the interpositus nucleus and possibly the red nucleus (e.g., Sears, Logue, & Steinmetz,

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1996). In addition, descending influences from higher brain areas are likely at many different points along the essential conditioning pathways including precerebellar nuclei, the cerebellum, the red nucleus, and the motor nuclei. Currently, the involvement of higher brain areas in conditioning is being studied in a number of laboratories (e.g., Simon et al., 2005; Weiss, Weible, Galvez, & Disterhoft, 2006). Given the large size of cerebellar cortex and the enormous number of neurons contained within it, it has been assumed over the years that cerebellar cortical regions also play an essential role in eyeblink conditioning. Results from cortical lesion experiments, however, have been mixed and not always supportive of an essential role for cerebellar cortex. For example, eyeblink conditioning (albeit at a reduced level of CR production) has been observed in pcd mice that have a complete loss of cerebellar Purkinje cells (L. Chen, Bao, Lockard, Kim, & Thompson, 1996). Also, Nolan and Freeman (2005) intraventricularly infused the immunotoxin OX7-Saporin, which selectively destroys Purkinje cells in the cerebellar cortex, after rats had acquired the conditioned eyeblink response. They showed that reacquisition of the CR was impaired but that the rats could show learned inhibitory responses when conditioned inhibition training was given after infusion. These data suggest differential roles for the cerebellar cortex in excitatory and inhibitory learning.

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One region of the cerebellar cortex that has received much attention is Larsell’s Lobule HVI, which is known to project to the critical region of the interpositus nucleus and receive inputs from precerebellar areas thought to be involved in CS and US processing. Results of lobule HVI lesions have been somewhat variable: Some research groups have reported complete abolition of CRs after removal (Yeo & Hardiman, 1992; Yeo, Hardiman, & Glickstein, 1985). Others have reported little or no effect of the lesion (Woodruff-Pak, Lavond, Logan, Steinmetz, & Thompson, 1993) while others have reported that the lesions affect CR amplitude and CR acquisition rate (Lavond & Steinmetz, 1989). From these data, it has been difficult to establish a precise role for lobule HVI in eyeblink conditioning, although some data have suggested it may be involved in memory consolidation processes (Cooke, Attwell, & Yeo, 2004). It is possible that other cerebellar cortical regions play a more critical role in conditioning. In a series of papers, Mauk and colleagues have shown that lesions of the anterior lobe of the cerebellum consistently affect the timing and sometimes the execution of CRs (Mauk & Buonomano, 2004; Perrett, Ruiz, & Mauk, 1993). Additional evidence for the critical involvement of the cerebellum in eyeblink conditioning has come from neural recording experiments (see Figure 26.2). Multiple-unit and

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Figure 26.2 Neural recording has been used to establish an important role for the cerebellum in eyeblink classical conditioning. Note: A: The performance of a CR recorded on a single trial in a rabbit (top trace) along with concomitantly recorded neural activity on that trial from the anterior lobe of the cerebellar cortex (bottom trace). The tone CS period is marked by the bar at the bottom. The insert shows a complex spike marked by the downward arrow, indicating that the action potentials shown are likely from Purkinje cells. Note the increase in Purkinje cell firing on this single trial when the tone CS was presented. B: Summarized

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data across a series of conditioning trials. The top panel shows individual behavioral responses recorded on several trials from a well-trained rabbit. The middle panel shows peristimulus time histograms created by summing the activity of the Purkinje cell shown in (A) across the training session. The bottom panel shows standardized scores of the unit activity. Note the increase in activity just after CS onset. From “Purkinje Cell Activity in the Cerebellar Anterior Lobe during Rabbit Eyeblink Conditioning,” by J. T. Green and J. E. Steinmetz, 2005, Learning and Memory, 12, 260–269. Adapted with permission.

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single-unit recordings taken from the dorsolateral interpositus nucleus have revealed populations of neurons that discharge in a CR-related pattern (Berthier & Moore, 1990; Gould & Steinmetz, 1996; McCormick & Thompson, 1984b; Tracy, Britton, & Steinmetz, 2001). Some neurons discharge to the presentation of the CS or US and after training neurons can be found that discharge in a pattern that forms an amplitude-time course model of the behavioral response. Importantly, the onset of the learningrelated interpositus firing precedes the behavioral response onset by 30 to 60 msec, indicating that these cells are candidates for neurons that drive the behavioral response via the brain-stem motor nuclei. Single-unit recordings taken from lobule HVI Purkinje cells have revealed a variety of response patterns including CS-activated, US activated, and CR related spiking patterns (Berthier & Moore, 1986; Gould & Steinmetz, 1996; Katz & Steinmetz, 1997). Surprisingly, these recording studies have shown that about two-thirds of the Purkinje cells in lobule HVI respond with increases in spiking (excitation) during the CS-US interval while about one-third show decreases in spiking (inhibition). Because all Purkinje cells inhibit deep nuclear cells on which they synapse, relative increases in populations of excitatory Purkinje cells would have the net effect of inhibiting deep nuclear activity (the opposite one would expect if increases in interpositus nucleus activity are necessary to drive motor neurons responsible for CR expression). Indeed, cerebellar cortical long-term depression (LTD; Ekerot & Kano, 1985; Hemart, Daniel, Jaillard, & Crepel, 1994; Ito, 1989) has been hypothesized to be an important cellular mechanisms involved in eyeblink conditioning (Thompson, 1986). LTD results in a decrease in Purkinje cell spiking. Recent recordings from the anterior lobe of the cerebellum have revealed that this region may be a better candidate for critical involvement in the acquisition and expression of eyeblink CRs (Green & Steinmetz, 2005). In this study, an ISI discrimination procedure was used to study temporal firing patterns of anterior lobe Purkinje cells. In this behavioral procedure, high and low frequency tone CSs were presented, each followed by an air puff US after either a short (250 ms) or long (750 ms) ISI. In essence, the rabbits learned to execute CRs at two different ISIs with each ISI signaled by a different CS. After training, Purkinje cells were isolated and recordings were made as the two CSs were presented. Similar to lobule HVI recordings, CS-, US- and CR-related neurons were found. Some neurons responded selectively on either long- or shortISI trials while other neurons responded equally to both CSs. Most importantly, the ratio of excitatory to inhibitory Purkinje cell firing patterns was reversed relative to lobule HVI recordings—about two-thirds of the neurons showed

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decreases in spiking during the CS-US interval. Further, it appears that the excitatory population tended to fire early in the CS period while the inhibitory population tended to fire later in the CS period. This is precisely the withintrial firing pattern that would be expected if this region is somehow modulating the interpositus nucleus during CR expression on a given trial. That is, on each trial excitation of a Purkinje cell population occurs early in the trial period to inhibit interpositus nucleus activity while later in the trial inhibition of a Purkinje cell population occurs to promote interpositus nucleus activation. These findings suggest that the anterior lobe of the cerebellum is critical for performance of the eyeblink CR. A number of studies have been conducted to define the neural pathways by which the CS and the US are projected from the periphery to the critical regions of the cerebellum (see Figure 26.3). It appears that the CS is projected via auditory brain stem nuclei to neurons within the pontine nuclei that send mossy fiber projections to cerebellar cortex and the interpositus nucleus. Auditory, light, and tactile neuronal responses were recorded from discrete regions of the pontine nuclei and lesions of these regions caused CSmodality-specific loss of eyeblink CRs (Steinmetz et al., 1987). Microstimulation of these regions can substitute for

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Figure 26.3 This figure shows two coronal sections of a rabbit brain with critical CS and US pathways identified. Note: The CS is projected to the cerebellum from neurons in the lateral pontine nuclei (LPN: bottom section) which terminate in the cerebellar cortex (HVI: top section) and interpositus nucleus (INP: top section). The US is projected to the cerebellum from neurons that originate in the inferior olive (IO: top section) which also terminate in the HVI and INP. In addition to the HVI projections, the CS and US are also projected to regions of the anterior lobe of cerebellar cortex. Both HVI and the anterior lobe send Purkinje cell axons to the INP where they inhibit unit activity.

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a peripheral CS and produce conditioned responding when paired with an air puff US (Steinmetz, 1990; Steinmetz, Rosen, Chapman, Lavond, & Thompson, 1986) and connections with critical regions of the interpositus nucleus and cerebellar cortex have been established in anatomical tract-tracing studies (e.g., Steinmetz & Sengelaub, 1992). In addition to the critical involvement of pontine neurons, the CS pathway may also involve neurons in the medial auditory thalamic nuclei (Campolattaro, Halverson, & Freeman, 2007; Halverson & Freeman, 2006). A similar series of experiments established the critical pathways for projecting the US to the cerebellum. For an air puff US, afferents in the cornea project input to the trigeminal nucleus, which, in turn, send projections to at least two locations. First, the trigeminal nucleus activates neurons in the reticular formation that then send axons to the brain stem motor nuclei that are responsible for generating eyeblinks. Second, the trigeminal nucleus sends projections to the medial region of the dorsal accessory inferior olive. The first pathway is thought to be the reflexive UR pathway while the second pathway is the source of input that activates the cerebellum (see Steinmetz, 2000, for review). Inferior olivary neurons send climbing fiber axons to cerebellar cortex and these axons have collaterals that terminate in the deep cerebellar nuclei. Lesions of the dorsal accessory olive caused extinction or abolition of the eyeblink CR, thus suggesting that this region was involved in projecting the US to the cerebellum (McCormick, Steinmetz, & Thompson, 1985; Voneida, Christie, Bogdanski, & Chopko, 1990; Yeo, Hardiman, & Glickstein, 1986). Stimulation of the dorsal accessory olive can produce a variety of discrete responses, including eyeblinks, and these can be conditioned when preceded by either a peripheral CS or a pontine-stimulation CS (Mauk, Steinmetz, & Thompson, 1986; Steinmetz, Lavond, & Thompson, 1989). Olivary recordings revealed neurons that respond to the presentation of the air puff US, although these responses diminish as CRs are formed (Sears & Steinmetz, 1991), presumably because of inhibitory feedback from the interpositus nucleus (Andersson, Garwicz, & Hesslow, 1988; Kim, Krupa, & Thompson, 1998). Although most of the experiments concerning the neuronal substrates of eyeblink conditioning have concentrated on the conditioning of somatic responses (e.g., the eyeblink), the paradigm has also been used to study the neuronal substrates of autonomic conditioning. Because eyeblink conditioning is an aversive learning procedure, learning-related changes in autonomic activity occur during the conditioning process. In rabbits, conditioned heart-rate changes have been studied most often. For example, Buchanan and Powell (1988) showed that when

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connections between the mediodorsal nucleus of the thalamus and prefrontal cortex are severed or when ibotenic acid is used to destroy cells in the mediodorsal nucleus, the late-occurring tachycardiac component of the conditioned heart rate response is abolished. These data suggest that circuitry that includes the prefrontal cortex and thalamus are involved in the sympathetic control of the conditioned autonomic responding that occurs during eyeblink conditioning. Lesions of the interpositus nucleus do not affect conditioned heart rate responding (Lavond, Lincoln, McCormick, & Thompson, 1984). For a comprehensive review of the neuronal basis of heart rate conditioning during the eyeblink conditioning procedure, see Powell, McGlaughlin, and Chachic (2000). Models of Eyeblink Conditioning Figure 26.4 depicts a schematic representation of the essential circuitry thought to be critical for eyeblink classical conditioning. Cerebellar models of eyeblink classical conditioning have generally posited that acquisition and performance of the CR is dependent on learning-related changes in neuronal firings in regions of the cerebellum where the CS and the US converge (e.g., Thompson, 1986). There is good evidence that this convergence occurs in at least three locations: the interpositus nucleus, lobule HVI, and the anterior lobe. The cerebellar conditioning models differ, however, in the relative contributions each region makes to the conditioning process. Yeo (2004) and his colleagues have emphasized the critical role played by lobule HVI in the conditioning process, including in the acquisition, performance, and between-session consolidation of the learned response. Mauk and associates have developed an elegant computational model, constrained by available data, which hypothesizes a role for the anterior lobe in the induction of learning-related activity in the interpositus nucleus and performance of the CR (Medina & Mauk, 2000; Ohyama, Nores, Medina, Riusech, & Mauk, 2006). Their models also address critical CR timing features through the regulation of inferior olivary activity by the interpositus nucleus and also include roles for LTD and LTP in cortex. Steinmetz (2000) and colleagues have hypothesized that plasticity is independently established in the two cortical areas and in the interpositus nucleus. Cortical activity is thought to modulate the responsiveness of the deep nuclear cells in a manner that provides gain and critical timing components of the response. All models seem to agree that neurons in the interpositus nucleus are responsible for the CR generation—that is, increases in activity in neurons in the interpositus nucleus that eventually activate the motor neurons responsible for CR execution are the central neuronal representation of the behavioral CR.

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Figure 26.4 Schematic drawing of the critical brain-stem and cerebellar circuitry involved in eyeblink classical conditioning. Note: Lines with arrowheads depict excitatory synapses while lines with solid circles depict inhibitory synapses. The CS is project to the cerebellum via mossy fibers (mf) from the pontine nuclei while the US is projected to the cerebellum via climbing fibers (cf) from the inferior olive. Conditioning is thought to occur as a result of the convergence of the CS and US in the cerebellar cortex and interpositus nucleus. A, B, C, and D depict key structures in this basic circuitry that have been studied using a variety of techniques. A: Lesions of the pontine nuclei abolish CR in a CS-modality-specific manner; stimulation here can substitute for a peripheral CS; neural recordings made here show CS-related activation

Neural Development and Eyeblink Classical Conditioning Because both the cerebellum and hippocampus undergo substantial postnatal maturation in many mammals (Altman, 1972; Altman & Bayer, 1997), eyeblink classical conditioning provides a highly informative means for studying the relationship between the ontogeny of learning and neural development. Experimental analysis of the behavioral expression of learning and the maturation of the underlying neural circuitry has benefited from the comparison of two forms of eyeblink conditioning, delay and trace (Ivkovich, Paczkowski, & Stanton, 2000). Delay conditioning is first successfully acquired between postnatal day (PD) 17 and 24 in rats when a 280 ms ISI is used (Stanton, Freeman, & Skelton, 1992). Nonassociative, reflex blinking is present at an even earlier age, indicating that the reason associative learning does not emerge earlier than PD 17 is not simply the result of underdeveloped sensory or motor function (Andrews, Freeman, Carter, & Stanton, 1995; Freeman, Spencer, Skelton, & Stanton, 1993). Interestingly, whereas rat pups can acquire the CS-US association on PD 17, they often are not yet able to express what they have learned (Stanton, Fox, & Carter, 1998). When trained again on PD 20, however, rats that received the previous training demonstrated facilitated learning relative to naïve controls.

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Inferior olive patterns. B: Lesions of the IO eventually abolish conditioned responding; stimulation here produces discrete movements that can be conditioned; neural recordings made here show US-related activation patterns. C: Lesions of the interpositus nucleus permanently abolish CRs; stimulation here can produce discrete eyeblinks; recordings made here show patterns of activation that are closely linked with the execution of the CR. D: Dependent on size and location, lesions of cerebellar cortex have caused a variety of effects of CR performance that ranged from complete abolition to no effects; stimulation here at high intensities can produce eyeblinks and other discrete movements; recordings from Purkinje cells here show a variety of CS-, US- and CR-related patterns of activation (see text for details).

These data indicate that the maturation of the neural circuit responsible for associative learning precedes maturation of the neural mechanisms controlling its behavioral expression. Trace conditioning, which incorporates a temporal gap between the offset of the CS and onset of the US, is the simplest of the higher order forms of eyeblink conditioning. Whereas delay conditioning is mediated solely by the cerebellum and brain stem, trace conditioning also engages the hippocampus and other forebrain structures (Thompson, 2005; Woodruff-Pak & Steinmetz, 2000). Trace conditioning is first successfully observed between PD 19 and 40 in rats (Ivkovich et al., 2000), with hippocampal lesions impairing its expression (Ivkovich & Stanton, 2001). Intriguingly, delay conditioning does not emerge until the same postnatal period if the conditioning ISI is of the same long duration (880 ms) as that used in trace conditioning (Ivkovich et al., 2000). The results suggest that it is the interval of time between CS onset and US onset, and not the conditioning paradigm, that most affects when learning is first observed, possibly due to the difficulty of forming associations over the long time intervals. Contrary to the CR production results, however, Ivkovich-Claflin, Garrett, and Buffington (2005) reported that the timing characteristics of the CR was affected by the type of conditioning employed. Infant rats (PD 21–23

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or PD 29–31) trained with the delay paradigm were able to accurately time CR expression at three different conditioning ISIs (630, 880, and 1,130 ms), whereas rats trained with the trace paradigm were able to accurately time CR expression with the shortest ISI only. The authors hypothesize that the hippocampal-related timing circuits engaged during trace conditioning fail, in infant rats, when the ISI is exceptionally long. The maturation of neuronal and synaptic interactions within and between the cerebellum and brain stem is proposed to influence the ontogeny of cerebellar plasticity and eyeblink conditioning. The neural loops that exist between the cerebellum and brain stem are not yet fully functional in young subjects. Consequently, the CS and US sensory input and feedback mechanisms are still immature, dampening synaptic plasticity processes and limiting learning (Freeman & Nicholson, 2004). Activity in the interpositus nucleus, which drives CR production is known to regulate learning-related changes in US-mediated climbing fiber activity through inhibitory feedback to the inferior olive (Medina, Nores, & Mauk, 2002; Sears & Steinmetz, 1991). The inhibitory regulation of the US pathway helps maintain equilibrium in the climbing fiber spontaneous firing rate during periods when the CS and US are not presented (Medina et al., 2002). Weakened negative cerebellar feedback in infant rats results in less robust learning because the increments in cerebellar plasticity that are produced during conditioning diminish over time (Freeman & Nicholson, 2004). Developmental changes in the interactions between the CS pathway and the cerebellum must likewise occur for successful eyeblink conditioning. As detailed above, CS auditory information is relayed from the pontine nuclei to the cerebellum via mossy fibers (Steinmetz et al., 1987; Steinmetz & Sengelaub, 1992). CS-mediated neural activity in the pontine nuclei may be enhanced, in turn, by positive feedback from the cerebellum that emerges as CRs develop (Clark, Gohl, & Lavond, 1997). Less excitatory feedback in immature rats results in weaker CS salience, leading to less learning-specific plasticity in the cerebellum and impaired learning (Freeman & Muckler, 2003). Pontine electrical stimulation, used in place of an auditory CS, can overcome the developmental limitations inherent in pontine responsiveness in infant rats, resulting in successful eyeblink conditioning (Freeman, Rabinak, & Campolattaro, 2005). Synaptic plasticity in Purkinje cells is also known to be influenced by the conjoint activity of the mossy/parallel fibers and climbing fibers, relaying CS and US information, respectively (Gould & Steinmetz, 1996; Sakurai, 1987). In infant rats, developmental differences in CS and US neural propagation might not allow for the same strengthening of

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Purkinje cell synaptic plasticity that is observed in adults. Changes in Purkinje cell inhibition of the deep nuclei may, in turn, lead to reductions in the induction and perseveration of the learning specific interpositus nucleus-mediated synaptic plasticities that underlie the acquisition and expression of eyeblink classical conditioning (Freeman & Nicholson, 2004). Neural Substrates of Other Associative Learning Procedures While eyeblink classical conditioning has been the model used most widely over the last 25 years to explore the neuronal basis of learning, there have been a number of other paradigms and procedures involving mammals that have broadened our insights into how the brain is involved in learning and memory. The paradigms and procedures all share a common feature—they are simple model systems that have allowed the brain correlates of learning to be explored in a systematic and productive manner. Eyeblink classical conditioning is considered an aversive learning task given that the air puff is considered an aversive US. A number of years ago, Gormezano and his colleagues developed an appetitive classical conditioning task in rabbits known as classical jaw-movement conditioning (Coleman, Patterson, & Gormezano, 1966; Sheafor & Gormezano, 1972; Smith, DiLollo, & Gormezano, 1966). For this task, a tone or light CS is presented before a rewarding intraoral water or saccharin US. The US in this procedure causes a rhythmic jaw movement that leads to the consumption of the liquid. With CS-US pairings, presentation of the CS elicits the jawmovement response. Early studies used this procedure to study motivational influences on learning (e.g., Mitchell & Gormezano, 1970) while later studies have used the procedure to explore the neural bases of appetitive learning (Berry, Seager, Asaka, & Borgnis, 2000; Berry, Seager, Asaka, & Griffin, 2001). While CS and US pathways have not been completely delineated for this type of conditioning, experiments have yielded data concerning the involvement of central brain regions. Jaw-movement conditioning is not critically dependent on the interpositus nucleus of the cerebellum. Gibbs (1992) showed that lesions of the interpositus nucleus completely abolished conditioned eyeblink responses but had no effect on the performance of jaw-movement CRs recorded from the same rabbit. Berry and colleagues have conducted many studies that have examined the involvement of the hippocampus in this form of appetitive learning. They have demonstrated the formation of learning-related activity in the hippocampus (e.g., Oliver, Swain, & Berry, 1993; Seager, Borgnis, & Berry, 1997) although the within-trial pattern of activity recorded

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during jaw-movement conditioning differed from activity recorded during eyeblink conditioning when both types of training were given to the same rabbit (Berry et al., 2000). In other work, Berry and colleagues have used the jawmovement conditioning procedure to study the involvement of the cholinergic system in learning, especially as related to normal aging processes. For example, they showed that systemic injections of cholinergic blockers like scopolamine retarded learning, suppressed CS-evoked hippocampal activity, and affected the rhythmicity of the jaw-movement CR (Salvatierra & Berry, 1989). The same behavioral and neural patterns are seen in aging rabbits thus suggesting a deficit in cholinergic function as a possible explanation of the deficits in behavior seen with aging (Seager et al., 1997; Woodruff-Pak, Lavond, Logan, & Thompson, 1987). Over the years, some investigators have had success using instrumental conditioning procedures to study the neuronal basis of learning. These instrumental conditioning procedures differ from classical conditioning procedures along one very important dimension: The response made by the conditioning subject affects the delivery of the stimuli used in training. Arguably, Gabriel and associates have had the most success using instrumental procedures to study the neural substrates of learning (see Gabriel & Talk, 2001, for review). In their discriminative instrumental avoidance procedure, rabbits are placed in a large rotating wheel apparatus and are typically presented with two different tone CSs. One of the tones (the CS+) is followed by a foot-shock US while the second tone (CS-) is not. If the rabbit steps forward in the wheel when the CS+ is presented, the shock is not delivered (i.e., it is avoided by the rabbit). Eventually, the rabbits learn to step to the CS+ and not respond to the CS-. These researchers have also developed a parallel behavioral paradigm to study appetitive learning (e.g., Freeman, Cuppernell, Flannery, & Gabriel, 1996). In an extensive and elegant series of studies, Gabriel and his associates have described the neural systems involved in the aversive and appetitive instrumental learning tasks (Gabriel & Talk, 2001). Unlike the basic circuitry for eyeblink conditioning, which appears to have a few critical sites of plasticity confined to the cerebellum, the instrumental learning circuitry can best be described as modular with many critical sites of plasticity that encode critical functional features of the CS-US convergence. Using mainly lesion and neural recording techniques, Gabriel and associates have defined and studied these modules. It appears that the cingulate cortex and associated thalamic nuclei play an important part in this learning, processing associative attention and retrieval of information when task-relevant cues are presented (Freeman & Gabriel, 1999; Gabriel, 1990; Gabriel, Sparenborg, & Kubota, 1989). The hippocampus

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has also been implicated in this learning and appears to be involved in context-based information processing (Kang & Gabriel, 1998). The amygdala has been demonstrated to play a role in the learning and was identified as important for initiating learning-relevant plasticity in other areas of the brain (Poremba & Gabriel, 1999). Interestingly, collaborative work involving the Gabriel and Steinmetz laboratories has shown that the critical neuronal bases of the two types of learning are completely dissociable—lesions of the cerebellum do not affect the aversive instrumental task while abolishing eyeblink conditioning and lesions of the limbic thalamus severely impair aversive instrumental learning while having no affect on eyeblink conditioning (Gabriel et al., 1996; Steinmetz, Sears, Gabriel, Kubota, & Poremba, 1991). A rat instrumental procedure was developed by Steinmetz and colleagues to study similarities and differences in aversive and appetitive learning (Steinmetz, Logue, & Miller, 1993). In the aversive procedure, a tone CS is presented for 4 to 6 sec at which time a foot-shock US is presented. Early in training, the rats learn to terminate the shock by pressing a response bar. With additional paired training, the rats learn to press the bar before the presentation of the shock, which prevents the shock delivery (i.e., a conditioned avoidance response). In the appetitive procedure, a tone CS is presented for 4 to 6 sec. If the rat presses the bar during the tone period, food pellet reinforcement is delivered (i.e., a conditioned approach or appetitive response). The same tone or different tones can be used during the aversive and appetitive training. This preparation has been used in within-subject design experiments, where the same rat is given both appetitive and aversive versions of the tasks often using the same tone CS, same training context, same response requirement (bar-pressing), and same CS-US timing parameters. Variations of the task have included training in conjunction with autonomic recording, partial reinforcement schedules, and delay of reinforcement schedules. In an initial study, it was demonstrated that bilateral lesions of the interpositus nuclei prevented acquisition of the aversive learning procedures but had no effect on appetitive learning when relatively short CS-US intervals were used (Steinmetz et al., 1993). These data suggest that there may be some similarities in the neuronal circuits involved in the aversive instrumental conditioning and eyeblink classical conditioning tasks, but that the appetitive task engages different neuronal systems. This result is similar to the results of the Gibbs study (1992) where it was established that classical eyeblink conditioning and classical jaw-movement conditioning involved different critical neural circuits. The instrumental bar-pressing tasks have also been used in studies designed to assess clinical

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Fear Conditioning

pathologies such as the learning and memory capabilities of rats specifically bred for differential alcohol preference (e.g., Blankenship, Finn, & Steinmetz, 1998; Blankenship, Finn, & Steinmetz, 2000; Rorick, Finn, & Steinmetz, 2003a, 2003b) and cognitive impairments associated with the administration of antiepileptic compounds (e.g., Banks, Mohr, Besheer, Steinmetz, & Garraghty, 1999). It is possible to use multiple conditioning procedures to explore the neuronal basis of learning. An example of this approach is a recent series of studies published by RorickKehn and Steinmetz (2005). In these studies, neural activity in the amygdala was recorded during three behavioral tasks presented to separate groups of rats: eyeblink classical conditioning, classical fear conditioning, and aversive signaled bar-press conditioning. Robust learning-related activation of the central nucleus of the amygdala was seen during all three tasks. The basolateral nucleus, however, showed little activation during the eyeblink conditioning tasks but significant activation during the fear conditioning and instrumental tasks. In general, the amount of learningrelated activity appeared to be related to the relative intensity of the US presented during the task and the somatic requirements of the task. Given the results of these experiments, the use of multiple learning procedures to study the function of a given brain structure or system would seem to be useful for advancing our understanding of the neuronal basis of learning and memory.

FEAR CONDITIONING The ability of organisms to associate environmental stimuli with emotionally charged events allows for the coordination of defensive reactions when faced with potential threats. Fear conditioning, which utilizes this innate propensity, refers to an experimental procedure wherein a subject learns that certain aversive stimuli are accurately predicted by other initially innocuous stimuli. It is a simple, reductive form of learning, expressed across the phylogenetic spectrum. As such, it is a highly amendable model for studying the neurobiological mechanisms of associative learning and memory. Moreover, fear conditioning research can inform a variety of clinical disorders related to emotion and anxiety, many of which are thought to result from disturbances in fear learning (reviewed in LeDoux, 2000). The Behavioral Procedure In a typical fear conditioning experiment, a neutral CS, such as a tone or light, is paired with a mildly aversive US, such as a brief electric foot shock. After one or more

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pairings, a conditioned fear response is elicited when the CS is presented, even in the absence of the US. Conditioned fear is manifest via a variety of fear CRs, including alterations in autonomic responses (changes in heart rate, blood pressure; papillary dilation), defensive responses (immobility or freezing, ultrasonic vocalizations), endocrine responses (pituitary-adrenal hormone release), pain sensitivity (analgesia), and reflex facilitation (whole-body startle, the R1 component of the blink response). Whereas the CS-US association is learned, the particular CRs emitted in response to the CS are not—they are species-specific responses that are expressed automatically in the presence of appropriate stimuli (Fanselow, 1997). In most fear conditioning studies, training typically employs simple contiguity or temporal overlap of the CS and US. As detailed in a series of studies from the late 1960s, however, it is actually the contingency, or informational relationship, that exists between the CS and US that is the critical factor underlying the associative learning (Kamin, 1968; Rescorla, 1968; Wagner, Logan, Haberlandt, & Price, 1968). The Involvement of the Amygdala in Fear Conditioning Located in the medial temporal lobe, the amygdala— named, for its shape, after the Greek word for almond (Burdach, 1819)—plays a critical role in the development and expression of conditioned fear (Brown et al., 2003; LeDoux, 2000; Maren, 2005). The amygdaloid nuclear complex (see Figure 26.5) is composed of 12 functionally and anatomically distinct nuclei (McDonald, 1982;

PR ACe LA BL BM

Figure 26.5 Coronal rat brain slice detailing the locations of multiple amygdala nuclei and the perirhinal cortex (PR). Note: CS-US information is propagated directly from the thalamus and indirectly through cortex, including PR, converging in the lateral nucleus (LA) of the amygdala. CS-related information is relayed to the central nucleus (ACe) directly from the LA, and indirectly through the basolateral (BL), and basomedial (BM) nuclei. The behavioral expression of conditioned fear is regulated through projection outputs from the ACe.

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Swanson & Petrovich, 1998). Massive reciprocal interconnections exist among the amygdala’s multiple nuclei, with information primarily flowing from lateral to medial (Pitkanen, Savander, & LeDoux, 1997). Sensory information related to the CS and US enters the amygdala through the lateral, basal, and basolateral nuclei (Aggleton, 2000), collectively termed the basolateral complex. Neural activity initiated by an auditory signal, probably the most commonly employed CS, is transmitted through the auditory system to the level of the auditory thalamus (LeDoux, Sakaguchi, & Reis, 1984). From the thalamus, auditory stimuli can reach the basolateral complex through a direct monosynaptic thalamic projection and a polysynaptic, thalamo-cortical projection (Campeau & Davis, 1995a; McDonald, 1998; Romanski & LeDoux, 1992). Somatosensory information related to the US has likewise been proposed to reach the amygdala directly from the thalamus and indirectly though cortex (McDonald, 1998; Shi & Davis, 1999; but see Brunzell & Kim, 2001). While the monosynaptic thalamic input to lateral amygdala is capable of supporting fear conditioning to simple pure tone CSs (Phillips & LeDoux, 1995; Romanski & LeDoux, 1992), it appears more limited, relative to the thalamo-cortical input, in its capacity to represent complex acoustic stimuli (Bordi & LeDoux, 1994a, 1994b). Indeed, aspiration lesions of the perirhinal cortex—the major cortical input to the lateral nucleus—impair acquisition of conditioned fear when the CS is a prerecorded rat 22 kHz ultrasonic distress call. Fear learning in the same animals is normal, however, when the CS is a 4 or 22 kHz pure tone, suggesting it is the spatio-temporal complexity or temporal discontinuity of the cue and not simply ultrasonic frequency that mandates cortical processing (Lindquist, Jarrard, & Brown, 2004). The motor efferents responsible for the expression of conditioned fear originate in the central nucleus of the amygdala. The central nucleus receives direct and indirect projections from many amygdala nuclei, including the lateral and basolateral nuclei (Jolkkonen & Pitkanen, 1998). In turn, the central nucleus projects to a variety of brain stem and hypothalamic regions, allowing it to modulate motor and autonomic outputs (Hopkins & Holstege, 1978; LeDoux, Iwata, Cicchetti, & Reis, 1988). The amygdala and surrounding structures have long been recognized to play a crucial role in regulating emotive experiences (e.g., Kluver & Bucy, 1937; Weiskrantz, 1956). And yet, the functional role of the amygdala in fear conditioning remains a matter of debate to the present. One line of research suggests that the amygdala, when activated by the arousal associated with an emotionally charged event, modulates fear memories stored in other areas of the brain (McGaugh, 2002; Packard & Cahill, 2001).

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Alternatively, other research firmly establishes a critical role for the amygdala in the development and long-term storage of fear memories (Fanselow & LeDoux, 1999; Maren, 2005). As an example, lesions of basolateral complex, whether 1 day or 16 months following the acquisition of conditioned fear, completely abolish its expression (Gale et al., 2004). Electrical lesions, neurotoxic lesions, and drug-induced reversible inactivation of the basolateral complex, or individual nuclei within it, all produce severe deficits in both the learning and expression of Pavlovian fear conditioning, independent of the particular CS used or CR monitored (Campeau & Davis, 1995b; Cousens & Otto, 1998; Lindquist & Brown, 2004; Muller, Corodimas, Fridel, & LeDoux, 1997). Importantly, the resulting fear conditioning deficits are not due to secondary changes in motor performance or sensory processing on the part of the rat (Choi & Brown, 2003; Maren, 1998; Wallace & Rosen, 2001). Electrophysiological studies have revealed that amygdala neuronal activity increases in response to both unconditioned and conditioned aversive stimuli (Applegate, Frysinger, Kapp, & Gallagher, 1982; Quirk, Repa, & LeDoux, 1995). Associative long-term potentiation (LTP), the leading candidate synaptic substrate for acquiring and expressing conditioned fear (Brown & Lindquist, 2003), requires the convergence of CS and US inputs onto single neurons. Electrophysiological recording have confirmed that individual neurons within the lateral nucleus respond to both the CS and US (Romanski, Clugnet, Bordi, & LeDoux, 1993). Following fear conditioning, CS-responsive amygdala neurons show learning-dependent increases in neuronal firing (Rogan, Stäubli, & LeDoux, 1997; Rorick-Kehn & Steinmetz, 2005), allowing for the enhanced transmission responsible for CR production. The central amygdala projects to hypothalamic and brain stem areas that mediate specific fear responses (Hopkins & Holstege, 1978; LeDoux et al., 1988). Accordingly, damage to the central nucleus interferes with the expression of multiple fear CRs (Choi & Brown, 2003; Choi, Lindquist, & Brown, 2001; Hitchcock & Davis, 1986; van der Karr, Piechowski, Rittenhouse, & Gray, 1991). On the other hand, damage restricted to particular central nucleus projection areas can selectively interrupt the expression of specific CRs. For example, perturbations of arterial blood pressure, but not freezing, result from damage to the lateral hypothalamus, whereas freezing, but not blood pressure, is disrupted by lesions to the periaqueductal gray (LeDoux et al., 1988). Recent research has expanded on the functional role of the central amygdala in acquiring conditioned fear. Wilensky, Schafe, Kristensen, and LeDoux (2006), for instance, temporarily inactivated the central nucleus with

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Fear Conditioning

Hippocampus

Medial prefrontal cortex

Primary sensory cortex

Association cortex

Thalamus

CS

US

Amygdala basolateral complex

including impaired conditioned galvanic skin responses (Bechara et al., 1995; LaBar, LeDoux, Spencer, & Phelps, 1995). Neuroimaging studies have likewise revealed increased regional cerebral blood flow in the amygdala when a subject acquires and later expresses conditioned fear (Büchel, Morris, Dolan, & Friston, 1998; Cheng, Knight, Smith, Stein, & Helmstetter, 2003). Intriguingly, the increase in blood flow is most pronounced when the experimental contingencies of the CS are altered, or, more precisely, early in conditioning and in the early parts of extinction (Knight, Smith, Cheng, Stein, & Helmstetter, 2004).

Intercalated cell mass

The Involvement of the Cerebellum in Fear Conditioning

Amygdala central nucleus

Reflex facilitation

Autonomic responses Defensive behavior

519

Hormone release

Pain sensitivity

Figure 26.6 Schematic drawing of the brain circuitry involved in classical fear conditioning. Note: CS-US sensory information is propagated through the thalamic and cortical pathways, converging in the amygdala basolateral complex. Recent work suggests that CS and US information may also converge directly in the amygdala central nucleus. The central nucleus is the major source of output pathways for multiple conditioned fear responses. Lines with arrowheads depict excitatory synapses while lines with solid circles depict inhibitory synapses.

the GABA agonist muscimol just before conditioning, impairing the acquisition of conditioned fear. In the same study, posttraining central nucleus injections of the protein synthesis inhibitor anisomycin resulted in fear memory consolidation deficits. In line with previous work (e.g., Killcross, Robbins, & Everitt, 1997), the data suggest that the central amygdala, rather than simply mediating fear expression, plays a larger role in the formation and consolidation of fear conditioning memories than heretofore generally appreciated. The neural circuitry thought to be involved in fear conditioning is summarized in Figure 26.6.

In addition to its well-known role in eyeblink classical conditioning, the cerebellum is also involved in emotional processing. Early results linking the cerebellum with emotion found that stimulating the cerebellar vermis, which is connected to hypothalamic and brain stem areas by way of the fastigial nucleus, produced a repertoire of behavioral responses indicative of emotional arousal (Snider & Maiti, 1976). The cerebellar cortex has been implicated in the expression of various affective and fear-related behaviors (Bobee, Mariette, Tremblay-Leveau, & Caston, 2000; Frings et al., 2002). The cerebellum may play a more complex role, as part of an integrated network, in regulating fear behavior. Injecting the Na+ channel blocker tetradotoxin into the vermis at various postconditioning time delays impaired the long-term retention of both contextual and cued fear memories (Sacchetti, Baldi, Lorenzini, & Bucherelli, 2002), suggesting the vermis is part of the neural substrate subserving the consolidation of fear conditioning. Cerebellar involvement in fear memory consolidation is also supported by the long-term increase of synaptic efficacy between parallel fibers and Purkinje cells observed following fear conditioning (Sacchetti, Scelfo, Tempia, & Strata, 2004; Zhu, Scelfo, Hartell, Strata, & Sacchetti, 2007). Reconsolidation, following fear memory retrieval, seems to rely on a functional cerebellum as well. Reversible inactivation of the vermis immediately, but not one hour, after recall of specific fear memories interferes with their subsequent retrieval (Sacchetti, Sacco, & Strata, 2007).

Fear Conditioning in Humans Neuropsychological and neuroimaging studies in humans have substantiated and extended fear conditioning research findings from nonhuman animal subjects (Delgado, Olsson, & Phelps, 2006). People with amygdala damage, for instance, exhibit deficits in Pavlovian fear conditioning,

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Contextual Fear Conditioning To this point, fear conditioning has been discussed in terms of the CS-US association that forms over the course of training. Nevertheless, it is well established that the US also forms associations with the context in which conditioning

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takes place. The hippocampus is proposed to bind the multimodal sensory information related to the physical elements of the conditioning environment into a single conjunctive representation that can then be associated with the US (Fanselow, 2000; Rudy, Huff, & Matus-Amat, 2004). The hippocampus consists of a dorsal and a ventral pole. The dorsal hippocampus is strongly interconnected with various cortices and plays a key role in cognitive function and spatial processing. The ventral hippocampus is connected to the amygdala and hypothalamus and participates in innate and conditioned emotional behavior. Lesion work indicates that the dorsal pole is involved in contextual, but not cued, fear conditioning, whereas the ventral pole appears to be involved in both forms of learning (Maren & Holt, 2004; Richmond et al., 1999). Hippocampal place cells encode spatial information, yet it is not the case that the hippocampus merely inputs processed spatial information to the amygdala (Sanders, Wiltgen, & Fanselow, 2003). For example, dorsal hippocampal lesioned rats show impairments in contextual fear when required to disambiguate different contexts based solely on a single sensory (olfaction) cue (Otto & Poon, 2006). The results reinforce the hypothesis that the hippocampus encodes generalized contextual information, independent of its well-recognized role in spatial processing. The hippocampus is necessary for contextual fear memory for only a limited time following conditioning. Hippocampal lesions impair recent (e.g., 1 day) but not remote (e.g., 28 or 50 days) contextual fear memories, without weakening fear responsiveness to the tone CS (Anagnostaras, Maren, & Fanselow, 1999; Kim & Fanselow, 1992). If made prior to training, hippocampal lesions impair context conditioning to a lesser degree, however. Taken together, the data suggest that the hippocampus is required for the rapid, incidental learning that underlies context conditioning, whereas cortical structures are responsible for the slower learning that integrates across multiple experiences in order to extract generalities (O’Reily & Rudy, 2001). Fanselow and colleagues (Sanders et al., 2003) have suggested that while both the hippocampus and neocortex are capable of forming contextual representations, an intact hippocampus normally inhibits the neocortex from forming a redundant representation. Only if the hippocampus is damaged or inactivated prior to training can compensatory learning in other structures proceed unimpeded, and animals are able to learn. In addition to the hippocampus, the perirhinal and postrhinal cortices are also involved in contextual fear conditioning. Neurotoxic lesions of each cortical region, at training-to-lesion intervals ranging from 1 to 100 days, impair the expression of contextual fear (Burwell, Bucci, Sanborn, & Jutras, 2004). Such long-term impairments

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differ from the posttraining hippocampal lesion results, suggesting that the two rhinal cortices may play an ongoing role in the storage or retrieval of contextual representations. Conditioned Fear Extinction In the real world, newly formed associations rarely remain static—the CS may over time lose its ability to accurately predict the US, a process termed extinction. The resulting reductions in conditioned responding are not due to simple forgetting, however. Extinction requires new learning on the part of the organism, learning that the CS is no longer predictive of the US (Myers & Davis, 2002). Results from several behavioral phenomena make clear that extinction is not simply the result of unlearning the CS-US association. First, relearning the CS-US association is significantly faster following extinction than during the original acquisition. Second, over time an extinguished CR can spontaneously recover if the CS is represented. Third, an extinguished CR can reappear following exposure to unsignaled presentations of the US. Fourth, extinguished CRs can reappear if subjects are tested in a context different from the one in which extinction training took place. All of the findings support the idea that the original CS-US association remains intact, though inhibited, once extinguished. While the hippocampus plays an important role in mediating the contextual modulation of extinction (Bouton, 2004), the memory trace underlying extinction is thought to be stored in the amygdala (Ji & Maren, 2007). It is not entirely clear, however, how the hippocampus regulates CS-evoked amygdala activity. Evidence points to the medial prefrontal cortex as an important component of the neural circuit for fear extinction. The medial prefrontal cortex is known to receive strong hippocampal inputs and to exert strong inhibitory control over the amygdala (Grace & Rosenkranz, 2002; Ishikawa & Nakamura, 2003). Extinction is impaired following lesions of ventral medial prefrontal cortex (Morgan, Romanski, & LeDoux, 1993), while single units in the infralimbic subregion of the medial prefrontal cortex show potentiation of shortlatency conditioned responses during the expression, but not acquisition, of extinction (Milad & Quirk, 2002). The data are consistent with the Pavlov-Konorski hypothesis (Konorski, 1967; Pavlov, 1927) that extinction potentiates excitatory neuronal activity in structures involved in inhibiting the conditioned response (Quirk, Garcia, & González-Lima, 2006). The infralimbic subregion has robust projections to clusters of intercalated cells interposed between the central and basolateral amygdala nuclei (Cassell & Wright, 1986; McDonald, Mascagni, & Guo, 1996). The GABAergic

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References 521

intercalated cells, in turn, project to the central nucleus and are responsible for feed-forward inhibition of its output neurons (Royer, Martina, & Pare, 1999). Neuronal activity in central amygdala projection neurons is decreased by electrical stimulation of the infralimbic subregion (Quirk, Likhtik, Pelletier, & Pare, 2003), whereas chemical stimulation of the infralimbic subregion increases c-Fos expression in the intercalated cells (Berretta, Pantazopoulos, Caldera, Pantazopoulos, & Paré, 2005). As a final point, the firing pattern of infralimbic subregion neurons in response to conditioned tones has been analyzed and mimicked, via electrical stimulation, resulting in reductions in the conditioned freezing response (Milad & Quirk, 2002; Milad, Vidal-Gonzalez, & Quirk, 2004). All told, the evidence suggests that extinction-induced potentiation of tone CS neuronal responses in the infralimbic subregion of the medial prefrontal cortex causes feed-forward inhibition of central amygdala efferent projections, via the intercalated cells, thereby preventing the expression of conditioned fear. SUMMARY Eyeblink classical conditioning and fear conditioning are two forms of associative learning, each of which evolved to solve a specific function. Eyeblink conditioning is slowly learned and characterized by the precise timing of a very specific motor response. Fear conditioning, on the other hand, is rapidly learned and characterized by emotional responses that are relatively diffuse in timing and topography. Enormous progress over the past few decades has been made in delineating the neural circuits underlying each form of conditioning and linking learning-related cellular and behavioral changes. These two model systems, along with a number of other models that have been developed, have unquestionably advanced our understanding of the neuronal basis of associative learning. Considering that most forms of learning involve changes at multiple sites throughout the brain, future research will benefit from analyses that investigate how the various groups of interconnected learning and memory neural systems interact, cooperatively and competitively, to influence ongoing and future behavior.

REFERENCES Abel, T., & Lattal, K. M. (2001). Molecular mechanisms of memory acquisition, consolidation and retrieval. Current Opinion in Neurobiology, 11, 180–187.

c26.indd 521

Altman, J. (1972). Postnatal development of the cerebellar cortex in the rat. Journal of Comparative Neurology, 145, 353–398. Altman, J., & Bayer, S. A. (1997). Development of the cerebellar system in relation to its evolution, structure, and functions. Boca Raton, FL: CRC Press. Anagnostaras, S. G., Maren, S., & Fanselow, M. S. (1999). Temporally graded retrograde amnesia of contextual fear after hippocampal damage in rats: Within subjects examination. Journal of Neuroscience, 19, 1106–1114. Andersson, G., Garwicz, M., & Hesslow, G. (1988). Evidence for a GABA-mediated cerebellar inhibition of the inferior olive in the cat. Experimental Brain Research, 72, 450–456. Andrews, S. J., Freeman, J. H., Carter, C. S., & Stanton, M. E. (1995). Ontogeny of eyeblink conditioning in the rat: Auditory frequency and discrimination learning effects. Developmental Psychobiology, 28, 307–320. Applegate, C. D., Frysinger, R. C., Kapp, B. S., & Gallagher, M. (1982). Multiple unit activity recorded from amygdala central nucleus during Pavlovian heart rate conditioning in rabbit. Brain Research, 238, 457–462. Asaad, W. F., Rainer, G., & Miller, E. K. (1998). Neural activity in the primate prefrontal cortex during associative learning. Neuron, 21, 1399–1407. Banks, M. K., Mohr, N. L., Besheer, J., Steinmetz, J. E., & Garraghty, P. E. (1999). The effects of phenytoin on instrumental appetitive-to-aversive transfer in rats. Pharmacology Biochemistry and Behavior, 63, 465–472. Bechara, A., Tranel, D., Damasio, H., Adolphs, R., Rockland, C., & Damasio, A. R. (1995, August 25). Double dissociation of conditioning and declarative knowledge relative to the amygdala and hippocampus in humans. Science, 269, 1115–1118. Berger, T. W., Alger, B., & Thompson, R. F. (1976, April 30). Neuronal substrate of classical conditioning in the hippocampus. Science, 192, 483–485. Berger, T. W., Rinaldi, P. C., Weisz, D. J., & Thompson, R. F. (1983). Single-unit analysis of different hippocampal cell types during classical conditioning of rabbit nictitating membrane response. Journal of Neurophysiology, 50, 1197–1219. Berger, T. W., & Thompson, R. F. (1978a). Neuronal plasticity in the limbic system during classical conditioning of the rabbit nictitating membrane response: Pt. I. The hippocampus. Brain Research, 145, 323–346. Berger, T. W., & Thompson, R. F. (1978b). Neuronal plasticity in the limbic system during classical conditioning of the rabbit nictitating membrane response: Pt. II. Septum and mammillary bodies. Brain Research, 156, 293–314. Berretta, S., Pantazopoulos, H., Caldera, M., Pantazopoulos, P., & Paré, D. (2005). Infralimbic cortex activation increases c-Fos expression in intercalated neurons of the amygdala. Neuroscience, 132, 943–953. Berry, S. D., Seager, M. A., Asaka, Y., & Borgnis, R. L. (2000). Motivational issues in aversive and appetitive conditioning paradigms. In D. S. Woodruff-Pak & J. E. Steinmetz (Eds.), Eyeblink classical conditioning: Animal models (Vol. 2, pp. 287–312). Boston: Kluwer Press. Berry, S. D., Seager, M. A., Asaka, Y., & Griffin, A. L. (2001). The septohippocampal system and classical conditioning. In J. E. Steinmetz, M. A. Gluck, & P. R. Solomon (Eds.), Model systems and the neurobiology of associative learning (pp. 79–110). Mahwah, NJ: Erlbaum.

Aggleton, J. P. (2000). The amygdala: A functional analysis. Oxford: Oxford University Press.

Berthier, N. E., & Moore, J. W. (1986). Cerebellar purkinje cell activity related to the classically conditioned nictitating membrane response. Experimental Brain Research, 63, 341–350.

Alger, B. E., & Teyler, T. J. (1976). Long-term and short-term plasticity in the CA1, CA3, and dentate regions of the rat hippocampal slice. Brain Research, 110, 463–480.

Berthier, N. E., & Moore, J. W. (1990). Activity of deep cerebellar nuclear cells during classical conditioning of nictitating membrane extension in rabbits. Experimental Brain Research, 83, 44–54.

8/18/09 6:23:50 PM

522

Neuronal Basis of Learning

Blankenship, M. R., Finn, P. R., & Steinmetz, J. E. (1998). A characterization of approach and avoidance learning in alcohol preferring (P) and non-preferring (NP) rats. Alcoholism: Clinical and Experimental Research, 22, 1227–1233. Blankenship, M. R., Finn, P. R., & Steinmetz, J. E. (2000). A characterization of approach and avoidance learning in high alcohol drinking (HAD) and low alcohol drinking (LAD) rats. Alcoholism: Clinical and Experimental Research, 24, 1778–1784. Bobee, S., Mariette, E., Tremblay-Leveau, H., & Caston, J. (2000). Effects of early midline cerebellar lesion on cognitive and emotional functions in the rat. Behavioral Brain Research, 112, 107–117. Bordi, F., & LeDoux, J. E. (1994a). Response properties of single units in areas of rat auditory thalamus that project to the amygdala: Pt. I. Acoustic discharge patterns and frequency receptive fields. Experimental Brain Research, 98, 261–274. Bordi, F., & LeDoux, J. E. (1994b). Response properties of single units in areas of rat auditory thalamus that project to the amygdala: Pt. II. Cells receiving convergent auditory and somatosensory inputs and cells antidromically activated by amygdala stimulation. Experimental Brain Research, 98, 275–286. Bouton, M. E. (2004). Context and behavioral processes in extinction. Learning and Memory, 11, 485–494. Bracha, V., Irwin, K. B., Webster, M. L., Wunderlich, D. A., Stachowiak, M. K., & Bloedel, J. R. (1998). Microinjections of anisomycin into the intermediate cerebellum during learning affect the acquisition of classically conditioned responses in the rabbit. Brain Research, 788, 169–178. Brown, T. H., Byrne, J. H., LaBar, K., LeDoux, J., Lindquist, D. H., Thompson, R. F., et al. (2003). Learning and memory: Basic mechanisms. In J. H. Byrne & J. L. Roberts (Eds.), From molecules to networks: An introduction to cellular and molecular neuroscience (pp. 499–574). San Diego, CA: Academic Press. Brown, T. H., & Lindquist, D. H. (2003). Long-term potentiation: Amygdala. In J. H. Byrne (Ed.), Learning and Memory (2nd ed., pp. 342–346). Farmington Hills, MI: Macmillan. Browning, P. G. F., Easton, A., Buckley, M. J., & Gaffan, D. (2005). The role of the prefrontal cortex in object-in-place learning in monkeys. European Journal of Neuroscience, 22, 3281–3291. Brunzell, D. H., & Kim, J. J. (2001). Fear conditioning to tone, but not to context, is attenuated by lesions of the insular cortex and posterior extension of the intralaminar complex in rats. Behavioral Neuroscience, 115, 365–375. Buchanan, S. L., & Powell, D. A. (1988). Parasagittal thalamic knife cuts retard Pavlovian eyeblink conditioning and abolish the tachycardiac component of the heart rate conditioned response. Brain Research Bulletin, 21, 723–729. Büchel, C., Morris, J., Dolan, R. J., & Friston, K. J. (1998). Brain systems mediating aversive conditioning: An event-related fMRI study. Neuron, 20, 947–957. Burdach, K. F. (1819). Vom Baue und Leben des Gehirns. Leipzig. Burwell, R. D., Bucci, D. J., Sanborn, M. R., & Jutras, M. J. (2004). Perirhinal and postrhinal contributions to remote memory for context. Journal of Neuroscience, 24, 11023–11028. Campeau, S., & Davis, M. (1995a). Involvement of the central nucleus and basolateral complex of the amygdala in fear conditioning measured with fear-potentiated startle in rats trained concurrently with auditory and visual conditioned stimuli. Journal of Neuroscience, 15, 2301–2311. Campeau, S., & Davis, M. (1995b). Involvement of subcortical and cortical afferents to the lateral nucleus of the amygdala in fear conditioning measured with fear-potentiated startle in rats trained concurrently with auditory and visual conditioned stimuli. Journal of Neuroscience, 15, 2312–2327.

c26.indd 522

Campolattaro, M. M., Halverson, H. E., & Freeman, J. H. (2007). Medial auditory thalamic stimulation as a conditioned stimulus for eyeblink conditioning in rats. Learning and Memory, 14, 152–159. Carew, T. J., & Sahley, C. L. (1986). Invertebrate learning and memory: From behavior to molecules. Annual Review of Neuroscience, 9, 435–487. Cassell, M. D., & Wright, D. J. (1986). Topography of projections from the medial prefrontal cortex to the amygdala in the rat. Brain Research Bulletin, 17, 321–333. Cegavske, C. F., Patterson, M. M., & Thompson, R. F. (1979). Neuronal unit activity in the abducens nucleus during classical conditioning of the nictitating membrane response in the rabbit (Oryctolagus cuniculus). Journal of Comparative Physiological Psychology, 93, 595–609. Cegavske, C. F., Thompson, R. F., Patterson, M. M., & Gormezano, I. (1976). Mechanisms of efferent neuronal control of the reflex nictitating membrane response in rabbit (Oryctolagus cuniculus). Journal of Comparative and Physiological Psychology, 90, 411–423. Chapman, P. F., Steinmetz, J. E., Sears, L. L., & Thompson, R. F. (1990). Effects of lidocaine injection in the interpositus nucleus and red nucleus on conditioned behavioral and neuronal responses. Brain Research, 537, 149–156. Chen, G., & Steinmetz, J. E. (2000a). Intra-cerebellar infusion of NMDA receptor antagonist AP5 disrupts classical eyeblink conditioning in rabbits. Brain Research, 887, 144–156. Chen, G., & Steinmetz, J. E. (2000b). Microinfusions of protein kinase inhibitor H7 in the cerebellum impairs the acquisition but not retention of classical eyeblink conditioning in rabbits. Brain Research, 865, 193–201. Chen, L., Bao, S., Lockard, J. M., Kim, J. J., & Thompson, R. F. (1996). Impaired classical eyeblink conditioning in cerebellar-lesioned and Purkinje cell degeneration (pcd) mutant mice. Journal of Neuroscience, 16, 2829–2838. Cheng, D. T., Knight, D. C., Smith, C. N., Stein, E. A., & Helmstetter, F. J. (2003). Functional MRI of human amygdala activity during Pavlovian fear conditioning: Stimulus processing versus response expression. Behavioral Neuroscience, 117, 3–10. Choi, J.-S., & Brown, T. H. (2003). Central amygdala lesions block ultrasonic vocalization and freezing as conditional but not unconditional responses. Journal of Neuroscience, 23, 8713–8721. Choi, J.-S., Lindquist, D. H., & Brown, T. H. (2001). Amygdala lesions prevent conditioned enhancement of the rat eyeblink reflex. Behavioral Neuroscience, 115, 764–775. Clark, R. E., Gohl, E. B., & Lavond, D. G. (1997). The learning-related activity that develops in the pontine nuclei during classical eyeblink conditioning is dependent on the interpositus nucleus. Learning and Memory, 3, 532–544. Clark, R. E., & Squire, L. R. (1998, April 3). Classical conditioning and brain systems: The role of awareness. Science, 280, 77–81. Coleman, S. R., Patterson, M. M., & Gormezano, I. (1966). Conditioned jaw movement in the rabbit: Deprivation procedure and saccharin concentration. Psychonomic Science, 6, 39–40. Cooke, S. F., Attwell, P. J. E., & Yeo, C. H. (2004). Temporal properties of cerebellar-dependent memory consolidation. Journal of Neuroscience, 24, 2934–2941. Coulter, D. A., Lo Turco, J. J., Kubota, M., Disterhoft, J. F., Moore, J. W., & Alkon, D. L. (1989). Classical conditioning reduces amplitude and duration of calcium-dependent afterhyperpolarization in rabbit hippocampal pyramidal cells. Journal of Neurophysiology, 61, 971–981. Cousens, G., & Otto, T. (1998). Both pre- and post-training excitotoxic lesions of the basolateral amygdala abolish the expression of olfactory and contextual fear conditioning. Behavioral Neuroscience, 112, 1092–1103.

8/18/09 6:23:50 PM

References 523 Delgado, M. R., Olsson, A., & Phelps, E. A. (2006). Extending animal models of fear conditioning to humans. Biological Psychology, 73, 39–48. Disterhoft, J. F., Golden, D. T., Read, H. L., Coulter, D. A., & Alkon, D. L. (1988). AHP reduction in rabbit hippocampal neurons during conditioning correlate with acquisition of the learned response. Brain Research, 462, 118–125. Disterhoft, J. F., & McEchron, M. D. (2000). Cellular alterations in hippocampus during acquisition and consolidation of hippocampus-dependent trace eyeblink conditioning. In D. S. Woodruff-Pak & J. E. Steinmetz (Eds.), Eyeblink classical conditioning: Animal models (Vol. 2, pp. 313–334). Boston: Kluwer Press. Disterhoft, J. F., Quinn, K. J., Weiss, C., & Shipley, M. T. (1985). Accessory abducens nucleus and conditioned eye retraction nictitating membrane extension in rabbit. Journal of Neuroscience, 5, 941–950. Ekerot, C. F., & Kano, M. (1985). Long-term depression of parallel fibre synapses following stimulation of climbing fibres. Brain Research, 342, 357–360. Fanselow, M. S. (1997). Species-specific defense reactions: Retrospect and prospect. In M. E. Bouton (Ed.), Learning, motivation, and cognition (pp. 321–341). Washington, DC: American Psychological Association. Fanselow, M. S. (2000). Contextual fear, gestalt memories, and the hippocampus. Behavioral Brain Research, 110(1–2), 73–81. Fanselow, M. S., & LeDoux, J. E. (1999). Why we think plasticity underlying Pavlovian fear conditioning occurs in the basolateral amygdala. Neuron, 23, 2239–2232. Freeman, J. H., & Nicholson, D. A. (2004). Developmental changes in the neural mechanisms of eyeblink conditioning. Behavioral and Cognitive Neuroscience Reviews, 3, 3–13. Freeman, J. H., Spencer, C. O., Skelton, R. W., & Stanton, M. E. (1993). Ontogeny of eyeblink conditioning in the rat: Effects of US intensity and interstimulus interval on delay conditioning. Psychobiology, 21, 233–242. Freeman, J. H., Jr., Cuppernell, C., Flannery, K., & Gabriel, M. (1996). Context-specific multi-site cingulate cortical, limbic thalamic, and hippocampal neuronal activity during concurrent discriminative approach and avoidance training in rabbits. Journal of Neuroscience, 16, 1538–1549. Freeman, J. H., Jr., & Gabriel, M. (1999). Changes of cingulothalamic topographic excitation patterns and avoidance response incubation over time following initial discriminative conditioning in rabbits. Neurobiology of Learning and Memory, 72, 259–272.

Gale, G. D., Anagnostaras, S. G., Godsil, B. P., Mitchell, S., Nozawa, T., Sage, J. R., et al. (2004). Role of the basolateral amygdala in the storage of fear memories across the adult lifetime of rats. Journal of Neuroscience, 24, 3810–3815. Gibbs, C. M. (1992). Divergent effects on deep cerebellar lesions on two different conditioned somatomotor responses in rabbits. Brain Research, 585, 395–399. Goldman-Rakic, P. S. (1995). Cellular basis of working memory. Neuron, 14, 477–485. Gomi, H., Sun, W., Finch, C. E., Itohara, S., Yoshimi, K., & Thompson, R. F. (1999). Learning induces a CDC2-related protein kinase, KKIAMRE. Journal of Neuroscience, 19, 9530–9537. Gould, T. J., & Steinmetz, J. E. (1996). Changes in rabbit cerebellar cortical and interpositus nucleus activity during acquisition, extinction and backward classical conditioning. Neurobiology of Learning and Memory, 65, 17–34. Grace, A. A., & Rosenkranz, J. A. (2002). Regulation of conditioned responses of basolateral amygdala neurons. Physiology and Behavior, 77(4–5), 489–493. Green, J. T., & Steinmetz, J. E. (2005). Purkinje cell activity in the cerebellar anterior lobe during rabbit eyeblink conditioning. Learning and Memory, 12, 260–269. Haley, D. A., Thompson, R. F., & Madden, J. (1988). Pharmacological analysis of the magnocellular red nucleus during classical conditioning of the rabbit nictitating membrane response. Brain Research, 454, 131–139. Halverson, H. E., & Freeman, J. H. (2006). Medial auditory thalamic nuclei are necessary for eyeblink conditioning. Behavioral Neuroscience, 120, 880–887. Hawkins, R. D., Kandel, E. R., & Bailey, C. H. (2006). Molecular mechanisms of memory storage in Aplysia. Biological Bulletin, 210, 174–191. Hebb, D. O. (1949). The organization of behavior. New York: Wiley. Hemart, N., Daniel, H., Jaillard, D., & Crepel, G. (1994). Properties of glutamate receptors are modified during long-term depression in rat cerebellar Purkinje cells. Neuroscience Research, 19, 213–221.

Freeman, J. H., Jr., & Muckler, A. S. (2003). Developmental changes in eyeblink conditioning and neuronal activity in the pontine nuclei. Learning and Memory, 10, 337–345.

Hitchcock, J., & Davis, M. (1986). Lesions of the amygdala, but not of the cerebellum or red nucleus, block conditioned fear as measured with the potentiated startle paradigm. Behavioral Neuroscience, 100, 11–22.

Freeman, J. H., Jr., Rabinak, C. A., & Campolattaro, M. M. (2005). Pontine stimulation overcomes developmental limitations in the neural mechanisms of eyeblink conditioning. Learning and Memory, 12, 255–259.

Hopkins, D. A., & Holstege, G. (1978). Amygdaloid projections to the mesencephalon, pons and medulla oblongata in the cat. Experimental Brain Research, 32, 529–547.

Frings, M., Maschke, M., Erichsen, M., Jentzen, W., Muller, S. P., Kolb, F. P., et al. (2002). Involvement of the human cerebellum in fearconditioned potentiation of the acoustic startle response: A PET study. NeuroReport, 13, 1275–1278.

Ishikawa, A., & Nakamura, S. (2003). Convergence and interaction of hippocampal and amygdalar projections with the prefrontal cortex in the rat. Journal of Neuroscience, 23, 9987–9995.

Gabriel, M. (1990). Functions of anterior and posterior cingulate cortex during avoidance learning in rabbits. Progress in Brain Research, 85, 467–483. Gabriel, M., Kang, E., Poremba, A., Kubota, Y., Allen, M. T., Miller, D. P., et al. (1996). Neural substrates of discrimination avoidance learning and classical eyeblink conditioning in rabbits: A double dissociation. Behavioral Brain Research, 82, 23–30. Gabriel, M., Sparenborg, S., & Kubota, Y. (1989). Anterior and medial thalamic lesions, discriminative avoidance learning, and cingular cortical neuronal activity in rabbits. Experimental Brain Research, 76, 441–457.

c26.indd 523

Gabriel, M., & Talk, A. C. (2001). A tale of two paradigms: Lessons learned from parallel studies of discriminative instrumental learning and classical eyeblink conditioning. In J. E. Steinmetz, M. A. Gluck, & P. R. Solomon (Eds.), Model systems and the neurobiology of associative learning (pp. 149–186). Mahwah, NJ: Erlbaum.

Ito, M. (1989). Long-term depression. Annual Review of Neuroscience, 12, 85–102. Ivkovich, D., Paczkowski, C. M., & Stanton, M. E. (2000). Ontogeny of delay versus trace eyeblink conditioning in the rat. Developmental Psychobiology, 36, 148–160. Ivkovich, D., & Stanton, M. E. (2001). Effects of early hippocampal lesions on trace, delay, and long-delay eyeblink conditioning in developing rats. Neurobiology: Learning and Memory, 76, 426–446. Ivkovich, D., & Thompson, R. F. (1997). Motor cortex lesions do not affect learning or performance of the eyelid response in rabbits. Behavioral Neuroscience, 111, 727–738.

8/18/09 6:23:51 PM

524

Neuronal Basis of Learning

Ivkovich-Caflin, D., Garrett, T., & Buffington, M. L. (2005). A developmental comparison of trace and delay eyeblink conditioning in rats using matching interstimulus intervals. Developmental Psychobiology, 47, 77–88.

Lavond, D. G., & Steinmetz, J. E. (2003). Handbook of classical conditioning. New York: Kluwer Press.

Ji, J., & Maren, S. (2007). Hippocampal involvement in contextual modulation of fear extinction. Hippocampus, 17, 749–758.

LeDoux, J. E., Iwata, J., Cicchetti, P., & Reis, D. J. (1988). Different projections of the central amygdaloid nucleus mediate autonomic and behavioral correlates of conditioned fear. Journal of Neuroscience, 8, 2517–2529.

Jolkkonen, E., & Pitkanen, A. (1998). Intrinsic connections of the rat amygdaloid complex: Projections originating in the central nucleus. Journal of Comparative Neurology, 395, 53–72. Kamin, L. J. (1968). Aversive stimulation. In M. R. Jones (Ed.), Miami symposium on the prediction of behavior (pp. 9–33). Coral Gables, FL: University of Miami Press. Kang, E., & Gabriel, M. (1998). Hippocampal modulation of cingulothalamic neuronal activity and discriminative avoidance learning in rabbits. Hippocampus, 8, 491–510. Katz, D. B., & Steinmetz, J. E. (1997). Single-unit evidence for eyeblink conditioning in cerebellar cortex is altered, but not eliminated, by interpositus nucleus lesions. Learning and Memory, 4, 88–104. Killcross, S., Robbins, T. W., & Everitt, B. J. (1997, July 24). Different types of fear-conditioned behavior mediated by separate nuclei within amygdala. Nature, 388, 377–380.

LeDoux, J. E. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 23, 155–184.

LeDoux, J. E., Sakaguchi, A., & Reis, D. J. (1984). Subcortical efferent projections of the medial geniculate nucleus mediate emotional responses conditioned to acoustic stimuli. Journal of Neuroscience, 4, 683–698. Lincoln, J. S., McCormick, D. A., & Thompson, R. F. (1982). Ipsilateral cerebellar lesions prevent learning of the classically conditioned nictitating membrane/eyelid response. Brain Research, 17, 190–193. Lindquist, D. H., & Brown, T. H. (2004). Amygdalar NMDA receptors control the expression of associative reflex facilitation and three other conditional responses. Behavioral Neuroscience, 18, 36–52. Lindquist, D. H., Jarrard, L. E., & Brown, T. H. (2004). Perirhinal cortex supports delay fear conditioning to rat ultrasonic social signals. Journal of Neuroscience, 24, 3610–3617.

Kim, J. J., & Fanselow, M. S. (1992, May 1). Modality-specific retrograde amnesia of fear. Science, 256, 675–677.

Maren, S. (1998). Overtraining does not mitigate contextual fear conditioning deficits produced by neurotoxic lesions of the basolateral amygdala. Journal of Neuroscience, 18, 3088–3097.

Kim, J. J., Krupa, D. J., & Thompson, R. F. (1998, January 23). Inhibitory cerebello-olivary projections and blocking effect in classical conditioning. Science, 279, 570–573.

Maren, S. (2005). Building and burying fear memories in the brain. Neuroscientist, 11, 89–99.

Kleim, J. A., Freeman, J. H., Bruneau, R., Nolan, B. C., Cooper, N. R., Zook, A., et al. (2002). Synapse formation is associated with memory storage in the cerebellum. Proceedings of the National Academy of Sciences, USA, 99, 13228–13231. Kluver, H., & Bucy, P. C. (1937). ‘Psychic blindness’ and other symptoms following bilateral temporal lobectomy in rhesus monkeys. American Journal of Physiology, 119, 352–353. Knight, D. C., Smith, C. N., Cheng, D. T., Stein, E. A., & Helmstetter, F. J. (2004). Amygdala and hippocampal activity during acquisition and extinction of human fear conditioning. Cognitive, Affective, and Behavioral Neuroscience, 4, 317–325. Konorski, J. (1967). Integrative activity of the brain. Chicago: University of Chicago Press. Kronforst-Collins, M. A., & Disterhoft, J. F. (1998). Lesions of the caudal area of rabbit medial prefrontal cortex impair trace eyeblink conditioning. Neurobiology of Learning and Memory, 69, 147–162. Krupa, D. J., Thompson, J. K., & Thompson, R. F. (1993, May 14). Localization of a memory trace in the mammalian brain. Science, 260, 989–991. LaBar, K. S., LeDoux, J. E., Spencer, D. D., & Phelps, E. A. (1995). Impaired fear conditioning following unilateral temporal lobectomy in humans. Journal of Neuroscience, 15, 6846–6855. Lashley, K. S. (1930). Basic neural mechanisms in behavior. Psychological Review, 37, 1–24. Lashley, K. S. (1950). In search of the engram in psychological mechanisms in animal behaviour. New York: Academic Press. Lavond, D. G., Hembree, T. L., & Thompson, R. F. (1985). Effect of kainic acid lesions of the cerebellar interpositus nucleus on eyelid conditioning in the rabbit. Brain Research, 326, 179–182. Lavond, D. G., Lincoln, J. S., McCormick, D. A., & Thompson, R. F. (1984). Effect of bilateral lesions of the dentate and interpositus cerebellar nuclei on conditioning of heart-rate and nictitating membrane/ eyelid responses in the rabbit. Brain Research, 305, 323–330. Lavond, D. G., & Steinmetz, J. E. (1989). Acquisition of classical conditioning without cerebellar cortex. Behavioural Brain Research, 33, 113–164.

c26.indd 524

Maren, S., & Holt, W. G. (2004). Hippocampus and Pavlovian fear conditioning in rats: Muscimol infusions into the ventral, but not dorsal, hippocampus impair the acquisition of conditional freezing to an auditory conditional stimulus. Behavioral Neuroscience, 118, 97–110. Mauk, M. D., & Buonomano, D. V. (2004). The neural basis of temporal processing. Annual Review of Neuroscience, 27, 307–340. Mauk, M. D., Steinmetz, J. E., & Thompson, R. F. (1986). Classical conditioning using stimulation of the inferior olive as the unconditioned stimulus. Proceedings of the National Academy of Sciences, USA, 83, 5349–5353. Mauk, M. D., & Thompson, R. F. (1987). Retention of classically conditioned eyelid responses following acute decerebration. Brain Research, 403, 89–95. McCormick, D. A., Lavond, D. G., Clark, G. A., Kettner, R. R., Rising, C. E., & Thompson, R. F. (1981). The engram found? Role of the cerebellum in classical conditioning of nictitating membrane and eyelid responses. Bulletin of the Psychonomic Society, 18, 103–105. McCormick, D. A., Steinmetz, J. E., & Thompson, R. F. (1985). Lesions of the inferior olivary complex cause extinction of the classically conditioned eyelid response. Brain Research, 359, 120–130. McCormick, D. A., & Thompson, R. F. (1984a, January 20). Cerebellum essential involvement in the classically conditioned eyelid response. Science, 223, 296–299. McCormick, D. A., & Thompson, R. F. (1984b). Neuronal responses of the rabbit cerebellum during acquisition and performance of a classically conditioned nictitating membrane-eyelid responses. Journal of Neuroscience, 4, 2811–2822. McDonald, A. J. (1982). Cell types and intrinsic connections of the amygdala. In J. P. Aggleton (Ed.), The amygdala: Neurobiological aspects of emotion, memory, and mental dysfunction (pp. 67–96). New York: Wiley-Liss. McDonald, A. J. (1998). Cortical pathways to the mammalian amygdala. Progress in Neurobiology, 55, 257–332. McDonald, A. J., Mascagni, F., & Guo, L. (1996). Projections of the medial and lateral prefrontal cortices to the amygdala: A Phaseolus vulgaris leucoagglutinin study in the rat. Neuroscience, 71, 55–75.

8/18/09 6:23:51 PM

References 525 McGaugh, J. L. (2002). Memory consolidation and the amygdala: A systems perspective. Trends in Neuroscience, 25, 456–461.

Otto, T., & Poon, P. (2006). Dorsal hippocampal contributions to unimodal contextual conditioning. Journal of Neuroscience, 26, 6603–6609.

McLaughlin, J., Skaggs, H., Churchwell, J., & Powell, D. A. (2002). Medial prefrontal cortex and Pavlovian conditioning: Trace versus delay conditioning. Behavioral Neuroscience, 116, 37–47.

Packard, M. G., & Cahill, L. (2001). Affective modulation of multiple memory systems. Current Opinion in Neurobiology, 11, 752–756.

Medina, J. F., & Mauk, M. D. (2000). Computer simulation of cerebellar information processing. Nature Neuroscience, 3, 1205–1211. Medina, J. F., Nores, W. L., & Mauk, M. D. (2002, March 21). Inhibition of climbing fibres is a signal for the extinction of conditioned eyelid responses. Nature, 416, 270–273. Milad, M. R., & Quirk, G. J. (2002, November 7). Neurons in medial prefrontal cortex signal memory for fear extinction. Nature, 420, 70–74. Milad, M. R., Vidal-Gonzalez, I., & Quirk, G. J. (2004). Electrical stimulation of medial prefrontal cortex reduces conditioned fear in a temporally specific manner. Behavioral Neuroscience, 118, 389–394. Miller, D. P., & Steinmetz, J. E. (1997). Hippocampal activity during discrimination/reversal eyeblink conditioning in rabbits. Behavioral Neuroscience, 111, 70–79. Mitchell, D. S., & Gormezano, I. (1970). Effects of water deprivation on classical appetitive conditioning of the rabbit’s jaw movement response. Learning and Motivation, 1, 199–206. Morgan, M. A., Romanski, L. M., & LeDoux, J. E. (1993). Extinction of emotional learning: Contribution of medial prefrontal cortex. Neuroscience Letters, 163, 109–113. Morris, R. G. M., Anderson, E., Lynch, G. S., & Baudry, M. (1986). Selective impairment of learning and blockade of long-term potentiation by an N-methyl-D-aspartate receptor antagonist, AP5. Nature, 319, 774–776. Moyer, J. R., Deyo, R. A., & Disterhoft, J. F. (1990). Hippocampectomy disrupts trace eye-blink conditioning in rabbits. Behavioral Neuroscience, 104, 243–252. Muller, J., Corodimas, K. P., Fridel, Z., & LeDoux, J. E. (1997). Functional inactivation of the lateral and basal nuclei of the amygdala by muscimol infusion prevents fear conditioning to an explicit conditioned stimulus and to contextual stimuli. Behavioral Neuroscience, 111, 863–891. Myers, K. M., & Davis, M. (2002). Behavioral and neural analysis of extinction. Neuron, 36, 567–584. Nolan, B. C., & Freeman, J. H. (2005). Purkinje cell Loss by OX7-saporin impairs excitatory and inhibitory eyeblink conditioning. Behavioral Neuroscience, 119, 190–201. Oakley, D. A., & Russell, I. S. (1972). Neocortical lesions and Pavlovian conditioning. Physiology and Behavior, 8, 915–926. Oakley, D. S., & Russell, I. S. (1974). Differential and reversal conditioning in partially neodecorticate rabbits. Physiology and Behavior, 13, 221–230. Oakley, D. S., & Russell, I. S. (1976). Subcortical nature of Pavlovian differentiation in the rabbit. Physiology and Behavior, 17, 947–954. Oakley, D. A., & Russell, I. S. (1977). Subcortical storage of Pavlovian conditioning in the rabbit. Physiology and Behavior, 18, 931–937.

c26.indd 525

Patterson, M. M., Cegavske, C. F., & Thompson, R. F. (1973). Effects of classical conditioning paradigm on hindlimb flexor nerve response in immobolized spinal cat. Journal of Comparative and Physiological Psychology, 84, 88–97. Pavlov, I. P. (1927). Conditioned reflexes (G. V. Anrep, Trans.). London: Oxford University Press. Perrett, S. P., Ruiz, B. P., & Mauk, M. D. (1993). Cerebellar cortex lesions disrupt learning-dependent timing of conditioned eyelid responses. Journal of Neuroscience, 13, 1708–1718. Phillips, R. G., & LeDoux, J. E. (1995). Lesions of the fornix but not the entorhinal or perirhinal cortex interfere with contextual fear conditioning. Journal of Neuroscience, 15, 5308–5315. Pitkanen, A., Savander, V., & LeDoux, J. E. (1997). Organization of intra-amygdaloid circuitries in the rat: An emerging framework for understanding functions of the amygdala. Trends in Neuroscience, 20, 517–523. Poremba, A., & Gabriel, M. (1999). Amygdala neurons mediate acquisition but not maintenance of instrumental avoidance behavior in rabbits. Journal of Neuroscience, 19, 9635–9641. Port, R. L., Romano, A. G., Steinmetz, J. E., Mikhail, A. A., & Patterson, M. M. (1986). Retention and acquisition of classical trace conditioned responses by rabbits with hippocampal lesions. Behavioral Neuroscience, 100, 745–752. Powell, D. A., Churchwell, J., & Burriss, L. (2005). Medial prefrontal lesions and Pavlovian eyeblink and heart rate conditioning: Effects of partial reinforcement on delay and trace conditioning in rabbits (Oryctolagus cuniculus). Behavioral Neuroscience, 119, 180–189. Powell, D. A., McLaughlin, J., & Chachich, M. (2000). Classical conditioning of autonomic and somatomotor responses and their central nervous system substrates. In D. S. Woodruff-Pak & J. E. Steinmetz (Eds.), Eyeblink classical conditioning: Animal models (Vol. II, pp. 257–286). New York: Kluwer Press. Quirk, G. J., Garcia, R., & González-Lima, F. (2006). Prefrontal mechanisms in extinction of conditioned fear. Biological Psychiatry, 60, 337–343. Quirk, G. J., Likhtik, E., Pelletier, J. G., & Pare, D. (2003). Stimulation of medial prefrontal cortex decreases the responsiveness of central amygdala output neurons. Journal of Neuroscience, 23, 8800–8807. Quirk, G. J., Repa, J. C., & LeDoux, J. E. (1995). Fear conditioning enhances short-latency auditory responses of lateral amygdala neurons: Parallel recordings in the freely behaving rat. Neuron, 15, 1029–1039. Rescorla, R. A. (1968). Probability of shock in the presence and absence of CS in fear conditioning. Journal of Comparative and Physiological Psychology, 66, 1–5.

Ohyama, T., Nores, W. L., Medina, J. F., Riusech, F. A., & Mauk, M. D. (2006). Learning-induced plasticity in deep cerebellar nucleus. Journal of Neuroscience, 26, 12656–12663.

Richmond, M. A., Yee, B. K., Pouzet, B., Veenman, L., Rawlins, J. N. P., Feldon, J., et al. (1999). Dissociating context and space within the hippocampus: Effects of complete, dorsal, and ventral excitotoxic hippocampal lesions on conditioned freezing and spatial learning. Behavioral Neuroscience, 113, 1189–1203.

Olds, J., Disterhoft, J. F., Segal, M., Kornblith, C. L., & Hirsh, R. (1972). Learning centers in the brain mapped by measuring latencies of conditioned unit responses. Journal of Neurophysiology, 35, 202–219.

Rogan, M. T., Stäubli, U. V., & LeDoux, J. E. (1997, December 11). Fear conditioning induces associative long-term potentiation in the amygdala. Nature, 390, 604–607.

Oliver, C. G., Swain, R. A., & Berry, S. D. (1993). Hippocampal plasticity during jaw movement conditioning in the rabbit. Brain Research, 608, 150–154.

Romano, A. G., & Patterson, M. M. (1987). The rabbit in Pavlovian conditioning. In I. Gormezano, W. F. Prokasy, & R. F. Thompson (Eds.), Classical conditioning (3rd ed., pp. 1–36). Hillsdale, NJ: Erlbaum.

O’Reily, R. C., & Rudy, J. W. (2001). Conjunctive representations in learning and memory: Principles of cortical and hippocampal function. Psychological Review, 108, 311–345.

Romanski, L. M., Clugnet, M. C., Bordi, F., & LeDoux, J. E. (1993). Somatosensory and auditory convergence in the lateral nucleus of the amygdala. Behavioral Neuroscience, 107, 444–450.

8/18/09 6:23:52 PM

526

Neuronal Basis of Learning

Romanski, L. M., & LeDoux, J. E. (1992). Equipotentiality of thalamoamygdala and thalamo-cortico-amygdala circuits in auditory fear conditioning. Journal of Neuroscience, 12, 4501–4509. Rorick, L. M., Finn, P. R., & Steinmetz, J. E. (2003a). High alcohol-drinking (HAD) rats exhibit persistent freezing responses to discrete cues following Pavlovian fear conditioning. Pharmacology, Biochemistry and Behavior, 76, 223–230. Rorick, L. M., Finn, P. R., & Steinmetz, J. E. (2003b). Moderate doses of ethanol partially reverse avoidance learning deficits in high-alcoholdrinking (HAD) rats. Pharmacology, Biochemistry and Behavior, 75, 89–102. Rorick-Kehn, L. M., & Steinmetz, J. E. (2005). Amygdala unit activity during three learning tasks: Eyeblink classical conditioning, Pavlovian fear conditioning and signaled avoidance conditioning. Behavioral Neuroscience, 119, 1254–1276. Royer, S., Martina, M., & Pare, D. (1999). An inhibitory interface gates impulse traffic between the input and output stations of the amygdala. Journal of Neuroscience, 19, 10575–10583. Rudy, J. W., Huff, N. C., & Matus-Amat, P. (2004). Understanding contextual fear conditioning: Insights from a two-process model. Neuroscience Biobehavioral Review, 28, 675–685. Sacchetti, B., Baldi, E., Lorenzini, C., & Bucherelli, C. (2002). Cerebellar role in fear-conditioning consolidation. Proceedings of the National Academy of Sciences, USA, 99, 8406–8411. Sacchetti, B., Sacco, T., & Strata, P. (2007). Reversible inactivation of amygdala and cerebellum but not perirhinal cortex impairs reactivated fear memories. European Journal of Neuroscience, 25, 2875–2884. Sacchetti, B., Scelfo, B., Tempia, F., & Strata, P. (2004). Long-term synaptic changes induced in the cerebellar cortex by fear conditioning. Neuron, 42, 973–982. Sakurai, M. (1987). Synaptic modification of parallel fibre-Purkinje cell transmission in in vitro guinea-pig cerebellar slices. Journal of Physiology, 394, 463–480. Salvatierra, A. T., & Berry, S. D. (1989). Scopolamine disruption of septo-hippocampal activity and classical conditioning. Behavioral Neuroscience, 103, 715–721. Sanders, M. J., Wiltgen, B. J., & Fanselow, M. S. (2003). The place of the hippocampus in fear conditioning. European Journal of Pharmacology, 463(1–3), 217–223. Schmajuk, N. A., & DiCarlo, J. J. (1992). Stimulus configuration, classical conditioning, and hippocampal function. Psychological Review, 99, 268–305.

Sheafor, P. J., & Gormezano, I. (1972). Conditioning of the rabbit’s (Oryctolagus cuniculus) jaw movement response: US magnitude effects on URs, CRs, and pseudo-CRs. Journal of Comparative and Physiological Psychology, 81, 449–456. Sherrington, C. S. (1906). Integrated action of the nervous system. New Haven, CT: Yale University Press. Shi, C., & Davis, M. (1999). Pain pathways involved in fear conditioning measured with fear-potentiated startle: Lesion studies. Journal of Neuroscience, 19, 420–430. Simon, B., Knuckley, B., Churchwell, J., & Powell, D. A. (2005). Posttraining lesions of the medial prefrontal cortex interfere with subsequent performance of trace eyeblink conditioning. Journal of Neuroscience, 25, 10740–10746. Smith, M. C., DiLollo, V., & Gormezano, I. (1966). Conditioned jaw movement in the rabbit. Journal of Comparative and Physiological Psychology, 62, 479–483. Snider, R. S., & Maiti, A. (1976). Cerebellar contributions to the Papez circuit. Journal of Neuroscience Research, 2, 133–146. Solomon, P. R., & Moore, J. W. (1975). Latent inhibition and stimulus generalization of the classically conditioned nictitating membrane response in rabbits (Oryctolagus cuniculus) following dorsal hippocampal ablation. Journal of Comparative and Physiological Psychology, 89, 1192–203. Solomon, P. R., Solomon, S. D., Van der Schaaf, E. V., & Perry, H. E. (1983, April 15). Altered activity in the hippocampus is more detrimental to classical conditioning than removing the structure. Science, 220, 329–331. Solomon, P. R., Van der Schaaf, E. V., Thompson, R. F., & Weisz, D. J. (1986). Hippocampus and trace conditioning of the rabbit’s classically conditioned nictitating membrane response. Behavioral Neuroscience, 100, 729–744. Squire, L. R. (1992). Declarative and nondeclarative memory: Multiple brain systems supporting learning and memory. Journal of Cognitive Neuroscience, 4, 232–243. Stanton, M. E., Fox, G. D., & Carter, C. S. (1998). Ontogeny of the conditioned eyeblink response in rats: Acquisition or expression? Neuropharmacology, 37, 623–632. Stanton, M. E., Freeman, J. H., Jr., & Skelton, R. W. (1992). Eyeblink conditioning in the developing rat. Behavioral Neuroscience, 106, 657–665. Steinmetz, J. E. (1990). Neuronal activity in the cerebellar interpositus nucleus during classical NM conditioning with a pontine stimulation CS. Psychological Science, 1, 378–382.

Schmaltz, L. W., & Theios, J. (1972). Acquisition and extinction of a classically conditioned response in hippocampectomized rabbit (Oryctolagus cuniculus). Journal of Comparative Physiological Psychology, 79, 328–333.

Steinmetz, J. E. (2000). Brain substrates of classical eyeblink conditioning: A highly localized but also distributed system. Behavioural Brain Research, 110, 13–24.

Schreurs, G. G., Oh, M. M., & Alkon, D. L. (1996). Pairing-specific longterm depression of Purkinje cells excitatory postsynaptic potentials results from classical conditioning procedure in the rabbit cerebellar slice. Journal of Neurophysiology, 75, 1051–1060.

Steinmetz, J. E., Lavond, D. G., Ivkovich, D., Logan, C. G., & Thompson, R. F. (1992). Disruption of classical eyelid conditioning after cerebellar lesions: Damage to a memory trace system or a simple performance deficit? Journal of Neuroscience, 12, 4403–4426.

Seager, M. A., Borgnis, R. L., & Berry, S. D. (1997). Delayed acquisition of behavioral and hippocampal responses during jaw movement conditioning in aging rabbits. Neurobiology of Aging, 18, 631–639.

Steinmetz, J. E., Lavond, D. G., & Thompson, R. F. (1989). Classical conditioning in rabbits using pontine nucleus stimulation as a conditioned stimulus and inferior olive stimulation as an unconditioned stimulus. Synapse, 3, 225–233.

Sears, L. L., Logue, S. F., & Steinmetz, J. E. (1996). Involvement of the ventrolateral thalamus in rabbit classical eyeblink conditioning. Behavioural Brain Research, 74, 105–117. Sears, L. L., & Steinmetz, J. E. (1990). Acquisition of classically conditioned-related activity in the hippocampus is affected by lesions of the cerebellar interpositus nucleus. Behavioral Neuroscience, 104, 681–692. Sears, L. L., & Steinmetz, J. E. (1991). Dorsal accessory inferior olive activity diminishes during acquisition of the rabbit classically conditioned eyelid response. Brain Research, 545, 114–122.

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Steinmetz, J. E., Logan, C. E., Rosen, D. J., Thompson, J. K., Lavond, D. G., & Thompson, R. F. (1987). Initial localization of the acoustic conditioned stimulus projection system to the cerebellum during classical eyelid conditioning. Proceedings of the National Academy of Sciences, USA, 84, 3531–3535. Steinmetz, J. E., Logue, S. F., & Miller, D. P. (1993). Using signaled barpressing tasks to study the neural substrates of appetitive and aversive learning in rats: Behavioral manipulations and cerebellar lesions. Behavioral Neuroscience, 107, 941–954.

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References 527 Steinmetz, J. E., Logue, S. F., & Steinmetz, S. S. (1992). Rabbit classically conditioned eyelid responses do not reappear after interpositus nucleus lesion and extensive post-lesion training. Behavioural Brain Research, 51, 103–114. Steinmetz, J. E., Rosen, D. J., Chapman, P. F., Lavond, D. G., & Thompson, R. F. (1986). Classical conditioning of the rabbit eyelid response with a mossy fiber stimulation CS: Pt. I. Pontine nuclei and middle cerebellar peduncle stimulation. Behavioural Neuroscience, 100, 871–880. Steinmetz, J. E., Sears, L. L., Gabriel, M., Kubota, Y., & Poremba, A. (1991). Cerebellar interpositus nucleus lesions disrupt classical nictitating membrane conditioning but not discriminative avoidance learning in rabbits. Behavioural and Neural Biology, 57, 103–115. Steinmetz, J. E., & Sengelaub, D. R. (1992). Possible CS pathway for classical eyelid conditioning in rabbits: Pt. I. Anatomical evidence for direct projections from the pontine nuclei to the cerebellar interpositus nucleus. Behavioral and Neural Biology, 57, 103–115. Swanson, L. W., & Petrovich, G. D. (1998). What is the amygdala? Trends in Neuroscience, 21, 323–331. Thompson, R. F. (1976). The search for the engram. American Psychologist, 31, 209–227. Thompson, R. F. (1986, August 29). The neurobiology of learning and memory. Science, 233, 941–947. Thompson, R. F. (2005). In search of memory traces. Annual Review of Psychology, 56, 1–23. Thompson, R. F., & Spencer, W. A. (1966). Habituation: A model phenomenon for the study of neuronal substrates of behavior. Psychological Review, 173, 16–43. Tracy, J. A., Britton, G. B., & Steinmetz, J. E. (2001). Comparisons of single unit responses to tone, light and compound conditioned stimuli during rabbit classical eyeblink conditioning. Neurobiology of Learning and Memory, 76, 253–267. Tsien, J. Z., Huerta, P. T., & Tonegawa, S. (1996). The essential role of hippocampal CA1 NMDA receptor-dependent synaptic plasticity in spatial memory. Cell, 87, 1327–1338. van der Karr, L. D., Piechowski, R. A., Rittenhouse, P. A., & Gray, T. S. (1991). Amygdaloid lesions: Differential effect on conditioned stress and immobilization-induced increases in corticosterone and renin secretion. Neuroendocrinology, 54, 89–95. Voneida, T. J., Christie, D., Bogdanski, R., & Chopko, B. (1990). Changes in instrumentally and classically conditioned limb flexion responses following inferior olivary lesions and olivocerebellar tractotomy in the cat. Journal of Neuroscience, 10, 3583–3593.

c26.indd 527

Weible, A. P., McEchron, M. D., & Disterhoft, J. F. (2000). Cortical involvement in acquisition and extinction of trace eyeblink conditioning. Behavioral Neuroscience, 114, 1058–1067. Weiskrantz, L. (1956). Behavioral changes associated with ablation of the amygdaloid complex in monkeys. Journal of Comparative and Physiological Psychology, 49, 381–391. Weiss, C., Weible, A. P., Galvez, R., & Disterhoft, J. F. (2006). Forebraincerebellar interactions during learning. Cell Science Reviews, 3, 200–230. Wilensky, A. E., Schafe, G. E., Kristensen, M. P., & LeDoux, J. E. (2006). Rethinking the fear circuit: The central nucleus of the amygdala is required for the acquisition, consolidation, and expression of Pavlovian fear conditioning. Journal of Neuroscience, 26, 12387–12396. Wise, R. A. (2004). Dopamine, learning and motivation. Nature Reviews Neuroscience, 5, 1–12. Woodruff-Pak, D. S., Lavond, D. G., Logan, C. G., Steinmetz, J. E., & Thompson, R. F. (1993). Cerebellar cortical lesions and reacquisition in classical conditioning of the nictitating membrane response in rabbits. Brain Research, 608, 67–77. Woodruff-Pak, D. S., Lavond, D. G., Logan, C. G., & Thompson, R. F. (1987). Classical conditioning in 3-, 30-, and 45-month old rabbits: Behavioral learning and hippocampal unit activity. Neurobiology of Aging, 8, 101–108. Woodruff-Pak, D. S., & Steinmetz, J. E. (Eds.). (2000). Eyeblink classical conditioning: Vol. 1. Human applications. Boston: Kluwer Press. Woody, C. D. (1986). Understanding the cellular basis of memory and learning. Annual Review of Psychology, 37, 433–493. Woody, C. D., & Black-Cleworth, P. (1973). Differences in excitability of cortical neurons as a function of motor projection in conditioned cats. Journal of Neurophysiology, 36, 1004–1116. Woody, C. D., & Engel, J., Jr. (1972). Changes in unit activity and thresholds to electrical microstimulation at coronal-precurciate cortex of cat with classical conditioning of different facial movements. Journal of Neurophysiology, 31, 851–864. Yeo, C. H. (2004). Memory and the cerebellum. Current Neurology and Neuroscience Reports, 4, 87–89. Yeo, C. H., & Hardiman, M. J. (1992). Cerebellar cortex and eyeblink conditioning: A reexamination. Experimental Brain Research, 88, 623–638. Yeo, C. H., Hardiman, M. J., & Glickstein, M. (1985). Classical conditioning of the nictitating membrane response of the rabbit: Pt. II. Lesions of the cerebellar cortex. Experimental Brain Research, 63, 81–92.

Wagner, A. R., Logan, F. A., Haberlandt, K., & Price, T. (1968). Stimulus selection in animal discrimination learning. Journal of Experimental Psychology, 76, 171–180.

Yeo, C. H., Hardiman, M. J., & Glickstein, M. (1986). Classical conditioning of nictitating membrane response of the rabbit: Pt. IV. Lesions of the inferior olive. Experimental Brain Research, 63, 81–92.

Wallace, K. J., & Rosen, J. B. (2001). Neurotoxic lesions of the lateral nucleus of the amygdala decrease conditioned fear but not unconditioned fear of a predator odor: Comparison with electrolytic lesions. Journal of Neuroscience, 21, 3619–3627.

Zhu, L., Scelfo, B., Hartell, N. A., Strata, P., & Sacchetti, B. (2007). The effects of fear conditioning on cerebellar LTP & LTD. European Journal of Neuroscience, 26, 219–227.

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Chapter 27

Synaptic and Cellular Basis of Learning CRAIG H. BAILEY AND ERIC R. KANDEL

Studies of a variety of memory systems, ranging in complexity from elementary forms of implicit memory in invertebrates and mammals to more complex forms of hippocampal-based explicit memory, suggest that the storage of long-term memory is associated with altered gene expression, the synthesis of new proteins, and the growth of new synaptic connections (Kandel, 2001). For both forms of memory storage, the synaptic growth is thought to represent a final cellular change that stabilizes the long-term process (Bailey, Bartsch, & Kandel, 1996; Bailey & Kandel, 1993; Bailey, Kandel, & Si, 2004). Despite the association of synaptic growth with various forms of long-term memory, surprisingly little is known about the cell biological mechanisms that regulate and couple the structural changes to the molecular changes and the relative functional contribution each may make to the initiation of the long-term process on the one hand and its stable maintenance on the other (Bailey & Kandel, 1993; Bliss, Collingridge, & Morris, 2003; Kandel, 2001). This in turn raises two questions central to an understanding of the molecular biology of memory storage: (1) Do the enduring alterations in synaptic strength that characterize long-term memory result from a structural change in preexisting connections, for example, from the conversion of nonfunctional (silent) synapses to functional synapses, from the addition of newly formed functional synapses, or from perhaps both? (2) Is the maintenance of long-term memory achieved, at least in part, because of the relative stability of synaptic structure? If so, what are the mechanisms that can survive molecular turnover and thereby serve to stabilize learning-induced changes in synapse number and structure? We address these questions by focusing on recent molecular and structural studies of long-term memory in Aplysia. We begin by examining the structural remodeling and growth of identified sensory neuron synapses that accompany long-term sensitization—an elementary form of implicit memory. We then turn to in vitro studies

of the sensory-to-motor neuron synapse reconstituted in dissociated cell culture that have provided some of the first molecular insights into both the signaling pathways and mechanisms that underlie the initiation of these structural changes and their functional contribution to the different temporal phases of long-term facilitation, as well as the role of local protein synthesis and activation of translational regulators in the stabilization of learning-related synaptic growth for the persistence of memory storage. Finally, we consider how the molecules and mechanisms that regulate alterations in the structure of the synapse that are induced by learning in Aplysia may relate to those that govern de novo synapse formation during development. MEMORY’S TWO MAJOR FORMS Modern studies in cognitive psychology have demonstrated that learning and memory are not unitary faculties of mind but consist of distinct mental processes (for review, see Squire & Zola-Morgan, 1991). In the most general sense, learning can be considered as the process by which new information is acquired, and memory can be considered as the process by which that knowledge is retained. Memory can be divided into at least two general categories, each with its own rules. Explicit or declarative memory is the conscious recall of knowledge about people, places, and things, and is particularly well developed in the vertebrate brain (see also Chapter 28). The second category, implicit or nondeclarative memory, relates to motor and perceptual skills as well as other tasks and is expressed through performance, without conscious recall of past experience. Implicit memory includes simple associative forms of memory such as classical and operant conditioning, and nonassociative forms such as sensitization and habituation. Explicit and implicit memory have been localized to different neural systems within the brain (Milner, 1985; Polster, Nadel, & Schachter, 1991; Squire, 1992). As first shown by Brenda Milner in her neuropsychological studies of the patient H.M., the establishment of explicit memory is critically

Research in this review was supported in part by National Institutes of Health grant MH37134 (to C.H.B.), the Howard Hughes Medical Institute (to E.R.K.), and the Kavli Institute for Brain Sciences. 528

Handbook of Neuroscience for the Behavioral Science, edited by Gary G. Berntson and John T. Cacioppo. Copyright # 2009 John Wiley & Sons, Inc. c27.indd 528

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Long-Term Synaptic Plasticity 529

dependent on structures in the medial temporal lobe of the cerebral cortex, including the hippocampal formation. Implicit memory is a family of different processes that are represented in a number of brain systems including the cerebellum, striatum, amygdala, and, in the simplest cases, the sensory and motor pathways recruited during the learning process for particular perceptual or motor skills. As a result, implicit memory can be studied in a variety of simple reflex systems, including those of higher invertebrates, whereas explicit memory is best studied in mammals. Experimental results from clinical studies in humans as well as a variety of studies on different animal systems suggest that each form of memory has distinct stages: a short-term form that lasts seconds, minutes, or hours and a long-term form that can persist for days, weeks, and even a lifetime. Early in this century, studies of human memory described a consolidation period in the transition from short-term to long-term memory. During this consolidation period, memory storage is labile and highly sensitive to disruption. Recent molecular studies of implicit and explicit forms of learning suggest that this transition corresponds to a central program of altered gene expression. This molecular cascade converts a transient short-term process, which involves the covalent modification of preexisting proteins and a change in the effectiveness of preexisting synapses, into a stable, self-maintained long-term process that is accompanied by the structural remodeling of preexisting synapses and the growth of new synaptic connections. Two experimental model systems have been extensively studied as representative examples of these two forms of memory storage: long-term sensitization in the marine snail Aplysia californica as an example of implicit memory and hippocampal long-term potentiation (LTP) as an example of long-lasting synaptic plasticity thought to contribute to explicit memory storage in mammals. An enduring increase in the strength of synaptic connections can be induced by high-frequency stimulation of specific afferent pathways. The phenomenon of LTP is currently thought to be a cellular correlate or, at least, a requirement for certain types of explicit memory formation in the mammalian hippocampus. In this chapter, we focus primarily on the cellular, molecular, and structural mechanisms that underlie longterm memory in Aplysia but refer to recent studies of LTP and spatial memory formation in mammals as points of comparison to consider similarities and differences in implicit and explicit memory storage.

LONG-TERM SYNAPTIC PLASTICITY The central nervous system of the marine snail Aplysia californica has proven useful as a model system for studying

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the cellular and molecular bases of learning and memory. It contains only approximately 20,000 large, identifiable nerve cells, clustered into 10 major ganglia. The ability to identify individual neurons and record their activity has made it possible to define the major components of the neuronal circuits of specific behaviors and to delineate the critical synaptic sites and underlying mechanisms used to store memory-related representations. The molecular mechanisms contributing to implicit memory storage have been most extensively studied for the gill-withdrawal reflex of Aplysia (Kandel, 2001). As is true for other types of defensive reflexes, the gillwithdrawal reflex can be modified by several different forms of implicit learning. We focus here on sensitization, an elementary nonassociative form of learned fear by which an animal learns about the properties of a single noxious stimulus (Figure 27.1A). The animal learns to strengthen its defensive reflexes and to respond vigorously to a variety of previously neutral stimuli after it has been exposed to a potentially threatening stimulus. In Aplysia, sensitization of the gill-withdrawal reflex can be induced by a strong stimulus applied to the tail. This activates facilitatory interneurons that synapse on identified sensory neurons and strengthen the synaptic connection between the sensory neurons and their target motor neurons (Figure 27.1B). As is the case for other defensive withdrawal reflexes, the behavioral memory for sensitization of the gill-withdrawal reflex is graded and retention is proportional to the number of training trials. A single stimulus to the tail gives rise to short-term sensitization lasting minutes to hours. Repetition of this stimulus produces long-term behavioral sensitization that can last for days or weeks (Frost, Castellucci, Hawkins, & Kandel, 1985; Figure 27.2). Short- and long-term sensitization lead to enhanced synaptic transmission at the monosynaptic connection between identified mechanoreceptor sensory neurons and motor neurons. Although this component accounts for only a part of the behavioral modification measured in the intact animal, its simplicity has facilitated the cellular and molecular analysis of both the short- and long-term forms of sensitization. The monosynaptic sensory to motor neuron connection, which is thought to be glutamatergic (Conrad, Wu, & Schacher, 1999; Dale & Kandel, 1993; Trudeau & Castellucci, 1993), can be reconstituted in dissociated cell culture in which serotonin (5-hydroxytryptamine [5HT]), a modulatory neurotransmitter normally released by sensitizing stimuli, can substitute for the tail shock used during behavioral training in the intact animal (Montarolo et al., 1986). In parallel to behavioral sensitization, a single application of 5-HT produces short-term changes in synaptic effectiveness, whereas five spaced applications given over a period of 1.5 hour produce long-term changes lasting

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Synaptic and Cellular Basis of Learning

(A)

Standard Gill-Withdrawal Reflex

Enhanced Gill-Withdrawal after Sensitization

Mantle shelf

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Siphon

Tactile stimulus

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Figure 27.1 Sensitization withdrawal reflex in Aplysia.

of

the

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Note. A: Dorsal view of Aplysia showing the gill, mantle shelf, and siphon. A light touch to the siphon causes the siphon to contract and the gill to withdraw under the protection of the mantle shelf (shown here retracted for a clearer view). Sensitization of the gillwithdrawal reflex is produced by applying a noxious stimulus to another part of the body (such as the tail) and leads to an enhancement of the withdrawal reflex of both the siphon and gill. B: Neural circuit of the gill-withdrawal reflex. The siphon is innervated by 24 sensory neurons that connect directly with the 6 motor neurons. The sensory neurons also connect to populations of excitatory and inhibitory interneurons that in turn connect with the motor neurons. Stimulating the tail activates modulatory interneurons that act on the terminals of the sensory neurons as well as on those of the excitatory interneurons. Three classes of modulatory neurons are activated by tail stimulation. The most important modulatory action is mediated by serotonin (5-HT). Blocking the action of these serotonergic cells blocks the effects of sensitizing stimuli. From “The Molecular Biology of Memory Storage: A Dialogue between Genes and Synapses,” by E. R. Kandel, 2001, Science, 294, p. 1031. Adapted with permission.

IN Motor Neuron (6)

EX Interneurons

Gill

Duration of Withdrawal (percentage of control)

1000

4 x 4 shocks a day for 4 days

Note. A summary of the effects of long-term sensitization training on the duration of gill and siphon withdrawal in Aplysia. The retention of the memory for sensitization is a graded function proportional to the number of training trials. Before sensitization, a weak touch to the siphon causes only a brief siphon- and gill-withdrawal reflex. Following a single noxious, sensitizing, shock to the tail, that same weak touch elicits a much larger response that lasts about 1 hour. More tail shocks increase the size and duration of the response. Application of protein synthesis inhibitors blocks the long-term but not the short-term memory for sensitization. From “Monosynaptic Connections Made by the Sensory Neurons of the Gill- and Siphon-Withdrawal Reflex in Aplysia Participates in the Storage of LongTerm Memory for Sensitization,” by W. N. Frost, V. F. Castellucci, R. D. Hawkins, and E. R. Kandel, 1985, Proceedings of the National Academy of Sciences, United States, 82, p. 8267. Adapted with permission.

500

4 shocks

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Figure 27.2 The behavioral memory for long-term sensitization of the gill-withdrawal reflex in Aplysia is graded and retention is proportional to the number of training trials.

Inhibitor of protein synthesis

0 0

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Days after Training

1 or more days. These findings of an elementary, cellular representation of the short- and long-term memory for sensitization have allowed us to address directly the following question: What are the molecular substrates and regulatory mechanisms that underlie memory storage?

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Biophysical and biochemical studies of the connections between sensory and motor neurons in both the intact animal and cells in culture indicate that the short-term and long-term changes share aspects of a common molecular mechanism. Both processes are initiated by 5-HT, and a

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Learning-Induced Growth of New Sensory Neuron Synapses 531

component of the increase in synaptic strength observed during both the short- and long-term is due to enhanced transmitter release by the sensory neuron. This presynaptic increase in transmitter release is due, in part, to the spike broadening that results from the modulation by 5-HT of specific sets of potassium channels (Dale, Kandel, & Schacher, 1987; Frost et al., 1985; Klein & Kandel, 1980; Montarolo et al., 1986; Scholz & Byrne, 1987). Despite these several similarities, the short-term cellular changes differ from the long-term modifications in two important ways. First, the short-term change involves only covalent modification of preexisting proteins and an alteration of preexisting connections. Both short-term behavioral sensitization in the animal and short-term facilitation in dissociated cell culture do not require ongoing macromolecular synthesis (Montarolo et al., 1986; Schwartz, Castellucci, & Kandel, 1971). By contrast, inhibitors of transcription or translation block the induction of the long-term changes in both the semi-intact preparation (Castellucci, Blumenfeld, Goelet, & Kandel, 1989) and primary cell culture (Montarolo et al., 1986). Most striking is the finding that the induction of long-term facilitation at this single synapse in Aplysia exhibits a requirement for protein and RNA synthesis during a critical time window or consolidation period. A variety of forms of memory in both vertebrates and invertebrates share this requirement for macromolecular synthesis during the consolidation period. From a molecular perspective, these studies indicate that the long-term behavioral and cellular changes require the expression of genes and proteins not required for short-term processes. The identification of the gene products required for this consolidation remains a major goal of molecular research into memory processes. Second, the finding in Aplysia that long-term sensitization training is associated with the growth of new synaptic connections between the sensory neurons and their follower cells demonstrated that the long-term but not the short-term process involves a structural change (Bailey & Chen, 1983, 1988a; Bailey & Kandel, 1993).

LEARNING-INDUCED GROWTH OF NEW SENSORY NEURON SYNAPSES In the early 1980s, studies in Aplysia first began to explore the morphological basis of the synaptic plasticity that might underlie the transition from short-term to long-term memory. By combining selective intracellular labeling techniques with the analysis of serial thin sections and transmission electron microscopy, complete reconstructions of unequivocally identified sensory neuron synapses were quantitatively analyzed from both control and behaviorally

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modified animals. The storage of long-term memory for sensitization (lasting several weeks) was accompanied by a family of distinct structural changes at identified sensory neuron synapses. These changes reflected a learning-induced remodeling of the functional architecture of presynaptic sensory neuron varicosities at two different levels of synaptic organization: (1) alterations in focal regions of membrane specialization of the synapse that mediate transmitter release—the number, size, and vesicle complement of sensory neuron active zones were larger in sensitized animals than in controls (Bailey & Chen, 1983, 1988b) and (2) a growth process that appeared similar to synaptogenesis during development and led to a pronounced increase in the total number of presynaptic varicosities per sensory neuron (Bailey & Chen, 1988a). Thus, sensory neurons from long-term sensitized animals exhibited a twofold increase in the number of synaptic varicosities, as well as an enlargement in the linear extent of each neuron’s axonal arbor when compared to sensory neurons from untrained animals (Figure 27.3). To determine which class of structural changes at sensory neuron synapses might contribute to the retention of long-term sensitization, Bailey and Chen (1989) compared the time course for each morphological change with the behavioral duration of the memory. They found that not all of the structural changes persisted as long as the memory. The increase in the size and synaptic vesicle complement of sensory neuron active zones present 24 hours following the completion of behavioral training returned to control levels when tested 1 week later. These data indicated that, insofar as this relatively transient modulation of active zone size and associated synaptic vesicles is one of the structural mechanisms underlying long-term sensitization, it is associated with the initiation and early expression of the long-term process and not with its persistence. By contrast, the duration of changes in varicosity and active zone number, which persisted unchanged for at least 1 week and were partially reversed at the end of 3 weeks, paralleled the behavioral time course of memory storage indicating that only the learning-induced increase in the number of sensory neuron synapses contributes to the stable maintenance of long-term sensitization. These results directly linked a change in synaptic structure to a long-lasting behavioral memory and suggested that the morphological alterations could represent an anatomical substrate for memory consolidation. In addition, the finding that some components of the learning-induced changes in synaptic architecture were transient whereas others endured suggested that not all of these modifications were regulated synchronously. At the structural level, the sensory neuron appears to have multiple mechanisms and parameters of plasticity available to it. Thus, during the later phases of long-term memory

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532

Synaptic and Cellular Basis of Learning Pericardial N. Branchial N. Gential N. Siphon N. 1 2 3

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100 µm

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Figure 27.3 Growth of sensory neurons induced by long-term sensitization in Aplysia. Note. Serial reconstruction of identified sensory neurons labeled with horseradish peroxidase (HRP; Bailey, Thompson, Castellucci, & Kandel, 1979) from long-term-sensitized and control animals. Total extent of the axonal arbors of sensory neurons from one control (untrained) and two long-termsensitized animals are shown. In each case, the rostral (row 3) to caudal (row 1) extent of the arbor is divided roughly into thirds. Each panel was produced by the super-imposition of camera lucida tracings of all HRP-labeled processes present in 17 consecutive slab-thick Epon sections and represents a linear segment through the ganglion of roughly 340 ␮m.

storage for sensitization, although there are more synapses, each individual synapse may recruit all of the mechanisms of plasticity that were present before training. Unlike the extensive anatomical changes observed at sensory neuron synapses following long-term training, the structural correlates of short-term memory in Aplysia (lasting minutes to hours rather than days to weeks) are far less pronounced (Bailey & Chen, 1988c). For example, the decrease in the strength of the sensory to motor neuron connection that accompanies short-term habituation is not associated with any detectable alterations in either the number of sensory neuron presynaptic varicosities or the number of active zones within the presynaptic varicosities. Nor does it alter the size of active zones or the total number of synaptic vesicles within the presynaptic varicosity.

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For each composite, ventral is up, dorsal is down, lateral is to the left, and medial is to the right. By examining images across each row (rows 1, 2, and 3), the viewer is comparing similar regions of each sensory neuron. In all cases, the axonal arbor of long-term-sensitized cells is markedly increased compared to cells from control (untrained) animals and parallels the concomitant learning-induced increase in the number of sensory neuron presynaptic varicosities. From “Long-Term Memory in Aplysia Modulates the Total Number of Varicosities of Single Identified Sensory Neurons,” by C. H. Bailey and M. Chen, 1988a, Proceedings of the National Academy of Sciences United States, 85, p. 2375. Reprinted with permission.

Rather, there is a reduction in the number of vesicles that are docked at the active zones and thus there are fewer packets of transmitter ready to be released. Taken together, these initial morphological studies of short- and long-term memory in Aplysia began to suggest a clear difference in the nature, extent, and time course of changes in the functional architecture of the synapse that may underlie memories of differing durations. The transient durations of short-term memories involving covalent modifications of preexisting proteins (proteins that turn over slowly) are accompanied only by modest structural rearrangements that appear to be restricted to shifts in the proximity of synaptic vesicle populations contiguous to the release site. By contrast, the prolonged durations of longterm memories depend on altered gene expression and the

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Functional Contribution of Presynaptic Structural Changes to Long-Term Facilitation

synthesis of new proteins and are associated with more substantial and potentially more enduring structural alterations that are reflected by frank changes in both the number of synaptic contacts and their active zone morphology. These studies also demonstrated, for the first time, that learning-induced structural changes could be detected at the level of specific, identified synaptic connections known to be critically involved in the behavioral modification and provided evidence for an intriguing notion—that active zones are plastic rather than immutable components of the synapse. Even elementary forms of learning can remodel the basic anatomical scaffolding of the neuron, in this case by altering the number and organization of transmitter release sites in the presynaptic compartment, to modulate the functional expression of synaptic connections. Long-term sensitization also induces a parallel set of anatomical changes in the postsynaptic motor neuron L7. For example, long-term training increases the number of postsynaptic spines in contact with the sensory neuron presynaptic varicosities (Bailey & Chen, 1988b). Whereas these learning-related structural changes are considerably regulated and involve the remodeling and growth of both the pre- and postsynaptic compartment, we limit ourselves in this review to the presynaptic changes. Complete serial reconstructions of identified sensory neuron varicosities in untrained (naive) animals revealed that approximately 60% of these presynaptic terminals lacked a structurally detectable active zone suggesting the possibility of nascent synapses in the adult brain. The extent to which learning and memory can convert these immature, and presynaptically silent synapses into mature and functionally competent synaptic connections is discussed next. Finally, these initial studies in Aplysia suggested that the growth of new sensory neuron synapses may represent the final and perhaps most stable phase of long-term memory storage, and raised the possibility that the stability of the long-term process might be achieved, at least in part, because of the relative stability of synaptic structure. The long-lasting growth of new synaptic connections between sensory neurons and their follower cells during long-term sensitization can be reconstituted in sensorymotor neuron co-cultures by five repeated applications of 5-HT (Bailey, Montarolo, Chen, Kandel, & Schacher, 1992; Glanzman, Kandel, & Schacher, 1990) as well as induced in the intact ganglion by the intracellular injection of cAMP, a second messenger activated by 5-HT (Nazif, Byrne, & Cleary, 1991). In culture, the synaptic growth can be correlated with the long-term (24 to 72 hr) enhancement in synaptic effectiveness and depends on the presence of an appropriate target cell similar to the synapse formation that occurs during development.

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FUNCTIONAL CONTRIBUTION OF PRESYNAPTIC STRUCTURAL CHANGES TO LONG-TERM FACILITATION In most model learning systems, the functional contribution of the structural changes that accompany long-lasting forms of synaptic plasticity remains largely unknown. We would like to know if changes in the number or structure of synaptic connections induced by learning are functionally effective and capable of contributing to the storage of long-term memory. Both technical and experimental limitations prevented the earlier behavioral studies in Aplysia discussed in the previous section from examining whether the increase in synaptic strength during long-term sensitization resulted from the conversion of preexisting but nonfunctional (silent) synapses to active synapses, or from the addition of newly formed functional synapses, or perhaps both. To address these issues directly, in vitro studies of the sensory-to-motor neuron synapse in Aplysia culture have monitored both functional and structural changes simultaneously so as to follow remodeling and growth at the same specific synaptic varicosities continuously over time and to examine the functional contribution of these presynaptic structural changes to the different time-dependent phases of long-term facilitation. Kim et al. (2003) combined time-lapse confocal imaging of individual presynaptic varicosities of sensory neurons labeled with three different fluorescent markers: the whole cell marker Alexa-594, and two presynaptic marker proteins: synaptophysin-eGFP that monitors changes in the distribution of synaptic vesicles within individual varicosities and synapto-PHluorin (synPH), a monitor of active transmitter release sites (Miesenbock, De Angelis, & Rothman, 1998). They found that repeated pulses of 5-HT induce two temporally, morphologically, and molecularly distinct classes of presynaptic changes: (1) the rapid activation of silent presynaptic terminals through the filling of preexisting empty varicosities with synaptic vesicles, which requires translation but not transcription; and (2) the generation of new synaptic varicosities that occurs more slowly and requires both transcription and translation. The enrichment of preexisting but empty varicosities with synaptophysin is completed within 3 to 6 hours, parallels intermediate-term facilitation, and accounts for approximately 32% of the newly activated synapses evident at 24 hours. By contrast, the new sensory neuron varicosities, which account for 68% of the newly activated synapses at 24 hours, do not form until 12 to 18 hours after exposure to five pulses of 5-HT. The rapid activation of silent presynaptic terminals suggests that in addition to its role in longterm facilitation, this modification of preexisting synapses may also contribute to the intermediate phase of synaptic

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Synaptic and Cellular Basis of Learning 0 hr

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MN

*

* Newly Activated Presynaptic Terminals MN⫽ Motor Neuron SN⫽ Sensory Neuron

Figure 27.4 Time course and functional contribution of two distinct presynaptic structural changes associated with intermediate- and long-term facilitation in Aplysia. Note. Repeated pulses of 5-HT in sensory to motor neuron co-cultures trigger two distinct classes of presynaptic structural changes: (1) the rapid clustering of synaptic vesicles to preexisting silent sensory neuron varicosities (3 to 6 hr), and (2) the slower generation of new sensory neuron synaptic varicosities (12 to 18 hr). The resultant newly filled and newly formed varicosities are functionally competent (capable of evoked

plasticity and memory storage (Ghirardi, Montarolo, & Kandel, 1995; Mauelshagen, Parker, & Carew, 1996; Sutton, Masters, Bagnall, & Carew, 2001; Figure 27.4). In this study, Kim and colleagues (2003) employed a reduced 5-HT protocol to induce selectively facilitation in the intermediate-term time domain without inducing longterm facilitation (Ghirardi et al., 1995). They found that isolated intermediate-term facilitation was also accompanied by the redistribution and clustering of synaptic vesicle proteins into empty sensory neuron varicosities at 0.5 hour and 3 hours similar to what occurred when intermediate- and long-term facilitation were recruited together. However, the presynaptic structural changes induced by the reduced 5-HT protocol differed from those induced by long-term training in at least two ways. First, there was no growth of new sensory neuron varicosities in the isolated intermediate phase. Second, unlike the filling of preexisting empty varicosities during the intermediate-term phase induced by the long-term protocol, the newly filled varicosities did not persist for 24 hours and were unaffected by inhibitors of protein synthesis suggesting that the structural remodeling induced by the reduced 5-HT protocol involved only a simple rearrangement of preexisting synaptic components. This may reflect a fundamental difference in the molecular mechanisms recruited by the two 5-HT protocols. Although both protocols induce intermediate-term facilitation, the long-term protocol may activate

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transmitter release) and contribute to the synaptic enhancement that underlies LTF. The rapid filling and activation of silent presynaptic terminals at 3 hours suggests that, in addition to its role in LTF, this modification of preexisting varicosities may also contribute to the intermediate phase of synaptic plasticity. Triangles represent functionally competent transmitter release sites (active zones). From “Presynaptic Activation of Silent Synapses and Growth of New Synapses Contribute to Intermediate and Long-Term Facilitation in Aplysia,” by J.-H. Kim et al., 2003, Neuron, 40, p. 162. Adapted with permission.

additional molecular events (including the machinery for translational activation) required to set up the long-term phase, perhaps by stabilizing the intermediate phase. At present, it is not known how the covalent modifications that lead to the rearrangement of preexisting synaptic proteins at empty varicosities is converted by the long-term protocol to a more stable, protein-synthesis dependent process. The activation of silent synapses also seems to play a major role in long-term potentiation (LTP)—a more complex form of explicit memory storage in the hippocampus of mammals. Although in mammals the term refers to a very specific molecular configuration found in synapses in different regions of the CNS of vertebrates (Malinow, Mainen, & Hayashi, 2000; Malinow & Malenka, 2002). In this case, the term silent synapse refers to excitatory glutamatergic synapses whose postsynaptic membrane contains NMDARs but no AMPARs. Found on the surface of the postsynaptic neuron, these receptors bind and are activated by the amino acid glutamate. There are two basic classes of glutamate receptors: ionotropic and metabotropic. Ionotropic receptors include AMPA receptors (AMPAR) that mediate fast synaptic transmission at excitatory synapses and NMDA receptors (NMDAR) that are permeable to calcium and regulate synaptic plasticity. Unlike ionotropic receptors, metabotropic glutatmate receptors (mGluRs) are not directly linked to ion channels but can affect them through an indirect process involving the activation of biochemical cascades.

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5-HT-Induced Regulation of the Presynaptic Actin Network

The lack of AMPAR-mediated signaling renders these synapses inactive, or “silent,” under normal conditions. Synaptic stimulation activates these silent synapses through the insertion of AMPARs into the postsynaptic membrane, a phenomenon sometimes referred to as AMPAfication. Calcium/calmodulin-dependent protein kinase II (CaMKII) plays a critical role in this process. Once this kinase is activated by high-frequency stimulation, it phosphorylates AMPARs or associated proteins, triggering their insertion into the postsynaptic membrane. The synapse is then no longer silent and postsynaptic responses are, by consequence, enhanced.

5-HT-INDUCED REGULATION OF THE PRESYNAPTIC ACTIN NETWORK One clue to the underlying molecular mechanisms responsible for these two discrete learning-related presynaptic structural changes comes from a study by Ahmari, Buchanan, and Smith (2000) who demonstrated that fluorescent puncta labeled by the synaptic vesicle marker VAMP-GFP are transported only at those synapses defined by the activity-dependent marker FM4-64. Moreover, these puncta contained not only synaptic vesicles but also other molecular components of the presynaptic active zone. Thus, the 5-HT-induced clustering of synaptic vesicle proteins to sensory neuron varicosities might represent a recruitment of not only synaptic vesicles but also the molecular precursors for active zone assembly. This redistribution of synaptic vesicle proteins and active zone components in both preexisting and newly formed sensory neuron synapses is also likely to involve cytoskeleton rearrangement (Benfenati, Onofri, & Giovedi, 1999; Matus, 2000). For example, structural remodeling of synapses in response to physiological activity requires reorganization of the actin network (Colicos, Collins, Sailor, & Goda, 2001; Huntley, Benson, & Colman, 2002) and the inhibition of actin function blocks synapse formation and interferes with long-term synaptic plasticity (Hatada, Wu, Sun, Schacher, & Goldberg, 2000; Krucker, Siggins, & Halpain, 2000; Zhang & Benson, 2001). Furthermore, several synaptic proteins such as synapsin can bind to the actin cytoskeleton and participate in synaptic vesicle trafficking (Humeau et al., 2001). How does an extracellular signal such as 5-HT lead to a reorganization of the actin cytoskeleton? The balance between actin polymerization and depolymerization is tightly regulated by extracellular signaling molecules, many of which act through the Rho family of GTPases (Hall, 1998). These small GTPases are thought to participate at different stages during the development of the central nervous system, for example, in the establishment of

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535

polarity, axon guidance, dendritic growth, and maintenance of dendritic spines (Bradke & Dotti, 1999; Nakayama, Harms, & Luo, 2000; Sin, Haas, Ruthhazer, & Cline, 2002; Threadgill, Bobb, & Ghosh, 1997; Yuan et al., 2003). Their participation, in turn, can be regulated by neuronal activity in vivo (Li, Aizenman, & Cline, 2002). In Aplysia, Udo et al. (2005) found that the application of toxin B, a general inhibitor of the Rho family, blocks 5-HT-induced long-term facilitation, as well as growth of new synapses in sensory-motor neuron co-cultures. Moreover, repeated pulses of 5-HT selectively induce the spatial and temporal regulation of the activity of only one of the small GTPases—Cdc42—at a subset of sensory neuron presynaptic varicosities. The activation of ApCdc42 induced by 5-HT is dependent on both the P13K and PLC pathways and, in turn, recruits the downstream effectors PAK (p21-Cdc42/Rac-activated kinase) and N-WASP (neuronal Wiskott-Aldrich syndrome protein) to regulate the presynaptic actin network. This initial molecular cascade leads to the outgrowth of filopodia, some of which represent morphological precursors for the growth of new sensory neuron varicosities associated with the storage of long-term facilitation. Initiation of Long-Term Facilitation As mentioned, the inhibition of transcription or translation does not affect short-term memory, but blocks the formation of long-term memory in a variety of model learning systems, suggesting that the stabilization of memory traces depends on de novo gene expression (Kandel, 2001). In Aplysia, 5-HT, released in vivo during sensitization or applied directly to cultured sensory neurons, regulates transmitter release. 5-HT binds to cell surface receptors on the sensory neurons that activate the enzyme adenylyl cyclase that converts ATP to the diffusible second messenger cAMP, thereby activating the cAMP-dependent protein kinase (PKA). PKA is a tetramer containing two catalytic subunits and two regulatory subunits. Binding of the second messenger cAMP to the regulatory subunit frees the active catalytic subunit of PKA that can then add phosphate groups to serine and threonine residues on target proteins, thereby altering their activity. Studies in Aplysia first revealed the participation of the camp/PKA-signaling pathway in behavioral sensitization and synaptic facilitation (Brunelli, Castellucci, & Kandel, 1976). PKA plays a central role in both short- and long-term facilitation: cAMP can evoke both short- and long-term facilitation, and inhibitors of PKA block both forms of facilitation. Insights into how PKA participates in both the short- and long-term process were provided by experiments in which fluorescently tagged PKA subunits were injected into sensory cells in

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culture. Using this technique to measure the amount of free PKA catalytic subunit, Bacskai et al. (1993) found that a single pulse of 5-HT, which produces short-term facilitation, increased the amount of active catalytic subunit in the presynaptic terminal of the sensory neurons. In the presynaptic terminals of the sensory cells, PKA phosphorylates target proteins such as ion channels, leading to a transient enhancement of transmitter release. By contrast, during long-term facilitation induced by repeated applications of 5-HT, the free catalytic subunit of PKA translocates to the cell body of the sensory neurons and enters the nucleus, where it phosphorylates transcription factors and thereby regulates gene expression. Both cAMP and PKA are essential components of the signal-transduction pathway for consolidating memories, not only in Aplysia but also for certain types of memory in Drosophila and mammals. Several olfactory learning mutants in Drosophila map to the cAMP pathway (Davis, 1996; Davis, Cherry, Dauwalder, Han, & Skoulakis, 1995; Drain, Folkers, & Quinn, 1991), indicating that blocking PKA function blocks memory formation in flies. In parallel, the late but not the early phase of LTP of the CA3-to-CAl synapse in the hippocampus is impaired by pharmacological or genetic interference with PKA (Abel et al., 1997; Frey, Huang, & Kandel, 1993; Y.-Y Huang, Li, & Kandel, 1994). However, the role of PKA seems to be different in hippocampal neurons than during LTF formation in Aplysia sensory neurons. In the hippocampus, PKA does not translocate to the nucleus and plays only a synaptic role: it can phosphorylate different targets, such as the GluR1 subunit of AMPAR (H. K. Lee, Barbarosie, Kameyama, Bear, & Huganir, 2000) and it favors the induction of LTP by counteracting the activity of protein phosphatases (Abel et al., 1997; Winder, Mansuy, Osman, Moallem, & Kandel, 1998). Finally, it also tags the synapse enabling the consolidation of the long-term process (Barco, Alarcon, & Kandel, 2002). In addition to protein kinases, synaptic protein phosphatases also play a key role in regulating the initiation of long-term synaptic changes. Various protein phosphatases, such as PP1 and calcineurin, oppose the local activity of PKA and act as inhibitory constraints on memory formation. Recent experiments in cultured Aplysia neurons indicate that calcineurin may act as a memory suppressor for sensitization (Sharma, Bagnall, Sutton, & Carew, 2003). In the mammalian brain, an increase in calcineurin activity also causes defects in long-term memory and L-LTP (Mansuy, Mayford, Jacob, Kandel, & Bach, 1998; Winder et al., 1998) whereas a reduction has the opposite effect (Malleret et al., 2001). Similarly, a reduction in PP1 activity improves memory in mice (Genoux et al., 2002). Therefore, in both systems a balance between phosphatase

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and kinase activities at a given synapse gates the synaptic signals that eventually reach the nucleus, and can regulate both memory storage and retrieval (Abel et al., 1998). These studies suggest that the long-term regulation of transmitter release requires PKA-related gene activation. PKA activates gene expression by the phosphorylation of transcription factors that bind to the cAMP-responsive element (CRE). One of the major transcription factors that recognize the CRE is a protein called CRE-binding protein (CREB1), which functions as a transcriptional activator only after it is phosphorylated by PKA or another second messenger kinase. Microinjection of CRE containing oligonucleotides into sensory neurons inhibits the function of CREB1 and blocks long-term facilitation but has no effect on the short-term process (Dash, Hochner, & Kandel, 1990). Not only is CREB1 activation necessary for longterm facilitation, it is also sufficient to induce long-term facilitation, albeit in reduced form and in a form that is not maintained beyond 24 hours. Thus, sensory cell injection of recombinant CREB1a phophorylated in vivo by PKA led to an increase in EPSP amplitude at 24 hours in the absence of any 5-HT stimulation (Bartsch et al., 2000). Bartsch and associates (1995) have found that the genetic switch that converts short- to long-term facilitation is not only composed of the CREB1 regulatory unit but also another member of the CREB gene family, ApCREB2, a CRE-binding transcription factor constitutively expressed in sensory neurons. ApCREB2 resembles human CREB2 and mouse ATF4 (Hai, Liu, Coukos, & Green, 1989; Karpinski, Morle, Huggenvik, Uhler, & Leiden, 1992), and functions as a repressor of long-term facilitation. Thus, injection of anti-ApCREB2 antibodies into Aplysia sensory neurons causes a single pulse of 5-HT, which normally induces only short-term facilitation lasting minutes, to evoke facilitation that lasts more than 1 day. This response requires both transcription and translation and is accompanied by the growth of new synaptic connections. That both positive and negative regulators govern long-term synaptic changes suggests the transition from short-term facilitation to long-term facilitation requires the simultaneous removal of transcriptional repressors and activation of transcriptional activators. These transcriptional repressors and activators can interact with each other both physically and functionally and it is likely that the transition is a complex process involving temporally distinct phases of gene activation, repression, and regulation of signal transduction. The complete set of genes regulated by a transcription factor in a specific cell type is still not known. In Aplysia sensory neurons, the activity of ApCREB1 leads to the expression of several immediate-response genes, such as ubiquitin hydrolase that stabilize short-term facilitation (Hegde et al.,

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Chromatin Remodeling and Epigenetic Changes During Long-Term Memory Storage 537

1997), and the transcription factor CCAAT-box-enhanced binding-protein (C/EPB), whose induction has been shown to be critical for LTF (Alberini, Ghirardi, Metz, & Kandel, 1994). This induced transcription factor (in concert with other constitutively expressed molecules such as ApAF; Bartsch et al., 2000) activate a second wave of downstream genes that can ultimately lead to the growth of new synaptic connections. These genes represent only a few of the family of gene products generated by CREB activity. The participation of the cAMP/CREB pathway appears to be a general feature of long-term memory formation throughout the animal kingdom. The first genetic screenings designed to identify learning mutants in Drosophila revealed two interesting mutants, dunce and rutabaga, with specific defects in memory formation (Dudai, Jan, Byers, Quinn, & Benzer, 1976; Duerr & Quinn, 1982) that were subsequently shown to affect genes in the cAMP signaling pathway (Byers, Davis, & Kiger, 1981; Waddell & Quinn, 2001). Experiments in transgenic flies have confirmed that the balance between CREB activator and repressor isoforms is critical for long-term behavioral memory. Thus, overexpression of an inhibitory form of CREB (dCREB-2b) blocked long-term olfactory memory but did not alter shortterm memory (Perazzona, Isabel, Preat, & Davis, 2004; Yin et al., 1994). Indeed, most of the upstream signaling cascade leading to CREB activation appears to be conserved through evolution, and many aspects of the role of CREB in synaptic plasticity described in invertebrates have also been observed in the mammalian brain. However, the role of CREB in explicit forms of memory appears to be more complex than in implicit forms of memory in invertebrates (see reviews by Barco, Pittenger, & Kandel, 2003; Lonze & Ginty, 2002). In mammals, CREB has been shown to regulate the expression of more than one hundred genes, but it is still not clear how many of these putative downstream genes are actually regulated during learning and required for memory storage (Lonze & Ginty, 2002; Mayr & Montminy, 2001). The current list of target genes is heterogeneous and includes genes with very diverse functions, from regulation of transcription and metabolism to genes affecting cell structure or signaling. Many CREB targets, such as c-fos, EGR-1, or C/EBPb are themselves transcription factors, whose induction may trigger a second wave of gene expression. Although we have focused on CREB- dependent gene expression because of its conserved role in memory formation through evolution, other transcription factors, such as ApAF and C/EBP in Aplysia and SRF, C/EBPb, c-fos, or EGR-1 in mice (Albensi & Mattson, 2000; Izquierdo & Cammarota, 2004; Ramanan et al., 2005; Tischmeyer & Grimm, 1999) are also likely to contribute to the transcriptional regulation that accompanies long-lasting forms of synaptic plasticity.

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The CREB-mediated response to extracellular stimuli can be modulated by a number of kinases (PKA, CaMKII, CaMKIV, RAK2, MAPK, and PKC) and phosphatases (PP1 and calcineurin). The CREB regulatory unit may therefore serve to integrate signals from various signal transduction pathways. This ability to integrate signaling as well as mediate activation or repression may explain why CREB is so central to memory storage in different contexts (Martin & Kandel, 1996).

CHROMATIN REMODELING AND EPIGENETIC CHANGES DURING LONG-TERM MEMORY STORAGE Guan et al. (2002) used chromatin immunoprecipitation techniques to examine directly the role of CREB-mediated responses in the integration of synaptic signaling by studying the long-term interactions of two opposing modulatory transmitters important for behavioral sensitization in Aplysia. Toward that end, they utilized a single bifurcated sensory neuron that contacts two spatially separated postsynaptic neurons (Martin, Casadio, et al., 1997). They found that when a neuron receives 5-HT, and at the same time receives input from the inhibitory transmitter FMRFamide at another set of synapses, the synapsespecific long-term depression produced by FMRFamide dominates. These opposing inputs are integrated in the neuron’s nucleus and are evident in the repression of C/EPB, a transcription regulator downstream from CREB that is critical for long-term facilitation. Whereas 5-HT induces C/EPB by activating CREB1 and recruiting the CREBbinding protein, a histone acetylase, to acetylate histones, FMRFamide displaces CREB1 with CREB2 that recruits a histone deacetylase to deacetylate histones. When 5-HT and FMRFamide are given together, FMRFamide overrides 5-HT by recruiting CREB2 and the deacetylase to displace CREB1 and CBP, thereby inducing histone deacetylation and repression of C/EBP. Thus, both the facilitatory and inhibitory modulatory transmitters that are important for long-term memory in Aplysia activate signal transduction pathways that alter nucleosome structure bidirectionally through acetylation and deacetylation of histone residues in chromatin (Figure 27.5). The epigenetic marking of chromatin, by histone modifications, chromatin methylation, and the activity of retrotransposons, may have long-term consequences on transcriptional regulation of specific gene loci involved in long-term synaptic changes, and thus adds a new layer of complexity to our view of how nuclear function and synaptic activity affect one another (Guan et al., 2002; Hsieh & Gage, 2005; Levenson & Sweatt, 2005). As detailed,

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Figure 27.5 5-HT and FMRFamide alter nucleosome structure bidirectionally through acetylation and deacetylation of chromatin.

(A) Control C/EBP TATA box CREB-1

C/EBP CRE Ac C/EBP Enhancer

C/EBP Coding

(B) 5-HT Alone PKA

5-HT

Ac CREB-1 P

CBP Pol II

TBP CRE C/EBP Enhancer

C/EBP Ac C/EBP Coding

(C) FMRFa Alone FMRFa

Note. A: At the basal level, CREB1a resides on the C/ EBP promoter some lysine residues of histones are acetylated. B: 5-HT, through PKA, phosphorylates CREB1 that binds to the C/EBP promoter. Phosphorylated CREB1 then forms a complex with CBP at the promoter. CBP then acetylates lysine residues of the histones (e.g., KS of H4). Acetylation modulates chromatin structure, enabling the transcription machinery to bind and induce gene expression. C: FMRFamide activates CREB2, which displaces CREB1 from the C/EBP promoter. HDAC5 is then recruited to deacetylate histones. As a result, the gene is repressed. D: If the neuron is exposed to both FMRFamide and 5-HT, CREBa is replaced by CREB2 at the promoter even though it might still be phosphorylated through the 5-HT-PKA pathway, and HDAC5 is then recruited to deacetylate histones, blocking gene induction. From “Integration of Long-Term-Memory-Related Synaptic Plasticity Involves Bidirectional Regulation of Gene Expression and Chromatin Structure,” by Z. Guan et al., 2002, Cell, 111, p. 490. Reprinted with permission.

CREB-2 P

HDAC-5

p38 C/EBP TATA box X

C/EBP CRE C/EBP Enhancer

C/EBP Coding

(D) FMRFa + 5-HT P 5-HT

PKA

CBP

CREB-1 p38 CREB-2 P

HDAC-5

FMRFa

C/EBP TATA box X

C/EBP CRE C/EBP Enhancer

C/EBP Coding

the contribution of histone tail acetylation, a modification that favors transcription and is associated with active loci was first revealed for long-term facilitation by Guan et al. (2002) in Aplysia. In addition to finding that facilitatory and inhibitory stimuli alter, bidirectionally, the acetylation stage and structure of promoters driven by the expression of genes involved in the maintenance of long-term facilitation, such as C/EBP, this study also demonstrated that

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enhancing histone acetylation with deacetylase (HDAC) inhibitors facilitates the induction of long-term facilitation. HDAC inhibitors have now been shown to enhance L-LTP in the Schaffer collateral pathway of mammals and memory formation in hippocampus-dependent tasks (Alarcon et al., 2004; Korzus, Rosenfeld, & Mayford, 2004; Levenson & Sweatt, 2005; Yeh, Lin, & Gean, 2004). Conversely, mice with reduced histone acetyltransferase

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Consolidation of Long-Term Memory 539

activity have deficits in both long-lasting forms of memory and LTP (Alarcon et al., 2004; Bourtchouladze et al., 2003; Korzus et al., 2004; Wood et al., 2005). These results indicate that critical chromatin remodeling occurs during the formation of long-term memory, and that these nuclear changes are required for the stable maintenance of memory storage. Synapse to Nucleus Signals The transcriptional switch for the conversion of shortto long-term memory requires not only the activation of CREB1 but also the removal of the repressive action of CREB2, which lacks consensus sites for PKA phosphorylation (Bartsch et al., 1995). ApCREB2 does, however, have both protein kinase C and mitogen-activated protein kinase (MAPK) phosphorylation sites and MAPK is activated by 5-HT in Aplysia neurons. Martin, Michael, et al., (1997) and Michael and Martin (1998) examined the subcellular localization of an Aplysia ERK2 homologue in sensory-to-motor neuron co-cultures during short- and long-term facilitation. Whereas MAPK immunoreactivity was predominantly localized to the cytoplasm in both sensory and motor neurons during short-term facilitation, MAPK translocated into the nucleus of the presynaptic sensory neuron but not in the postsynaptic motor cell during 5-HT-induced long-term facilitation. Presynaptic but not postsynaptic nuclear translocation of MAPK was also triggered by elevations in intracellular cAMP, indicating that the cAMP pathway activates the MAPK pathway in a neuron-specific manner. Injection of either anti-MAPK antibodies or MAPK inhibitors (PD98059) into the presynaptic sensory cell selectively blocked long-term facilitation without affecting short-term facilitation. Thus, like PKA, MAPK translocates to the nucleus with prolonged 5-HT treatment so as to activate the activators (CREB1) and relieve the repressors (CREB2; Martin, Michael, et al., 1997). The involvement of MAPK in long-term plasticity may be quite general: Martin, Michael, et al., (1997) found that cAMP also activated MAPK in mouse hippocampal neurons, suggesting that MAPK may play a role in hippocampal long-term potentiation. The requirement for MAPK during hippocampal LTP has been shown by English and Sweatt (1996, 1997), who demonstrated that ERK1 is activated in CAl pyramidal cells during LTP and that bath application of MAPK kinase inhibitors blocks LTP. CONSOLIDATION OF LONG-TERM MEMORY The activation of adenylyl cyclase by 5-HT, the increase in cAMP concentration with the resultant dissociation of

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the catalytic subunit of PKA and its translocation to the nucleus, as well as the phosphorylation of CREB1 are all unaffected by inhibitors of RNA or protein synthesis. Where then does the RNA and protein synthesis–dependent step that characterizes the consolidation phase of long-term memory appear? It requires an additional step—the synthesis of proteins encoded by the genes whose expression is induced by CREB1 and repressed by CREB2. To examine which genes are downstream from CREB1, Alberini et al. (1994) characterized the intermediary, immediate-early genes induced by cAMP and CREB. In a search for possible cAMP-dependent regulatory genes that might be interposed between constitutively expressed transcription factors and stable effector genes, Alberini and colleagues (1994) focused on the CCAAT-box-enhancerbinding protein (C/EBP) transcription factors. They cloned an Aplysia C/EBP homologue (ApC/EBP) and found that its expression was induced by exposure to 5-HT. Inhibition of ApC/EBP activity blocked long-term facilitation but had no effect on short-term facilitation. Thus, the induction of ApC/EBP seems to serve as an intermediate component of a molecular switch activated during the consolidation period. The existence of C/EBP, a cAMP-regulated immediateearly gene that is itself a transcription factor and regulates other genes, leads to a model of sequential gene activation. CREB1a, CREB1b, CREB1c, and CREB2 represent the first level of control because all are constitutively expressed. Stimuli that lead to long-term facilitation disturb the balance between CREB1-mediated activation and CREB2-mediated repression, through the action of PKA, MAPK, and possibly other kinases. This leads to the upregulation of a family of immediate-early genes. Some of these immediate-early genes are transcription factors such as C/EBP; others are effectors, such as ubiquitin hydrolase, that contribute to consolidation by either extending the inducing signal or initiating the changes at the synapse that cause long-term facilitation. Activity-Dependent Modulation of Cell Adhesion Molecules and the Initiation of Learning-Related Synaptic Growth How does this sequential gene activation lead to the growth of new sensory neuron synapses? Since the functional and structural changes that accompany long-term sensitization in Aplysia require new protein synthesis, Barzilai, Kennedy, Sweatt, and Kandel (1989) utilized quantitative two-dimensional gels and [355] methionine incorporation to examine changes in specific proteins in the sensory neurons in response to 5-HT. They found that 5-HT initiates a large increase in overall protein synthesis during training. Moreover, beyond these overall effects, 5-HT also

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produces three temporally discrete sets of changes in specific proteins that could be resolved on two-dimensional gels. First, 5-HT induces a rapid and transient increase at 30 minutes in the rate of synthesis of 10 proteins and a transient decrease in five proteins that subside within 1 hour and are in all cases dependent on transcription. These early changes are followed by at least two further rounds of changes in the expression of specific proteins, some of which are transient, and some of which persist for at least 24 hours. The 15 early proteins induced by repeated exposure to 5-HT can also be induced by cAMP. Of the 15 early proteins Barzilai et al. (1989) observed to be specifically altered in expression during the acquisition of long-term facilitation, six have now been identified. Two proteins that increase (clathrin and tubulin) and four proteins that decrease their level of expression (NCAM-related cell adhesion molecules) all seem to relate to the 5-HT-induced structural changes. Mayford, Barzilai, Keller, Schacher, and Kandel (1992) first focused on the four proteins, D1 to D4, that decrease their expression in a transcriptionally dependent manner following the application of 5-HT or cAMP and found that they encoded different isoforms of an immunoglobulin-related cell adhesion molecule, which is homologous to NCAM in vertebrates and Fasciclin II in Drosophila. Imaging of fluorescently labeled MAbs to apCAM indicates that not only is there a decrease in the level of expression but that even preexisting protein is lost from the surface membrane of the sensory neurons within 1 hour after the addition of 5-HT (Mayford et al., 1992). This transient modulation by 5-HT of cell adhesion molecules, therefore, may represent one of the early molecular steps required for initiating learning-related growth of synaptic connections. Blocking the expression of the antigen by MAb causes defasciculation, a step that appears to precede synapse formation during development in Aplysia (Keller & Schacher, 1990). To examine the mechanisms that underlie the 5-HTinduced down-regulation of apCAM and, in particular, how these relate to the initiation of synaptic growth, Bailey, Chen, Keller, and Kandel (1992) combined thinsection electron microscopy with immunolabeling using a gold-conjugated MAb specific to apCAM. They found that a 1-hour application of 5-HT led to a 50% decrease in the density of gold-labeled apCAM complexes at the surface membrane of the sensory neuron. This down-regulation was particularly prominent at adherent processes of the sensory neurons and was achieved by a heterologous, protein synthesis–dependent activation of the endosomal pathway, leading to internalization and apparent degradation of apCAM. As is the case for the down-regulation at the level of expression, the 5-HT-induced internalization of apCAM can be simulated by cAMP. Concomitant with the

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down-regulation of apCAM, Hu, Barzilai, Chen, Bailey, and Kandel (1993) further demonstrated that, as part of this coordinated program for endocytosis, 5-HT and cAMP also induce an increase in the number of coated pits and coated vesicles in the sensory neurons and an increase in the expression of the light chain of clathrin (apClathrin). Because the apClathrin light chain contains the important functional domains of both LCa and LCb of mammalian clathrin thought to be essential for the coated pit assembly and disassembly, the increase in clathrin may be an important component in the activation of the endocytic cycle required for the internalization of apCAM. The learning-induced internalization of apCAM is thought to have at least two major structural consequences: (1) disassembly of homophilically associated fascicles of the sensory neurons (defasciculation), a process that may destabilize adhesive contacts normally inhibiting growth; and (2) endocytic activation that may lead to a redistribution of membrane components to sites where new synapses form. Thus, aspects of the initial steps in the learningrelated growth of synaptic connections that is a hallmark of the long-term process may eventually be understood in the context of a novel and targeted form of receptormediated endocytosis. To further define the mechanisms whereby 5-HT leads to apCAM down-regulation, Bailey and colleagues (1997) used epitope tags to examine the fate of the two apCAM isoforms (transmembrane and GPI-linked) and found that only the transmembrane form (TM-apCAM) is internalized (Figure 27.6). This internalization was blocked by overexpression of TM-apCAM with a point mutation in the two MAPK phosphorylation consensus sites, as well as by injection of a specific MAPK antagonist into sensory neurons. These data suggest that activation of the MAPK pathway is important for the internalization of TMapCAM and may represent one of the initial and perhaps permissive stages of learning-related synaptic growth in Aplysia. Furthermore, the combined actions of MAPK both in the cytoplasm and in the nucleus suggest that MAPK plays multiple roles in long-lasting synaptic plasticity and appears to regulate each of the two distinctive processes that characterize the long-term process: activation of transcription and growth of new synaptic connections. Han, Lim, Kandel, and Kaang (2004) examined more closely the relationship between the 5-HT-induced downregulation of TM-apCAM and synaptic growth by overexpressing various HA-epitope tagged recombinant apCAMs in Aplysia sensory neurons. They found that overexpression of TM-apCAM, but not the GPI-linked isoform of apCAM, blocked both long-term facilitation as well as the associated increase in the number of sensory neuron varicosities. By interrupting the adhesive function of apCAM

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Consolidation of Long-Term Memory 541

MN

Transmembrane Isoform

tail portion of apCAM alone. These studies indicated that the extracellular domain of TM-apCAM has an inhibitory function that is neutralized by internalization to induce long-term facilitation and suggested that the cytoplasmic domain provides an interactive platform for both signal transduction and the internalization machinery.

SN

Nuclear Translocation of apCAM-Associated Protein (CAMAP) and Induction of Long-Term Facilitation

GPI-Linked Isoform

5-HT MN

SN

Transmembrane Isoform of apCAM

GPI-Linked Isoform of apCAM

Figure 27.6 Regional specific down-regulation of the transmembrane isoform of apCAM. Note. This model is based on the assumption that the relative concentration of the GPI-linked versus transmembrane isoforms of apCAM is highest at points of synaptic contact between the sensory neuron and motor neuron and reflects the results of studies done in dissociated cell culture. Thus, previously established connections might remain intact following exposure to 5-HT since they would be held in place by the adhesive, homophilic interactions of the GPI-linked isoforms and the process of outgrowth from sensory neuron axons would be initiated by down-regulation of the transmembrane form at extrasynaptic sites of membrane apposition. In the intact ganglion, the axons of sensory neurons are likely to fasciculate not only with other sensory neurons but also with the processes of other neurons and perhaps even glia. One of the attractive features of this model is that the mechanism for down-regulation is intrinsic to the sensory neurons. Thus, even if some of the sensory neuron axonal contacts in the intact ganglion were heterophilic in nature, that is, with other neurons or glia, we would still expect the selective internalization of apCAM at the sensory neuron surface membrane at these sites of heterophilic apposition to destabilize adhesive contacts and to facilitate disassembly. From “Mutation in the Phosphorylation Sites of MAP Kinase Blocks LearningRelated Internalization of apCAM in Aplysia Sensory Neurons,” by C. H. Bailey et al., 1997, Neuron, 18, p. 921. Reprinted with permission.

with an anti-HA antibody, this inhibition of long-term facilitation induced by the overexpression of TM-apCAM was restored. Moreover, long-term facilitation could be completely blocked by overexpression of the cytoplasmic

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S. H. Lee at al. (2007) examined the 5-HT-induced signaling interactions mediated by the cytoplasmic domain of TM-apCAM and found an additional, and novel role for this cell adhesion molecule in synapse-specific forms of long-lasting plasticity. As outlined, long-term facilitation at the sensory to motor neuron synapse requires the activation of CREB1 in the nucleus of the sensory neuron (Bartsch, Casadio, Karl, Serodio, & Kandel, 1998). Activated CREB1 induces the transcription factor ApC/ EBP that in turn acts on downstream genes encoding proteins important for synaptic growth and the stable maintenance of long-term facilitation (Alberini et al., 1994). An initial step, thought to be permissive, for the initiation of learning-related growth is the clathrinmediated internalization and consequent down-regulation of TM-apCAM. To examine directly how the internalization of TMapCAM is related to the initiation of nuclear transcription, S. H. Lee et al. (2007) first looked for molecules that could bind to the cytoplasmic tail of TM-apCAM and cloned an apCAM-associated protein (CAMAP) by yeast two-hybrid screening. They found that 5-HT signaling at the synapse activates PKA which in turn phosphorylates CAMAP to induce the dissociation of CAMAP from apCAM and that this dissociation is a prerequisite for the internalization of apCAM. The 5-HT-induced dissociated CAMAP is subsequently translocated to the nucleus of the sensory neurons. In the nucleus, CAMAP acts as a transcriptional co-activator for CREB1 that is essential for the activation of ApC/ EBP required for the initiation of long-term facilitation. Combined, these data suggest that CAMAP is one of the retrograde signals from the synapse to the nucleus where it acts as a co-regulator of the presynaptic gene expression associated with the induction of long-term facilitation in Aplysia. In addition, these findings demonstrate the importance, for learning-related synaptic plasticity, of signal propagation into the nucleus from the surface membrane of activated synaptic sites mediated by a molecule directly interacting with a cell surface adhesion molecule and suggest a novel presynaptic molecular mechanism to turn on the gene transcription required for long-term memory.

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STABILIZATION OF LEARNING-RELATED SYNAPTIC GROWTH In addition to transcription in the nucleus and protein synthesis in the cell body, long-term memory also requires a second site of local protein synthesis at the synapse. A number of distinct mRNAs have been localized in the axons of Aplysia and in the dendrites of rodent hippocampal neurons (for review, see Steward & Schuman, 2001, 2003). The molecular mechanisms that target these mRNAs to the synapse are largely unknown, but some are carried by the kinesin motors, the key anterograde transport machinery (Puthanveettil et al., 2008). Some of these mRNAs are thought to involve the recognition of cis-acting elements in their 3⬘untranslated region by specific RNA-binding proteins that interact with the cytoskeleton. Once transported to the synaptic compartments, these mRNAs are translated only after docking at active synaptic sites, a process frequently referred to as synaptic or local protein synthesis. Regulation of local protein synthesis plays a major role in the control of synaptic strength at the sensory to motor neuron connection in Aplysia and during L-LTP in the hippocampus. Martin, Casadio, et al. (1997) first investigated the role of local protein synthesis in an Aplysia culture system in which a single bifurcated sensory neuron was plated in contact with two spatially separated gill motor neurons. In this system, repeated application of 5-HT to one synapse produces a CREB-mediated, synapse-specific long-term facilitation that can be blocked by the local application of inhibitors of translation, suggesting that local protein synthesis at the synapse is required as part of the retrograde signaling cascade for the initiation of synapse-specific long-term facilitation. Subsequent studies by Casadio et al. (1999) found, in addition, that long-term synapse-specific facilitation induced by 5-HT in Aplysia requires local protein synthesis for the stable maintenance of learning-induced synaptic growth. Similarly, in the hippocampus, the induction of LTP in the Schaffer collateral pathway is accompanied by the transport of polysomes from dendritic shafts to active spines of CA1 neurons, suggesting a critical role for local protein synthesis in the morphological changes associated with LTP (Ostroff, Fiala, Allwardt, & Harris, 2002), and local inhibition of protein synthesis blocks L-LTP in the Schaffer collateral pathway (Bradshaw, Emptage, & Bliss, 2003; Cracco, Serrano, Moskowitz, Bergold, & Sacktor, 2005). Following the sending of a retrograde signal to the nucleus and the subsequent transcriptional activation, newly synthesized gene products, both mRNAs and proteins, have to be delivered by kinesin-mediated fast axonal transport (Puthanveettil & Kandel, 2006) specifically to the synapses whose activation originally triggered

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the wave of gene expression. To explain how this specificity can be achieved in a biologically economical way given the massive number of synapses in a single neuron, Martin, Casadio, et al. (1997) and Frey and Morris (1997) proposed the synaptic capture hypothesis. This hypothesis, also referred to as synaptic tagging, proposes that the products of gene expression are delivered throughout the cell, but are only functionally incorporated in those specific synapses that have been tagged by previous synaptic activity. The synaptic tag model has now been supported by a number of studies both Aplysia (Casadio et al., 1999; Martin, Casadio, et al., 1997) and in the rodent hippocampus (Barco et al., 2002; Dudek & Fields, 2002; Frey & Morris, 1997, 1998). Studies of synaptic capture at the synapses between the sensory and motor neurons of the gill-withdrawal reflex in Aplysia have further demonstrated that the production of CRE-driven gene products in the nucleus is not sufficient to achieve synapse-specific long-term facilitation. One also needs a PKA-mediated covalent signal to mark the stimulated synapses, and consequent local protein synthesis to stabilize that mark (Casadio et al., 1999; Martin, Casadio, et al., 1997). Thus, injection into the cell body of phosphorylated CREB-1 gives rise to long-term facilitation at all the synapses of the sensory neuron, but this facilitation is not maintained beyond 24 to 48 hours unless one of the synapses is also marked by triggering the short-term process with a single pulse of 5-HT (Casadio et al., 1999). Once marked that synapse and only that synapse shows maintained facilitation and growth. Experiments in the rat hippocampus by Frey and Morris have demonstrated, in turn, that once transcription-dependent LTP has been induced at one pathway, the long-term process can be “captured” at a second pathway receiving a single train that would normally produce only E-LTP. The stimulus for the short-term process causes a transient potentiation and, in addition, marks the synaptic terminals, enabling the capture of the newly expressed gene products. The properties of synaptic capture observed for intracompartmental capture in hippocampal CA1 neurons are similar to those described in the bifurcated sensory neurons of Aplysia (Martin, Casadio, et al., 1997). However, in mammals, where there are two dendritic compartments—apical and basal, the tag appears to be restricted to specific dendritic compartments, and additional mechanisms are required to capture across compartments (Alarcon, Barco, & Kandel, 2006). The finding of two distinct components for the marking signal in Aplysia first suggested that there is a mechanistic distinction between the initiation of long-term synaptic plasticity and synaptic growth (which requires only nuclear transcription and central translation but does not require

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Stabilization of Learning-Related Synaptic Growth

local protein synthesis) and the stable maintenance of the long-term functional and structural changes that requires, in addition, local protein synthesis at the synapse. How might this local protein synthesis at the synapse, which is necessary for stabilizing synaptic growth and long-term plasticity, be regulated? The control of local translation at the synapse is likely to be complex and involve several different mechanisms, including different types of mRNA transport and docking, cytoplasmic poly adenylation, mTOR which is the target of the selective protein synthesis inhibitor rapamycin (Cammalleri et al., 2003; Purcell, Sharma, Bagnall, Sutton, & Carew, 2003), and the phosphorylation of different translation factors (see review by Sutton & Schuman, 2005). Since mRNAs are made in the cell body, the need for the local translation of some mRNAs suggests that these mRNAs may be dormant before they reach the activated synapse. If that were true, one way of activating protein synthesis at the synapse would be to recruit a regulator of translation that is capable of activating translationally dormant mRNAs. Si, Lindquist, and Kandel (2003) began to search for such a molecule by focusing on the Aplysia homolog of CPEB (cytoplasmic polyadenylation elementbinding protein), a protein capable of activating dormant mRNAs through the elongation of their polyA tail. CPEB was first identified in oocytes and subsequently in hippocampal neurons. In Aplysia, a novel, neuron-specific isoform of CPEB is present in the processes of sensory neurons and stimulation with 5-HT increases the amount of CPEB protein at the synapse. The induction of CPEB is independent of transcription but requires new protein synthesis and is sensitive to rapamycin and to inhibitors of P13 kinase. Moreover, the induction of CPEB coincides with the polyadenylation of neuronal actin, and blocking CPEB locally at the activated synapse blocks the long-term maintenance of synaptic facilitation but not its early expression at 24 hours. Thus, CPEB has all the properties required of the local protein synthesis–dependent component of marking and supports the idea that there are separate mechanisms for initiation of the long-term process and its stabilization. Moreover, these data suggest that the maintenance but not the initiation of long-term synaptic plasticity requires a new set of molecules in the synapse and some of these new molecules are made by CPEB-dependent translational activation. Interestingly, a structurally similar neuronal isoform of CPEB, CPEB-3, has been found in mouse hippocampal neurons, where it is induced by the neurotransmitter dopamine (Theis, Si, & Kandel, 2003). How might CPEB stabilize the late phase of long-term facilitation? As outlined above, the stability of long-term facilitation seems to result from the persistence of structural changes at sensory neuron synapses, the decay of which

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543

parallels the decay of the behavioral memory. These 5-HT-induced structural changes at the synapses between sensory and motor neurons include the remodeling of preexisting facilitated synapses, as well as the growth and establishment of new synaptic connections. The reorganization and growth of new synapses have two broad requirements: (1) structural (changes in shape, size, and number) and (2) regulatory (where and when to grow). The genes involved in both of these aspects of synaptic growth might be potential targets of apCPEB. The structural aspects of the synapses are dynamically controlled by reorganization of the cytoskeleton, which can be achieved either by redistribution of preexisting cytoskeletal components or by their local synthesis. Construction of cDNA libraries from the isolated axonal neurites of Aplysia sensory neurons has facilitated identification of mRNAs that encode structural proteins such as ␣1-tubulin and N-actin as well as translational elements including CPEB, the elongation factor eEF1␣ and several ribosomal proteins (Moccia et al., 2003; Moroz et al., 2006). Most of these transcripts are localized in the distal axonal processes of the sensory neurons and are inactive before synaptic stimulation. ApCPEB is capable of activating dormant mRNAs by elongating their polyA tails. Stimulation with 5-HT increases the amount of ApCPEB protein at the synapse and this in turn could lead to the local activation of mRNAs encoding both structural proteins (tubulin and N-actin) and regulatory molecules such as EphA2, CAMKII, and members of the ephrin family (Brittis, Lu, & Flanagan, 2002). Thus, CPEB might contribute to the stabilization of learning-related synaptic growth by controlling the local synthesis of both the cytoskeletal components of the synapse as well as regulatory molecules important for synaptic maturation. Biological molecules have a relatively short half-life (hours to days) compared to the duration of memory (days, weeks, even years). How then can the learning-induced alterations in the molecular composition of a synapse be maintained for such a long time? Most answers to this elusive question rely on some type of self-sustained mechanism that can somehow modulate synaptic strength and synaptic structure. For example, Malinow and colleagues have proposed that two regulatory pathways control the insertion and removal of AMPA receptors at the synapse: the maintenance pathway is always on and controls the constant turnover of receptor subunits, whereas the constructive pathway is only turned on during LTP induction (Malinow et al., 2000; Malinow & Malenka, 2002). The activation of the constructive pathway and insertion of new AMPARs would cause the growth and/or maturation of postsynaptic densities enabling the formation of new memories, whereas the maintenance pathway would be responsible for their stabilization (Hayashi et al., 2000;

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Lisman & Zhabotinsky, 2001). Another interesting model for long-term memory storage was suggested by Crick (1984) who proposed that autocatalytic kinases might provide the molecular mechanism for long-lasting, selfmaintained changes in synaptic function. Lisman further developed this idea based on the autocatalytic properties of the calcium/calmodulin-dependent protein kinase II (CaMKII; Lisman & Zhabotinsky, 2001). Kandel and Si proposed a model based on the prionlike properties of the Aplysia neuronal isoform of CPEB to explain how a population of unstable molecules can produce a stable change in synaptic form and function (Si, Lindquist, et al., 2003). CPEB has two conformational states: one is inactive or acts as a repressor, while the other is active. In a naive synapse, the basal level of CPEB expression is low and its state is inactive or repressive. However, if a given threshold is reached, CPEB switchs to the prion-like state, which activates the translation of dormant mRNAs through the elongation of their poly-A tail (Si, Giustetto, et al., 2003). Once the prion state is established at an activated synapse, dormant mRNAs, made in the cell body and distributed cell-wide, would be translated only at the activated synapses. Because the activated CPEB can be self-perpetuating, it could contribute to a self-sustaining, synapse-specific long-term molecular change and provide a mechanism for the stabilization of learning-related synaptic growth and the persistence of memory storage. These molecular mechanisms are not mutually exclusive: the synaptic translation of CaMKII mRNAs can be regulated by CPEB, and the synthesis and trafficking of new AMPAR subunits may require CaMKII activity as well as enhanced protein synthesis (Burgin et al., 1990; Y. S. Huang, Jung, Sarkissian, & Richter, 2002; Ouyang, Rosenstein, Kreiman, Schuman, & Kennedy, 1999).

SUMMARY Molecular genetics has brought about a dramatic unification within the biological sciences. A major advancement in our understanding of genes, their expression, and the structure of the proteins they encode has led to a refined appreciation of the conservation of cellular function at the molecular level that now provides a common conceptual framework for several, previously unrelated, disciplines: cell biology, biochemistry, development, immunology, and neurobiology. A parallel and potentially more profound unification is occurring between cognitive psychology—the science of mind—and neural science—the science of the brain. The ability to study the biological basis of mental function is providing a new paradigm for examining cognitive processes such as perception, language, learning, and

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memory. As we outlined in this chapter, one of the key unifying findings emerging from the molecular study of both implicit and explicit memory processes is the unexpected realization that these distinct forms of memory, that differ not only in the neural systems involved, but also in the nature of the information stored, nevertheless may recruit the same restricted set of molecular logic for their longterm representation. Thus, whereas animals and humans are capable of a wide variety of learning processes that utilize a number of different second messenger and signaling cascades, they may share a common set of molecular mechanisms for the storage of long-term memory. In Aplysia, these mechanisms include a core sequence of three steps. First, the initiation step involves the PKA-mediated activation of CREB1 and the concomitant MAPK-mediated derepression of CREB2. Second, the consolidation step involves the induction by CREB1 of a set of immediate early genes such as the C-terminal ubiquitin hydrolase and the transcription factor ApC/EBP. Third, the stabilization step involves the down-regulation of apCAMs and the consequent remodeling of preexisting synapses and the growth of new synaptic connections. For synapse-specific forms of long-term facilitation, the local generation of a retrograde signaling cascade at the synapse travels to the nucleus to activate the transcriptional machinery. Newly synthesized gene products, both mRNAs and proteins, are then delivered specifically to the synapses whose activation originally triggered the wave of gene expression (Figure 27.7). Since many studies in the vertebrate brain have now found that immediate early genes are induced in the hippocampus and certain regions of the neocortex by treatments that lead to LTP, it will be of particular interest to investigate whether genes induced by CREB, perhaps of the C/ EBP family, are also required for long-term synaptic modifications in mammals. That the late phase of mossy fiber, Schaffer collateral, and perforant pathway LTP involves cAMP raises the additional, attractive possibility that, in the hippocampus as well, cAMP and PKA are recruited because they may be able to access the signaling pathways and transcriptional machinery required for synaptic growth and the persistence of memory storage. Perhaps the most striking findings to emerge from the cellular and molecular studies of memory storage in Aplysia and the mammalian brain are that long-term memory involves both transcription in the nucleus and structural changes at the synapse. The structural changes associated with the storage of long-term memory can be grouped into two general categories: remodeling of preexisting synapses and growth of new synapses (Bailey & Kandel, 1993; Bailey et al., 2004; Greenough & Bailey, 1988; Lamprecht & LeDoux, 2004; Yuste & Bonhoeffer,

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Summary

CRE

CREB-2

CRE Early 6

Early

5

CREB-1 MAPK

5HT 1

Ubiquitin Hydrolase

cAMP

AC Tail

3

Nucleus TAAC Late

CAAT Late C/EBP

4

C/EBP+ AF AF

C/EBP

Persistent Kinase

Effectors for Synaptic Growth

G 2 PKA

545

7 apCAM

K+ Channel

8

9 Ca2+ Channel 10

11 AMPA

NMDA

NMDA

Note. Long-term synaptic plasticity contributing to learning and memory involves a sequence of cellular, molecular, and structural mechanisms including 1: neurotransmitter release and short-term strengthening of synaptic connections, 2: equilibrium between kinase and phosphatase activities at the synapse, 3: retrograde transport from the synapse to the nucleus, 4: activation of nuclear transcription factors, 5: activity-dependent induction of gene expression, 6: chromatin alteration and epigenetic

changes in gene expression, 7: synaptic capture of newly synthesized gene products, 8: local protein synthesis at active synapses, 9: synaptic growth and the formation of new synapses, 10: activation of preexisting silent synapses, and 11: self-perpetuating mechanisms and the molecular basis of memory persistence. The location of these events, which may act in part to stabilize some of the changes that occur during short- and intermediate-term plasticity, moves from the synapse (1–2) to the nucleus (3–6) and then back to the synapse (7–11). Molecular details are discussed in the text.

2001). Despite an increasing body of evidence for changes in the number or structure of synaptic connections and long-term memory, it has so far proven difficult to follow individual structural changes at the same synapse over time and to relate directly this remodeling to physiological function and memory storage. Studies have shown that activity-dependent remodeling of preexistingsynapsesandthegrowthofnewsynapticconnections occurs in the mammalian CNS (Buchs & Muller, 1996; Colicos et al., 2001; De Paola, Arber, & Caroni, 2003; Engert & Bonhoeffer, 1999; Greenough & Bailey, 1998; Maletic-Savatic, Malinow, & Svoboda, 1999; Toni, Buchs, Nikonenko, Bron, & Muller, 1999). However, in the mammalian brain, these structural changes are difficult to study because the effects are often modest. Moreover, the specific role of this structural plasticity

remains unclear because the functional contribution of individual synapses to memory processes in these more complex neuronal networks is not yet well defined (Hayashi & Majewska, 2005; Lamprecht & LeDoux, 2004; Segal, 2005). For example, although the generation and enlargement of dendritic spines has been associated with the production of LTP and synaptic activity in organotypic hippocampal slices (Matsuzaki, Honkura, Ellis-Davies, & Kasai, 2004; Nagerl, Eberhorn, Cambridge, & Bonhoeffer, 2004) and acute slices of neonatal animals (Zhou, Homma, & Poo, 2004), these structural changes are much more subtle in the adult brain (Lang et al., 2004). In adults, there is only a modest production of new spines (Zuo, Lin, Chang, & Gan, 2005), and learning-related plasticity seems to rely more on subcellular changes than on anatomical changes. Thus, neuronal activity

Figure 27.7 Mechanisms of long-term memory formation.

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AMPA

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regulates the transport of polysomes from dendritic shafts to active spines (Ostroff et al., 2002), as well as the trafficking of neurotransmitter receptors (Malinow & Malenka, 2002). By contrast, in Aplysia the learning-induced structural changes that accompany long-term sensitization in vivo and long-term facilitation in vitro are robust, highly reproducible, and easy to study and can be shown to be both functionally effective and capable of contributing to memory storage. Time-lapse imaging studies of the sensory to motor neuron synapse in culture have revealed that LTF is accompanied by two temporally and morphologically distinct classes of presynaptic structural change: the rapid activation of silent preexisting varicosities by filling with synaptic vesicles and the slower growth of new functional varicosities. These findings, the first to be made on individually identified presynaptic varicosities, suggest that the duration of the changes in synaptic effectiveness that accompany memory storage may be reflected by the differential regulation of two fundamentally disparate forms of presynaptic compartment: (1) nascent (silent) varicosities that can be rapidly and reversibly remodeled into active transmitter release sites and (2) mature, more stable, and functionally competent varicosities that following long-term training may undergo a process of fission to form new stable synaptic contacts. The increasing morphological correspondence between the studies of long-term sensitization in Aplysia and LTP in the mammalian hippocampus indicates that learning may resemble a process of neuronal growth and differentiation across a broad segment of the animal kingdom and suggests that new synapse formation may be a highly conserved feature for the storage of both implicit and explicit forms of long-term memory. One of the unifying principles emerging from these studies is that despite the different ways by which each form of memory is induced, the subsequent steps required for conversion of their short-term memory to one of longer duration may be similar. This apparent similarity in some of the molecular steps may be because for both implicit and explicit memory storage the synaptic growth is likely to represent the final and self-sustaining change that stabilizes the long-term process. Despite this association, surprisingly little is known about the molecular mechanisms that underlie learning-related changes in the structure of the synapse. Recent studies of the synaptic growth that accompanies long-term memory in Aplysia have begun to characterize the sequence of molecular events responsible for both the initiation and persistence of the structural change. This in turn has revealed that specific molecules and mechanisms important for de novo synapse formation during the development of the nervous system can be reutilized in the adult for the purposes of synaptic plasticity and memory storage.

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These studies indicate that long-term memory involves the flow of information from receptors on the cell surface to the genome, as seen in other processes of cell differentiation and growth. Such changes may reflect the recruitment by environmental stimuli of developmental processes that are latent or inhibited in the fully differentiated neuron. An increasing body of evidence suggests that the cell and molecular changes accompanying long-term memory storage share several features in common with the cascade of events that underlie synapse formation during neuronal development. In both cases, the structural change exhibits a requirement for new protein and mRNA synthesis. These alterations in transcriptional and translational state can be initiated in the long-term process by the repeated or prolonged exposure to modulatory transmitters that, in this respect, appear to mimic the effects of growth factors and hormones during the cell cycle and differentiation. Thus, modulatory transmitters important for learning and memory activate not only the cytoplasmic second-messenger cascades required for the short-term process, but also activate a nuclear messenger system by which the transmitter can exert long-term regulation over the excitability and ultimately, the architecture of the neuron through changes in gene expression. Studies in Aplysia have further demonstrated that the earliest stages of long-term memory formation are associated with modulation of an immunoglobulin-related cell adhesion molecule homologous to NCAM. With the emergence of the nervous system, the Aplvsia NCAM becomes expressed exclusively in neurons and is specifically enriched at synapses. These cell adhesion molecules are maintained into adulthood, at which point they can be down-regulated by 5-HT, a modulatory transmitter important for both sensitization and classical conditioning in Aplysia and by cAMP, a second-messenger activated by 5HT. This down-regulation appears to serve as a preliminary and permissive step for the growth of synaptic connections that accompany the long-term process. Thus, a molecule used during development for cell adhesion and axon outgrowth is retained into adulthood, at which point it seems to restrain or inhibit growth until the molecule is rapidly and transiently decreased at the cell surface by a modulatory transmitter important for learning. The finding that 5HT leads to the rapid down-regulation of only one isoform of apCAM (the transmembrane isoform) and not the others (the GPI-linked isoforms) raises the interesting possibility that learning-related synaptic growth in the adult may be initiated by an activity-dependent recruitment of specific isoforms of adhesion molecules, similar to the modulation of cell-surface receptors during the fine-tuning of synaptic connections in the developing nervous system. One consequence of isoform recruitment is that it would allow

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References 547

neuronal activity to regulate the surface expression of each isoform, a process that might take on additional functional significance if these surface molecules were distributed differentially along the three-dimensional extent of the neuron. These studies also suggest that processing and storage of information in the nervous system may rely on the same mechanisms utilized by other cells in the body to organize and regulate membrane trafficking important for growth. Findings in other invertebrate systems and the mammalian brain suggest that the modulation of cell adhesion molecules is important for long-lasting forms of both developmental and learning-related synaptic plasticity (Benson, Schnapp, Shapiro, & Huntley, 2000; Fields & Itoh, 1996; Martin & Kandel, 1996; Murase & Schuman, 1999; Washbourne et al., 2004). Indeed, a number of studies in vertebrates have now shown that at critical developmental stages, the refinement of synaptic connections, both their growth and regression, is determined by an activitydependent process that seems related to LTP in the hippocampus (Antonini & Stryker, 1993; Constantine-Paton, Cline, & Debski, 1990; Goodman & Shatz, 1993). Finally, insights from the molecular studies of learning and memory in Aplysia suggest that the critical time window for new macromolecular synthesis that is a ubiquitous feature of long-term memory storage may be explained by a cascade of gene activation whereby one or more immediate-early genes control the transcription of late effector genes. The biological significance of an immediateearly–gene-dependent response in long-term plasticity may reside in the necessity of a convergent checkpoint that turns on a genetic program similar to the cascade of gene activation during cell differentiation. As is the case for development, in long-term memory, a convergent checkpoint and cascade of gene activation may be critical to preserve important functions that ultimately rely on a small number of cells. Critical time windows have been previously described in other contexts, especially as part of developmental processes. For example, establishment of the differentiated state in DNA viruses often requires a sequence of gene activation whereby early regulatory genes lead to the maintained expression of later effector genes. A similar time window is evident in the later stages of Drosophila development where the steroid hormone ecdysone induces growth and moulting by altering the expression of early genes that turn on the expression of later genes (Ashburner, 1990). The similarity between these critical periods and the one found in long-term memory suggests that aspects of the regulatory mechanisms underlying learning-related synaptic plasticity in the adult may eventually be understood in the context of the basic molecular program used to refine synaptic connections during the later stages of neuronal

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development. Both processes appear to share a cascade of gene activation, with a critical time window during which the differentiated state is still labile and can be modified. That this feature is particularly well-developed in neurons, which characteristically remain plastic throughout most of their life cycle, and can grow and retract their synaptic connections on appropriate target cells in an activitydependent fashion, may underlie the unique ability of neurons to respond to environmental stimuli that is essential for learning and memory storage.

REFERENCES Abel, T., Martin, K. C., Bartsch, D., & Kandel, E. R. (1998, January 16). Memory suppressor genes: Inhibitory constraints on the storage of long- term memory. Science, 279, 338–341. Abel, T. P. V., Nguyen, V., Barad, M., Deuel, T. A., Kandel, E. R., & Bourtchouladze, R. (1997). Genetic demonstration of a role for PKA in the late phase of LTP and in hippocampus-based long-term memory. Cell, 88, 615–626. Ahmari, S. E., Buchanan, J., & Smith, S. J. (2000). Assembly of presynaptic active zones from cytoplasmic transport packets. Nature Neuroscience, 3, 445–451. Alarcon, J. M., Barco, A., & Kandel, E. R. (2006). Capture of the late phase of long-term potentiation within and across the apical and basilar dendritic compartments of CA1 pyramidal neurons: Synaptic tagging is compartment restricted. Journal of Neuroscience, 26, 256–264. Alarcon, J. M., Malleret, G., Touzani, K., Vronskaya, S., Ishii, S., Kandel, E. R., et al. (2004). Chromatin acetylation, memory, and LTP are impaired in CBP+/- mice: A model for the cognitive deficit in Rubinstein-Taybi syndrome and its amelioration. Neuron, 42, 947–959. Albensi, B. C., & Mattson, M. P. (2000). Evidence for the involvement of TNF and NF-kappaB in hippocampal synaptic plasticity. Synapse, 35, 151–159. Alberini, C. M., Ghirardi, M., Metz, R., & Kandel, E. R. (1994). C/EBP is an immediate-early gene required for the consolidation of long-term facilitation in Aplysia. Cell, 76, 1099–1114. Antonini, A., & Stryker, M. P. (1993, June 18). Rapid remodeling of axonal arbors in the visual cortex. Science, 260, 1819–1821. Ashburner, M. (1990). Puff, genes and hormones revisited. Cell, 61, 1–3. Bacskai, B. J., Hochner, B., Mahaut-Smith, M., Adams, S. R., Kaang, B. K., Kandel, E. R., et al. (1993, April 9). Spatially resolved dynamics of cAMP and protein kinase A subunits in Aplysia sensory neurons. Science, 260, 222–226. Bailey, C. H., Bartsch, D., & Kandel, E. R. (1996). Toward a molecular definition of long-term memory storage. Proceedings of the National Academy of Sciences, USA, 93, 13445–13452. Bailey, C. H., & Chen, M. (1983, April 1). Morphological basis of longterm habituation and sensitization in Aplysia. Science, 220, 91–93. Bailey, C. H., & Chen, M. (1988a). Long-term memory in Aplysia modulates the total number of varicosities of single identified sensory neurons. Proceedings of the National Academy of Sciences, USA, 85, 2373–2377. Bailey, C. H., & Chen, M. (1988b). Long-term sensitization in Aplysia increases the number of presynaptic contacts onto the identified gill motor neuron L7. Proceedings of the National Academy of Sciences, USA, 85, 9356–9359.

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548

Synaptic and Cellular Basis of Learning

Bailey, C. H., & Chen, M. (1988c). Morphological basis of short-term habituation in Aplysia. Journal of Neuroscience, 8, 2452–2459. Bailey, C. H., & Chen, M. (1989). Time course of structural changes at identified sensory neuron synapses during long-term sensitization in Aplysia. Journal of Neuroscience, 9, 1774–1780. Bailey, C. H., Chen, M., Keller, F., & Kandel, E. R. (1992). Serotoninmediated endocytosis of apCAM: An early step of learning-related synaptic growth in Aplysia. Science, 256, 645–649. Bailey, C. H., Kaang, B. K., Chen, M., Marin, C., Lim, C. S., Casadio, A., et al. (1997). Mutation in the phosphorylation sites of MAP kinase blocks learning-related internalization of apCAM in Aplysia sensory neurons. Neuron, 18, 913–924. Bailey, C. H., & Kandel, E. R. (1993). Structural changes accompanying memory storage. Annual Review of Physiology, 55, 397–426. Bailey, C. H., Kandel, E. R., & Si, K. (2004). The persistence of long-term memory: A molecular approach to self-sustaining changes in learninginduced synaptic growth. Neuron, 44, 49–57. Bailey, C. H., Montarolo, P. G., Chen, M., Kandel, E. R., & Schacher, S. (1992). Inhibitors of protein and RNA synthesis block the structural changes that accompany long-term heterosynaptic plasticity in the sensory neurons of Aplysia. Neuron, 9, 749–758. Bailey, C. H., Thompson, E. B., Castellucci, V. F., & Kandel, E. R. (1979). Ultrastructure of the synapses of sensory neurons that mediate the gillwithdrawal reflex in Aplysia. Journal of Neurocytology, 8, 415–444. Barco, A., Alarcon, J. M., & Kandel, E. R. (2002). Expression of constitutively active CREB protein facilitates the late phase of long-term potentiation by enhancing synaptic capture. Cell, 108, 689–703. Barco, A., Pittenger, C., & Kandel, E. R. (2003). CREB, memory enhancement and the treatment of memory disorders: Promises, pitfalls and prospects. Expert Opinion on Therapeutic Targets, 7, 101–114. Bartsch, D., Casadio, A., Karl, K. A., Serodio, P., & Kandel, E. R. (1998). CREB1 encodes a nuclear activator, a repressor, and a cytoplasmic modulator that form a regulatory unit critical for long-term facilitation. Cell, 95, 211–223. Bartsch, D., Ghirardi, M., Casadio, A., Giustetto, M., Karl, K. A., Zhu, H., et al. (2000). Enhancement of memory-related long-term facilitation by ApAF, a novel transcription factor that acts downstream from both CREB1 and CREB2. Cell, 103, 595–608. Bartsch, D., Ghirardi, M., Skehel, P. A., Karl, K. A., Herder, S. P., Chen, A., et al. (1995). Aplysia CREB2 represses long-term facilitation: Relief of repression converts transient facilitation into long-term functional and structural changes. Cell, 83, 979–992. Barzilai, A., Kennedy, T. E., Sweatt, J. D., & Kandel, E. R. (1989). 5-HT modulates protein synthesis and the expression of specific proteins during long-term facilitation in Aplysia sensory neurons. Neuron, 2, 1577–1586. Benfenati, F., Onofri, F., & Giovedi, S. (1999). Protein-protein interactions and protein modules in the control of neurotransmitter release. Philosophical Transactions of the Royal Society of London: Biological Sciences, 354, 243–257. Benson, D. L., Schnapp, L. M., Shapiro, L., & Huntley, G. W. (2000). Making memories stick: Cell adhesive molecules in synaptic plasticity. Trends in Cell Biology, 10, 473–480. Bliss, T. V., Collingridge, G. L., & Morris, R. G. (2003). Introduction: Longterm potentiation and structure of the issue. Philosophical Transactions of the Royal Society of London: Biological Sciences, 358, 607–611. Bourtchouladze, R., Lidge, R., Catapano, R., Stanley, J., Gossweiler, S., Romashko, D., et al. (2003). A mouse model of Rubinstein-Taybi syndrome: Defective long-term memory is ameliorated by inhibitors of phosphodiesterase 4. Proceedings of the National Academy of Sciences, USA, 100, 10518–10522. Bradke, F., & Dotti, C. G. (1999, March 19). The role of local actin instability in axon assembly. Science, 283, 1931–1934.

c27.indd Sec10:548

Bradshaw, K. D., Emptage, N. J., & Bliss, T. V. (2003). A role for dendritic protein synthesis in hippocampal late LTP. European Journal of Neuroscience, 18, 3150–3152. Brittis, P. A., Lu, Q., & Flanagan, J. G. (2002). Axonal protein synthesis provides a mechanism for localized regulation at an intermediate target. Cell, 110, 223–235. Brunelli, M., Castellucci, V., & Kandel, E. R. (1976, December 10). Synaptic facilitation and behavioral sensitization in Aplysia: Possible role of serotonin and cyclic AMP. Science, 194, 1178–1181. Buchs, P. A., & Muller, D. (1996). Induction of long-term potentiation is associated with major ultrastructural changes of activated synapses. Proceedings of the National Academy of Sciences, USA, 93, 8040–8045. Burgin, K. E., Waxham, M. N., Rickling, S., Westgate, S. A., Mobley, W. C., & Kelly, P. T. (1990). In situ hybridization histochemistry of Ca2+/ calmodulin-dependent protein kinase in developing rat brain. Journal of Neuroscience, 10, 1788–1798. Byers, D., Davis, R. L., & Kiger, J. A., Jr. (1981, January 1). Defect in cyclic AMP phosphodiesterase due to the dunce mutation of learning in drosophila melanogaster. Nature, 289, 79–81. Cammalleri, M., Lutjens, R., Berton, F., King, A. R., Simpson, C., Francesconi, W., et al. (2003). Time-restricted role for dendritic activation of the mTOR-p70S6K pathway in the induction of late-phase longterm potentiation in the CA1. Proceedings of the National Academy of Sciences, USA, 100, 14368–14373. Casadio, A., Martin, K. C., Giustetto, M., Zhu, H., Chen, M., Bartsch, D., et al. (1999). A transient, neuron-wide form of CREB-mediated longterm facilitation can be stabilized at specific synapses by local protein synthesis. Cell, 99, 221–237. Castellucci, V. F., Blumenfeld, H., Goelet, P., & Kandel, E. R. (1989, April 1). Inhibitor of protein synthesis blocks long-term behavioral sensitization in the isolated gill-withdrawal reflex of Aplysia. Science, 220, 91–93. Colicos, M. A., Collins, B. E., Sailor, M. J., & Goda, Y. (2001). Remodeling of synaptic actin induced by photoconductive stimulation. Cell, 107, 605–616. Conrad, P., Wu, F., & Schacher, S. (1999). Changes in functional glutamate receptors on a postsynaptic neuron accompany formation and maturation of an identified synapse. Journal of Neurobiology, 39, 237–248. Constantine-Paton, M., Cline, H. T., & Debski, E. (1990). Patterned activity, synaptic convergence, and the NMDA receptor in developing visual pathways. Annual Review of Neuroscience, 13, 129–154. Cracco, J. B., Serrano, P., Moskowitz, S. I., Bergold, P. J., & Sacktor, T. C. (2005). Protein synthesis-dependent LTP in isolated dendrites of CA1 pyramidal cells. Hippocampus, 15, 551–556. Crick, F. (1984, November 8). Memory and molecular turnover. Nature, 312, 101. Dale, N., & Kandel, E. R. (1993). L-glutamate may be the fast excitatory transmitter of Aplysia sensory neurons. Proceedings of the National Academy of Sciences, USA, 90, 7163–7167. Dale, N., Kandel, E. R., & Schacher, S. (1987). Serotonin produces longterm changes in the excitability of Aplysia sensory neurons in culture that depend on new protein synthesis. Journal of Neuroscience, 7, 2232–2238. Dash, P. K., Hochner, B., & Kandel, E. R. (1990, June 21). Injection of the cAMP-responsive element into the nucleus of Aplysia sensory neurons blocks long-term facilitation. Nature, 345, 718–721. Davis, R. L. (1996). Physiology and biochemistry of Drosophila learning mutants. Physiological Reviews, 76, 299–317. Davis, R. L., Cherry, J., Dauwalder, B., Han, P. L., & Skoulakis, E. (1995). The cyclic AMP system and Drosophila learning. Molecular and Cellular Biochemistry, 149–150, 271–278. De Paola, V., Arber, S., & Caroni, P. (2003). AMPA receptors regulate dynamic equilibrium of presynaptic terminals in mature hippocampal networks. Nature Neuroscience, 6, 491–500.

8/18/09 6:25:17 PM

References 549 Drain, P., & Folkers E., & Quinn, W. G. (1991). CAMP-dependent protein kinase and the disruption of learning in transgenic flies. Neuron, 6, 71–82. Dudai, Y., Jan, Y. N., Byers, D., Quinn, W. G., & Benzer S. (1976). Dunce, a mutant of Drosophila deficient in learning. Proceedings of the National Academy of Sciences, USA, 73, 1684–1688. Dudek, S. M., & Fields, R. D. (2002). Somatic action potentials are sufficient for late-phase LTP-related cell signaling. Proceedings of the National Academy of Sciences, USA, 99, 3962–3967. Duerr, J. S., & Quinn, W. G. (1982). Three drosophila mutations that block associative learning also affect habituation and sensitization. Proceedings of the National Academy of Sciences, USA, 79, 3646–3650. Engert, F., & Bonhoeffer, T. (1999, May 6). Dendritic spine changes associated with hippocampal long-term synaptic plasticity. Nature, 399, 66–70. English, J. D., & Sweatt, J. D. (1996). Activation of p42 mitogenactivated protein kinase in hippocampal long-term potentiation. Journal of Biological Chemistry, 271, 24329–24332. English, J. D., & Sweatt, J. D. (1997). A requirement for the mitogen-activated protein kinase cascade in hippocampal long-term potentiation. Journal of Biological Chemistry, 272, 19103–19106. Fields, R. D., & Itoh, K. (1996). Neural cell adhesion molecules in activitydependent development and synaptic plasticity. Trends in Neuroscience, 19, 473–480. Frey, U., Huang, Y.-Y., & Kandel, E. R. (1993, June 11). Effects of cAMP simulate a late stage of LTP in hippocampal CA1 neurons. Science, 260, 1661–1664. Frey, U., & Morris, R. G. (1997, February 6). Synaptic tagging and longterm potentiation. Nature, 385, 533–536. Frey, U., & Morris, R. G. (1998). Weak before strong: Dissociating synaptic tagging and plasticity-factor accounts of late-LTP. Neuropharmacology, 37, 545–552. Frost, W. N., Castellucci, V. F., Hawkins, R. D., & Kandel, E. R. (1985). Monosynaptic connections made by the sensory neurons of the gill- and siphon-withdrawal reflex in Aplysia participates in the storage of longterm memory for sensitization. Proceedings of the National Academy of Sciences, USA, 82, 8266–8269. Genoux, D., Haditsch, U., Knobloch, M., Michalon, A., Storm, D., & Mansuy, I. M. (2002, August 29). Protein phosphatase 1 is a molecular constraint on learning and memory. Nature, 418, 970–975. Ghirardi, M., Montarolo, P. G., & Kandel, E. R. (1995). A novel intermediate stage in the transition between short- and long-term facilitation in the sensory to motor neuron synapse of Aplysia. Neuron, 14, 413–420. Glanzman, D. L., Kandel, E. R., & Schacher, S. (1990, August 17). Targetdependent structural changes accompanying long-term synaptic facilitation in Aplysia neurons. Science, 249, 779–802. Goodman, C. S., & Shatz, C. J. (1993). Development mechanisms that generate precise patterns of neuronal connectivity. Cell, 72, 77–98. Greenough, W. T., & Bailey, C. H. (1988). The anatomy of a memory: Convergence of results across a diversity of tests. Trends in Neuroscience, 11, 142–147. Guan, Z., Giustetto, M., Lomvardas, S., Kim, J. H., Miniaci, M. C., Schwartz, J. H., et al. (2002). Integration of long-term-memory-related synaptic plasticity involves bidirectional regulation of gene expression and chromatin structure. Cell, 111, 483–493. Hai, T. W., Liu, F., Coukos, W. J., & Green, M. K. (1989). Transcription factor ATF cDNA clones: An extensive family of leucine zipper proteins able to selectively form DNA-binding heterodimers [published erratum appears in Genes and Development, 1990, April 4(4), 682]. Genes and Development, 3, 2083–2090. Hall, A. (1998, January 23). Rho GTPases and the actin cytoskeleton. Science, 279, 509–514.

c27.indd Sec10:549

Han, J. H., Lim, Y. S., Kandel, E. R., & Kaang, B. K. (2004). Role of Aplysia cell adhesion molecules during 5-HT-induced long-term functional and structural changes. Learning and Memory, 11, 421–435. Hatada, Y., Wu, F., Sun, Z. Y., Schacher, S., & Goldberg, D. J. (2000). Presynaptic morphological changes associated with long-term synaptic facilitation are triggered by actin polymerization at preexisting varicosities. Journal of Neuroscience, 20, RC82. Hayashi, Y., & Majewska, A. K. (2005). Dendritic spine geometry: Functional implication and regulation. Neuron, 46, 529–532. Hayashi, Y., Shi, S. H., Esteban, J. A., Piccini, A., Poncer, J. C., & Malinow, R. (2000, March 24). Driving AMPA receptors into synapses by LTP and CaMKII: Requirement for GluR1 and PDZ domain interaction. Science, 287, 2262–2267. Hegde, A. N., Inokuchi, K., Pei, W., Casadio, A., Ghirardi, M., Chain, D. G., et al. (1997). Ubiquitin C-terminal hydrolase is an immediate-early gene essential for long-term facilitation in Aplysia. Cell, 89, 115–126. Hsieh, J., & Gage, F. H. (2005). Chromatin remodeling in neural development and plasticity. Current Opinions in Cell Biology, 17, 664–671. Hu, Y., Barzilai, A., Chen, M., Bailey, C. H., & Kandel, E. R. (1993). 5HT and cAMP induce the formation of coated pits and vesicles and increase the expression of clathrin light chain in sensory neurons of Aplysia. Neuron, 10, 921–929. Huang, Y. S., Jung, M. Y., Sarkissian, M., & Richter, J. D. (2002). N-methylD-aspartate receptor signaling results in Aurora kinase-catalyzed CPEB phosphorylation and alpha CaMKII mRNA polyadenylation at synapses. EMBO Journal, 21, 2139–2148. Huang, Y.-Y., Li, X. C., & Kandel, E. R. (1994). CAMP contributes to mossy fiber LTP by initiating both a covalently mediated early phase and macromolecular synthesis-dependent late phase. Cell, 79, 69–79. Humeau, Y., Doussau, F., Vitiello, F., Greengard, P., Benfenati, F., & Poulain, B. (2001). Synapsin controls both reserve and releasable synaptic vesicle pools during neuronal activity and short-term plasticity in Aplysia. Journal of Neuroscience, 21, 4195–4206. Huntley, G. W., Benson, D. L., & Colman, D. R. (2002). Structural remodeling of the synapse in response to physiological activity. Cell, 108, 1–4. Izquierdo, I., & Cammarota, M. (2004, May 7). Neuroscience: Zif and the survival of memory. Science, 304, 829–830. Kandel, E. R. (2001, November 2). The molecular biology of memory storage: A dialogue between genes and synapses. Science, 294, 1030–1038. Karpinski, B. A., Morle, G. D., Huggenvik, J., Uhler, M. D., & Leiden, J. M. (1992). Molecular cloning of human CREB-2: An ATF/CREB transcription factor that can negatively regulate transcription from the cAMP response element. Proceedings of the National Academy of Sciences, USA, 89, 4820–4824. Keller, Y., & Schacher, S. (1990). Neuron-specific membrane glycoproteins promoting neurite fasciculation in Aplysia Californica. Journal of Cell Biology, 111, 2637–2650. Kim, J.-H., Udo, H., Li, H.-L., Youn, T. Y., Chen, M., Kandel, E. R., et al. (2003). Presynaptic activation of silent synapses and growth of new synapses contribute to intermediate and long-term facilitation in Aplysia. Neuron, 40, 151–165. Klein, M., & Kandel, E. R. (1980). Mechanism of calcium current modulation underlying presynaptic facilitation and behavioral sensitization in Aplysia. Proceedings of the National Academy of Sciences, USA, 77, 6912–6916. Korzus, E., Rosenfeld, M. G., & Mayford, M. (2004). CBP histone acetyltransferase activity is a critical component of memory consolidation. Neuron, 42, 961–972. Krucker, T., Siggins, G. R., & Halpain, S. (2000). Dynamic actin filaments are required for stable long-term potentiation (LTP) in area CA1 of the

8/18/09 6:25:18 PM

550

Synaptic and Cellular Basis of Learning

hippocampus. Proceedings of the National Academy of Sciences, USA, 97, 6856–6861. Lamprecht, R., & LeDoux, J. (2004). Structural plasticity and memory. Nature Reviews Neuroscience, 5, 45–54. Lang, C., Barco, A., Zablow, L., Kandel, E. R., Siegelbaum, S. A., & Zakharenko, S. S. (2004). Transient expansion of synaptically connected dendritic spines upon induction of hippocampal long-term potentiation. Proceedings of the National Academy of Sciences, USA, 101, 16665–16670. Lee, H. K., Barbarosie, M., Kameyama, K., Bear, M. F., & Huganir, R. L. (2000, June 22). Regulation of distinct AMPA receptor phosphorylation sites during bidirectional synaptic plasticity. Nature, 405, 955–959. Lee, S.-H., Lim, C.-S., Park, H., Lee, J.-A., Han, J.-H., Kim, H., et al. (2007). Nuclear translocation of CAM-associated protein activates transcription for long-term facilitation in Aplysia. Cell, 129, 801–812. Levenson, J. M., & Sweatt, J. D. (2005). Epigenetic mechanisms in memory formation. Nature Reviews Neuroscience, 6, 108–118. Li, Z., Aizenman, C. D., & Cline, H. T. (2002). Regulation Rho GTPases by crosstalk and neuronal activity in vivo. Neuron, 33, 741–750. Lisman, J. E., & Zhabotinsky, A. M. (2001). A model of synaptic memory: A CaMKII/PP1 switch that potentiates transmission by organizing an AMPA receptor anchoring assembly. Neuron, 31, 191–201. Lonze, B. E., & Ginty, D. D. (2002). Function and regulation of CREB family transcription factors in the nervous system. Neuron, 35, 605–623. Maletic-Savatic, M., Malinow, R., & Svoboda, K. (1999, March 19). Rapid dendritic morphogenesis in CAl hippocampal dendrites induced by synaptic activity. Science, 283, 1923–1927. Malinow, R., Mainen, Z. F., & Hayashi, Y. (2000). LTP mechanisms: From silence to four-lane traffic. Current Opinion in Neurobiology, 10, 352–357. Malinow, R., & Malenka, R. C. (2002). AMPA receptor trafficking and synaptic plasticity. Annual Review of Neuroscience, 25, 103–126. Malleret, G., Haditsch, U., Genoux, D., Jones, M. W., Bliss, T. V., Vanhoose, A. M., et al. (2001). Inducible and reversible enhancement of learning, memory, and long-term potentiation by genetic inhibition of calcineurin. Cell, 104, 675–686. Mansuy, I. M., Mayford, M., Jacob, B., Kandel, E. R., & Bach, M. E. (1998). Restricted and regulated overexpression reveals calcineurin as a key component in the transition from short-term to long-term memory. Cell, 92, 39–49. Martin, K. C., Casadio, A., Zhu, H.,Yaping, E., Rose, J., Chen, M., et al. (1997). Synapse-specific long-term facilitation of Aplysia sensory somatic synapses: A function for local protein synthesis memory storage. Cell, 91, 927–938. Martin, K. C., & Kandel, E. R. (1996). Cell adhesion molecules, CREB and the formation of new synaptic connections during development and learning. Neuron, 17, 567–570. Martin, K. C., Michael, D., Rose, J. C., Barad, M., Casadio, A., Zhu, H., et al. (1997). MAP kinase translocates into the nucleus of the presynaptic cell and is required for long-term facilitation in Aplysia. Neuron, 18, 899–912. Matsuzaki, M., Honkura, N., Ellis-Davies, G. C. R., & Kasai, H. (2004, June 17). Structural basis of long-term potentiation in single dendritic spines. Nature, 429, 761–766.

Mayr, B., & Montminy, M. (2001). Transcriptional regulation by the phosphorylation-dependent factor CREB. Nature Reviews Molecular Cell Biology, 2, 599–609. Michael, D., & Martin, K. C. (1998). Repeated pulses of serotonin required for long-term facilitation activate mitogen-activated protein kinase in sensory neurons of Aplysia. Proceedings of the National Academy of Sciences, USA, 95, 1864–1869. Miesenbock, G., De Angelis, D. A., & Rothman, J. E. (1998, July 9). Visualizing secretion and synaptic transmission with pH-sensitive green fluorescent proteins. Nature, 394, 192–195. Milner, B. (1985). Memory and the human brain. In M. Shafto (Ed.), How we know (pp. 1864–1869). San Francisco: Harper & Rowe. Moccia, R., Chen, D., Lyles, V., Kapuya, E. E. Y., Kalachikov, S., Spahn, C. M., et al. (2003). An unbiased cDNA library prepared from isolated: Aplysia sensory neuron processes is enriched for cytoskeletal and translational mRNAs. Journal of Neuroscience, 23, 9409–9417. Montarolo, P. G., Goelet, P., Castellucci, V. F., Morgan, J., Kandel, E. R., & Schacher, S. (1986, December 5). A critical period for macromolecular synthesis in long-term heterosynaptic facilitation in Aplysia. Science, 234, 1249–1254. Moroz, L. L., Edwards, J. R., Puthanveettil, S. V., Kohn, A. B., Ha, T., Heyland, A., et al. (2006). Neuronal transcriptome of Aplysia: Neuronal compartments and circuitry. Cell, 127, 1453–1467. Murase, S., & Schuman, E. M. (1999). The role of cell adhesion molecules in synaptic plasticity and memory. Current Opinions in Cell Biology, 11, 549–553. Nagerl, U. V., Eberhorn, N., Cambridge, S. B., & Bonhoeffer, T. (2004). Bidirectional activity-dependent morphological plasticity in hippocampal neurons. Neuron, 44, 759–767. Nakayama, A. Y., Harms, M. B., & Luo, L. (2000). Small GTPases Rac and Rho in maintenance of dendritic spines and branches in hippocampal pyramidal neurons. Journal of Neuroscience, 15, 5329–5338. Nazif, F. A., Byrne, J. H., & Cleary, L. J. (1991). CAMP induces long-term morphological changes in sensory neurons of Aplysia. Brain Research, 539, 324–327. Ostroff, L. E., Fiala, J. C., Allwardt, B., & Harris, K. M. (2002). Polyribosomes redistribute from dendritic shafts into spines with enlarged synapses during LTP in developing rat hippocampal slices. Neuron, 35, 535–545. Ouyang, Y., Rosenstein, A., Kreiman, G., Schuman, E. M., & Kennedy, M. B. (1999). Tetanic stimulation leads to increased accumulation of Ca(2+)/ calmodulin-dependent protein kinase II via dendritic protein synthesis in hippocampal neurons. Journal of Neuroscience, 19, 7823–7833. Perazzona, B., Isabel, G., Preat, T., & Davis, R. L. (2004). The role of cAMP response element-binding protein in Drosophila long-term memory. Journal of Neuroscience, 24, 8823–8828. Polster, M. R., Nadel, L., & Schachter, D. L. (1991). Cognitive neuroscience: An analysis of memory: A historical perspective. Journal of Cognitive Neuroscience, 3, 95–116. Purcell, A. L., Sharma, S. K., Bagnall, M. W., Sutton, M. A., & Carew, T. J. (2003). Activation of a tyrosine kinase-MAPK cascade enhances the induction of long-term synaptic facilitation and long-term memory in Aplysia. Neuron, 37, 473–484.

Matus, A. (2000, October 27). Actin-based plasticity in dendritic spines. Science, 290, 754–758.

Puthanveettil, S. V., Monje, F.J., Miniaci, M.C., Choi, Y.B., Karl, K.A., Khandros, E., et al. (2008). A new component in synaptic plasticity: Upregulation of kinesin in the neurons of the gill-withdrawal reflex. Cell, 135, 960–973.

Mauelshagen, J., Parker, G. R., & Carew, T. J. (1996). Dynamics of induction and expression of long-term synaptic facilitation in Aplysia. Journal of Neuroscience, 16, 7099–7108.

Ramanan, N., Shen, Y., Sarsfield, S., Lemberger, T., Schutz, G., Linden, D. J., et al. (2005). SRF mediates activity-induced gene expression and synaptic plasticity but not neuronal viability. Nature Neuroscience, 8, 759–767.

Mayford, M., Barzilai, A., Keller, F., Schacher, S., & Kandel, E. R. (1992, May 1). Modulation of an NCAM-related adhesion molecule with long-term synaptic plasticity in Aplysia. Science, 256, 638–644.

Scholz, K. P., & Byrne, J. H. (1987, February 6). Long-term sensitization in Aplysia: Biophysical correlates in tail sensory neurons. Science, 235, 685–687.

c27.indd Sec10:550

8/18/09 6:25:18 PM

References 551 Schwartz, H., Castellucci, V. F., & Kandel, E. R. (1971). Functions of identified neurons and synapses in abdominal ganglion of Aplysia in absence of protein synthesis. Journal of Neurophysiology, 34, 9639–9653.

Toni, N., Buchs, P. A., Nikonenko, I., Bron, C. R., & Muller, D. (1999, November 25). LTP promotes formation of multiple spine synapses between a single axon terminal and a dendrite. Nature, 402, 421–425.

Segal, M. (2005). Dendritic spines and long-term plasticity. Nature Reviews Neuroscience, 6, 277–284.

Trudeau, L. E., & Castellucci, V. F. (1993). Excitatory amino acid neurotransmission of sensory-motor and interneuronal synapses of Aplysia Californica. Journal of Neurophysiology, 70, 1221–1230.

Sharma, S. K., Bagnall, M. W., Sutton, M. A., & Carew, T. J. (2003). Inhibition of calcineurin facilitates the induction of memory for sensitization in Aplysia: Requirement of mitogen-activated protein kinase. Proceedings of the National Academy of Sciences, USA, 100, 4861–4866. Si, K., Giustetto, M., Etkin, A., Hsu, R., Janisiewicz, A. M., Miniaci, M. C., et al. (2003). A neuronal isoform of CPEB regulates local protein synthesis and stabilizes synapse-specific long-term facilitation in aplysia. Cell, 115, 893–904. Si, K., Lindquist, S., & Kandel, E. R. (2003). A neuronal isoform of the aplysia CPEB has prion-like properties. Cell, 115, 879–891. Sin, W. C., Haas, K., Ruthhazer, E. S., & Cline, H. T. (2002, October 3). Dendrite growth increased by visual activity requires NMDA receptor and Rho GTPases. Nature, 419, 475–480. Squire, L. R. (1992). Memory and the hippocampus: A synthesis from findings with rats, monkeys, and humans. Psychological Review, 99, 195–231. Squire, L. R., & Zola-Morgan, S. (1991, September 20). The medial temporal lobe memory system. Science, 253, 1380–1386. Steward, O., & Schuman, E. M. (2001). Protein synthesis at synaptic sites on dendrites. Annual Review of Neuroscience, 24, 299–325. Steward, O., & Schuman, E. M. (2003). Compartmentalized synthesis and degradation of proteins in neurons. Neuron, 40, 347–359. Sutton, M. A., Masters, S. E., Bagnall, M. W., & Carew, T. J. (2001). Molecular mechanisms underlying a unique intermediate phase of memory in Aplysia. Neuron, 31, 143–154. Sutton, M. A., & Schuman, E. M. (2005). Local translational control in dendrites and its role in long-term synaptic plasticity. Journal of Neurobiology, 64, 116–131. Theis, M., Si, K., & Kandel, E. R. (2003). Two previously undescribed members of the mouse CPEB family of genes and their inducible expression in the principal cell layers of the hippocampus. Proceedings of the National Academy of Sciences, USA, 100, 9602–9607.

c27.indd Sec10:551

Udo, H., Jin, I., Kim, J.-H., Li, H.-L., Youn, T., Hawkins, R. D., et al. (2005). Serotonin-induced regulation of the actin network for learningrelated synaptic growth requires CdC42, N-WASP and PAK in Aplysia sensory neurons. Neuron, 45, 887–901. Waddell, S., & Quinn, W. G. (2001). Flies, genes, and learning. Annual Review of Neuroscience, 24, 1283–1309. Washbourne, P., Dityatev, A., Scheiffele, P., Biederer, T., Weiner, J. A., Christopherson, K. S., et al. (2004). Cell adhesion molecules in synapse formation. Journal of Neuroscience, 24, 9244–9249. Winder, D. G., Mansuy, I. M., Osman, M., Moallem, T. M., & Kandel, E. R. (1998). Genetic and pharmacological evidence for a novel, intermediate phase of long-term potentiation suppressed by calcineurin. Cell, 92, 25–37. Wood, M. A., Kaplan, M. P., Park, A., Blanchard, E. J., Oliveira, A. M., Lombardi, T. L., et al. (2005). Transgenic mice expressing a truncated form of CREB-binding protein (CBP) exhibit deficits in hippocampal synaptic plasticity and memory storage. Learning and Memory, 12, 111–119. Yeh, S. H., Lin, C. H., & Gean, P. W. (2004). Acetylation of nuclear factorkappaB in rat amygdala improves long-term but not short-term retention of fear memory. Molecular Pharmacology, 65, 1286–1292. Yin, J. C., Wallach, J. S., Del Vecchio, M., Wilder, E. L., Zhou, H., Quinn, W. G., et al. (1994). Induction of a dominant negative CREB transgene specifically blocks long-term memory in Drosophila. Cell, 79, 49–58. Yuan, X. B., Jin, M., Xu, X., Song, Y. Q., Wu, C. P., Poo, M. M. (2003). Signaling and crosstalk of GTPases in mediating axon guidance. Nature Cell Biology, 5, 38–45. Yuste, R., & Bonhoeffer, T. (2001). Morphological changes in dendrititic spines associated with long-term synaptic plasticity. Annual Review of Neuroscience, 24, 1071–108. Zhang, W., & Benson, D. L. (2001). Stages of synapse development defined by dependence on F-actin. Journal of Neuroscience, 21, 5169–5181.

Threadgill, R., Bobb, K., & Ghosh, A. (1997). Regulation of dendritic growth and remodeling by Rho, Rac and Cdc42. Neuron, 19, 625–634.

Zhou, Q., Homma, K. J., & Poo, M. M. (2004). Shrinkage of dendritic spines associated with long-term depression of hippocampal synapses. Neuron, 44, 749–757.

Tischmeyer, W., & Grimm, R. (1999). Activation of immediate early genes and memory formation. Cellular and Molecular Life Sciences, 55, 564–574.

Zuo, Y., Lin, A., Chang, P., & Gan, W. B. (2005). Development of longterm dendritic spine stability in diverse regions of cerebral cortex. Neuron, 46, 181–189.

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Chapter 28

Memory HOWARD EICHENBAUM

memory capacities in animals with selective hippocampal damage and recordings of hippocampal neuronal activity in behaving animals and humans, have begun to reveal how the hippocampus mediates episodic memory. In this chapter, I outline some of the defining features of episodic memory and focus on aspects of its anatomical and physiological basis in the functional organization of the medial temporal lobe. Episodic memory is supported by a large network of brain areas, including prominently widespread neocortical areas that contribute to episodic memory by virtue of various aspects of cognitive and perceptual processing. The involved cortical areas include the prefrontal cortex and other areas that mediate working memory, effortful retrieval, source monitoring, and other cognitive processing functions that are essential to recollection (e.g., Aggleton & Brown, 2006; Henson, Rugg, Shallice, Josephs, & Dolan, 1999; Yonelinas, Otten, Shaw, & Rugg, 2005; see Chapters 29 and 30). Also, areas of the parietal and temporal cortex are involved in complex perceptual processing essential to configuration of the conceptual contents of information that is the subject of recollection (e.g., Uncapher, Otten, & Rugg, 2006). Projections from these areas strongly converge onto the medial temporal lobe, which also sends strong projections back to these cortical areas, suggesting a central role in organizing or extending the persistence of cortical representations. Outputs of these areas converge on, and are also the primary output targets of, the medial temporal lobe, and in particular, the hippocampus (Eichenbaum, 2000). The medial temporal lobe is special in this organization because, unlike neocortical areas, it plays a fully selective role in memory and not other cognitive or perceptual functions. Therefore, the following considerations about the neurobiology of episodic memory focus on the role of the medial temporal lobe, and in particular the hippocampus. Episodic memory is the capacity to remember unique personal experiences. Tulving (2002) distinguishes episodic memory by what should be considered subjective features of

The understanding of memory is one of the major objectives of cognitive and neuroscience research. Behavioral and neurobiological studies extending over the past 100 years have revealed that there are multiple types of memory and that different forms of memory are supported by distinct brain systems (Eichenbaum & Cohen, 2001). Most prominent among these is the system for declarative memory, our capacity to store and bring to consciousness everyday facts and experiences. An essential brain substrate of declarative memory was identified nearly 50 years ago in the case study of H. M., a man who became amnesic following removal of the medial temporal lobe to alleviate his epileptic seizures (Corkin, 1984; Scoville & Milner, 1957). H. M. was severely impaired in declarative memory, whereas his perceptual and cognitive abilities were intact, as were his capacities for other forms of memory, including short-term and working memory, and perceptual and motor skill learning. Furthermore, the deficit in the acquisition of new declarative memories was accompanied by a temporally graded retrograde amnesia, such that H. M. could recall information obtained remotely in life but he was impaired in recalling events that occurred shortly before the onset of amnesia. These observations suggest that the memory processing mediated by the medial temporal lobe begins during learning and continues to contribute to the consolidation of declarative memories over a prolonged period. Succeeding neuropsychological analyses on amnesic patients and functional imaging studies on normal humans elaborated the domain of capacities that are dependent on the medial temporal region and, in particular, the hippocampus (Eichenbaum & Cohen, 2001; Squire, Stark, & Clark, 2004). These studies have emphasized the critical role of the hippocampus in two components of declarative memory: (1) The hippocampus plays a critical role in episodic memory, our capacity for recollection of unique personal experiences, and (2) the hippocampus is involved in particular aspects of the acquisition of semantic or factual knowledge. These studies, plus detailed characterizations of spared and impaired 552

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Role of the Hippocampus in Recollection and Familiarity

experience of remembering. Most prominent is autonoetic awareness, a sense of having had a particular experience. Also highlighted in Tulving’s conception are the memory of one’s self-involvement in the remembered episode and the capacity to mentally “replay” the experience. Importantly, these features all involve internalized subjective qualities of remembering, accessible only by verbal report and not through any objective measures of behavior or by assessing the contents of remembered events. This places key aspects of episodic memory difficult to characterize by objective assessments and entirely outside the province of testing in animals, severely limiting the kinds of neurobiological studies that could reveal the fundamental roles and nature of information coding in areas of the brain. For these reasons, these subjective features of episodic memory, including autonoetic awareness, self-involvement, and mental replay, are rarely examined in research on episodic memory in humans or its analogues in animals. Instead, studies on episodic memory typically evaluate objective features of the contents of memory, most commonly the context or source of remembered items, such as when or where an event occurred. The range of objective features of episodic memories is perhaps best illustrated by the common experience in which we sometimes meet someone who looks familiar, but cannot remember who he is or why we know him. A conversation ensues and eventually a critical reminder surfaces that generates a rich and complex memory. The memory includes contextual information about where and when we last encountered the person. A vivid recollection unfolds as a series of events that constitute the full encounter. Also, we are often able to recall and distinguish other encounters with that person and with related experiences. Features that are prominent in this example of episodic recollection are the basis of the following discussion of the role of the hippocampus and other medial temporal areas. HIPPOCAMPUS AND FEATURES OF EPISODIC MEMORY The previous anecdote reflects the fundamental features of episodic recollection in daily life. First, recollection of previous experiences is distinguished from a sense of familiarity, even when that sense of familiarity can be quite strong and provide a clue about the recency of prior experience. Second, a defining feature of recollection is that episodic memories for events involve the context in which they occurred, specifically when and where the event occurred. Third, a vivid episodic memory is structured by a temporal organization involving the flow of events in a unique experience. Fourth, specific episodic memories are distinguished

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and related to other specific memories that contain substantial common information. In this chapter, I consider these four fundamental distinguishing features of episodic recollection, ask whether they characterize the memory capacities of animals as they do humans, and explore the role of the hippocampus in each. ROLE OF THE HIPPOCAMPUS IN RECOLLECTION AND FAMILIARITY As the incident described suggests, one of the ways familiarity and recollection are distinguished is by their retrieval dynamics. Familiarity occurs quickly and is graded in strength. Items from our past can generate a slight sense of familiarity or an intensely held belief that we have experienced them before. By contrast, recollection is qualitative. Its goodness is characterized by the number of associations we retrieve and we tend to retrieve each one in an all-ornone fashion. How can these properties be dissociated in the performance of human and animal subjects? The retrieval dynamics of recollection and familiarity have been distinguished in humans by the analysis of receiver operating characteristic (ROC) functions during recognition memory performance (Yonelinas, 2001). In a typical experiment, subjects study a list of words, then are tested for their capacity to identify the same words plus a set of words that were not studied as “old” or “new.” The resulting ROC analysis plots “hits,” that is, correct identifications of old items, against “false alarms,” incorrect identifications of new items as if they were old, across a range of confidence levels. This analysis typically reveals an asymmetric function characterized by an above-zero threshold of recognition at the most conservative criterion (zero false alarm rate) and thereafter a curvilinear performance function (Yonelinas, 2001; Figure 28.1A). The positive Y-intercept is viewed as an index of the recollection in the absence of measurable familiarity, whereas the degree of curvature reflects familiarity as typical of a signal-detection process (Macmillan & Creelman, 1991). Consistent with this view, under different experimental demands that favor one of these processes, the shape of the ROC curve takes on distinguishable functions. During performance that favors recollection, the ROC curve highlights the threshold component of recognition with performance at successively higher confidence levels characterized by a linear function (Figure 28.1B). In contrast, during performance that favors familiarity, the ROC curve is symmetrical and curvilinear (Figure 28.1C). Yonelinas et al. (2002) used ROC analysis to show that mild hypoxia that causes damage largely confined to the hippocampus resulted in a severe deficit in recollection

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but normal familiarity. The distinction between impaired recollection and spared familiarity was verified by measures of subjective experiences in recognition reflected in “remember” versus “know” judgments by the same patients. In addition, structural equation modeling methods used on a large sample of hypoxic patients revealed that hypoxic severity predicted the degree to which recollection, but not familiarity, was impaired. A similar pattern of deficient recollection and preserved familiarity was reported in a patient with relatively selective hippocampal atrophy related to meningitis (Aggleton et al., 2005). These studies indicate the hippocampus plays a selective role in recollection. However, other interpretations of the data on ROC analyses in normal human subjects have led to the view that recollection and familiarity reflect differences in strength of a single memory function (Wixted & Stretch, 2004) and many reports are mixed on whether ROC curves are more consistent with single or dual processes in recognition, suggesting that the dissociation of these processing functions may be dependent on parameters of testing and assumptions in the data analysis (Parks & Yonelinas, 2007; Wixted, 2007). In addition, another ROC study reported deficits in

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(Fortin et al., 2004). (D) Normal rats tested with a 30-min delay. Insets: F ⫽ Familiarity estimates; R ⫽ recollection estimates. (E) Postoperative performance with a 30-min delay, including an estimated curve for controls based on familiarity alone (con F). (F) Control rats tested with a 75min memory delay. Diagonal dotted lines represent chance performance across criterion levels. C ⫽ control group; H ⫽ hippocampal group. Error bars indicate SEM. * p < .05.

both recollection and familiarity in hypoxic patients with identified hippocampal damage (Wais, Wixted, Hopkins, & Squire, 2006) and several other studies also reflect a mixture of results on whether the hippocampus is selectively involved in recollection or involved in both recollection and familiarity. Differences in the localization of damage in different patients as well as differences in the task demands across studies might account for the variability in results across these studies. To address whether recollection and familiarity can be distinguished in ROC functions by selective hippocampal damage, we developed an ROC protocol for assessing recollection and familiarity in rats and for examining the effects of highly selective experimental lesions of the hippocampus. Our recognition task exploited rats’ superb odor memory capacities (Fortin, Wright, & Eichenbaum, 2004). On each daily test session, rats initially sampled 10 common household scents mixed in with playground sand in a plastic cup containing a cereal reward. When each sample was presented, the animal would dig for the reward and incidentally smell the odor of the sand. Following a 30minute memory delay, the same odors plus 10 additional odors were presented one at a time in random order.

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Role of the Hippocampus in Recollection and Familiarity

On each recognition test, the animal followed a nonmatchto-sample rule such that it could dig in the target odor to obtain a reward if the target was “new” (a nonmatch) or could refrain from digging if the odor was “old” (a match) and instead obtain a reward in an empty cup on the opposite end of the test chamber. Initially, animals were trained with short lists of odors, and the list length was gradually increased to 10 items. In addition, in the final phase of training and testing, a different response criterion was encouraged for each daily session using a combination of variations in the height of the test cup, making it more or less difficult to respond to that cup, and manipulations of the reward magnitudes associated with correct responses to the test and the unscented cup. Notably, the use of a method for explicitly varying the animal’s bias is different from the use of confidence judgments in experiments on recognition in humans (Yonelinas, 2001); nevertheless, both methods successfully vary the subject’s criterion along the full range required to compute ROC curves. The ROC curve of intact control rats was asymmetric (Figure 28.1D), containing both a threshold component (above-zero Y-intercept) and a strong curvilinear component. This pattern is remarkably similar to the ROC of humans in verbal recognition performance (Figure 28.1A), consistent with a combination of recollection-like and familiarity-based components of recognition in animals. To explore the role of the hippocampus in recollection, subjects were subsequently divided into two groups matched on both performance components; one group received selective lesions of the hippocampus whereas the other group received sham control operations. After recovery, we again tested recognition performance at each response criterion. The ROC of control rats continued to reflect both recollection-like and familiarity components, whereas the ROC of animals with selective hippocampal lesions was fully symmetrical and curvilinear (Figure 28.1E), characteristic of familiarity-based recognition in humans (Figure 28.1C). To describe these patterns quantitatively, we calculated indices of recollection and familiarity (Figure 28.1E inset). Whereas familiarity remains normal in rats with hippocampal lesions, recollection is severely impaired. The overall level of performance (averaged across biases) on the task is slightly worse in the hippocampal group (66%, compared to 73% in controls). Given that any performance deficit would be expected to result in an ROC closer to the diagonal (chance performance; dashed line in Figure 28.1E), it is possible that the alteration in their ROC pattern resulting from the hippocampal lesion reflect a generalized decline in memory. To compare the ROC of hippocampal rats with the pattern of forgetting in normal animals, we challenged the memory of control rats by increasing the memory delay to 75 minutes. This manipulation

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succeeded in reducing the overall level of performance of control animals to 64%, equivalent to that of the hippocampal rats. Yet, further testing of the controls showed that their ROC continued to have an asymmetrical threshold component, as indicated by an above-zero Y-intercept (Figure 28.1F). Notably, the controls’ ROC was distinctly more linear than that of both the hippocampal rats and the controls when tested at the shorter memory delay. This pattern of performance suggests that, in normal rats, familiarity fades more quickly than recollection, similar to observations on humans (Yonelinas, 2002). Moreover, comparison of the ROC curve in normal rats at the 75-minutes delay versus that of rats with hippocampal damage at the 30-min delay emphasizes the distinction between these two groups in their differential emphasis on recollection and familiarity, respectively, even when the overall levels of recognition success are equivalent. These findings strongly suggest that rats exhibit two distinct processes in recognition, one that is marked by a threshold retrieval dynamic characteristic of episodic recollection in humans, and another that follows a symmetrical and curvilinear processing function characteristic of familiarity in humans. These observations suggest comparable dual retrieval mechanisms underlying recognition in animals and humans, and strongly support the notion that the hippocampus plays a critical role only in the recollective processes that contribute to recognition. Memory for Where and When Events Occurred Several investigators have argued that animals are indeed capable of remembering the spatial and temporal context in which they experienced specific stimuli (Clayton, Bussey, & Dickinson, 2003; Day, Langston, & Morris, 2003). To further explore these aspects of episodic memory, we developed a task that assesses memory for events from a series of events that each involve the combination of an odor (“what”), the place in which it was experienced (“where”), and the order in which the presentations occurred (“when”; Ergorul & Eichenbaum, 2004). On each of a series of events, rats sampled an odor in a unique place along the periphery of a large open field (Figure 28.2A). Then, memory for when those events occurred was tested by presenting a choice between an arbitrarily selected pair of the odor cups in their original locations. We identified both the stimulus initially approached and the final choice in which the rat dug for food. Over a series of shaping phases, rats were trained to select the earlier presented odor of a pair randomly selected from the series. Rats performed well above chance (76.2% correct) in their choices on the test phase, indicating that they can remember the order of unique sequences of odors and places

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Memory

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Figure 28.2 A: An example (B versus C) trial for a whatwhere-when test and odor and spatial probes. B: Comparison of performance (mean ⫾ SEM) versus percentage of correct first approaches on the what-where-when probe tests. C: Postsurgery performance of sham-control (n ⫽ 7) and hippocampal lesion (n ⫽ 7) groups. Note: (A) In the sample phase of every trial, rats were presented with four odors in series (A⫹ → B⫹ → C⫹ → D⫹), each at a different location

(Figure 28.2B). In addition, we also found that rats first approached the correct stimulus at well above chance level, indicating they remembered the sequence of places where the cups were presented prior to perceiving information about the odor at that location; importantly, separate tests showed that rats cannot accurately identify the odor in a cup until they arrive within at the edge of the cup. However, performance was not as accurate in the first approach as it was in the final choice, suggesting that rats begin by guessing the location of the earlier experienced cup, then confirm this choice using the smell of the cup. This hypothesis was confirmed in a control condition in which we presented the test cups without odors. In this condition, performance of intact animals fell to chance, indicating that when the selected location is not confirmed by the associated odor, performance is disrupted (Figure 28.2B). This pattern of results strongly suggests rats normally use a combination of “where” and “what” information to judge “when” the events occurred. To examine the role of the hippocampus, animals were subsequently separated into matched groups, one of which received selective hippocampal lesions. Subsequently, intact rats continued to choose well on the standard “what-wherewhen” trials (Figure 28.2C). By contrast, the performance of animals with hippocampal lesions was no better than chance. In addition, whereas intact rats continued to perform well on the initial approach, rats with hippocampal lesions approached the correct choice less often than expected by chance. Contrary to the strategy of normal rats and the reinforcement contingency of the test phase, rats with hippocampal damage were inclined to visit the more recently

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on a platform. In the following test phase, odors B and C were presented in their sample locations in the what-where-when choice test, or next to each other in the odor probe, or two nonodorous stimuli were presented in the sample locations of B and C in the spatial probe. ⫹ ⫽ Reinforced stimulus; arrow on the platform: position of the rat at the starting-point (arrowhead corresponds to the rat’s head); star symbol: the experimenter ’s fixed position throughout testing. (B) Presurgery performance of normal rats (n ⫽ 14). (C) Dashed line ⫽ Chance level. * p ⬍ .05.

presented and rewarded place rather than the earlier visited locus. This observation indicates an intact spatial memory in rats with hippocampal damage and this memory was employed despite its maladaptive consequences. Rats with hippocampal damage could detect the earlier presented odor when presented without spatial cues (odor probe test; Figure 28.2A, data in Figure 28.2C) and the observation of a preference for the most recently presented odor indicates that hippocampal lesioned animals could identify and remember the odors and places in some way. These findings, combined with the failure of rats with hippocampal damage in the standard condition, indicate that the hippocampus is critical for effectively combining the “what,” “when,” and “where” qualities of each experience to compose the retrieved memory. Normal rats initially employ their memory of the places of presented cups and approach the location of the earlier experience. Then they confirm the presence of the correct odor in that location. Animals with hippocampal damage fail on both aspects of this task and, instead, their behavior is guided by another form of memory that leads to the incorrect first approach. That they can initially approach the most recently rewarded location indicated their spatial memory is intact. However, it appears they are driven to approach the last rewarded cup rather than combine the what-wherewhen cues to select the earlier event. Events Are Represented as Items in Context Additional insights about the fundamental properties of memory representation can be gained through the analysis

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Role of the Hippocampus in Recollection and Familiarity

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of neural activity patterns associated with the critical stimuli and behavioral events that occur in animals performing memory tasks. These studies can confirm the evidence from tests of brain damage by providing evidence of normal coding of features of memory that are lost following selective damage of the same brain areas. These studies can provide insights about where and how particular types of information are encoded within the circuitry of the hippocampus and associated brain structures. A wealth of studies have shown that hippocampal neurons fire associated with the ongoing behavior and the context of events as well as the animal’s location (Eichenbaum, 2004). The combination of spatial and nonspatial features of events captured by hippocampal neuronal activity is consistent with the view that the hippocampus encodes many features of events and the places where they occur. Two studies provide examples that highlight the rapid associative coding of events and places by hippocampal neurons. In one study rats were trained on an auditory fear conditioning task in which a tone was paired with shock to produce conditioned freezing to subsequent tone presentations (Moita, Moisis, Zhou, LeDoux, & Blair, 2003). Prior to fear conditioning, few hippocampal cells were activated by the auditory stimulus. Following pairings of tone presentations and shocks, many cells fired briskly to the tone and did so only when the animal was in a particular place where the cell had fired above baseline prior to conditioning. Another study examined the firing properties of hippocampal neurons in monkeys performing a task where they rapidly learned new scene-location associations (Wirth et al., 2003). Just as the monkeys acquired a new response to a location in the scene, neurons in the hippocampus changed their firing patterns to become selective to particular scenes. Additional studies have directly examined the extent to which hippocampal neurons encode specific stimuli and places where they occur by training subjects to perform the same memory judgments at many locations in the environment. In one study, rats performed a task in which they had to recognize any of nine olfactory cues placed in any of nine locations (Wood, Dudchenko, & Eichenbaum, 1999). On each trial, a reward was available when the rat responded to a cue that differed from (was a nonmatch to) the immediately preceding stimulus. Because the location of the discriminative stimuli was varied systematically, cellular activity related to the stimuli and behavior could be dissociated from that related to the animal’s location. Some hippocampal cells encoded particular odor stimuli, others were activated when the rat sampled any odor at a particular place, and yet others fired associated with whether the odor matched or differed from the previous cue (Figure 28.3). However, the largest subset of hippocampal neurons

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Figure 28.3 Incidence of hippocampal neurons that encode odors, places where odors were sampled, the match/nonmatch status of the odor, or combinations of odor, place, and match/nonmatch status. Note: From “The Global Record of Memory in Hippocampal Neuronal Activity,” by E. Wood, P. A. Dudchenko, H. Eichenbaum, 1999, Nature, 97, pp. 613–616. Reprinted with permission.

fired only when associated with a particular combination of the odor, the place where it was sampled, and the matchnonmatch status of the odor. In a similar task created for humans, Ekstrom et al. (2003) recorded the activity of hippocampal neurons as people played a taxi driver game, searching for passengers picked up and dropped off at various locations in a virtual reality town. Some cells encoded particular cues or fired as the subject traversed specific locations. Also, many of these cells fired selectively when the subject viewed of a particular scene from a particular place or passed a location while pursuing a particular goal. Hippocampal cells that represent specific salient objects in the context of a particular environment have also been observed in studies of rats engaged in foraging (Gothard, Skaggs, Moore, & McNaughton, 1996; Rivard et al., 2004) and place learning (Hollup, Molden, Donnett, Moser, & Moser, 2001) in open fields. Furthermore, parallel evidence from functional imaging has shown that the human hippocampus is selectively activated during association of item and the context in which it was experienced (e.g., Davachi, Mitchell, & Wagner, 2003; Ranganath et al., 2003; for reviews, see Davachi, 2006; Eichenbaum, Yonelinas, & Ranganath, 2007). Thus, in rats, monkeys, and humans, a prevalent property of hippocampal firing patterns involves the representation of unique associations of stimuli and their significance, specific behaviors, and the places where these events occurred. Memory for the Order of Events within a Unique Experience In addition to memory for the spatial and temporal context of distinct events, a vivid recollection often involves recalling the flow of events within a single experience.

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Figure 28.4 Memory for order and item in rats. Note: A: On each trial the animal was presented with a series of 5 odors (e.g., odors A through E), the animal was then either probed for its memory of the order of elements in the series (top) or its memory of the individual items presented (bottom). ⫹ ⫽ Rewarded odor; – ⫽ Nonrewarded odor. B: Hippocampal animals were impaired on all sequential order probes. Performances on different probes are grouped according to the lag (number

To investigate the memory for the order of events in a unique experience, we developed a behavioral protocol that assesses memory for episodes composed of a unique sequence of olfactory stimuli presented to the animal as it remains in its cage (Fortin, Agster, & Eichenbaum, 2002; see also Kesner, Gilbert, & Barua, 2002). In addition, our design allowed us to directly compare memory for the sequential order of odor events with recognition of the odors in the list independent of memory for their order. On each trial, rats were presented with a series of five odors, selected randomly from a large pool of common household scents. Memory for each series was subsequently probed by a choice test where the animal was reinforced for selecting the earlier of two of the odors that had appeared in the series. For example, the rat might be initially presented with odors A then B then C then D then E. Following the delay, two nonadjacent odors, for example B and D, were presented and the animal would be rewarded for selecting odor that appeared earlier (in this case, B). On each trial, any pair of nonadjacent odors might be presented as the probe, so the animal had to remember the entire sequence in order to perform well throughout the testing session. After training over many days, rats performed sequential order judgments well above chance levels (Figure 28.4), indicating they can remember the order of a sequence of events in unique experiences. To examine the role of the

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of intervening elements). C: Hippocampal animals performed as well as controls on the recognition probes. X ⫽ Randomly selected odor that was not presented in the series and used as the alternative choice. From “Critical Role of the Hippocampus in Memory for Sequences of Events,” by N. J. Fortin, K. L. Agster, and H. Eichenbaum, 2002, Nature Neuroscience, 5, pp. 459 & 460. Reprinted with permission. * p < .05.

hippocampus in memory for the order of events in unique experiences, these subjects were divided into two groups matched for performance, then animals in one group were given selective hippocampal lesions whereas those in the other group received sham operations. After recovery, all animals were tested again on memory for the order of odors in unique odor sequences. Intact rats continued to perform well whereas rats with hippocampal lesions were severely impaired, performing no better than chance except when the judgment was easiest (when the odors were first and last in the series). The same rats were then also tested on their ability to recognize odors that were presented in the series. On each trial, a series of five odors was presented in a format identical to that used in the previous testing. Then recognition was probed using a choice test in which the animal was presented with one of the odors from the series and another odor from the pool that was not in the series, and food was buried in the odor not presented in the series. For example, the rat might instead be presented with the series A through E then, following a delay, an odor selected randomly from those initially sampled and an odor not presented in the sequence, for example, A and X, were presented. The rat would be rewarded for choosing X. Both intact rats and rats with selective hippocampal damage acquired the task rapidly and there was no overall performance difference

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Role of the Hippocampus in Recollection and Familiarity

between the groups in acquisition rate or final level of recognition performance (Figure 28.4). Furthermore, in both groups, recognition scores were consistently superior on probes involving odors that appeared later in the series, suggesting some forgetting of items that had to be remembered for a longer period and through more intervening items. A potential confound in any study that employs time as a critical dimension in episodic memory is that memories obtained at different times are likely to differ in the strength of their memory traces, due to the inherent decremental nature of memory traces. To what extent could normal animals be using differences in the relative strengths of memory traces for the odors to judge their sequential order? The observation of a temporal gradient in recognition performance by normal animals suggests that memories were in fact stronger for the more recently presented items in each sequence. These differences in trace strength potentially provide sufficient signals for the animals to judge the order of their presentation. However, the observation of the same temporal gradient of recognition performance in rats with hippocampal damage indicated that they had normal access to the differences in trace strengths for the odors. Yet these intact trace-strength differences were not sufficient to support above chance performance in the order probes. These considerations strongly suggest that normal rats also could not utilize the relative strengths of memories for the recently experienced odors, and instead based their sequential order judgments directly on remembering the odor sequence. The findings indicate that animals have the capacity to recollect the flow of events in unique experiences and that the hippocampus plays a critical role in this capacity. Episodes Are Represented as Sequences of Events Another common observation across species and many different behavioral protocols is that different hippocampal neurons fire during each successive event that composes task performance. Some cells are active during simple behaviors such as foraging for food (e.g., Muller, Kubie, & Ranck, 1987) and learned behaviors directed at relevant stimuli that have to be remembered (e.g., Hampson, Heyser, & Deadwyler, 1993), and the firing patterns have been observed across a broad range of learning protocols, from classical conditioning, discrimination learning, and nonmatching or matching to sample tasks to a variety of spatial learning and memory tasks (for review, see Eichenbaum, 2004). In each of these paradigms, a substantial proportion of hippocampal neurons show time-locked activations associated with each sequential event. Many of these cells show striking specificities corresponding to particular combinations of stimuli, behaviors, and the spatial location of the event.

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These sequential firing patterns can be envisioned to represent a series of events and their places that compose a meaningful episode, and the information contained in these representations both distinguishes and links related episodes. Consider, for example, a study in which rats were trained on the classic spatial alternation task in a modified T-maze (Wood, Dudchenko, Robitsek, & Eichenbaum, 2000). Performance on this task requires that the animal distinguish left-turn and right-turn episodes and that it remember the immediately preceding episode to guide the choice on the current trial, and in that way, the task is similar in demands to those of episodic memory (Figure 28.5). We found that hippocampal neurons encode each sequential behavioral event and its locus within one type of episode, with most cells firing only when the rat is performing within either the left-turn or the right-turn type of episode. This was particularly evident for cells that fired when the rat was on the “stem” of the maze, that is, when the it traversed the same locations on both types of trials (Figure 28.5). Indeed, virtually all cells that fired when the rat was on the maze stem fired differentially on left-turn versus right-turn trials. The majority of cells showed strong selectivity, some firing almost exclusively as the rat performed one of the trial types, suggesting they were part of the representations of only one kind of episode. Other cells fired substantially on both trial types, potentially providing a link between left-turn and right-turn representations by the common places traversed on both trial types. These findings indicated that separate ensembles of neurons encoded the sequences of events that composed left-turn and rightturn trials. Notably there were also some cells that fired similarly on both trial types; these might serve to link the two types of episodes. Functional imaging studies in humans have also revealed hippocampal involvement in both spatial and nonspatial sequence representation. Several studies have shown that the hippocampus is active when people recall routes between specific start points and goals, but not when subjects merely follow a set of cues through space (Hartley, Maguire, Spiers, & Burgess, 2003). Evidence from functional imaging studies also indicates that the specific role of the hippocampus is to represent sequences of places traversed in a route, rather than a mapping of the environment. In a study examining navigation from a route perspective (person centered) and survey perspective (looking down from above; Shelton & Gabrieli, 2002), the hippocampus was more activated in the route condition, where navigation requires the association and continuous updating of different views and movements throughout the environment. In postscanning tests, subjects were asked to draw a map that described how they navigated the virtual environment in the route and survey conditions. All of the subjects

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Figure 28.5 Activity of hippocampal neurons in the T-maze spatial alternation task. Note: A: T-maze alternation task. Rats performed a continuous alternation task in which they traversed the central stem of the apparatus on each trial and then alternated between left and right turns at the T junction. ITI, Inter-trial interval. B: Examples of hippocampal neurons distinguishing left and right-turn episodes in the central stem. The stem was divided into four sectors for data analyses. In each example, the paths taken by the animals on the central stem are plotted in the left panel (light gray, left-turn trial; dark gray, right-turn trial). In the middle panels,

used a sequential strategy in their drawings of the route, but not survey task. As discussed by Shelton and Gabrieli (2002), route building required that subjects link together the sequences and views experienced while navigating the environment, engaging the hippocampus as a result of its purported role in mediating a memory space in both humans and animals (Eichenbaum, Dudchencko, Wood, Shapiro, & Tanila, 1999). In addition, the hippocampus is selectively activated when people learn sequences of pictures (Kumaran & Maguire, 2006). Even greater hippocampal activation is observed when subjects must disambiguate picture sequences that overlap, parallel to our findings on hippocampal cells that disambiguate spatial sequences (Wood et al., 2000). Networking Memories A third defining quality of recollection is our capacity to bring to mind multiple related memories, that is, memories that have common elements, and to make inferences from the information contained in those memories. To examine the extent to which animals can link memories

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the location of the rat when individual spikes occurred is indicated separately for left-turn trials (black dots) and right-turn trials (black dashes). In the right panel, the mean firing rate of the cell for each sector, adjusted for variations in firing associated with covariates, is shown separately for left-turn trials (light gray) and right-turn trials (dark gray). From “Hippocampal Neurons Encode Information about Different Types of Memory Episodes Occurring in the Same Location,” by E. Wood, P. Dudchenko, R. J. Robitsek, and H. Eichenbaum, 2000, Neuron, 27, p. 626. Reprinted with permission. ** p ⬍ .01; *** p ⬍ .001.

that share common elements, we studied whether rats can learn a set odor problems that share elements, and then tested whether they had integrated the memories into networks that support inferential judgments. One experiment compared the ability of normal rats and rats with selective damage to the hippocampus on their ability to learn a set of paired associate problems that contained common elements, and to interleave the representations of these problems in support of novel inferential judgments (Bunsey & Eichenbaum, 1996). Animals were initially trained on two sets of overlapping odor paired associates (e.g., A goes with B, B goes with C). Then the rats were given probe tests to determine if they could infer the relationships between items that were only indirectly associated through the common elements (A goes with C ?). Normal rats learned the paired associates and showed strong transitivity in the probe tests (Figure 28.6). Rats with selective hippocampal lesions also learned the pairs over several trials but were severely impaired in the probes, showing no evidence of transitivity. In another experiment, rats learned a hierarchical series of premises that involved odor choice judgments between overlapping elements (e.g., A > B, B > C, C > D, D > E),

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Role of the Hippocampus in Recollection and Familiarity

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then were probed on the relationship between indirectly related items (B > D ?; Figure 28.6). Normal rats learned the series and showed robust transitive inference on the probe tests. Rats with hippocampal damage also learned each of the initial premises but failed to show transitivity (Dusek & Eichenbaum, 1997). The combined findings from these studies show that rats with hippocampal damage can learn even complex associations, such as those embodied in the odor paired–associates and conditional discriminations. However, without a hippocampus, they do not interleave the distinct experiences by their common elements to form a relational network that supports inferential memory expression. Importantly, according to the present view, the hippocampus does not compute or directly mediate transitive judgments. Rather, the hippocampus mediates only the encoding and retrieval of information about previous experiences on which cortical areas might accomplish the critical judgment. One neocortical association area that receives hippocampal outputs and is likely critical to inferential judgments is prefrontal cortex (Waltz et al., 1999). Hippocampus Encodes Events That Can Link Related Memories In virtually all the studies described, some hippocampal neurons encode features that are common among different experiences—these representations could provide links between distinct memories. For example, in Moita and colleagues’ (2003) study of auditory fear conditioning, whereas some cells only fired to a tone when the animal was in a particular place, others fired associated with the

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with hippocampal lesions do not show transitivity, indicating they have not represented the indirect relations. Hippo ⫽ Hippocampal lesion. Data source: Bunsey and Eichenbaum (1996).

tone wherever it was presented across trials. In the Wood et al. (1999) study on odor recognition memory, whereas some cells showed striking associative coding of odors, their match/nonmatch status, and places, other cells fired associated with one of those features across different trials. Some cells fired during a particular phase of the approach toward any stimulus cup. Others fired differentially as the rat sampled a particular odor, regardless of its location or match-nonmatch status. Other cells fired only when the rat sampled the odor at a particular place, regardless of the odor or its status. Yet, other cells fired differentially associated with the match and nonmatch status of the odor, regardless of the odor or where it was sampled. Similarly, in Ekstrom and colleagues’ (2003) study on humans performing a virtual navigation task, whereas some hippocampal neurons fired associated with combinations of views, goals, and places, other cells fired when subjects viewed particular scenes, occupied particular locations, or had particular goals in findings passengers or locations for drop off. Also, Rivard, et al., (2004) studied rats exploring objects in open fields, finding that whereas some cells fired selectively associated with an object in one environment, others fired associated with the same object across environments. The notion that these cells might reflect the linking of important features across experiences and the abstraction of common information was highlighted in more recent studies on monkeys and humans. Hampson, Pons, Stanford, and Deadwyler (2004) trained monkeys on matching to sample problems, then probed the nature of the representation of

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stimuli by recording from hippocampal cells when the animals were shown novel stimuli that shared features with the trained cues. They found many hippocampal neurons that encoded meaningful categories of stimulus features and appeared to employ these representations to recognize the same features across many situations. Kreiman, Koch, and Fried (2000) characterized hippocampal firing patterns in humans during presentations of a variety of visual stimuli. They reported a substantial number of hippocampal neurons that fired when the subject viewed specific categories of material, for example, faces, famous people, animals, scenes, houses, across many exemplars of each. A subsequent study showed that some hippocampal neurons are activated when a subject views any of a variety of different images of a particular person, suggesting these cells could link the recollection of many specific memories related to that person (Quiroga, Reddy, Kreiman, Koch, & Fried, 2005). This combination of findings across species provides compelling evidence for the notion that some hippocampal cells represent common features among the various episodes that could serve to link memories obtained in separate experiences. Furthermore, recent functional imaging studies have associated activation of the hippocampus in humans to the performance of performing transitive inference tasks similar to those described above as dependent on the hippocampus in animals. In one study, subjects learned overlapping paired associations between faces and houses or direct face-face associations (Preston, Shrager, Dudukovic, & Gabrieli, 2004). The hippocampus was selectively activated when people identified the indirect associations between faces that were paired with the same house as compared with direct face-face associations. In another study, subjects were trained on the task which involves a hierarchical series of judgments (A ⬎ B, B ⬎ C, C ⬎ D, D ⬎ E) or a series of nonoverlapping judgments (K ⬎ L, M ⬎ N, O ⬎ P, Q ⬎ R; Heckers, Zalezak, Weiss, Ditman, & Titone, 2004). The hippocampus was activated when subjects performed transitive judgments as compared to novel judgments between items taken from the nonoverlapping pairs. Under some circumstances, it may be possible to indirectly relate items without a memory network (O’Reilly & Rudy, 2001; Van Elzakker, O’Reilly, & Rudy, 2003), but the previous described results provide compelling evidence that the hippocampus is indeed involved in binding-related memories and in using these memories to make novel inferential judgments.

EPISODIC MEMORY AND THE HIPPOCAMPAL MEMORY SYSTEM A consideration of the anatomical organization of the major circuitry involving the hippocampus and neocortex

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Figure 28.7 Functional organization of the hippocampal system.

provides further insights into basic mechanisms that underlie recollection across diverse species. In primates, the hippocampus receives an enormous variety of information from virtually every cortical association area, and this information is funneled into the hippocampus via the parahippocampal region, which is subdivided into the perirhinal cortex, parahippocampal cortex, and entorhinal cortex (Figure 28.7). The cortical outputs of hippocampal processing involve feedback connections from the hippocampus successively back to the entorhinal cortex, then the perirhinal and parahippocampal cortex, and finally, the neocortical areas from which the inputs to the hippocampus originated (Amaral & Witter, 1995). To what extent is the organization of this system similar in mammalian species? The internal circuitry of the hippocampus itself is largely conserved across mammalian species (Manns & Eichenbaum, 2006). The subdivisions of the hippocampus are connected by a serial, unidirectional path, starting with the dentate gyrus, and continuing through CA3, then CA1, and then the subiculum. Furthermore, anatomical details involving several topographical and parallel organizations are highly similar in species including rats, cats, and monkeys, as well as other species (see Amaral & Witter, 1995; Witter, Wouterlood, Naber, & Van Haeften, 2000, for reviews). There is also considerable conservation of the areas of the parahippocampal region. The perirhinal, parahippocampal (called postrhinal cortex in rats), and entorhinal subdivisions of the parahippocampal region are similar in cytoarhcitechture in rats, mice, and monkeys, and the connectivity among these areas is also remarkably

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Episodic Memory and the Hippocampal Memory System 563

similar (Burwell, Witter, & Amaral, 1995). In contrast to the conservation of hippocampal and parahippocampal circuitry, the neocortical regions that are the ultimate origin of hippocampal inputs differ substantially from species to species. For example, there are numerous dissimilarities in the neocortex that reflect general differences between small-brained and big-brained mammals, such as cortical size, laminar stratification, and number of polymodal association areas (Krubitzer & Kaas, 2005; Manns & Eichenbaum, 2006). Further, the extent of cortical areas devoted to a particular sensory modality also varies substantially between species. Despite major species differences in the neocortex, the organization of cortical inputs to the hippocampus is remarkably similar in rodents and primates. Across species, most of the neocortical input to the perirhinal cortex comes from association areas that process unimodal sensory information about qualities of objects (i.e., “what” information), whereas most of the neocortical input to the parahippocampal cortex comes from areas that process polymodal spatial (“where”) information (Burwell et al., 1995; Suzuki & Amaral, 1994). There are connections between the perirhinal cortex and parahippocampal cortex, but the “what” and “where” streams of processing remain largely segregated as the perirhinal cortex projects primarily to the lateral entorhinal area whereas the parahippocampal cortex projects mainly to the medial entorhinal area. Similarly, there are some connections between the entorhinal areas, but the “what” and “where” information streams mainly converge within the hippocampus. These anatomical considerations suggest a functional organization of the flow of information into and out of the hippocampus. Substantial evidence indicates that neurons in the perirhinal cortex and lateral entorhinal cortex are involved in the representation of individual perceptual stimuli. Electrophysiological studies on monkeys and rats performing simple recognition tasks have shown that many cells in the perirhinal cortex exhibit enhanced or suppressed responses to stimuli when they reappear in a recognition test (Suzuki & Eichenbaum, 2000). Similarly, in humans, among all areas within the medial temporal lobe, the perirhinal area selectively shows suppressed responses to familiar stimuli (Henson, Cansino, Herron, Robb, & Rugg, 2003). Complementary studies in animals with damage to the perirhinal cortex indicate that this area may be critical to memory for individual stimuli in the delayed nonmatching to sample task in rats (Otto & Eichenbaum, 1992) and monkeys (Suzuki, Zola-Morgan, Squire, & Amaral, 1993). These and other data have led several investigators to the view that the perirhinal cortex is specialized for identifying the memory strength of individual stimuli (e.g., Brown & Aggleton, 2001; Henson et al., 2003).

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By contrast, the parahippocampal cortex and medial entorhinal area may be specialized for processing spatial context. Whereas perirhinal and lateral entorhinal neurons have poor spatial coding properties, parahippocampal and medial entorhinal neurons show strong spatial coding (Hargreaves, Rao, Lee, & Knierim, 2005). In addition, whereas object recognition is impaired following perirhinal damage, object-location recognition is deficient following parahippocampal cortex damage in rats (Gaffan, Healey, & Eacott, 2004) and monkeys (Alvarado & Bachevalier, 2005). Similarly, perirhinal cortex damage results in greater impairment in memory for object pairings whereas parahippocampal cortex lesions results in greater impairment in memory for the context in which an object was presented (Norman & Eacott, 2005). Parallel findings from functional imaging studies in humans have dissociated object processing in the perirhinal cortex from spatial processing in the parahippocampal cortex (Pihlajamaki et al., 2004). Furthermore, whereas the perirhinal cortex is activated in association with the memory strength of specific stimuli (Henson et al., 2003), the parahippocampal cortex is activated during recall of spatial and nonspatial context (Bar & Aminoff, 2003). Compelling support for differentiation of functions associated with episodic recollection comes from withinstudy dissociations that reveal activation of the perirhinal cortex selectively is associated with familiarity and activity in the hippocampus as well as the parahippocampal cortex is selectively associated with recollection (Eichenbaum et al., 2007). These and many other results summarized in this review suggest a functional dissociation between the perirhinal cortex, where activation changes are consistently associated with familiarity, and the hippocampus and parahippocampal cortex, where activation changes are consistently associated with recollection. An outstanding question in these studies is whether the parahippocampal cortex and hippocampus play different roles in recollection. The findings on parahippocampal activation associated with viewing spatial scenes suggests the possibility that this area is activated during recollection because recall involves retrieval of spatial contextual information (Bar & Aminoff, 2003). By contrast, the hippocampus may be activated associated with the combination of item and context information. These findings are consistent with the anatomically guided hypothesis about the functional organization of the hippocampal system presented in Figure 28.6 and suggest mechanisms by which the anatomical components of this system interact in support of the phenomenology of recollection. Following experience with a stimulus, the perirhinal and lateral entorhinal areas may match a memory cue to a stored template of the stimulus, reflected in suppressed

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activation that signals familiarity. Outputs from perirhinal and lateral entorhinal areas back to neocortical areas may be sufficient to generate the sense of familiarity without participation of the hippocampus. In addition, during the initial experience, information about the to-be-remembered stimulus, processed by the perirhinal and lateral entorhinal areas, and about the spatial and possibly nonspatial context of the stimulus, is processed by the parahippocampal and medial entorhinal areas, converge in the hippocampus. During subsequent retrieval, presentation of the cue may drive the recovery of object-context representations in the hippocampus that, via back projections, regenerates a representation of the contextual associations in parahippocampal and medial entorhinal areas, which cascades that information back to neocortical areas that originally processed the item and contextual information. This processing pathway may constitute a principle mechanism for recollection of unique events across species (Eichenbaum et al., 2007).

a reconciliation of the findings on animals and humans. The objectively observable features of episodic recollection are supported by interactions between the cortex and hippocampus similarly in all mammalian species. Where species differ most is in the elaboration of the cerebral cortex, including those areas implicated in representation of the self. Therefore, consistent with Moscovitch’s proposal, the information processing and neural system that supports episodic recollection appears to be conserved across species, but the contents of episodic memories may differ among species, including the nature and extent of self-awareness as a part of the information that is encoded and retrieved in an episodic memory. Therefore, future investigations on animals about how the cortical-hippocampal system supports episodic memory are entirely valid, whereas investigations on self-awareness in memory can be considered a distinct question to be pursued independently in analyses of the relevant cortical networks.

SUMMARY REFERENCES The findings reviewed in this chapter indicate that humans and animals possess the same objectively observable features of episodic memory, and that the hippocampus plays a critical role in each. Animals as well as humans can remember where and when events occurred, and their retrieval dynamics indicates this remembering is similar to that in human recollection. Animals and humans can remember the order of events in unique experiences, and they can disambiguate overlapping experiences. Furthermore, each of these capacities is as dependent on the hippocampus in animals as it is in humans. Indeed, the cortical-hippocampal system that mediates each of these features of recollection is remarkably conserved in mammalian species, including humans. In an effort to focus on cross-species comparisons, the current review omitted consideration of Tulving’s (2002) requirement for subjectively experienced features of recollection, specifically the inclusion of one’s awareness of participation in a remembered episode. As stated at the outset, autonoetic awareness in episodic memory is beyond investigation in animals, and this would seem to preclude a definitive conclusion about whether animals have the full set of features of episodic memory. However, functional imaging studies have identified a network of cortical areas that is engaged in autobiographical memory (Cabeza & St. Jacques, 2007) and the sense of self (Northoff & Bermpohl, 2004). Combining these anatomical findings with the proposal by Moscovitch (1995) that self-awareness in episodic memory is constituted as the encoding and retrieval of information about one’s personal participation in the episode, suggests

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Aggleton, J. P., & Brown, M. W. (2006). Interleaving brain systems for episodic and recognition memory. Trends in Cognitive Sciences, 10, 455–463. Aggleton, J. P., Vann, S. D., Denby, C., Dix, S., Mayes, A. R., et al. (2005). Sparing of the familiarity component of recognition memory in a patient with hippocampal pathology. Neuropsychologia, 43, 1810–1823. Alvarado, M. C., & Bachevalier, J. (2005). Comparison of the effects of damage to the perirhinal and parahippocampal cortex on transverse patterning and location memory in rhesus macaques. Journal of Neuroscience, 25, 1599–1609. Amaral, D. G., & Witter, M. P. (1995). Hippocampal formation. In G. Pacinos (Ed.), The rat nervous system (2nd ed., pp. 443–493). San Diego, CA: Academic Press. Bar, M., & Aminoff, E. (2003). Cortical analysis of visual context. Neuron, 38, 347–358. Brown, M. W., & Aggleton, J. P. (2001). Recognition memory: What are the roles of the perirhinal cortex and hippocampus? Nature Reviews Neuroscience, 2, 51–61. Bunsey, M., & Eichenbaum, H. (1996, January 18). Conservation of hippocampal memory function in rats and humans. Nature, 379, 255–257. Burwell, R. D., Witter, M. P., & Amaral, D. G. (1995). Perirhinal and postrhinal cortices of the rat: A review of the neuroanatomical literature and comparison with findings from the monkey brain. Hippocampus, 5, 390–408. Cabeza, R., & St. Jacques, P. (2007). Functional neuroimaging of autobiographical memory. Trends in Cognitive Sciences, 11, 219–227. Clayton, N. S., Bussey, T. J., & Dickinson, A. (2003). Can animals recall the past and plan for the future? Nature Reviews Neuroscience, 4, 685–691. Corkin, S. (1984). Lasting consequences of bilateral medial temporal lobectomy: Clinical course and experimental findings in HM. Seminars in Neurology, 4, 249–259. Davachi, L. (2006). Item, context and relational episodic encoding in humans. Current Opinion in Neurobiology, 16, 693–700.

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References 565 Davachi, L., Mitchell, J. P., & Wagner, A. D. (2003). Multiple routes to memory: Distinct medial temporal lobe processes build item and source memories. Proceedings of the National Academy of Sciences, USA, 100, 2157–2162. Day, M., Langston, R., & Morris, R. G. M. (2003, July 10). Glutamatereceptor-mediated encoding and retrieval of paired-associate learning. Nature, 424, 205–209. Dusek, J. A., & Eichenbaum, H. (1997). The hippocampus and memory for orderly stimulus relations. Proceedings of the National Academy of Sciences, USA, 94, 7109–7114. Eichenbaum, H. (2000). A cortical-hippocampal system for declarative memory. Nature Reviews Neuroscience, 1, 41–50.

Hollup, S. A., Molden, S., Donnett, J. G., Moser, M. B., & Moser, E. I. (2001). Accumulation of hippocampal place fields at the goal location in an annular watermaze task. Journal of Neuroscience, 21, 1635–1644. Kesner, R. P., Gilbert, P. E., & Barua, L. A. (2002). The role of the hippocampus in memory for the temporal order of a sequence of odors. Behavioral Neuroscience, 116, 286–290. Kreiman, K., Koch, C., & Fried, I. (2000). Category specific visual responses of single neurons in the human medial temporal lobe. Nature Neuroscience, 3, 946–953.

Eichenbaum, H. (2004). Hippocampus: Cognitive processes and neural representations that underlie declarative memory. Neuron, 44, 109–120.

Krubitzer, L., & Kaas, J. (2005). The evolution of the neocortex in mammals: How is phenotypic diversity generated? Current Opinions in Neurobiology, 15, 444–453.

Eichenbaum, H., & Cohen, N. J. (2001). From conditioning to conscious recollection: Memory systems of the brain. New York: Oxford University Press.

Kumaran, D., & Maguire, E. A. (2006). The dynamics of hippocampal activation during encoding of overlapping sequences. Neuron, 49, 617–629.

Eichenbaum, H., Dudchencko, P., Wood, E., Shapiro, M., & Tanila, H. (1999). The hippocampus, memory, and place cells: Is it spatial memory or a memory space? Neuron, 23, 209–226.

Macmillan, N. A., & Creelman, C. D. (1991). Detection theory: A user ’s guide. New York: Cambridge University Press.

Eichenbaum, H., Yonelinas, A. R., & Ranganath, C. (2007). The medial temporal lobe and recognition memory. Annual Review of Neuroscience, 20, 123–152. Ekstrom, A. D., Kahana, M. J., Caplan, J. B., Fields, T. A., Isham, E. A., Newman, E. L., et al. (2003, September 11). Cellular networks underlying human spatial navigation. Nature, 425, 184–187.

Manns, J. R., & Eichenbaum, H. (2006). Evolution of the hippocampus. In J. H. Kaas (Ed.), Evolution of nervous systems (Vol 3. pp. 465–490). New York: Academic Press. Moita, M. A. P., Moisis, S., Zhou, Y., LeDoux, J. E., & Blair, H. T. (2003). Hippocampal place cells acquire location specific responses to the conditioned stimulus during auditory fear conditioning. Neuron, 37, 485–497.

Ergorul, C., & Eichenbaum, H. (2004). The hippocampus and memory for “What,” “Where,” and “When.”Learning and Memory, 11, 397–405.

Moscovitch, M. (1995). Recovered consciousness: A hypothesis concerning modularity and episodic memory. Journal of Clinical Experimental Neuropsychology, 17, 276–290.

Fortin, N. J., Agster, K. L., & Eichenbaum, H. (2002). Critical role of the hippocampus in memory for sequences of events. Nature Neuroscience, 5, 458–462.

Muller, R. U., Kubie, J. L., & Ranck, J. B., Jr. (1987). Spatial firing patterns of hippocampal complex spike cells in a fixed environment. Journal of Neuroscience, 7, 1935–1950.

Fortin, N. J., Wright, S. P., & Eichenbaum, H. (2004, September 9). Recollection-like memory retrieval in rats is dependent on the hippocampus. Nature, 431, 188–191.

Norman, G., & Eacott, M. J. (2005). Dissociable effects of lesions to the perirhinal cortex and the postrhinal cortex on memory for context and objects in rats. Behavioral Neuroscience, 119, 557–566.

Gaffan, E. A., Healey, A. N., & Eacott, M. J. (2004). Objects and positions in visual scenes: Effects of perirhinal and postrhinal cortex lesions in the rat. Behavioral Neuroscience, 118, 992–1010.

Northoff, G., & Bermpohl, F. (2004). Cortical midline structures and the self. Trends in Cognitive Science, 8, 102–107.

Gothard, K. M., Skaggs, W. E., Moore, K. M., & McNaughton, B. L. (1996). Binding of hippocampal CA1 neural activity to multiple reference frames in a landmark-based navigation task. Journal of Neuroscience, 16, 823–835. Hampson, R. E., Heyser, C. J., & Deadwyler, S. A. (1993). Hippocampal cell firing correlates of delayed-match-to-sample performance in the rat. Behavioral Neuroscience, 107, 715–739. Hampson, R. E., Pons, T. P., Stanford, T. R., & Deadwyler, S. A. (2004). Categorization in the monkey hippocampus: A possible mechanism for encoding information into memory. Proceedings of the National Academy of Sciences, USA, 101, 3184–3189. Hargreaves, E. L., Rao, G., Lee, I., & Knierim, J. J. (2005, June 17). Major dissociation between medial and lateral entorhinal input to dorsal hippocampus. Science, 308(5729), 1792–1794. Hartley, T., Maguire, E. A., Spiers, H. J., & Burgess, N. (2003). The wellworn route and the path less traveled: Distinct neural bases of route following and wayfinding in humans. Neuron, 37, 877–888. Heckers, S., Zalezak, M., Weiss, A. P., Ditman, T., & Titone, D. (2004). Hippocampal activation during transitive inference in humans. Hippocampus, 14, 153–162. Henson, R. N., Cansino, S., Herron, J. E., Robb, W. G., & Rugg, M. D. (2003). A familiarity signal in human anterior medial temporal cortex? Hippocampus, 13, 301–304. Henson, R. N., Rugg, M. D., Shallice, T., Josephs, O., & Dolan, R. J. (1999). Recollection and familiarity in recognition memory: An

c28.indd 565

event- related functional magnetic resonance imaging study. Journal of Neuroscience, 19, 3962–3972.

O’Reilly, R. C., & Rudy, J. W. (2001). Conjunctive representations in learning and memory: Principles of cortical and hippocampal function. Psychological Reviews, 108, 311–345. Otto, T., & Eichenbaum, H. (1992). Complementary roles of orbital prefrontal cortex and the perirhinal-entorhinal cortices in an odor-guided delayed non-matching to sample task. Behavioral Neuroscience, 106, 763–776. Parks, C. M., & Yonelinas, A. P. (2007). Moving beyond signal detection models: Comment on Wixted. Psychological Review, 114, 188–202. Pihlajamaki, M., Tanila, H., Kononen, M., Hanninen, A., Soininen, H., & Aronen, H. J. (2004). Visual presentation of novel objects and new spatial arrangements of objects differentially activates the medial temporal lobe areas in humans. European Journal of Neuroscience, 19, 1939–1949. Preston, A., Shrager, Y., Dudukovic, N. M., & Gabrieli, J. D. E. (2004). Hippocampal contribution to the novel use of relational information in declarative memory. Hippocampus, 14, 148–152. Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005, June 23). Invariant visual representation by single neurons in the human brain. Nature, 435(7045), 1102–1107. Ranganath, C., Yonelinas, A. P., Cohen, M. X., Dy, C. J., Tom, S. M., & D’Esposito, M. (2003). Dissociable correlates of recollection and familiarity within the medial temporal lobes. Neuropsychologia, 42, 2–13. Rivard, B., Li, Y., Lenck-Santini, P.-P., Poucet, B., & Muller, R. U. (2004). Representation of objects in space by two classes of hippocampal pyramidal cells. Journal of General Physiology, 124, 9–25.

8/18/09 6:26:21 PM

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Scoville, W. B., & Milner, B. (1957). Loss of recent memory after bilateral hippocampal lesions. Journal of Neurology, Neurosurgery, and Psychiatry, 20, 11–12.

Wirth, S., Yanike, M., Frank, L. M., Smith, A. C., Brown, E. N., & Suzuki, W. A. (2003, June 6). Single neurons in the monkey hippocampus and learning of new associations. Science, 300, 1578–1581.

Shelton, A. L., & Gabrieli, J. D. E. (2002). Neural correlates of encoding space from route and survey perspectives. Journal of Neuroscience, 22, 2711–2717.

Witter, M. P., Wouterlood, F. G., Naber, P. A., & Van Haeften, T. (2000). Anatomical organization of the parahippocampal-hippocampal network. Annals of the New York Academy of Science, 911, 1–24.

Squire, L. R., Stark, C. E. L., & Clark, R. E. (2004). The medial temporal lobe. Annual Review of Neuroscience, 27, 279–306.

Wixted, J. T. (2007). Dual process theory and signal detection theory of recognition memory. Psychological Review, 114, 152–176.

Suzuki, W. A., & Amaral, D. G. (1994). Perirhinal and parahippocampal cortices of the macaque monkey: Cortical afferents. Journal of Comparative Neurology, 350, 497–533.

Wixted, J. T., & Stretch, V. (2004). In defense of the signal detection interpretation of remember/know judgments. Psychonomic Bulletin and Review, 11, 616–641.

Suzuki, W. A., & Eichenbaum, H. (2000). The neurophysiology of memory. Annals of the New York Academy of Science, 911, 175–191.

Wood, E., Dudchenko, P. A., & Eichenbaum, H. (1999, February 18). The global record of memory in hippocampal neuronal activity. Nature, 397, 613–616.

Suzuki, W. A., Zola-Morgan, S., Squire, L. R., & Amaral, D. G. (1993). Lesions of the perirhinal and parahippocampal cortices in the monkey produce long-lasting memory impairment in the visual and tactual modalities. Journal of Neuroscience, 13, 2430–2451. Tulving, E. (2002). Episodic memory: From mind to brain. Annual Review Psychology, 53, 1–25. Uncapher, M. R., Otten, L. J., & Rugg, M. D. (2006). Episodic encoding is more than the sum of its parts: An fMRI investigation of multifeatural contextual encoding. Neuron, 52, 547–556. Van Elzakker, M., O’Reilly, R. C., & Rudy, J. W. (2003). Transitivity, flexibility, conjunctive representations, and the hippocampus: Pt. I. An empirical analysis. Hippocampus, 13, 334–340.

Wood, E., Dudchenko, P. A., Robitsek, R. J., & Eichenbaum, H. (2000). Hippocampal neurons encode information about different types of memory episodes occurring in the same location. Neuron, 27, 623–633. Yonelinas, A. P. (2001). Components of episodic memory: The contribution of recollection and familiarity. Philosophical Transcripts of the Royal Society of London: Biological Sciences, 356, 1363–1374. Yonelinas, A. P. (2002). The nature of recollection and familiarity: A review of 30 years of research. Journal of Memory and Language, 46, 441–517.

Wais, P. E., Wixted, J. T., Hopkins, R. O., & Squire, L. R. (2006). The hippocampus supports both the recollection and the familiarity components of recognition memory. Neuron, 49, 459–466.

Yonelinas, A. P., Kroll, N. E., Quamme, J. R., Lazzara, M. M., Sauve, M. J., Widaman, K. F., et al. (2002). Effects of extensive temporal lobe damage or mild hypoxia on recollection and familiarity. Nature Neuroscience, 5, 1236–1241.

Waltz, J. A., Knowlton, B. J., Holyoak, K. J., Boone, K. B., Mishkin, F. S., Sanbtos, M. M., et al. (1999). A system for relational reasoning in human prefrontal cortex. Psychological Science, 10, 119–125.

Yonelinas, A. P, Otten, L. J., Shaw, K. N., & Rugg, M. D. (2005). Separating the brain regions involved in recollection and familiarity in recognition memory. Journal of Neuroscience, 25, 3002–3008.

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Chapter 29

Psychological and Neural Mechanisms of Short-Term Memory CINDY LUSTIG, MARC G. BERMAN, DEREK EVAN NEE, RICHARD L. LEWIS, KATHERINE SLEDGE MOORE, AND JOHN JONIDES

To comprehend this sentence, you must hold the beginning phrase in mind while reading and processing the rest of the words. The ability to remember and process information over a short time is essential to almost any activity, making short-term memory essential for cognition. In this chapter, we integrate psychological constructs of short-term memory that are drawn from behavioral data with their likely neural bases, especially as revealed by studies of patients and studies that use neuroimaging. Our discussion is organized around three questions that any account of short-term memory must address:

3. What causes forgetting? A complete theory of shortterm memory must describe how information learned only seconds ago can be forgotten. We consider the behavioral and neurophysiological evidence for the two dominant accounts of forgetting (interference and decay) and we suggest a possible mechanism for short-term forgetting that may underlie both proposed accounts. After addressing these questions, we sketch out a model that illustrates the links between psychological constructs and neural structures as an item moves through the stages of short-term memory from initial perception, maintenance over time and in the face of interfering information, and ultimate retrieval back into the focus of attention. To presage that model, we argue that short-term memory exhibits the following properties:

1. What is the structure of short-term memory? A proper theory must describe an architecture that implements the short-term storage of representations. The dominant answer to this question has long been a model consisting of short-term storage buffers that are coordinated by a central executive and that are dissociable from long-term storage. Recently, there has been a shift toward models that do not distinguish short- and longterm stores. Instead, these models posit that short-term memory consists of a focus of attention that operates on perceptual and long-term memory representations. Our review focuses on these recent models and their likely neural underpinnings. 2. What processes operate on the stored information? A proper theory must articulate the processes that create and operate on representations, and how these processes can be implemented within the structure described. These processes may include encoding and maintenance operations, shifts of the attentional focus, and retrieval of items into the focus of attention. Although rehearsal is often colloquially associated with short-term memory, we argue that it represents a strategic use of retrieval rather than a primary process.

• Short-term memory consists of the temporary activation of long-term memory or perceptual representations. • This temporary activation is severely limited to at most four representations. • There are elementary processes that operate on these representations to encode, maintain, and retrieve them. • Forgetting is largely accounted for by interference among competing representations. STRUCTURE OF SHORT-TERM MEMORY Classic Model: Short-Term and Long-Term Memory as Separate Stores Any discussion of short-term memory (STM) must begin with the highly influential model developed by Baddeley and colleagues (e.g., Baddeley, 1986, 1992; Baddeley & Hitch,

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1974; Repov & Baddeley, 2006). This model is the prototypical example of multistore models of short-term memory. The defining feature of these models is that they describe STM as separate from long-term memory (LTM). That is, information held in mind over the course of a few seconds is stored separately from information held over the course of long periods of time. Other common features include the separation of STM into different buffers based on information modality, and the separation between storage buffers and the executive control processes that coordinate the buffers and operate on the material within them. Figure 29.1 illustrates Baddeley’s working memory model (Baddeley, 2000; Baddeley & Hitch, 1974) and the brain structures that have been linked to each component (Smith & Jonides, 1999). Fundamental components of the model include the short-term storage buffers, which are different for different types of information and from long-term storage. The phonological loop is assumed to hold information that can be rehearsed verbally (e.g., letters, digits). A visuospatial sketchpad is assumed to maintain visual information and can be further fractionated into visual/object and spatial stores (Repov & Baddeley, 2006; Smith et al., 1995). An episodic buffer that draws on the other buffers and LTM has been added to account for the retention of multimodal information (Baddeley, 2000). A separate central executive is responsible for working memory processes that require operations on the items stored in the buffers. This central executive is also thought to be responsible for coordinating the interplay among the various buffers and their interactions with LTM. The earliest evidence for buffers that vary by modality came from studies showing that secondary verbal tasks interfered with verbal STM but not visual STM, and vice versa (e.g., Brooks, 1968; den Heyer & Barrett, 1971). This double dissociation implied uniquely verbal processes

for verbal STM, and uniquely visual processes for visual STM, arguing for separate stores. More recent neuroimaging research has further investigated the neural correlates of the reputed independence of STM buffers. Verbal STM has been shown to rely primarily on left inferior frontal and left parietal cortices, spatial STM on right posterior dorsal frontal and right parietal cortices, and object/visual STM on left inferior frontal, left parietal, and left inferior temporal cortices (e.g., Awh et al., 1996; Jonides et al., 1993; Smith & Jonides, 1997; see review by Wager & Smith, 2003). Verbal STM shows a marked left-hemisphere preference, whereas spatial and object STM can be distinguished mainly by a dorsal versus ventral separation in posterior cortices (consistent with Ungerleider & Haxby, 1994; see Baddeley, 2003, for an account of the function of these regions in the service of STM). These neural dissociations provide further evidence for separable short-term stores, and they are illustrated as well in Figure 29.2 from Fuster (2001) who laid out an argument for the separability of storage by modality and the interaction of these storage systems with frontal mechanisms. The idea of separate storage by modality is still wellaccepted, especially with regard to the posterior regions (e.g., left parietal for verbal, right parietal for spatial). However, the rest of Baddeley’s model—which argues for separable STM and LTM systems—is less well supported. Initially, the most compelling data to motivate a separation of STM from LTM came from brain-injured patients who seemed to show a double dissociation between the two systems. Patients with parietal and temporal lobe damage showed impaired short-term phonological capabilities but

Visual Associated brain regions: right posterior dorsal frontal right parietal cortices

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Figure 29.1 Baddeley’s working memory model.

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Figure 29.2 Cortical interactions in working memory. Note: Fuster emphasized the reciprocal and reentrant connections between the prefrontal cortex and sensory regions in working memory; note that similar connectivity applies in long-term memory. From “The Prefrontal Cortex: An Update: Time Is of the Essence,” by J. M. Fuster, 2001, Neuron, 30, p. 329. Reprinted with permission.

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intact long-term memory (Shallice & Warrington, 1970; Vallar & Papagano, 2002). Conversely, patients with medial temporal lobe (MTL) damage were often claimed to demonstrate impaired long-term memory but preserved short-term memory (e.g., Baddeley & Warrington, 1970; Scoville & Milner, 1957). However, some research suggests that MTL patients’ real problem may be forming new associations and bindings, a process preferentially tapped by long-term episodic memory tests, but that can also be endemic in STM tests. For example, if the task requires associating an item with a particular spatial location, these patients show profound deficits even after very short delays (Olson, Page, Moore, Chatterjee, & Verfaellie, 2006). On the other side of the STM/LTM dissociation, patients with left perisylvian damage that results in STM deficits also have deficits in phonological processing in general, suggesting a deficit that extends beyond STM per se (e.g., Martin, 1993). Finally, functional neuroimaging data from healthy adults also suggest that STM and LTM have more commonalities than differences (e.g., Braver et al., 2001; Cabeza, Dolcos, Graham, & Nyberg, 2002; Ranganath & Blumenfeld, 2005). Thus, the view that STM and LTM are separable based on studies of patients is open to reinterpretation. Another argument for separate STM and LTM systems arose from early work using single-unit recordings that appeared to identify cortical regions specialized for STM, consistent with the assumption that the two types of memory are stored in separate areas of the brain. This work showed single-unit activity in dorsolateral prefrontal cortical regions (principal sulcus, inferior convexity) that was selectively responsive to memoranda during the delay (retention interval) of STM tasks. This delay activity was interpreted as evidence that these regions were the storage sites for STM (e.g., Funahashi, Bruce, & Goldman-Rakic, 1989; Fuster, 1973; Wilson, O’Scalaidhe, & GoldmanRakic, 1993; see Jacobsen, 1936, for preceding lesion work). However, the sustained activation of frontal cortex during the delay period does not necessarily mean that this region is a site of STM storage. As we review in the following section, many other regions of neocortex also show activation that outlasts the physical presence of a stimulus and provides a possible neural basis for STM storage. The alternative view that we promote is that prefrontal cortical involvement in STM reflects the operation of processes that guide the use (encoding, maintenance, and retrieval) of information that is primarily perceived and stored via posterior regions (the same areas of purported LTM storage). This view receives support from studies showing that prefrontal cortical involvement may not be necessary for STM except in the face of distraction (Malmo, 1942; Postle & D’Esposito, 1999). By contrast, patients with left temporo-parietal damage show deficits in

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phonological storage, regardless of the effects of interference (Vallar & Baddeley, 1984; Vallar & Papagano, 2002). One item on which multistore and unitary-store models agree is that central executive control processes are primarily implemented by prefrontal cortex (see, e.g., Figure 29.3). A meta-analysis of 60 functional neuroimaging studies indicated that increased demand for executive processing recruits the dorsolateral frontal cortex and posterior parietal cortex (Wager & Smith, 2003). By contrast, storage processes recruit predominately posterior areas in the primary and secondary association cortex. These results corroborate the evidence from lesion studies and support the distinction between storage and executive processing. Unitary-Store Models: Shared Representations in Perception, STM, and LTM Figure 29.4 illustrates several unitary-store models. The shared assumption of these models is that STM consists of a temporary activation of the same representations used for initial perception and LTM. As shown in Figure 29.4, the types of activation associated with STM may include both a privileged status in the focus of attention (most likely IFS

IFS

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Figure 29.3 (Figure C.29 in color section) Executive functions are largely localized in prefrontal cortex. Note: Each color indicates data from studies of a different type of executive process. Green  Response conflict; Pink  Task novelty; Yellow  Working memory load; Yellow  Working memory load; Red  Working memory delay; Blue  Perceptual difficulty. From “Common Regions of the Human Frontal Lobe Recruited by Diverse Cognitive Demands,” by J. Duncan and A. M. Owen, 2000, Trends in Neuroscience, 23, pp. 475–483. Adapted with permission.

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LTM with varying levels of activation

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Note: A: From “Working Memory and Focal Attention,” by B. McElree, 2001, Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, pp. 817–835. Adapted with permission. B: From “The Magical Number 4 in Short-Term Memory: A Reconsideration of

Mental Storage Capacity,” by N. Cowan, 2000, Behavioral and Brain Sciences, 24, pp. 87–185. Adapted with permission. C: From “Access to Information in Working Memory: Exploring the Focus of Attention,” by K. Oberauer, 2002, Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, pp. 411–421. Adapted with permission.

implemented by active firing of the neurons involved in the representation) and out-of-focus but highly activated representations within LTM (perhaps implemented by short-term plasticity and synchronization of spontaneous activity in the neurons composing the representation). As we elaborate in the following section, the major distinction among models within the unitary-store family concerns the size or capacity of the attentional focus. Early versions of unitary-store models (e.g., Anderson, 1983; Atkinson & Shiffrin, 1971; Hebb, 1949) fell out of favor during the predominance of the multistore account. Recent developments have called some of the assumptions of the multistore model into doubt (see Jonides et al., 2008; Postle, 2006, for a more detailed discussion of the issues). At the same time, unitary-store models have been revived and elaborated (Anderson et al., 2004; Cowan, 1988, 1995, 2000; McElree, 2001; Oberauer, 2002; Verhaeghen, Cerella, & Basak, 2004, and others). When an item is initially perceived, it activates a distributed set of neurons throughout the brain regions involved in processing its different feature components: For a visually presented item, these would include some neurons in V4 that code color information about the object, some in the inferotemporal cortex that code shape information, and so on. Medial temporal lobe structures are involved in processing contextual information, including those aspects later needed for episodic memory. Depending on consolidation processes, the coordinated pattern of activation among the different feature components ultimately results in synaptic changes and long-term storage. Where does STM fit into this picture? Our take on this question is most like Oberauer’s (2002; see Figure 29.4C). One representation is in the focus of attention, either as the

result of recent perception or retrieval from LTM. The network of neurons involved in this representation is actively firing in conjunction with frontal and parietal networks involved in attention to that representation. This representation is immediately available and accessible; functionally, this means that it may be used to guide immediate action. A limited set of other recently perceived/retrieved representations are not in the focus, but maintain a relatively high level of activation and availability, perhaps implemented by shortterm plasticity mechanisms such as increased coordination of spontaneous activity (Destexhe & Contreras, 2006; Sussillo Toyoizumi, & Maass, 2007). This is the state that Oberauer (2002) terms “the region of direct access” (region here does not refer to brain region but a functional state), where roughly three items are in a heightened state of activation and can be accessed faster than those in the activated portions of LTM can, but slower than the one in the focus of attention. In short, unitary-store models posit that perception, STM, and LTM use the same underlying representations, but the state of those representations (active firing, short-term plasticity, long-term plasticity) may differ depending on which of these functions is involved. This contrasts with multistore models, which posit STM buffers that are separate from LTM storage. Unitary- and multistore models agree that posterior regions are clearly differentiated by information type (e.g., auditory, visual, spatial). However, they differ in their view of frontal activations: Unitary-store models view these as primarily related to processes operating on the representations, especially those involved in selecting a representation for the focus of attention and keeping it there. From the multistore perspective, frontal activations are coding the content of the representation itself—they are the site of the STM buffers. The current weight of evidence, from

Figure 29.4 Unitary STM models.

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Structure of Short-Term Memory 571 IPS tracks wm load to 4 VSTM

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Figure 29.5 (Figure C.30 in color section) In a visual shortterm memory (VSTM) task, the intraparietal sulcus (IPS) tracks capacity up to four items. Note: Pictured on the left are IPS regions (left and right) activated during the memory task. Pictured on the right is a timecourse of IPS activity

neuroimaging studies, lesion data, and modeling work (see reviews by Damasio, 1989; McClelland, McNaughton, & O’Reilly, 1995; Reuter-Lorenz & Jonides, 2007), favors the unitary-store models.

Controversies over Capacity Regardless of whether one subscribes to multi- or unitarystore models, an important question is how much information can be held within a storage buffer (multistore models) or the focus of attention (unitary-store models). Multistore models describe capacity limits as dependent on the individual buffers, in particular the speed with which information can be rehearsed in that buffer versus the speed with which information is forgotten (Baddeley, 1986, 1992; Repov & Baddeley, 2006). In the verbal domain, for example, it has been shown that approximately two seconds’ worth of verbal information can be recirculated successfully (e.g., Baddeley, Thomson, & Buchanan, 1975). In unitary-store models, there may be some constraints imposed by the material-specific aspects of the representation, but the critical questions surround the capacity of the focus of attention. Even Miller’s (1956) classic paper acknowledged that the traditional estimate of “seven plus or minus two” is too large because it is based on studies that allowed participants to engage in processes of rehearsal and chunking, and therefore reflects contributions of the focus of attention, selectively activated representations, and LTM (see also, Cowan, 2000; Waugh & Norman, 1965.) Current models differ on whether the capacity is about four items (Cowan, 2000) or only one (Garavan, 1998; McElree, 2001; Verhaeghen & Basak, in press). Figure 29.4 shows the slight variations among these unitary STM models; we next review some of the evidence and issues surrounding this capacity debate.

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following stimulus presentation. Different patterned lines indicate the number of items to be remembered (1, 2, 3, 4, 6, or 8) on each trial. From “Capacity Limit of Visual Short-Term Memory in Human Posterior Parietal Cortex,” by J. J. Todd and R. Marois, 2004, Nature, 428, pp. 751–754. Adapted with permission.

Behavioral and Neural Evidence for the Magic Number 4 Cowan (2000) reviewed an impressive body of evidence leading to his conclusion that the capacity limit is four items, plus or minus one (e.g., Sperling, 1960; see Cowan’s table 1). An important line of evidence comes from change-detection and other tasks that do not require the serial recall of individual items that may lead to interference in output and therefore underestimate capacity. For example, Luck and Vogel (1997) presented subjects with 1 to 12 colored squares in an array. After a blank interval, another array of squares was presented, in which one square may have changed color. Subjects were to respond whether the arrays were identical. In these experiments and others (e.g., Pashler, 1988), there are sharp drop-offs in performance after approximately four items. Electrophysiological and neuroimaging studies also support the idea of a four-item capacity limit. The first such report was by Vogel and Machizawa (2004) who recorded event-related potentials (ERPs) from subjects as they performed a visual change-detection task. ERPs recorded shortly after the onset of the retention interval in this task indicated a negative-going wave over parietal and occipital sites that persisted for the duration of the retention interval and was sensitive to the number of items held in memory. Importantly, this signal plateaued when the array size reached between three and four items. The amplitude of this activity was strongly correlated with estimates of each subject’s memory capacity and was less pronounced on incorrect than correct trials, indicating that it was strongly related to performance. Subsequent functional magnetic resonance imaging (fMRI) studies have observed similar load- and accuracy-dependent activations, especially in intraparietal and intraoccipital sulci (see Figure 29.5:

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were retrieved at similar rates (which McElree deems a measure of accessibility, the speed of retrieval) despite differing in accuracy (which McElree describes as a measure of availability, the probability of successful retrieval). Oberauer (2002) suggested a compromise solution to the “one versus four” debate. In his model, up to four items can be highly accessible, but only one of these items can be in the focus of attention. This model is similar to that of Cowan (2000), but adds the assumption that an important method of accessing short-term memories is to focus attention on one item, depending on task demands. Thus, in tasks that serially demand attention to several items (such as those of Garavan, 1998, or McElree, 2001), the mechanism that accomplishes this involves changes in the focus of attention among temporarily activated representations in LTM.

Todd & Marois, 2004, 2005). These particular regions have been implicated by others (e.g., Yantis & Serences, 2003) in the control of attentional allocation, supporting the idea that one rate-limiting step in STM capacity has to do with the allocation of attention (Cowan, 2000; McElree, 1998, 2001; Oberauer, 2002). Evidence for More Severe Limits on Focus Capacity Others argue for a much more limited capacity of just one item (e.g., Garavan, 1998; McElree, 2001; Verhaeghen & Basak, in press). This estimate is based on studies using a combination of response time and accuracy as measures of performance. For example, Garavan (1998) required subjects to keep two running counts in STM—one for triangles and one for squares—as shape stimuli appeared one after another in random order. This task can be seen in Figure 29.6. Subjects controlled their own presentation rate, and Garavan measured the time spent processing each figure before moving on to the next. Responses to a figure of one category (e.g., a triangle) that followed a figure from the other category (e.g., a square) were fully 500 ms longer than responses to the second of two figures from the same category (e.g., a triangle followed by another triangle). These findings suggested that attention could be focused on only one internal counter in STM at a time. Switching attention from one counter to another incurred a substantial cost in time. Other evidence comes from McElree (1998) who found that the last item in a list was retrieved substantially faster than other items, suggesting that it was still in the focus. Other items were retrieved at a rate that was substantially slower than the last item. Those other items, however,

Alternatives to Capacity Limits Based on Number of Items Some researchers disagree with fixed item-based limits, in part because these limits seem mutable. For example, practice may improve subjects’ ability to use processes such as chunking to allow greater capacities by tying together individual items into a single unit (McElree, 1998; Verhaeghen et al., 2004, but see Oberauer, 2006). However, proponents of the fixed-capacity view might retort that practice alters the amount of information that can be coded into a single representation, not the total number of representations that can be held in STM (Miller, 1956). Another attack on fixed-capacity views comes from questioning the assumption that items are the appropriate unit for expressing capacity limits. Wilken and Ma (2004) demonstrated that a signal-detection account of STM, in

Square counter: 1 Triangle counter: 0 Trial 1 500 ms counter switch cost

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Figure 29.6 The counter-updating task.

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Three Core Processes of STM: Encoding, Maintenance, and Retrieval

which STM capacity is primarily constrained by noise, better fit behavioral data than an item-based fixed capacity model. Recent data from change-detection tasks suggest that object complexity (Eng, Chen, & Jiang, 2005) and similarity (Awh, Barton, & Vogel, 2007) play an important role in determining capacity. Xu and Chun (2006) offer neuroimaging evidence that may reconcile the item-based and complexity accounts: In a change-detection task, they found that activation of inferior parietal sulcus tracked a capacity limit of four, but nearby regions were sensitive to the complexity of the memoranda, as were the behavioral results. Building on these findings, we suggest a new view of capacity. The fundamental idea that attention can be allocated to one piece of information in memory is correct, but the definition of what that one piece is needs to be clarified. It cannot be that just one physical item is in the focus of attention at any one time, because if that were so, hardly any computation would be possible. How could one add 34, for example, if attention could be allocated only to the “3” or the “4” or the “” operation? We propose that what attention focuses on is what is bound together into a single functional context, whether that context is defined by time, space, or some other stimulus characteristic such as similarity or task relevance. By this account, attention can be placed on the whole problem “34,” allowing relevant computations to be made. Put another way, the critical unit is at the level of representation as perceived by the subject. This is not necessarily the same as the physical “item” presented by an experimenter. Chunking is one special case of a single representation holding multiple items. One can also think of more everyday examples: In considering this chapter, the level of representation could be the entire chapter, the current page, a single word, or a single letter. Letters are bound together by the functional context of a word, and so on. Complexity comes into play by limiting the number of subcomponents that can be bound into one functional context. This approach has the advantage of permitting novel relations to be established among familiar items to form new representations. This addresses one of the criticisms of the purest form of the unitary models: If STM is strictly limited to an activated portion of LTM, then the system can never entertain new thoughts. Summary What is the structure of STM? We favor the unitary-store model, in which the representational bases for perception, STM, and LTM are identical. That is, the same neocortical representations that are the repository of semantic knowledge are activated when a piece of information is main-

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tained for the short-term, whether that activation is due to perceiving that information or retrieving it from LTM (Wheeler, Peterson, & Buckner, 2000). Different regions of neocortex represent different types of information (e.g., verbal, spatial), and it is therefore to be expected that STM is also organized by information-type. Empirically, STM often cashes out as the four or so items whose representations can be temporarily activated and processed simultaneously. However, this item-based limit is flexible and dependent on factors such as complexity and experience. The critical feature of unitary-store models is the severely limited focus of attention. Although the capacity of that focus is still under debate, we believe it is one representation, although this representation may consist of several items bound together into one functional context. For the relatively simple stimuli used in laboratory experiments, this limit also appears to be around four items, suggesting that it may be related to the factors that place a similar four-item limit on subitizing, another attention-demanding process.

THREE CORE PROCESSES OF STM: ENCODING, MAINTENANCE, AND RETRIEVAL How does this structure work—that is, what are the processes of STM? Many have been suggested, including rehearsal, attention shifts, updating, and interference resolution. However, we argue that these complex processes represent combinations or special cases of three basic types, which govern the transition of memory representations into and out of the focus of attention: Encoding processes select sensory information and transform it into the representation that occupies the focus, maintenance processes keep the representation in the focus and protect it from interference or decay, and retrieval processes bring information from the past back into the focus. Encoding Items into the Focus Although detailed accounts of encoding processes are usually left to theories of perception, most accounts of STM make several assumptions about how encoding occurs. First, perceptual information is assumed to have immediate but capacity-limited access to the focus of attention. Perceptual information can serve as the object of the focus just as information from the past does. Several of the experiments cited by Cowan (2000) as evidence for a capacity of four involved representations of objects presented currently or less than a second ago. These include visual tracking experiments (Pylyshyn et al., 1994), enumeration

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(Trick & Pylyshyn, 1993), and whole-report of spatial arrays and spatiotemporal arrays (Darwin, Turvey, & Crowder, 1972; Sperling, 1960). Similarly, in McElree’s (2006) and Garavan’s (1998) experiments, each incoming item in the stream of material (words or letters or objects) is assumed to be momentarily represented in the focus. Second, current theories assume that encoding new representations into the focus results in the displacement of other representations. For example, in McElree’s singleitem focus model, each incoming item has its turn in the focus and replaces the previous item. The work reviewed earlier showing performance discontinuities after the putative limit of STM capacity has been reached appears to support the idea of whole-item displacement (e.g., Cowan, 2000; Garavan, 1998; McElree, 2001; Oberauer, 2002). However, it is not clear how simple item-based displacement accounts for the effects of similarity or complexity on capacity estimates. One possibility is that these factors influence how items compete with each other for access to the focus. Another possibility is that complexity and similarity influence the set of featural components needed to represent an item, and items compete with each other for this limited feature-based representational resource. In other words, the more overlap there is between the patterns of activation that represent two items (in V4, inferotemporal cortex, fusiform gyrus, etc.), the more likely those items are to interfere with each other. We expand further on these ideas in the section on forgetting. Third, all current accounts assume that perceptual information does not have automatic, obligatory access to the focused state. Instead, given the severe limits on capacity, attentional control is required to ensure that task-relevant items are included in the focus, and task-irrelevant items are excluded. Postle (2005) found that while subjects maintained information in STM, increased activity in the dorsolateral prefrontal cortex during the presentation of distracting material was accompanied by a selective decrease in inferior temporal regions involved in object representation. This pattern suggests that prefrontal regions selectively modulated posterior perceptual areas to prevent incoming sensory input from disrupting the representation of task-relevant memoranda. Just as Postle (2005) found evidence to suggest that prefrontal activations prevent distracting sensory information from being encoded, we suggest that frontal and parietal areas are responsible for selective attention toward relevant inputs. This involves biasing posterior sensory regions toward important target stimuli. As these items are encoded, the medial temporal lobe binds each item to a functional context (e.g., a temporal and/or spatial context). Simultaneously, short-term synaptic plasticity works across cortical areas to maintain the representation

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(including contextual information) even once it is no longer active in the attentional focus. Zucker and Regehr (2002) identified at least three distinct plasticity mechanisms which begin to operate on this time scale (tens of milliseconds), and which together are sufficient to produce memories lasting several seconds. (For the use of this mechanism in a prominent neural network model of STM, see Burgess & Hitch, 1999, 2005, 2006). Maintaining Items in the Focus Once an item is in the focus of attention, what keeps it there? If the item is in the perceptual present, the answer is clear: attentionally-modulated perceptual encoding. The more pressing question is: What keeps something in the cognitive focus when it is not currently perceived? For many neuroscientists, this is the central question of STM—how is information held in mind for the purpose of future action after the perceptual input is gone? Extensive evidence from both humans and nonhuman primates supports the idea that prefrontal-posterior circuits underlie active maintenance. The role of posterior perceptual regions is relatively clear; activity in these regions likely recapitulates the initial perceptual encoding of the representation, but what of the activation in prefrontal circuits? For example, consider the classical evidence, introduced earlier, that some neurons fire selectively during the delay period in delayed-match-to-sample tasks (e.g., Funahashi et al., 1989; Fuster, 1973). Does this activation (and its counterpart in human neuroimaging studies (e.g., Jha & McCarthy, 2000) suggest that a representation of the information is also held in the prefrontal cortex, or does it reflect some other process? Early interpretations of these frontal activations linked them directly to STM representations (Goldman-Rakic, 1987). By contrast, more recent theories suggest that they subserve attentional control processes that maintain representations in posterior areas (Ranganath, 2006; Ruchkin, Grafman, Cameraon, & Berndt, 2003). For example, maintenance operations may modulate perceptual encoding to prevent incoming perceptual stimuli from disrupting the focused representation in posterior cortex (Postle, 2005). Mechanistic descriptions of how maintenance might occur are found in computational neural-network models hypothesizing that prefrontal cortical circuits support attractors, which are selfsustaining activation patterns observed in certain classes of recurrent networks (Hopfield, 1982; Polk, Simen, & Lewis, 2002). A major challenge is to develop computational models that are able to engage in active maintenance of representations in posterior cortex while simultaneously processing, to some degree, incoming perceptual material (see Renart, Paraga, & Rolls, 1999, for one example).

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Three Core Processes of STM: Encoding, Maintenance, and Retrieval 5.0 4.0 3.0 d´

We thus adopt the following view of maintenance operations: prefrontal and parietal regions perform attentional control processes by signaling posterior sensory regions to continue a high level of activation for the representation that is currently in the focus of attention even after it is no longer physically presented. These control regions may differ by type of material (e.g., Smith & Jonides, 1997). The attentional focus on this representation protects it from interference and decay and keeps it in an immediately accessible state. Other representations that are maintained in STM, but not in the focus, are supported by short-term plasticity mechanisms that increase the coordination or connection weights between the features of these representations. As we describe next, stochastic variability in the neurons that make up these representations may eventually lead them to decay, and they are also vulnerable to competitive interference from each other, other items in memory, or incoming stimuli (Zucker & Regehr, 2002).

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Figure 29.7 Results from McElree and Dosher (1989). Note: Observed average d’values as a function of total processing time for serial positions one (SP1) through five (SP5). (Smooth functions are derived from the estimated parameters of exponential model fits.) From “Serial Position and Set Size in Short-Term Memory: Time Course of Recognition,” by B. McElree and B. A. Dosher, 1989, Journal of Experimental Psychology: General, 118, p. 357. Reprinted with permission.

Retrieval into the Focus Most major STM theories do not include detailed treatments of retrieval, although the limited-focus models assume that there is some way of bringing information from LTM into the focus. This process can be labeled “retrieval” (c.f., McElree, 2006; Sternberg, 1966), but that label does not imply the spatial metaphor of moving items from one store to another. Instead, it is important to keep in mind our assumption that the same underlying neural representations subserve both STM and LTM, and that the question is whether that representation is currently in the highly activated state that constitutes the focus. There is now considerable evidence, mostly from mathematical models of behavioral data, that STM retrieval of item-information is a rapid, parallel, content-addressable process. Early models of STM retrieval (e.g., Sternberg, 1966) postulated a serial search process. However, current models favor a parallel search process because it can better account for reaction time data such as those shown in Figure 29.7. McElree and Dosher (1989) manipulated the response deadline in a standard item-recognition task, in which participants are presented with a rapid sequence of to-be-remembered verbal items (e.g., letters or digits), followed by a probe item. The task was to identify whether the probe was a member of the memory set. The speed at which an item was retrieved was thought to measure its accessibility (exhibited in Figure 29.7 by the rise to asymptote), whereas accuracy measured availability (exhibited in Figure 29.7 by the asymptote). As described previously, the last item (which is the most recent) is accessed more quickly than the others, suggesting that it is already in the focus. The other items are accessed at a uniform rate,

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suggesting a parallel search among them to bring them into the focus (see also Hockley, 1984; see review by McElree, 2006). What are the neural underpinnings of STM retrieval, and does it differ—if at all—from LTM retrieval? As described earlier, similar activations in posterior perceptual regions support the idea that STM and LTM both operate on the same neural representations—that is, that they are similar in structure with regard to how representations are stored. STM and LTM may also be similar in process, at least when it comes to retrieval: The retrieval processes described for STM are isomorphic with those posited for LTM (e.g., Anderson et al., 2004; Gillund & Shiffrin, 1984; Murdock, 1982; Plaut, 1997). This isomorphism logically follows from the idea that out-of-focus STM representations are simply LTM representations in a special state of activation, which may include short-term plasticity. This unification is one of the principal theoretical virtues of the recent STM retrieval models such as those championed by McElree (2006). Extensive studies have delineated a network of medial temporal lobe (MTL) regions, lateral prefrontal regions, and anterior prefrontal regions active in long-term retrieval tasks (e.g., Buckner, Koutstaal, Schacter, Wagner, & Rosen, 1998; Cabeza & Nyberg, 2000; Fletcher & Henson, 2001). As described earlier, although MTL structures were originally thought not to play a role in STM, recent work has shown that they come into play when the task demands remembering novel information or making associations, regardless of the time scale.

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Like the MTL, the frontal cortex is used similarly in retrieval for STM and LTM, as evidenced in numerous neuroimaging studies (see Figure 29.8). For example, eventrelated studies of a standard STM probe-recognition task find activations in lateral prefrontal regions (e.g., D’Esposito, Postle, Jonides, & Smith, 1999; D’Esposito & Postle, 2000) and anterior prefrontal regions (Badre & Wagner, 2005) often implicated in LTM retrieval. Some of these studies used retention intervals that were somewhat longer than the typical behavioral STM task, making them vulnerable to the criticism that the activations in fact represented LTM retrieval. However, a meta-analysis of studies that involved bringing very recently presented items into the focus of attention likewise found specific involvement of the lateral and anterior prefrontal cortex (Johnson et al., 2005). Therefore, these regions appear to be involved in retrieval, regardless of time scale. Even stronger evidence derives from recent imaging studies that directly compare short- versus long-term retrieval tasks using within-subjects designs. The two types of tasks activate highly overlapping regions in the dorsolateral, ventrolateral, and anterior prefrontal cortex (Cabeza, Dolcos, Graham, & Nyberg, 2002; Ranganath, Johnson, D’Esposito, 2003; Talmi, Grady, Goshen-Gottstein, & Moscovitch, 2005). In some cases, STM and LTM tasks involve the same regions but differ in the relative amount of activation shown within these regions. For example, Cabeza et al. (2002) reported similar engagement of medial temporal regions in both types of task, but greater anterior and ventrolateral activation in the long-term episodic tasks. However, Talmi et al. (2005) reported greater activation in both medial temporal and lateral frontal cortices for recognition probes of the earliest items in a 12-item list (where LTM would be more prominent) versus the last or secondBA 47/45

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Figure 29.8 (Figure C.31 in color section) Overlap between regions involved in short-term memory (red) and long-term memory (blue). Note: From “Similarities and Differences in the Neural Correlates of Episodic Memory Retrieval and Working Memory,” by R. Cabeza, F. Dolcos, R. Graham, and L. Nyberg, 2002, NeuroImage, 16, pp. 317–330. Adapted with permission.

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to-last items (where STM would be more prominent). This discrepancy might be explained if items at the end of the list were still in the focus of attention, and thus did not require cue-based retrieval processes. Notably, the end-oflist items preceded the probe by less than 2 seconds, within the time span classically suggested for verbal STM (e.g., Baddeley et al., 1975). Summary The bulk of the neuroimaging evidence points to the conclusion that the recruitment of frontal and medial temporal regions depends on whether the information is currently in or out of focus, not whether the task nominally tests short or long time spans (see Sakai, 2003, for a more extensive review) because these regions are not involved in memory storage per se. Thus, MTL regions will increase activation in response to items in a typical LTM or STM task if these items have fallen out of the focus of attention in order to retrieve their functional context. Likewise, frontal regions will increase activation when any item (from an LTM or STM task) is being retrieved into the focus of attention during rehearsal or in preparation to make a response. Frontal regions are thought to perform several operations during retrieval including initiating retrieval, accessing stored representations, and selecting among competing representations (Badre & Wagner, 2007; Sakai, 2003). Relationship of STM Processes to Rehearsal Rehearsal intuitively seems like the prototypical STM process. However, many formal and computational theories of STM exclude rehearsal from their list of core processes (e.g., Anderson & Matessa, 1997; Burgess & Hitch, 2006; Meyer & Kieras, 1997). Cowan (2000) describes evidence that first-grade children do not use verbal rehearsal strategies, but nevertheless have measurable STM capacities. In fact, Cowan (2000) uses young children’s failure to use rehearsal to argue that their performance is indicative of the fundamental capacity limits of STM. We take the view that rehearsal is simply a controlled sequence of retrievals and re-encodings of items into the focus of attention (c.f., Baddeley, 1986; Cowan, 1995). The theoretical force of this idea becomes apparent when it is coupled with our other assumptions about the structures and processes of the underlying STM architecture. We briefly sketch here two interesting sets of empirical predictions that follow from this view. When coupled with the idea of a single-item focus, the assumption that rehearsal is a sequence of retrievals into the focus makes a clear prediction: A just-rehearsed item should display the same retrieval dynamics as a justperceived item. This prediction was directly tested by

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Why Do We Forget?

McElree (2006) using a version of his response-deadline recognition task. A retention interval occurred between the list-presentation and the probe, and subjects were trained to rehearse the list at a particular rate during that interval. Knowing the rate at which subjects rehearsed made it possible to know when each item was rehearsed, and thus when it was hypothetically re-established in the focus. The results were compelling: A just-rehearsed item showed the same fast retrieval dynamics that typify a just-perceived item in experiments without a retention interval (see previous section). In other words, the difference in speed-accuracy tradeoff functions for in-focus versus out-of-focus items was apparent regardless of whether the dichotomy was established by internally controlled rehearsal or externally controlled perception. The assumption that rehearsal is a controlled strategy also yields interesting predictions. If rehearsal is the controlled composition of more primitive STM processes, then rehearsal should activate the same brain circuits as the primitive processes, along with additional (frontal) circuits associated with their control. In other words, there should be overlap of rehearsal with brain areas subserving retrieval and initial perceptual encoding. Likewise, there should be control areas distinct from those of the primitive processes. Both predictions receive support from neuroimaging studies. The first prediction is broadly confirmed: There is now considerable evidence for the re-activation of areas associated with initial perceptual encoding in tasks that require rehearsal (see Jonides, Lacey, & Nee, 2005, for a recent review; note also that there is evidence for reactivation in LTM retrieval: Wheeler et al., 2000, 2006). The second prediction—that rehearsal engages additional control areas beyond those participating in maintenance, encoding, and retrieval— receives support from two effects. One is that verbal rehearsal engages a set of frontal structures associated with articulation and its planning: supplementary motor, premotor, inferior frontal, and posterior parietal areas (e.g., Chein & Fiez, 2001; Jonides, Smith, Marshuetz, Koeppe, & Reuter-Lorenz, 1998; Smith & Jonides, 1999). The other is that spatial rehearsal engages attentionally mediated occipital regions, suggesting rehearsal processes that include retrieval of spatial information (Awh, Jonides, & Reuter-Lorenz, 1998; Awh & Jonides, 2001). Summary There is substantial evidence supporting the idea that rehearsal is a process composed of more fundamental STM processes, namely retrieval and encoding. In addition, a just-perceived item is functionally equivalent to a just-rehearsed item, showing that the focus of attention has similar properties in these two cases.

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WHY DO WE FORGET? Forgetting in STM is a vexing problem: What accounts for failures to retrieve something encoded just seconds ago? There are two major explanations for forgetting, often placed in opposition: time-based decay and similaritybased interference. Next, we describe some of the major findings in the literature related to each of these explanations, and we suggest that they may ultimately result from the same underlying principles. Decay Theories: Intuitive but Problematic The central claim of decay theory is that as time passes, information in memory erodes, and so it is less available for later retrieval. This explanation has strong intuitive appeal. However, theories of STM that rely on decay to explain forgetting face two strong criticisms. First, experiments attempting to demonstrate decay can seldom eliminate confounds and alternative explanations. Second, most psychological theories that posit decay do not include a mechanistic explanation of how it might occur. Without such an explanation, it is difficult to see decay theories as any more than a restatement of the phenomenon. Next we review the debates on this issue and ultimately suggest a possible mechanism for decay. Retention-Interval Confounds: Controlling for Rehearsal and Interference The classic Brown-Peterson procedure (J. Brown, 1958; Peterson & Peterson, 1959) illustrates many of the difficulties in providing evidence for decay. In this procedure, participants were asked to learn consonant trigrams (e.g., DPW). Each trigram was followed by a retention interval during which participants counted backward to prevent rehearsal, followed by their attempt to recall the trigram. Performance decreased as retention interval increased, apparently providing good evidence for time-based decay. However, Keppel and Underwood (1962) showed that almost no forgetting occurs for the earliest trials, regardless of the retention interval. The effects of the retention interval became apparent only after several trials had passed, suggesting that proactive interference from previous memoranda was the major mechanism of forgetting, and that it was this influence that increased over time. A major problem in testing decay theories is controlling for what occurs during the retention interval, especially with human subjects. One common method is to attempt to prevent rehearsal by requiring subjects to perform another attention-demanding task during the interval— for example, requiring them to count backwards during the Brown-Petersen task. However, the difficulty of the

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retention-interval task does not appear to influence the amount of forgetting that occurs, raising the possibility that the retention-interval task relies on a different resourcepool than does the primary memory task, and thus may not ultimately be effective in preventing rehearsal (Roediger, Knight, & Kantowitz, 1977). Another problem is that most tasks that fill the retention interval require subjects to use STM. This could lead to active displacement of items from the focus of attention (e.g., McElree, 2001). Thus, the problem with retentioninterval tasks is that they are questionable in preventing rehearsal of the to-be-remembered information, and they also introduce new, distracting information that may engage STM. Several attempts have been made to escape the rehearsal conundrum by using stimuli that are not easily converted to verbal codes (e.g., pure tones; Harris, 1952) or by varying the retention interval during implicit memory procedures, where participants do not know that their memory is being tested, and so they would have no reason to rehearse (McKone, 1995). These experiments provide some of the best behavioral evidence for decay, although they are still somewhat vulnerable to Keppel and Underwood’s (1962) criticism about prior trials. Another potential problem is that even if they are not deliberately rehearsing, participants’ brains and minds are not inactive during the retention interval (Raichle et al., 2001). There is increasing evidence that the processes ongoing during nominal “resting states” are related to memory, including STM (Hampson, Driesen, Skudlarski, Gore, & Constable, 2006). Spontaneous retrieval of other memories during the retention interval could interfere with memory for the experimental items. So, although experiments that reduce the influence of rehearsal provide some of the best evidence of decay, they are not definitive. What Happens Neurally during the Delay? Stimulus-associated neural activity usually declines during a retention interval. This decline seems like a prime candidate for a mechanism of decay. However, it has been more difficult than expected to show a relation between reduced neural activity and reduced memory. Single-cell results like those of Fuster (1973, 1995) are often cited as evidence for decay. In monkeys performing a delayed-response task, delay-period activity in inferotemporal cortex steadily declined over 18 seconds (see also, Pasternak & Greenlee, 2005). At a molar level, human neuroimaging studies often show delay-period activity in prefrontal and posterior regions, and this activity is often thought to support maintenance or storage (see review by Smith & Jonides, 1999). As reviewed earlier, it is likely that the posterior regions support storage, and that

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frontal regions support processes related to interferenceresolution, control, attention, response preparation, motivation, and reward. Consistent with the primate data, Jha and McCarthy (2000) found a general decline in activation in posterior regions over a delay period, which suggests some neural evidence for decay. However, this decline in activation was not obviously related to performance, which suggests two (not mutually exclusive) possibilities: (1) the decline in activation was not representative of decay, so it did not correlate with performance; or (2) these regions might not have been storage regions (but see Todd & Marios, 2004; Xu & Chun, 2006, for evidence more supportive of load sensitivity in posterior regions). The idea that neural activity decays also faces a serious challenge from the classic results of Malmo (1942), who found that a monkey with frontal lesions was able to perform a delayed response task extremely well (97% correct) if visual stimulation and motor movement (and therefore associated interference) were restricted during a 10-second delay. By contrast, in unrestricted conditions, performance was as low as 25% correct (see also, D’Esposito & Postle, 1999; Postle & D’Esposito, 1999). In summary, evidence for time-based declines in neural activity that would naturally be thought to be part of a decay process is mixed. Is There a Mechanism for Decay? At least two key empirical results (Harris, 1952; McKone, 1998) do seem to implicate some kind of time-dependent decay. If one assumes that decay happens, how might it occur? One possibility—perhaps most compatible with results like those of Malmo (1942)—is that what changes over time is not the integrity of the representation itself, but the likelihood that attention will be attracted away from it. This explanation is also compatible with the focus-ofattention view of STM. By this explanation, the representation within the focus does not decay. However, as more time passes, there is a greater likelihood that attention is attracted away from this representation and toward external stimuli or other memories. For recently presented items outside of the focus, decay may occur because of stochastic variability in the activity of the neurons that make up an item’s representation. The temporal synchronization of neuronal activity is an important part of the representation (e.g., Deiber et al., 2007; Jensen, 2006; Lisman & Idiart, 1995), and it is possible that being in the focus helps to maintain this synchrony. As time out of the focus increases, variability in the firing rates of individual neurons may cause them to fall increasingly out of synchrony, unless they are reset by rehearsal. By this hypothesis, as the neurons fall out of synchrony,

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Why Do We Forget?

the pattern that forms the representation becomes increasingly difficult to discriminate from surrounding neural noise. See Lustig, Matell, and Meck (2005) for an example that integrates neural findings with computational (Frank, Loughry, & O’Reilly, 2001) and behaviorally based (G. D. A. Brown, Preece, & Hulme, 2000) models of STM. Interference Theories: Comprehensive but Complex Interference effects play several roles in memory theory: First, they are the dominant explanation of forgetting. Second, some have suggested that STM capacity and its variation among individuals are largely determined by the ability to overcome interference (e.g., Hasher & Zacks, 1988; Unsworth & Engle, 2007). Finally, differential interference effects in short- and long-term memory have been used to justify the idea that they are separate systems, and common interference effects have been used to justify the idea that they are a unitary system. Interference theory has a problem opposite that of decay: It is comprehensive but complex (Crowder, 1976). The basic principles are straightforward. Items in memory compete, with the amount of interference determined by the similarity, number, and strength of the competitors. The complexity stems from the fact that interference may occur at multiple stages (encoding, retrieval, and possibly storage) and at multiple levels (the representation itself, or its association with a cue or a response). Interference from the past (proactive interference, PI) may affect both the encoding and the retrieval of new items, and it often increases over time. By contrast, interference from new items onto older memories (retroactive interference, RI) frequently decreases over time, and may not be as reliant on similarity (see discussion by Wixted, 2004). Retrieval Interference It can be difficult to select between items that are similar to each other. For example, if participants learn and recall four lists from the same category (e.g., flowers), recall performance shows typical PI effects: decreasing performance across the lists. However, if the category of the fourth list is changed, even subtly (e.g., wildflowers) memory for this list can be nearly as high as on the very first trial (Wickens, 1970). Importantly, this “release from PI” occurs even if the subject is only made aware of the category shift after the list has been learned (Gardiner, Craik, & Birtwist, 1972). This suggests that the effects of category-change occur largely at retrieval, by helping participants differentiate and thus select recent-list items from others in memory. Selection and retrieval processes remain an important topic in interference research. Functional neuroimaging

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studies consistently identify a region in left inferior frontal gyrus (LIFG) as active during interference-resolution, at least for verbal materials (see the review by Jonides & Nee, 2006). This region appears to be generally important for selection among competing alternatives, for example, in semantic memory as well as in STM (Thompson-Schill, D’Esposito, Aguirre, & Farah, 1997). In STM, LIFG is most prominent during the test phase of interference trials, and its activation during this phase often correlates with behavioral measures of interference-resolution (D’Esposito, et al., 1999; Jonides et al., 1998; Reuter-Lorenz et al., 2000; Thompson-Schill et al., 2002). These findings attest to the importance of processes for resolving retrieval interference. The commonality of the neural substrate for interference-resolution across short-term and long-term tasks provides yet further support for the hypothesis of shared retrieval processes for the two types of memory. Interference effects occur at multiple levels and it is important to distinguish between interference at the level of representations and interference at the level of responses. The LIFG effects described previously appear to be familiarity-based and to occur at the level of representations. Items on a current trial must be distinguished and selected from among items on previous trials that are familiar because of prior exposure, but are currently incorrect. A separate contribution occurs at the level of responses: An item associated with a positive response on a prior trial may now be associated with a negative response, or vice versa. This response-based conflict can be separated from the familiarity-based conflict, and its resolution appears to relate more to activity in the anterior cingulate (Nelson, Reuter-Lorenz, Sylvester, Jonides, & Smith, 2003; see Figure 29.9). Other Mechanisms for Interference Effects Many studies examining encoding in STM have focused on retroactive interference (RI): how new information disrupts previous memories. Early theorists described this disruption in terms of displacement of entire items from STM, perhaps by disrupting consolidation (e.g., Waugh & Norman, 1965). However, rapid serial visual presentation (RSVP) studies suggest that this type of consolidation is complete within a very short time—less than 500 ms, and in some situations as short as 50 ms (Vogel, Woodman, & Luck, 2006). What about interference effects beyond this time window? As reviewed previously, most current focus-based models implicitly assume something like whole-item displacement is at work. It is not clear how these models account for similarity-based interference. Two recent models (Nairne, 2002; Oberauer, 2006) suggest a possible modification.

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(A)

4 mPFC/ACC

Familiarity conflict Response conflict

z  10

IFG

Average t-score

3 2 1 0 1 z  30

2

IFG

mPFC

Figure 29.9 Dissociations between stimulus (familiarity) and response-based conflict. Note: A: mPFC/ACC region of interest is associated with response-conflict, and IFG is associated with familiarity-based conflict. B: Average t-scores in the IFG and mPFC for familiarity- and response-conflict conditions. From “Dissociable Neural Mechanisms Underlying Response-Based and Familiarity-Based Conflict in Working Memory,” by J. K. Nelson, P. A. Reuter-Lorenz, C. Y. C. Sylvester, J. Jonides, and E. E. Smith, 2003, Proceedings of the National Academy of Sciences, 100, pp. 11171–11175. Reprinted with permission.

Rather than a competition at the item level for a singlefocus resource, these models posit a lower-level similarity-based competition for “feature units.” By this idea, representations are composed of bundles of features (e.g., color, shape, spatial location, temporal location), which are in turn distributed over multiple units. The more two items overlap, the more they compete for these feature units, resulting in greater interference. This proposed mechanism fits well with the idea that perception, STM, and LTM rely on representations that are distributed throughout sensory, semantic, and motor cortex (Postle, 2006). As we describe next, it is also congruent with the stochastic mechanism we suggested earlier for decay. Interference-Based Decay The mechanism we earlier proposed for decay is based on the idea that stochastic variability causes the neurons making up a representation to fall out of synchrony. Using the terminology of Nairne (2002) and Oberauer (2006), the feature units become less tightly bound. Feature units that are not part of a representation also show some random activity due to their own stochastic variability, creating a noise distribution. Over time, there is an increasing likelihood that the feature units making up the to-be-remembered item’s representation will overlap with those of the noise distribution, making them increasingly difficult to distinguish. This increasing overlap with the noise distribution and loss of feature binding could lead to the smooth forgetting functions often interpreted as evidence for decay. Such a mechanism could also account for strength-oflearning effects and similarity-based interference. Poorly learned items might have fewer differentiating features and be less tightly bound, thus making their representations

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more difficult to discriminate from the noise distribution to begin with, and faster to lose their integrity by falling out of synchrony. McKone (1998) found that nonwords decayed faster than words, and were also more susceptible to interference. Similarity-based interference could occur because of competition between representations for control over shared feature units, increasing the rate at which any given representation would lose integrity. In summary, although it is still speculative, this model of neural representations and how they change over time due to intrinsic variability in neuronal activity supplies a unified mechanism for interference and decay.

SKETCH OF SHORT-TERM MEMORY AT WORK In our review thus far, we brought together the literature on behavioral and neuroscience data concerned with shortterm memory. Here, we sketch out how this integration might work on a moment-to-moment basis throughout a typical STM task: an N-back probe recognition task. Figure 29.10 illustrates the task events in terms of the stimulus display and the subject’s response. The stages of the task are displayed in a more abstract form in Figure 29.11, with the task events at the bottom of the figure and the putative cognitive events at the top. In the task, the participant sees a letter presented for 700 ms and must respond “yes” if the letter matches a letter seen 4th-back, and respond “no” if the letter does not match the letter 4thback. Therefore, in this task, the participant must actively maintain four items to match the current probe against the 4th-back item. The participant must also keep track of the other items for future trials.

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Sketch of Short-Term Memory at Work

We adopt the STM architecture of Oberauer (2002) along with our elaboration of the processes involved in STM and forgetting to explain how this task would be accomplished. To reiterate, the focus of attention consists of frontal and parietal regions biasing posterior cortical areas that are involved in perception and storage of LTM representations. Items outside the focus are either in a highly accessible state, or are more dormant in the posterior representational cortices. In Figure 29.10, the participant first encodes the letter B, activating posterior perceptual areas, presumably in left inferior temporal cortex (Polk et al., 2002). This item moves into the focus of attention, and the MTL initiates its contextual binding. B does not match the 4th-back item, so the subject responds “No.” Next, the letter D is shown, which displaces the letter B from the focus of attention, and D is encoded using the same process as described for

“No”

Matches the 4th-back item

B D

“No”

M

“No”

“No”

J “Yes”

B

Figure 29.10 Sample STM task: The N-Back task (where N 4)

1. Encode 1st item ‘B’ in the focus.

B

2. Encode 2nd item ‘D’ in the focus. Item B moves into the region of Direct Access, but is brought back into the focus periodically with rehearsal.

B

B. The participant must still keep the letter B active, even though it is not the focus of attention, and thus it remains in a highly active state outside the focus of attention. MTL activation persists for B, maintaining the item’s context; however, this activation is greatly decreased due to stochastic drift as outlined previously, possibly leading to decay of the representation. This process continues for the rest of the items. The participant responds with “No” to the first four items (B, D, M, and J) because they do not match the letter 4th-back. Moreover, each item displaces the previous item from the focus of attention. In addition, throughout the task, the participant rehearses the items, which periodically brings the items that are out of the focus back into the focus as illustrated in panel 2 of Figure 29.11. Frontal and parietal areas increase biasing in the posterior regions to retrieve these items back into the focus during rehearsal. Rehearsal itself is mediated by premotor and inferior frontal gyrus regions. Finally, the participant gets the letter B, which does match the item 4th-back. Before we move on to the retrieval process, notice the following depicted in Figure 29.11. First, the representations of items 1 and 2 (B and D) overlap due to their featural and contextual similarities (shape, phonology, and temporal context). Second, items 3 and 4 (M and J) are much farther away from items 1 and 2. This is because these items do not share many features with items 1 and 2. In addition, item 3 is no longer within the activated/highly available state. This item has suffered from proactive interference from items 1 and 2 and retroactive interference from item 4.

rd 3. Encode 3 item ‘M’ in the focus Item ‘D’ moves into the region of Direct Access and has many overlapping Features with item ‘B’.

B M D

D

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4. Encode 4th item ‘J’ in the focus. Item ‘M’ moves into the region of Direct Access, but does not share as many features with items ‘B’ and ‘D’.

B J D M

Note: this rehearsal occurs for all the items, but will only be illustrated for item 1 (i.e., ‘B’). 5. Encode the 5th item ‘B’ into the focus. This item is the cue and warrants a ‘yes’ response, unlike all the other stimuli that necessitated ‘no’ responses. Item ‘J’ moves into the region of Direct Access, but does not share many features with the other items.

6. The cue ‘B’ (i.e., the 5th item) matches item ‘B’ (i.e., the 1st item), but does not match any of the other items. Therefore, this item is the item selected from the cue-based parallel retrieval, which brings item 1 back into the focus to form one functional context and warrants a yes response. In addition, item ‘D’ may be retrieved incorrectly from time to time because it is similar to the first item ‘B’ both in visual features and in temporal features (both occurred around the same time).

B B J D M

Figure 29.11

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B J D B M

Model of STM performing the N-Back task.

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In addition to this interference, this item has also lost representational fidelity due to stochastic decline in neural firing when it was not in focus. When the cue letter B is presented, the participant performs a cue-based retrieval of that item. The cue best matches item 1, but it also may be subject to some similarity-based interference from item 2, which could induce an incorrect response or delay the correct response, “Yes.” There is also similarity-based interference from items 3 and 4, but this interference is much weaker. Item 1 is then brought back into the focus, replacing the cue, and the participant responds affirmatively.

SUMMARY Let us step away from this particular example and take stock of what we now know about short-term memory, both the psychological and the neural mechanisms. Our review of the structure, processes, and forgetting mechanisms of STM lead us to the following synthesis of the facts of the matter. It is this synthesis with which we close.

Neural Mechanisms of Short-Term Memory • Frontal and parietal systems mediate the control of the focus of attention by their connections with and modulations of activity in posterior regions that represent the features of representations within the focus. • The (largely posterior) systems that represent item features for perception, action, or LTM storage also represent those features for STM. Items within the focus of attention are represented by patterns of heightened, synchronized firing of neurons in these (verbal, spatial, motor, etc.) regions. • Medial temporal structures are important for binding items to their context (including information about time and spatial location), and for retrieving items whose context is no longer in the focus of attention (an STM function) or fully consolidated in neocortex (an episodic LTM function). • The inherent variability of neuronal activity may contribute to the loss of integrity of neural representations, and thus lead to forgetting.

References Psychological Mechanisms of Short-Term Memory • The core of short-term memory is a focus of attention containing a single functional context and the items bound within it. • The representations that the focus of attention operates on in STM are isomorphic with those that form the basis of initial perception and storage in LTM. • These focused representations consist of bundles of features for stored information. Those features can include those that tie an item to its functional context— for example, serial order, time, or location—and novel relations among familiar items. • Representations enter the focus of attention via perceptual encoding or via cue-based retrieval from LTM. • Controlled, active maintenance processes are required to keep a representation in the focus, especially in the face of other distracting material. • Rehearsal is not a core STM maintenance process, in that it does not keep a representation consistently within the focus. Instead, it consists of controlled but sequential retrieval of highly activated but out-of-focus LTM representations into the focus. • Forgetting occurs when the fidelity of a representation declines over time due to stochastic processes (“pure” decay), or because of similarity-based competition between representations for features (interference-based decay). Similarity also influences competition between representations for the focus of attention (retrieval or selection-based interference).

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Anderson, J. R. (1983). Retrieval of information from long-term memory. Science, 200, 25–30. Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of mind. Psychological Review, 111, 1036–1060. Anderson, J. R., & Matessa, M. (1997). A production system theory of serial memory. Psychological Review, 104, 728–748. Atkinson, R. C., & Shiffrin, R. M. (1971). The control of short-term memory. Scientific American, 224, 82–90. Awh, E., Barton, B., & Vogel, E. K. (2007). Visual working memory represents a fixed number of items regardless of complexity. Psychological Science, 18, 622–628. Awh, E., & Jonides, J. (2001). Overlapping mechanisms of attention and spatial working memory. Trends in Cognitive Science, 5, 119–126. Awh, E., Jonides, J., & Reuter-Lorenz, P. A. (1998). Rehearsal in spatial working memory. Journal of Experimental Psychology: Human Perception and Performance, 24, 780–790. Awh, E., Jonides, J., Smith, E. E., Buxton, R. B., Frank, L. R., Love, T., et al. (1999). Rehearsal in spatial working memory: Evidence from neuroimaging. Psychological Science, 10, 433–437. Awh, E., Jonides, J., Smith, E. E., Schumacher, E. H., Koeppe, R. A., & Katz, S. (1996). Dissociation of storage and rehearsal in verbal working memory: Evidence from PET. Psychological Science, 7, 25–31. Baddeley, A. D. (1986). Working memory. Oxford, England: Clarendon Press. Baddeley, A. D. (1992). Working memory. Science, 225, 556–559. Baddeley, A. D. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Sciences, 4, 417–423. Baddeley, A. D. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4, 829–839. Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. A. Bower (Ed.), Recent advances in learning and motivation (Vol. 8, pp. 47–90). New York: Academic Press.

8/17/09 2:27:13 PM

References 583 Baddeley, A. D., Thomson, N., & Buchanan, M. (1975). Word length and structure of short-term memory. Journal of Verbal Learning and Verbal Behavior, 14, 575–589.

den Heyer, K., & Barrett, B. (1971). Selective loss of visual and verbal information in STM by means of visual and verbal interpolated tasks. Psychonomic Science, 25, 100–102.

Baddeley, A. D., & Warrington, E. K. (1970). Amnesia and the distinction between long- and short-term memory. Journal of Verbal Learning and Verbal Behavior, 9, 176–189.

D’Esposito, M., & Postle, B. R. (1999). The dependence of span and delayedresponse performance on the prefrontal cortex. Neuropsychologia, 37, 1303–1315.

Badre, D., & Wagner, A. D. (2005). Frontal lobe mechanisms that resolve proactive interference. Cerebral Cortex, 15, 2003–2012.

D’Esposito, M., & Postle, B. R. (2000). Neural correlates of processes contributing to working-memory function: Evidence from neuropsychological and pharmacological studies. In S. Monsell & J. Driver (Eds.), Control of cognitive processes (pp. 580–602). Cambridge, MA: MIT Press.

Badre, D., & Wagner, A. D. (2007). Left ventrolateral prefrontal cortex and the cognitive control of memory. Neuropsychologia, 45, 2883–2901. Braver, T. S., Barch, D. M., Kelley, W. M., Buckner, R. L., Cohen, N. J., Miezin, F. M., et al. (2001). Direct comparison of prefrontal cortex regions engaged by working and long-term memory tasks. Neuroimage, 14, 48–59. Brooks, L. R. (1968). Spatial and verbal components of the act of recall. Canadian Journal of Psychology, 22, 349–368. Brown, G. D. A., Preece, T., & Hulme, C. (2000). Oscillator-based memory for serial order. Psychological Review, 107, 127–181. Brown, J. (1958). Some tests of the decay theory of immediate memory. Quarterly Journal of Experimental Psychology, 10, 12–21. Buckner, R. L., Koutstaal, W., Schacter, D. L., Wagner, A. D., & Rosen, B. R. (1998). Frontal-anatomic study of episodic retrieval using fMRI: Pt. I. Retrieval effort versus retrieval success. NeuroImage, 7, 151–162. Burgess, N., & Hitch, G. J. (1999). Memory for order: A network model off the phonological loop and its timing. Psychological Review, 106, 551–581. Burgess, N., & Hitch, G. J. (2005). Computational models of working memory: Putting long-term memory into context. Trends in Cognitive Sciences, 9, 535–541. Burgess, N., & Hitch, G. J. (2006). A revised model of short-term memory and long-term learning of verbal sequences. Journal of Memory and Language, 55, 627–652. Cabeza, R., Dolcos, F., Graham, R., & Nyberg, L. (2002). Similarities and differences in the neural correlates of episodic memory retrieval and working memory. Neuroimage, 16, 317–330. Cabeza, R., & Nyberg, L. (2000). Imaging cognition: Pt. II. An empirical review of 275 PET and fMRI studies. Journal of Cognitive Neuroscience, 9, 254–265. Chein, J. M., & Fiez, J. A. (2001). Dissociation of verbal working memory systems components using a delayed serial recall task. Cerebral Cortex, 11, 1003–1014. Cowan, N. (1988). Evolving conceptions of memory storage, selective attention, and their mutual constraints within the human information processing system. Psychological Bulletin, 104, 163–191. Cowan, N. (1995). Attention and memory: An integrated framework. New York: Oxford University Press. Cowan, N. (2000). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–185. Crowder, R. (1976). Principles of learning and memory. Hillsdale, NJ: Erlbaum.

Destexhe, A., & Conterars, D. (2006). Neuronal computation with stochastic network states. Science, 314, 85–90. Duncan, J., & Owen, A. M. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neuroscience, 23, 475–483. Eng, H. Y., Chen, D. Y., & Jiang, Y. H. (2005). Visual working memory for simple and complex visual stimuli. Psychonomic Bulletin and Review, 12, 1127–1133. Fletcher, P. C., & Henson, R. N. A. (2001). Frontal lobes and human memory: Insights from functional neuroimaging. Brain, 124, 849–881. Frank, M. J., Loughry, B., & O’Reilly, R. C. (2001). Interactions between the frontal cortex and basal ganglia in working memory: A computational model. Cognitive, Affective, and Behavioral Neuroscience, 1, 137–160. Funahashi, S., Bruce, C. J., & Goldman-Rakic, P. S. (1989). Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. Journal of Neurophysiology, 61, 331–349. Fuster, J. M. (1973). Unit activity in prefrontal cortex during delayed response performance: Neuronal correlates of transient memory. Journal of Neurophysiology, 36, 61–78. Fuster, J. M. (1995). Memory in the cerebral cortex. Cambridge, MA: MIT Press. Fuster, J. M. (2001). The prefrontal cortex: An update. Time is of the essence. Neuron, 30, 319–333. Garavan, H. (1998). Serial attention within working memory. Memory and Cognition, 26, 263–276. Gardiner, J. M., Craik, F. I. M., & Birtwist, J. (1972). Retrieval cues and release from proactive inhibition. Journal of Verbal Learning and Verbal Behavior, 11, 778–783. Gillund, G., & Shiffrin, R. M. (1984). A retrieval model for both recognition and recall. Psychological Review, 91, 1–67. Goldman-Rakic, P. S. (1987). Circuitry of primate pre-frontal cortex and regulation of behavior by representational memory. In F. Plum (Ed.), Handbook of physiology: The nervous system (Vol. 5, pp. 373–417). Bethesda, MD: American Physiological Society. Hampson, M., Driesen, N. R., Skudlarski, P., Gore, J. C., & Constable, R. T. (2006). Brain connectivity related to working memory performance. Journal of Neuroscience, 26, 13338–13343.

Damasio, A. R. (1989). Time-locked multiregional retroactivation: A system-level proposal for the neuronal substrates of recall and recognition. Cognition, 33, 25–62.

Harris, J. D. (1952). The decline of pitch discrimination with time. Journal of Experimental Psychology, 43, 96–99.

Darwin, C. J., Turvey, M. T., & Crowder, R. G. (1972). Auditory analogue of Sperling partial report procedure: Evidence for brief auditory storage. Cognitive Psychology, 3, 255–267.

Hasher, L., & Zacks, R. T. (1988). Working memory, comprehension, and aging: A review and a new view. In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 22, pp. 193–225). New York: Academic Press.

Deiber, M. P., Missonnier, P., Bertrand, O., Gold, G., Fazio-Costa, L., Ibanez, V., et al. (2007). Distinction between perceptual and attentional processing in working memory tasks: A study of phase-locked and induced oscillatory brain dynamics. Journal of Cognitive Neuroscience, 19, 158–172.

c29.indd 583

D’Esposito, M., Postle, B. R., Jonides, J., Smith, E. E., & Lease, J. (1999). The neural substrate and temporal dynamics of interference effects in working memory as revealed by event-related fMRI. Proceedings of the National Academy of Science, USA, 96, 7514–7519.

Hebb, D. O. (1949). The organization of behavior. New York: Wiley. Hockley, W. E. (1984). Analysis of response-time distributions in the study of cognitive processes. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 598–615.

8/17/09 2:27:14 PM

584

Psychological and Neural Mechanisms of Short-Term Memory

Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, USA, 70, 2554–2558. Jacobsen, C. F. (1936). The functions of the frontal association areas in monkeys. Computational Psychology Monograph, 13, 1–60. Jensen, O. (2006). Maintenance of multiple working memory items by temporal segmentation. Neuroscience, 139, 237–249. Jha, A. P., & McCarthy, G. (2000). The influence of memory load upon delay-interval activity in a working-memory task: An event-related functional MRI study. Journal of Cognitive Neuroscience, 12, 90–105. Johnson, M. K., Raye, C. L., Mitchell, K. J., Greene, E. J., Cunningham, W. A., & Sanislow, C. A. (2005). Using fMRI to investigate a component process of reflection: Prefrontal correlates of refreshing a just-activated representation. Cognitive, Affective, and Behavioral Neuroscience, 5, 339–361. Jonides, J., Lacey, S. C., & Nee, D. E. (2005). Processes of working memory in mind and brain. Current Directions in Psychological Science, 14, 2–5. Jonides, J., Lewis, R. L., Nee, D. E., Lustig, C. A., Berman, M. G., Moore, K. S. (2008). The mind and brain of short-term memory. Annual Review of Psychology, 59, 193–224. Jonides, J., & Nee, D. E. (2006). Brain mechanisms of proactive interference in working memory. Neuroscience, 139, 181–193. Jonides, J., Smith, E. E., Koeppe, R. A., Awh, E., Minoshima, S., & Mintun, M. A. (1993, June 17). Spatial working memory in humans as revealed by PET. Nature, 363, 623–625. Jonides, J., Smith, E. E., Marshuetz, C., Koeppe, R. A., & Reuter-Lorenz, P. A. (1998). Inhibition in verbal working memory revealed by brain activation. Proceedings of the National Academy of Sciences, USA, 95, 8410–8413. Keppel, G., & Underwood, B. J. (1962). Proactive-inhibition in shortterm retention of single items. Journal of Verbal Learning and Verbal Behavior, 1, 153–161.

McKone, E. (1998). The decay of short-term implicit memory: Unpacking lag. Memory and Cognition, 26, 1173–1186. Meyer, D. E., & Kieras, D. E. (1997). A computational theory of executive cognitive processes and multiple-task performance: Pt. 1. Basic mechanisms. Psychological Review, 104, 3–65. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97. Murdock, B. B. (1982). A theory for the storage and retrieval of item and associative information. Psychological Review, 89, 609–626. Nairne, J. S. (2002). Remembering over the short-term: The case against the standard model. Annual Review of Psychology, 53, 53–81. Nelson, J. K., Reuter-Lorenz, P. A., Sylvester, C. Y. C., Jonides, J., & Smith, E. E. (2003). Dissociable neural mechanisms underlying responsebased and familiarity-based conflict in working memory. Proceedings of the National Academy of Sciences, USA, 100, 11171–11175. Oberauer, K. (2002). Access to information in working memory: Exploring the focus of attention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 411–421. Oberauer, K. (2006). Is the focus of attention in working memory expanded through practice? Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 197–214. Olson, I. R., Page, K., Moore, K. S., Chatterjee, A., & Verfaellie, M. (2006). Working memory for conjunctions relies on the medial temporal lobe. Journal of Neuroscience, 26, 4596–5601. Pashler, H. (1988). Familiarity and visual change detection. Perception and Psychophysics, 44, 369–378. Pasternak, T., & Greenlee, M. W. (2005). Working memory in primate sensory systems. Nature Reviews Neuroscience, 6, 97–107. Peterson, L. R., & Peterson, M. J. (1959). Short-term retention of individual verbal items. Journal of Experimental Psychology, 58, 193–198.

Lisman, J. E., & Idiart, M. A. P. (1995). Storage of 7/-2 short-term memories in oscillatory subcycles. Science, 267, 1512–1515.

Plaut, D. C. (1997). Structure and function in the lexical system: Insights from distributed models of word reading and lexical decision. Language and Cognitive Processes, 12, 765–805.

Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390, 279–281.

Polk, T. A., Simen, P., & Lewis, R. L. (2002). A computational approach to control in complex cognition. Cognitive Brain Research, 15, 71–83.

Lustig, C., Matell, M. S., & Meck, W. H. (2005). Not “just” a coincidence: Frontal-striatal interactions in working memory and interval timing. Memory, 13, 441–448.

Polk, T. A., Stallcup, M., Aguirre, G. K., Alsop, D. C., D’Esposito, M., Detre, J. A., et al. (2002). Neural specialization for letter recognition. Journal of Cognitive Neuroscience, 14, 145–159.

Malmo, R. B. (1942). Interference factors in delayed response in monkeys after removal of frontal lobes. Journal of Neurophysiology, 5, 295–308.

Postle, B. R. (2005). Delay-period activity in the prefrontal cortex: One function is sensory gating. Journal of Cognitive Neuroscience, 17, 1679–1690.

Martin, R. C. (1993). Short-term memory and sentence processing: Evidence from neuropsychology. Memory and Cognition, 21, 176–183. McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419–457. McElree, B. (1998). Attended and non-attended states in working memory: Accessing categorized structures. Journal of Memory and Language, 38, 225–252. McElree, B. (2001). Working memory and focal attention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 817–835.

Postle, B. R. (2006). Working memory as an emergent property of the mind and brain. Neuroscience, 139, 23–38. Postle, B. R., & D’Esposito, M. (1999). “What” then “where” in visual working memory: An event-related, fMRI study. Journal of Cognitive Neuroscience 11, 585–597. Pylyshyn, Z., W. (1994). Some primitive mechanisms of spatial attention. Cognition, 50, 363–384. Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, USA, 98, 676–682.

McElree, B. (2006). Accessing recent events. Psychology of Learning and Motivation, 46, 155–200.

Ranganath, C. (2006). Working memory for visual objects: Complementary roles of inferior temporal, medial temporal, and prefrontal cortex. Neuroscience, 139, 277–289.

McElree, B., & Dosher, B. A. (1989). Serial position and set size in shortterm memory: Time course of recognition. Journal of Experimental Psychology: General, 118, 346–373.

Ranganath, C., & Blumenfeld, R. S. (2005). Doubts about double dissociations between short- and long-term memory. Trends in Cognitive Sciences, 9, 374–380.

McKone, E. (1995). Short-term implicit memory for words and nonwords. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 1108–1126.

Ranganath, C., Johnson, M. K., & D’Esposito, M. (2003). Prefrontal activity associated with working memory and episodic long-term memory. Neuropsychologia, 41, 378–379.

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References 585 Renart, A., Parga, N., & Rolls, E. T. (1999). Backward projections in the cerebral cortex: Implications for memory storage. Neural Computation, 11, 1349–1388. Repov, G., & Baddeley, A. D. (2006). The multi-component model of working memory: Explorations in experimental cognitive psychology. Neuroscience, 139, 5–21. Reuter-Lorenz, P. A., & Jonides, J. (2007). The executive is central to working memory: Insights from age, performance and task variations. In A. R. Conway, C. Jarrold, M. J. Kane, A. Miyake, & J. N. Towse (Eds.), Variations in working memory (pp. 250–270). New York: Oxford University Press. Reuter-Lorenz, P. A., Jonides, J., Smith, E. E., Hartley, A., Miller, A., Marshuetz, C., et al. (2000). Age differences in the frontal lateralization of verbal and spatial working memory revealed by PET. Journal of Cognitive Neuroscience, 12, 174–187. Roediger, H. L., Knight, J. L., & Kantowitz, B. H. (1977). Inferring decay in short-term-memory: The issue of capacity. Memory and Cognition, 5, 167–176. Ruchkin, D. S., Grafman, J., Cameron, K., & Berndt, R. S. (2003). Working memory retention systems: A state of activated long-term memory. Behavioral and Brain Sciences, 26, 709–777. Sakai, K. (2003). Reactivation of memory: Role of medial temporal lobe and prefrontal cortex. Reviews in Neurosciences, 14, 241–251. Scoville, W. B., & Milner, B. (1957). Loss of recent memory after bilateral hippocampal lesions. Journal of Neurological and Neurosurgical Psychiatry, 20, 11–21. Shallice, T., & Warrington, E. K. (1970). Independent functioning of verbal memory stores: A neuropsychological study. Quarterly Journal of Experimental Psychology, 22, 261–273. Smith, E. E., & Jonides, J. (1997). Working memory: A view from neuroimaging. Cognitive Psychology, 33, 5–42. Smith, E. E., & Jonides, J. (1999). Neuroscience: Storage and executive processes in the frontal lobes. Science, 283, 1657–1661. Smith, E. E., Jonides, J., Koeppe, R. A., Awh, E., Schumacher, E. H., & Minoshima, S. (1995). Spatial versus object working-memory: PET investigations. Journal of Cognitive Neuroscience, 7, 337–356. Sperling, G. (1960). The information available in brief visual presentations. Psychological Monographs, 74, Whole No. 498. Sternberg, S. (1966). High speed scanning in human memory. Science, 153, 652–654. Sussillo, D., Toyoizumi, T., & Maass, W. (2007). Self-tuning of neural circuits through short-term synaptic plasticity. Journal of Neurophysiology, 97, 4079–4095. Talmi, D., Grady, C. L., Goshen-Gottstein, Y., & Moscovitch, M. (2005). Neuroimaging the serial position curve. Psychological Science, 16, 716–723. Thompson-Schill, S. L., D’Esposito, M., Aguirre, G. K., & Farah, M. J. (1997). Role of left inferior prefrontal cortex in retrieval of semantic knowledge: A reevaluation. Proceedings of the National Academy of Sciences, USA, 94, 14792–14797. Thompson-Schill, S. L., Jonides, J., Marshuetz, C., Smith, E. E., D’Esposito, M., Kan, I. P., et al. (2002). Effects of frontal lobe damage on interference effects in working memory. Journal of Cognitive, Affective and Behavioral Neuroscience, 2, 109–120. Todd, J. J., & Marois, R. (2004). Capacity limit of visual short-term memory in human posterior parietal cortex. Nature, 428, 751–754. Todd, J. J., & Marois, R. (2005). Posterior parietal cortex activity predicts individual differences in visual short-term memory capacity. Cognitive, Affective, and Behavioral Neuroscience, 5, 144–155.

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Trick, L. M., & Pylyshyn, Z. W. (1993). What enumeration studies can show us about spatial attention: Evidence for limited capacity preattentive processing. Journal of Experimental Psychology: Human Perception and Performance, 19, 331–351. Ungerleider, L. G., & Haxby, J. V. (1994). “What” and “where” in the human brain. Current Opinion in Neurobiology, 4, 157–165. Unsworth, N., & Engle, R. W. (2007). The nature of individual differences in working memory capacity: Active maintenance in primary memory and controlled search from secondary memory. Psychological Review, 114, 104–132. Vallar, G., & Baddeley, A. D. (1984). Fractionation of working memory: Neuropsychological evidence for a phonological short-term store. Journal of Verbal Learning and Verbal Behavior, 23, 151–161. Vallar, G., & Papagano, C. (2002). Neuropsychological impairments of verbal short-term memory. In A. D. Baddeley, M. D. Kopelman, & B. A. Wilson (Eds.), The handbook of memory disorders (2nd ed., pp. 249–270). Chichester, West Sussex, England: Wiley. Verhaeghen, P., & Basak, C. (in press). Aging and switching of the focus of attention in working memory: Results from a modified N-Back task. Quarterly Journal of Experimental Psychology. Verhaeghen, P., Cerella, J., & Basak, C. (2004). A working memory workout: How to expand the focus of serial attention from one to four items in 10 hours or less. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 1322–1337. Vogel, E. K., & Machizawa, M. G. (2004, December 18). Neural activity predicts individual differences in visual working memory capacity. Nature, 426, 748–751. Vogel, E. K., Woodman, G. F., & Luck, S. J. (2006). The time course of consolidation in visual working memory. Journal of Experimental Psychology: Human Perception and Performance, 32, 1436–1451. Wager, T. D., & Smith, E. E. (2003). Neuroimaging studies of working memory: A meta-analysis. NeuroImage, 3, 255–274. Waugh, N. C., & Norman, D. A. (1965). Primary memory. Psychological Review, 72, 89–104. Wheeler, M. E., Peterson, S. E., & Buckner, R. L. (2000). Memory’s echo: Vivid remembering reactivates sensory-specific cortex. Proceedings of the National Academy of Sciences, USA, 97, 11125–11129. Wheeler, M. E., Shulman, G. L., Buckner, R. L., Miezin, F. M., Velanova, K., & Peteresen, S. E. (2006). Evidence for separate perceptual reactivation and search processes during remembering. Cerebral Cortex, 16, 949–959. Wickens, D. D. (1970). Encoding categories of words: Empirical approach to meaning. Psychological Review, 77, 1–15. Wilken, P., & Ma, W. J. (2004). A detection theory account of change detection. Journal of Vision, 4, 1120–1135. Wilson, F. A. W., O’Scalaidhe, S. P., & Goldman-Rakic, P. S. (1993). Dissociation of object and spatial processing domains in primate prefrontal cortex. Science, 260, 1955–1958. Wixted, J. T. (2004). The psychology and neuroscience of forgetting. Annual Review of Psychology, 55, 235–269. Xu, Y., & Chun, M. M. (2006). Dissociable neural mechanisms supporting visual short-term memory for objects. Nature, 440, 91–95. Yantis, S., & Serences, J. T. (2003). Cortical mechanisms of space-based and object-based attentional control. Current Opinion in Neurobiology, 13, 187–193. Zucker, R. S., & Regehr, W. G. (2002). Short-term synaptic plasticity. Annual Review of Physiology, 64, 355–405.

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Chapter 30

Forgetting and Retrieval BRICE A. KUHL AND ANTHONY D. WAGNER

Retrieval of episodic memories—conscious memories of past events—often provides critical information that can shape current thought and behavior. Although successful remembering is generally thought of as far more desirable than forgetting, it is likely that forgetting is also an important component of an adaptive memory system (M. C. Anderson, 2003; Bjork, 1989; Schacter, 1999). To fully understand the functioning of episodic memory, it is important to consider both the situations and mechanisms that lead to successful remembering as well as those that contribute to forgetting. Indeed, the phenomena of remembering and forgetting are intimately related—that is, we often forget precisely because we have remembered some other information. What we ultimately remember and forget is influenced both by our prior mnemonic experiences as well as the functioning of neurobiological mechanisms that guide mnemonic retrieval. In particular, the frontal lobes—which are known to play an important role in goaldirected attention and behavior—are central to the ability to direct retrieval toward those memories that are relevant and away from those that are irrelevant. We consider two broad classes of forgetting and their corresponding relations to frontal lobe function. First, we review evidence that our ability to remember is often complicated by interference from competing memories, and that these situations (a) increase the likelihood of forgetting and (b) increase demands on the prefrontal cortex (PFC). Second, we consider situations in which our mnemonic activities require selecting against, or avoiding, particular memories, describing evidence that such acts of selection (a) increase the likelihood of later forgetting selected-against memories and (b) are supported by the PFC. We conclude by situating the relationship between the PFC and forgetting within the broader context of the PFC and the control of cognition and behavior.

GROSS ANATOMY AND CONNECTIVITY OF THE PREFRONTAL CORTEX In this chapter, we primarily focus on the role of the PFC in regulating episodic retrieval and forgetting. Thus, before considering specific classes of forgetting and their relation to the PFC, it is worth briefly describing the gross anatomy of the frontal lobes—namely, subregions within the PFC that putatively support distinct functional mechanisms. The prefrontal cortex is generally divided into ventrolateral, dorsolateral, frontopolar, and medial subregions (Figure 30.1). In the human, the ventrolateral PFC (VLPFC; Figure 30.1A) corresponds to the inferior frontal gyrus, which includes, from the caudal to rostral extent, inferior frontal pars opercularis (Brodmann Area [BA] 44), inferior frontal pars triangularis (BA 45), and inferior frontal pars orbitalis (an area Petrides & Pandya, 2002, term area 47/12). Although Petrides and Pandya (2002) refer to area 47/12 and BA 45 collectively as the mid-VLPFC, distinguishing these regions from caudally situated BA 44, in this review, we highlight functional dissociations between area 47/12 and area 45. Thus, we refer to inferior frontal pars orbitalis (area 47/12) as the anterior VLPFC, pars triangularis (BA 45) as the mid-VLPFC, and pars opercularis (BA 44) as the posterior VLPFC. The VLPFC is separated from the dorsolateral PFC (DLPFC) by the inferior frontal sulcus in humans (in monkeys, the principal sulcus marks this boundary). Although DLPFC has been used to refer to a broad range of lateral PFC regions, we use DLPFC to refer to the middle frontal gyrus. As we discuss, episodic retrieval and forgetting have been linked with activity in BA’s 46 and 9/46 (Figure 30.1A)—subregions of DLPFC that Petrides and Pandya (1999) refer to as mid-DLPFC. Rostral to DLPFC and VLPFC is the frontopolar cortex (FPC; BA 10; Figure 30.1A-B). A final area of interest for the present chapter is the anterior cingulate cortex (ACC; BA’s 24 and 32; Figure 30.1B), which is situated along the medial wall of the PFC, immediately superior to the corpus callosum. Importantly, PFC subregions are both interconnected and connected with posterior cortical sites, suggesting that

Supported by the National Institute of Mental Health (5R01MH080309 and 5R01-MH076932) and the Alfred P. Sloan Foundation. The authors thank Ben Levy for insightful discussion. 586

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retrosplenial cortex, DLPFC may interact with hippocampal and parahippocampal structures (Petrides, 2005). The FPC is connected both with the DLPFC and VLPFC as well as with the superior temporal cortex, cingulate cortex, and retrosplenial cortex, suggesting that the FPC may be particularly well suited to incorporate diverse sources of information (Petrides, 2005; Petrides & Pandya, 2007). The ACC is also widely connected with cortical sites, including multiple lateral PFC sites (DLPFC, in particular), the posterior parietal cortex, and the medial temporal lobe cortex (Pandya, Van Hoesen, & Mesulam, 1981). Thus, whereas DLPFC and VLPFC have fairly distinct patterns of connectivity with the posterior cortical sites, the FPC and ACC may each interact with both DLPFC and VLPFC structures in coordinating goal-directed behavior.

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Figure 30.1 A: Lateral view of the PFC and corresponding cytoarchitectonic areas. B: Medial view of the PFC. Note. (A) DLPFC ⫽ Areas 46 and 9/46. VLPFC ⫽ Areas 47/12, 45, and 44. FPC ⫽ Area 10. (B) ACC ⫽ Areas 32 and 24. From “Dorsolateral Prefrontal Cortex: Comparative Cytoarchitectonic Analysis in the Human and the Macaque Brain and Corticocortical Connection Patterns,” by M. Petrides and D. N. Pandya, 1999, European Journal of Neuroscience, 11, pp. 1011–1036. Copyright 1999 by Blackwell Publishing. Reprinted with permission.

PFC subregions are well equipped to coordinate diverse cognitive operations. For example, the VLPFC is strongly connected with cortical areas in the lateral and medial temporal lobe, including (but not limited to) the inferotemporal cortex, superior temporal cortex, and, more medially, the perirhinal and parahippocampal cortex (Petrides & Pandya, 2002). The DLPFC has substantial reciprocal connections with posterior parietal cortex, superior temporal cortex, retrosplenial cortex, anterior and posterior cingulate cortex, as well as connectivity with VLPFC (Petrides & Pandya, 1999). Notably, through its connections with the

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Perhaps the most widely accepted and well-documented cause of forgetting is interference. Interference occurs whenever irrelevant memories compete with relevant memories (Mensink & Raaijmakers, 1988; for reviews, see M. C. Anderson & Spellman, 1995; Wixted, 2004). The extent to which interference contributes to forgetting is related both to the number of irrelevant memories that compete as well as the strength of these irrelevant memories. Most typically, interference is thought to occur during the act of retrieval, creating situations of retrieval competition. Retrieval competition has been particularly well studied in three classic behavioral paradigms. First, memories of past experiences often interfere with our ability to retrieve memories of more recent experiences—a situation termed proactive interference. Conversely, the ability to retrieve memories of past experiences is often subject to interference from more recent memories—retroactive interference. Finally, even when the order of learning is not relevant, the general principle that associates of a retrieval cue compete with each other during retrieval has been studied in the fan effect (J. R. Anderson, 1974). In the following sections, we briefly review first the classic behavioral evidence concerning these three situations of interference and then potential neurobiological mechanisms that serve to overcome interference. Classic Interference Phenomena Proactive interference (PI) and retroactive interference (RI) have been the subject of extensive behavioral research (for reviews, see M. C. Anderson & Spellman, 1995; Wixted, 2004), and have been best illustrated in classic A-B, A-C paradigms (Figure 30.2). In a standard A-B, A-C

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paradigm, an initial list of A-B cue-associate pairs (e.g., SHOE-HOUSE) is studied, followed by a second list of A-C pairs in which some of the previously studied cues are paired with new associates (e.g., SHOE-ROPE). When memory for pairs from the second list is tested, the influence of proactive interference is reflected in poorer recall for those pairs that overlap with pairs from the first list, relative to pairs in the second list that are completely unrelated to pairs in the first list. Thus, learning of new information is impaired when previously learned information interferes. Retroactive interference, on the other hand, is evidenced by poorer recall of A-B pairs as a result of subsequently learning overlapping A-C pairs (e.g., memory for SHOE-HOUSE is impaired by learning SHOE-ROPE). Thus, in an A-B, A-C paradigm, either retroactive or proactive interference may occur, depending on which pairs (A-B or A-C) are tested. Several features of PI and RI are of note. First, the magnitude of interference-related forgetting observed depends on the extent to which retrieval cues reference items from both study lists—an observation made in the earliest RI studies (Müller & Pilzecker, 1900). For example, RI is greater when A-B study is followed by A-C study than C-D study (i.e., a new cue and new associate). Second, PI and RI are maximally observed at different points in time. Specifically, PI is maximal when the lag between A-C study and A-C recall is long, and may be negligible when the delay is very short. RI, on the other hand, is maximal when the delay between A-C study and A-B recall is short,

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with the magnitude of RI decreasing as the delay increases (Postman, Stark, & Fraser, 1968). Importantly, both of these properties of PI/RI can be well explained in terms of retrieval competition that occurs during cued recall (McGeoch, 1942; Mensink & Raaijmakers, 1988). That is, an A-B, A-C paradigm elicits greater RI than an A-B, C-D paradigm because the A-B, A-C paradigm creates a situation in which a single retrieval cue (A) is linked to two associates—thereby enhancing retrieval competition and, therefore, the likelihood of forgetting. Similarly, changes in the relative magnitude of RI and PI at different delays can be explained in terms of changes in the relative salience of B and C terms and, therefore, changes in retrieval competition. For example, some models suggest differential decay rates for B and C terms following A-C study, meaning that the relative strengths of the associate terms change with time (J. R. Anderson, 1983b). Other models suggest that changes in the availability of contextual cues contribute to relative changes in the accessibility of B and C terms (Estes, 1955; Mensink & Raaijmakers, 1988). In either case, at very short delays following A-C study, A-C pairs are thought to be highly salient relative to A-B pairs, meaning that while RI will be high (if B terms are tested), PI will be low (if C terms are tested). At longer delays, the relative salience of A-C pairs decreases, thereby reducing RI, but increasing the potential for PI. Retrieval competition has also been studied in the context of the fan effect (J. R. Anderson, 1974). The classic finding in fan effect paradigms is that as the amount of related information stored in long-term memory grows, the time it takes to verify that one recognizes a particular piece of that information increases (similarly, increases in fan size may also decrease accuracy). In a typical fan effect task, subjects study a series of propositions (e.g., “A doctor is in the bank,” “A fireman is in the park,” “A lawyer is in the park,” etc.). Importantly, individual elements may appear in multiple propositions (e.g., “park” is associated with both “lawyer” and “fireman”). When elements are associated with multiple propositions (a “fan”), the time it takes to recognize a proposition containing those elements (i.e., “high fan” propositions) increases (J. R. Anderson, 1983a). The fan effect has proven to be a highly consistent finding and has inspired influential models of human memory (J. R. Anderson, 1976). According to the standard account of the fan effect, during recognition memory a finite amount of activation is shared between all the elements in a fan. When the fan size is high, relevant elements receive correspondingly less activation, reflecting increased competition, and retrieval time is therefore slowed (J. R. Anderson, 1976, 1983a). Thus far, we have considered a single mechanism— retrieval competition—to explain forgetting in RI and PI paradigms and to account for recognition memory slowing

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Interference and Memory Retrieval

in the fan paradigm. However, alternate accounts have been advanced for RI and PI (for reviews, see M. C. Anderson, 2003; Wixted, 2004). In an influential two-factory theory, Melton and Irwin (1940) argued that some factor in addition to response competition contributes to RI, noting that substantial RI is often observed even when there is little behavioral evidence that A-C pairs actually compete with retrieval of A-B pairs. They speculated that a second factor contributing to RI (in addition to response competition) is the unlearning, or direct weakening, of original (A-B) associations. While our focus on retrieval competition as the primary mechanism of interference-related forgetting reflects more recent arguments that classic interference phenomena can be fully accounted for by retrieval competition alone (Mensink & Raaijmakers, 1988), we later consider a mechanism of forgetting—inhibition—that bears many similarities to Melton and Irwin’s (1940) unlearning mechanism. Specifically, inhibition shares with unlearning the idea that irrelevant memories may be directly weakened. Neurobiological Mechanisms of Interference Resolution A hallmark of frontal lobe damage is increased distractibility or perseveration upon irrelevant information. Consistent with this general observation, frontal lobe patients suffer an exaggerated susceptibility to PI (e.g., Shimamura, Jurica, Mangels, Gershberg, & Knight, 1995; Smith, Leonard, Crane, & Milner, 1995). Specifically, whereas frontal lobe patients typically learn list 1 items (e.g., A-B pairs) as well as controls, after studying a second list, their recall for list 2 items (e.g., A-C pairs) is impaired, relative to controls. The selective impairment for A-C pairs indicates that frontal lobe patients are relatively unimpaired at encoding information when interference is not present, but are particularly impaired when prior learning interferes with memory for subsequently encountered information. Indeed, during A-C cued recall, frontal lobe patients often show a greater tendency to generate B terms (intrusions), highlighting the sensitivity of frontal lobe patients to competition from prior learning (Shimamura et al., 1995). While exaggerated susceptibility to PI has frequently been associated with frontal lobe damage, frontal lobe patients vary widely in the location and extent of their damage. As a result, initial studies of frontal lobe patients yielded considerable variability in the subregions of PFC implicated in resolving PI. For example, whereas some reports suggested a greater sensitivity to PI in patients with left frontal damage (Moscovitch, 1982), others revealed greater PI in patients with right frontal damage (Turner, Cipolotti, Yousry, & Shallice, 2007). Similarly, there were reports in which left and right frontal patients show comparable

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increases in sensitivity to PI relative to controls (Smith et al., 1995), but also reports of frontal patients displaying relatively normal sensitivity to PI despite impairments on other “frontal tests” (Janowsky, Shimamura, Kritchevsky, & Squire, 1989). Thus, while initial studies of frontal patients highlighted that interference resolution likely depends on the integrity of the frontal lobes, this work yielded ambiguity regarding the specific PFC subregions that are critical for overcoming mnemonic competition. Progress on this important issue has greatly accelerated over the past decade, largely because the higher resolution of functional neuroimaging methods—positron emission tomography (PET) and functional magnetic resonance imaging (fMRI)—has enabled researchers to begin to examine whether interference resolution is differentially associated with functional responses in specific PFC subregions. As we next review, considerable neuroimaging evidence, accumulated over the past decade, now indicates that at least some forms of interference resolution are associated with activation in the left ventrolateral PFC (VLPFC; Figure 30.1A). Moreover, recent neuropsychological investigations of patients with damage that specifically includes the left VLPFC also highlight the necessity of this region for overcoming mnemonic competition. In a classic PET study of proactive interference (Dolan & Fletcher, 1997), subjects learned an initial set of word pairs (e.g., DOG-BOXER) followed by a second list that either contained completely new word pairs (e.g., CLOTHVELVET), previously studied word pairs (e.g., DOGBOXER), or word pairs that contained previously studied words paired with new associates (e.g., DOG-DALMATION or SPORTSMAN-BOXER). When list 2 contained completely new associates, relative to conditions that contained at least one old word, enhanced activation was observed in the hippocampus and medial temporal lobe cortex, suggesting that the medial temporal lobes preferentially respond to the novelty of to-be-learned information (Figure 30.3). In contrast, when list 2 contained previously studied words paired with new associates (a situation of interference equivalent to the A-C condition described previously), activation was observed in a region of the left lateral PFC that encompassed the mid/posterior VLPFC and DLPFC. Importantly, this left lateral PFC response was driven not by the novelty of individual words, but by the extent to which list 2 learning was complicated by interference from memory for list 1 pairs. Subsequent neuroimaging work provided additional evidence that the left VLPFC, in particular, plays a critical role in resolving PI. For example, in an fMRI study of PI during episodic encoding (Henson, Shallice, Josephs, & Dolan, 2002), activation in the left VLPFC decreased as a word pair (A-B) was repeatedly studied, but increased

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when one of the pair members changed (A-C). Similarly, in another study (Fletcher, Shallice, & Dolan, 2000), left VLPFC activation increased when previously studied word pairs were rearranged, relative to their initial study configuration. While the relationship between PI and the left VLPFC has primarily been evidenced during encoding of A-C pairs, Henson et al. (2002) also observed greater activation in the left VLPFC, along with the anterior cingulate cortex (ACC), when retrieval occurred in the face of PI (neuroimaging data from the fan effect paradigm further implicate the left VLPFC in competitive retrieval, as described next). Studies of retrieval from semantic memory and working memory also implicate the left VLPFC in guiding interference-laden mnemonic processing. Specifically, neuroimaging studies of semantic retrieval have consistently found greater engagement of the left VLPFC—particularly the left mid-VLPFC—when retrieval involves selecting between competing alternatives (e.g., Badre, Poldrack, Pare-Blagoev, Insler, & Wagner, 2005; Thompson-Schill, D’Esposito, Aguirre, & Farah, 1997; for review, see Badre & Wagner, 2007). Moreover, when PFC damage includes the left mid-VLPFC, the ability to retrieve relevant semantic representations from among competitors is impaired (Martin & Cheng, 2006; Metzler, 2001; Thompson-Schill et al., 1998), establishing the necessity of this region for interference resolution during semantic retrieval. Similarly, within working memory, imaging studies have consistently implicated the left mid-VLPFC in overcoming PI that accumulates across trials (for review, see Jonides & Nee, 2006). Moreover, PFC lesions that include damage

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PFC is maximally engaged when the word pair being encoded partially overlaps with a previous pair (i.e., when interference is present). In contrast, medial temporal lobe activation is maximal when the word pair being encoded is completely novel. From “Dissociating Prefrontal and Hippocampal Function in Episodic Memory Encoding,” by R. J. Dolan and P. C. Fletcher, 1997, Nature, 388, pp. 582–585. Copyright 1997 by Macmillan Publishers. Adapted with permission.

to the left mid-VLPFC (Thompson-Schill et al., 2002) and focal transient disruption of the left mid-VLPFC with transcranial magnetic stimulation (Feredoes, Tononi, & Postle, 2006) impair working memory performance in the face of PI. Collectively, these convergent findings across episodic, semantic, and working memory contexts indicate that the left mid-VLPFC contributes to interference resolution. While the left mid-VLPFC appears to play a critical role in resolving interference, there remains the question of how, in mechanistic terms. A prominent hypothesis, derived primarily from neuroimaging and patient data, is that the left mid-VLPFC supports the selection of task-relevant representations when competition is present (Thompson-Schill et al., 1997, 1998). That is, when multiple semantic—or episodic—representations become simultaneously active, a left mid-VLPFC bias mechanism is posited to favor relevant representations over irrelevant representations (Badre & Wagner, 2007). Accordingly, when viewed through this light, many instances of forgetting may reflect failures of mnemonic selection, as opposed to retrieval, per se. Notably, within semantic and working memory paradigms, selection has typically been studied—and left mid-VLPFC activation has typically been observed—during retrieval (of either semantic information or working memory contents). Within episodic memory, however, left mid-VLPFC activation has most frequently been observed in PI paradigms during A-C encoding. Thus, it has been argued that A-C encoding engages the same selection mechanism that is observed during semantic and working memory retrieval (Henson et al., 2002). However, it should be noted that left mid-VLPFC activation during encoding

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Inhibition as a Cause of Forgetting

might also be recast in terms of retrieval-related activation. That is, A-C associations may become differentiable from A-B associations through an elaborative encoding process in which semantic properties unique to A-C associations are selectively favored during A-C study—a process that would amount to competitive semantic retrieval. In either case, competition from irrelevant associations drives left mid-VLPFC activation during A-C encoding. During episodic retrieval, left mid-VLPFC engagement has also been observed when competition is present. In particular, the link between left VLPFC engagement and retrieval competition has been well established in studies of the fan effect. In fan paradigms, the increase in reaction time that is associated with “high fan” recognition is thought to directly correspond to prolonged engagement of mechanisms that guide retrieval in the face of competition (Sohn, Goode, Stenger, Carter, & Anderson, 2003). Consistent with this perspective, a pair of fMRI studies revealed that high fan, relative to low fan, recognition is associated with increased engagement of a region of the left lateral PFC, inclusive of the left mid-VLPFC (Sohn et al., 2003, 2005). This neural correlate of the fan effect provides a compelling link between recent neuroimaging work and classic interference theory, indicating that direct manipulations of retrieval competition increase the engagement of the left mid-VLPFC. Moreover, left midVLPFC engagement has been observed in other situations of competitive episodic retrieval, such as when the retrieval task requires recollection of specific (criterial) details of an encoding event (Dobbins, Foley, Schacter, & Wagner, 2002; Dobbins & Wagner, 2005; Kostopoulos & Petrides, 2003; Lundstrom, Ingvar, & Petersson, 2005). Summary Initial observations of increased sensitivity to interference following frontal lobe damage have now been complemented by substantial evidence that the left mid-VLPFC, in particular, plays a fundamental role in resolving interference. From a mechanistic perspective, the left midVLPFC is thought to resolve interference by selecting goal-relevant representations in the face of competition from irrelevant representations. This putative selection mechanism—and the many situations in which left midVLPFC-mediated selection has been observed—accords well with the perspective from classic interference theory that forgetting is well accounted for in terms of retrieval competition. In other words, retrieval competition powerfully influences the likelihood of forgetting, and it is in precisely these situations of enhanced retrieval competition that left mid-VLPFC selection resolves interference.

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INHIBITION AS A CAUSE OF FORGETTING In the previous section, we highlighted the potential for retrieval competition from irrelevant memories to obscure access to currently relevant memories and thereby produce retrieval failures, or forgetting. However, overcoming competition from irrelevant memories can also have consequences for what is remembered in the future. That is, when competing memories are selected against, there is a decreased likelihood that these memories will later be remembered (if they later become relevant). This form of forgetting is related to retrieval competition—it is a reaction to, and consequence of, competition from irrelevant memories—but the mechanism of forgetting is thought to reflect the direct weakening, or inhibition, of competing memories. We next consider two situations in which the relationship between forgetting and memory inhibition has been studied: (1) when the act of remembering a target memory requires selecting against closely related, but irrelevant, memories, and (2) when there is an explicit intention to forget or to keep out of mind individual memories or sets of memories. In each case, we consider the behavioral evidence supporting the occurrence of inhibition as well as the neurobiological mechanisms through which inhibition may occur. Retrieval-Induced Forgetting Competition that is present during the act of retrieval can compromise successful retrieval of target memories. Although we previously emphasized the demand to resolve competition such that successful retrieval, or selection, may occur, it has also been argued that retrieval competition is resolved through the inhibition of those memories that compete with the target memories (M. C. Anderson, Bjork, & Bjork, 1994; M. C. Anderson & Spellman, 1995; for reviews, see M. C. Anderson, 2003; Levy & Anderson, 2002). Functionally, the inhibition of irrelevant, competing memories is thought to be adaptive in that it reduces competition during the retrieval of target memories (M. C. Anderson, 2003; Bjork, 1989). However, to the extent that previously irrelevant memories later become relevant, the inhibition that they suffered increases the likelihood that they will be forgotten (for review, see Levy & Anderson, 2002). That the retrieval of target memories can produce forgetting of related memories has been termed retrievalinduced forgetting and has been demonstrated in a variety of situations (for review, see Levy & Anderson, 2002). In a standard retrieval-induced forgetting paradigm, participants study a series of cue-associate pairs with multiple associates studied with each cue (e.g., “FRUIT-banana,”

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“FRUIT-apple,” “DRINK-whiskey,” “DRINK-scotch”). After study, participants engage in selective retrieval practice of some of the associates of some of the cues. For example, participants might receive “FRUIT-a_” as a probe to remember “apple.” Typically, half of the associates of half of the cues are practiced, three times each, in this manner. Finally, all associates—both practiced and unpracticed—are tested in a final, cued recall phase where cues are presented along with the first letters of individual associates. Not surprisingly, practiced associates (e.g., “apple”; referred to as RP⫹ items) are better remembered during the final test than unpracticed associates (Figure 30.4). However, some of the unpracticed associates are related to practiced associates (e.g., “banana”; RP⫺ items), whereas other unpracticed associates are related to a cue for which none of the associates were practiced (e.g., “scotch” is related to “DRINK,” but none of the associates of “DRINK” receive practice; NRP items). Of critical interest, RP⫺ items—the associates that are related to practiced items—are more poorly remembered than NRP (baseline) items (Figure 30.4). In other words, practice retrieving “apple” can make it more difficult to remember “banana”—evidence for retrieval-induced forgetting. This forgetting is thought to occur precisely because “banana” is related to “apple”—that is, during retrieval of “apple,” “banana” competes and is subject to inhibition as a means of reducing this competition. This inhibition is manifested, behaviorally, in an increased rate of forgetting. That RP⫺ items are more likely to be forgotten than NRP items does not, on its own, indicate that RP⫺ items are necessarily inhibited. Instead, given that RP⫺ items are tested using the same cues (e.g., “FRUIT-”) as RP⫹ items,

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it is possible that the strengthening of RP⫹ items creates enhanced retrieval competition during RP⫺ recall, thereby blocking or occluding access to RP⫺ items (Mensink & Raaijmakers, 1988). Evidence of memory inhibition comes from the critical observation that retrieval-induced forgetting also occurs even when RP⫹ items are tested using novel cues (e.g., “MONKEY-b” for “banana”; Aslan, Bäuml, & Pastotter, 2007; Johnson & Anderson, 2004; Levy, McVeigh, Marful, & Anderson, 2007; MacLeod & Saunders, 2005; Saunders & MacLeod, 2006) or even when RP⫺ items are tested in simple item recognition tests (Hicks & Starns, 2004). Importantly, both of these tests avoid the problem of retrieval competition between RP⫺ and RP⫹ items, as the cue that they share in common is eliminated during the test procedure. This property of retrieval-induced forgetting is referred to as cue-independence. Further evidence for memory inhibition comes from the finding that retrieval-induced forgetting is most likely to occur when mnemonic competition is high. Specifically, if competing memories are weak they are less likely to be forgotten (inhibited); whereas competing memories that are strong are more likely to be forgotten (M. C. Anderson et al., 1994; Bäuml, 1998). Similarly, if retrieval practice of RP⫹ items is replaced with noncompetitive extra study exposures, forgetting of “competitors” (i.e., RP⫺ items) does not occur (M. C. Anderson, Bjork, & Bjork, 2000). Together, these data provide strong support for the argument that retrieval-induced forgetting is a response to retrieval competition—a property we refer to as competitiondependence. Thus, the observation that retrieval-induced forgetting is cue-independent provides important evidence that competing memories are actually inhibited, while the observation that retrieval-induced forgetting is competition-dependent provides a constraint on when inhibition should occur.

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Figure 30.4 Schematic of retrieval-induced forgetting. Note. Practiced items (RP⫹) are typically better remembered than baseline (NRP) or competing (RP⫺) items (numbers reflect percentage recall). Critically, RP⫺ items are typically more poorly recalled than NRP items. The recall impairment for RP⫺ items, relative to NRP items, reflects the magnitude of retrieval-induced forgetting. From “Rethinking Interference Theory: Executive Control and the Mechanisms of Forgetting,” by M. C. Anderson, 2003, Journal of Memory and Language, 49, pp. 415–445. Copyright 2003 by Elsevier Press. Adapted with permission.

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Previously, we discussed the role of PFC in guiding retrieval in the face of competition. With respect to retrieval-induced forgetting, there is an additional phenomenon to explain: the weakening or inhibition of competing memories. On the one hand, inhibition may be a by-product of PFC control mechanisms that guide attention toward task-relevant representations—a form of biased competition (Miller & Cohen, 2001). On the other hand, inhibition may be a distinct form of control, implemented by an independent PFC control mechanism that directly weakens competing representations (M. C. Anderson et al., 2004; Levy & Anderson, 2002). Although current evidence does not clearly favor one of these possibilities over the other, both perspectives emphasize that PFC influences what is retrieved and what

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is inhibited; we therefore review general evidence that the PFC is engaged during retrieval in situations that ultimately result in inhibition. The key behavioral properties of retrieval-induced forgetting are well captured in a detailed neural network model developed by Norman, Newman, and Detre (2007). Central to the model is an algorithm in which oscillations in memory activation levels allow for the identification of target memories that are weak and competitors that are strong. Although the details of the model are beyond the scope of this chapter, it is of note that the model involves feedback mechanisms through which weak targets can be strengthened and strong competitors can be weakened. Of particular interest, the model does not contain a layer representing the contribution of the PFC. Rather, inhibition is explained in terms of local learning through feedback within memory-dedicated systems (i.e., within the medial temporal lobes). As long as competition exists, feedback mechanisms will punish competing memories. The model accounts for cue-independent forgetting in that individual items that compete for retrieval are directly weakened, and it accounts for competition-dependence in that competing items are only weakened if they become active (i.e., if they compete) during target retrieval. While the Norman et al. (2007) model does not contain a layer representing PFC—and therefore does not explain inhibition, itself, in terms of PFC cognitive control operations—the authors argue that the PFC nonetheless plays an important role in retrieval-induced forgetting. By their view, the critical role of the PFC is that it supports the selection of relevant memories—a function that is particularly needed when competition is high. More specifically, they suggest that when a retrieval cue leads to the co-activation of both relevant and irrelevant memories, competition occurs, which is detected by ACC. The ACC then triggers the engagement of other PFC mechanisms that selectively increase the activation of goal-relevant memories, or increase attention to goal-relevant features, thereby resolving competition. By detecting competition and guiding retrieval toward target representations, the PFC can select target memories to be strengthened and, as a consequence, the PFC influences which memories are weakened. Thus, the Norman et al. (2007) model explains inhibition as a by-product of PFCmediated biased competition (Miller & Cohen, 2001). The relationship between retrieval competition, inhibition, and the PFC was recently addressed in an fMRI study of retrieval-induced forgetting (Kuhl, Dudukovic, Kahn, & Wagner, 2007) that focused on the neural responses within the PFC during selective retrieval practice (i.e., across the three retrieval practice attempts of each RP⫹ item). Of critical interest was whether PFC engagement across retrieval practice is related to the inhibition of competing (RP⫺) memories, as revealed by behavioral performance on the

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final test of all items. As Norman et al. (2007) suggest, the PFC should be differentially necessary when competition is high. Thus, the PFC should be maximally engaged during initial retrieval practice attempts (i.e., before targets are strengthened and competitors are weakened), with PFC engagement decreasing as targets are repeatedly practiced and competitors are suppressed. Consistent with this prediction, Kuhl et al. (2007) observed robust decreases in PFC engagement during repeated (third) relative to initial (first) retrieval practice trials. To directly test for a relationship between these decreases in PFC engagement and the phenomenon of competitor weakening (inhibition), the relative magnitude of competitor weakening was computed for each participant [(NRP accuracy—RP⫺ accuracy)/NRP accuracy] and then regressed upon the magnitude of PFC disengagement that each participant displayed across retrieval practice trials. If the weakening of competing memories reduces the demands on PFC, then the decrease in PFC engagement across retrieval practice trials should be positively correlated with the amount of competitor weakening. Indeed, such a relationship was observed in two PFC foci: the ACC and right anterior VLPFC (Figure 30.5). The finding that ACC disengagement was related to the weakening of competing items is consistent with the hypothesis of Norman et al. (2007) that the ACC should serve to detect competition between target and competing memories, and is also consistent with a much broader literature implicating the ACC in detecting conflict between competing representations (Botvinick, Braver, Barch, Carter, & Cohen, 2001; Braver, Barch, Gray, Molfese, & Snyder, 2001; MacDonald, Cohen, Stenger, & Carter, 2000; van Veen & Carter, 2002). In other words, the relative strength of target versus competing memories should increase as a function of retrieval practice repetition, meaning that with successive retrieval practice repetitions, to the extent that competitors are successfully weakened, there should be less retrieval competition and thus less ACC engagement. The right anterior VLPFC was also clearly sensitive to the weakening of competing memories, with this sensitivity potentially taking two forms. On the one hand, the right anterior VLPFC may serve to increase activation of target memories or features of target memories—a function Norman et al. (2007) ascribe to PFC—with this function maximally required when competition is highest. On the other hand—and in contrast to the role of the PFC suggested by Norman et al. (2007)—the right anterior VLPFC may serve to directly inhibit competing memories. While these possibilities are difficult to disambiguate, we later return to potential mechanistic contributions of the right anterior VLPFC.

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Figure 30.5 Neural activation reductions in the ACC (A) and the right anterior VLPFC (B) during retrieval practice correlated with behavioral measure of retrieval-induced forgetting. Note. Subjects that showed the greatest magnitude of suppression (of RP⫺ items) displayed the greatest reductions in anterior cingulate cortex

It remains ambiguous whether the right anterior VLPFC and ACC directly or indirectly contribute to the forgetting of competing memories, however, it also might be asked whether the forgetting observed by Kuhl et al. (2007) is best explained in terms of inhibition. As described previously, support for inhibition comes from evidence that retrieval-induced forgetting is cue-independent and competition-dependent. With respect to cue-independence, the key feature is that the forgetting of competing memories should not be accounted for in terms of strengthened, practiced memories interfering with RP⫺ recall at test. Consistent with this prediction, the decreased engagement of the ACC and right anterior VLPFC across retrieval practice trials—which was correlated with RP forgetting— was not correlated with RP⫹ strengthening, suggesting that it was, in fact, the weakening of competing memories and not the strengthening of practiced memories, that reduced demands on these PFC subregions during retrieval practice. With respect to competition-dependence, it should be predicted that, if the ACC indexes competition, the initial engagement of the ACC should be a signal that competition is present, thereby triggering competitor inhibition. Indeed, those participants that showed the most retrievalinduced forgetting demonstrated greater initial engagement of the ACC during retrieval practice. In support of

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and right anterior VLPFC activation during retrieval practice. From “Decreased Demands on Cognitive Control Reveal the Neural Processing Benefits of Forgetting,” by B. A. Kuhl, N. M. Dudukovic, I. Kahn, and A. D. Wagner, 2007, Nature Neuroscience, 10, p. 911. Reprinted with permission.

the claim that the ACC was driven by mnemonic competition and that mnemonic competition triggered inhibition, it was also observed that initial hippocampal activation was positively correlated with both initial ACC activation and the magnitude of inhibition. Thus, engagement of the hippocampus likely reflected successful retrieval of both target and competing memories, with robust hippocampal activation perhaps signaling inefficient, competition-laden retrieval—a situation that triggers competitor inhibition. The competition-dependent role of the PFC in retrievalinduced forgetting is also supported by a recent event-related potential (ERP) study (Johansson, Aslan, Bäuml, Gabel, & Mecklinger, 2007). In this study, ERPs were compared during selective retrieval practice versus a control condition in which retrieval practice was replaced by extra study exposures. As noted earlier, behavioral data indicate that retrieval-induced forgetting is not observed when retrieval practice is replaced by extra study (M. C. Anderson et al., 2000), with the explanation being that extra study exposures are noncompetitive, or are at least much less competitive than retrieval practice. Replicating this dissociation, Johansson et al. (2007) observed that retrieval practice resulted in subsequently lower recall of competing memories than did extra study exposures. At the neural level, ERPs associated with retrieval practice were more positive-going than ERPs associated with extra study, with this

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difference restricted to frontal electrode sites. Critically, the magnitude of this difference in frontal electrodes between retrieval practice and extra study was greater for participants who showed the most retrieval-induced forgetting, relative to those that showed the least retrieval-induced forgetting. These data are consistent with the theme that retrieval competition is associated with both the engagement of the PFC and the inhibition of competing memories. Moreover, these data, like those reported by Kuhl et al. (2007), demonstrate a coupling between the PFC’s response to competition and the inhibition of competing memories. While these findings support the relationship between the PFC and retrieval-induced forgetting, several researchers have also attempted to establish whether intact PFC functioning is necessary for retrieval-induced forgetting to occur. For example, retrieval-induced forgetting has been probed both in patients with frontal lobe damage as well as in older adults (a population in which frontal lobe dysfunction is common, e.g., Moscovitch & Winocur, 1992; Raz et al., 1997). In one such study, Conway and Fthenaki (2003) compared patients with frontal lobe damage (either left or right lateral PFC damage) to control participants. Although the PFC has frequently been implicated in inhibitory control, the frontal patients displayed a normal pattern of retrieval-induced forgetting, relative to controls. From these data, Conway and Fthenaki argued that retrievalinduced forgetting reflects a form of unintentional inhibition and that intact PFC functioning is not necessary for producing such inhibition. Paralleling these findings, Aslan and colleagues (2007) observed normal retrieval-induced forgetting in older adults, compared with younger adults. Although these observations perhaps suggest that normal PFC functioning is not necessary for retrieval-induced forgetting to occur, there are several issues complicating this conclusion. As discussed earlier, in a standard retrieval-induced forgetting study, inhibition is not the only potential cause of forgetting. That is, if the test procedure allows the strengthened, practiced memories (RP⫹) to interfere with retrieval of competing memories (RP⫺), then retrieval-induced forgetting can be explained simply in terms of retrieval competition that arises at test. Moreover, as Norman et al. (2007) note, the PFC may be particularly important for resolving competition during the test phase of a retrieval-induced forgetting study, given that retrieval cues are linked to multiple associates and damage to the PFC is known to increase sensitivity to retrieval competition. While the potential contamination of retrieval competition during the test phase can be eliminated if an independent-probe test procedure is used (M. C. Anderson & Spellman, 1995), Conway and Fthenaki (2003) did not use such a procedure. Thus, their observation of normal retrieval-induced forgetting

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in frontal patients may simply reflect robust sensitivity to retrieval competition at test, as opposed to the actual inhibition of competing memories. Aslan and colleagues (2007), however, used both a standard test procedure as well as an independent probe test procedure, with older adults showing normal retrieval-induced forgetting in both cases. Although this result is intriguing, and, at first pass, may seem consistent with the hypothesis from Norman et al. (2007) that the PFC does not directly support inhibition, Aslan and colleagues did not explicitly address frontal lobe integrity among the older adults that were tested. Thus, it is unclear that these older adults suffered from any PFC dysfunction. Indeed, it is noteworthy that the older and younger adults tested by Aslan and colleagues demonstrated equivalent retrieval practice success. Given that the PFC is known to make necessary contributions to selective retrieval (Badre & Wagner, 2007; Dobbins & Wagner, 2005), this finding raises the possibility that the older adults tested may have had little, if any, PFC dysfunction. Summary Recent neurobiological evidence supports the claim that the PFC plays an important role in overcoming competition during selective retrieval and influencing what ultimately becomes inhibited. Moreover, the PFC is engaged in response to the presence of competition and the PFC directly benefits from the inhibition of competing memories. Thus, neurobiological evidence supports both behavioral evidence concerning when inhibition should occur (M. C. Anderson & Spellman, 1995; Levy & Anderson, 2002) as well as theoretical explanations of why inhibition is adaptive (M. C. Anderson, 2003; Bjork, 1989). Although progress in understanding the functional neurobiology of forgetting has been made, a fundamental ambiguity that awaits further clarification concerns the precise mechanism through which inhibition occurs. As noted earlier, inhibition may occur because: (a) the PFC biases competition toward (selects) relevant memories (Miller & Cohen, 2001) and, as a consequence, competing memories are inhibited; or (b) the PFC directly weakens competing memories (Levy & Anderson, 2002). Disambiguating these possibilities is particularly difficult because both hypotheses predict that PFC function will be related to the phenomena of inhibition and selection. For example, if inhibition is a consequence of PFC selection, then damage to the PFC should impair the ability to select target memories and, as a consequence, competitors should not be inhibited. On the other hand, if the PFC directly contributes to inhibition as a means of facilitating selective retrieval (Levy & Anderson, 2002), then damage to the PFC should impair the ability to inhibit irrelevant memories, which should, as a consequence, compromise the ability to select target

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memories. Thus, by either account, damage to the PFC should disrupt both the inhibition of irrelevant memories as well as the selection of relevant memories. One approach to distinguish between the mechanisms of selection and inhibition is to examine whether distinct PFC subregions contribute to each. As will be recalled from the previous section, mnemonic selection has repeatedly been associated with the left mid-VLFPC (Badre & Wagner, 2007). In the study by Kuhl et al. (2007), right anterior VLPFC engagement, but not left mid-VLPFC engagement, was correlated with the inhibition of competing memories. Although this may suggest a dissociation between the left mid-VLPFC (selection) and right anterior VLPFC (inhibition), there remain alternative explanations. For example, the right anterior VLPFC may support the allocation of attention toward properties of the retrieval cue—a form of attentional selection—which is particularly necessary when competition is high. By this view, the right anterior VLPFC would support a form of selection that is distinct from the selection supported by the left midVLPFC, but would not directly support inhibition. As we describe in the following sections, there is, in fact, some evidence in support of a selective-attention account of the right anterior VLPFC. However, given the limited data at present, mechanistic dissociations between the left midVLFPC and right anterior VLPFC remain tentative. Stopping Retrieval While attempts to remember a target memory can trigger inhibition of competing memories, it has also been argued that inhibition can occur as a result of deliberate attempts to forget something or even deliberate attempts to simply keep something out of mind. For example, Bjork (1970) describes the predicament of a short-order cook, for whom it is highly advantageous to forget an order once it is complete. The advantage to forgetting a completed order, of course, is that it reduces confusion (proactive interference) when trying to remember a current order. The situation of deliberately trying to forget, or discard, something that has already been learned has been studied using the directed forgetting paradigm. Directed forgetting studies are generally divided into two main classes. In the first type, the list method, there are typically two lists of stimuli, studied one after the other. In some cases, or for some participants, there is an instruction immediately following list 1 learning (and before list 2 learning) to forget the entire list that was just studied. After list 2 learning, memory is assessed for both list 2 items and list 1 items. When participants are instructed to forget list 1, there are typically two results of interest, relative to when a forget instruction was not issued: (1) recall of list 1 items is worse, and (2) recall of list 2 items is better.

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The impaired recall of list 1 items suggests that recall for already learned material can be volitionally influenced, whereas the improved recall of list 2 items suggests that proactive interference can be reduced, as would be the goal of the short-order cook. The second procedure used in directed forgetting studies is the item method, in which individual items (e.g., single words) are presented one at a time, followed by an instruction to either remember or forget the item. Importantly, the remember/forget instruction typically does not appear until after the relevant item has disappeared, thus ensuring that the item is at least initially encoded. In item method directed forgetting studies, forget items are, again, more poorly recalled than remember items. Although the two methods of directed forgetting are seemingly similar, the forgetting that is observed (of forget items) may have different causes. In the item method, evidence suggests that remember items benefit from preferential encoding, relative to forget items, with inhibition thought to play little role (Basden, Basden, & Gargano, 1993). In other words, remember items likely benefit from the remember instruction, but it is not clear that forget items actually suffer from the forget instruction. In the list method, however, preferential encoding in the control condition does not seem to account for the forgetting of list 1 items in the forget condition (Basden et al., 1993; Geiselman, Bjork, & Fishman, 1983). Rather, forgetting in the list method following a forget instruction has been explained in terms of either inhibition (e.g., Bjork, 1989) or an internal context change in response to the forget instruction (Sahakyan & Kelley, 2002). Although these two accounts of list-method directed forgetting are not mutually exclusive, the contextual change account has proven to hold substantial explanatory power (Sahakyan, Delaney, & Waldum, 2008). Although directed forgetting has received considerable attention given its potential application to the control of real-life memories, the mechanistic ambiguity concerning the phenomenon creates challenges for studying memory inhibition. By contrast, a more recently developed paradigm—the Think/No-Think paradigm (M. C. Anderson & Green, 2001)—has allowed for a more direct assessment of the relationship between memory control and inhibition. In the Think/No-Think paradigm, participants first study a series of cue-associate pairs (e.g., “ordeal-roach,” “journey-pants”) and are trained to recall the associate word (e.g., “roach”) when presented with the cue (e.g., “ordeal”; Figure 30.6A). Next, participants engage in the Think/NoThink phase, in which they are presented with cues (the left-hand member of a cue-associate pair; e.g., “ordeal-”) from some of the previously studied pairs. For some of these cues, participants are instructed to retrieve (Think) of the corresponding associate. For other cues, participants are instructed to prevent the corresponding associate from

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entering awareness (No-Think). Critically, participants are instructed that it is not enough to simply withhold a response on No-Think trials; rather, they are instructed to do their best to completely avoid thinking of the associate. Think and No-Think cues are repeated a varying numbers of times (e.g., 0, 1, 8, or 16 repetitions for each cue). Importantly, some of the cues never appear in the Think/ No-Think phase, functioning as baseline items (0 repetitions). Finally, recall of all associates (Think, No-Think, Baseline) is tested. Cued recall reveals that Think items are, not surprisingly, better remembered than No-Think items (Figure 30.6B). This result is essentially equivalent to the comparison of remember versus forget conditions in an item-method– directed forgetting study. However, to identify whether No-Think items actually suffered a cost, test-phase recall performance for No-Think items is compared to recall of Baseline items (i.e., items that were initially studied and trained, but that did not appear during the Think/No-Think phase). The Baseline condition provides a critical comparison condition (one that is not present in directed forgetting studies) for assessing whether No-Think items actually suffer a cost. That is, if No-Think instructions impair memory for No-Think items, then these items should be more poorly recalled than Baseline items. This is what is typically observed, with memory for No-Think items decreasing as a function of the number of No-Think repetitions that an item received (Figure 30.6B; M. C. Anderson & Green, 2001). Although a cued-recall impairment for No-Think items relative to Baseline items is suggestive of memory inhibition, it is alternatively possible that the impaired recall of No-Think items simply reflects retrieval competition (interference) that arises at test—a concern that we considered above with respect to retrieval-induced forgetting. In

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Figure 30.6 A: Outline of the Think/ No-Think paradigm. B: Recall performance at test for “Think” items increases as a function of “Think” repetitions. Note. (A) Critically, during the Think/No-Think phase, subjects are cued to think of the corresponding associate for “Think” items, but to avoid thinking of the response for “No-Think” items. Adapted from M. C. Anderson et al. (2004). (B) Recall performance for “No-Think” items decreases as a function of “NoThink” repetitions. This pattern is apparent both in the Same Probe and Independent Probe tests. From “Suppressing Unwanted Memories by Executive Control,” by M. C. Anderson and C. Green, 2001, Nature, 410, pp. 366–369. Copyright 2001 by Macmillan Publishers. Adapted with permission.

retrieval-induced forgetting, the concern over an interference explanation is perhaps more obvious, given that retrieval practice involves strengthening RP⫹ items that share a retrieval cue with RP⫺ items. In the Think/NoThink paradigm, however, it is possible that when presented with No-Think trials, participants direct their thought away from the trained associate by thinking of something else; with repetition, this new, self-generated associate may be strengthened, relative to the originally trained associate. Accordingly, at test, the cue may elicit this self-generated memory, which would interfere with target recall. As with retrieval-induced forgetting, the independent probe technique has been applied to the Think/No-Think paradigm in order to establish whether inhibition has actually occurred. For example, rather than testing the associate “roach” with the original cue “ordeal,” a new, independent probe such as “Insect-r” can be used. Critically, below-baseline forgetting of No-Think items is evident when independent probes are used at test (Figure 30.6; M. C. Anderson & Green, 2001; M. C. Anderson et al., 2004). Thus, although the Think/No-Think paradigm bears a procedural similarity to directed forgetting, the forgetting observed in the TNT paradigm has a clearer mechanistic cause—namely, deliberate attempts to keep a memory out of mind when presented with a reminder can result in inhibition of that memory. It should be noted, however, that, to the extent that participants approach No-Think trials in the Think/ No-Think paradigm by actively remembering something else, the Think/No-Think paradigm and retrievalinduced forgetting may reduce to a common phenomenon. Consistent with this view, it has been demonstrated that inhibition in the Think/No-Think paradigm is most likely to occur when participants approach No-Think trials by generating diversionary thoughts (Hertel & Calcaterra, 2005).

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Neurobiological Mechanisms of Stopping Retrieval Given that the directed forgetting paradigm was developed 3 decades prior to the Think/No-Think paradigm, there have been considerably more attempts to understand the neurobiological basis of directed forgetting than inhibition in Think/ No-Think. However, because the mechanisms of directed forgetting are more ambiguous, we briefly review several examples of the potential relationship between the PFC and directed forgetting before more fully considering the role of the PFC in the context of Think/No-Think paradigms. Zacks, Radvansky, and Hasher (1996) compared directed forgetting between older and younger adults across multiple experiments, using both item- and list-method procedures, and consistently observed that older adults were poorer at directed forgetting than young adults, consistent with the hypothesis of an inhibitory deficit associated with aging. Specifically, relative to their baseline retrieval rate, older adults were more likely than young adults to retrieve items that had previously received a forget instruction. Although suggestive of an inhibitory deficit, there are, as the authors note, alternative accounts. For example, older adults might have been poorer at encoding remember/ forget instructions, and/or as the experiment progressed, older adults may have had greater difficulty keeping track of which items were supposed to be remembered versus forgotten, potentially leading to inadvertent rehearsal of forget items. Thus, while the impairment of older adults in this context is in contrast to normal retrieval-induced forgetting among older adults (Aslan et al., 2007), it is not clear what accounts for this dissociation. Directed forgetting has also been examined in frontal patients, but with somewhat variable results. For example, Conway and Fthenaki (2003) found impaired directed forgetting among frontal patients using both list and item method designs, with the impairment restricted to those patients with right frontal damage. However, Andrés, Van der Linden, and Parmentier (2007) reported normal item-method directed forgetting among frontal patients. Unfortunately, given the variability in the size and location of lesions across these studies, it is difficult to reconcile the discrepancies in the data or to draw conclusions about the mechanisms involved. Rather, it seems that, as with aging, frontal lobe damage may, at least in some cases, disrupt directed forgetting. Finally, item-method directed forgetting has also been assessed using both ERPs and fMRI. In an ERP study, Paz-Caballero, Menor, and Jimenez (2004) observed an early (100 to 200 ms) frontal positivity for forget instructions, relative to remember instructions, that was only observed for those participants that showed a high amount of directed forgetting. The authors suggest that this frontal

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positivity may reflect the engagement of the PFC in order to inhibit or stop processing of forget items. Although this interpretation involves an inhibitory component, it does not demand that forget items themselves are inhibited; rather, it could simply be that the processing of forget items is discontinued. Thus, this interpretation is compatible with the argument that item-method directed forgetting reflects preferential encoding of remember items. By contrast, Wylie, Foxe, and Taylor (2008) used an item-method– directed forgetting paradigm with fMRI and found that the right anterior VLPFC was more active for forget items that were actually forgotten, whereas a reverse pattern was observed for items that received a remember instruction. The authors argue that the positive relationship between the right anterior VLPFC and the forgetting of forget items suggests an active mechanism of forgetting, challenging the selective rehearsal account of item-method– directed forgetting. As previously described, activation in the right anterior VLPFC was also correlated with the forgetting of competing memories in the context of retrieval-induced forgetting (Kuhl et al., 2007), perhaps suggesting a common mechanistic contribution across these two contexts. While the discussed ERP and fMRI studies of itemmethod–directed forgetting suggest an active mechanism is involved in stopping retrieval and, potentially, in inhibiting competing memories, these possibilities have been more directly assessed in a pair of fMRI studies using the Think/No-Think paradigm. These studies used emotionally neutral word pairs (M. C. Anderson et al., 2004) or emotionally valenced images (Depue, Curran, & Banich, 2007), and yielded several convergent outcomes. A key theoretical claim of M. C. Anderson’s is that inhibition reflects the engagement of active control processes supported by the PFC (Levy & Anderson, 2002). Thus, in each study it was predicted that No-Think trials would not simply reflect the failure to engage retrieval mechanisms, but rather that No-Think trials would engage PFC control mechanisms to a greater extent than Think trials. M. C. Anderson et al. (2004) observed greater activation during No-Think versus Think trials in several PFC subregions, including bilateral DLPFC, VLPFC (inclusive of right anterior VLPFC), and ACC. In contrast, Think trials were associated with greater activation in the hippocampus—consistent with the role of the hippocampus in retrieving episodic memories (e.g., Eldridge, Knowlton, Furmanski, Bookheimer, & Engel, 2000; Kirwan & Stark, 2004). Similarly, Depue et al. (2007) observed greater No-Think than Think activation in the right DLPFC, right frontopolar cortex, and right anterior VLPFC; greater Think than No-Think activation was again observed in the hippocampus. Thus, with respect to the contrast of No-Think versus Think, both studies revealed activation in the right DLPFC and right anterior VLPFC.

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Figure 30.7 A: Activation in the bilateral DLPFC during “NoThink” trials was positively correlated with the inhibition score (i.e., forgetting of No-Think items at test). B: Participants who displayed the greatest DLPFC activation during No-Think trials were characterized by lower recall accuracy for No-Think items at test, relative to Baseline items.

Note. From “Neural Systems Underlying the Suppression of Unwanted Memories,” by M. C. Anderson et al., 2004, Science, 303, pp. 232–235. Copyright 2004 by the American Association for the Advancement of Science. Adapted with permission.

Strikingly, both studies also found that the engagement of the PFC during No-Think trials was related to the fate of the to-be-avoided memories. Specifically, the magnitude of activation in the DLPFC (bilateral in M. C. Anderson et al., 2004; Figure 30.7; right lateralized in Depue et al., 2007) positively correlated with the magnitude of inhibition (forgetting) of No-Think items. These data indicate that the DLPFC is recruited during attempts to stop retrieval, and that this recruitment is associated with a cost for those memories that are avoided. Within the hippocampus, an intriguing pattern of data was observed. During Think trials, both Depue et al. (2007) and M. C. Anderson et al. (2004) observed that the hippocampus tended to be more active for items that were later remembered, relative to those that were later forgotten. By contrast, during No-Think trials, M. C. Anderson et al. (2004) reported a trend toward greater hippocampal activation for No-Think items that were later forgotten. This finding of greater hippocampal activation for No-Think items later forgotten compared to those later remembered was particularly robust among those participants who exhibited the most inhibition. If hippocampal activation is typically associated with remembering, then why is greater hippocampal activation on No-Think trials associated with forgetting? M. C. Anderson et al. suggest that such activation may reflect momentary intrusions of the to-be-avoided memories, noting that hippocampal activation during No-Think trials was also correlated with DLPFC engagement. Consistent with this interpretation, Depue et al. (2007) reported that hippocampal activation tended to

decrease across repetitions of No-Think items (presumably reflecting a practice-related decrease in intrusions), but increased across repetitions of Think trials. Moreover, this decrease in hippocampal activation across No-Think repetitions was apparent to a greater degree for the items later forgotten than those later remembered. Together, these data suggest that hippocampal activation during No-Think trials may reflect inadvertent remembering, thereby triggering DLPFC-mediated control that results in the eventual inhibition of intruding memories. As such, these data are consistent with the competitiondependent property of retrieval-induced forgetting (i.e., that competition triggers inhibition) and are potentially compatible with the observation by Kuhl et al. (2007) that greater hippocampal activation during initial retrieval practice attempts was associated with greater inhibition of competing memories. While M. C. Anderson et al. (2004) and Depue et al. (2007) found compelling evidence that the DLPFC was related to memory inhibition in Think/No-Think paradigms, Kuhl et al. (2007) observed a relationship between the right anterior VLPFC (and ACC) and memory inhibition in retrieval-induced forgetting. Although this apparent discrepancy in the foci of lateral PFC activations may, at first pass, suggest different mechanisms of inhibition in the two paradigms, there is a notable difference in the analyses reported by Kuhl et al. (2007) and those reported by M. C. Anderson et al. (2004) and Depue et al. (2007). Specifically, M. C. Anderson et al. and Depue et al. found that DLPFC activation, collapsed across all No-Think repetitions,

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predicted memory inhibition, whereas Kuhl et al. (2007) found that activation changes in right anterior VLPFC activation (i.e., repetition-related reductions) predicted memory inhibition. Although M. C. Anderson and colleagues (2004) did not consider their data as a function of repetition, Depue and colleagues (2007) separately considered activation in each of four quartiles (each quartile contained three repetitions of Think/No-Think items). Importantly, right anterior VLPFC activation during No-Think trials tended to decrease across repetitions—indeed, this region was engaged, above Baseline, only during No-Think trials in the first two quartiles. Although Dupue et al. did not report whether the magnitude of this decrease was related to the magnitude of inhibition, the data are at least consistent with the view that right anterior VLPFC engagement is decreasingly necessary as No-Think items are inhibited. By contrast, right DLPFC activation did not decrease across quartiles; in fact, right DLPFC only displayed above-Baseline activation during No-Think trials in the last three quartiles. Moreover, a negative correlation was observed between DLPFC and hippocampal activation that was maximal during the last quartile. Thus, while DLPFC activation was correlated with memory inhibition and hippocampal activation, the temporal profile of DLPFC activation raises interesting questions about its mechanistic contribution. If intrusions during No-Think trials are most likely to occur during initial No-Think attempts, and these intrusions trigger DLPFC-mediated inhibition, as argued by M. C. Anderson and colleagues (2004), then why is the DLPFC most active during later repetitions, relative to initial repetitions? Moreover, why are hippocampal and DLPFC activation uncorrelated during initial No-Think repetitions (when intrusions are presumably highest), but strongly negatively correlated during late repetitions (when intrusions are presumably low)? These two aspects of the data seem to indicate that DLPFC engagement is highest when the demand for inhibition is actually lowest. Although not discussed by Depue and colleagues (2007), perhaps the increase in DLPFC activation across repetitions, and the increasingly negative relationship between the DLPFC and the hippocampus, reflects a practice-related improvement in the ability to engage the DLPFC. That is, during initial No-Think attempts, there may be a failure to engage the DLPFC to inhibit No-Think items; with practice, the DLPFC is successfully recruited and this is reflected in the down-regulation of the hippocampus. Importantly, this view suggests that DLPFC engagement is not an obligatory response to competition, but may be flexibly engaged to regulate competition. Regardless of why DLPFC engagement onsets later than the right anterior VLPFC, the dissociation between

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these regions is intriguing, particularly in light of evidence implicating each of these regions in other contexts that putatively involve memory inhibition. Moreover, it is also of note that the left mid-VLPFC, which has repeatedly been implicated in resolving mnemonic competition (for review, see Badre & Wagner, 2007), has not been implicated in the inhibition of episodic memories using either retrieval-induced forgetting or Think/No-Think paradigms. Although additional work is clearly necessary in order to better elucidate the relationship between these various PFC control mechanisms and the mechanisms of selective retrieval and inhibition, in the next section we attempt to synthesize the evidence reviewed thus far, situating this evidence in the broader context of how the PFC contributes to selective attention and goal-oriented behavior.

PREFRONTAL CORTEX CONTRIBUTIONS TO RETRIEVAL AND FORGETTING Although our treatment of forgetting is grouped into two main themes—interference and inhibition—it should be clear that these are not two, independent causes of forgetting. Rather, the presence of competition can directly interfere with retrieval, thereby causing forgetting, but competition can also trigger the inhibition of competing memories, again contributing to forgetting. In other words, both forms of forgetting are ultimately related to the presence of competition and the mechanisms through which competition is resolved. Understanding the way in which competition is resolved is not, of course, a question that is specific to the domain of memory, as several influential models of PFC function are principally focused on mechanisms of competition resolution (e.g., Desimone & Duncan, 1995; Miller & Cohen, 2001; Shimamura, 2000). Thus, understanding the control processes that guide retrieval and forgetting should benefit from a consideration of the ways in which the PFC guides attention and goaldirected behavior. In this final section, we briefly consider how attention and cognitive control may be implemented through coordinated, but distinct, contributions from the ACC, DLPFC, and VLPFC. By some accounts, attentional control may be implemented via two distinct frontoparietal networks (Corbetta & Shulman, 2002). At a first level, attention-grabbing changes in sensory stimuli, across multiple modalities, tend to activate a network of ventral fronto-parietal regions, with the right VLPFC perhaps the most frequently activated PFC subregion (e.g., Downar, Crawley, Mikulis, & Davis, 2000; for review, see Corbetta & Shulman, 2002). For example, right anterior VLPFC activation has been associated with the reorienting of attention in response to, and in order to overcome,

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Prefrontal Cortex Contributions to Retrieval and Forgetting

distraction (Weissman, Roberts, Visscher, & Woldorff, 2006). This ventral fronto-parietal attentional system has been dissociated from a dorsal fronto-parietal system that is thought to support top-down orienting of attention, perhaps integrating bottom-up inputs with attentional task sets (Corbetta & Shulman, 2002). Although the frontal component of this dorsal system most frequently involves the frontal eye fields, the DLPFC may also be a component of this same system, particularly when considering attentional control outside the domain of visual attention (e.g., Luks, Simpson, Dale, & Hough, 2007). For example, in a now classic study, the role of the DLPFC in implementing control in a modified Stroop task was contrasted with that of the ACC (MacDonald et al., 2000). Critically, during task preparation, the DLPFC, but not the ACC, was modulated by the task instruction. During the trial itself, the ACC—but not the DLPFC—was modulated by the level of conflict (greater ACC engagement for incongruent versus congruent trials). Conceptually, similar dissociations between the DLPFC and ACC have since been reported (e.g., Weissman, Warner, & Woldorff, 2004), and from these and other observations, it has been argued that the DLPFC supports the top-down implementation of control. Thus, with respect to attentional control, the VLPFC appears to be engaged in response to distracting or unexpected stimuli or events and serves to reorient attention. In a complementary manner, the DLPFC appears to play a critical role in volitionally engaging attention; this topdown allocation of attention may occur in preparation for a demanding cognitive task, but may also occur during task execution, to the extent that attended information interacts with task goals (Corbetta & Shulman, 2002). Distinctions between the VLPFC and DLPFC have also been drawn in other domains, where a putatively hierarchical relationship between the VLPFC and DLPFC has often been emphasized. For example, with respect to the use of rules, it has been argued that the VLPFC supports the retrieval and maintenance of task rules, whereas the DLPFC may support flexible rule use or rule selection (for review, see Bunge, 2004). This view is supported by evidence that the VLPFC tends to be continuously engaged during rule maintenance, whereas the DLPFC tends to be engaged in preparation for a response (Bunge, 2004). Within the context of working memory paradigms, the DLPFC has frequently been implicated in response selection and top-down control, as opposed to simply maintaining information (e.g., Rowe, Toni, Josephs, Frackowiak, & Passingham, 2000; for review, see Curtis & D’Esposito, 2003). The higher-order role of the DLPFC in working memory has been contrasted with the role of the VLPFC, which is thought to support retrieval or simple maintenance of information (D’Esposito et al., 1998; D’Esposito, Postle,

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Ballard, & Lease, 1999; Petrides, 1996). For example, the VLPFC is engaged during rote rehearsal and during elaborative rehearsal that requires the manipulation or updating of working memory contents, whereas the DLPFC is selectively engaged by elaborative rehearsal (Wagner, Maril, Bjork, & Schacter, 2001). Moreover, DLPFC activation may lag VLPFC activation, consistent with the idea that the DLPFC operates on the products of information maintained/retrieved by VLPFC (Wagner et al., 2001). Similarly, within episodic memory, the VLPFC has been implicated in maintaining and elaborating on retrieval cues, whereas DLPFC has been implicated in monitoring the products of retrieval and their relation to decision rules (Dobbins et al., 2002; Dobbins & Wagner, 2005). Returning to the theme of this chapter, a central question is how do selective retrieval and forgetting relate to these PFC processing distinctions? As we review, retrieval competition has been associated with the engagement of the VLPFC—both the left mid-VLPFC (Badre & Wagner, 2007; Thompson-Schill et al., 1997) and the right anterior VLPFC (Kuhl et al., 2007). However, the VLPFC has also been implicated in stopping retrieval (M. C. Anderson et al., 2004; Depue et al., 2007; Wylie et al., 2008), suggesting that VLPFC is engaged in response to competition from irrelevant memories, rather than remembering, per se. Indeed, it is a critical point that VLPFC engagement appears to be more tightly coupled with retrieval competition than with the actual phenomenon of retrieval. For example, repeated successful retrieval of the same information—which is associated with behavioral facilitation—is associated with robust decreases in the engagement of the bilateral VLPFC, but relatively little modulation of the DLPFC; in contrast, the actual phenomenon of retrieval success is associated with robust engagement of the DLPFC, but more limited activation of the VLPFC (Kuhl et al., 2007). Similarly, when task demands explicitly require stopping the act of retrieval, the right anterior VLPFC is engaged during initial attempts, but is less engaged with practice, presumably reflecting decreasing competition from to-be-avoided memories; DLPFC engagement, on the other hand, does not decrease across repeated attempts to stop retrieval, and may even tend to increase (Depue et al., 2007). These dissociations between the VLPFC and DLPFC are potentially compatible with dual-system theories of attention (Corbetta & Shulman, 2002). As discussed, the VLPFC is thought to support reflexive orienting to distracting stimuli. Compatible with this perspective, in the context of mnemonic control, competing memories may serve as distracting representations that help reorient attention via VLPFC engagement. The DLPFC, on the other hand, may support the top-down allocation of attention. In situations of mnemonic control, it may be that the DLPFC

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is not directly engaged in response to mnemonic competition, but rather is engaged to help bias mnemonic processing such that mnemonic goals are achieved. For example, the DLPFC may evaluate retrieval products with respect to task goals (Dobbins et al., 2002; Henson, Rugg, Shallice, & Dolan, 2000), and may therefore be sensitive to retrieval success. Alternatively, or additionally, the DLPFC may implement attentional biases that, once in place, effectively reduce mnemonic competition. Although a distinction between the VLPFC and DLPFC based on reflexive versus top-down control, respectively, may hold some explanatory power, it should be noted that the left VLPFC has also been implicated in implementing top-down control during retrieval (e.g., Badre et al., 2005). Thus, further evidence is necessary in order to better specify the mechanistic distinctions between VLPFC and DLPFC control processes and their relation to mnemonic processing. Although the distinction between the VLPFC and DLPFC has been of particular interest in theories of PFCmediated control, it is worth emphasizing that these regions (a) act in concert with other prefrontal structures (e.g., ACC and frontopolar cortex), and (b) can likely be further subdivided into distinct functional units. With respect to other PFC control mechanisms, the ACC may support an initial component of cognitive control, in that it can detect competition between multiple, coactive representations (Botvinick et al., 2001; Braver et al., 2001; van Veen & Carter, 2002). Importantly, ACC engagement has frequently been shown to correlate with DLPFC engagement (Badre & Wagner, 2004; Bunge, Burrows, & Wagner, 2004; Kondo, Osaka, & Osaka, 2004), leading to the hypothesis that ACC-mediated competition detection triggers DLPFCmediated control. Such couplings have been observed in the context of competitive remembering (Bunge et al., 2004; Kuhl et al., 2007), with one possibility being that the computation performed by the DLPFC, in response to ACC signaling, is to increase activation of goal-relevant memories (Miller & Cohen, 2001). The frontopolar cortex, on the other hand, may be situated at the top of the PFC processing hierarchy (Koechlin & Summerfield, 2007), coordinating VLPFC/DLPFC operations with specific subgoals (Braver & Bongiolatti, 2002). Consistent with a supervisory role of the frontopolar cortex, initial attempts to stop retrieval result in coupled activation between the right anterior VLPFC and frontopolar cortex, whereas later attempts are associated with coupling between the DLPFC and frontopolar cortex (Depue et al., 2007). Finally, while the organizing principles of the VLPFC and DLPFC that we consider here may be useful in terms of constraining hypotheses of how the PFC implements control, both the VLPFC and DLPFC can likely be further decomposed into distinct functional units (e.g., Badre

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et al., 2005; Dobbins et al., 2002; Gold et al., 2006). For example, within the VLPFC, the left mid-VLPFC has been implicated in selecting between multiple, active representations, whereas the left anterior VLPFC has been implicated in controlled retrieval of semantic information through direct interaction with posterior semantic stores (for review, see Badre & Wagner, 2007). In other words, there are likely multiple ways in which the VLPFC responds to competition and multiple ways in which the DLPFC coordinates mnemonic processing. Future work will undoubtedly advance understanding of both the specific mechanisms supported by PFC subregions as well as the way in which these mechanisms act in concert such that mnemonic competition is resolved.

SUMMARY In this chapter, we highlighted the interrelated nature of remembering and forgetting, and the substantial impact that prefrontal function has on each. Specifically, the prefrontal cortex serves to guide retrieval toward goal-relevant memories and away from those memories that prove irrelevant. These prefrontal-mediated operations have important consequences both for what we presently remember as well as what we later forget. Moreover, multiple, functionally distinct prefrontal subregions are involved in coordinating these mnemonic operations, likely reflecting the engagement of broader cognitive control mechanisms that allow for the flexible allocation of attention. Accordingly, a complete telling of the story of forgetting and remembering will ultimately entail full specification of the many ways in which the frontal lobes shape acts of retrieval.

REFERENCES Anderson, J. R. (1974). Retrieval of propositional information from longterm memory. Cognitive Psychology, 6, 451–474. Anderson, J. R. (1976). Language, memory, and thought. Hillsdale, NJ: Erlbaum. Anderson, J. R. (1983a, April 1). Retrieval of information from long-term memory. Science, 220, 25–30. Anderson, J. R. (1983b). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22, 261–295. Anderson, M. C. (2003). Rethinking interference theory: Executive control and the mechanisms of forgetting. Journal of Memory and Language, 49, 415–445. Anderson, M. C., Bjork, E. L., & Bjork, R. A. (2000). Retrieval-induced forgetting: Evidence for a recall-specific mechanism. Psychonomic Bulletin and Review, 7, 522–530. Anderson, M. C., Bjork, R. A., & Bjork, E. L. (1994). Remembering can cause forgetting: Retrieval dynamics in long-term memory. Journal

8/18/09 6:27:49 PM

References of Experimental Psychology: Learning, Memory, and Cognition, 20, 1063–1087.

Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222.

Anderson, M. C., & Green, C. (2001, March 15). Suppressing unwanted memories by executive control. Nature, 410, 366–369.

D’Esposito, M., Aguirre, G. K., Zarahn, E., Ballard, D., Shin, R. K., & Lease, J. (1998). Functional MRI studies of spatial and nonspatial working memory. Cognitive Brain Research, 7, 1–13.

Anderson, M. C., Ochsner, K. N., Kuhl, B., Cooper, J., Robertson, E., Gabrieli, S. W., et al. (2004, January 9). Neural systems underlying the suppression of unwanted memories. Science, 303, 232–235. Anderson, M. C., & Spellman, B. A. (1995). On the status of inhibitory mechanisms in cognition: Memory retrieval as a model case. Psychological Review, 102, 68–100. Andrés, P., Van der Linden, M., & Parmentier, F. B. (2007). Directed forgetting in frontal patients’ episodic recall. Neuropsychologia, 45, 1355–1362. Aslan, A., Bäuml, K. H., & Pastotter, B. (2007). No inhibitory deficit in older adults’ episodic memory. Psychological Science, 18, 72–78. Badre, D., Poldrack, R. A., Pare-Blagoev, E. J., Insler, R. Z., & Wagner, A. D. (2005). Dissociable controlled retrieval and generalized selection mechanisms in ventrolateral prefrontal cortex. Neuron, 47, 907–918. Badre, D., & Wagner, A. D. (2004). Selection, integration, and conflict monitoring: Assessing the nature and generality of prefrontal cognitive control mechanisms. Neuron, 41, 473–487. Badre, D., & Wagner, A. D. (2007). Left ventrolateral prefrontal cortex and the cognitive control of memory. Neuropsychologia, 45, 2883–2901. Basden, B. H., Basden, D. R., & Gargano, G. J. (1993). Directed forgetting in implicit and explicit memory tests: A comparison of methods. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19, 603–616. Bäuml, K. H. (1998). Strong items get suppressed, weak items do not: The role of item strength in output interference. Psychonomic Bulletin and Review, 5, 459–463. Bjork, R. A. (1970). Positive forgetting: The noninterference of items intentionally forgotten. Journal of Verbal Learning and Verbal Behavior, 9, 255–268. Bjork, R. A. (1989). Retrieval inhibition as an adaptive mechanism in human memory. Hillsdale, NJ, and England: Erlbaum. Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108, 624–652. Braver, T. S., Barch, D. M., Gray, J. R., Molfese, D. L., & Snyder, A. (2001). Anterior cingulate cortex and response conflict: Effects of frequency, inhibition and errors. Cerebral Cortex, 11, 825–836. Braver, T. S., & Bongiolatti, S. R. (2002). The role of frontopolar cortex in subgoal processing during working memory. NeuroImage, 15, 523–536. Bunge, S. A. (2004). How we use rules to select actions: A review of evidence from cognitive neuroscience. Cognitive, Affective, and Behavioral Neuroscience, 4, 564–579. Bunge, S. A., Burrows, B., & Wagner, A. D. (2004). Prefrontal and hippocampal contributions to visual associative recognition: Interactions between cognitive control and episodic retrieval. Brain and Cognition, 56, 141–152. Conway, M. A., & Fthenaki, A. (2003). Disruption of inhibitory control of memory following lesions to the frontal and temporal lobes. Cortex, 39, 667–686. Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3, 201–215. Curtis, C. E., & D’Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in Cognitive Sciences, 7, 415–423. Depue, B. E., Curran, T., & Banich, M. T. (2007, July 13). Prefrontal regions orchestrate suppression of emotional memories via a two-phase process. Science, 317, 215–219.

c30.indd Sec4:603

603

D’Esposito, M., Postle, B. R., Ballard, D., & Lease, J. (1999). Maintenance versus manipulation of information held in working memory: An eventrelated fMRI study. Brain and Cognition, 41, 66–86. Dobbins, I. G., Foley, H., Schacter, D. L., & Wagner, A. D. (2002). Executive control during episodic retrieval: Multiple prefrontal processes subserve source memory. Neuron, 35, 989–996. Dobbins, I. G., & Wagner, A. D. (2005). Domain-general and domainsensitive prefrontal mechanisms for recollecting events and detecting novelty. Cerebral Cortex, 15, 1768–1778. Dolan, R. J., & Fletcher, P. C. (1997, August 7). Dissociating prefrontal and hippocampal function in episodic memory encoding. Nature, 388, 582–585. Downar, J., Crawley, A. P., Mikulis, D. J., & Davis, K. D. (2000). A multimodal cortical network for the detection of changes in the sensory environment. Nature Neuroscience, 3, 277–283. Eldridge, L. L., Knowlton, B. J., Furmanski, C. S., Bookheimer, S. Y., & Engel, S. A. (2000). Remembering episodes: A selective role for the hippocampus during retrieval. Nature Neuroscience, 3, 1149–1152. Estes, W. K. (1955). Statistical theory of spontaneous recovery and regression. Psychological Review, 62, 145–154. Feredoes, E., Tononi, G., & Postle, B. R. (2006). Direct evidence for a prefrontal contribution to the control of proactive interference in verbal working memory. Proceedings of the National Academy of Sciences, USA, 103, 19530–19534. Fletcher, P. C., Shallice, T., & Dolan, R. J. (2000). “Sculpting the response space”: An account of left prefrontal activation at encoding. Neuroimage, 12, 404–417. Geiselman, R. E., Bjork, R. A., & Fishman, D. L. (1983). Disrupted retrieval in directed forgetting: A link with posthypnotic amnesia. Journal of Experimental Psychology: General, 112, 58–72. Gold, B. T., Balota, D. A., Jones, S. J., Powell, D. K., Smith, C. D., & Andersen, A. H. (2006). Dissociation of automatic and strategic lexicalsemantics: Functional magnetic resonance imaging evidence for differing roles of multiple frontotemporal regions. Journal of Neuroscience, 26, 6523–6532. Henson, R. N., Rugg, M. D., Shallice, T., & Dolan, R. J. (2000). Confidence in recognition memory for words: Dissociating right prefrontal roles in episodic retrieval. Journal of Cognitive Neuroscience, 12, 913–923. Henson, R. N., Shallice, T., Josephs, O., & Dolan, R. J. (2002). Functional magnetic resonance imaging of proactive interference during spoken cued recall. Neuroimage, 17, 543–558. Hertel, P. T., & Calcaterra, G. (2005). Intentional forgetting benefits from thought substitution. Psychonomic Bulletin and Review, 12, 484–489. Hicks, J. L., & Starns, J. J. (2004). Retrieval-induced forgetting occurs in tests of item recognition. Psychonomic Bulletin and Review, 11, 125–130. Janowsky, J. S., Shimamura, A. P., Kritchevsky, M., & Squire, L. R. (1989). Cognitive impairment following frontal lobe damage and its relevance to human amnesia. Behavioral Neuroscience, 103, 548–560. Johansson, M., Aslan, A., Bäuml, K. H., Gabel, A., & Mecklinger, A. (2007). When remembering causes forgetting: Electrophysiological correlates of retrieval-induced forgetting. Cerebral Cortex, 17, 1335–1341. Johnson, S. K., & Anderson, M. C. (2004). The role of inhibitory control in forgetting semantic knowledge. Psychological Science, 15, 448–453. Jonides, J., & Nee, D. E. (2006). Brain mechanisms of proactive interference in working memory. Neuroscience, 139, 181–193.

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Kirwan, C. B., & Stark, C. E. (2004). Medial temporal lobe activation during encoding and retrieval of novel face-name pairs. Hippocampus, 14, 919–930. Koechlin, E., & Summerfield, C. (2007). An information theoretical approach to prefrontal executive function. Trends in Cognitive Sciences, 11, 229–235. Kondo, H., Osaka, N., & Osaka, M. (2004). Cooperation of the anterior cingulate cortex and dorsolateral prefrontal cortex for attention shifting. Neuroimage, 23, 670–679. Kostopoulos, P., & Petrides, M. (2003). The mid-ventrolateral prefrontal cortex: Insights into its role in memory retrieval. European Journal of Neuroscience, 17, 1489–1497. Kuhl, B. A., Dudukovic, N. M., Kahn, I., & Wagner, A. D. (2007). Decreased demands on cognitive control reveal the neural processing benefits of forgetting. Nature Neuroscience, 10, 908–914. Levy, B. J., & Anderson, M. C. (2002). Inhibitory processes and the control of memory retrieval. Trends in Cognitive Sciences, 6, 299–305. Levy, B. J., McVeigh, N. D., Marful, A., & Anderson, M. C. (2007). Inhibiting your native language: The role of retrieval-induced forgetting during second-language acquisition. Psychological Science, 18, 29–34. Luks, T. L., Simpson, G. V., Dale, C. L., & Hough, M. G. (2007). Preparatory allocation of attention and adjustments in conflict processing. Neuroimage, 35, 945–958. Lundstrom, B. N., Ingvar, M., & Petersson, K. M. (2005). The role of precuneus and left inferior frontal cortex during source memory episodic retrieval. Neuroimage, 27, 824–834. MacDonald, A. W., III, Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000, June 9). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science, 288, 1835–1838. MacLeod, M. D., & Saunders, J. (2005). The role of inhibitory control in the production of misinformation effects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 964–979. Martin, R. C., & Cheng, Y. (2006). Selection demands versus association strength in the verb generation task. Psychonomic Bulletin and Review, 13, 396–401. McGeoch, J. A. (1942). The psychology of human learning: An introduction. New York: Longmans, Green. Melton, A. W., & Irwin, J. M. (1940). The influence of degree of interpolated learning on retroactive inhibition and the overt transfer of specific responses. American Journal of Psychology, 3, 173–203. Mensink, G., & Raaijmakers, J. G. (1988). A model for interference and forgetting. Psychological Review, 95, 434–455. Metzler, C. (2001). Effects of left frontal lesions on the selection of context-appropriate meanings. Neuropsychology, 15, 315–328. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202. Moscovitch, M. (1982). Multiple dissociations of function in amnesia. In L. Cermak (Ed.), Human memory and amnesia (pp. 337–370). Hillsdale, NJ: Erlbaum. Moscovitch, M., & Winocur, G. (1992). The neuropsychology of memory and aging. In F. I. M. Craik & T. A. Salthouse (Eds.), The handbook of aging and cognition (pp. 315–372). Hillsdale, NJ: Erlbaum. Müller, G. E., & Pilzecker, A. (1900). Experimentalle Beitrage zur Lehre com Gedachtnis. Zeitschrift fur Psychologie, 1, 1–300. Norman, K. A., Newman, E. L., & Detre, G. (2007). A neural network model of retrieval-induced forgetting. Psychological Review, 114, 887–953. Pandya, D. N., Van Hoesen, G. W., & Mesulam, M. M. (1981). Efferent connections of the cingulate gyrus in the rhesus monkey. Experimental Brain Research, 42, 319–330. Paz-Caballero, M. D., Menor, J., & Jimenez, J. M. (2004). Predictive validity of event-related potentials (ERPs) in relation to the directed forgetting effects. Clinical Neurophysiology, 115, 369–377.

c30.indd Sec4:604

Petrides, M. (1996). Specialized systems for the processing of mnemonic information within the primate frontal cortex. Philosophical Transactions of the Royal Society of London, B: Biological Sciences, 351, 1455–1461. Petrides, M. (2005). Lateral prefrontal cortex: Architectonic and functional organization. Philosophical Transactions of the Royal Society of London, B: Biological Sciences, 360, 781–795. Petrides, M., & Pandya, D. N. (1999). Dorsolateral prefrontal cortex: Comparative cytoarchitectonic analysis in the human and the macaque brain and corticocortical connection patterns. European Journal of Neuroscience, 11, 1011–1036. Petrides, M., & Pandya, D. N. (2002). Comparative cytoarchitectonic analysis of the human and the macaque ventrolateral prefrontal cortex and corticocortical connection patterns in the monkey. European Journal of Neuroscience, 16, 291–310. Petrides, M., & Pandya, D. N. (2007). Efferent association pathways from the rostral prefrontal cortex in the macaque monkey. Journal of Neuroscience, 27, 11573–11586. Postman, L., Stark, K., & Fraser, J. (1968). Temporal changes in interference. Journal of Verbal Learning and Verbal Behavior, 7, 672–694. Raz, N., Gunning, F. M., Head, D., Dupuis, J. H., McQuain, J., Briggs, S. D., et al. (1997). Selective aging of the human cerebral cortex observed in vivo: Differential vulnerability of the prefrontal gray matter. Cerebral Cortex, 7, 268–282. Rowe, J. B., Toni, I., Josephs, O., Frackowiak, R. S., & Passingham, R. E. (2000, June 2). The prefrontal cortex: Response selection of maintenance within working memory? Science, 288, 1656–1660. Sahakyan, L., Delaney, P. F., & Waldum, E. R. (2008). Intentional forgetting is easier after two “shots” than one. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34, 408–414. Sahakyan, L., & Kelley, C. (2002). A contextual change account of the directed forgetting effect. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 1064–1072. Saunders, J., & MacLeod, M. D. (2006). Can inhibition resolve retrieval competition through the control of spreading activation? Memory and Cognition, 34, 307–322. Schacter, D. L. (1999). The seven sins of memory: Insights from psychology and cognitive neuroscience. American Psychologist, 54, 182–203. Shimamura, A. P. (2000). The role of the prefrontal cortex in dynamic filtering. Psychobiology, 28, 207–218. Shimamura, A. P., Jurica, P. J., Mangels, J. A., Gershberg, F. B., & Knight, R. T. (1995). Susceptibility to memory interference effects following frontal lobe damage: Findings from tests of paired-associate learning. Journal of Cognitive Neuroscience, 7, 144–152. Smith, M. L., Leonard, G., Crane, J., & Milner, B. (1995). The effects of frontal- or temporal-lobe lesions on susceptibility to interference in spatial memory. Neuropsychologia, 33, 275–285. Sohn, M. H., Goode, A., Stenger, V. A., Carter, C. S., & Anderson, J. R. (2003). Competition and representation during memory retrieval: Roles of the prefrontal cortex and the posterior parietal cortex. Proceedings of the National Academy of Sciences, USA, 100, 7412–7417. Sohn, M. H., Goode, A., Stenger, V. A., Jung, K. J., Carter, C. S., & Anderson, J. R. (2005). An information-processing model of three cortical regions: Evidence in episodic memory retrieval. Neuroimage, 25, 21–33. Thompson-Schill, S. L., D’Esposito, M., Aguirre, G. K., & Farah, M. J. (1997). Role of left inferior prefrontal cortex in retrieval of semantic knowledge: A reevaluation. Proceedings of the National Academy of Sciences, USA, 94, 14792–14797. Thompson-Schill, S. L., Jonides, J., Marshuetz, C., Smith, E. E., D’Esposito, M., Kan, I. P., et al. (2002). Effects of frontal lobe damage on interference effects in working memory. Cognitive, Affective, and Behavioral Neuroscience, 2, 109–120.

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References Thompson-Schill, S. L., Swick, D., Farah, M. J., D’Esposito, M., Kan, I. P., & Knight, R. T. (1998). Verb generation in patients with focal frontal lesions: A neuropsychological test of neuroimaging findings. Proceedings of the National Academy of Sciences, USA, 95, 15855–15860.

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605

Weissman, D. H., Roberts, K. C., Visscher, K. M., & Woldorff, M. G. (2006). The neural bases of momentary lapses in attention. Nature Neuroscience, 9, 971–978. Weissman, D. H., Warner, L. M., & Woldorff, M. G. (2004). The neural mechanisms for minimizing cross-modal distraction. Journal of Neuroscience, 24, 10941–10949.

Turner, M. S., Cipolotti, L., Yousry, T., & Shallice, T. (2007). Qualitatively different memory impairments across frontal lobe subgroups. Neuropsychologia, 45, 1540–1552.

Wixted, J. T. (2004). The psychology and neuroscience of forgetting. Annual Review of Psychology, 55, 235–269.

van Veen, V., & Carter, C. S. (2002). The anterior cingulate as a conflict monitor: FMRI and ERP studies. Physiology and Behavior, 77, 477–482.

Wylie, G. R., Foxe, J. J., & Taylor, T. L. (2008). Forgetting as an active process: An fMRI investigation of item-method-directed forgetting. Cerebral Cortex, 18, 670–682.

Wagner, A. D., Maril, A., Bjork, R. A., & Schacter, D. L. (2001). Prefrontal contributions to executive control: FMRI evidence for functional distinctions within lateral Prefrontal cortex. Neuroimage, 14, 1337–1347.

Zacks, R. T., Radvansky, G., & Hasher, L. (1996). Studies of directed forgetting in older adults. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 143–156.

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Chapter 31

Emotional Modulation of Learning and Memory LARRY F. CAHILL

This chapter addresses our current understanding of the brain/body mechanisms subserving the influence of emotional arousal on long-term memory. The focus is on memory enhancement for acute, emotionally stressful events in healthy males and females. A great deal of evidence from both animal and human subject studies converges on the conclusion that endogenous stress hormones, released during and after emotionally arousing events, interact with the amygdala (an almond-shaped structure in the medial temporal lobe) to influence memory storage processes for the emotional events that occur in other brain regions (McGaugh, 2004). This mechanism is thought to provide an evolutionarily adaptive way to adjust memory strength to memory importance. It is also increasingly clear that biological sex can influence neuronal function from the level of single neurons in vivo to the level of functioning humans (Cahill, 2006). Relatively recent research is beginning to reveal influences of sex on neural mechanisms of emotionally influenced memory. These discoveries challenge a fundamental, but generally unexamined assumption on which most research in this field has been built, namely, that the sex of the subjects tested will not significantly influence experimental findings. They also have substantial clinical implications for a host of disorders, such as posttraumatic stress disorder (PTSD) and clinical depression. We present some of the recent evidence regarding sex influences, and consider the implications for future research. This work already forces the conclusion that studies of emotional memory (at least involving human subjects) may no longer safely assume that subject sex will not significantly influence experimental findings, hence conclusions about brain mechanisms.

BASOLATERAL AMYGDALA: THE BRAIN’S FOCAL POINT FOR MODULATION OF MEMORY Central to our current understanding of brain mechanisms of emotional memory is the amygdala complex, a collection of nuclei in the medial temporal lobe implicated in both producing and reacting to the body’s stress response. A central concept developed by McGaugh and colleagues (McGaugh, 2004; McGaugh, Cahill, & Roozendaal, 1996) holds that the amygdala modulates the storage of different forms of memory, in particular conscious (“declarative”) memory, and does so via extensive interactions with the endogenous stress response produced by emotionally salient events. One may immediately grasp the plausibility of this view of amygdala function by first considering the anatomical connectivity of the primate amygdala. A meta-analysis of cortico-cortical connectivity in the monkey by Young and Scannell (1994) revealed a quite striking and unique aspect of the amygdala, in particular its basolateral nuclei. This analysis demonstrated convincingly that the primate basolateral amygdala region possesses an extremely widespread and unique pattern of connectivity with the cortex. The overwhelming majority of these connections with the cortex are amygdalofugal, that is, from the amygdala to the cortical regions. Thus, the amygdala is evidently extremely well suited to exert a diffuse, modulatory influence on cortical function. Its anatomical architecture also belies the simplistic, albeit popular notion that the amygdala possesses an “in” door through which Pavlovian stimuli enter, and an “out” door through which “emotional memories” made in the amygdala leave (LeDoux, 2000). Across many species, learning tasks, and laboratories, stimulation of the amygdala (and in particular its basolateral complex) potently modulates—enhances or

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impairs—memory storage processes. Most often, stimulation has been given immediately after learning, allowing the conclusion that the effects of the stimulation on memory resulted from an influence on consolidation processes. Evidence also indicates that the amygdala’s ability to modulate memory consolidation depends crucially on endogenous stress hormones. For example, amygdala stimulation may improve or impair memory storage depending on adrenal gland function (McGaugh, 2004). It is remarkably consistent that across many laboratories and learning paradigms essentially all peripherally administered drugs and hormones require the basolateral amygdala to affect memory. This is among the best-supported conclusions in all of the neurobiology of learning and memory. Lesions or functional inactivation of the key amygdala nuclei (the basolateral amygdala) block the memory enhancing and impairing effects of all drugs and hormones tested to date. Even the amnesia induced by some general anesthetics is blocked by lesions of the basolateral amygdala (Alkire, Vazdarjanova, Dickinson-Anson, White, & Cahill, 2001). If a major amygdala function is to interact with endogenous stress hormones to influence memory, then we should find a disproportionate effect of amygdala lesions in learning situations that are relatively arousing, that is, stress response activating. We examined this possibility in a study involving lesions of the amygdala in rats (Cahill & McGaugh, 1990). The results from this study suggested that amygdala lesions impaired memory only in the relatively arousing circumstances, leading us to conclude that “the degree of arousal produced by the unconditioned stimulus, and not the aversive nature per se, determines the level of amygdala involvement (in a learning situation). The amygdala appears to participate in learning especially when the reinforcement is of a highly arousing nature.” Importantly, this conclusion has been confirmed by four human brain imaging studies that examined responses of the amygdala in human subjects to stimuli that varied across the arousing (arousing-calming) and valence dimensions (pleasant-unpleasant) (Anderson et al., 2003; Kensinger & Corkin, 2004; Lewis, Critchley, Rotshtein, & Dolan, 2006; Small et al., 2003). In all four studies, the amygdala responded selectively to the arousing qualities of the stimuli. Thus, both animal and human subject work converge on the view that arousal (sympathetic activation), and not valence or any particular emotion such as fear, is critical to amygdala activation. Although the amygdala appears to be required for enhanced memory for arousing events, evidence indicates it is not always the site of storage of such memories. This fact is made most clear by experiments involving stimulation of the amygdala immediately after animals are trained

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in tasks known to be dependent on other brain regions, namely the hippocampus and caudate nucleus (McGaugh, 2004; Packard, Cahill, & McGaugh, 1994). These studies show that postlearning stimulation of the amygdala can modulate memory for hippocampus-dependent and caudate-dependent tasks. Crucially, however, it fails to do so if the relevant “downstream” structure (hippocampus for a hippocampal task, caudate for a caudate task) is simultaneously inactivated. Furthermore, inactivation of the amygdala prior to retrieval testing does not affect performance in these tasks. Taken together, these findings argue that the amygdala acts in the period soon after an emotionally arousing event to modulate the storage of memories in other brain regions, such as the hippocampus and caudate. Thus, extensive research involving both animal and human subjects converges on a neurobiological mechanism by which emotional arousal sculpts the contents of memory (McGaugh, 2004). Endogenous stress hormones, released during and after an emotionally arousing event, influence the postevent storage of memory by actions requiring the amygdala. Without an amygdala, modulation of memory by stress hormones fails to occur. The amygdala in turn does not necessarily serve as a site of storage of memory, but as a modulator of memory processing occurring in other brain regions. This postlearning memory modulating mechanism can serve as an evolutionarily adaptive way to create memory strength that is, in general, proportional to memory importance.

AMYGDALA-BASED EMOTIONAL MODULATION OF ATTENTION In addition to its well-established, postlearning (“offline”) modulatory role in memory, evidence suggests that the focusing of attention during emotional events (“online” modulation) also involves the BLA (see Anderson, 2005; Anderson & Phelps, 2001). Anderson and colleagues have found, for example, that whereas in healthy subjects presentation of an emotional stimulus reduces the “attentional blink” (a reduced awareness of a stimulus presented in the same location and very shortly after another stimulus), this effect fails to occur in patients with bilateral amygdala damage. Indeed, there is a striking asymmetry in amygdalo-fugal and amygdalo-petal projections, whereby the amygdala sends many more projections throughout neocortical regions, including perceptual cortices, than it receives. It has been known since the early 1950s that stimulation of the amygdala activates the cortical EEG as effectively as does the reticular activating system. The amygdala also has projections to subcortical structures

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important for attention and eye movements—the thalamic pulvinar nucleus and the superior colliculus, and receives input from sensory thalamic nuclei. This arrangement is consistent with the view that the amygdala receives a relatively rapid and coarse representation of the state of the world and, via its robust feedback projections to the cortex, alters the perception of stimuli, upregulating cortical encoding of stimuli of emotional importance. It has been speculated that these stimuli may then be tagged in some way that allows the relatively sluggish, postlearning hormonal responses to selectively enhance consolidation of the tagged information (Cahill, Gorski, & Le, 2003).

AMYGDALA ACTIVITY AND EMOTIONAL MEMORY IN HUMANS—EMERGENCE OF SEX EFFECTS If the amygdala functions, at least in part, to modulate the storage of memory for emotional events, then it should be possible to detect a relationship between the degree to which the amygdala is activated in response to emotional events, and the degree to which those events are subsequently recalled. Such a relationship is now well established. Cahill et al. (1996) scanned healthy male subjects with PET for regional cerebral glucose while they viewed either a series of relatively emotionally arousing (negative) films, or a matched but much more emotionally neutral set of films, and examined memory for the films 3 weeks later. The results showed that right hemisphere amygdala activity while viewing the emotional films correlated significantly with long-term recall, but not with recall of emotionally neutral films. Several other laboratories have now confirmed this finding, providing additional support for the view that the amygdala plays a special, presumably modulatory, role in memory storage for emotional events, as predicted by animal research. However, each human imaging study contained unexplained hemispheric asymmetries in the amygdala relationship to subsequent memory for emotional material. It was at this point that I observed that studies reporting amygdala effects predominantly or exclusively on the right side of the brain involved only male subjects, whereas those studies reporting amygdala effects predominantly or exclusively on the left side of the brain involved only female subjects, raising the possibility that subject sex determined, at least in part, the hemispheric lateralization of amygdala function. But because the studies differed along many other dimensions as well (e.g., type of scanning, type of to-be-remembered material), this conclusion was clearly speculative.

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Sex-Related Hemispheric Lateralization of the Amygdala Relationship to Long-Term Memory for Emotional Events We sought to determine whether subject sex was influencing lateralization of the amygdala relationship to long-term memory for emotional material by directly comparing activity in the brains of men and women within a single study. In our first study (Cahill et al., 2001), 11 men and 11 women received two PET scans for regional cerebral glucose utilization—one while watching a series of emotionally arousing films clips, another while watching a series of more emotionally neutral clips. Memory for the films was assessed in a surprise free recall test 3 weeks later. The results showed that a large area of right, but not left hemisphere amygdala activity was significantly related to enhanced memory for the emotional film clips in men. Yet, in women, a large area of left, and not right, hemisphere amygdala activity related to enhanced memory for the emotional films. Canli, Desmond, Zhao, and Gabrieli (2002) confirmed this sex-related lateralization in an fMRI study of amygdala function. Subjects in this study were scanned while viewing a series of emotionally arousing or neutral slides. Activity of the right, and not left, amygdala in males related significantly to memory for the most emotional slides, whereas activity of the left, and not right, amygdala related to memory for the emotional slides in women. Canli et al. (2002) observed in addition that “both correlations were so robust that they were present even with multiple comparisons across the brain and without selecting the amygdala as a region of interest.” Perhaps the most compelling demonstration of a sexrelated hemispheric lateralization to date comes from an fMRI study by Cahill, Uncapher, Kilpatrick, Alkire, and Turner (2004), who also employed fMRI to study amygdala activity at encoding and subsequent memory for emotional images. Consistent with the previous studies, these authors report that activity of the right hemisphere amygdala was significantly more related to subsequent memory for the emotional images in men than in women, but activity of the left hemisphere amygdala was significantly more related to subsequent memory for the emotional images in women than in men (see Figure 31.1). Unlike the studies just mentioned, Cahill et al. (2004) also documented a significant crossover interaction between the variables of hemisphere and sex in the amygdala relationship to memory for emotional material. A fourth study directly comparing amygdala function in men and women further documents the sex-related lateralization (Mackiewicz, Sarinopoulos, Cleven, & Nitschke, 2006). These investigators argue that the effect was evident for more ventral amygdala aspects, which correspond largely to the

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Amygdala Activity and Emotional Memory in Humans—Emergence of Sex Effects

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Figure 31.1 Sex-related hemispheric lateralization of amygdala function in long-term memory for emotionally arousing films. Note. Activity of the right, and not left, amygdala in males while viewing emotionally arousing films related significantly to memory for the films 2 weeks later. Activity of the left, and not right, amygdala in women related significantly to memory for the films. From “Sex-Related Hemispheric Lateralization of Amygdala Function in Emotionally-Influenced Memory: An fMRI Investigation,” by L. Cahill, M. Uncapher, L. Kilpatrick, M. Alkire, and J. Turner, 2004, Learning and Memory, 11, pp. 261–266. Copyright 2004 by Cold Spring Harbor Laboratory Press. Reprinted with permission.

basolateral nuclei. As discussed earlier, it is the basolateral nuclei that animal research indicates is key to the amygdala’s modulatory role in memory. Thus, a sex-related hemispheric lateralization of amygdala function with respect to long-term memory for emotional events is now evident across many studies of amygdala function from many laboratories, including four studies that have directly compared amygdala function in men and women in this context. The sex-related hemispheric lateralization of amygdala function in emotional memory raises a simple question: What does it mean? Before addressing that question, it is helpful to consider the profound importance of the sex influence issue for neuroscience. Sex Influences on Brain Function More Generally Considered Biological sex influences brain function to a far greater extent than neuroscience has recognized to date (Cahill,

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2006). Pronounced neurobiological differences between males and females are increasingly reported outside of the traditional domain of reproduction. Sex differences exist in every brain lobe, including in cognitive brain regions such as the amygdala, hippocampus, and even the neocortex. The advent of modern imaging techniques has revealed sex-related differences in brain correlates of emotional processing, facial processing, working memory, auditory processing, and language processing, to name just a few. Even the cellular correlates of neuronal death in cell culture differ depending on whether the neurons were derived from male or female brains (Li et al., 2005). Sex-related differences also exist in stress hormone function. As one example, Wolf, Schommer, Hellhammer, McEwen, and Kirschbaum (2001) reported a negative correlation between a cortisol response to a stressor and subsequent memory in a sample of men and women, but found further that this effect resulted from a highly significant correlation found only in men. As conceptual blinders that sex does not matter fall off, more investigators are looking, and finding, sex influences on memory and its neural correlates. Three examples: First, Milad and colleagues (2006) determined whether sex differences exist in the acquisition and extinction of Pavlovian fear conditioning in healthy men and women. Acquisition was significantly faster in men than in women. During extinction, no overall sex difference was found, but the menstrual cycle significantly influenced the rate of extinction in females. A second example: Yonker and colleagues (2003) examined sex influences on episodic memory in healthy subjects using a series of tasks. They reported a large (Cohen’s d > .70) overall female advantage in performance on these episodic memory tests. Interestingly, and in contrast to the conditioning study just mentioned, this advantage appeared to be unrelated to circulating levels of estradiol. Third, in a series of recent studies employing null mutant and microarray genetic methods, Mizuno and colleagues have reported “male specific” molecular mechanisms within the hippocampus related to memory formation (see Mizuno et al., 2007). Male and female mice learning the same tasks appear not to be using the same molecular neural machinery to do so. Clearly grasping the impressive quantity and diversity of sex influences on brain function should, by itself, challenge investigators to carefully consider potential sex influences on emotional memory, the issue to which we now return. Sex Difference in Human Amygdala Functional Connectivity at Rest Returning to the issue of the amygdala and neural mechanisms of emotional memory, we next wondered whether the sex-difference in amygdala function in response to

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Figure 31.2 Amygdala seed voxels displaying significant sexrelated differences in amygdala functional connectivity during resting conditions. Note. Left amygdala areas reveal greater functional connectivity in women than in men. Right amygdala areas reveal greater functional connectivity in men than in women. From “Sex-Related Differences in Amygdala Functional Connectivity during Resting Conditions,” by Kilpatrick L. A., Zald D. H., Pardo J. V., & L. F. Cahill, 2006, Neuroimage, 30, pp. 452– 461. Copyright 2006 by Elsevier Press. Reprinted with permission.

emotional stimuli arose, at least in part, from a preexisting sex difference in the functional connectivity of the amygdala at rest, before stimulation. We studied the patterns of functional covariance between the left and right hemisphere amygdalae and the rest of the brain in a large sample of men and women given blood-flow PET scans while resting with their eyes closed (Kilpatrick, Zald, Pardo, & Cahill, 2006). The results of this analysis revealed that activity of the right hemisphere amygdala covaried to a much larger extent with other brain regions in men than it did in women; conversely, activity of the left hemisphere amygdala covaried with other brain regions far more in women than in men (Figure 31.2). Consistent with findings from several earlier investigations, no difference existed between the sexes in the overall levels of amygdala activity; rather, the sexes differed in the pattern of amygdala connectivity with the rest of the brain. Thus, it appears from these findings that the sex-related hemispheric lateralization of amygdala function in relation to memory for emotional stimuli results in part from a preexisting sex difference in the amygdala’s functional connectivity. The findings of Kilpatrick et al. (2006) also indicate that sex can no longer be ignored by any investigators of human amygdala function, since pronounced sex differences in its function presumably must exist in all experimental situations. Potential Relationship of the Sex-Related Amygdala Hemispheric Specialization to Hemispheric Global/Local Processing Bias We also sought to better understand what the sex-related lateralization of amygdala function may mean by integrating

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it with other knowledge about hemisphere lateralization of function. In particular, a good deal of evidence suggests that the two cerebral hemispheres differentially process more global versus local aspects of a stimulus or scene. Evidence from a variety of sources indicates that the right hemisphere is biased toward the processing of more global, holistic aspects of a stimulus or scene, while the left hemisphere is biased toward more local, finer detail processing of the same stimulus or scene (Beeman & Bowden, 2000; Fink et al., 1996; Fink, Marshall, Halligan, & Dolan, 1999). Combining our evidence of a sex-related hemispheric laterality of amygdala function in memory for emotional material (males/right, females/left) with the view that the hemispheres differentially process global versus local information (holistic/right, detail/left) allowed us to posit that there may exist a sex-related difference in the effects of a ␤-adrenergic blockade on emotional memory. We know from animal research that amygdala function is impaired by beta-blockers, drugs that induce blockade of ␤-adrenergic receptors. We also know, on anatomical grounds, that each amygdala largely modulates its own hemisphere. Hence, we reasoned that a beta-blocker, by impairing amygdala function, might impair the presumed modulatory effect of the right hemisphere amygdala on the more global processing of the right hemisphere in men, thereby reducing their memory for the more global (central) aspects of an emotional story. Similarly, we reasoned that the same betablocker might impair the presumed modulatory effect of the left hemisphere amygdala on the more local processing of the left hemisphere, thereby reducing memory for the details of the same emotional story in women. To test this hypothesis, we then reanalyzed published data from two studies demonstrating an impairing effect of β-adrenergic blockade on memory for an emotionally arousing story (Cahill & van Stegeren, 2003; Figure 31.3). Note in particular the results for story phase 2 (P2 on the X-axis) in which the emotional story elements were introduced (concerning severe injuries to a small boy in an accident while his mother watched), and for which the hypothesis at issue most clearly holds. The P2 results reveal a double dissociation of gender and type of to-be-remembered information (central versus peripheral) on propranolol’s impairing effect on memory: Propranolol significantly impaired P2 memory of central information in men but not women, yet impaired P2 memory of peripheral detail in women but not men. These results are consistent with the hypothesis that, under emotionally arousing conditions, activation of right amygdala/hemisphere function produces a relative enhancement of memory for central information in males, and activation of left amygdala/hemisphere function in females produces a relative enhancement of memory for peripheral details in women.

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Amygdala Activity and Emotional Memory in Humans—Emergence of Sex Effects Men 100

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Note. A: Values for questions defined as pertaining to central information. B: Values for questions defined as pertaining to peripheral detail. Values represent mean percentage correct (±SEM) on the recognition test in each experimental group. P1, P2, P3 indicate story phases 1, 2, 3, respectively. Emotional story elements were introduced in P2. From “SexRelated Impairment of Memory for Emotional Events with ␤-Adrenergic Blockade,” by L. Cahill and A. van Stegeren, 2003, Neurobiology of Learning and Memory, 79, pp. 81–88. Copyright 2003 by Elsevier Press. Reprinted with permission. * p < .01 placebo compared with corresponding P2 propranolol group (posthoc, two-tailed, unpaired t-test comparison).

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Uncovering Sex Influences on Emotional Memory Because the assumption that subject sex will not significantly influence findings, hence conclusions, is increasingly viewed by investigators as questionable at best, many are beginning to explicitly examine the issue in studies of emotional memory. For example, Gasbarri, Arnone, Lucchese, Pacitti, and Cahill (2007) examined EEG responses to emotional and neutral stimuli in healthy men and women. The P300 response was assessed from electrodes located over the left and right hemispheres as men and women viewed images taken from the International Affective Picture System. The results showed that, for the negative (and presumably most arousing) slides, the P300 was greater when recorded over the left hemisphere in women than it was in men. Conversely, the P300 was greater when recorded over the right hemisphere in men than it was in women. This pattern (women left/men right) parallels that observed in earlier studies (described previously) regarding the amygdala. Additionally, they suggest that sex-related differences in how the brain processes memory for emotional events begin within 300 ms of the onset of an emotional event. Other work examined the effects of a postlearning stressor (cold pressor stress, CPS, induced by forearm immersion in ice water) on memory consolidation. In one study (Andreano & Cahill, 2006), subjects received CPS or a control procedure immediately after hearing a short

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story. Memory for the story was assessed in an incidental, free recall test 1 week later. CPS produced a retrograde enhancing effect on memory in men, but not in women, despite having produced a similar cortisol response in both groups. Ongoing work in our laboratory is examining menstrual cycle influences on the mnemonic effects of CPS. But again, the findings force the important conclusion that subject sex cannot be assumed not to matter any longer in studies of emotional memory. As a final example, we are also uncovering menstrual cycle influences on rumination that occurs after subjects view emotional films. In as yet unpublished work, we find that women who view emotionally graphic films while in the luteal phase of their menstrual cycle ruminate significantly more on the films than do women who view them while in the follicular phase of the cycle. Furthermore, progesterone levels are significantly positively correlated with rumination. In the same study (Ferree & Cahill, in preparation), we also demonstrate a highly significant relationship between the degree of postevent rumination after viewing arousing films and the strength of subsequent memory strength for the films. These new findings are thus suggesting that rumination occurring after emotionally arousing events may help strengthen memory for that event, and that this process is accelerated in women who experience the emotional event with high levels of progesterone.

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SUMMARY There is now very strong evidence from converging animal and human subject work that endogenous stress hormones, released during and after emotionally arousing events, interact with the amygdala (especially its basolateral nuclei) to modulate memory consolidation processes. It is becoming increasingly clear that, while these neural events may be broadly similar in men and women, they also differ in significant ways that can no longer be avoided by our field. Understanding disorders of emotional memory such as PTSD with established sex differences in their incidence and/or nature requires that we better understand sex influences on emotional memory in our basic science. We suggest that greater attention to potential sex influences is also likely to better inform future studies of “flashbulb memory.”

REFERENCES Alkire, M., Vazdarjanova, A., Dickinson-Anson, H., White, N. S., & Cahill, L. (2001). Selective basolateral amygdala lesions block propofol-induced amnesia. Anesthesiology, 95, 708–715. Amaral, D. G., & Price, J. L. (1984). Amygdalo-cortical projections in the monkey (Macaca fascicularis). Journal of Comparative Neurology, 230, 465–496. Anderson, A. K. (2005). Affective influences on the attentional dynamics supporting awareness. Journal of Experimental Psychology: General, 134, 258–281. Anderson, A. K., Christoff, K., Stappen, I., Panitz, D., Ghahremani D. G., Glover, G., et al. (2003). Dissociated neural representations of intensity and valence in human olfaction. Journal of Neuroscience, 6, 96–202. Anderson, A., & Phelps, E. (2001, May 17). Lesions of the human amygdala impair enhanced perception of emotionally salient events. Nature, 411, 305–309. Andreano, J. M., & Cahill, L. L. (2006). Glucocorticoid release and memory consolidation in men and women. Psychological Science, 17, 466–470. Beeman, M. J., & Bowden, E. M. (2000). The right hemisphere maintains solution-related activation for yet-to-be-solved problems. Memory and Cognition, 28, 1231–1241. Cahill, L. (2006). Why sex matters for neuroscience. Nature Reviews: Neuroscience, 7, 477–484.

Cahill, L., Uncapher, M., Kilpatrick, L., Alkire, M., & Turner, J. (2004). Sex-related hemispheric lateralization of amygdala function in emotionally-influenced memory: An fMRI investigation. Learning and Memory, 11, 261–266. Cahill, L., & van Stegeren, A. (2003). Sex-related impairment of memory for emotional events with ␤-adrenergic blockade. Neurobiology of Learning and Memory, 79, 81–88. Canli, T., Desmond, J. E., Zhao, Z., & Gabrieli, J. D. (2002). Sex differences in the neural basis of emotional memories. Proceedings of the National Academy of Sciences, USA, 99, 10789–10794. Canli, T., Zhao, Z., Brewer, J., Gabrieli, J. D., & Cahill, L. (2000). Eventrelated activation in the human amygdala associates with later memory for individual emotional experience. Journal of Neuroscience, 20, RC99. Fink, G. R., Halligan, P. W., Marshall, J. C., Frith, C. D., Frackowiak, R. S., & Dolan, R. J. (1996, August 15). Where in the brain does visual attention select the forest and the trees? Nature, 382, 626–628. Fink, G. R., Marshall, J. C., Halligan, P. W., & Dolan, R. J. (1999). Hemispheric asymmetries in global/local processing are modulated by perceptual salience. Neuropsychologia, 37, 31–40. Gasbarri, A., Arnone, B., Lucchese, F., Pacitti, F., & Cahill, L. (2007). Sex - related hemispheric laterality of emotional picture processing: An event - related potential study. Brain Research, 1139, 178 – 186. Kensinger, E. A. & Corkin S. (2004). Two routes to emotional memory: Distinct neural processes for valence and arousal. Proceedings of the National Academy of Sciences, USA, 101, 3310–3315. Kilpatrick, L. A., Zald, D. H., Pardo, J. V., & Cahill, L. F. (2006). Sexrelated differences in amygdala functional connectivity during resting conditions. Neuroimage, 30, 452–461. LeDoux, J. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 23, 155–184. Lewis, P. A., Critchley, H. D., Rotshtein, P., & Dolan, R. J. (2006, May 22). Neural correlates of processing valence and arousal in affective words. Cerebral Cortex, 17(3), 742-748. Li, H., Pin, S., Zeng, Z., Wang, M. M., Andreasson, K. A., & McCullough, L. D. (2005). Sex differences in cell death. Annals of Neurology, 58, 317–321. Mackiewicz, K. L., Sarinopoulos, I., Cleven, K. L., & Nitschke, J. B. (2006, September 8). The effect of anticipation and the specificity of sex differences for amygdala and hippocampus function in emotional memory. Proceedings of the National Academy of Sciences, USA, 103, 14200–14205. McGaugh, J. L. (2004). The amygdala modulates the consolidation of memories of emotionally arousing experiences. Annual Review of Neuroscience, 27, 1–28.

Cahill, L., Gorski, L., & Le, K. (2003). Enhanced human memory consolidation with post-learning stress: Interaction with the degree of arousal at encoding. Learning and Memory, 10, 270–274.

McGaugh, J. L., Cahill, L., & Roozendaal, B. (1996). Involvement of the amygdala in memory storage: Interaction with other brain systems. Proceedings of the National Academy of Sciences, USA, 93, 13508–13514.

Cahill, L., Haier, R. J., Fallon, J., Alkire, M. T., Tang, C., Keator, D., et al. (1996). Amygdala activity at encoding correlated with long-term, free recall of emotional information. Proceedings of the National Academy of Sciences, USA, 93, 8016–8021.

Milad, M.R., Goldstein, J.M., Orr, S.P., Wedig, M.M., Klibanski, A., Pitman, R.K. & Rauch, S.L. (2006) Fear conditioning and extinction: influence of sex and menstrual cycle in healthy humans. Behavioral Neuroscience. 120(6), 1196–203.

Cahill, L., Haier, R. J., White, N. S., Fallon, J., Kilpatrick, L., Lawrence, C., et al. (2001). Sex-related difference in amygdala activity during emotionally influenced memory storage. Neurobiology of Learning and Memory, 75, 1–9.

Mizuno, K., Antunes-Martins, A., Ris, L., Peters, M., Godaux, E., & Giese, K. P. (2007). Calcium/calmodulin kinase kinase beta has a malespecific role in memory formation. Neuroscience, 145, 393–402.

Cahill, L., & McGaugh, J. L. (1990). Amygdaloid complex lesions differentially affect retention of tasks using appetitive and aversive reinforcement. Behavioral Neuroscience, 104, 532–543.

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Packard, M., Cahill, L., & McGaugh, J. L. (1994). Amygdala modulation of hippocampal-dependent and caudate nucleus-dependent memory processes. Proceedings of the National Academy of Sciences, USA, 91, 8477–8481.

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References

c31.indd Sec4:613

613

Small, D. M., Gregory, M. D., Mak, Y. E., Gitelman, D., Mesulam, M. M., & Parrish, T. (2003, August 14). Dissociation of neural representation of intensity and affective valuation in human gustation. Neuron, 39(4), 701–711.

Wolf, O. T., Schommer, N. C., Hellhammer, D. H., McEwen, B. S., & Kirschbaum, C. (2001). The relationship between stress induced cortisol levels and memory differs between men and women. Psychoneuro endocrinology, 26, 711–720.

Young, M. P., & Scannell, J. W. (1994). Analysis of connectivity: Neural systems in the cerebral cortex. Reviews in the Neurosciences, 5, 227–250.

Yonker, J. E., Eriksson, E., Nilsson, L. G., & Herlitz, A. (2003). Sex differences in episodic memory: Minimal influence of estradiol. Brain and Cognition, 52(2), 231–238.

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Chapter 32

Evaluative Processes GARY G. BERNTSON, GREG J. NORMAN, AND JOHN T. CACIOPPO

evaluative systems and the increasing complexity of these networks at higher levels of the neuraxis that can sustain at least partially independent activations. Such patterns allow for more flexible outputs, such as cautious approach during anxiety-like states (see Chapter 36), capable of developing over different temporal dimensions. We review evidence that evaluative processes are well conserved throughout ontogeny and phylogeny, represented throughout multiple levels of the neuraxis, and organized along a cardinal dimension of evaluative bivalence (i.e., approach vs. avoidance, positivity vs. negativity).

Natural selection has tailored the computational capacities of the brain to promote survival and maximize reproduction. This evolutionary pressure has led to the ability to quickly evaluate situations in which an organism must delineate between hostile and hospitable stimuli and select appropriate responses. The behavioral output of such evaluations may manifest in approach or avoidance dispositions that promote survival and minimize negative consequences. Although approach and avoidance dispositions often synergistically promote a common behavioral outcome, at times they may come into conflict (e.g., tolerating an unpalatable taste in order to obtain nutrients). Moreover, evaluative processes are represented in distributed systems at multiple levels of the neuraxis, and this multiple-level processing may also give rise to conflicts (e.g., suppressing pain-withdrawal reflexes to remove an embedded sliver). Despite potential complexities of central evaluative substrates, behavioral manifestations are constrained—an organism cannot simultaneously approach and avoid a goal object. Such physical constraints may belie the underlying structure of central evaluative processes and have led to theoretical models, typically based on behavioral measures that characterize evaluative processes as points along a bipolar (positive to negative) dimension of valence (Osgood, Suci, & Tannenbaum, 1957; Posner, Russell, & Peterson, 2005; Russell, 2003; Watson, Wiese, Vaidya, & Tellegen, 1999). This is often considered to be mediated by a single neural integrator responsible for valence integration (Allport, 1935). Although useful in many contexts, models of evaluative processes that assume reciprocity among positive and negative valence and homogeneity of neural substrates are likely too simplistic. Based on evolutionary, neurobiological, and psychological considerations, Cacioppo and Berntson (1994; Cacioppo, Gardner, & Berntson, 1997; Larsen, McGraw, & Cacioppo, 2001) have proposed a more complex, bivariate space model of evaluative processes. This model recognizes distinct positive and negative evaluative systems that can function in a reciprocal or coactive fashion (e.g., in ambivalence) and embraces the multiple-level representations of

LEVELS OF ORGANIZATION IN THE NERVOUS SYSTEM Levels of Evaluative Function: Lower-Level and Spinal Reflexes Spinal reflexes are among the lowest levels of organization in the central nervous system, and their relative simplicity allows for fast and efficient adaptive response to environmental stimuli. Although capable of operating independently of higher levels, spinal reflexes also provide critical functional support for higher-level functions, an issue to which we return later. In his treatise The Integrative Action of the Nervous System (1906), Sir Charles Sherrington detailed spinal organizations that contribute to postural regulation and provide the basic neurological support for locomotion. He also described spinal substrates for basic, low-level evaluative reactions. Among the most salient of spinal reflexes is the flexor (pain) withdrawal reflex, which represents a primitive but effective evaluative mechanism for protection against noxious or injurious stimuli. Nociceptive signals carried by somatosensory afferents activate flexor neuron pools via interneuron circuits within the spinal cord, resulting in flexor withdrawal responses (Craig, 2003; Lundberg, 1979; Sandrini et al., 2005; Schouenborg, Holmberg, & Weng, 1992). 617

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Although appetitive reflexes may be less obvious than aversive reflexes at the level of the spinal cord, primitive approach/engagement dispositions are also apparent in spinal extensor reflexes. Sherrington (1906) described extensor thrust reflexes to Palmer contact that represent low-level reflexive dispositions promoting contact and engagement with the external environment. These approach/engagement reflexes are supplemented by suckling and ingestive reflexes of brain stem origin, which are considered later in this chapter. At a trivial level, flexor and extensor reflexes promote diametrically opposing motoric dispositions. The spinal circuits for these reflexes are distinct and separately organized, and they include differences in peripheral sensory receptors, afferent axonal populations, central interneuronal pathways, and motoneuron output pools. This is not to say that flexor/extensor reflexes are entirely independent. Although the primary neural circuits underlying flexor and extensor reflexes are parallel and distinct, there are rich interactions among these networks—an organizational pattern that Sherrington referred to as the alliance of reflexes. Examples include the crossed-extension reflex, in which activation of the flexor reflex in one limb is associated with a reflex extension of the opposite limb. Sherrington also described interactions among networks for opponent flexor and extensor reflexes for a given limb as a pattern of reciprocal innervation. Reciprocal innervation is the property by which spinal reflex networks that activate a specific outcome (e.g., limb flexion) also tend to inhibit opponent (e.g., extensor) muscles, which synergistically promote the target response. These organizational patterns are not unique to spinal circuits but represent general neuroarchitectural features that may inform the operations of higher-level systems as well. Behavioral manifestations of the principle of reciprocal innervation, for example, can be seen even at a cognitive level. One example comes from the cognitive dissonance literature, where the mere selection of an item from among several choices results in increased cognitive valuation of the chosen item and concurrent devaluation of the nonselected items (Aronson & Carlsmith, 1963; Egan, Santos, & Bloom, 2007). The integrative outputs of spinal approach/withdrawal circuits may provide a basic model for understanding higher-level evaluative processes. For example, flexor withdrawal and extensor approach reflexes are not symmetrical in strength because flexor withdrawal reflexes are significantly more potent than their antagonistic extensor (approach) reflexes and recover more rapidly after spinal transection. As is considered later, asymmetric strength of evaluative systems is also apparent at higher levels of the neuraxis where avoidance reactions (anxiety, fear) tend to

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have a stronger hold on affect when compared to approach reactions (incentive, reward). This makes adaptive sense because a single failure of the avoidance system can lead to subsequent injury or death. Natural selection may have tuned the avoidance system for preferential control of behavior. The bias toward avoidance reactions represents a occurring theme at all levels of the neuraxis and has been termed the negativity bias (Cacioppo & Berntson, 1999; Cacioppo, Larsen, Smith, & Berntson, 2004). Despite this negativity bias, flexor/withdrawal reflexes are not always dominant over their opponent processes because extensor/approach reflexes can take precedence over withdrawal processes at lower levels of stimulation or activation. This disposition toward approach behaviors in the context of low levels of activation has been termed the positivity offset (Cacioppo & Berntson, 1999; Cacioppo et al., 2004) and characterizes the operations of evaluative processes at multiple levels of the neuraxis. As we consider later, the asymmetry of neurobehavioral dispositions can lead to a context-dependent outcome because approach dispositions may predominate at lower levels of evaluative activation but can be trumped by avoidance or withdrawal (negativity bias) at higher levels of evaluative activation. Spinal flexor and extensor reflexes have separate, although interacting, circuitries and thus can operate in parallel within the constraints of those neural interactions. Despite this underlying bivalence, the behavioral output of opponent extensor/flexor networks may lie along a bipolar continuum from flexion to extension, the output being constrained by the mechanical coupling of the extensor and flexor muscles around a specific point of articulation at a joint. Neural Hierarchies Multilevel perspectives of neuronal organization have been emphasized by scientists and philosophers alike, among the more influential of whom was the nineteenthcentury neurologist John Hughlings Jackson. In his essay “Evolution and Dissolution of the Nervous System,” Jackson (1884/1958) laid the groundwork for multilevel characterization of neuronal organization. Jackson argued that the evolutionary emergence of higher levels of neuronal organizations does not involve a replacement or displacement of lower levels. Rather, evolutionary development entails a re-representation and elaboration of functions at progressively higher levels of the nervous system. Although rostral levels were thought to be characterized by elaborate networks capable of more sophisticated functions, they were not seen to replace lower levels but in fact remain highly dependent on lower neuraxial substrates. For example, the critical spinal networks and related locomotor reflexes for stepping constitute essential lower

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processing circuits that support outputs from higher motor systems. In Jackson’s view, the proper interpretation of the consequences of brain injuries is that these injuries are not optimally defined by the functions that are lost but rather in the reversion (dissolution) of those functions to lower levels of neural organization. It is now apparent that the neuraxis is replete with hierarchical organizations composed of simple reflexlike circuits at the lowest levels, such as the brain stem and spinal cord, and neural networks for more integrative computations at higher levels (for reviews, see Berntson, Boysen, & Cacioppo, 1993; Berntson & Cacioppo, 2000; Berridge, 2004). The relatively simple neural circuitry characteristic of lower levels of the neuraxis is essential for survival because it allows for rapid computations and subsequent motor outputs. The adaptive function of such circuits is obvious because it may be more important in some circumstances to perform a rapid but imperfect response rather than a more elaborate and protracted performance that may produce a more elaborate outcome. The additional time consumed by such processes could lead to a negative outcome. As environmental challenges grow increasingly complex, more integrated neuronal processing may be more adaptive, and higher level analytical and response mechanisms may come into play. Moreover, learned anticipatory processes may promote more strategic avoidance of adaptive challenges prior to their occurrence. The increasing amount of information that must be processed and integrated by progressively higher-level systems may lead to neurocomputational bottlenecks that require a slower and more serial mode of processing. Based on hierarchical interconnections, higher-level systems may depend heavily on lower-level systems for the transmission and preliminary processing and filtering of afferent sensory and perceptual data and for the implementation of sensorimotor subroutines that support executive outputs. The advantages and disadvantages associated with higher-level (integrative, flexible, but capacity-limited) and lower-level (rapid, efficient, but rigid) processing were a likely source of evolutionary pressure for the preservation of lower-level substrates, despite higher-level elaborations and re-representations (Berntson & Cacioppo, in press). Together these interacting hierarchical structures allow neural systems to rapidly respond through low-level processing (e.g., pain-withdrawal reflexes), whereas more rostral neural substrates permit a more elaborate response over time and allow for evaluation of future strategies and subsequent consequences. Hierarchical representations do not merely reflect theoretical models or cognitive curiosities but are empirically documented by neuroanatomical and functional analyses of neural systems throughout the brain (Berntson et al., 1993; Figure 32.1).

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Figure 32.1 Hierarchical and heterarchical organizations. Note: A heterarchy differs from a hierarchy (illustrated by solid arrows) by the additional presence of long ascending and descending pathways that span intermediate levels (dashed arrows). Properties of the levels in both classes of organizations lie along the illustrated continua of processing mode, integrative capacity, and output repertoire. Heterarchical organizations have greater integrative capacity and output flexibility because the long ascending and descending projections provide inputs and outputs that are not constrained by intermediate levels.

Neural Heterarchies Additional neuroarchitectural complexities exist beyond strict hierarchical organization patterns because long descending pathways exist that bypass intermediate levels and directly synapse onto lower levels of the neuraxis (Porter, 1987; Wakana, Jiang, Nagae-Poetscher, Zijl, & Mori, 2004). This type of organization is documented by the existence of direct, long descending projections from higher neuraxial systems to lower motor neurons, effectively bypassing intermediate levels. In addition to the well-known anatomy of somatomotor systems (Porter, 1987; Wakana et al., 2004), this pattern of organization is also apparent in the autonomic nervous system (Berntson & Cacioppo, 2000). As illustrated in Figure 32.2, for example, the baroreflex is a tightly organized brain stem–mediated reflex system that serves to maintain blood pressure homeostasis. Increases in blood pressure activate specialized cardiovascular mechanoreceptors, which then feed back into brain stem reflex circuitry, leading to reciprocal increases in vagal cardiac output and decreases in sympathetic cardiac and vascular tone. These responses collectively lead to decreases in heart rate, cardiac output, and vascular tone, which synergistically compensate for the blood pressure perturbation. In contrast to this lower-level, homeostatic reflex regulation, higher-level systems (e.g., with even mild psychological stress) are capable of overriding the baroreflex and yielding concurrent increases in

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blood pressure and heart rate. This nonhomeostatic modulation of cardiovascular may arise in part from descending inhibition of brain stem baroreflex networks. It also likely reflects the actions of long descending projections from higher neurobehavioral substrates that bypass intermediate reflex circuits and project monosynpatically to lower autonomic source nuclei (see Figure 32.3). As a result, cortical and limbic structures are able to bypass intermediate hierarchical elements and directly control lower levels (see Berntson et al., 1994). The presence of long ascending and descending pathways in neural organizational patterns, combined with lateral interconnections between levels, has previously been described as a neural heterarchy (see Berntson et al., 1993; Berntson & Cacioppo, 2000). Heterarchical organization patterns have the components of hierarchical systems, as higher levels are in continuous communication with lower-level systems via intermediate levels, but they have the additional capacity to interact over widely separated levels via direct connections. Direct neuronal projections from higher brain systems to lower-level systems allow for manifestations of higher computational re-representative networks that are not constrained by intermediate-level organizations. This affords cognitive and behavioral flexibility when needed but also allows for intermediate-level processing when necessary. The multiple levels of organization and associated functional flexibility come with a disadvantage because a heterarchical organization opens the possibility for functional conflicts between distinct levels of processing (e.g., when an organism must inhibit pain

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Figure 32.2 (Figure C. 34 in color section) Summary of brain stem systems underlying the baroreceptor cardiac reflex. Note: Baroreceptor afferents project to nucleus tractus solitarius (NTS), which in turn leads to activation of parasympathetic motor neurons in the nucleus ambiguus (nA) and dorsal motor nucleus of the vagus (DMX). The NTS also activates the caudal ventrolateral medulla (Cvlm), which in turn inhibits the rostral ventrolateral medulla (Rvlm), leading to a withdrawal of excitatory drive on the sympathetic motor neurons in the intermediolateral cell column of the spinal cord (IML). CAs ⫽ Catecholamines; PGi ⫽ Nucleus paragigantocellaris (coextensive with Rvlm).

withdrawal to achieve a higher-order goal). We return to this issue later. Levels of Evaluative Function: Intermediate Levels—Decerebration Although primitive approach/withdrawal dispositions are represented at spinal levels, they are substantially developed and elaborated at brain stem levels. Classical demonstrations of the functional capacity of brain stem networks come from studies of experimental isolation of the brain stem and spinal cord (i.e., decerebration) and from tragic cases of human decerebration (Berntson & Micco, 1976; Berntson, Tuber, Ronca, & Bachman, 1983; Harris, Kelso, Flatt, Bartness, & Grill, 2006; Ronca, Berntson, & Tuber, 1986; Tuber, Berntson, Bachman, & Allen, 1980; Yates, Jakus, & Miller, 1993). Although acute postsurgical somatomotor rigidity historically obscured the behavioral capacities of the experimental decerebrate, with longer survival times and the resolution of this rigidity a great deal of organizational capacity is apparent at brain stem levels (Bard & Macht, 1958; Berntson & Micco, 1976; Norman, Buchwald, & Villablanca, 1977). Decerebrate animals, for example, can right themselves and locomote; eat and drink on encountering appropriate goal objects; groom; and display aggressive, defensive, and escape behaviors to noxious stimuli (see Adams, 1979; Berntson & Micco, 1976; Norman et al., 1977). Considerable functional capacity is also apparent in tragic cases of human decerebration (anencephaly and

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Figure 32.3 (Figure C. 33 in color section) Expansion of the baroreflex circuit of Figure 32.2 to illustrate the ascending and descending pathways to and from rostral neural areas such as the medial prefrontal cortex, hypothalamus, and amygdala. Note: Ascending systems include routes from the rostral ventrolateral medulla (Rvlm) and the nucleus of the tractus solitarius (NTS) to the locus coeruleus (LC) noradrenergic system and indirectly to the basal forebrain (BF) cortical cholinergic system. CAs = Catecholamines; Cvlm = Caudal ventrolateral medulla; DMX = Dorsal motor nucleus of the vagus; IML = Intermediolateral cell column of the spinal cord; nA = Nucleus ambiguus; PGi = Paragigantocullar nucleus (partially coextensive with Rvlm).

hydranencephaly), generally resulting from a failure of cell migration early in neurodevelopment. Although these infants generally do not survive for more than a few weeks after birth, they show a relatively intact array of infantile reflexes, including flexor and extensor reflexes, stepping reflexes, and a wide range of brain stem reflexes including tonic neck reflexes and suckling reflexes, among others. Figure 32.4 illustrates transillumination of the scalp and a representative CAT scan from a decerebrate human infant. Despite the virtual lack of any neural tissue above the diencephalon, this infant showed basic manifestations of evaluative processing. In addition to displaying typical pain-withdrawal reflexes, she would fuss and cry in

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Note: Top: Results of transillumination of the head (viewed posteriorly— tip of left ear on leftward side). The dark region toward the base of the head is the cerebellum; note that there is little to occlude the light above that level. Bottom: Results of CAT scan at four horizontal planes of the head (front of the head is up) from dorsal (left) to ventral (right). Light areas indicate more radiodense areas such as bone and neural tissue, dark regions more radiolucent areas. Note the clear appearance of the skull but the absence of brain tissue on the left. In the two middle planes, the cerebellum is apparent posteriorly (bottom of the cranial vault). In the lowest plane (right), diencephalic and other brain stem tissue is present.

response to noxious stimuli and could be quieted and comforted with contact and rocking. This infant also showed typical appetitive responses and would suckle and ingest milk sufficient to maintain body weight. It is worth noting that brain stem neurobehavioral substrates do not entail a mere assemblage of rigidly regulated and tightly organized reflex networks because both decerebrate animals (Mauk & Thompson, 1987; Norman et al., 1977) and humans (Berntson et al., 1983; Tuber et al., 1980) display neural plasticity and associative learning. Intake/Rejection Responses and Taste Hedonics Among the more thoroughly studied brain stem evaluative processes are those supporting approach/avoidance action dispositions related to taste hedonics. Similar to the organization of the spinal cord, the neuroarchitectures underlying approach vs. avoidance dispositions appear to be relatively independent and under separate control in brain stem circuitry (Berntson et al., 1993; Berridge & Grill,

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1984; Steiner, Glaser, Hawilo, & Berridge, 2001). Taste hedonics and associated intake/rejection responses offer a prime example of brain stem evaluative systems. Orofacial displays to taste, represented by stereotyped, reflex-like negative rejection/ejection responses to aversive stimuli (gaping, tongue protrusion) and positive intake responses (smiling, licking, swallowing) are well conserved in mammals. Such responses can be seen early in development and are readily apparent in decerebrate organisms. The positive and negative responses to gustatory stimuli mirror the evaluative reflexes of the spinal cord in that they reflect opposing patterns of approach/avoidance dispositions. Similar to spinal reflexes, the behavioral output of these systems cannot be interpreted as lying along a single bipolar continuum extending from approach (highly positive) to avoidance (highly negative). Although this depiction can be useful, it belies the underlying complexity of hedonic processes because experimental evidence suggests that gustatory approach/withdrawal systems are partially independent and do not converge on a single hedonic integrator (Berridge & Grill, 1984). Just as a person can tighten extensor and flexor muscles simultaneously, intake and rejection responses are not incompatible and can become coactive. For example, although the probability of rejection responses to a glucose solution increases following the addition of a bitter compound, this can occur without a reciprocal reduction in probability of intake responses. Similarly, increasing both bitter and sweet concurrently leads to increases in both intake and rejection responses. Thus, it is clear that taste preference, as measured by behavioral consumption and represented on a bipolar scale, does not always represent the underlying bivariate hedonic state. This does not rule out interaction between the approach/avoidance responses, but suggests that the mixing positive and negative valences of hedonic stimuli do not simply yield a null average of the two or a state of indifference (Berridge & Grill, 1984). Gustatory approach/avoidance responses are represented by distinct positive and negative hedonic dimensions that conform to the positivity offset and negativity bias as described previously. Gustatory evaluative processes mediated by brain stem systems are more complex than their behavioral output (total intake continuum), and knowledge of this fact facilitates a more accurate description of evaluative processes based on the underlying bivariate substrates. Levels of Function: Higher-Level Rerepresentations As we move to the highest levels of the neuraxis, the rerepresentation and elaboration of evaluative processes becomes ever more apparent, and neuron-organizational

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complexity expands dramatically. The brain stem and spinal cord are highly sensitive to aversive and hedonic stimuli and can yield appropriate behavioral responses, but this so-called reptilian brain (MacLean, 1985) lacks much of the behavioral flexibility and adaptability characteristic of intact organisms. Although decerebrates may ingest palatable foods, they do not display typical goal-seeking behavior in the absence of a food stimulus but rather are prisoners of the momentary stimulus or environmental context (see Berntson et al., 1993; Berntson & Micco, 1976). Decerebrates lack the flexibility and variety of behavior seen in intact animals because of the devolution of the nervous system to its more primitive representations. It is not until the development of the paleomammalian brain (limbic system and archicortex) and the neomammalian brain (neocortex) that we see the full evolution and elaboration of evaluative processes (MacLean, 1985). It is with the development of rostral brain structures that we begin to see the emergence of goal-directed behaviors that reflect anticipatory processes and expectancies that liberate the organism from the immediate exigencies of this stimulus or that. In view of the expanding complexity of rostral evaluative substrates, it seems unlikely that these networks would simplify from the basic bivariate evaluative structure of lower substrates to become a single bipolar hedonic integrator. In contrast, with the expanding cognitive and computational complexity of evaluative processes at higher neuraxial levels, there is a parallel expansion of the complexity of the underlying mediating neural systems. Higher evaluative processes entail planning, strategizing, and engaging in anticipatory processes that can require access to associative networks, attentional and computational resources, and so on. Moreover, whereas lower evaluative substrates may entail simple approach/withdrawal dispositions, higher motivational processes become further differentiated and nuanced. Berridge (1996) characterized the “liking” aspects of motivation as those that entail the hedonic and response-eliciting properties of a stimulus or motivational context. These are apparent in the orofacial intake/ ingestive responses to positive hedonic tastes as described previously for the decerebrate organism. The decerebrate, however, largely lacks what Berridge termed the wanting aspects of motivation, which entail an attentional focus on, and goal-seeking behaviors directed toward, a desired stimulus, state, or context. This latter aspect of evaluative processes is heavily dependent on the increased computational capacity of higher levels of the neuraxis and is mediated by more elaborate neural circuitry. It should not be surprising that the neuroarchitecture of higher evaluative processes entails more complex and distributed networks that are not as readily dichotomized into positive and negative substrates as is the case with

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Levels of Organization in the Nervous System

lower level representations. Indeed, many computational, attentional, and memorial processes may be commonly deployed for positive and/or negative evaluative processing. Moreover, the further development and elaboration of evaluative systems, such as that between “liking” and “wanting,” may entail added neuroanatomical complexity. Historically, the nucleus accumbens (nACC) has been depicted as a neural integrator of reward and positive hedonic states (Berridge & Grill, 1984; Hoebel, Rada, Mark, & Pothos, 1999; Koob, 1992). In the 1940s, Robert Heath, working on psychiatric patients with indwelling electrical brain stimulators, showed that patients would report pleasurable states and would self-administer stimulation to various brain regions, especially areas in and around the nACC (Heath, 1972). More recently, electrical stimulation of the nACC has been reported to elicit a smile associated with euphoric responses (Okun et al., 2004). It is now clear that nearly all rewarding stimuli or positive hedonic states are associated with dopamine release in the nACC, and lesions or blockage of dopamine receptors in the nACC reduces rewards and positive hedonics (Hoebel et al., 1999; Robinson & Berridge, 2003; Wise, 2006; see also Chapter 40). In this regard, the nACC contrasts with the amygdala, which has generally been implicated in fear conditioning, negative affect, and aversive states (see Chapter 39), a topic to which we return. Although these findings are consistent with a differentiation of positive and negative neural substrates at higher levels of the neuraxis, similar to that seen at lower levels, there are added complexities in higher substrates. The nACC, in fact, may not be a simple monolithic reward integrator. Recent work has suggested important phenomenological and computational distinctions within the nACC. For example, the liking (positive hedonic effect, reward) and wanting (incentive salience, goal striving) aspects of hedonic states are mediated by distinct anatomical regions of the nACC (Berridge, 1996; Pecina & Berridge, 2005). Moreover, negative stimuli may also activate the nACC, and other distinct areas may be involved in suppression of negative evaluative processing (Pecina & Berridge, 2005). These complexities caution against the overly simplistic ascription of discrete neural loci to the mediation of complex neuropsychological phenomena. Nevertheless, there remain clear differentiations between higher neural substrates mediating positive and negative evaluative processes. A hemispheric lateralization of positive and negative evaluative processes has been reported, with the right hemisphere implicated more in negative affective processing or avoidance dispositions and the left hemisphere involved more in positive affect or approach dispositions (Cacioppo & Gardner, 1999; Davidson, 1990; Harmon-Jones, Vaughn,

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Mohr, Sigelman, & Harmon-Jones, 2004). For example, positive affective stimuli induce greater activation in the left hemisphere (Canli, Desmond, Zhao, Glover, & Gabrieli, 1998; Davidson, 1998, 2004; Lee et al., 2004; Nitschke, Sarinopoulos, Mackiewicz, Schaefer, & Davidson, 2006; Pizzagalli, Sherwood, Henriques, & Davidson, 2005), and patients with damage to the left hemisphere have a higher probability of experiencing depression and overall negative affect (Davidson, 1998). Similarly, facial expression and reaction time data suggest a left hemisphere predominance for positive affect and a greater right hemisphere representation for negative affect (Davidson, Shackman, & Maxwell, 2004; Root, Wong, & Kinsbourne, 2006). The relative right hemispheric bias for withdrawal/avoidance reactions may be related to the right lateralization of visceral/nociceptive afferent projections (Craig, 2005) and is consistent with the finding that left insula stimulation gives rise to parasympathetic cardiac activation whereas right insula stimulation induces sympathetic activation (Oppenheimer, 1993, 2006). Furthermore, within-hemisphere differentiation is also apparent in cortical representations. Pleasantness rating of odors, for example, was related to the degree of medial orbitofrontal activation as measured by fMRI, whereas unpleasantness was more related to activation of the dorsal anterior cingulate (Grabenhorst, Rolls, Margot, da Silva, & Velazco, 2007). Similarly, deciding on the lesser of two punishments yielded greater activation in the dorsal anterior cingulate, whereas deciding between the larger of two rewards yielded greater activation in the ventromedial prefrontal cortex (Blair et al., 2006). The amygdala has been especially implicated in fear and negative affect since the classic studies of Walter Rudolf Hess (1954) on brain stimulation in the waking animal. The amygdala appears to be a critical nodal point in subcortical circuits that allow for rapid detection and response to threat and for the learning of fear-related cues (LeDoux, 1996; Öhman & Mineka, 2001). These circuits allow for more elaborate processing of threat-related cues than do lower-level brain stem substrates but remain highly efficient because they can operate without the need for extensive cortical processing (Larson et al., 2006; LeDoux, 1996; Öhman & Mineka, 2001; Tooby & Cosmides, 1990). Although the amygdala may also participate in classical thalamo-cortical-limbic circuits, direct thalamo-amygdala pathways are a sufficient substrate for fear reactions and simple fear conditioning, providing for a “quick and dirty transmission route” (LeDoux, 2000). The thalamoamygdala subcortical circuit may support simple fear conditioning and fear reactions in the absence of awareness (“blindsight”) following visual cortical injuries (see De Gelder, Vroomen, Pourtois, & Weiskrantz, 1999; Pegna,

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Visual Cortex

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Figure 32.5 Schematic representation of the classical thalamocortical visual pathway, where afferent information is conveyed to the cortex via the relay nucleus of the thalamus (lateral geniculate nucleus). Note: Also illustrated is an alternative thalamo-amygdala route that can bypass the cortex and mediate rapid fear and defensive responses to certain classes of aversive stimuli (see LeDoux, 2003).

Khateb, Lazeyras, & Seghier, 2005; Weiskrantz, 1986). In contrast, relational learning (e.g., contextual conditioning) and the processing of more complex threat-related cues may be more dependent on higher-level cortical processing (Berntson, Sarter, & Cacioppo, 1998; see also Chapter 39). Recent research supports this heterarchical organization showing that auditory fear conditioning induces plasticity in amygdala neurons prior to apparent changes in cortical areas, suggesting that early plasticity in amygdala neurons results from direct thalamo-amygdala projections (Öhman & Mineka, 2001; Quirk, Armony, & LeDoux, 1997; Quirk, Repa, & LeDoux, 1995; see Figure 32.5). The more direct, efficient, but relatively limited direct thalamo-amygdala and the more elaborate, integrative, and flexible thalamo-cortical-amygdala circuits represent distinct heterarchical levels of processing. Fear versus Anxiety Fear is a reaction to an explicit threatening stimulus, with escape or avoidance the outcome of increased cue proximity (see Chapter 49). Anxiety is a more general state of distress, typically longer lasting, prompted by less explicit or more generalized cues, and involving physiological arousal but often without organized functional behavior (Berntson et al., 1998; Lang, Davis, & Ohman, 2000). The amygdala appears to be especially critical for simple fear conditioning and fear potentiation of startle (LeDoux, 2003; Phelps & LeDoux, 2005; Walker, Toufexis, & Davis, 2003). Inactivation of the lateral nucleus of the

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amygdala, for example, blocks the conditioned fear response in rats and attenuates fear-potentiated startle (LeDoux, 2003; Walker et al., 2003). Conversely, although the amygdala may play a role in anxiety-like responses, inactivation of the central nucleus of the amygdala does not attenuate anxiety-like behavior in mice (Walker & Davis, 1997). Rather, the bed nucleus of the stria terminalis and the medial prefrontal cortex may be more specifically involved in anxiety-like reactions to longer-lasting, more generalized threat cues. Lesions of the bed nucleus of the stria terminalis, for example, disrupt light-induced startle potentiation (which has been suggested to be a model for anxiety) but largely spare simple, conditioned fear-potentiated startle (Walker & Davis, 1997; Walker et al., 2003). Furthermore, lesions of the basal forebrain cortical cholinergic pathway or its termination in the medial prefrontal cortex disrupt anxiety-like responses but spare simple fear conditioning (Berntson et al., 1998; Hart, Sarter, & Berntson, 1999). Whereas cortical systems may not be necessary in explicit fear responses, they appear to be critical for the processing of more complex stimuli and for contextual fear conditioning (Knox & Berntson, 2006; LeDoux, 2000; Phillips & LeDoux, 1992; Stowell, Berntson, & Sarter, 2000). Mental imagery or anticipation of aversive or anxiogenic contexts induces activation in the bed nucleus of the stria terminalis as well as in cortical areas, including the medial prefrontal cortex and the anterior cingulate cortex (Kosslyn et al., 1996; Shin et al., 2004; Straube, Mentzel, & Miltner, 2007). Gray and McNaughton (2000) incorporated much of this information into a two-dimensional defense system model that makes clear anatomical, behavioral, and functional distinctions between fear and anxiety. The first dimension is a qualitative distinction between systems controlling defensive avoidance (fear) and defensive approach (anxiety). The two states often display opposite characteristics—fear produces speed toward or away from a stimulus whereas anxiety produces slowness, caution, and deliberation (see also Chapter 36). The second dimension is based on functional and organizational properties inherent to the neuroarchitectural substrates involved in the two qualitative distinctions. These distinctions are characterized in a hierarchical manner whereby substantial overlap between the two systems exists at caudal levels (periaqueductal gray and medial hypothalamus). As one moves rostrally, some differentiation may emerge (e.g., anterior cingulate for defensive avoidance and posterior cingulate for defensive approach), but the more significant perspective concerns the level of requisite processing (see Chapter 36). This is consistent with the present heterarchical model. The multiple heterarchical levels represent at least partially distinct processing substrates and may function in

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Levels of Organization in the Nervous System

partial independence from other levels (Berntson et al., 1998). This is an issue to which we return (see “Multilevel Organization and Its Conflicts”). Generally, however, different levels are in constant reciprocal communication with one another and are capable of shifting from approach to avoidance defensive strategies at a moment’s notice and displaying coactivity of substrates (Gray & McNaughton, 2000). The multiplicity in heterarchical levels may preclude simple isomorphic mappings between affect in the psychological domain and neural substrates in the biological domain (Berntson, 2006). The complexity in brain– behavior mapping in affective processes is illustrated by recent findings on the role of the amygdala. The Amygdala The amygdala is one of the most well-studied neural structures. It has been the subject of neuroscientific as well as psychological research for decades and is central to many theories of affect and evaluative processing. In general accord with animal studies, imaging studies in humans have reported amygdala activation during emotion, especially with negative emotions (Critchley et al., 2005; Irwin et al., 1996; Sabatinelli, Bradley, Fitzsimmons, & Lang, 2005; Zald & Pardo, 1997), and patients with amygdala damage show attenuated negative affect (Tranel, Gullickson, Koch, & Adolphs, 2006) and deficits in emotional memory (Buchanan, Tranel, & Adolphs, 2006; LaBar & Cabeza, 2006; Phelps, 2006; Phelps & LeDoux, 2005). Although the amygdala has been implicated in a range of processes extending from fear conditioning to emotional memory to aversive reactions, the precise role of this structure has not been fully clarified. This issue was pursued in a recent study of patients with amygdala damage (Berntson, Berchara, Damasio, Tranel, & Cacioppo, 2007). Participants rated a set of images from the International Affective Picture System (Lang, Bradley, & Cuthbert, 1999) on perceived valence (extending from highly positive to highly negative picture content) and on affective intensity (i.e., how aroused the images made them feel). As illustrated in Figure 32.6, patients with damage to the amygdala were comparable on their ratings of valence of the picture content to persons in a norm group and to control patients with lesions that spared the amygdala. Patients were quite capable of recognizing and appropriately labeling positive and negative aspects of the stimuli. When compared to other groups, however, amygdala lesion patients significantly differed on their ratings of emotional arousal or intensity (see Figure 32.6). Control patients and the norm group showed the expected increases in arousal ratings as the images approached either positive or negative extremes. Amygdala patients also displayed an increase in arousal to the more positive images. They did

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not, however, show a parallel arousal gradient to negative stimuli. Although the amygdala patients clearly recognized and labeled the negative images, they did not display the expected affective response. These findings are in agreement with a previously reported double dissociation between cognitive and affective processes in brain-damaged patients (Bechara et al., 1995). Consistent with the animal literature, a patient with amygdala damage failed to develop a typical conditioned autonomic response to a conditioned stimulus that was paired with a loud noise, despite the fact that this patient acquired declarative knowledge about the relation between the conditioned stimulus and the noise. This parallels the dissociation between the cognitive and arousal dimensions in the affective picture task of the Berntson et al. (2007) study. In contrast, a patient with damage to the hippocampus (sparing the amygdala) developed a conditioned autonomic response to the conditioned stimulus but could not cognitively describe the experimental contingencies (Bechara et al., 1995). These dissociations between cognitive knowledge and affective/autonomic responses reflect the multiple levels at which evaluative processing can occur. They also document the further differentiation between dimensions of evaluative processing, even within a given valence, at higher neural levels. In view of this elaboration and differentiation, it is highly unlikely the basic delineation between positive/approach and negative/withdrawal dispositions would devolve into a single affective continuum. Although there may be a perceived continuum between positive and negative affect, this perception may not accurately reflect the distinct neural substrates for these affective dimensions. In a recent fMRI study, Grabenhorst et al. (2007) reported that pleasant (jasmine) and unpleasant (indole) odors resulted in similar activations in primary olfactory areas (pyriform cortex), with these activations being correlated with odor intensity. The pleasant and unpleasant odors, however, differentially activated other distinct brain regions (e.g., medial orbitofrontal cortex and dorsal anterior cingulate cortex, respectively). Although a mixture of the two odors was rated as pleasant, it continued to show distinct activations in both the medial orbitofrontal cortex (where activations were correlated with pleasantness) and the anterior cingulate (where activations were correlated with unpleasantness). The authors concluded, “Mixtures that are found pleasant can have components that are separately pleasant and unpleasant, and the brain can separately and simultaneously represent the positive and negative hedonic value.” (p. 13532). Differential activation of positive and/or negative evaluative substrates may guide behavior even in the absence

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Figure 32.6 A: Lesions and arousal and valence ratings in the evaluative picture-rating study; B: Mean (SEM) arousal (I) and valence (II) ratings across stimulus categories for patients with amygdala lesions compared with the clinical contrast group and normative control data. Note: (A) (I) Illustrative bilateral lesion of the amygdala secondary to herpes simplex encephalitis. (II) Example of one of the smaller lesions in the lesion contrast group that spared the amygdala. All groups effectively discriminated the stimulus categories and applied valance ratings accordingly. (B) All groups also displayed comparable arousal functions to positive stimuli, but the amygdala group showed diminished arousal selectively to the negative stimuli. Neg = Negative; Pos = Positive. From “Amygdala Contribution to Selective Dimensions of Emotion,” by G. G. Berntson, A. Bechara, H. Damasio, D. Tranel, and J. T. Cacioppo, 2007, Social Cognitive and Affective Neuroscience, 2, pp. 123–129, pp. 3 & 5. Reprinted with permission.

of awareness. This is consistent with a report that a patient with damage to the primary gustatory cortex (and other areas) was unable to recognize or even distinguish sweet (positive) from saline (aversive) solutions and would drink either avidly. When given a choice between the two, however, this patient would consistently choose the sweet solution, although he could not explain why (Adolphs, Tranel, Koenigs, & Damasio, 2005). In this case, higher-level substrates for cognitive recognition and labeling were disrupted, but lower heterarchical systems were able to guide behavioral choice in the absence of cognitive awareness.

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MULTILEVEL ORGANIZATIONS AND THEIR CONFLICTS Evaluative processes evidence a cardinal feature of bivalence in their functional architecture and are represented at multiple levels of the neuraxis. Although these bivalent substrates may interact, they retain at least some degree of independence and separability. Substrates at differing levels of the neuraxis also interact in a heterarchical fashion, but they, too, entail at least partially distinct organizations with differential processing capacities and

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differential access to sensory, perceptual, memorial, and cognitive information. The lowest heterarchical levels provide for rapid, albeit rather inflexible, information processing and adaptive reflexive reactions. Higher levels are capable of broader integration of information, expanded neural computations, and a richer and more flexible array of actions and outputs. An important question concerns the determinants of the level or levels of processing that are deployed in a given situation. In their elaboration likelihood model, Cacioppo and Petty (1984; Petty, Cacioppo, Strathman, & Priester, 2005) distinguish between what they term central and peripheral routes to persuasion and attitude dispositions. The central route is characterized by higher-level cognitive deliberation, whereas the peripheral route is less processing dependent and entails appeal to authority, reliance on preexisting biases or prejudices, and so on. In this model, important determinants of which route will predominate are the availability information as well as cognitive resources and motivation for deliberative consideration that are necessary to support the central, as opposed to the peripheral, processing route. Processing may also occur at multiple levels, with the output or action reflecting some aggregate manifestation or the predominance of one or another level. As discussed previously, higher neural systems can inhibit or override lower-level substrates, but more typically, complex interactions and recurrent processing may occur across levels. In their iterative processing model, Cunningham and Zelazo (2007) propose recurrent, reciprocal communications across processing levels. In this scheme, lower level substrates may provide affectively laden information regarding the valance and the arousal dimensions of a particular stimulus or context to higher evaluative processing substrates, which in turn can then modulate lower-level processing systems. In some cases, the bivalent organization and multiple levels in evaluative processing substrates may lead to conflicts. The coexistence of both positive and negative attributes to an object or outcome does not necessarily result in a neutral dispositional state, as might be implied by a bipolar evaluative model. Ambivalence is not the simple equivalent of indifference. Ambivalence may reflect the coactivation of both positive and negative evaluations. In his classic studies on conflict, Neal Miller (1959, 1961) used behavioral measures (e.g., running speed or the strength of pull on a tether to approach a reward or avoid a noxious stimulus) to assess motivational dispositions in rats. A typical gradient of an approach disposition to a food reward is illustrated in Figure 32.7 as a function of the proximity of the animal to the goal box. Similarly

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Figure 32.7 Miller ’s (1959, 1961) approach/avoidance conflict. Note: Approach (solid line) and avoidance (dashed line) gradients as a function of distance from the goal. Goal items include food (positive incentive) and shock (negative incentive). The avoidance gradient has a steeper slope and predominates as the goal box is approached (negativity bias), whereas at more remote loci, the approach gradient is higher than the avoidance gradient (positivity offset). The intersection of the gradients represents the maximal conflict point, where approach and avoidance dispositions are equivalent.

illustrated is the avoidance disposition away from a shock grid at the goal box, as measured independently. Miller generally observed that the slope of the avoidance gradient tended to be steeper than that of the approach gradient, so that at a distance from the goal, the approach disposition was greater than the avoidance disposition, and vice versa at proximate locations. The two motivational dispositions (approach and avoidance) were then invoked simultaneously, by the presence of both the food and the shock grid. This introduced what Miller termed an approach/avoidance conflict. The animal would approach the goal box if placed remotely in the apparatus, but as it approached the goal, the relative strength of the avoidance disposition increased (see Figure 32.7) and the approach disposition was overcome by avoidance. At that point, the animal was in what Miller referred to as a stable conflict. Any further approach would lead to an increment in avoidance, and any movement away would lead to a relative predominance of approach. Indeed, animals showed agitation and vacillation at an intermediate distance from the goal, and that point could be predicted by the relative magnitudes of the approach and avoidance gradients as measured independently. For Miller ’s rats, the aggregate effect of a positive motivation and a concurrent negative disposition was not evaluative neutrality and sanguinity—it was ambivalence, agitation, and vacillation. Conceptually similar findings have emerged from studies on humans (Larsen, McGraw, Mellers, & Cacioppo, 2004). Good outcomes that could have been better (i.e., disappointing wins) and bad outcomes that could have been worse (i.e., relieving losses) are rated by participants toward the middle or neutral point of bipolar emotion scales (i.e., ratings along a positive to negative continuum). This

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might suggest that such outcomes are associated with the absence of affect or indifference. If participants are presented with continuous unipolar measures of positive and negative affect, however, a very different picture emerges. When rating positive and negative separately, participants indicate the coactivation of both positive and negative affect. The participants are not indifferent—they are ambivalent (from the Latin, “both valences or vigors”). There is a conceptual parallel in this study with Miller ’s (1959, 1961) rats. Although behavioral or measurement constraints may make it appear that positive and negative evaluative dispositions lie on a continuum, these appearances may belie the underlying bivalence of the neurobehavioral substrates. In the Miller study, physical constraints precluded the concurrent motor expressions of approach and avoidance. A rat cannot simultaneously approach and avoid the same place at the same time, although it may serially express the underlying bivalent affect states in its vacillation around the equilibrium point of two opposing evaluative dispositions. In the Larsen, McGraw, Mellers, & Cacioppo (2004) study, behavioral constraints were not imposed because the two (positive and negative) unipolar affect ratings were done sequentially. With a bipolar rating scale, however, a constraint is imposed by the measurement instrument, which is grounded on a spurious bipolar theory about the underlying evaluative structure. Because of the inherent complexity in higher levels of evaluative processes, as well as physical and measurement constraints, basic positive (generally associated with approach) and negative (generally associated with avoidance) evaluative systems may not always be readily discernable in behavior. Although affective states may at times appear to lie along a continuum from positive to negative, the fundamental underlying substrates evidence a bivalent organization, even at the highest levels of the neuraxis. The cortical system represents the ultimate level of neuronal complexity and processing capacity. The mammalian neocortical system includes networks responsible for the most complex of sensory and perceptual processes, associative learning, memory, attentional focus, contextual awareness, strategizing, and outcome monitoring. In primates, the expanded cortex allows for even more elaborate processing. The additional computational power of primate neocortical structures allows for intricate social interactions that are dependent on the ability to anticipate future outcomes, run cognitive simulations, and manage social alliances. Although such complex neuropsychological phenomena would not be possible without the highestlevel brain systems, these functions have more primitive representations at lower levels of the neuraxis and, in many cases, are derivative of the lower substrates.

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EVALUATIVE SPACE AND THE NEUROARCHITECTURE OF EVALUATIVE PROCESSES Wundt (1896) and Thurstone (1931) were early champions of the bipolar model of affect, in which the momentary affective states could be characterized as lying along a bipolar continuum extending from positive to negative. This view has also been incorporated into contemporary models of emotion, including the circumplex model of Russell and others (Russell, 1980, 1983; see also Posner et al., 2005). Cacioppo and Berntson have proposed an alternative, bivariate model of affect whereby the positive and negative dimensions are at least partially independent in both their conceptual and neurological bases (Cacioppo & Berntson, 1994; Cacioppo, Gardner, & Berntson, 1997; Cacioppo et al., 2004). As illustrated in Figure 32.8, this evaluative space model subsumes the bipolar model as the reciprocal diagonal and also offers a more comprehensive representative of affective states. Whereas bipolar models are unable to represent states of ambivalence, the evaluative space model readily accounts for such states as a manifestation of coactivation of both positive and negative affect. It is also in accord with the finding that positive and negative emotions are not always correlated (Larsen et al., 2004). The evaluative space model illustrates how neuroevolutionary and neurobehavioral frameworks can guide and constrain theories and models of higher neuropsychological functioning. Moreover, behavioral findings and features may inform neurobehavioral theories as well, in a reciprocal fashion. The fact that neuronal substrates of approach and avoidance are at least partially independent allows for evolutionary pressures to sculpt these circuits independently. Because the driving force in evolution is the ability to pass on genetic information, avoiding noxious or potentially lethal stimuli may assume greater adaptive importance, especially at close proximities, than approaching positive or rewarding stimuli. The latter can always be pursued subsequently if the organism lives to see another day. This may be the evolutionary basis for the negativity bias in evaluative processing, as is apparent in lower reflex substrates discussed previously. It is also apparent from the steeper slope of the avoidance gradient in Neal Miller ’s (1959, 1961) behavioral studies of conflict. Additional research utilizing event-related potentials has demonstrated a similar negativity bias in early stages of evaluative processing in humans (Cacioppo et al., 2004). Miller also observed what has been termed a positivity offset in his conflict paradigm. This refers to the fact that the approach gradient often surpasses the avoidance gradient as the distance to the goal increases beyond the equilibrium point (see Figure 32.9). Both the positivity offset and

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Multilevel Interactions: Examples from the Autonomic Nervous System 629 (B)

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Figure 32.8 Bivariate evaluative space. A: The bivariate evaluative plane. B: A three-dimensional depiction of evaluative space, where the surface overlying the bivariate plane represents the net approach/avoidance disposition for any location on that plane. Note: (A) The y axis represents the level of activation of positive evaluative processes (Positivity), and the x axis represents the level of activation of the negative evaluative process (Negativity). The reciprocity diagonal represents the classical bipolar model of valence that extends from high positivity (upper left) to high negativity (lower right) along a single evaluative continuum. The coactivity diagonal represents an alternative mode where both evaluative dimensions (conflict, ambivalence) are coactivated.

negativity bias are apparent in numerous behavioral contexts (see Ito & Cacioppo, 2005, for a review and empirical studies). As depicted in Figure 32.8, the negativity bias and the positivity offset are reflected in the differential slopes of the positivity and negativity functions in the evaluative space model. The overlying surface of Figure 32.8 represents the net action dispositions on both the positivity and negativity continua. Movement along the positivity axis represents the positive or approach gradient, movement along the negativity axis represents the negative or avoidance gradient, and the surface in between these extremes represents varying degrees of ambivalence. The evaluative space model in Figure 32.8 is useful in describing and explaining overall action dispositions. It should be noted, however, that this characterization could be applied to distinct levels within the evaluative heterarchy, with the overall action disposition representing a composite or aggregate of the multiple processing levels. This aggregate function, therefore, may well be dynamic, as differing levels of processing may come into play depending on the situation or context.

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The arrows outside of the box represent uncoupled changes in positive or negative evaluative processing. This evaluative plane provides a more comprehensive model of evaluative processes that subsumes the bipolar model as one reciprocal. (B) The insert on this figure illustrates activation functions along the positivity and negativity axes. Differences in the slopes and intercepts of these functions depict the positivity offset (higher intercept) and negativity bias (higher slope). From “Relationship between Attitudes and Evaluative Space: A Critical Review, with Emphasis on the Separability of Positive and Negative Substrates,” by J. T. Cacioppo and G. G. Berntson, 1994, Psychological Bulletin, 115, p. 412. Adapted with permission.

MULTILEVEL INTERACTIONS: EXAMPLES FROM THE AUTONOMIC NERVOUS SYSTEM The evaluative space model in Figure 32.8 may provide a broader framework for conceptualizing other neurobiological processes that have a fundamental bivariate structure. One example is the autonomic nervous system (ANS) and its neurobehavioral control (Berntson et al., 1998). Mirroring the bipolar conceptualizations of evaluative processes, historical depictions of the sympathetic and parasympathetic branches of the ANS have been of a reciprocally regulated system, with increases in activity of one branch associated with decreases in the other (Berntson, Cacioppo, & Quigley, 1991). This bipolar conceptualization arose largely out of research on basic autonomic reflexes that, like the flexor–extensor circuits, are rather rigid and lack the range and flexibility of control characteristic of more rostral systems. The efferent arm of the baroreceptor heart rate reflex, for example, entails a notable reciprocal regulation of the sympathetic and parasympathetic branches of the ANS. Baroreceptor afferents

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Figure 32.9 Distribution of normalized parasympathetic cardiac control (as indexed by HF [HFz]) and sympathetic cardiac control (as indexed by PEP [-PEPz]) scores across the CHASRS population, and their relation to the derived CAR and CAB metrics. Note: The overall distribution deviates considerably from the reciprocal diagonal representing a bipolar model. Individuals in the reciprocal parasympathetic quadrant would have relatively high CAB scores, whereas those in the reciprocal sympathetic quadrant would have relatively low CAB scores. An additional dimension is reflected along the coactivity diagonal. Individuals in the coactivation quadrant would have relatively high CAR scores, whereas those in the co-inhibition quadrant would have relatively low CAR scores. CAB = Cardiac autonomic balance; CAR = Cardiac autonomic regulatory capacity; CHASRS = Chicago Health and Social Relations Study; HF = High-frequency heart rate variability; PEP = Preejection period. From “Cardiac Autonomic Balance versus Cardiac Regulatory Capacity,” by G. G. Berntson, G. J. Norman, L. C. Hawkley, and J. T. Cacioppo, 2008, Psychophysiology, 45, p. 646. Reprinted with permission.

increase their rate of firing in response to mechanical distortion associated with an increase in blood pressure, and this afferent signal is conveyed to the nucleus tractus solitarius in the medulla, which is the primary visceral receiving area of the brain. The nucleus tractus solitarius subsequently issues direct and indirect excitatory projections to vagal motor neurons in the nucleus ambiguous and dorsal motor nucleus, leading to a reflexive increase in parasympathetic outflow. This yields a decrease in heart rate and a reduction in cardiac output, which tends to normalize or oppose the pressor perturbation. Projections from the nucleus tractus solitarius also indirectly suppress sympathetic outflow via inhibition of the sympathoexcitatory neurons in the rostral ventrolateral medulla. This sympathetic withdrawal acts to further slow the beat of the heart as well as to decrease myocardial contractility. Thus, the reciprocal actions of the individual branches of the ANS synergistically contribute to the homeostatic regulation of blood pressure.

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The baroreceptor heart rate reflex represents a prototypic, reciprocally regulated system, having a bipolar action disposition extending from sympathetic to parasympathetic dominance. Although descriptive of some basic autonomic reflexive circuits, this characterization belies the true complexity of autonomic control of cardiovascular function. Higher-level brain structures are capable of modulating ANS activity via direct projections from forebrain structures such as the cingulate cortex (Critchley et al., 2005), amygdala (LeDoux, Iwata, Cicchetti, & Reis, 1988), and insular cortex (Oppenheimer, 1993) to autonomic brain stem nuclei (see also Berntson et al., 1998). Stimulation and lesion studies of rostral structures have shown that higher systems can facilitate, inhibit, or even bypass basic brain stem autonomic reflexes and thereby modulate autonomic outflow directly (Sévoz-Couche, Comet, Hamon, & Laguzzi, 2003). It is not likely a coincidence that many of these same brain structures may be the substrates for higher-level functions, including evaluative processes. These descending pathways are the conduit by which psychological stressors can yield anti-homeostatic effects on the ANS, including concurrent increases in blood pressure and heart rate (in opposition to baroreflex control). Direct stimulation of the hypothalamus, for example, can invoke each of the basic modes (see Figure 32.9) of reciprocal, coactive, or independent changes in the activity of the autonomic branches (Koizumi & Kollai, 1981; Shih, Chan, & Chan, 1995). The ability of higher-level systems to flexibly modulate activities in the autonomic branches has required an expansion of simple, reciprocally regulated homeostatic models. Given the research on evaluative processes, it has now also become clear that simple bipolar conceptualizations of the ANS are inadequate. Contemporary systems models recognize the basic bivariate organization of the ANS and include concepts such as heterostasis, allostasis, and allodynamic regulation that recognize the greater breadth and flexibility of autonomic control associated with rostral regulatory substrates (Berntson, Norman, Hawkley, & Cacioppo, 2008; McEwen, 2004). Theories about both evaluative processes and autonomic control have significance for the kinds of data scientists collect and for scientists’ understanding of the basic neurobiology of these processes. Bipolar theories of affect, for example, lead to the development of bipolar scales of valence that obscure the underlying bivariate nature of evaluative processes. Similarly, the reciprocal model of autonomic control biases toward particular conceptions of psychosomatic relations that may impact research and understanding of disease processes. Concepts of a reciprocally regulated, homeostatic system have limited understanding of the ANS and lead to models of autonomic contributions to disease states as

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Summary

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reflecting a homeostatic failure. Although there are homeostatic features to some aspects of autonomic control, the ANS is not a universally homeostatic system. The concurrent increase in blood pressure and heart rate during stress is not a homeostatic response—it is explicitly anti-homeostatic. But it may be nevertheless highly adaptive, at least in the short term, in preparing for action. The multiplicity of the modes of autonomic control (see Figure 32.9) may have important health implications. Berntson et al. (1994) found substantial individual differences in patterns of stress reactivity, and such differences may play an important role in the susceptibility to disease (see Cacioppo et al., 1998). The understanding and measurement of these patterns, however, is heavily dependent on models of autonomic control. An index of autonomic balance could be derived from a bipolar conception of autonomic control as a scale extending from maximal sympathetic activation at one end to maximal parasympathetic activation at the other (i.e., along the reciprocal diagonal of Figure 32.9). A measure of cardiac autonomic balance (CAB) was so derived from normalized measures of high-frequency heart rate variability (which provides a relatively pure index of parasympathetic cardiac control) and preejection period (which provides a relatively pure index of sympathetic cardiac control). Although this index was not correlated with most aspects of health and disease in a population-based sample (i.e., the Chicago Health and Social Relations Study), it was predictive of diabetes mellitus and independent of demographics and health behaviors (Berntson et al., 2008). Other conceptualizations of psychosomatic relations have emphasized not so much the state of sympathetic/ parasympathetic balance but rather the overall capacity for autonomic control as indexed by autonomic flexibility and variability. This concept, together with the demonstration of the basic bivariate structure of autonomic control, suggests an alternative metric to CAB. An index of cardiac autonomic regulatory capacity (CAR) was derived as the sum of activities of the autonomic branches, again based on normalized high-frequency heart rate variability and preejection period measures. In contrast to the reciprocal diagonal represented by CAB, CAR as a metric captures the coactivity diagonal of Figure 32.9. Analysis of the Chicago Health and Social Relations Study sample revealed that CAR was a better predictor of overall health status and was a significant predictor of the prior occurrence of myocardial infarction, whereas the reciprocity metric (CAB) was not (Figure 32.10). These results suggest that distinct patterns of modes of autonomic control may be associated with distinct health dimensions. A bipolar conception of autonomic control, however, admits theory and measurement only of CAB and would occlude the relationships between CAR and health. In contrast, the broader and more comprehensive bivariate model of auto-

631

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Figure 32.10 CAR and CAB in disease states. Note: Data points illustrate means and standard errors of CAR and CAB as a function of participant group, relative to the population. Compared to other participants, those with a prior myocardial infarction (MI) had lower CAR scores, indicating lower overall cardiac regulatory capacity, but were not highly deviant on CAB. In contrast, those with diabetes showed a lower CAB score, reflective of a predominant sympathetic balance, but were not highly deviant on CAR. CAB = Cardiac autonomic balance; CAR = Cardiac autonomic regulatory capacity. From “Cardiac Autonomic Balance versus Cardiac Regulatory Capacity,” by G. G. Berntson, G. J. Norman, L. C. Hawkley, and J. T. Cacioppo, 2008, Psychophysiology, 45, p. 649. Reprinted with permission.

nomic control subsumes CAB as one diagonal (reciprocal diagonal of Figure 32.9) and captures CAR as the coactivity diagonal. Theories impact understandings, and specious or oversimplified theories may obscure lawful relationships.

SUMMARY With recent theoretical and technological advances, scientifically relevant conceptualizations of affective processes and their neural substrates are now possible. Utilizing strong evidence from fields such as genetics, evolutionary biology, neurobiology, and psychology provides points of convergence where interdisciplinary perspectives complement one another. The bivariate multilevel model of evaluation allows for the inclusion of new theoretical constructs and empirical evidence that can resolve competing hypotheses, generate new and testable hypotheses, and increase theoretical breadth and depth, leading to better conceptualizations of affective phenomena. Theories that assume strictly bipolar (valence) mechanisms underlying affective responses have difficulty accounting for evidence from the neurosciences that shows distinct neural substrates are coactivated in the presence of appetitive and aversive stimuli. Nor do these theories incorporate the influence of evaluative mechanisms organized at lower levels of the neuraxis. The evaluative space model provides a more comprehensive conception of evaluative processes and

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subsumes, rather than discards, more simplistic models based on bipolar conceptualizations of affect.

Berridge, K. C. (2004). Motivation concepts in behavioral neuroscience. Physiology and Behavior, 81, 179–209. Berridge, K. C., & Grill, H. J. (1984). Idohedonic tastes support a twodimensional hypothesis of palatability. Appetite, 5, 221–231.

REFERENCES Adams, D. B. (1979). Brain mechanisms for offense, defense, and submission. Behavioral and Brain Sciences, 2, 201–241. Adolphs, R., Tranel, D., Koenigs, M., & Damasio, A. R. (2005). Preferring one taste over another without recognizing either. Nature Neuroscience, 7, 860–861.

Blair, K., Marsh, A. A., Morton, J., Vythilingam, M., Jones, M., Mondillo, K., et al. (2006). Choosing the lesser of two evils, the better of two goods: Specifying the roles of ventromedial prefrontal cortex and dorsal anterior cingulate in object choice. Journal of Neuroscience, 26, 11379–11386. Buchanan, T. W., Tranel, D., & Adolphs, R. (2006). Memories for emotional autobiographical events following unilateral damage to medial temporal lobe. Brain, 129, 115–127.

Allport, G. W. (1935). Attitudes. In C. Murchison (Ed.), Handbook of social psychology (Vol. 2, pp. 798–884). Worchester, MA: Clark University Press.

Cacioppo, J. T., & Berntson, G. G. (1994). Relationship between attitudes and evaluative space: A critical review, with emphasis on the separability of positive and negative substrates. Psychological Bulletin, 115, 401–423.

Aronson, E., & Carlsmith, J. M. (1963). Effect of severity of threat on the devaluation of forbidden behavior. Journal of Abnormal and Social Psychology, 66, 584–588.

Cacioppo, J. T., & Berntson, G. G. (1999). The affect system: Architecture and operating characteristics. Current Directions in Psychological Science, 8, 133–137.

Bard, P., & Macht, M. B. (1958). The behavior of chronically decerebrate cats. In G. E. W. Wolsten-Holme & C. M. O’Connor (Eds.), Neurological basis of behavior (pp. 55–75). London: Churchill.

Cacioppo, J. T., Berntson, G. G., Malarkey, W. B., Kiecolt-Glaser, J. K., Sheridan, J. F., Poehlmann, K. M., et al. (1998). Autonomic, neuroendocrine, and immune responses to psychological stress: The reactivity hypothesis. Annals of the New York Academy of Sciences, 840, 664–673.

Bechara, A., Tranel, D., Damasio, H., Adolphs, R., Rockland, C., & Damasio, A. R. (1995, August 25). Double dissociation of conditioning and declarative knowledge relative to the amygdala and hippocampus in humans. Science, 269, 1115–1118. Berntson, G. G. (2006). Reasoning about brains. In J. T. Cacioppo, P. S. Visser, & C. L. Pickett (Eds.), Social neuroscience: People thinking about thinking people (pp. 1–11). Cambridge, MA: MIT Press. Berntson, G. G., Bechara, A., Damasio, H., Tranel, D., & Cacioppo, J. T. (2007). Amygdala contribution to selective dimensions of emotion. Social Cognitive and Affective Neuroscience, 2, 123–129. Berntson, G. G., Boysen, S. T., & Cacioppo, J. T. (1993). Neurobehavioral organization and the cardinal principle of evaluative bivalence. Annals of the New York Academy of Science, 702, 75–102. Berntson, G. G., & Cacioppo, J. T. (2000). From homeostasis to allodynamic regulation. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (pp. 459–481). Cambridge, England: Cambridge University Press. Berntson, G. G., & Cacioppo, J. T. (2008). The neuroevolution of motivation. In J. Shah & W. Gardner (Eds.), Handbook of motivation science (pp. 188–200). New York: Guilford Press. Berntson, G. G., Cacioppo, J. T., Binkley, P. F., Uchino, B. N., Quigley, K. S., & Fieldstone, A. (1994). Autonomic cardiac control: III. Psychological stress and cardiac response in autonomic space as revealed by pharmacological blockades. Psychophysiology, 31, 599–608. Berntson, G. G., Cacioppo, J. T., & Quigley, K. S. (1991). Autonomic determinism: The modes of autonomic control, the doctrine of autonomic space, and the laws of autonomic constraint. Psychological Reviews, 98, 459–487. Berntson, G. G., & Micco, D. J. (1976). Organization of brainstem behavioral systems. Brain Research Bulletin, 1, 471–483. Berntson, G. G., Norman, G. J., Hawkley, L. C., & Cacioppo, J. T. (2008). Cardiac autonomic balance versus cardiac regulatory capacity. Psychophysiology, 45, 643–652. Berntson, G. G., Sarter, M., & Cacioppo, J. T. (1998). Anxiety and cardiovascular reactivity: The basal forebrain cholinergic link. Behavioural Brain Research, 94, 225–248. Berntson, G. G., Tuber, D. S., Ronca, A. E., & Bachman, D. S. (1983). The decerebrate human: Associative learning. Experimental Neurology, 81, 77–88. Berridge, K. C. (1996). Food reward: Brain substrates of wanting and liking. Neuroscience Biobehavioral Review, 20, 1–25.

c32.indd 632

Cacioppo, J. T., & Gardner, W. L. (1999). Emotion. Annual Review of Psychology, 50, 191–214. Cacioppo, J. T., Gardner, W. L., & Berntson, G. G. (1997). The affect system has parallel and integrative processing components: Form follows function. Journal of Personality and Social Psychology, 76, 839–855. Cacioppo, J. T., Larsen, J. T., Smith, N. K., & Berntson, G. G. (2004). The affect system: What lurks below the surface of feelings. In A. S. R. Manstead, N. Frijda, & A. Fischer (Eds.), Feelings and emotions (pp. 221–240). Cambridge, England: Cambridge University Press. Cacioppo, J. T., & Petty, R. E. (1984). The elaboration likelihood model of persuasion. Advances in Consumer Research, 11, 673–675. Canli, T., Desmond, J. E., Zhao, Z., Glover, G., & Gabrieli, J. D. (1998). Hemispheric asymmetry for emotional stimuli detected with fMRI. NeuroReport, 9, 3233–3239. Craig, A. D. (2003). Pain mechanisms: Labeled lines versus convergence in central processing. Annual Review of Neuroscience, 26, 1–30. Craig, A. D. (2005). Forebrain emotional asymmetry: A neuroanatomical basis? Trends in Cognitive Sciences, 9, 566–571. Critchley, H. D., Taggart, P., Sutton, P. M., Holdright, D. R., Batchvarov, V., Hnatkova, K., et al. (2005). Activity in the human brain predicting differential heart rate responses to emotional facial expressions. NeuroImage, 24, 751–762. Cunningham, W. A., & Zelazo, P. D. (2007). Attitudes and evaluations: A social cognitive neuroscience perspective. Trends in Cognitive Sciences, 11, 97–104. Davidson, R. J. (1990). Approach-withdrawal and cerebral asymmetry: Emotional expression and brain physiology. Journal of Personality and Social Psychology, 58, 330–341. Davidson, R. J. (1998). Anterior electrophysiological asymmetries, emotion, and depression: Conceptual and methodological conundrums. Psychophysiology, 35, 607–614. Davidson, R. J. (2004). What does the prefrontal cortex “do” in affect: Perspectives on frontal EEG asymmetry research. Biological Psychology, 67, 219–233. Davidson, R. J., Shackman, A. J., & Maxwell, J. S. (2004). Asymmetries in face and brain related to emotion. Trends in Cognitive Science, 8, 389–391. De Gelder, B., Vroomen, J., Pourtois, G., & Weiskrantz, L. (1999). Non-conscious recognition of affect in the absence of striate cortex. NeuroReport, 10, 3759–3763.

8/19/09 4:12:22 PM

References 633 Egan, L. C., Santos, L. R., & Bloom, P. (2007). The origins of cognitive dissonance: Evidence from children and monkeys. Psychological Science, 18, 978–983.

Larsen, J. T., McGraw, A. P., & Cacioppo, J. T. (2001). Can people feel happy and sad at the same time? Journal of Personality and Social Psychology, 81, 684–696.

Grabenhorst, F., Rolls, E. T., Margot, C., da Silva, M. A., & Velazco, M. I. (2007). How pleasant and unpleasant stimuli combine in different brain regions: Odor mixtures. Journal of Neuroscience, 27, 13532–13540.

Larsen, J. T., McGraw, A. P., Mellers, B. A., & Cacioppo, J. T. (2004). The agony of victory and thrill of defeat: Mixed emotional reactions to disappointing wins and relieving losses. Psychological Science, 15, 325–330.

Gray, J. A., & McNaughton, N. (2000). The neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system. Oxford, England: Oxford University Press. Harmon-Jones, E., Vaughn, K., Mohr, S., Sigelman, J., & Harmon-Jones, C. (2004). The effect of manipulated sympathy and anger on left and right frontal cortical activity. Emotion, 4, 95–101. Harris, R. B., Kelso, E. W., Flatt, W. P., Bartness, T. J., & Grill, H. J. (2006). Energy expenditure and body composition of chronically maintained decerebrate rats in the fed and fasted condition. Endocrinology, 147, 1365–1376. Hart, S., Sarter, M., & Berntson, G. G. (1999). Cholinergic inputs to the rat medial prefrontal cortex mediate potentiation of the cardiovascular defensive response by the anxiogenic benzodiazepine receptor partial inverse agonist FG 7142. Neuroscience, 94, 1029–1038. Heath, R. G. (1972). Pleasure and brain activity in man: Deep and surface electroencephalograms during orgasm. Journal of Nervous and Mental Diseases, 154, 3–18. Hess, W. R. (1954). Diencephalon: Autonomic and extrapyramidal functions. Monographs in biology and medicine: Vol. III. New York: Grune & Stratton. Hoebel, B. G., Rada, P. V., Mark, G. P., & Pothos, E. N. (1999). Neural systems for reinforcement and inhibition of behavior: Relevance to eating, addiction, and depression. In D. Kahneman, E. Diener, & N. Schwarz (Eds.), Well-being: The foundations of hedonic psychology (pp. 558–572). New York: Russell Sage Foundation. Irwin, W., Davidson, R. J., Lowe, M. J., Mock, B. J., Sorenson, J. A., & Turski, P. A. (1996). Human amygdala activation detected with echoplanar functional magnetic resonance imaging. NeuroReport, 7, 1765–1769. Ito, T. A., & Cacioppo, J. T. (2005). Variations on a human universal: Individual differences in positivity offset and negativity bias. Cognition and Emotion, 19, 1–26. Jackson, J. H. (1958). Evolution and dissolution of the nervous system (Croonian Lectures). In J. Taylor (Ed.), Selected writings of John Hughlings Jackson (pp. 45–63). New York: Basic Books. (Original work published 1884.) Knox, D., & Berntson, G. G. (2006). Effect of nucleus basalis magnocellularis cholinergic lesions on fear-like and anxiety-like behavior. Behavioral Neuroscience, 120, 307–312. Koizumi, K., & Kollai, M. (1981). Control of reciprocal and non-reciprocal action of vagal and sympathetic efferents: Study of centrally induced reactions. Journal of the Autonomic Nervous System, 3, 483–501. Koob, G. F. (1992). Drugs of abuse: Anatomy, pharmacology, and function of reward pathways. Trends in Pharmacological Sciences, 13, 177–184. Kosslyn, S. M., Shin, L. M., Thompson, W. L., McNally, R. J., Rauch, S. L., Pitman, R. K., et al. (1996). Neural effects of visualizing and perceiving aversive stimuli: A PET investigation. NeuroReport, 7, 1569–1576. LaBar, K. S., & Cabeza, R. (2006). Cognitive neuroscience of emotional memory. Nature Reviews Neuroscience, 7, 54–64. Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1999). International affective picture system (IAPS): Instruction manual and affective ratings. [Technical report A-4.] Gainsville, FL: University of Florida, Center for Research in Psychophysiology. Lang, P. J., Davis, M., & Ohman, A. (2000). Fear and anxiety: Animal models and human cognitive psychophysiology. Journal of Affective Disorders, 61, 137–159.

c32.indd 633

Larson, C. L., Schaefer, H. S., Siegle, G. J., Jackson, C. A., Anderle, M. J., & Davidson, R. J. (2006). Fear is fast in phobic individuals: Amygdala activation in response to fear-relevant stimuli. Biological Psychiatry, 60, 410–417. LeDoux, J. E. (1996). The emotional brain: The mysterious underpinnings of emotional life. New York: Simon & Schuster. LeDoux, J. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 23, 155–184. LeDoux, J. (2003). The emotional brain, fear, and the amygdala. Cellular and Molecular Neurobiology, 23, 727–738. LeDoux, J. E., Iwata, J., Cicchetti, P., & Reis, D. J. (1988). Different projections of the central amygdaloid nucleus mediate autonomic and behavioral correlates of conditioned fear. Journal of Neuroscience, 8, 2517–2529. Lee, G. P., Meador, K. J., Loring, D. W., Allison, J. D., Brown, W. S., Paul, L. K., et al. (2004). Neural substrates of emotion as revealed by functional magnetic resonance imaging. Cognitive and Behavioral Neuroscience, 17, 9–17. Lundberg, A. (1979). Multisensory control of spinal reflex pathways. Progress in Brain Research, 50, 11–28. MacLean, P. D. (1985). Evolutionary psychiatry and the triune brain. Psychological Medicine, 15, 219–221. Mauk, M., & Thompson, R. F. (1987). Retention of classically conditioned eyelid responses following acute decerebration. Brain Research, 403, 89–95. McEwen, B. (2004). Protection and damage from acute and chronic stress: Allostasis and allostatic overload and relevance to the pathophysiology of psychiatric disorders. Annals of the New York Academy of Sciences, 1032, 1–7. Miller, N. E. (1959). Liberalization of basic S-R concepts: Extensions to conflict behavior, motivation and social learning. In S. Koch (Ed.), Psychology: A study of a science (pp. 196–292). New York: McGraw-Hill. Miller, N. E. (1961). Some recent studies on conflict behavior and drugs. American Psychologist, 16, 12–24. Nitschke, J. B., Sarinopoulos, I., Mackiewicz, K. L., Schaefer, H. S., & Davidson, R. J. (2006). Functional neuroanatomy of aversion and its anticipation. NeuroImage, 29, 106–116. Norman, R. J., Buchwald, J. S., & Villablanca, V. J. (1977, April 29). Classical conditioning with auditory discrimination of the eye blink in decerebrate cats. Science, 196, 551–553. Öhman, A., & Mineka, S. (2001). Fears, phobias, and preparedness: Toward an evolved module of fear and fear learning. Psychological Review, 108, 438–522. Okun, M. S., Bowers, D., Springer, U., Shapira, N. A., Malone, D., & Rezai, A. R. (2004). What’s in a ‘smile’? Intra-operative observations of contralateral smiles induced by deep brain stimulation. Neurocase, 10, 271–279. Oppenheimer, S. (1993). The anatomy and physiology of cortical mechanisms of cardiac control. Stroke, 24, 13–15. Oppenheimer, S. M. (2006). Cerebrogenic cardiac arrhythmias: Cortical lateralization and clinical significance. Clinical Autonomic Research, 16, 1619–1560. Osgood, C., Suci, G., & Tannenbaum, P. (1957). The measurement of meaning. Urbana: University of Illinois.

8/19/09 4:12:23 PM

634

Evaluative Processes

Pecina, S., & Berridge, K. C. (2005). Hedonic hot spot in the nucleus accumbens shell: where do mu-opiods cause increased hedonic impact of sweetness? Journal of Neuroscience, 25, 11777–11787. Pegna, A. J., Khateb, A. A., Lazeyras, F., & Seghier, M. L. (2005). Discriminating emotional faces without primary visual cortices involves the right amygdala. Nature Neuroscience, 8, 24–25. Petty, R. E., Cacioppo, J. T., Strathman, A. J., & Priester, J. R. (2005). To think or not to think: Exploring two routes to persuasion. In S. Shavitt & T. C. Brock (Eds.), Persuasion: Psychological insights and perspectives (2nd ed., pp. 81–116). New York: Allyn & Bacon. Phelps, E. A. (2006). Emotion and cognition: Insights from studies of the human amygdala. Annual Review of Psychology, 57, 27–53. Phelps, E. A., & LeDoux, J. E. (2005). Contributions of the amygdala to emotion processing: From animal models to human behavior. Neuron, 48, 175–187. Phillips, R. G., & LeDoux, J. E. (1992). Differential contribution of amygdala and hippocampus to cued and contextual fear conditioning. Behavioral Neuroscience, 106, 274–285. Pizzagalli, D. A., Sherwood, R. J., Henriques, J. B., & Davidson, R. J. (2005). Frontal brain asymmetry and reward responsiveness: A sourcelocalization study. Psychological Science, 16, 805–813. Porter, R. (1987). Functional studies of motor cortex. Ciba Foundation Symposium, 132, 83–97. Posner, J., Russell, J. A., & Peterson, B. S. (2005). The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and Psychopathology, 173, 715–734. Quirk, G. J., Armony, J. L., & LeDoux, J. E. (1997). Fear conditioning enhances different temporal components of tone-evoked spike trains in auditory cortex and lateral amygdala. Neuron, 19, 613–624. Quirk, G. J., Repa, C., & LeDoux, J. E. (1995). Fear conditioning enhances short-latency auditory responses of lateral amygdala neurons: Parallel recordings in the freely behaving rat. Neuron, 15, 1029–1039. Robinson, T. E., & Berridge, K. C. (2003). Addiction. Annual Review of Psychology, 54, 25–53. Ronca, A. E., Berntson, G. G., & Tuber, D. A. (1986). Cardiac orienting and habituation to auditory and vibrotactile stimuli in the infant decerebrate rat. Developmental Psychobiology, 18, 79–83. Root, J. C., Wong, P. S., & Kinsbourne, M. (2006). Left hemisphere specialization for response to positive emotional expressions: A divided output methodology. Emotion, 6, 473–483. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161–1178. Russell, J. A. (1983). Pancultural aspects of human conceptual organization of emotions. Journal of Personality and Social Psychology, 45, 1281–1288. Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110, 145–172. Sabatinelli, D., Bradley, M. M., Fitzsimmons, J. R., & Lang, P. J. (2005). Parallel amygdala and inferotemporal activation reflect emotional intensity and fear relevance. NeuroImage, 24, 1265–1270. Sandrini, G., Serrao, M., Rossi, P., Romaniello, A., Cruccu, G., & Willer, J. C. (2005). The lower limb flexion reflex in humans. Progress in Neurobiology, 77, 353–395. Schouenborg, J., Holmberg, H., & Weng, H. R. (1992). Functional organization of the nociceptive withdrawal reflexes: II. Changes of excitability and receptive fields after spinalization in the rat. Experimental Brain Research, 90, 469–478. Sévoz-Couche, C., Comet, M. A., Hamon, M., & Laguzzi, R. (2003). Role of nucleus tractus solitarius 5-HT3 receptors in the defense

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reaction-induced inhibition of the aortic baroreflex in rats. Journal of Neurophysiology, 90, 2521–2530. Sherrington, C. S. (1906). The integrative action of the nervous system. New Haven, CT: Yale University Press. Shih, C. D., Chan, S. H., & Chan, J. Y. (1995). Participation of hypothalamic paraventricular nucleus in locus ceruleus-induced baroreflex suppression in rats. American Journal of Physiology: Heart and Circulatory Physiology, 269, H46–H52. Shin, L. M., Orr, S. P., Carson, M. A., Rauch, S. L., Macklin, M. L., Lasko, N. B., et al. (2004). Regional cerebral blood flow in the amygdala and medial prefrontal cortex during traumatic imagery in male and female Vietnam veterans with PTSD. Archives of General Psychiatry, 61, 168–176. Steiner, J. E., Glaser, D., Hawilo, M. E., & Berridge, K. C. (2001). Comparative expression of hedonic impact: Affective reactions to taste by human infants and other primates. Neuroscience and Biobehavioral Reviews, 25, 53–74. Stowell, J. R., Berntson, G. G., & Sarter, M. (2000). Attenuation of the bidirectional effects of chlordiazepoxide and FG 7142 on conditioned response suppression and associated cardiovascular reactivity by loss of cortical cholinergic inputs. Psychopharmacology, 150, 141–149. Straube, T., Mentzel, H. J., & Miltner, W. H. (2007). Waiting for spiders: Brain activation during anticipatory anxiety in spider phobics. NeuroImage, 37, 1427–1436. Thurstone, L. L. (1931). The measurement of attitudes. Journal of Abnormal Psychology, 26, 249–269. Tooby, J., & Cosmides, L. (1990). The past explains the present: Emotional adaptations and the structure of ancestral environment. Ethology and Sociobiology, 11, 375–424. Tranel, D., Gullickson, G., Koch, M., & Adolphs, R. (2006). Altered experience of emotion following bilateral amygdala damage. Cognitive Neuropsychiatry, 11, 219–232. Tuber, D. S., Berntson, G. G., Bachman, D. S., & Allen, J. N. (1980, November 28). Associative learning in premature hydranencephalic and normal twins. Science, 210, 1035–1037. Wakana, S., Jiang, H., Nagae-Poetscher, L. M., Zijl, P. C., & Mori, S. (2004). Fiber tract based atlas of human white matter anatomy. Radiology, 230, 77–87. Walker, D. L., & Davis, M. (1997). Double dissociation between the involvement of the bed nucleus of the stria terminalis and the central nucleus of the amygdala in light-enhanced versus fear potentiated startle. Journal of Neuroscience, 17, 9375–9383. Walker, D. L., Toufexis, D. J., & Davis, M. (2003). Role of the bed nucleus of the stria terminalis versus the amygdala in fear, stress, and anxiety. European Journal of Pharmacology, 463, 199–216. Watson, D., Wiese, D., Vaidya, J., & Tellegen, A. (1999). The two general activation systems of affect: Structural findings, evolutionary considerations, and psychobiological evidence. Journal of Personality and Social Psychology, 76, 820–838. Weiskrantz, L. (1986). Blindsight: A case study and implications. Oxford, England: Oxford University Press. Wise, R. A. (2006). Role of brain dopamine in food reward and reinforcement. Philosophical Transactions of the Royal Society of Biological Sciences, 361, 1149–1158. Wundt, W. (1896). Outlines of psychology. Leipzig, Germany: Engelmann. Yates, B. J., Jakus, J., & Miller, A. D. (1993). Vestibular effects on respiratory outflow in the decerebrate cat. Brain Research, 3, 209–217. Zald, D. H., & Pardo, J. V. (1997). Emotion, olfaction, and the human amygdala: Amygdala activation during aversive olfactory stimulation. Proceedings of the National Academy of Sciences, USA, 94, 4119–4124.

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Chapter 33

Pain: Mechanisms and Measurement JOSÉE GUINDON AND ANDREA G. HOHMANN

DEFINITION OF PAIN

circuits implicated in pain transmission and modulation and ongoing improvements in the evaluation of its effects. It is now generally acknowledged that pain comprises sensory-discriminative, motivational-affective, and cognitive-evaluative dimensions (Figure 33.1). In the mid1990s, pain was defined by the International Association of the Study of Pain (IASP) as “an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage” (Merskey & Bogduk, 1994, pp. 209–214). This definition raised questions because it is possible to experience an injury without pain and pain can also be experienced in the absence of any apparent injury. For example, people born with congenital analgesia exhibit profound insensitivity to pain even in the presence of serious injury (e.g., fractures, burns, appendicitis; Comings & Amromin, 1974; Manfredi et al., 1981; Waxman, 2007). The cause underlying congenital analgesia until recently has remained elusive.

The word pain comes from the Latin word peona meaning punishment or penalty. Pain is an unpleasant, complex, personal, and subjective experience that can range in intensity from slight through severe to indescribable. In the general population of the United States, the two most common forms of pain involve headaches and back pain that affect 45 and 9 million people, respectively. The management of moderate and severe chronic pain is the main concern and burden of patients and clinicians. Despite improvements in our understanding of neural circuits contributing to pain transmission and modulation, the need for safe and effective approaches for pain relief remains predominant. The mission of defining pain is a complicated one (for review, see Brennan, Carr, & Cousins, 2007; Price, 1999). This definition has evolved through time together with advances in both our understanding of the neural

Multidimensional Experience

ive l ect a Aff vation ti mo

Cog eva nitive lua tive

Sensory-discriminative Dimensions

Pain Neurophysiological Unpleasantness Affective motivational aspect

Psychophysical

Psychological

Figure 33.1

Algosity Pain intensity (psychophysical properties)

Neuropsychological

Cultural

Social

Environmental

Pain is a multidimensional phenomenon. 635

Handbook of Neuroscience for the Behavioral Science, edited by Gary G. Berntson and John T. Cacioppo. Copyright # 2009 John Wiley & Sons, Inc. c33.indd 635

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A mutation in the SCN9A gene, which is linked to chromosome 2q24.3 and results in nonfunctional Nav1.7 channels, has been implicated in congenital insensitivity to pain (Cox et al., 2006; Waxman, 2007). Furthermore, causes underlying the two most common forms of pain in the population, headaches (Moskowitz, 1992; Villalon, Centurion, Valdivia, de Vries, & Saxena, 2003) and back pain (Loeser, 2001), remain poorly understood. In many cases, pain is felt without any sign of injury causing controversy and speculation about the pathophysiology of these conditions. Furthermore, it is also possible for motorcycle accident victims to experience pain after healing the avulsion of the brachial plexus (Wynn Parry, 1980). Therefore, another definition of pain was proposed by Price (1999). According to this definition, pain is a somatic perception containing: (a) a bodily sensation with qualities like those reported during tissue-damaging stimulation, (b) an experienced threat associated with this sensation, and (c) a feeling of unpleasantness or other negative emotion based on this experienced threat. This definition doesn’t require the demonstration of tissue damage or the association between sensation and tissue lesion. However, unpleasant somatic sensation (e.g., itch) is not necessarily associated with pain. Therefore, pain may more appropriately be defined with the association of two somatosensory qualities: unpleasantness (affective-motivational aspect) and algosity (a unique quality of pain that allows it to be unequivocally identified). The psychophysics and neural mechanisms may differ for each of these dimensions. These dimensions are distinct in their intensity-based sensory discriminations. Moreover, the magnitude of unpleasantness can be dissociated from the pain intensity (algosity magnitude; Fields, 1999). Pain remains a complex multidimensional experience related to sensory-discriminative, cognitive-evaluative, and motivational-affective dimensions which, by its complexity, cannot be solely explained by social, cultural, environmental, neurophysiological, psychophysical, psychological, or neuropsychological aspects (Melzack & Katz, 1999; Melzack & Wall, 1991; Price, 1999). Interactions between these aspects are also likely to occur (Figure 33.1).

THE CONCEPTUALIZATION OF PAIN HAS EVOLVED The concept of pain has evolved over time (for a review, see Melzack & Wall, 1991). The ancient Greeks grouped pain with the emotions or appetites and not with sensation. They considered pain to be the opposite of pleasure (Dallenbach, 1939; Livingstone, 1998; Marshall, 1894). The view of pain as pure emotion went into decline in the

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seventeenth century with the advent of specificity theory. This theory postulates that a specific pain system carries a message from pain receptors in the skin to a pain center in the brain. The best classical description of this theory comes from Descartes (1664) who conceived of the pain system as a straight-through channel from the skin to the brain (Figure 33.2). Different qualities of sensation were not recognized in this early conceptualization. Müller ’s (1842) doctrine of specific nerve energies postulated that the qualities of experience were associated with the properties of sensory nerves. Müller proposed that the brain receives information about external objects only by way of the sensory nerves and five classical senses were recognized: seeing, hearing, taste, smell, and touch. The theory of cutaneous senses was established by Max von Frey between 1894 and 1895. Von Frey’s designation of the free nerve endings as pain receptors is the basis of the specificity theory (Boring, 1942; Figure 33.2). A “solution” to the puzzle of pain was explained by the existence of specific pain receptors in the body tissue traveling via pain fibers and a pain pathway to a pain center in the brain (Boring, 1942). It was later proposed that the amount and quality of perceived pain were modulated by many psychological variables in addition to the sensory input. For example, dogs that received electric shocks, burns, or cuts followed by the presentation of food eventually responded to these noxious stimuli as signals for food and most notably failed to show any signs of pain (Pavlov, 1927). Goldscheider (1894) was the first to propose that stimulus intensity and central summation are the critical determinants of pain. This conceptualization represented the origin of pattern theory. Goldscheider concluded that mechanisms of central summation, which were postulated to be localized in the dorsal horn of the spinal cord, were essential for any understanding of pain mechanisms. Livingstone (1943) proposed that pathological stimulation of sensory nerves (e.g., peripheral nerve damage) initiates activity in reverberatory circuits in the gray matter of the spinal cord. This abnormal activity could be triggered by normally nonnoxious inputs and generates volleys of nerve impulses that are interpreted centrally as pain (Figure 33.2). The simplest form of pattern theory deals with peripheral instead of central patterning. According to this theory, the pattern of pain is produced by an intense stimulation of nonspecific receptors (Sinclair, 1955; Weddell, 1955). Note that in this conceptualization, all fiber endings (except those innervated by hair cells) were believed to be alike. The sensory interaction theory developed by Noordenbos (1959) suggests that the small diameter fibers exist to carry nerve impulse patterns that produce pain, whereas the large fibers inhibit this transmission. Therefore, a shift in

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The Conceptualization of Pain Has Evolved 637 Central control processes Motivationalaffective system

L

⫹

S L S

⫹ ⫹ Theory of cutaneous senses

Descartes 1664

Pain Appetites ⫺

Emotions

Opposite

Summation theory Goldscheider 1894

S

Model of reverberatory circuits Livingston 1943

1927 Pavlov

1842 Müller Doctrine of specific nerve energies

CR (salivation)

US (food) UR (salivation)

US (food) paired with CS (electric, burn, cut)

1955 Sinclair: Weddell Peripheral pattern theory

Sensory-discriminative system

T

(Spatio-temporal analysis)

Sensory interaction theory Noordenbos 1959

Melzack & Casey 1968

1965 Melzack & Wall

1990 Melzack

Gate-control system

Input

Pain perception and action systems

Central control

L ⫹ SG

⫺ S

A

E

⫺⫹ T

Action system

S

⫺⫹

Historical time line showing the evolution of pain

1990; SG ⫽ substantia gelatinosa; T ⫽ transmission; US ⫽ unconditioned stimuli; UR ⫽ unconditioned response.

Note: A ⫽ Affective-motivational; CR ⫽ conditioned response; CS ⫽ conditioned stimuli; E ⫽ evaluative-cognitive; L ⫽ large diameter fiber; S ⫽ small diameter fiber ⫽ SD ⫽ sensory-discriminative for Melzack,

Data source: aBoring (1942), Melzack and Wall (1991); bMarshall (1894), Dallenbach (1939), Livingstone (1998).

the ratio toward small fibers would result in an increase in neural transmission, summation, and excessive pathological pain. Finally, the gate-control theory postulated that the perception of pain is determined by interactions between different types of fibers, both small-diameter pain transmitting and large-diameter nonpain transmitting fibers. This theory asserted that activation of the large diameter (fast-conducting) nonpain transmitting fibers could indirectly inhibit signals from small-diameter (slowconducting) pain transmitting fibers and block the transmission and perception of pain (Melzack & Wall, 1965; Figure 33.2). Under pathological conditions, the fast system loses its dominance over the slow one, resulting in slow pain (Bishop, 1946), diffuse burning pain (Bishop, 1959), or hyperalgesia (Noordenbos, 1959). This model was updated by Melzack and Casey (1968) as a conceptual model of the sensory, motivational, and central control determinants of pain. In this conceptualization, the

output of the transmission (T) cells of the gate-control system projects to the sensory-discriminative system (via the neospinothalamic fibers) and the motivational-affective system (via the paramedical ascending system). The central control trigger projects back to the gate-control system, the sensory-discriminative system, and the motivationalaffective system. These three systems interact and project to the motor system to influence motor responses to pain (Figure 33.2). Body-self matrix theory developed from attempts to explain the neurophysiology of phantom limb pain (Melzack, 1990a, 1999). According to this theory, a genetically built-in matrix of neurons for the whole body comprises a widely distributed neural network that incorporates somatosensory, limbic, and thalamocortical components. These components contain smaller parallel networks that contribute to sensory-discriminative, affective-motivational, and evaluative-cognitive dimensions of pain experience as the neuromatrix. According to this

Figure 33.2 theories.

c33.indd 637

Gate control system

Pattern theory

CS (electric, burn, cut)

Pleasure ⫹

L

⫹

Max von Freya 1894–1895

Specificity theory Ancient Greeksb

⫹

S

Motor mechanisms

(Central intensity monitor)

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theory, the cyclical processing and synthesis of nerve impulses in the neuromatrix imposes a characteristic output pattern or a neurosignature that is perceived as pain (Melzack, 1990a; Figure 33.2). The conceptualization of pain is likely to continue to evolve over time together with advances in our understanding of pain transmission and modulation.

TRANSIENT PAIN Transient pain is defined by the brief duration of the experienced pain sensation. It is the feeling commonly experienced with minor injuries (e.g., a stubbed toe, a mild burn, the itching of sunburn). Transient pain has no long-term consequence because it is associated with almost no tissue damage. Transient pain is not typically accompanied by anxiety (Melzack & Wall, 1991). In this situation, a first pain is felt which is well localized and relatively mild (Table 33.1). This is followed shortly by a second pain that distracts attention from the person’s previous activity and decreases in intensity until it fades away (Marchand, 1998; Melzack & Wall, 1991; Table 33.1).

ACUTE PAIN Acute pain (e.g., such as that felt after twisting an ankle or cutting your finger) is a more intense sensation than transient pain. This experience is marked by intense consciousness of the event and a penetrating sensation that is accompanied by alertness and orientation toward the affected region. This painful experience contains a sequence of perceptions, evaluations, and emotions (Price, 1999). Perception and evaluation are mostly related to examination of the injury itself but can be influenced by previous experiences (e.g., a prior history of similar experiences) or the personality of the injured (Melzack & Wall, 1991; Price, 1999). The emotional component of acute pain may be related to fear or anxiety emanating from the body following the injury and includes autonomic activation. Furthermore, after some delay, thoughts and concerns regarding pain become more elaborated, reflective, and directed toward the long-term consequences of this injury (e.g., activities and responsibilities that will be unattended) and anxiety about the healing process. The immediate and late stage of this acute pain experience involves two main aspects: the desire to avoid harm due to the injury and the expectation that you will succeed in preventing harm (Price, 1999). Expectations can also be focused on the healing process and anxiety/fear can be experienced

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if these expectations are not met. Thus, acute pain can reiterate some of the complexities of the multidimensional experience that represents persistent pain.

PAIN AS A RECUPERATIVE HEALING MECHANISM Transient and acute pain is adaptive in that it serves a protective function and enables the affected individual to learn to avoid serious injuries in the future. A sequence of change occurs after an injury on several levels: physiological, biological, neurological, and behavioral. A relationship exists between the behavioral and biological events to ensure subsequent recuperation and healing after injury. Individuals born with congenital insensitivity to pain often sustain serious injuries, providing a rather extreme illustration of the useful functions of pain to warn, protect, and heal injured tissue (Fields, 1987; Melzack & Wall, 1983). Wall (1979) proposed that three phases of pain behavior— immediate, acute, and chronic—follow an injury in both animals and humans. The immediate phase of pain behavior is the first period corresponding to the activation of nociceptive afferent neurons and is related to autonomic responses (e.g., fight-or-flight responses) combined with emotions (e.g., fear or anger). It is possible that pain is not felt at this moment if the subject is caught in a stressful situation where it is necessary to escape or find safety. The acute phase of pain behavior corresponds to the behavior associated with the recovery process. At this point, the subject will feel pain and will have to cope with it (i.e., find treatment and prepare for recovery). This phase can be accompanied by anxiety and distress about the injury. Finally, the chronic phase of pain consists of quiet inactivity and related behavior related to rest, inactivation, recuperation, and healing. Long-term changes in the nervous system induced by the failure or delays in complete healing of the injury where pain is no longer a symptom of an injury, but rather becomes a serious medical syndrome, may contribute to the development of chronic pain syndromes in humans (Price, 1999; Wall, 1979).

LONG-TERM OR CHRONIC PAIN The fact that long-term or chronic pain is accepted and recognized as a distinct medical entity represents a major breakthrough in the field of pain (Bonica, 1953, 1974). A few decades ago, people who felt pain long after healing had occurred were frequently sent to psychiatric hospitals. Misdiagnosis and mistreatment resulted from an utter lack

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TABLE 33.1

Yes

Yes

No

A␤

A␦

C

A␦

C

Myelinated

Fibre Type

30 – 100 m/sec

6 – 30 m/s

1.0 – 2.5 m/s

5 – 15 ␮m

1 – 5 ␮m

0.25 – 1.5 ␮m

First pain A␦ fiber

Conduction Velocity

Diameter

Characterization of different types of afferent fibers and their relationship to first and second pain

Free

Free

Second pain C fiber

Specialized and free

Receptor Type

Light pressure Heavy pressure Heat (45˚C ⫹) Chemicals Warmth

Light pressure Heavy pressure Heat (45˚C ⫹)

Light pressure or touch

Respond To

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Pain: Mechanisms and Measurement

of knowledge regarding the pathophysiology of chronic pain. Chronic pain is defined as pain that persists after all possible healing has occurred or long after pain can serve any useful function. Thus, chronic pain is no longer a symptom of injury or disease but is a medical problem in its own right that requires urgent attention to alleviate unnecessary suffering (Melzack & Wall, 1991). Feelings of fear, anxiety, and anger characterizing the earliest phase of the pain experience are transformed into despair, frustration, hopelessness, and depression that can develop later (Cohen, Patel, Khetpal, Peterson, & Kimmel, 2007; Price, 1999; Scholl & Allen, 2007). Such changes are understandable and may be related to reflections about the interference of pain in everyday life, difficulty of enduring such pain, and the ultimate negative consequences of enduring this persistent pain (Price, 1988, 1999). Moreover, treatments that are usually beneficial for most acute pains are not necessarily effective for chronic pain (Guindon, Walczak, & Beaulieu, 2007). Chronic pain is perceived as intractable and becomes intolerable because existing pharmacotherapies show only limit efficacy for pain management and adverse side-effects constrain therapeutic dosing. Many factors interact and negatively contribute to chronic pain including psychological factors that can lead to depression in both adults (Chenot et al., 2008; Cohen et al., 2007) and children (Scholl & Allen, 2007). Future research aimed at further elucidating mechanisms underlying transmission and modulation of pain, especially chronic pain, are required to eliminate unnecessary suffering in affected patients.

PATHWAYS CONTRIBUTING TO THE TRANSMISSION AND MODULATION OF PAIN The trajectory of nociceptive information traveling from the periphery through sensory nerve fibers to the central nervous system after an injury is characterized by a series of chemical and electrical reactions. These reactions may be divided into four steps (Fields, 1987). The first step is transduction, which corresponds to the transformation of the chemical, thermal, or mechanical stimulus into energy (action potentials) in sensory nerve endings. For example, when an individual burns a finger, intense heat energy from the burn will be converted into electrical nerve impulses at the free nerve endings in the skin (Melzack & Wall, 1991; Price, 1999). In this case, any skin damage will activate these networks and initiate the transmission (second step) of trains of neural impulses along sensory nerve fibers (known as primary afferent neurons) running from the periphery (skin) to the spinal cord. Then, the signal moves along secondary projection neurons via ascending pathways that originate in the spinal cord and innervate the

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brain stem and thalamus. Thalamic neurons subsequently convey this information to diverse cortical regions (Fields, 1987). The third step is the modulation of neurons responsible for the transmission of nociceptive information from the periphery to the central nervous system by descending projections from the brain. These descending processes may either inhibit or facilitate pain (Bie & Pan, 2007; Garcia-Larrea & Magnin, 2008; Mason, 2005). The fourth step refers to the perception of pain. This phenomenon corresponds to the finality of the nociceptive experience that can be influenced by emotional state and previous experience (Fields, 1987).

Transduction Nociceptive information is perceived by the application of a stimulus capable of harming the integrity of the organism (e.g., burning a finger with fire). Note that the same stimulus will be ignored by some receptors and perceived by others. The latter receptors, better known as nociceptors (responding to noxious stimulation), are bushy networks of fibers that penetrate many layers of the skin, muscles, articulations, and visceral structures (Melzack & Wall, 1991; Price, 1999). These nociceptive receptors are sensitive to stimuli of various kinds (chemical, thermal, or mechanical) and transduce this stimulus energy into actions potentials. Transmission of this information is linked to the intensity of nociceptive stimulation and is encoded by the frequency and pattern of firing of nociceptive afferent neurons (Fields, 1987; Julius & Basbaum, 2001; Price, 1999). These primary afferents consequently mediate both transduction and transmission of pain (Fields, 1987; Millan, 1999). These nociceptive receptors are also described as nerve endings linked to sensory nerve fibers. The primary afferent sensory fibers include A (large myelinated), A␤ (small myelinated), and C fibers (thin unmyelinated), which are classified based on their fiber diameters and conduction velocities (Table 33.1). These fibers are tuned precisely to begin firing nerve impulses when a particular event occurs (depending on the fiber recruited) in the region of their terminals. For example, A␤ fibers start firing when skin is cooled by a fraction of a degree and A␤/C fibers largely respond to an increase in temperature. A single painful stimulus evokes two successive and qualitatively different pain sensations, termed first and second pain. First pain is mediated by A␤ fibers and is characterized by a brief, well-localized sensation whereas second pain is mediated by C fibers and is associated by a later, longer lasting burning and more diffusely localized sensation (Melzack & Wall, 1991; Price, Hu, Dubner, & Gracely, 1977; Table 33.1).

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Pathways Contributing to the Transmission and Modulation of Pain 641

Transmission The transmission of nociceptive information from the periphery to the central nervous system constitutes a first line of defense to minimize damage to the organism. This process is more complex than described previously by early pain theories due to the multiplicity of different receptors, overlap in receptive fields of afferent fibers, and involvement of multiple ascending nociceptive pathways (for a review, see Julius & Basbaum, 2001; Melzack & Wall, 1991; Millan, 1999; Price, 1999). Skin damage initiates trains of nerve impulses along primary afferent fibers that are running from the periphery to the spinal cord. The primary nociceptive afferent neurons enter the spinal cord via the dorsal roots to converge on and synaptically excite neurons in the grey matter of the dorsal horn of the spinal cord. The cells in the grey matter of the spinal cord are arranged in laminae (or layers) in a dorsal ventral direction running the entire length of the spinal cord (Rexed, 1952). A total of 10 laminae have been described, of which 6 are found in the dorsal horn (Figure 33.3). Laminae II may be further subdivided into laminae II inner and outer, which receive different primary afferent inputs (Julius & Basbaum, 2001; Marker, Lujan, Colon, & Wickman, 2006). In general, nociceptive afferent C fibers and some A␤ fibers terminate in laminae

Sensory cortex

I and II, whereas other A fibers penetrate deeper into the dorsal horn (laminae V). The second-order neurons in the dorsal horn of the spinal cord cross over (decussate) to the contralateral side and ascend to innervate the thalamus via the spinothalamic tract (neospinothalamic and paleospinothalamic), the major ascending central pathway for pain (Melzack & Wall, 1991; Price, 1999). The neospinothalamic pathway originates in part from laminae I of the spinal cord that receives input from A␤ fibers responsible for rapid and well-localized pain (for review, see Besson & Chaouch, 1987; Julius & Basbaum, 2001; Kandel, 1985; Melzack, 1990b). At least four classes of spinal and medullary dorsal horn neurons have been identified. These include low threshold mechanosensitive (LTM), thermoreceptive (warm and cold), wide dynamic range (WDR), and nociceptive specific (NS) cells. WDR neurons respond with increasing action potential frequency to stimulation ranging from nonnoxious to noxious. These neurons, which receive input from both large diameter (A) and small diameter (A␦ and C) fibers, code information about stimulus intensity. Nociceptive specific cells, which receive input from small diameter (A␦ mechanosensitive, A␦ heat, and C polymodal), respond exclusively to noxious stimuli. By contrast, low-threshold mechanosensitive cells, which receive input

Sensory cortex Frontal lobe

Limbic System

Limbic System Thalamus

0

0

1 2 3 4 5 6

Frontal lobe Thalamus

PAG

0

NRM

0

⫹ 3 4 5 6 0

0

Transmission

Modulation

Spinothalamic tract

Figure 33.3 Circuitry mediating transmission (from the periphery to the brain) and modulation (from the brain to the periphery) of pain.

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Pain: Mechanisms and Measurement

from large-diameter A fibers, respond to light touch, pressure, and hair movement. Few of these fibers project into the spinothalamic tract. Thermoreceptive cells respond exclusively to either warming or cooling of the receptive field. Several lines of evidence specifically support a role for WDR and NS cells in pain discrimination (see Price & Dubner, 1977, for a review): (a) selective stimulation of these fibers produces sensations of pain, (b) these neurons exhibit maximal responses to noxious (as opposed to nonnoxious) levels of stimulation, (c) manipulations that reduce neural responses in these cells produce concomitant reductions in pain sensation, and (d) these neurons exhibit anatomical connections consistent with a role in pain transmission. Nociceptive dorsal horn neurons project to the ventroposterolateral (VPL) nucleus of the thalamus. VPL neurons subsequently relay the transmitted information to the sensory cortex. The rapid conduction of A␤ fibers and the small receptive fields of the neospinothalamic pathway are essential qualities that permit the physical or sensorydiscriminative aspects of pain (localization and perception; Figure 33.3). The paleospinothalamic tract is located on the median position of the thalamus. This tract is mainly innervated by dorsal horn neurons that receive afferent input from C fibers that transmit slow and diffuse pain. Synapses converge principally on the nucleus of the reticular formation of the cerebral trunk and the median nucleus of the thalamus whose neurons subsequently project to the frontal cortex and limbic system. These latter regions are also implicated in emotion and memory (Kandel, 1985; Melzack, 1990b). The slow conduction of C fibers, the diffuse aspect of the receptive fields, and the higher cerebral structures implicated in the paleospinothalamic pathway support a role for this pathway in motivationalaffective (e.g., unpleasantness) aspects of pain perception (Figure 33.3). Modulation Nociceptive information traveling from the periphery to the central nervous system can be modulated by inhibition or facilitation from pathways that descend from the brain to the spinal cord. Descending inhibition occurs at any moment of the transmission of nerve impulse. Three main mechanisms describe this modulation: the gatecontrol theory (Melzack & Wall, 1965), the descending inhibitory control system (Basbaum & Fields, 1978; Bie & Pan, 2007; Garcia-Larrea & Magnin, 2008; Millan, 2002; Reynolds, 1969), and the inhibitory control produced by higher centers of the central nervous system (Bie & Pan, 2007; Craig & Bushnell, 1994; Garcia-Larrea & Magnin,

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Verbal Rating Scale No pain 0

Score

0

1

2

Mild

Moderate

1

Severe 3

2

Numerical Rating Scale 3 4 5 6 7

8

9

No pain

0

10 Worst possible pain

10

20

30

40

50

60

70

80

90

100

Visual Analogue Scale Worst possible pain

No pain

McGill Pain Questionnaire 102 words divided in three categories: sensory, affective, evaluative Pain Rating Index Number of Words Chosen Actual Pain Intensity: Sum of ranking of each words chosen in all 20 subclasses

Sum the words chosen in all 20 subclasses

• • • • • •

No pain (0) Mild (1) Discomforting (2) Distressing (3) Horrible (4) Excruciating (5)

Figure 33.4 Four valid tools (or instruments) to measure pain in humans: (1) Verbal Rating Scale, (2) Numerical Rating Scale, (3) Visual Analogue Scale, and (4) McGill Pain Questionnaire.

2008; Hagbarth & Kerr, 1954; Mason, 2005; Melzack, Stotler, & Livingston, 1958; Figure 33.3). The gate-control theory (see Figure 33.2) proposes the existence of a rapidly conducting fiber system (referring to A fibers), which inhibits the synaptic transmission at laminae I and II of the dorsal horn of the spinal cord of a more slowly conducting system (A␤ and C fibers) that carries the signal for pain (Melzack & Wall, 1965). The existence of a descending inhibitory control system was first described by Reynolds (1969), who hypothesized that electrical stimulation of a small area of the grey matter surrounding the cerebral aqueduct—the periaqueductal grey area (PAG)—could enhance descending inhibition and produce analgesia. His hypothesis was borne out as electrical stimulation of the PAG induced sufficient analgesia to perform an invasive surgery (laparotomy) on otherwise awake rats. Later, it was discovered that brain stem inhibitory fibers descend through a distinct pathway in the dorsolateral spinal cord called the dorsolateral funiculus (Basbaum, Marley, O’Keefe, & Clanton, 1977). The PAG was a key component of this descending system (Fields, Basbaum, & Heinricher, 2006). The PAG receives input from many different brain regions (e.g., hypothalamus, cortex, thalamus) and is implicated in the mechanism whereby cortical and other inputs act to control the nociceptive gate in the dorsal horn. PAG neurons activate neurons in the nucleus raphe magnus (NRM), an

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Pathways Contributing to the Transmission and Modulation of Pain 643

area of the rostral medulla close to the midline, which in turn project via the dorsolateral funiculus of the spinal cord to make synaptic connections on dorsal horn interneurons (Basbaum & Fields, 1978; Fields, Basbaum, & Heinricher, 2006). Inhibitory control produced by higher centers of the central nervous system was first demonstrated by the fact that responses evoked in the ventrolateral spinal cord could be virtually abolished by the stimulation of different brain structures including the reticular formation, the cerebellum, and the cerebral cortex (Hagbarth & Kerr, 1954). The clear implications of this demonstration were that these neural structures exert an inhibitory control over the transmission of pain in the dorsal horn. This hypothesis was confirmed by the demonstration that lesions of a small area of the reticular formation (the central tegmental tract adjacent to the lateral PAG) produced hyperalgesia in cats. This area was postulated to exert a tonic inhibitory control over the pain signals because ablation of this structure produced hyperresponsiveness to pain. Thus, removal of inhibition allows pain signals to travel unchecked to the brain and can even permit summation of nonnoxious signals to produce spontaneous pain (Melzack et al., 1958). Chronic pain is also known to be actively facilitated by descending projections from the nucleus raphe magnus (Bie & Pan, 2007; Garcia-Larrea & Magnin, 2008). Mechanisms underlying descending inhibition (see reviews by Bie & Pan, 2007; Fields, Basbaum, & Heinricher, 2006; Garcia-Larrea & Magnin, 2008; Mason, 2005; Melzack & Wall, 1991; Price, 1999) and descending facilitation (Garcia-Larrea & Magnin, 2008; Mason, 2005) of pain

TABLE 33.2

are reviewed elsewhere. Pathophysiologic modifications that can contribute to pain and the attenuation of spinal inhibition include selective neuronal loss and subsequent development of inflammatory phenomena (e.g., cytokine secretion by macrophages and glial cells). These observed changes in the dorsal horn can modify the activity of projections neurons to the brain stem, thereby increasing spinal hyperactivity (DeLeo & Yezierski, 2001; Garcia-Larrea & Magnin, 2008; Pruimboom & vanDam, 2007). Perception The personal interpretation of a nociceptive stimulus that is associated with an emotional state of mind (or situation) or past experiences is described as the perception of pain. Note that a similar stimulus may provoke different sensations in different individuals, suggesting that pain is highly modifiable. Many psychological, neurophysiological, and psychophysical factors can modulate and influence the perception of pain, thereby altering the effectiveness of the treatment (Greenwald, 1991; Guindon, Walczak, & Beaulieu, 2007; see Table 33.2). Thus, early imaging studies using positron emission tomography (PET) and, more recently, functional magnetic resonance imaging (fMRI) hold significant promise for identifying networks of interconnected cerebral structures implicated in the affective component of pain (Bingel, Schoell, & Büchel, 2007; Craggs, Price, Verne, Perlstein, & Robinson, 2007; Moisset & Bouhassira, 2007; Rainville, Duncan, Price, Carrier, & Bushnell, 1997).

Drugs commonly used in the treatment of pain

Classical analgesics and new adjuvants Opioids

NSAIDs

Antidepressants

Local anesthetics

• Morphine

• Diclofenac

• Venlaflaxine

• Bupivacaine

• Hydromorphone

• Ketorolac

• Imipramine

• Ropivacaine

• Fentanyl

• Ketoprofen

• Duloxetine

• Levobupivacaine

• Remifentanil

• Ibuprofen

• Bupropion

• Lidocaine 5%

• Alfentanil

• Naproxen

• Sufentanil • Meperidine

COXIBS

Anticonvulsants • Gabapentin

Others

• Pregabalin

• Acetaminophen

• Buprenorphine

• Celecoxib

• Lamotrigine

• Butorphanol

• Etoricoxib

Cannabinoids

• Ketamine

(paracetamol)

• Nalbuphine

• Lumiracoxib

• Cannabis

• Nefopam

• Oxycodone

• Parecoxib

• Δ9-THC/CBD

• Clonidine

• Tramadol

• Nabilone

• Neostigmine

• Methadone

• Dronabinol

• Magnesium

• Adjulemic Acid

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Pain: Mechanisms and Measurement

PAIN MEASUREMENT IN HUMANS The development of valid instruments for measuring pain in humans is critical to effectively quantify the intensity, quality, and duration of pain. Measurement of pain in humans is important for diagnosis, choice of therapy, and evaluation of the relative effectiveness of different therapies (Guarino & Myers, 2007; Melzack & Katz, 1999). The varieties of pain experience can be assessed by a description of the qualities of pain experienced within three (i.e., sensory-discriminative, motivational-affective, and cognitive-evaluative) dimensions (Melzack, 2005). However, it may be difficult to describe pain experience because these words are not often used and it seems impossible to actively capture such abstract sensations as the shooting pain felt by neuropathic patients (Melzack & Katz, 1999; Melzack & Wall, 1991). In the past, pain was measured with respect to a single unique quality: the variation in intensity (Beecher, 1959). Methods used included verbal rating, numerical rating, and visual analogue scales (Figure 33.4). Verbal rating scales consist of a series of verbal pain descriptors ordered from the least to the most intense sensation (no pain, mild, moderate, severe; Daoust, Beaulieu, Manzini, Chauny, & Lavigne, 2008; Jensen & Karoly, 1992). The patient reads the list and chooses the one word that most closely describes his momentary pain (e.g., a score of zero to the lowest rank descriptor, a score of one for the next lowest rank descriptor, and so on). Numerical rating scales are described as a series of numbers from 0 to 10 or 0 to 100 with endpoints intended to represent the extremes of the pain experience, such as no pain and worst possible pain, respectively (Jensen & Karoly, 1992; Molton, Jensen, Nielson, Cardenas, & Ehde, 2008). In this case, the patient has to decide the number that best corresponds to the intensity of his or her actual pain. These two methods are simple to administer, reliable, and valid. Visual analogue scales remain the measurement instrument of choice for pain assessment when unidimensional levels of pain are assessed. The visual analogue scale consists of a 10-cm horizontal (Daoust et al., 2008; Huskisson, 1983; Joyce, Zutshi, Hrubes, & Mason, 1975) or vertical (Sriwatanakul et al., 1983) line with the two endpoints marked as no pain and worst pain ever (or other similar descriptors). The patient is asked to place a mark on the line at the point that best describes the level of the pain intensity experienced. The distance from the low end to the mark is used as a numerical index. This method is particularly valuable because it can be used to assess unpleasantness of the pain experience (Nielsen, Price, Vassend, Stubhaug, & Harris, 2005; Price, Harkins, & Baker, 1987; Price, Harkins, Rafii, & Price, 1986) separately from intensity, is sensitive to pharmacological and

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nonpharmacological procedures that alter pain experience (Bélanger, Melzack, & Lauzon, 1989; Choinière, Melzack, Girard, Rondeau, & Paquin, 1990; Price, Harkins, & Baker, 1987), and correlates well with verbal and numerical rating scales measuring pain (Daoust et al., 2008; Ekblom & Hansson, 1988; Kremer & Atkinson, 1983). This method was improved by Choinière and Amsel (1996) with their development of a visual analogue thermometer. This instrument consists of a rigid plastic horizontal scale of 10 cm with a band that slides from the left (no pain) to the right (worst pain ever) with a tab on the back of the thermometer; the patient places the cursor at the point referring to his or her pain intensity. However, in some elderly patients who lack manual dexterity, this method is not optimal and numerical or verbal rating scales are more reliable (Gagliese & Melzack, 1997). The visual analogue scale has been used to measure pain affect (or unpleasantness), although it corresponds to only one dimension of pain experience leaving the two other dimensions to be assessed separately. The complexity of pain necessitates describing it in terms of three dimensions to adequately capture the pain experience. The McGill Pain Questionaire (MPQ; Melzack, 1975) was developed to better specify the qualities of pain. This questionnaire developed from choosing 102 words from the literature and categorizing them into three major classes related to the three dimensions of pain: (1) sensory descriptors that describe the sensory qualities of the pain experience in terms of temporal, spatial, pressure, thermal, or other such qualities; (2) affective descriptors that assess the affective qualities in terms of tension, fear, and autonomic properties that are part of the pain experience; and (3) evaluative descriptors that describe the subjective overall intensity of the total pain experience (Melzack, 2005; Melzack & Torgerson, 1971). The words were divided in three major classes, and further separated into 16 subclasses. The intensity of each word was subsequently rated using a numerical scale ranging from least to worst pain by physicians, patients, and university graduates. Some key words were considered missing by patients and a fourth supplementary class (adding 4 subclasses) called miscellaneous was added to the lists of pain descriptors (Melzack & Torgerson, 1971). The descriptor lists of the MPQ are read to the patient who is instructed to choose only the words that describe the feelings and sensations at that precise moment (Melzack, 1975). The questionnaire gives the clinicians three major indices: (1) the pain rating index that corresponds to the sum of ranking of each word chosen in all 20 subclasses (the word in each subclass implying the least pain is given a score of 1, the next word a score of 2, and so on); (2) the number of words that are chosen; (3) the actual pain intensity when the questionnaire is administered from no pain (score 0) to excruciating pain

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Pain Measurement in Animals

(score 5; Melzack, 1975, 2005). The MPQ has been demonstrated to be a reliable, consistent, valid, and useful tool for clinicians to assess pain qualities in each patient (Chapman et al., 1985; Love, Leboeuf, & Crisp, 1989; Melzack, 1983; Wilkie, Savedra, Holzemier, Tesler, & Paul, 1990). The MPQ is sensitive enough to measure decreases in pain behavior following analgesic treatments in patients with wounds (Briggs, 1996) or breast cancer (Eija, Tiina, & Pertti, 1996) pain and also measure decreases comparable to those detected using visual analogue or verbal rating scales following oral analgesics treatments in postoperative pain (Jenkinson et al., 1995). Since its introduction in 1975, the MPQ has been used in several hundred studies of pain and translated into several languages (Melzack & Katz, 1999). A short version of the MPQ was subsequently developed to better suit project research or emergency situations where the time available to obtain information is limited and critical (Melzack, 1987). This short version consists of 15 words taken from the longer version (11 words from the sensory and 4 words from the affective categories), and is accompanied by a present pain intensity score and a visual analogue scale rating (Cook et al., 2004; Melzack, 1987). Although pain is a private and personal experience, patients suffering the same or similar pain syndromes can characterized their pain by a distinctive constellation of words. Patients with similar conditions but sometimes divergent backgrounds show remarkable consistency in this choice of words (Grushka & Sessle, 1984; Katz, 1992; Katz & Melzack, 1991; Melzack, 2005). Thus, the MPQ enables different pain syndromes to be reliably discriminated (Dubuisson & Melzack, 1976). However, high levels of anxiety in a patient can diminish the discriminative capacity of the instrument (Atkinson, Kremer, & Ignelzi, 1982; Bélanger et al., 1989; Melzack, Wall, & Ty, 1982). Behavioral approaches such as those used to measure pain in animals are relied on to assess pain in infants and preverbal children, and also in adults with poor language capacity or mental confusion (Chapman et al., 1985; Marinov, Mandadjieva, & Kostianev, 2008; McGrath & Unruh, 2002; Ross & Ross, 1984). However, behavioral measures of pain should not replace self-rated measures if the patient is able to rate his or her subjective state of pain. Someone else’s pain cannot be described completely by anyone other than the inflicted individual (Melzack & Katz, 1999). Some patients may be stoic in response to pain and outwardly exhibit a calm demeanor even while experiencing excruciating pain accompanied by autonomic activation, whereas other patients may exaggerate pain behaviors but in fact experience less pain. Therefore, concordance in the assessment of pain may vary between the patient

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645

and health care professionals, effects that may be attributed to the personal and private nature of the pain experience (Choinière et al., 1990). In some cases, physiological approaches (e.g., heart rate, blood pressure, electrodermal activity) can also be used to better correlate pain experience with its behavioral counterpart in patients and elucidate mechanisms associated with the painful experience (al’Absi, Petersen, & Wittmers, 2002; Chapman et al., 1985; Price, 1988). Nonetheless, it must be noted that even though many physiological and endocrine events can be measured and occur concurrently with pain experience, many of these events are not exclusively related to pain and may appear in response to stress rather than pain per se (Christensen, Brandt, Rem, & Kehlet, 1982).

PAIN MEASUREMENT IN ANIMALS Early studies on pain were performed in anaesthetized animals and used transient stimuli to produce pain in order to avoid tissue damage. In the past decade, many animal pain models have been developed to study mechanisms underlying persistent pain in awake behaving animals (for a review, see Dubner & Ren, 1999). Animal subjects are necessary in pain research because they permit manipulation of experimental variables that can lead to a better understanding of pain mechanisms at cellular and subcellular levels and improve analgesic therapies. Moreover, animal models can be used to model certain human pathological conditions (Chapman et al., 1985). The main purpose of these studies is to further elucidate the physiopathological aspect of pain conditions found in humans and permit preclinical validation of novel analgesic targets. Experimental pain may be induced in animals with different modalities of noxious stimulation (e.g., heat, mechanical, electrical and chemical stimulation; for reviews, see Chapman et al., 1985; Dubner & Ren, 1999; Hogan, 2002; Ren & Dubner, 1999; Whiteside, Adedoyin, & Leventhal, 2008). Although animals lack the ability to verbally communicate their pain, they exhibit the same motor behaviors and physiological responses demonstrated by humans following pain stimulation (Chapman et al., 1985; Dubner & Ren, 1999; Whiteside et al., 2008). However, for ethical reasons, animals should be exposed to the minimal pain necessary to carry out the experiment (Dubner, 1983) and they should not be exposed to pain greater than humans themselves would tolerate (Bowd, 1980). Finally, animal studies on pain employ behavioral measures of two types: simple reflex measures and more complex voluntary and intentional behaviors that can be either unlearned or learned (Chapman et al., 1985).

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646

Pain: Mechanisms and Measurement

Simple Reflex Measures Simple reflex measures include the tail-flick, the hot-plate, the mechanical withdrawal reflex, the Hargreaves, and the paw pressure tests (Table 33.3). In most cases, latency to withdraw/escape from noxious stimulation is assessed. The tail-flick test measures the latency for a rodent to withdraw his tail from a heat source (radiant heat or hot water immersion) focused on the tail (D’Amour & Smith, 1941). The hot-plate test measures the latency to escape (licking, lifting, paw fluttering) when the animal is placed on a preheated plate (van Eick, 1967). In the mechanical withdrawal test, thermal, mechanical, or electrical stimuli may be employed (Chaplan, Bach, Pogrel, Chung, & Yaksh, 1994). The Hargreaves test measures the latency to paw withdrawal following application of radiant heat to the plantar surface of the paw through the floor of a glass platform (Hargreaves, Dubner, Brown, Flores, & Joris, 1988). One advantage of this latter method is that animals do not need to be restrained, but rather may be placed underneath an inverted plastic cage positioned on the glass platform so that they may move freely. The paw pressure test measures the latency to struggle or vocalization following application of a constant, or more frequently, an increasing mechanical pressure applied to the hind limb (Randall & Selitto, 1957). It is important to note that in all these simple reflex measures the animal has control over the intensity and duration of the stimulus ensuring that the animal is not exposed to intolerable levels of pain (Dubner & Ren, 1999). Organized Unlearned Behaviors Complex organized unlearned behaviors may be assessed to measure pain because these behaviors require supraspinal sensory processing rather than relying exclusively on simple reflexes (Chapman et al., 1985). Commonly associated behaviors are used to assess nocifensive manisfestation following inflammatory, neuropathic, visceral, cancer, or postoperative pain (Bennett & Xie, 1988; Brennan, Vandermeulen, & Gebhart, 1996; Hargreaves et al., 1988; Ness, 1999; Pogatzki, Niemeier, & Brennan, 2002; Schwei et al., 1999; Wacnik et al., 2003). In these models, pain is produced that cannot be controlled by the animal. Therefore, it is important that investigators assess the level of pain in these animals and provide analgesics when it doesn’t interfere with the experiment (Dubner & Ren, 1999). Organized Learned Behaviors Organized learned behaviors are considered as a separate category because pain is inferred from an animal’s learned or operant responses to escape noxious stimulation. Indeed,

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the most common method involves an animal escaping from a noxious stimulus by initiating a learned behavior such as crossing a barrier or pressing a bar. For example, electric shock can be delivered to a grid floor in a cage and the animal can learn to jump over a barrier partition to escape this stimulus. It is important to note that these learning procedures give the animal control over the painful stimulus of the experiment (Dubner & Ren, 1999). Tissue Injury Models of Persistent Pain Animal models of tissue injury and inflammation have been developed to reproduce features of clinical pain conditions (Whiteside et al., 2008). The formalin test (Dubuisson & Dennis, 1977) and the orofacial formalin test (Clavelou, Dallel, Orliaguet, Woda, & Raboisson, 1995; Clavelou, Pajot, Dallel, & Raboisson, 1989) are commonly employed examples. Other models of more persistent pain employ irritants (e.g., capsaicin, bee venom) or inflammatory agents (e.g., carrageenan or complete Freund’s adjuvant; Hargreaves et al., 1988; LaMotte, Shain, Simone, & Tsai, 1991; Stein, Millan, & Herz, 1988). Animal models of polyarthritis and arthritis have also been developed to attempt to mimic these human conditions (Coderre & Wall, 1987; De Castro Costa, DeSutter, Gybels, & Van Hees, 1981; Okuda, Nakahama, Miyakawa, & Shima, 1984; Schaible, Schmidt, & Willis, 1987; Table 33.4). Furthermore, visceral pain (Chernov, Wilson, Fowler, & Plummer, 1967; Ness & Gebhart, 1988; Table 33.5), cancer pain (Medhurst et al., 2002; Wacnik et al., 2001; Table 33.6), and postoperative pain (Brennan et al., 1996; Pogatzki et al., 2002; Table 33.7) models have been developed as well. These models are well characterized and remain sensitive to analgesics that are effective for suppressing similar pain states in humans. Nerve Injury Models of Persistent Pain Nerve injury models have been developed to better understand the mechanisms involved in the development of neuropathic pain in humans. Several models of nerve ligation have been developed over the past 20 years (Bennett & Xie, 1988; Decosterd & Woolf, 2000; Kim & Chung, 1992; Seltzer, Dubner, & Shir, 1990) and have improved our understanding of mechanisms (e.g., central sensitization; Woolf & Thompson, 1991) that contribute to neuropathic pain states (Table 33.8). Bennett and Xie (1988) developed the first animal model of neuropathic pain, known as the chronic constriction injury (CCI) of the sciatic nerve. The development of animal models of neuropathic pain has had a monumental impact on the pain field, both by spurring research on the underlying mechanisms, by encouraging the development of additional animal models, and by enabling preclinical validation of novel analgesics that have shown efficacy

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• Radiant heat is focused on the tail

Tail-flick test

• Application of filaments of different calibers (force) are typically applied to the plantar surface of the paw

Von Frey filament test

Paw pressure test

• Heat is applied to the plantar surface of the hind paw through the floor of a glass platform

Hargreaves test

• A mechanical simulator is used to apply a constant noxious pressure or, more often, an increasing pressure to the hind paw of the animal

• Filaments applied through the floor of a wire mesh platform

• Animal is placed on a metal surface that is either preheated to a noxious temperature or progressively increases in temperature

Hot-plate test

• The tail can also be immersed in hot water

Description

Methods for measurement of acute pain in animals

Test

TABLE 33.3

Thermal

Mechanical

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• Sensitive to analgesics

• Animal must be manually restrained for assessment

• Sensitive to analgesics

• Frequency of paw withdrawal to a given filament (force) • Behavioral assessments: freezing (no movement), withdrawal of the paw, and vocalization produced by the animal

• Animal is not manually restrained during assessment

• Sensitive to analgesics

• Animal is not manually restrained during assessment

• Sensitive to analgesics

• Jump reflects an integrated response at the supraspinal level

• Licking is a reflex

• Sensitive to analgesics

• Simple method that measures spinal nociceptive reflex

Commentary

• The threshold of paw withdrawal is related to the force applied by the filament

• Measure the latency for the animal to withdraw its paw from a heat source

• Measure the latency to the appearance of evoked responses from the animal (lick, bite, fluttering of hindpaws, and/or jump)

• Measure the latency for the animal to remove it’s tail from the heat source

Dependent Measures

Randall & Selitto, 1957

Chaplan et al, 1994

Hargreaves et al., 1988

van Eick, 1967

D’Amour & Smith, 1941

References

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• Subcutaneous (dorsal surface) or intraplantar (plantar surface) injection of formalin in the paw

Formalin test

• Intraplantar injection of complete Freund’s adjuvant (CFA) in rodents

• Intrajoint injection of sodium urate crystals, carrageenan, kaolin, or other irritants provoke acute inflammation of the joint

• CFA injected into the tail induces a delayed hypersensitivity

Complete Freund’s adjuvant

Acute arthritis

Polyarthritis

• Inflammation and hyperalgesia of multiple joints occurs after 10 days to 3 weeks

• Intraplantar injection of carrageenan

• Mechanical and thermal hypersensitivity

• Reduction in motor activity

• Scratching behavior

• Mechanical and thermal hypersensitivity

• Circumference of the joint

• Degree of flexion of the joint

• Mechanical and thermal hypersensivitiy • Edema

• Mechanical and thermal hypersensitivity • Edema

• Mechanical and thermal hypersensitivity (assessed in rodents with von Frey filaments and Hargreaves testing)

• Intradermal injection of capsaicin into forearm of humans

Carrageenan

• Nocifensive behavior (licking, biting, and flinching injected paw)

• Intraplantar injection of capsaicin in rodents

Capsaicin

• Duration of time spent rubbing the injected upper lip

• Injection of formalin into the upper lip of a rodent

• Systemic disease develops and includes: skin lesions, destruction of bones and cartilage, liver impairment, and lymphadenopathy

• Sodium urate crystal-induced arthritis is fully developed within 24 hr

• Inflammation appears 2 hr after injection and is maximal after 6–8 hr

• Used in rats and mice

• Behavioral changes maximal at 2 hr post-injection and typically persist for 24 to 96 hr

• This model is used in rats, mice and human and nonhuman primates

• Reliable method to assess trigeminal pain

• Biphasic response similar to that observed following formalin injection in the paw

• This model is commonly used in rats and in mice

• A weighted pain score may be calculated based on time spent in each category of behavior

Commentary • Biphasic pain response, characterized by acute and inflammatory phases

• Duration of licking/biting, lifting, and favoring the injected paw is measured

Dependent Measures

Orofacial formalin test

• Injection is unilateral

Description

Test

TABLE 33.4 Animal models of tissue injury-induced persistent pain References

De Castro Costa et al., 1981

Okuda et al., 1984; Coderre & Wall, 1987; Schaible et al., 1987

Stein, Millan, Herz, 1988; Iadarola et al., 1988

Hargreaves et al., 1988

Simone, Ngeow, Putterman, LaMotte, 1987; LaMotte et al., 1991

Clavelou et al., 1989; Clavelou et al., 1995

Dubuisson & Dennis, 1977; Watson, Sufka, Coderre, 1997

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Myocardial ischemia

Ureteral calculosis

• Cystitis is induced in mice, and rats by i.p. injection of cyclophosphamide (anticancer substance) with adverse effects affecting the bladder

Cystitis

• Temporary obstruction of coronary artery or application of algogenic substances to epicardium of anaesthetized animal

• Animals show discomfort (abdominal contraction and hind limb extension) during 4 days following the implantation

• Artificial stone (made using dental cement) is implanted in rat ureteral tract

• Mustard oil, acetic acid, and other compounds have been used to induce cystitis

• Distension of the bladder in anaesthetized animals

• Tachycardia grade related to increasing colorectal distension

• Additionally used on anaesthetized animals to measure tachycardia simultaneously

Bladder distension

• Muscle contraction or behavioral reaction is measured

• Colorectal distension induced by an inflatable device

Colorectal distension

• Recordings of neuronal electrical activity

• Muscular lumbar hyperalgesia is demonstrated by vocalization of the animal following electrical stimulation of lumbar muscle

• In mice, mechanical allodynia and locomotion behavior are commonly measured

• In rats, pain behavior (licking, abdominal contraction, piloerection, arched back) are commonly evaluated

• Physiological responses or activation of micturition reflex

• Abdominal contractions and stretching of the body

• Injection (i.p.) of noxious irritants such as acetic acid produces a characteristic response (e.g., arched back, abdominal contractions, rolling on one side)

Writhing test

Dependent Measures

Description

Test

TABLE 33.5 Animal models of visceral pain

• This model is used to elucidate pain mechanisms related to angina

• Strong clinical relevance

• Strong clinical relevance

• The stimulus is known to affect only one viscera organ, the bladder

• Observed cardiovascular or visceromotor responses are reliable and reproducible

• Attenuated by analgesics

• Strong clinical relevance

Kumar et al., 1970; Pan & Chen, 2002

Giamberardino, Vecchiet, Albe-Fessard, 1990; Giamberardino, Valente, de Bigontina, Vecchiet, 1995

McMahon & Abel, 1987; Lanteri-Minet, Don, de Pommery, Michiels, Menetrey, 1995

Gosling, Dixon, Dunn, 1977; Ness, 1999

Ness & Gebhart, 1988; Ness, Randich, Gebhart, 1991, Ness, 1999

Helfer & Jaques, 1970; Vyklicky, 1979

• Used in mice and rats • Stimulus more natural than injections of irritants

Chernov, Wilson, Fowler, Plummer, 1967;

References

• Inflammation of the visceral organs and abdominal wall

Commentary

650

Pain: Mechanisms and Measurement

TABLE 33.6 Animal models of cancer pain Test

Description

Dependent Measures

Commentary

References

Carcinoma

• Neoplasia cells (from mammary gland) are injected into the tibia

• Mechanical hypersensitivity

• This metastasis cancer model is proposed to study new therapeutic treatments for metastasis pain

Medhurst et al., 2002; Walker et al., 2002

• Bone destruction is observed between 10 and 14 days; bone integrity is compromised after 20 days Fibrosarcoma

• Malignant neoplasia cells are injected into the humerus of mice • Hyperalgesia appears on the third day after injecting the cells and progresses with tumor development

• Metastatic bone pain is difficult to control due to fast progression and difficulty in predicting onset and severity • Hypersensitivity related to movement • Mechanical and thermal hypersensitivity

• Humerus cancer model permits evaluation of hypersensitivity in movement

Wacnik et al., 2001, Wacnik et al., 2003

• Injecting cancer cells in the femur of mice was the first cancer pain model to be developed

Schwei et al., 1999; Wang & Wang, 2003

• Malignant neoplasia cells may also be injected into the calcaneus bone • Destruction is appearing after six days Osteosarcoma

• Malignant neoplasia cells are injected inside the femur of mice

• Mechanical hypersensitivity

• Cancer pain lesions are developing and are destroying the bone Neuropathic cancer pain

• Sarcoma cells are injected at the proximity of the sciatic nerve in mice

• This model suggests that cancer pain is different from inflammatory or neuropathic pain • Mechanical and thermal hypersensitivity

• Tumor growth compresses the nerve

• Nerve damage appears progressively by compression and better represents neuropathic cancer pain observed clinically

Shimoyama, Tanaka, Hasue, Shimoayama, 2002

TABLE 33.7 Animal models of postoperative pain Test

Description

Dependent Measures

Commentary

References

Plantar hindpaw incision

• 1 cm incision is made on the plantar surface of the hindpaw in rats

• Mechanical hypersensitivity

• Nociceptive behavior and mechanical hypersensitivity is similar to human reaction to wound

Brennan et al., 1996; Brennan, 1999

• Plantar muscle is cut parallel to muscle fiber

• Pain is elevated after surgery and decreases 7 to 10 days later • Local morphine or anesthetics reduce pain behavior

Gastrocnemius muscles paw incision

• Gastrocnemius muscles of the rat paw are sectioned under general anesthesia

• Thermal and mechanical hypersensitivity on the plantar face

• This model permits evaluation of mechanisms responsible for secondary hypersensitivity and its role in postoperative persistent pain

Pogatzki et al., 2002

• Opiate administration reduces pain behavior Ovariohysterectomy

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• Removal of ovaries and uterus in rodents

• Mechanical and thermal hypersensitivity

• Pain behavior associated with abdominal contractions and stretching of the body

• Painful behaviors are evaluated with a rating scale

• Strong clinical relevance • Visceral or peritoneal pain may increase painful behaviors • Analgesics reduce these abnormal behaviors

Lascelles, Waterman, Cripps, Livingston, Henderson, 1995; Gonzalez, Field, Bramwell, McCleary, Singh, 2000

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651

TABLE 33.8A Animal models of neuropathic pain: traumatic nerve injury Test

Description

Dependent Measures

Commentary

References

Chronic construction of sciatic nerve

• Three loosely constrictive ligatures are placed around the common sciatic nerve

• Mechanical and thermal hypersensitivity

Bennett & Xie, 1988

• Animals develop nociceptive behaviors (protection and reduction of weight on the affected limb and lameness)

• Cold allodynia

• Hypersensitivity develops between 10 and 14 days and persists for 8 weeks

• Spontaneous pain

• Used in rats and mice

• The sciatic nerve is exposed at highthigh level and 1/3–1/2 of the dorsal thickness of the nerve is trapped in a ligature

• Mechanical and thermal hypersensitivity

• Hypersensitivity can persist for many months

• Ipsilateral and contralateral deficits

• Used in mice and rats

• Mechanical and thermal hypersensitivity • Spontaneous pain

• Thermed SNL (Chung) model

• Mechanical and thermal hypersensitivity

• This model shows that sensitivity of intact small nerve is modified by section of an adjacent nerve fiber • Rapid onset of cutaneous hypersensitivity

Decosterd & Woolf, 2000

• This model assesses the contribution of inflammation to the development of neuropathic pain

Bennett et al., 2000; Bennett, Everhart, Hulsebosch, 2000

• This model is also used in mice; known as chronic constriction of the saphenous nerve

Walczak, Pichette, Leblond, Desbiens, Beaulieu, 2005, 2006

Partial sciatic nerve injury

• Termed CCI model Seltzer et al., 1990

• Animals develop nociceptive behaviors (protection and licking of the affected limb) Spinal nerve ligation

• L5 and L6 spinal nerves are ligated and sectioned distal to the dorsal root ganglion; L4 spinal nerve is intact • Animals develop nociceptive behaviors (protection and reduction of weight on the affected limb and lameness)

Spared nerve injury

• Two of the three terminal branches of the sciatic nerve (tibial and common peroneal nerves) are ligated and sectioned. The third branch, the sural nerve, is left intact

• Cold allodynia • Spontaneous pain

• Animals develop chronic nociceptive behaviors (modification of position and reduction of weight on the affected limb) Neuritis-induced neuropathic pain

Saphenous nerve partial ligation

• Application of complete Freund’s adjuvant to the nerve (usually at the periphery of the sciatic nerve)

• Mechanical and thermal hypersensitivity

• 1/3–1/2 of the saphenous nerve is ligated (via three loose ligatures around the circumference of the saphenous nerve)

• Mechanical and thermal hypersensitivity

• Cold allodynia

Kim & Chung, 1992

• The SNL model (with the CCI and partial ligation of the sciatic nerve) constitute the three most widely used neuropathic pain models

• Neuropathic pain behaviors are observed 3 to 5 days after surgery

TABLE 33.8B Animal models of neuropathic pain: metabolic and toxic neuropathies Test

Description

Dependent Measures

Commentary

References

Diabetic neuropathy

• Injection (i.p.) of streptozotocine in rats provokes destruction of ␤ cells in the pancreas and subsequent development of diabetes type 1

• Mechanical and thermal hypersensitivity

• This model is controversial because observations are difficult to attribute to neuropathy because rats show impaired health and obvious signs of discomfort

Rakieten, Rakieten, Nadkarni, 1963; Wuarin Bierman, Zahnd, Kaufmann, Burcklen, Adler, 1987

• Mechanical hypersensitivity always observed

• These models are useful for understanding neuropathy induced in patients by treatment with chemotherapeutic agents

Aley, Reichling, Levine, 1996; Authier, Coudore, Eschalier, Fialip, 1999; Polomano & Bennett, 2001

• Rats develop reductions in locomotor activity Chemotherapyevoked toxic neuropathy

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• Repeated injections of antitumor agents (vincristine, paclitaxel, or cisplatin) change responsiveness to mechanical and sometimes thermal (cold or heat) stimulation

• Thermal hyperalgesia or hypoalgesia may be absent or present depending on agent and dose used

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for treating neuropathic pain in humans (e.g., gabapentin). The development of neuropathy following neuritis (Bennett, Chastain, & Hulsebosch, 2000), metabolic challenges (Rakieten, Rakieten, & Nadkarni, 1963; Wuarin Bierman, Zahnd, Kaufmann, Burcklen, & Adler, 1987), and chemotherapeutic treatment (Aley, Reichling, & Levine, 1996; Authier, Coudore, Eschalier, & Fialip, 1999; Polomano & Bennett, 2001) have also been studied (Table 33.8).

PHARMACOLOGICAL MANAGEMENT OF PAIN The main concern of clinical health professionals is to improve the management of pain in their patients. However, available pharmacotherapies for the management of pain are inadequate and require that multiple factors (e.g., unwanted side-effect profiles) also be considered (Table 33.9) to optimize the efficacy of the treatment. Pain is a multifactorial phenomenon requiring interdisciplinary approaches. Multimodal analgesia is an approach that involves the combination of several analgesics administered by the same or different routes to achieve better, more effective relief than analgesics administered separately. Thus, the combination of desipramine (an antidepressant) with morphine enhances the efficacy of the narcotic analgesic for the control of postoperative pain (Levine, Gordon, Smith, & McBryde, 1986). Furthermore, low back pain is well relieved by the combination of tramadol with acetaminophen (Perrot, Krause, Crozes, & Naïm, 2006). Musculoskeletal pain is similarly reduced by the combination of acetaminophen with hydrocodone (Hewitt et al., 2007). Different pharmacological approaches exist for the treatment of pain such as the use of opioids or nonsteroidal anti-inflammatory drugs

TABLE 33.9 Multiple factors influencing pharmacological treatment of pain • Cultural and religious belief • Personal experience • Social/family environment • Previous medical history • Pain intensity • Reduced work status • Interference with leisure activity • Other diseases interacting • Interactions with other drugs • Toxicity • Cost • Patient acceptance and compliance • Patient expectations and beliefs about the cause of pain

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(NSAIDs), anticonvulsants, antidepressants, ketamine, and others (for review, see Guindon, Walczak, & Beaulieu, 2007) although adverse side-effects remain the main constraint (Table 33.10). Furthermore, along with standard routes of administration, novel drug delivery approaches have become available in the past few years, including transdermal patches, oral mucosal sprays, intranasal instillation, rectal suppositories, and others (Table 33.10). The literature has indicated that multimodality approaches are associated with an increase in patient satisfaction and a reduction in side-effects compared to those resulting from single analgesic techniques in pain management (Brodner et al., 2001; Mugabure Bujedo, Tranque Bizueta, Gonzalez Santos, & Adrian Garde, 2007; Pyati & Gan, 2007).

NONPHARMACOLOGICAL MANAGEMENT OF PAIN Less than 50 years ago, neurosurgery was a common approach employed to manage severe or chronic pain. Indeed, the destruction of peripheral nerves by a meticulous surgery that excise the injured area and include grafting of a new nerve section was performed frequently. However, this procedure failed to alleviate pain and in some cases exacerbated it (Noordenbos & Wall, 1981). In addition, the cordotomy procedure (cutting tracts of the spinal cord) was used on terminally ill cancer patients to reduce severe cancer pain (Ischia, Ischia, Luzzani, Toscano, & Steele, 1985; Ischia, Luzzani, Ischia, & Pacini, 1984). This procedure was recommended only to terminal cancer patients because the pain returns and is frequently accompanied by unpleasant sensations and incontinence (Ischia et al., 1984, 1985). However, surgery infrequently achieves long-term control of pain and resumption of pain is common. This finding is unsurprising because surgical section disrupts the normal patterns of input to the central nervous system (e.g., resulting in abnormal bursting activity in the deafferented central cells that persists long after the surgery; Melzack & Loeser, 1978). Morever, the complexity of brain activity and its plasticity contradict a simple surgical solution for pain problems. Therefore, a more promising approach consists of continuous nerve blockage to reduce evoked pain. This technique reduces nausea and vomiting in patients receiving continuous peripheral nerve blocks while increasing their satisfaction. In this case, rehabilitation is improved and incidence of postsurgery chronic pain syndromes is greatly decreased (Boezaart, 2006). In home treatments are also possible, increasing patient satisfaction and comfort (Ilfed & Enneking, 2005). Furthermore, the use of nonpharmacological options such as massage, acupuncture, heat therapy, relaxation, and

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• Oral

Relief of: • Osteoarthritis • Rheumatoid arthritis • Acute or postoperative pain

• Blocking evoked pain

Local anesthetics Lidocaïne, bupivacaine, and others

Local/regional Transdermal (patch) Intravenous Neuroaxial (spinal, epidural)

• Oral • Sublingual spray • Inhalation

• Acute • Chronic pain

Cannabinoids Cannabis, nabilone, dronabinol, ⌬9-THC/CBD • • • •

• Oral

• Diabetic neuropathy • Postherpetic neuralgia • Trigeminal neuralgia

Anticonvulsants Gabapentin, pregabalin, lamotrigine

• Oral

• Neuropathic pain

Antidepressants Tricyclic (imipramine) Newer (venlaflaxine, duloxetine, bupropion)

Coxibs: celecoxib, etoricoxib, lumiracoxib, parecoxib

• Oral • Topical

• Analgesics • Anti-inflammatory agents

Oral Intravenous Transdermal (patch) Sublingual spray Intranasal spray Oral transmucosal Pulmonary Microspheres

NSAIDs Traditional: diclofenac, ketorolac, ketoprofen, ibuprofen, naproxen

• • • • • • • •

Treatment of pain such as: • Neuropathic • Inflammatory • Cancer • Acute • Post-operative

Opioids Morphine (or alternative opioid: hydromorphone, fentanyl, remifentanil, alfentanil, sufentanil, meperidine, buprenorphine, butorphanol, etc.)

Route of Administration

Indications

Different pharmacological treatments and administrations for pain relief

Drug

TABLE 33.10

Respiratory depression Sedation Nausea and vomiting Constipation Cognitive dysfunction Pruritus Tolerance/dependence Euphoria

• Tachycardia

• Convulsions • Coma • Skin erythema • Rash • Cardiorespiratory depression with increasing doses

• Tolerance • Memory impairment

• Euphoria

• Edema • Weight gain

• Ataxia • Diplopia

Sedation Constipation Dry mouth Orthostatic hypotension Weight gain with tricyclic Ataxia, nausea, and anorexia using newer antidepressants • Sedation

• • • • • •

• Cardiac (myocardial infarction and stroke) • Gastrointestinal associated with long-term use • Renal (acute renal failure)

• Gastrointestinal disturbances • Renal • Skin reactions

• • • • • • • •

Adverse Effects

• Locoregional anesthesia problems: non-consenting patients, local infection, coagulation disorders, inadequate monitoring

• Patients with hypertension and ischemic heart disease

• Patients with renal dysfunction need a dose adjustment

• Patients with glaucoma and/or taking monoamine oxidase inhibitors • Duloxetine has been approved by US FDA for use in diabetic, neuropathy

• Patients with cardiovascular and cerebrovascular disease • Carefulness in patients with hypertension, hyperlipidaemia, diabetes, arterial disease, or smoking

• Patients with gastrointestinal and renal complications

• Screen patients for alcohol/substance abuse; co-administer preemptive stool softeners and antiemetics

Contraindications

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transcutaneous electrical nerve stimulation (TENS) as adjuvants to conventional analgesia can also be considered and incorporated to achieve an effective and successful pain management regimen in some patients (Pyati & Gan, 2007). It is also possible to reduce clinical pain by using cognitive therapies although these therapies are not exclusive and need to be used in conjunction with the proper medication. For example, in patients suffering from posttraumatic stress syndrome (Muse, 1985, 1986) and ovarian cancer (Montazeri, McEwen, & Gillis, 1996), psychological counseling is validated to improve quality of life and reduce pain. However, there are no perfect therapies and the effectiveness of each can vary depending on the disease and the patient. Although each therapy has its own specific limitations related to the disease/patient context (see Table 33.10), it is highly significant that the effects of two or more therapies given in combination can produce additive or synergistic effects.

SUMMARY Great advances have been made in the past several decades in defining pain and understanding its underlying mechanisms. Further research is nonetheless necessary to reduce unnecessary suffering in chronic pain patients. Although many treatment modalities are being used to reduce and alleviate pain, many basic research and clinical questions remain unanswered. These gaps in our knowledge base may be attributed to the complexity of pain mechanisms that are involved. Filling these gaps may identify previously unrecognized therapeutic targets. In the coming years, advances in our preclinical and clinical understanding of pain mechanisms are expected, which should provide an impetus for improving pharmacotherapies for chronic pain. Treatment paradigms are shifting from single to multiple therapies that combine medications with distinct mechanisms of action and/or combine medications with nontraditional therapies. Targeting multiple analgesic mechanisms simultaneously holds promise for attaining a more complete attenuation of pain with a more limited spectrum of unwanted side-effects.

REFERENCES al’Absi, M., Petersen, K. L., & Wittmers, L. E. (2002). Adrenocortical and hemodynamic predictors of pain perception in men and women. Pain, 96, 197–204. Aley, K. O., Reichling, D. B., & Levine, J. D. (1996). Vincristine hyperalgesia in the rat: A model of painful vincristine neuropathy in humans. Neuroscience, 73, 259–265.

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Atkinson, J. H., Kremer, E. F., & Ignelzi, R. J. (1982). Diffusion of pain language with affective disturbance confounds differential diagnosis. Pain, 12, 375–384. Authier, N., Coudore, F., Eschalier, A., & Fialip, J. (1999). Pain related behavior during vincristine-induced neuropathy in rats. NeuroReport, 10, 965–968. Basbaum, A. I., & Fields, H. L. (1978). Endogenous pain control mechanisms: Review and hypothesis. Annals of Neurology, 4, 451–462. Basbaum, A. I., Marley, N. J. E., O’Keefe, J., & Clanton, C. H. (1977). Reversal of morphine and stimulus produced analgesia by subtotal spinal cord lesions. Pain, 3, 43–56. Beecher, H. K. (1959). Measurement of subjective responses. New York: Oxford University Press. Bélanger, E., Melzack, R., & Lauzon, P. (1989). Pain of first-trimester abortion: A study of psychosocial and medical predictors. Pain, 36, 339–350. Bennett, A. D., Chastain, K. M., & Hulsebosch, C. E. (2000). Alleviation of mechanical and thermal allodynia by CGRP(8–37) in a rodent model of chronic central pain. Pain, 86, 163–175. Bennett, A. D., Everhart, A. W., & Hulsebosch, C. E. (2000). Intrathecal administration of an NMDA or a non-NMDA receptor antagonist reduces mechanical but not thermal allodynia in a rodent model of chronic central pain after spinal cord injury. Brain Research, 859, 72–82. Bennett, G. J., & Xie, Y. K. (1988). A peripheral mononeuropathy in rat that produces disorders of pain sensation like those seen in man. Pain, 33, 87–107. Besson, J. M., & Chaouch, A. (1987). Peripheral and spinal mechanisms of nociception. Physiological Reviews, 67, 67–186. Bie, B., & Pan, Z. Z. (2007). Trafficking of central opioid receptors and descending pain inhibition. Molecular Pain, 3, 37. Bingel, U., Schoell, E., & Büchel, C. (2007). Imaging pain modulation in health and disease. Current Opinion in Neurology, 20, 424–431. Bishop, G. H. (1946). Neural mechanisms of cutaneous sense. Physiological Reviews, 26, 77–102. Bishop, G. H. (1959). The relation between nerve fiber size and sensory modality: Phylogenetic implications of the afferent innervations of the cortex. Journal of Nervous and Mental Diseases, 128, 89–114. Boezaart, A. P. (2006). Perineural infusion of local anesthetics. Anesthesiology, 104, 872–880. Bonica, J. J. (1953). The management of pain. Philadelphia: Lea and Febiger. Bonica, J. J. (1974). Organization and function of a pain clinic. In J. J. Bonica (Ed.), Advances in neurology (pp. 433–443). New York: Raven Press. Boring, E. G. (1942). Sensation and perception in the history of experimental psychology. New York: Appleton-Century-Crofts. Bowd, A. D. (1980). Ethics and animal experimentation. American Psychologist, 35, 224–225. Brennan, F., Carr, D. B., & Cousins, M. (2007). Pain management: A fundamental human right. Anesthesia and Analgesia, 105, 205–221. Brennan, T. J. (1999). Postoperative models of nociception. Animal models of pain. ILAR Journal, 40, 129–136. Brennan, T. J., Vandermeulen, E. P., & Gebhart, G. F. (1996). Characterization of a rat model of incisional pain. Pain, 64, 493–501. Briggs, M. (1996). Surgical wound pain: A trial of two treatments. Journal of Wound Care, 5, 456–460. Brodner, G., Van Aken, H., Hertle, L., Fobker, M., Von Eckardstein, A., Goeters, C., et al. (2001). Multimodal perioperative management: Combining thoracic epidural analgesia, forced mobilization, and oral nutrition: Reduces hormonal and metabolic stress and improves convalescence after major urologic surgery. Anesthesia and Analgesia, 92, 1594–600.

8/17/09 3:05:09 PM

References 655 Chaplan, S. R., Bach, F. W., Pogrel, J. W., Chung, J. M., & Yaksh, T. L. (1994). Quantitative assessment of tactile allodynia in the rat paw. Journal of Neuroscience Methods, 53, 55–63. Chapman, C. R., Casey, K. L., Dubner, R., Foley, K. M., Gracely, R. H., & Reading, A. E. (1985). Pain measurement: An overview. Pain, 22, 1–31. Chenot, J. F., Leonhardt, C., Keller, S., Scherer, M., Donner-Banzhoff, N., Pfingsten, M., et al. (2008). The impact of specialist care for low back pain on health service utilization in primary care patients: A prospective cohort study. European Journal of Pain, 12, 275–283. Chernov, H. I., Wilson, D. E., Fowler, W. F., & Plummer, A. J. (1967). Non-specificity of the mouse writhing test. Archives Internationales de Pharmacodynamie et de Thérapie, 167, 171–178. Choinière, M., & Amsel, R. (1996). A visual analogue thermometer for measuring pain intensity. Journal of Pain and Symptom Management, 11, 299–311. Choinière, M., Melzack, R., Girard, N., Rondeau, J., & Paquin, M. J. (1990). Comparisons between patients and nurses assessments of pain and medication efficacy in severe burn injuries. Pain, 40, 143–152.

Descartes, R. (1664). L’homme translated by M. Foster in 1901. Lectures on the history of physiology during 16th, 17th, and 18th centuries. Cambridge: Cambridge University Press. Dubner, R. (1983). Pain research in animals. Annals of the New York Academy of Sciences, 406, 128–132. Dubner, R., & Ren, K. (1999). Assessing transient and persistent pain in animals. In P. D. Wall & R. Melzack (Eds.), Textbook of pain (4th ed., pp. 359–369). London: Churchill Livingstone. Dubuisson, D., & Dennis, S. G. (1977). The formalin test: A quantitative study of the analgesic effects of morphine, meperidine, and brain stem stimulation in rats and cats. Pain, 4, 161–174. Dubuisson, D., & Melzack, R. (1976). Classification of clinical pain descriptors by multiple group discriminant analysis. Experimental Neurology, 51, 480–487. Eija, K., Tiina, T., & Pertti, N. J. (1996). Amitriptyline effectively relieves neuropathic pain following treatment of breast cancer. Pain, 64, 293–302.

Christensen, P., Brandt, M. R., Rem, J., & Kehlet, H. (1982). Influence of extradural morphine on the adrenocortical and hyperglycaemic response to surgery. British Journal of Anaesthesia, 54, 23–27.

Ekblom, A., & Hansson, P. (1988). Pain intensity measurements in patients with acute pain receiving afferent stimulation. Journal of Neurology, Neurosurgery, and Psychiatry, 51, 481–486.

Clavelou, P., Dallel, R., Orliaguet, T., Woda, A., & Raboisson, P. (1995). The orofacial formalin test in rats: Effects of different formalin concentrations. Pain, 62, 295–301.

Fields, H. L. (1987). Pain. New York: McGraw-Hill.

Clavelou, P., Pajot, J., Dallel, R., & Raboisson, P. (1989). Application of the formalin test to the study of orofacial pain in the rat. Neuroscience Letters, 103, 349–353.

Fields, H. L., Basbaum, A. I., Heinricher, M. M. (2006). Central nervous system mechanisms of pain modulation. In S. B. McMahon & M. Koltzenburg (Eds.), Wall & Melzack’s Textbook of pain, 5th edition (pp. 125–142). Edinburgh: Elsevier.

Coderre, T. J., & Wall, P. D. (1987). Ankle joint urate arthritis (AJUA) in rats: An alternative animal model of arthritis to that produced by Freund’s adjuvant. Pain, 28, 379–393. Cohen, S. D., Patel, S. S., Khetpal, P., Peterson, R. A., & Kimmel, P. L. (2007). Pain, sleep disturbance, and quality of life in patients with chronic kidney disease. Clinical Journal of the American Society of Nephrology, 2, 919–925. Comings, D. E., & Amromin, G. D. (1974). Autosomal dominant insensitivity to pain with hyperplastic myelinopathy and autosomal dominant indifference to pain. Neurology, 24, 838–848. Cook, A. J., Roberts, D. A., Henderson, M. D., VanWinkle, L. C., Chastain, D. C., & Hamill-Ruth, R. J. (2004). Electronic pain questionnaires: A randomized, crossover comparison with paper questionnaires for chronic pain assessment. Pain, 110, 310–317. Cox, J. J., Reimann, F., Nicholas, A. K., Thornton, G., Roberts, E., Springell, K., et al. (2006, August 24). An SCN9A channelopathy causes congenital inability to experience pain. Nature, 444, 894–898. Craggs, J. G., Price, D. D., Verne, G. N., Perlstein, W. M., & Robinson, M. M. (2007). Functional brain interactions that serve cognitive-affective processing during pain and placebo analgesia. NeuroImage, 38, 720–729. Craig, A. D., & Bushnell, M. C. (1994, July 8). The thermal grill illusion: Unmasking the burn of cold pain. Science, 265, 252–255. Dallenbach, K. M. (1939). Pain: History and present status. American Journal of Psychology, 52, 331–347. D’Amour, F. E., & Smith, D. L. (1941). A method for determining loss of pain sensation. Journal of Pharmacology and Experimental Therapeutics, 72, 74–79. Daoust, R., Beaulieu, P., Manzini, C., Chauny, J. M., & Lavigne, G. (2008). Estimation of pain intensity in emergency medicine: A validation study. Pain, 138, 565–570. De Castro Costa, M., DeSutter, P., Gybels, J., & Van Hees, J. (1981). Adjuvant-induced arthritis in rats: A possible animal model of chronic pain. Pain, 10, 173–186. Decosterd, I., & Woolf, C. J. (2000). Spared nerve injury: An animal model of persistent peripheral neuropathic pain. Pain, 87, 149–158.

c33.indd 655

DeLeo, J. A., & Yezierski, R. P. (2001). The role of inflammation and neuroimmune activation in persistent pain. Pain, 201, 1–6.

Fields, H. L. (1999). Pain: An unpleasant topic. Pain (Suppl. 6), S61–S69.

Gagliese, L., & Melzack, R. (1997). Age differences in the quality of chronic pain: A preliminary study. Pain Research and Management, 2, 157–162. Garcia-Larrea, L., & Magnin, M. (2008). Pathophysiology of neuropathic pain: Review of experimental models and proposed mechanisms. Presse Médicale, 37, 315–340. Giamberardino, M. A., Valente, R., de Bigontina, P., & Vecchiet, L. (1995). Artificial ureteral calculosis in rats: Behavioral characterization of visceral pain episodes and their relationship with referred lumbar muscle hyperalgesia. Pain, 61, 459–469. Giamberardino, M. A., Vecchiet, L., & Albe-Fessard, D. (1990). Comparison of the effects of ureteral calculosis and occlusion on muscular sensitivity to painful stimulation in rats. Pain, 43, 227–234. Goldscheider, A. (1894). Uber den Schmerz in physiologischer und klinischer hinsichl. Berlin, Germany: Hirschwald. Gonzalez, M. I., Field, M. J., Bramwell, S., McCleary, S., & Singh, L. (2000). Ovariohysterectomy in the rat: A model of surgical pain for evaluation of pre-emptive analgesia? Pain, 88, 79–88. Gosling, J. A., Dixon, J. S., & Dunn, M. (1977). The structure of the rabbit urinary bladder after experimental distension. Investigative Urology, 14, 386–389. Greenwald, H. P. (1991). Interethnic differences in pain perception. Pain, 44, 157–163. Grushka, M., & Sessle, B. J. (1984). Applicability of the McGill Pain Questionnaire to the differentiation of toothache pain. Pain, 19, 49–57. Guarino, A. H., & Myers, J. C. (2007). An assessment protocol to guide opioid prescriptions for patients with chronic pain. Missouri Medicine, 104, 513–516. Guindon, J., Walczak, J. S., & Beaulieu, P. (2007). Recent advances in the pharmacological management of pain. Drugs, 67, 2121–2133. Hagbarth, K. E., & Kerr, D. I. (1954). Central influences on spinal afferent conduction. Journal of Neurophysiology, 17, 295–307. Hargreaves, K., Dubner, R., Brown, F., Flores, C., & Joris, J. (1988). A new and sensitive method for measuring thermal nociception in cutaneous hyperalgesia. Pain, 32, 77–88.

8/17/09 3:05:09 PM

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Pain: Mechanisms and Measurement

Helfer, H., & Jaques, R. (1970). The duration of action of antinociceptive agents ad determined in mice using the arachidonic acid writhing test. Pharmacology, 4, 163–168. Hewitt, D. J., Todd, K. H., Xiang, J., Jordan, D. M., Rosenthal, N. R., & CAPSS-216 Study Investigators. (2007). Tramadol/acetaminophen or hydrocodone/acetaminophen for the treatment of ankle sprain: A randomized, placebo-controlled trial. Annals of Emergency Medicine, 49, 468–480. Hogan, Q. (2002). Animal pain models. Regional Anesthesia and Pain Medicine, 27, 385–401. Huskisson, E. C. (1983). Visual analogue scales. In R. Melzack (Ed.), Pain measurement and assessment (pp. 33–37). New York: Raven Press. Iadarola, M. J., Brady, L. S., Draisci, G., & Dubner, R. (1988). Enhancement of dynorphin gene expression in spinal cord following experimental inflammation: Stimulus specificity, behavioral parameters and opioid receptor binding. Pain 1 35, 313–326. Ilfeld, B. M., & Enneking, F. K. (2005). Continuous peripheral nerve blocks at home: A review. Anesthesia and Analgesia, 100, 1822–1833. Ischia, S., Ischia, A., Luzzani, A., Toscano, D., & Steele, A. (1985). Results up to death in the treatment of persistent cervico-thoracic (Pancoast) and thoracic malignant pain by unilateral percutaneous cervical cordotomy. Pain, 21, 339–355. Ischia, S., Luzzani, A., Ischia, A., & Pacini, L. (1984). Role of unilateral percutaneous cervical cordotomy in the treatment of neoplastic vertebral pain. Pain, 19, 123–131. Jenkinson, C., Carroll, D., Egerton, M., Frankland, T., McQuay, H., & Nagle, C. (1995). Comparison of the sensitivity to change of long and short form pain measures. Quality of Life Research, 4, 353–357. Jensen, M. P., & Karoly, P. (1992). Self-report scales and procedures for assessing pain in adults. In D. C. Turk & R. Melzack (Eds.), Handbook of pain assessment (pp. 135–151). New York: Guilford Press. Joyce, C. R., Zutshi, D. W., Hrubes, V., & Mason, R. M. (1975). Comparison of fixed interval and visual analogue scales for rating chronic pain. European Journal of Clinical Pharmacology, 8, 415–420. Julius, D., & Basbaum, A. I. (2001, July 21). Molecular mechanisms of nociception. Nature, 413, 203–210. Katz, J. (1992). Psychophysical correlates of phantom limb experience. Journal of Neurology, Neurosurgery, and Psychiatry, 55, 811–821. Katz, J., & Melzack, R. (1991). Auricular TENS reduces phantom limb pain. Journal of Pain and Symptom Management, 6, 73–83. Kandel, E. R. (1985). Central representations of pain and analgesia. In E. R. Kandel & J. H. Schwartz (Eds.), Principles of neural science (pp. 331–343). New York: Elsevier. Kim, S. H., & Chung, J. M. (1992). An experimental model for peripheral neuropathy produced by segmental spinal nerve ligation in the rat. Pain, 50, 355–363. Kremer, E., & Atkinson, J. H. (1983). Pain language as a measure of effect in chronic pain patients. In R. Melzack (Ed.), Pain measurement and assessment (pp. 119–127). New York: Raven Press. Kumar, B., Hood, W. B., Jr., Joison, J., Gilmour, D. P., Norman, J. C., & Abelmann, W. H. (1970). Experimental myocardial infarction: VI. Efficacy and toxicity of digitalis in acute and healing phase in intact conscious dogs. Journal of Clinical Investigation, 49, 358–364. LaMotte, R. H., Shain, C. N., Simone, D. A., & Tsai, E. F. (1991). Neurogenic hyperalgesia: Psychophysical studies of underlying mechanisms. Journal of Neurophysiology, 66, 190–211. Lanteri-Minet, M., Bon, K., dePommery, J., Michiels, J. F., & Menetrey, D. (1995). Cyclophosphamide cystitis as a model of visceral pain in rats: Model elaboration and spinal structures involved as revealed by the expression of c-Fos and Krox-24 proteins. Experimental Brain Research, 105, 220–232.

c33.indd 656

Lascelles, B. D., Waterman, A. E., Cripps, P. J., Livingston, A., & Henderson, G. (1995). Central sensitization as a result of surgical pain: Investigation of the pre-emptive value of pethidine for ovariohysterectomy in the rat. Pain, 62, 201–212. Levine, J. D., Gordon, N. C., Smith, R., & McBryde, R. (1986). Desipramine enhances opiate postoperative analgesia. Pain, 27, 45–49. Livingstone, W. K. (1943). Pain mechanisms. New York: Macmillan. Livingstone, W. K. (1998). Pain and suffering. Seattle: IASP Press. Loeser, J. D. (2001). Tic douloureux. Pain Research and Management, 6, 156–165. Love, A., Leboeuf, D. C., & Crisp, T. C. (1989). Chiropractic chronic low back pain sufferers and self-report assessment methods: Pt. I. A reliability study of the Visual Analogue Scale, the pain drawing and the McGill Pain Questionnaire. Journal of Manipulative and Physiological Therapeutics, 12, 21–25. Manfredi, M., Bini, G., Cruccu, G., Accornero, N., Berardelli, A., & Medolago, L. (1981). Congenital absence of pain. Archives of Neurology, 38, 507–511. Marchand, S. (1998). Le phénomène de la douleur. Montreal, Ontario, Canada: Chenelière/McGraw-Hill. Marinov, B., Mandadjieva, S., & Kostianev, S. (2008). Pictorial and verbal category-ratio scales for effort estimation in children. Child: Care, Health, and Development, 34, 35–43. Marker, C. L., Lujan, R., Colon, J., & Wickman, K. (2006). Distinct populations of spinal cord lamina II interneurons expressing G-protein-gated potassium channels. Journal of Neuroscience, 26, 12251–12259. Marshall, H. R. (1894). Pain, pleasure and aesthetics. London: Macmillan. Mason, P. (2005). Ventromedial medulla: Pain modulation and beyond. Journal of Comparative Neurology, 493, 2–8. McGrath, P. J., & Unruh, A. M. (2002). The social context of neonatal pain. Clinics in Perinatology, 29, 555–572. McMahon, S. B., & Abel, C. (1987). A model for the study of visceral pain states: Chronic inflammation of the chronic decerebrate rat urinary bladder by irritant chemicals. Pain, 28, 109–127. Medhurst, S. J., Walker, K., Bowes, M., Kidd, B. L., Glatt, M., Muller, M., et al. (2002). A rat model of bone cancer pain. Pain, 96, 129–140. Melzack, R. (1975). The McGill Pain Questionnaire: Major properties and scoring methods. Pain, 1, 277–299. Melzack, R. (1983). Pain measurement and assessment. New York: Raven Press. Melzack, R. (1987). The short-form McGill Pain Questionnaire. Pain, 30, 191–197. Melzack, R. (1990a). Phantom limbs and the concept of a neuromatrix. Trends in Neuroscience, 13, 88–92. Melzack, R. (1990b). The tragedy of needless pain. Scientific American, 262, 27–33. Melzack, R. (1999). From the gate to the neuromatrix. Pain (Suppl. 6), S121–S126. Melzack, R. (2005). The McGill Pain Questionnaire: From description to measurement. Anesthesiology, 103, 199–202. Melzack, R., & Casey, K. L. (1968). Sensory, motivational, and central control determinants of pain: A new conceptual model. In D. Kenshalo (Ed.), The skin senses (pp. 422–443). Springfield: Thomas. Melzack, R., & Katz, J. (1999). Pain measurement in persons in pain. In P. D. Wall & R. Melzack (Eds.), Textbook of pain (4th ed., pp. 409–426). London: Churchill Livingstone. Melzack, R., & Loeser, J. D. (1978). Phantom body pain in paraplegics: Evidence for a central “pattern generating mechanism” for pain. Pain, 4, 195–210.

8/17/09 3:05:10 PM

References 657 Melzack, R., Stotler, W. A., & Livingston, W. K. (1958). Effects of discrete brainstem lesions in cats on perception of noxious stimulation. Journal of Neurophysiology, 21, 353–367.

Pan, H. L., & Chen, S. R. (2002). Myocardial ischemia recruits mechanically insensitive cardiac sympathetic afferents in cats. Journal of Neurophysiology, 87, 660–668.

Melzack, R., & Torgerson, W. S. (1971). On the language of pain. Anesthesiology, 34, 50–59.

Pavlov, I. P. (1927). Conditioned reflexes. Oxford: Humphrey Milford.

Melzack, R., & Wall, P. D. (1965, November 19). Pain mechanisms: A new theory. Science, 150, 971–979. Melzack, R., & Wall, P. D. (1983). The challenge of pain. New York: Basic Books. Melzack, R., & Wall, P. D. (1991). The challenge of pain. London: Penguin Books. Melzack, R., Wall, P. D., & Ty, T. C. (1982). Acute pain in an emergency clinic: Latency of onset and description patterns related to different injuries. Pain, 14, 33–43. Merskey, H., & Bogduk, N. (1994). Part III: Pain terms, a current list with definitions and notes on usage. In H. Merskey & N. Bogduk (Eds.), Classification of chronic pain, 2nd edition (pp. 209–214). Seattle, WA: IASP Press. Millan, M. J. (1999). The induction of pain: An integrative review. Progress in Neurobiology, 57, 1–164. Millan, M. J. (2002). Descending control of pain. Progress in Neurobiology, 66, 355–474. Moisset, X., & Bouhassira, D. (2007). Brain imaging of neuropathic pain. NeuroImage, 37 (Suppl. 1), S80–S88. Molton, I. R., Jensen, M. P., Nielson, W., Cardenas, D., & Ehde, D. M. (2008). A preliminary evaluation of the motivational model of pain selfmanagement in persons with spinal cord injury-related pain. Journal of Pain, 9, 606–612. Montazeri, A., McEwen, J., & Gillis, C. R. (1996). Quality of life in patients with ovarian cancer: Current state of research. Support Care Cancer, 4, 169–179. Moskowitz, M. A. (1992). Neurogenic versus vascular mechanisms of sumatriptan and ergot alkaloids in migraine. Trends in Pharmacological Sciences, 13, 307–311. Müller, J. (1842). Elements of physiology. London: Taylor. Mugabure Bujedo, B., Tranque Bizueta, I., Gonzalez Santos, S., & Adrian Garde, R. (2007). Multimodal approaches to postoperative pain management and convalescence. Revista Española de Anestesiología y Reanimación, 54, 29–40. Muse, M. (1985). Stress-related, postraumatic chronic pain syndrome: Criteria for diagnosis, and preliminary report on prevalence. Pain, 23, 295–300. Muse, M. (1986). Stress-related, postraumatic chronic pain syndrome: Behavioral treatment approach. Pain, 25, 389–394. Ness, T. J. (1999). Models of visceral nociception. ILAR Journal, 40, 119–128. Ness, T. J., & Gebhart, G. F. (1988). Colorectal distension as a noxious visceral stimulus: Physiologic and pharmacologic characterization of pseudaffective reflexes in the rat. Brain Research, 450, 153–169. Ness, T. J., Randich, A., & Gebhart, G. F. (1991). Further behavioral evidence that colorectal distension is a “noxious” visceral stimulus in rats. Neuroscience Letters, 131, 113–116. Nielsen, C. S., Price, D. D., Vassend, O., Stubhaug, A., & Harris, J. R. (2005). Characterizing individual differences in heat-pain sensitivity. Pain, 119, 65–74. Noordenbos, W. (1959). Pain. Amsterdam: Elsevier Press. Noordenbos, W., & Wall, P. D. (1981). The failure of nerve grafts to relieve pain following nerve injury. Journal of Neurology, Neurosurgery, and Psychiatry, 44, 1008–1073. Okuda, K., Nakahama, H., Miyakawa, H., & Shima, K. (1984). Arthritis induced in cats by sodium urate: A possible animal model for chronic pain. Pain, 18, 287–297.

c33.indd 657

Perrot, S., Krause, D., Crozes, P., Naïm, C., & GRTF-ZAL-1 Study Group. (2006). Efficacy and tolerability of paracetamol/tramadol (325 mg/37.5 mg) combination treatment compared with tramadol (50 mg) monotherapy in patients with subacute low back pain: A multicenter, randomized, double-blind, parallel-group, 10-day treatment study. Clinical Therapeutics, 28, 1592–1606. Pogatzki, E. M., Niemeier, J. S., & Brennan, T. J. (2002). Persistent secondary hyperalgesia after gastrocnemius incision in the rat. European Journal of Pain, 6, 295–305. Polomano, R. C., & Bennett, G. J. (2001). Chemotherapy-evoked painful peripheral neuropathy. Pain Medicine, 2, 8–14. Price, D. D. (1988). Psychological and neural mechanisms of pain. New York: Raven Press. Price, D. D. (1999). Psychological mechanisms of pain and analgesia: Progress in pain research and management. Seattle, WA: IASP Press. Price, D. D., & Dubner, R. (1977). Neurons that subserve the sensorydiscriminative aspects of pain. Pain, 3, 307–338. Price, D. D., Harkins, S. W., & Baker, C. (1987). Sensory-affective relationships among different types of clinical and experimental pain. Pain, 28, 297–307. Price, D. D., Harkins, S. W., Rafii, A., & Price, C. (1986). A simultaneous comparison of fentanyl’s analgesic effects on experimental and clinical pain. Pain, 24, 197–203. Price, D. D., Hu, J. W., Dubner, R., & Gracely, R. H. (1977). Peripheral suppression of first pain and central summation of second pain evoked by noxious heat pulses. Pain, 3, 57–68. Pruimboom, L., & vanDam, A. C. (2007). Chronic pain: A non-use disease. Medical Hypotheses, 68, 506–511. Pyati, S., & Gan, T. J. (2007). Perioperative pain management. CNS Drugs, 21, 185–211. Rainville, P., Duncan, G. H., Price, D. D., Carrier, B., & Bushnell, M. C. (1997, August 15). Pain affect encoded in human anterior cingulate but not somatosensory cortex. Science, 277, 968–971. Rakieten, N., Rakieten, L., & Nadkarni, M. V. (1963). Studies on the diabetogenic action of streptozotocin. Cancer Chemotherapy Reports, 29, 91–98. Randall, L. O., & Selitto, J. J. (1957). A method for measurement of analgesic activity on inflamed tissue. Archives Internationales de Pharmacodynamie et de Thérapie, 111, 409–419. Ren, K., & Dubner, R. (1999). Inflammatory models of pain and hyperalgesia. ILAR Journal, 40, 111–118. Rexed, B. (1952). The cytoarchitectonic organization of the spinal cord in the cat. Journal of Comparative Neurology, 96, 415–495. Reynolds, D. V. (1969, April 25). Surgery in the rat during electrical analgesia induced by focal brain stimulation. Science, 164, 444–445. Ross, D. M., & Ross, S. A. (1984). Childhood pain: The school-aged child’s viewpoint. Pain, 20, 179–191. Schaible, H. G., Schmidt, R. F., & Willis, W. D. (1987). Enhancement of the responses of ascending tract cells in the cat spinal cord by acute inflammation of the knee joint. Experimental Brain Research, 66, 489–499. Scholl, J., & Allen, P. J. (2007). A primary care approach to functional abdominal pain. Pediatric Nursing, 33, 247–254. Schwei, M. J., Honore, P., Rogers, S. D., Salak-Johnson, J. L., Finke, M. P., Ramnaraine, M. L., et al. (1999). Neurochemical and cellular reorganization of the spinal cord in a murine model of bone cancer pain. Journal of Neuroscience, 19, 10886–10897.

8/17/09 3:05:10 PM

658

Pain: Mechanisms and Measurement

Seltzer, Z., Dubner, R., & Shir, Y. (1990). A novel behavioral model of neuropathic pain disorders produced in rats by partial sciatic nerve injury. Pain, 43, 205–218. Shimoyama, M., Tanaka, K., Hasue, F., & Shimoyama, N. (2002). A mouse model of neuropathic cancer pain. Pain, 99, 167–174. Simone, D. A., Ngeow, J. Y., Putterman, G. J., & LaMotte, R. H. (1987). Hyperalgesia to heat after intradermal injection of capsaicin. Brain Research, 418, 201–203. Sinclair, D. C. (1955). Cutaneous sensation and the doctrine of specific nerve energies. Brain, 78, 584–614. Sriwatanakul, K., Kelvie, W., Lasagna, L., Calimlim, J. F., Weis, O. F., & Mehta, G. (1983). Studies with different types of visual analog scales for measurement of pain. Clinical Pharmacology and Therapeutics, 34, 234–239. Stein, C., Millan, M. J., & Herz, A. (1988). Unilateral inflammation of the hindpaw in rats as a model of prolonged noxious stimulation: Alterations in behavior and nociceptive thresholds. Pharmacology, Biochemistry, and Behavior, 31, 455–461. van Eick, A. J. (1967). A change in the response of the mouse in the “hot plate” analgesia-test, owing to a central action of atropine and related compounds. Acta Physiologica et Pharmacologica Neerlandica, 14, 499–500. Villalon, C. M., Centurion, D., Valdivia, L. F., de Vries, P., & Saxena, P. R. (2003). Migraine: Pathophysiology, pharmacology, treatment and future trends. Current Vascular Pharmacology, 1, 71–84. Vyklicky, L. (1979). Techniques for the study of pain in animals. In J. J. Bonica, J. C. Liebeskind, & D. G. Albe-Fessard (Eds.), Advances in pain research and therapy (Vol. 3, pp. 727–745). New York: Raven Press. Wacnik, P. W., Eikmeier, L. J., Ruggles, T. R., Ramnaraine, M. L., Walcheck, B. K., Beitz, A. J., et al. (2001). Functional interactions between tumor and peripheral nerve: Morphology, algogen identification, and behavioral characterization of a new murine model of cancer pain. Journal of Neuroscience, 21, 9355–9366. Wacnik, P. W., Kehl, L. J., Trempe, T. M., Ramnaraine, M. L., Beitz, A. J., & Wilcox, G. L. (2003). Tumor implantation in mouse humerus evokes movement-related hyperalgesia exceeding that evoked by intramuscular carrageenan. Pain, 101, 175–186. Walczak, J. S., Pichette, V., Leblond, F., Desbiens, K., & Beaulieu, P. (2005). Behavioral, pharmacological and molecular characterization of the saphenous nerve partial ligation: A new model of neuropathic pain. Neuroscience, 132, 1093–1102.

c33.indd 658

Walczak, J. S., Pichette, V., Leblond, F., Desbiens, K., & Beaulieu, P. (2006). Characterization of chronic constriction of the saphenous nerve, a model of neuropathic pain in mice showing rapid molecular and electrophysiological changes. Journal of Neuroscience Research, 83, 1310–1322. Walker, K., Medhurst, S. J., Kidd, B. L., Glatt, M., Bowes, M., Patel, S., et al. (2002). Disease modifying and anti-nociceptive effects of the bisphosphonate, zoledronic acid in a model of bone cancer pain. Pain, 100, 219–229. Wall, P. D. (1979). On the relation of injury to pain [The first John J. Bonica Lecture]. Pain, 6, 253–264. Wang, L. X., & Wang, Z. J. (2003). Animal and cellular models of chronic pain. Advanced Drug Delivery Reviews, 55, 949–965. Watson, G. S., Sufka, K. J., & Coderre, T. J. (1997). Optimal scoring strategies and weights for the formalin test in rats. Pain, 70, 53–58. Waxman, S. G. (2007). Nav1.7, its mutations, and the syndromes that they cause. Neurology, 69, 505–507. Weddell, G. (1955). Somesthesis and the chemical senses. Annual Review of Psychology, 6, 119–136. Whiteside, G. T., Adedoyin, A., & Leventhal, L. (2008). Predictive validity of animal pain models? A comparison of the pharmacokineticpharmacodynamic relationship for pain drugs in rats and humans. Neuropharmacology, 54, 767–775. Wilkie, D. J., Savedra, M. C., Holzemier, W. L., Tesler, M. D., & Paul, S. M. (1990). Use of the McGill Pain Questionnaire to measure pain: A metaanalysis. Nursing Research, 39, 36–41. Woolf, C. J., & Thompson, S. W. N. (1991). The induction and maintenance of central sensitization is dependent on N-methyl-D-aspartic acid receptor activation; implications for the treatment of post-injury pain insensitivity states. Pain, 44, 293–299. Wuarin Bierman, L., Zahnd, G. R., Kaufmann, F., Burcklen, L., & Adler, J. (1987). Hyperalgesia in spontaneous and experimental animal models of diabetic neuropathy. Diabetologia, 30, 653–658. Wynn Parry, C. B. (1980). Pain in avulsion lesions of the brachial plexus. Pain, 9, 41–53. You, H. J., Dahl Morch, C., Chen, J., & Arendt-Nielsen, L. (2003). Simultaneous recordings of wind up of paired spinal dorsal horn nociceptive neuron and nociceptive flexion reflex in rats. Brain Research, 960, 235–245.

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Chapter 34

Hunger TERRY L. POWLEY

produced with different methods have yet to be synthesized into a coherent account of hunger and satiety. Until there is better integration, effective treatments for eating disorders may continue to elude both researchers and those suffering from the disorders.

The neural basis of ingestive behavior is a central topic of behavioral neuroscience. Brain mechanisms of feeding have been discussed in clinical neurology for well over a century, and they have been the subject of intensive experimental scrutiny for more than six decades. Much has been learned. Crucial hypothalamic circuits have been delineated. Key brain stem mechanisms have been identified. Important limbic and telencephalic networks have been described. In addition, extensive autonomic control loops connecting the brain with the periphery have been defined, and batteries of gut-brain peptides, hormones, cytokines, and other signals reflecting energy balance have been identified and shown to affect feeding. Adipose tissue and other energy stores have also been shown to be innervated, to be actively regulated, and to generate feedback signals influencing feeding. But much remains to be explained. Perhaps the most sobering gauge of the adequacy of current models of central nervous system (CNS) mechanisms of ingestion is the fact that these accounts, to date, have not produced effective therapies for any of the major eating disorders, including anorexia, bulimia, and obesity that occur in epidemic proportions. Obesity, because of its prevalence, is the most widely targeted disorder. And, ironically, the most successful interventions for obesity yet devised are radical bariatric surgeries (Thomas, 1995). Such treatments do not draw on what is known about the brain mechanisms of feeding, rather they revise gastrointestinal (GI) physiology and feedback signals. The irony that these peripheral interventions were not formulated from an understanding of brain feeding circuits is accentuated by the likelihood that, as advances are made in understanding the neurobiology of ingestion, radical bariatric surgery will one day be considered gastroenterology’s frontal lobotomy. A survey of the research on the neural basis of energy intake underscores the conclusion that different neuroscience methods have generated different—in some cases conflicting, in some cases complementary—views of the neural mechanisms of feeding. These disparate perspectives

FOOD INTAKE: THE TERMINOLOGY AND CONSTRUCTS OF HUNGER AND SATIETY The behavioral neuroscience of ingestion employs, for the most part, a terminology of eating drawn from the popular vernacular (e.g., hunger, satiety, appetite, anorexia). With this lay vocabulary comes excess baggage—the terminology carries multiple connotations and incorporates assumptions, a “folk psychology” of feeding. These implications embedded in the language can have ramifications for investigations designed to produce a neuroscience of feeding. Early investigations of the neural bases of feeding focused on ingestion as a motivated behavior and used food intake as a prototype for motivations associated with homeostasis. With this traditional emphasis on explaining intake in terms of motivation, many experiments measured behavior (feeding or feeding cessation) while making assumptions about internal motivational processes (hunger or satiety, respectively) and, in turn, then making inferences about how those imputed motivations might be organized in terms of their neural circuitry. Both the concept of hunger, the motivation to seek and ingest food that occurs when an individual is in negative energy balance, and the concept of satiety, the motivation to refrain from ingesting nutrients in the face of either a net positive energy balance or a substantial energy load in the GI tract, are constructs or intervening variables (cf. Blundell, 1980). For research purposes, since hunger and satiety cannot typically, if ever, be directly observed, investigators rely on operational definitions. In the behavioral neuroscience of ingestion, hunger is usually gauged by the amount a subject will spontaneously consume (or the 659

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amount of time the subject is deprived); satiety is used synonymously with the cessation of consumption (or an absence of deprivation). These conventions are used provisionally in this chapter. It should be stressed, though, that factors other than what most investigators would recognize as the experiential elements of hunger and satiety can also affect food intake. If an experimenter holds the energy balance of an animal or human subject constant while varying the subject’s stress, fatigue, distractions, hydrational status, arousal, nocioceptive stimulation, social contingencies, or any number of other altered states, the investigator can affect the subject’s intake of nutrients and thus confound operational definitions of hunger and satiety. When a researcher does a feeding experiment, he or she tries, of course, to control the environment and hold extraneous variables constant. When an experimenter manipulates an animal’s brain or physiology, however, it is far more difficult to establish that the manipulation does not indirectly affect feeding by altering one or more of the myriad conditions that can circuitously influence energy balance or its effects on feeding.

DIFFERENT METHODS, DIFFERENT MODELS OF NEURAL FEEDING MECHANISMS This chapter emphasizes two aspects of the literature on the neural models of feeding, or hunger and satiety. The first aspect relates to methods; the second to the evolution of the problem. Both are useful in terms of understanding the sources of current ideas and appreciating the limitations of these ideas. It is an axiom of science that experimental answers reflect the techniques that are used in the experiment. Techniques limit what can be measured and hence what results are obtained, but they also mold the interpretations of those results. The point is as true for the behavioral neuroscience of ingestion as for any science. Assessing or rethinking a particular observation and its common interpretation is often aided by a reconsideration of the selectivities and biases inherent in the methods used to generate the observation. It is also axiomatic that the scientific understanding of a problem does not develop against a static background. Techniques evolve as do experimental and conceptual paradigms. Experimental questions reflect these changes. Understanding the context or state of the science at the time a particular model of feeding was developed is another way of appreciating both the limitations and strengths of that model. As neuroscience has grown exponentially, its ideas about neural organization and function have changed,

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and with the transformations, the models of behavioral neuroscience have also changed. Questions about the neural basis of behavior, for example, are not framed the same way they were decades ago, and newer results (e.g., Holstege, Bandler, & Saper, 1996; Janig, 2006; Swanson, 2000) are not interpreted the same way they would have been. Thinking in terms of CNS “centers” dominated the early work on the behavioral neuroscience of ingestion; “distributed circuits” and “networks” are more consonant with neuroscience’s present view of the CNS (e.g., Berthoud, 2002; Holstege et al., 1996; Sawchenko, 1998; van den Pol, 2003). These trends are illustrated in our survey of the neuroscience of feeding according to a rough chronology reflecting the introduction of different techniques.

HYPOTHALAMUS: FEEDING CENTER OR NETWORK NODE? The neuroscience of ingestive behavior has long focused on the hypothalamus as the hub of CNS feeding circuitry. Beginning with early clinical observations on Froehlich’s syndrome over a century ago and extending through the past six or more decades of experimental analysis, it is clear that damage to the basomedial hypothalamus can produce the classic “ventromedial hypothalamic syndrome” distinguished by hyperphagia and obesity. The syndrome is also characterized by a sensitivity of the hyperphagia to particular diets (e.g., high fat diets), a “finickiness,” as well as by other symptoms (e.g., disruptions of reproductive functions, changes in temperament; Corbit & Stellar, 1964; Hetherington & Ranson, 1942; King & Cox, 1973) that are usually presumed to be incidental to the ingestive disorder. In contrast, bilateral damage to the lateral hypothalamus often leads to an aphagia and/or anorexia with an associated reduction in body weight (Teitelbaum & Epstein, 1962). This lateral hypothalamic syndrome encompasses other symptoms including an exaggerated dependence of ingestion on the palatability of the diet and other consequences that are generally presumed to be incidental to the dramatic feeding effects (e.g., an adipsia). In a number of ways, these two classic hypothalamic syndromes appear to be mirror images or reciprocals of each other. And soon after the patterns of symptoms were initially characterized, investigators concluded that the intact ventromedial hypothalamus apparently comprises a “satiety center” and the lateral hypothalamus a “hunger center” (e.g., Anand & Brobeck, 1951). These observations and the center analysis were made not long after Sherrington’s (1906) seminal delineation of the reciprocal and antagonist operations of spinal motor neuron pools

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Hypothalamus: Feeding Center or Network Node? 661

innervating flexors and extensors had been incorporated into physiological and behavioral thinking, and the center models were cast in terms analogous to cross-linked and opposing Sherringtonian reflexes. These postulated reciprocally acting feeding centers were taken as a prototype for Stellar’s (1954) classical and highly influential description of hypothalamic mechanisms of motivated behaviors, and his schematic summary (Figure 34.1) captures the general outline articulated for feeding as well as other motivations. The signals most often postulated to supply the feedback to the hypothalamus from metabolic or energy balance were blood glucose (the “glucostatic” hypothesis formalized by Mayer, 1953) or lipids (the “lipostatic” hypothesis articulated by Kennedy, 1953). Another alternative was that the thermic effects of metabolism influenced hypothalamic functions not only to regulate body temperature, but also energy balance (the “thermostatic” hypothesis considered by Brobeck, 1957), but much of the evidence initially obtained [based on parabiosis experiments (discussed later in this chapter—see Figure 34.7), infusions of metabolites, glucoprivation experiments, gold thioglucose lesion selectivity, etc.] was used to support one or another variant of the glucostatic or lipostatic hypotheses. Though the initial analyses of the syndromes resulting from basomedial hypothalamic damage and lateral hypothalamic lesions emphasized the feeding alterations and interpreted the associated changes in body weight that occurred (obesity or excessive leanness, respectively) as secondary to changes in appetite or satiety and hunger, subsequent experimentation challenged the conclusions that the distorted motivation to feed was a primary effect of the lesion and the corresponding change in body weight was secondary. In the case of both the ventromedial hypothalamic and lateral hypothalamic syndromes, experimentally displacing an animal’s body weight to the plateau that would be achieved by the animal after it sustained hypothalamic damage, but doing so prior to the production of the lesions, was found to eliminate the dramatic hyperphagia or aphagia, respectively, that typically occurred after the hypothalamic damage (Hoebel & Teitelbaum, 1966; Powley & Keesey, 1970). The observations suggested that the affected hypothalamic areas might play a role in the long-term regulation of energy balance and body energy stores and that the areas might modulate, directly or indirectly, feeding behavior so as to regulate body weight or adiposity. The idea that hunger and satiety, the motivational substrates of ingestion, were organized within the hypothalamus in a manner similar to Sherringtonian motor neuron pools also frayed as additional experimentation on the feeding syndromes appeared. The ventromedial syndrome was often far from dramatic and robust, depending heavily on animal strain, gender, diet conditions, and other factors (Corbit &

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Stellar, 1964; Teitelbaum, 1955). Many of the motivational and behavioral changes associated with hypothalamic damage also appeared to be secondary consequences of more proximal autonomic and endocrine adjustments occasioned by the hypothalamic manipulation (Powley, 1977). The lateral hypothalamic syndrome in some cases seemed produced by or reinforced by either sensory neglect (Marshall, Turner, & Teitelbaum, 1971) or dyskinesias or akinesias resulting from disruptions of nigrostriatal circuitry in addition to hypothalamic circuitry (Marshall, Richardson, & Teritelbaum, 1974; Ungerstedt, 1970). Furthermore, the nature of the hypothalamic role in feeding was also questioned by the realization that hypothalamic outflows are only one or two synapses upstream of both pituitary endocrine effectors and autonomic preganglionic motor neurons, whereas hypothalamic efferents are far less directly linked to somatic or skeletal motor neuron pools. As discussed in more detail later in this chapter, subsequent neuroanatomical developments failed to delineate obvious structural counterparts of the “final common path” to behavior posited by Stellar and others (see Figure 34.1). The patterns of connectivity discovered raised the possibility that the hypothalamus might operate to effect autonomic and endocrine control of energy handling, that is, “physiological energy balance,” and that the influences on ingestion, that is, “behavioral energy balance,” might either be coordinated in parallel or might be secondary effects that occurred as energy partitioning changed (Figure 34.2). Any path to behavior was not a “final common path” but rather an output relayed circuitously through polysynaptic networks with myriad opportunities for further modulation or neural editing (Figure 34.3). Consistent with the idea that much of the hypothalamic role in feeding was secondary to endocrine or autonomic adjustments, pair-feeding experiments indicated that animals with basomedial hypothalamic lesions fattened, even when energy intake was tightly clamped at control levels and even when both the amount of food and pattern of meal taking were both controlled (Walgren & Powley, 1985). Similarly, animals with lateral hypothalamic damage defend their altered body weight levels with physiological energy balance responses when caloric intake is experimentally controlled (Hirvonen & Keesey, 1996; Keesey, Powley, & Kemnitz, 1976). Such assessments also make the point that feeding represents one side of the energy balance equation. Energy homeostasis is the product of both intake and expenditure, and expenditure is affected by behaviors other than feeding (e.g., activity, nursing young) and by various anabolic processes that promote energy conversation and storage (e.g., slowing metabolic rate and growth) and catabolic processes that stimulate energy expenditure (e.g., thermogenesis).

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Cortex & Thalamus Serial organization of pattern

Arousal of pattern

Hypothalamus Internal chemical & physical factors Hormones Blood temp Osmotic pressure Drugs

INHIB

EXCIT

Sensory stimuli Unlearned & learned

Final common path for behavior Regulation of internal balance

Feedback from consummatory behavior

Figure 34.1 Stellar ’s classical formulation (1954) of the hypothalamic control of motivated behaviors, including feeding. Note: Eliot Stellar developed his pivotal theory to account for motivated behaviors generally (hunger, sexual behavior, sleep, thirst, etc.), but he drew on early work concluding that the hypothalamus contained hunger and satiety centers. He also drew on the Sherringtonian physiology of spinal motor neurons, including the “final common path” concept. In the case specifically of feeding, the lateral hypothalamus was posited to issue an excitatory (EXCIT) command to feed by way of a “final common path for behavior.” In this explanation of feeding, the basomedial hypothalamus was postulated to inhibit (INHIB) feeding by acting as a brake on the excitatory outflow from the lateral hypothalamus. In this hypothalamic model, which dominated early experimentation on the neural basis of feeding, internal humoral signals, sensory information from the environment, arousal and patterning inputs from the forebrain were all envisioned to converge on the hypothalamic centers. These centers in turn were assumed to integrate these inputs and adjust feeding according to need. From “The Physiology of Motivation,” by E. Stellar, 1954, Psychological Review, 61, pp. 6. Reprinted with permission.

Two corollaries of the insight about energy balance are particularly relevant to attempts to understand feeding. First, not only can feeding not be fully appreciated without reference to the other factors in the equation, but, in terms of experimental analyses, it is important to recognize that alterations in feeding can in some cases be secondary to changes in other anabolic or catabolic adjustments. Second, the extensive participation of hypothalamic mechanisms in both endocrine and autonomic systems suggests that hypothalamic feeding effects will almost unavoidably be nested within more global neural programs of energy homeostasis broadly defined—energy homeostasis integrating the demands of growth, reproduction, thermogenesis, activity, and so on. This last point is particularly important in terms of the ongoing searches for therapeutic pharmacological interventions to manage feeding disorders because interventions targeted to the hypothalamic control of feeding

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may ramify to interact with the physiologies of growth, reproduction, arousal, and so on. Other sets of observations based on more recently introduced methodologies (see discussions that follow) have further reshaped our understanding of the role of the hypothalamus—as well as other brain mechanisms—in controlling food intake. Before surveying some of these contributions, however, it is useful to reconsider the way in which methods in behavioral neuroscience shape our explanations of behavior. Lesions, localized and nonspecific tissue damage, and macro stimulation, involving relatively large electrodes in the case of electrical stimulation or large cannulas in the case of chemical delivery, were the most widely used techniques in behavioral neuroscience during the era of the hypothalamic feeding center analyses. These techniques, however, in no small measure were responsible for generating the models. Just as to a hammer every problem is a nail, so to a lesion (or conventional stimulation procedure) every problem is a neural center. If not a center, then a fiber bundle that can be interrupted by a focal manipulation. The conventional lesion and stimulation techniques employed in early investigations of the hypothalamus were biased to locate concentrations or nexuses of neural tissue. At the same time, though, by their focal nature, they were also biased to overlook or miss diffusely distributed and redundantly organized neural systems. Both because of the dramatic nature of the symptoms that occurred following hypothalamic manipulations and because the hypothalamus was easily accessible and a convenient size for the neuroscience techniques of the time, it was particularly easy to ignore the limitations and complexities of interpreting lesions and to fall into the trap that every lesion “syndrome” uncovered a “center” (Glassman, 1978). And, additionally, because of the prevalence of the faculty psychology of the era, a psychology that posited centers for behavioral faculties or constructs such as hunger, the behavioral neuroscience of the feeding was preoccupied with the hypothalamus until roughly three decades ago.

FOOD INTAKE WITHOUT THE HYPOTHALAMUS: CAUDAL BRAIN STEM CIRCUITS The early monopoly of the hypothalamus in the behavioral neuroscience of ingestion was challenged by a key series of experiments that demonstrated the capacity of other CNS sites, specifically regions of the caudal brain stem, to organize feeding behavior without hypothalamic influence. In this series, to explore what controls of ingestion were still functional in long-term decerebrate rats, Grill and Norgren (1978) capitalized on the fact that the behavior of

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Food Intake without the Hypothalamus: Caudal Brain Stem Circuits

Catabolic path way s LHA ⴙ Response to satiety signals ⴚ A n ab olic pathways POMC

PFA

PVN

NPY

Fat mass

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NTS

ARC Leptin

Adiposity signals

Insulin

GI tract Vagus nerve

Satiety signals

Liver Mechanical Chemical Energy metabolism

CCK release

Note: This neuroaxis coordinates endocrine and autonomic responses participating in overall energy balance and thus, through its modulation of anabolic and catabolic conditions and the resulting energy homeostasis, generates many of the signals that determine feeding decisions. These signals include not only the exemplars of leptin, insulin, and CCK noted in the figure, but also an extensive battery of peptides released in both gut and brain. Furthermore, other signals such as chemical and mechanical signals from the GI tract are generated in the periphery and affect central integration by way of centripetal neural projections (e.g., afferents of the vagus nerve) through the visceral neuroaxis. Receptors for

gut-brain peptides are expressed throughout this visceral neuroaxis, thus establishing a distributed network involved in processing and integrating energy balance signals. This extended visceral neuroaxis in turn is reciprocally and extensively interconnected rostrally with the limbic and telencephalic sites (not illustrated) that have been implicated in feeding and energy balance. ARC ⫽ Arcuate nucleus; LHA ⫽ Lateral hypothalamic area; NPY ⫽ Neuropeptide Y; NTS ⫽ Nucleus of the solitary tract; PFA ⫽ Perifornical area; POMC ⫽ Proopiomelanocortin; PVN ⫽ Paraventricular nucleus ⫽ SNS ⫽ Sympathetic nervous system. From “Central Nervous System Control of Food Intake,” by M. W. Schartz, S. C. Woods, D. Porte Jr., R. J. Seeley, and Baskin, D. G., 2000, Nature, 404 pp. 668. Reprinted with permission.

chronically decerebrate animals cannot be organized exclusively by the hypothalamus (or any other diencephalic or telencephalic regions) and must reflect the potential of the caudal brain stem. Though such animals do not seem to evidence long-term regulation of body weight (a function that can potentially still be ascribed to the hypothalamus), they do display the capacity to increase and decrease their ingestion in response to a variety of the short-term signals that control intake in intact or control animals. Grill and Norgren, in their reassessment of what they described as the “hypothalamic hegemony” over the neuroscience of feeding, argued persuasively for a more hierarchical view of feeding circuitry, one more consistent with a Jacksonian hierarchy in which functions are redundantly organized or “re-represented” at multiple levels of the neuroaxis, than with a “center” organization.

Thus, the caudal brain stem possesses many of the capacities to generate feedings responses and affect energy homeostasis that had long been attributed exclusively to the hypothalamus. Though many more recent research efforts still—or again—focus on the ventral diencephalic networks and even though the hypothalamic melanocortin system and the rest of the ventral diencephalic ingestive circuitry is clearly critically implicated in feeding (see later discussion), it has also been established that that the caudal brain stem independently possesses many of the same capacities attributed to hypothalamic circuits. Stated differently, hypothalamic circuitry must not be necessary or uniquely organized for many ingestive functions since a decerebrate animal can perform the functions even when the hypothalamus (and indeed the forebrain) is no longer connected to the brain stem.

Figure 34.2 The endocrine and autonomic core of the visceral neuroaxis.

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Superior cervical ganglion

Cervical spine SNS afferents

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Figure 34.3 Neural networks responsible for feeding and energy balance. Note: The schematic summarizes numerous projections and inputs that have been identified and characterized with modern neuroscience mapping tools. Dotted lines with arrows are used to designate signals from the environment and/or the internal milieu that converge on the central nervous system. Solid lines with arrows identify centrifugal projections to effector organs or sites. Stippled lines with arrows designate motor pathways. ACB ⫽ Nucleus accumbens; AIC ⫽ Agranular insular cortex; AMY ⫽ Amygdala; AP ⫽ Area postrema; ARC ⫽ Arcuate nucleus; dmnX ⫽ Dorsal motor nucleus of the vagus; HIP ⫽ Hippocampus;

A second type of observation, one based on receptor mapping studies, reinforces the point that the caudal brain stem contains the circuitry necessary to control feeding. As more recent research (see discussion that follows) has focused on key roles of cholecystokinin (CCK), leptin, insulin, ghrelin, and other peripheral hormones in feeding, the initial tendency has naturally been to focus on the hypothalamus, given its well-established involvement in ingestive behavior. The lack of a complete blood-brain barrier in the basomedial hypothalamus, the early demonstrations of receptors for the metabolic hormones in arcuate and other hypothalamic nuclei, and the delineation of the melanocortin circuitry in the hypothalamus all converged on a hypothalamic account of the feeding effects elicited by humoral signals. In spite of these several observations tending to reinforce what has been characterized as a “hypothalamocentric” model of feeding, parallel observations on both the blood-brain barrier of the dorsal vagal complex and the key hormones signaling energy conditions indicated that

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LH ⫽ Lateral hypothalamus; MoN ⫽ Motor nuclei for oromotor control; NTS ⫽ Nucleus of the solitary tract; OLF ⫽ Olfactory bulb; PFC ⫽ Prefrontal cortex; PIR ⫽ Piriform cortex; PIT ⫽ Pituitary gland; PRL ⫽ Prelimbic cortex; PVN ⫽ Paraventricular nucleus of the hypothalamus; RF ⫽ Medullary reticular formation; RVLM rostroventrolateral medulla; SNS ⫽ Sympathetic nervous system; V1/V4 ⫽ Visual processing areas 1 and 4; VII ⫽ Facial nerve; V ⫽ Trigeminal nerve; IX ⫽ Glossopharyngeal nerve. From “Mind versus Metabolism in the Control of Food Intake and Energy Balance,” by H.-R. Berthoud, 2004, Physiology and Behavior, 81, pp. 785. Reprinted with permission.

this brain stem vagal trigone area possessed the same features as the basomedial hypothalamus. Like the basomedial hypothalamus, the nucleus of the solitary tract/area po-strema region possesses a leaky blood-brain area that gives circulating humoral factors access to the parenchyma of the dorsal vagal complex. In addition, like the basomedial hypothalamus, the dorsal vagal complex densely expresses receptors for CCK (Zarbin, Innis, Wamsley, Snyder, & Kuhar, 1983), leptin (Funahashi, Yada, Suzuki, & Shioda, 2003), ghrelin (Zigman, Jones, Lee, Saper, & Elmquist, 2006), insulin (Hill, Lesniak, Pert, & Roth, 1986; Kar, Chabot, & Quirion, 1993), melanocortin-4 (Kishi et al., 2003), and many of the other gut or gut-brain hormones and neuropeptides that also modulate central feeding systems. A complementary set of experiments, based on yet a different experimental strategy, also indicates that the receptors for gut-brain neuropeptides and metabolic hormones are not only present in the caudal brain stem, but that these receptors do apparently mediate many of the same ingestive responses previously ascribed to the basomedial

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Lessons from Anatomical Mapping and Tracing Technologies

hypothalamus. These experiments involve infusing the different neuropeptides and hormones directly into the brain stem or floor of the fourth ventricle and measuring ingestive responses. Indeed, in a series of experiments that have infused candidate signals directly into the fourth ventricle (since CSF flows caudally within the ventricular system, fourth ventricle infusions should produce negligible rostral effects) and compared the efficacies of this route of administration with those of more rostral infusions, Grill and colleagues (Grill & Kaplan, 1990) as well as others (reviewed in Blessing, 1997) have found that fourth ventricular stimulation is as effective or even, in some cases, more effective than third ventricular stimulation at mobilizing appropriate feeding responses. Such experiments find a foundation in the general point that many earlier infusion experiments designed to probe the role of the hypothalamus in organizing feeding in response to different humoral signals (and interpreted in terms of a hypothalamo-centric model) did not necessarily limit the administration of the signals to the hypothalamus. The most commonly used protocol for probing the humoral sensitivity of the hypothalamus has been to cannulate a lateral ventricle or the third ventricle and to infuse the signal, say leptin, into the ventricle. If we adopt a hypothalamo-centric model and assume the target receptors and target tissues are in the arcuate nucleus or infundibular hypothalamus, then such ventricular infusions will apparently have their effects at these diencephalic sites and diffusion or spillage to other sites will be inconsequential. Alternatively (if the hypothalamocentric assumption about limited distribution of receptors is wrong, as it has proven to be), since the flow of CSF is caudal toward the fourth ventricle, any receptors in dorsal vagal complex or other periventricular sites might also be activated. Two key perspectives emerge from the observations establishing that the lower brain stem contains the neural circuitry sufficient to control feeding: First, observations on the ingestive behavior evidence by decerebrate animals also suggest that metabolic signals are detected not exclusively by the hypothalamus, but also, in parallel, by other regions of the visceral neuroaxis including the caudal brain stem and even the primary afferents that innervate the viscera (see Figure 34.2; also see Figure 34.5). The evidence indicates that there is considerable redundancy in the CNS in terms of the metabolic signals that influence feeding— the role of the hypothalamus (and that of the caudal brain stem as well) is more one of a cooperative part of a distributed network, rather than a monolithic control center. Second, much of the neural apparatus controlling feeding must be organized in the caudal brain stem, thus suggesting

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that these particular elements of ingestive behavior are not uniquely and/or exclusively organized in the hypothalamus. By extension, the point suggests, as discussed previously, that the hypothalamic circuits implicated in feeding may be committed more to monitoring humoral and endocrine signals and integrating them into coordinated autonomic or neuroendocrine adjustments associated with energy balance.

LESSONS FROM ANATOMICAL MAPPING AND TRACING TECHNOLOGIES: DISTRIBUTED NEURAL NETWORKS COOPERATE TO CONTROL OF FEEDING AND ENERGY REGULATION Anatomical analyses have implications for functional interpretations and define boundary conditions within which behavioral systems presumably operate. It is possible, in broad terms, to reverse engineer the types of operations that are likely to occur from the structural organization that exists, that is, to infer function from form. If, for example, there are no neural connections between two regions, then any interactions or communications must either (1) be non-neural (e.g., hormonal or humoral), or (2) possibly not occur. Strong projections between a site and a target, on the other hand, suggest substantial neural interactions. Or, yet again, sites or circuits that express a particular receptor presumably respond to the corresponding ligand. Often such constraints of structure influence behavioral neuroscience analyses and models without much explicit discussion. For example, the limited anatomical information about hypothalamic projections and interconnections that was available at the middle of the past century made it reasonable at the time to consider hypothalamic sites implicated in feeding behavior as executive centers that operated more or less autonomously. Similarly, with relatively few energy-balance feedback signals recognized at the time, and with those known (e.g., the glucostatic and lipostatic mechanisms) seemingly effective in the hypothalamus, it was reasonable for investigators to conclude that the few humoral signals and an equally limited number of visceral afferent inputs converged on the hypothalamus that then controlled feeding and the physiology of energy balance in a top-down executive program. By comparison with today’s information, knowledge about brain circuitry was quite limited when the original hypothalamic feeding center models were developed. Much of what was then known about neural circuits was inferred from staining procedures that delineated Nissl patterns of conspicuous clusters of neurons, nuclei, and the

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more conspicuous and coherent fiber tracts, either in relief or in myelin staining. These nissl-and-myelin maps were supplemented by information from notoriously capricious silver methods for degeneration and limited tracing methods, mainly retrograde degeneration that is often illusive and rarely works in polysynaptic chains. Thus, the limited anatomical appreciation of the extent of the interconnectivity of the hypothalamus—and for that matter, the caudal brain stem—with other brain sites made it reasonable, and even necessary, to consider the hypothalamus in terms of overly simplified assumptions of inputs and outputs. These unrealistic expectations were then incorporated into many models and schematics (see, for example, Figure 34.1). The past decades, however, have seen the development of an enormous battery of tools for delineating the details of neural circuits. Hundreds of neural tracing techniques have been developed (e.g., Zaborszky, Wouterlood, & Lanciego, 2006) and used to specify myriad interconnections and projections that were unknown when lesion studies initially concentrated on the hypothalamus. Similarly, an equal or larger number of histochemical and immunocytochemical protocols have been developed and used to recognize pathways expressing particular neurotransmitters, peptides, or receptors (e.g., Bjorklund, Hokfelt, & Owman, 1988). As these mapping tools have been applied, much has been learned about just how extensive the interconnections of the different sites implicated in the control of feeding actually are. The developments include the recognition that the hypothalamus is reciprocally and multiply interconnected with the caudal brain stem sites implicated in feeding (Broberger & Hokfelt, 2001; Sawchenko, 1998). Furthermore, the mapping experiments indicate that both of these hubs of feeding circuitry are embedded in extensive, often parallel and redundant, circuitry that welds the two complex stations into a massively cross-linked system of re-entrant connections involving most, and probably all, of the brain (e.g., Berthoud, 2002, 2004; Holstege et al., 1996; see also Figure 34.3). Parallel discoveries, with the different mapping techniques have also produced a corresponding rethinking of the peripheral nervous system and have indicated both the complexity of autonomic circuitry in the viscera and the extensive afferent and efferent projections by which the CNS is linked to that peripheral circuitry. The enteric nervous system of the gut is now widely recognized as being complexly interconnected and containing so many neurons (equivalent to the total number in the spinal cord) that it is often considered a “second brain” or a “little brain” organized in a distributed fashion throughout the gut. Correspondingly, the extrinsic pathways that connect the CNS to the enteric nervous system or “brain” in the

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gut are now similarly realized to be both more numerous and more highly organized than previously presumed. Autonomic efferents project densely to the GI tract (Holst, Kelly, & Powley, 1997), and, within the organs of digestion, visceral afferents supply a profuse network formed of a number of different specialized endings (Powley & Phillips, 2002). The autonomic efferents and afferents interconnecting the brain of the CNS with the little brain of the GI tract are so extensive that, from the functional perspective, it is even in all likelihood misleading functionally to compartmentalize and separate CNS and peripheral nervous system (PNS). As structural experiments revealed the ubiquitous cross-linkages throughout both the central and peripheral components of the visceral neuroaxis, two other aspects of the changing views of energy-balance signaling have reinforced the conclusion that the visceral neural network is distributed and decentralized with cross-linked control loops controlling energy balance and feeding. One of the developments was the recognition that, in contrast to the assumptions of early models of feeding, the peripheral organs that participated in energy metabolism and energy regulation also have much richer local control networks of not only neural, but also endocrine and paracrine, coordination that effect regulation of the energy economy in the periphery, presumably without hypothalamic executive intervention. The second change in the perspective on energy balance to develop was the realization of how extensive a battery of hormones and cytokines the organs of digestion and metabolism release in the course of executing the local regulation of the different phases of metabolism. In the middle of the twentieth century, when the hypothalamic feeding center model was proposed, only two or three GI hormones had been identified and characterized. In contrast, it is now appreciated that the gut is the largest endocrine organ in the body and that it orchestrates much of energy balance with the releases of over 30 peptide hormones that serve as endocrine signals reflecting anabolic and catabolic events (e.g., Rehfeld, 1998; also see Figures 34.4 and 34.5). The GI tract, for example, elaborates, among others, CCK, leptin, ghrelin, glucagon-like peptide-1 (GLP1), peptide YY (PYY), gastrin, secretin, obestatin, numerous other hormones, and a number of cytokines. These hormones commonly serve as paracrine and neurocrine factors influencing local physiology and thus, indirectly, produce additional feedback and feed forward to neural circuits. Similarly, adipose tissue is now realized to be an active, dynamic system with its own regulatory loops as well as to be innervated (Badman & Flier, 2007; Powell, 2007). In its decentralized orchestration of metabolism, adipose tissue synthesizes and releases leptin,

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Lessons from Anatomical Mapping and Tracing Technologies

adiponectin, resistin, estradiol, angiotensin, and cytokines such as interleukin-6 (IL-6) and tumor-necrosis factoralpha (TNF-a). Concomitantly, with the recognition that there is considerable local integration and control and that a substantial number of potential signals is generated in the process of local control of the viscera, came recognition (a) that many peptide hormones produced by the gut were actually gut-brain hormones (Figure 34.4) elaborated by both the viscera and the brain and (b) that the receptors for many of these signals could be found throughout the visceral neuroaxis (Figure 34.5). As the enormously wide distributions of receptors for the multiplicity of endocrine and humoral factors influencing energy balance was recognized (Funahashi et al., 2003; Hill, Lesniak, Pert, & Roth, 1986; Kar et al., 1993; Kishi et al., 2003; Zarbin et al., 1983, 2006), the simplifying proposition that any one hormone might code for or signal a particular function (e.g., ghrelin for hunger or CCK for satiety) became untenable. The hormones affecting energy balance also operate in many physiological and behavioral systems, not merely ingestion (see Figure 34.6). Leptin, for example, does not simply modulate feeding, it participates in, inter alia, immune function, inflammation, learning processes, cardiovascular function, reproduction, and

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bone metabolism as well (Harvey & Ashford, 2003). That signaling associated with a single hormone cuts across so much physiology tends, of course, to confound the search for function-specific pharmacological treatments. With all of the new observations, the idea that the hypothalamus, among neural sites, might have near-exclusive access to the putative humoral signals and that it must therefore act as a top-down controller failed to square with (a) complex regulatory loops discovered in the periphery, (b) the rich flux of potential signals elaborated by these regulatory loops, (c) the fact that receptors for the putative signals are widely distributed throughout the visceral neuroaxis, and (d) the evidence that the signals set and bias the gains of the regulatory circuitry. Indeed, the amount of integration and local regulation that is now known to occur in the GI tract and other viscera makes it possible to assert persuasively that the control of feeding involves as much bottom-up regulation and integration (e.g., Cummings & Overduin, 2007) as it does top-down programming by hypothalamic circuits. What modern neuroanatomical methods have not revealed is also instructive. As mentioned, many early versions (and even recent versions) of the hypothalamic feeding center model implicitly or even explicitly (see Figure 34.1; also, for comparison, see Figure 34.8) considered the

Esophagus

Stomach Ghrelin Leptin GRP, NMB Duodenum CCK

Small intestine

Jejunum APO AIV Ileum GLP1 Oxyntomodulin PYY

Figure 34.4 Principal peripheral sites of synthesis of gut-brain peptides or gastrointestinal peptides that influence feeding. Note: Signals are depicted in terms of the main gut location of production, though many of the peptides are produced at multiple sites. Importantly, most of these peptides (i.e, CCK, APO AIV, GLP1, oxyntomodulin, PYY, enterostatin, ghrelin, gastrin-releasing peptide [GRP], neuromedin B [NMB], and possibly pancreatic polypeptide [PP]) are synthesized within the brain as well as within the gut. Gut peptides that influence feeding,

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Pancreas Amylin Enterostatin Glucagon Insulin PP Colon GLP1 Oxyntomodulin PYY

but that do not appear to be synthesized in CNS include leptin, insulin, glucagon, and amylin. Additional abbreviations are given in Figure 34.6. APO AIV ⫽ Apolipoprotein A-IV; CCK ⫽ Cholecystokinin; GLP1 ⫽ glucagon-like peptide-1 ; GRP ⫽ Gastrin-releasing peptide; NMB ⫽ Neuromedin B; PP ⫽ pancreatic polypeptide; PYY ⫽ peptide YY. “Gastrointestinal Regulation of Food Intake,” by D. E. Cummings and J. Overduin, 2007, Journal of Clinical Investigation, 117, pp. 14. Reprinted with permission.

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Selected GI and pancreatic peptides that regulate food intake Peptide

CCK GLP1 Oxyntomodulin PYY3⫺36 Enterostatin APO AIV PP Amylin GRP and NMB Gastric leptin Ghrelin

Main site of synthesis

Receptors mediating feeding effects

Proximal intestinal I cells Distal-intestinal L cells Distal-intestinal L cells Distal-intestinal L cells Exocrine pancreas Intestinal epithelial cells Pancreatic F cells Pancreatic ␤ cells Gastric myenteric neurons Gastric chief and P cells Gastric X/A–like cells

CCK1R GLP1R GLP1R and other Y2R F1-ATPase ␤ subunit Unknown Y4R, Y5R CTRs, RAMPs GRPR Leptin receptor Ghrelin receptor

Figure 34.5 A partial list of selected gastrointestinal and pancreatic gut-brain peptides that influence food intake. Note: The primary site of synthesis of the peptide and its receptor mediating its effects on ingestion are summarized, as is the orexic or anorexic influence that the peptide has on intake. In addition, known nervous system sites of action of the peptides are indicated with “Xs.” Even with an absence of evidence for some peptides at some sites (the blank spaces), as of yet, it is clear the majority of the peptides bind with their receptors

hypothalamic mechanisms an integrative center that generates executive motor decisions to feed or not to feed. These models, in the spirit of a Sherringtonian final common path or a command neuron output, often hypothesize a key output pathway to brain stem motor centers that would ultimately organize the behaviors. In contrast to the direct efferent access to the pituitary and autonomic preganglionics, such posited pathways from hypothalamic circuitry to brain stem and spinal cord motor neurons pools have not, however, been verified in the extensive tracing and mapping analyses that have now been done. Overall, the new insights to have emerged from modern mapping strategies have necessitated a rethinking of the neural basis of feeding. Such a reframing is still very much ongoing, though. For example, by one construction, each of the multiple sites associated with feeding could be considered a specialized processor or module—multiple specialized processors, each contributing unique analyses or syntheses to the enterprise of energy balance. Such a view has been adopted or discussed in recent reviews (Berthoud, 2002, 2004; Saper, Chou, & Elmquist, 2002; Williams et al., 2001). In contrast, however, another construction of the decentralized network delineated by the newer mapping analyses would be that there is an extensive amount of redundancy and overlap of functional capacity among the distributed sites. Presently available observations on the neural substrate of feeding neither firmly reject either of the constructions nor unequivocally establish the validity of either perspective.

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Effect on Sites of action of peripheral food intakeA peptides germane to feeding Hypothalamus Hindbrain Vagus nerve X X X X X? X? X X X X X X X X X X X X ? ? X X X X

throughout the visceral neuroaxis—in the vagus nerve and brain stem as well as in the hypothalamus. APO AIV ⫽ Apolipoprotein A-IV; CCK ⫽ Cholecystokinin; CTRs ⫽ Calcitonin receptors; GLP-1 ⫽ Glucagon-like peptide 1; GRP ⫽ Gastrin-releasing peptide; GRPR ⫽ GRP receptor; NMB ⫽ Neuromedin B; RAMPs ⫽ Receptor activity-modifying proteins. From “Gastrointestinal Regulation of Food Intake,” by D. E. Cummings and J. Overduin, 2007, Journal of Clinical Investigation, 117, pp. 15. Reprinted with permission.

NEUROPHYSIOLOGY OF SINGLE CELLS: ANOTHER DISTRIBUTED-NETWORK VIEW OF BRAIN FEEDING MECHANISMS Single-unit electrophysiology has provided another important window on how the nervous system integrates the signals of energy balance and organizes feeding responses. The contributions of single-cell recording experiments to the neurobiology of feeding might be viewed as paralleling chronologically the development and application of the neural tracing technologies just discussed. Like anatomical mapping techniques, electrophysiological analyses have evolved considerably in terms of their sensitivity and scope. For purposes of this brief survey, the evolution might be considered to encompass three stages: an initial period in which recording needed to be performed in animals that were extensively restrained or, even more often, anesthetized; a later phase in which unit recording was practical in awake, freely behaving animals; and a final stage in which multiple units of circuits or ensembles could be simultaneously recorded during behavior. In the earliest electrophysiological work, recording experiments were perhaps most commonly designed to corroborate the hypothalamic feeding center model. Recording typically did not take place during behavior, and the animal subject was anesthetized and/or paralyzed. Many of the experiments were focused on the hypothalamus, with little or no sampling of neurons in other regions, and often explored the types of signals (glucose, other metabolites,

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Neurophysiology of Single Cells: Another Distributed-Network View of Brain Feeding Mechanisms

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Selected appetite-modifying peptides, illustrating their central effects on energy balance and other physiological activities Peptide

Effects on Energy Balance Feeding

Thermogenesis

NPY

MCH

?

Orexin A Galanin Opioids ␣-MSH 5-HT GLP-1 CCK

Figure 34.6 Some of the most common neuropeptides implicated in the control of food intake. Note: These neuropeptides are widely expressed in the hypothalamus and rest of the CNS, as well as in the periphery. Arrows summarize dominant directions of effect exerted by the neuropeptides (decreases, increases, no clear increase or decrease). As the table organization illustrates, many of the neuropeptides influence both feeding (as well as body weight)

gustatory and visceral afferent inputs, etc.) that would affect hypothalamic unit activity. In this phase, most of the electrophysiology was designed to confirm and to elucidate the hypothalamic center model, and the resulting observations were generally consistent the proposition that the hypothalamus received (and therefore, at least in principle could integrate) humoral, gustatory, and visceral inputs (presumably in the service of decisions to feed or not to feed). As single-unit techniques evolved and could be used practically to monitor neuronal traffic in awake, behaving subjects, however, a picture of a more extensive circuitry of feeding emerged. In part perhaps because the power of the newer electrophysiological paradigms permitted recording from awake, behaving animals and in part because the proposition that feeding was virtually a hypothalamic prerogative had already begun to wane, unit recording began to describe a much more distributed network of sites participating in the control of feeding. Individual units throughout much of the limbic system, and particularly in the orbitofrontal cortex, were found (Rolls, 2005) to have firing patterns associated with deprivation, repletion, food choice, palatability, and other conditions classically ascribed to the hypothalamic feeding centers. Similarly, neurons throughout the gustatory and visceral neuroaxes,

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Body Weight

Ezamples of Other Physiological Actions

Blood pressure regulation, circadian rhythmicity, and memory processing Locomotion and regulation of skin colour Wakefulness and alertness Reproduction Locomotion and reproductive behavior Grooming and blood pressure regulation Mood regulation and behavioral responses Regulation of blood glucose and gut motility Grooming and blood pressure regulation

and energy expenditure or thermogenesis. In addition, notably, as summarized in the right-hand column, all of the neuropeptides also influence other physiological systems, not merely energy homeostasis. From “The Hypothalamus and the Control of Energy Homeostasis: Different Circuits, Different Purposes” by G. Williams et al., 2004, Physiology and Behavior, 81, p. 212. Reprinted with permission.

including even first-, but particularly second- and all higher-order neurons of the neuroaxes displayed evidence that their respective activities were modulated by signals (e.g., energy infusions) or conditions (e.g., deprivation or hunger, refeeding or satiety) that often had been attributed to hypothalamic processing (Scott, Yan, & Rolls, 1995). The organizational pattern suggested by the results was more consonant overall with the distributed network ideas that were emerging in the neural tracing developments (see previous discussion) occurring in parallel. Even more recently, in what might be considered the third stage of development, the further evolution of techniques for the recording of cells in behaving animals, the introduction of “multi-trode” or multiple electrode recording simultaneously from large numbers or ensembles of individual neurons combined with the increased availability of hardware and software for massive computation has made it feasible to monitor systems or networks of neurons in behaving animals. For example, Araujo and co-workers (2006), based on concomitant recordings from the lateral hypothalamus, amygdala, insular cortex, and orbitofrontal cortex through a complete feeding-satiety-feeding cycle, argued that hunger may be organized in terms of a “distributed population code.”

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Too few electrophysiological observations—certainly too few cases examining neurons in multiple sites for long intervals—are yet available to yield a complete perspective on the prospect of ensemble coding of hunger and satiety. Furthermore, the exquisitely high temporal and spatial resolution that unit recording achieves, often comes at the price of limited windows of time for observation (gauged by the length of a feeding bout or the duration of an inter-meal interval). Because of the temporal constraints and sampling limitations, only relatively phasic and potentially unrepresentative subsets of neurons from the entire population of the viscera neuroaxis can be readily characterized. Nonetheless, it is the case that as single-neuron electrophysiology has developed, it has come to portray an extensive and distributed network of neurons that are active during the execution of feeding behavior.

ECOLOGICAL OBSERVATIONS: ENVIRONMENTAL CONTINGENCIES HAVE SHAPED NEURAL MECHANISMS OF FEEDING Applications of newly developed neuroscience technologies have driven most of the evolution in ideas about the neurobiology of feeding. Nonetheless, advances have come from other biological fields as well. Two ecological analyses have been particularly instructive. The first analysis deals with rethinking regulatory mechanisms that determine hunger and satiety; the second addresses the issue of brain networks implicated in feeding behavior. These ecological perspectives developed initially independently of, and in parallel with, the neuroscience of feeding. More recently, though, the ecological and neural perspectives have begun to merge. Thrifty Gene Hypothesis The first ecological analysis challenges traditional views about the operations of the regulatory mechanisms controlling feeding. This viewpoint is associated with the thrifty gene hypothesis introduced by Neel (1962). The concept can be appreciated by contrasting it with the ideas about the control of energy balance that were common prior to Neel’s articulating the hypothesis. Early feeding models assumed, in effect, that hunger and satiety mechanisms are organized in a symmetrical manner, much like Sherrington’s agonists and antagonists or flexors and extensors, around a point of energy balance. Specifically, the models often assumed that the control function gains of the neural mechanisms translating energy perturbations into responses that correct deficits and surpluses, respectively, are comparable. Neel and other investigators who explored the thrifty gene concept noted, however, that a symmetry assumption

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is not consistent with observations on the biological adaptations of most species (Bjorntorp, 2001; Coleman, 1979; Schwartz et al., 2003). Behavioral and physiological mechanisms that redress energy deficits and their counterpart mechanisms that correct energy surpluses are not controls of comparable efficiency organized in mirror-image symmetry around an equilibrium point at which intake is matched to expenditure. While there presumably have been many imperative selection pressures to avoid energy deficits, analyses suggest that there have not been equally strong pressures to avoid positive energy balances. Even more specifically, Neel noted that evolution in demanding environments would have selected for genes that are “thrifty” and promote efficient storage of calories that may mitigate times of intermittent food availability or famine. The more unpredictable and/or hostile the environment, the more advantage in having such thrifty genes promoting energy storage. Procreation requires not starving to death before you can pass on your genes. Falling into energy deficit can easily be fatal and thus thwart propagating one’s genes, while having plenty—perhaps even excesses—of calories stored may well see members of the species through their reproductive age. Even if energy surpluses are not optimal for avoiding diseases of old age (Neel focused on Type II diabetes and obesity) or for longevity, they make good reproductive sense. Indeed, too effective a set of defenses mobilized against any stored energy excess would be maladaptive, and reserves that were so tightly regulated that they could not fluctuate would be something of an oxymoron. Hence, from a thrifty gene vantage point, animals benefit from stringent hunger mechanisms and defenses against energy deficits, while they also benefit (at least reproductively) from elastic, flexible, and more limited satiety mechanisms and defenses against positive energy balance and storage. These functional asymmetries can elucidate how control mechanisms are structured. They also can partially explain why numerous challenges from dietary manipulations to even subtle metabolic disorders easily produce obesity and excessive energy storage conditions but less readily yield anorexias and wasting disorders. Feeding Complexities, Brain Mass, and Brain Circuits The second perspective, which emerges from neuroecology, reinforces the inferences about distributed networks that have emerged from anatomical mapping experiments (see earlier section) and have been emerging from both electrophysiological mapping experiments (see earlier discussion) and functional scanning studies as well (discussed in the next section). The perspective grows out of the widely accepted evidence that the brain sizes of different

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Ecological Observations: Environmental Contingencies Have Shaped Neural Mechanisms of Feeding 671

species are proportional to the variety, complexity, or unpredictability in space and time of their respective food supplies. Omnivores, active predators, and generalist species living in problematic environments, all have relatively larger brains (even once the contribution of factors such as body mass that also predict a portion of brain mass are accounted for). Herbivores with simple diets, animals adapted to predictable environments with ready sources of nutrients, and monophagous species have relatively smaller brains for their respective body sizes. The brain-environmental-demand correlation can be decomposed into two more particular relationships, each with implications for a neurobiology of feeding behavior. First, there is a correlation between the behavioral specializations species use in feeding and the relative size of the different sensory, motor, and “cognitive” neural systems that hypertrophy in those species. Raptors and other predators that rely on sight, for example, have more extensive visual systems; caching species that must remember storage sites have larger hippocampi; species that devise novel feeding solutions have larger forebrains and cortical association areas. Such observations are numerous, and they have been documented for a variety of different taxa and families, including fish, birds, rodents, and primates (see, e.g., Iwaniuk & Hurd, 2005; Lefebvre, Reader, & Sol, 2004; Nicolakakis & Lefebvre, 2000; Timmermans, Lefebvre, Boire, & Basu, 2000). Conspicuously—and tellingly—in its absence, hypothalamic hypertrophy has not been correlated with demanding environments. The second, and more general, correlation between brain mass and feeding complexity is that, even after the increases associated with general factors such as body size and with the specific factors of sensory or motor or cognitive systems have all been partialled out, there is still an underlying residual positive correlation between overall brain mass and the complexity of the ecological niche for feeding. Bigger brains are found in species with complex feeding patterns adapted to challenging environments. This more general relationship also appears to hold for a variety of taxa and families (e.g., Aboitiz, 1996; Bernard & Nurton, 1993; but also see Healy & Rowe, 2007). Though there are a number of interpretations we might apply to these observations, this general correlation is consistent with the implication suggested by the neuroanatomical mapping, electrophysiological, and functional mapping (see discussion that follows) literatures implicating many distributed CNS sites as a neural network active and involved in hunger and satiety. The two neuroecological correlations taken together point to a complex neural network involved in ingestive behavior. They also serve as a reminder to neurobiological analyses of ingestion of just how pervasive feeding behavior is in most species’ lives and just how much of the CNS is preoccupied with ingestive behavior. Though

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animal or human subjects and nutritional neuroscientists who study them are, typically, buffered from the exigencies of their hunter-gatherer roots, most species expend most of their energy most of the time making feeding decisions in challenging environments where it is necessary to obtain nutrients while evading predation, conserving calories, balancing multiple homeostatic needs, maintaining physiological vigilance for microbes and toxins that often occur in potential food sources, and juggling resource unpredictabilities. A consideration of these environmental and physiological contingencies (for instructive discussions, see Collier & Johnson, 2004; Garcia, Hankins, & Coil, 1977; Harris & Ross, 1987; Rozin, 1976; Woods, 1991) and the multidimensional demands they place on sensory, motor, memory, and planning operations explains why a multiplicity of brain sites increase in mass in challenging environments. Neurobiology of Ingestive Mechanisms from the Environmental Perspective Ecology, in stepping back from a short-sighted focus on neural machinery and in considering the environment to which the circuitry is adapted, offers other lessons as well. Such examinations have forced a more general recognition that the mechanisms of energy homeostasis did not evolve in vacuums. Neural controls carry the stamp of the environment and the diets that species have evolved to exploit. This realization has spotlighted the fact that feeding strategies and the neural control mechanisms that were shaped for the Paleolithic era or before may be inefficient—or even pathological—in negotiating the dietary, nutritional and energetic contingencies of the twenty-first century. Just as the physiological disturbances that astronauts experience in zero gravity have emphasized that mechanisms selected for an earth-bound gravitational environment do not perform optimally in all environments, so the obesity epidemic and other modern feeding disorders would seem to suggest there are limits of energy-balance mechanisms that were selected for the environmental challenges faced by man’s hunter-gatherer ancestors (Eaton, Eaton, & Konner, 1999; Pollan, 2006). Certainly the environments in which feeding mechanisms evolved did not include ready surpluses of energy-dense, highly-processed sources of nutrients. Finally, ecological observations also add an instructive methodological footnote that applies to many feeding experiments in behavioral neuroscience. Laboratory experiments on feeding typically employ highly simplified, rigidly predictable environments and testing regimens. Such is the nature of good experimental control. Paradoxically, though, in establishing experimental control, laboratory research removes or clamps many of the challenges that the nervous system has evolved to negotiate when the individual

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feeds. Thus, experiments often remove many of the sensory, motor, memory, and planning demands the animal more typically faces. In essence, to study, say, the hypothalamus, we remove the challenges for the association cortex, frontal lobe, parietal lobe, cingulate cortex, and cerebellum from the contingencies the animal encounters. Not surprisingly (though seldom actually discussed), such a paradigm accentuates, or even exaggerates, the apparent role of the hypothalamus by deemphasizing the operations of the other stations of the neural network. Simplify the environment enough, engineer most of the normal environmental contingencies out of the task, and employ tests selected to tap hypothalamic processing, and it is likely that the hypothalamus will appear to operate like an executive center controlling feeding.

HYPOTHALAMIC CIRCUITS OF INGESTION AND THEIR SIGNALS CHARACTERIZED WITH MOLECULAR BIOLOGY As previously outlined, early behavioral neuroscience explained the control of food intake and energy balance in terms of hypothalamic hunger and satiety centers. These ideas, though, seemed to lose much of their explanatory validity in the research of the past three decades of the twentieth century. The unique and executive preeminence originally assigned to the hypothalamus became unsupportable as different technologies such as decerebration techniques, anatomical mapping, and single unit electrophysiology were applied. Simultaneously, the hypothalamic center idea also seemed less accurate as neuroscientific explanations of a variety of motivated behaviors (e.g., reproductive behavior—see Chapters 5, 6, 24, and 35), including feeding, evolved. As better understanding of motivational mechanisms accumulated (see also Chapter 36), there was a growing recognition that the uncritical invocation of hunger and satiety mechanisms, as frequently had occurred, amounted to invoking a folk or faculty psychology to account for feeding. Behavioral “faculties” were simply mapped, particularly in some of the earlier brain behavior analyses, onto regions of the brain in a one-to-one phrenological fashion. But, unless the operation of neural region can be specified at the neuronal level, treating a region as a proverbial black box and invoking the idea of a hunger center to explain hunger is tautological, and positing a satiety center to explain satiety is hollow. In the past decade or so, in terms of experimental focus, the research pendulum has swung back to a strongly renewed interest in the hypothalamus (Elmquist, Elias, & Saper, 1999; King, 2006; Marx, 2003; Schwartz, Woods,

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Porte, Seeley, & Baskin, 2000; Woods, Schwartz, Baskin & Seeley, 2000). Though the reversal is by no means complete, the hypothalamus has been implicated as a crucial hub in the control of feeding by new methodologies that provide some of the previously unavailable neuronal-level specification of mechanism, thus offering critical information to escape the circularity just discussed. At least three complementary types of investigations, emerging in large part from the revolution in molecular biology and applications of new genetic tools to the epidemic of obesity and other eating disorders, have again refocused considerable experimental effort on the hypothalamic mechanisms that affect ingestive behavior. An understanding of the circulating signals, the circuits, and the neuropeptides expressed in the hypothalamic network have developed synergistically. Earlier studies, and in particular experiments that combined genetically obese mutant ob/ob and db/db mice in parabiotic pairs (see Figure 34.7; see also Coleman, 1973; Coleman & Hummel, 1969), had implicated a lipostatic signal in the adiposity disorders, but the exact mechanism remained obscure. When the ob gene was eventually cloned and determined to encode leptin and the db gene was found to code for the leptin receptor (Tartaglia et al., 1995; Zhang et al., 1994), it rapidly emerged that the hormone leptin, produced primarily by adipocytes, served as a lipostatic signal, that receptors for the signal were expressed in the hypothalamus, that leptin was transported across the weak blood-brain barrier of the basomedial hypothalamus, and that appropriate manipulations of the hormone and receptor could variously produce phenocopies of the ob/ob mice or correct the disturbances caused by the mutation (Friedman & Halaas, 1998). With a key role for leptin established, receptor mapping studies of the hypothalamus indicated that leptin receptors were found through the tuberal hypothalamic nuclei implicated in feeding (including the ventromedial nucleus and lateral hypothalamus as well as the paraventricular, dorsomedial, and arcuate nuclei), with particularly heavy expression found in the arcuate nucleus (Barsh & Schwartz, 2002; Elmquist et al., 1999; Sawchenko, 1998; van den Pol, 2003). As investigations focused on the arcuate nucleus, the outlines of the intrahypothalamic circuitry implicated in energy balance, a network now recognized as a melanocortin system, emerged (see Figure 34.8). The arcuate nucleus contains two distinct types of neurons, both expressing the leptin receptor and both releasing the inhibitory transmitter gamma-aminobutyric acid (GABA), which through the neuropeptide modulators they produce and also release, have opposite effects (through the melanocortin system) on feeding. One class of arcuate neurons expresses both proopiomelanocortin (POMC) and cocaine-amphetamine

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Hypothalamic Circuits of Ingestion and Their Signals Characterized with Molecular Biology

regulated transcript (CART). POMC is cleaved into a number of products including melanocyte-stimulating hormones, adrenocorticotropic hormone, and ␤-endorphin. When these POMC/CART neurons are activated, they affect melanocortin receptors (particularly the subtypes 3 and 4, or MC3R and MC4R) expressed through the paraventricular nucleus, lateral hypothalamus, dorsal hypothalamic nucleus, and arcuate. Such activation when elicited by leptin or other stimulation or when achieved by the appropriate pharmacological challenges reduces food

ob

db

Figure 34.7 The parabiosis method and a classical illustration of the technique used to demonstrate a lipostatic signal (i.e., leptin). Note: Parabiosis is the condition in which two animals are conjoined surgically, typically side-to-side. The surgical union is commonly performed so that the animals exchange blood through vascular anastomoses. This experimental analogue of Siamese twins thus provides a preparation in which hormonal and other blood-borne signals pass between the pair of animals (but, of course, their neural pathways remain separate). The demanding surgical and maintenance requirements have limited the use of the technique, but the method can, in some cases, provide particularly definitive tests. In classical experiments performed 25 years before the ob and db genes were cloned and determined to code for leptin and the leptin receptor, respectively, Coleman and his colleagues (Coleman, 1973; Coleman & Hummel, 1969) were able to predict the existence of the adipocyte hormone and its receptor and partially describe the unknown hormone’s physiology through experiments using parabiosis. Three of the surgical pairings that the Coleman laboratory employed are illustrated in the top row of this figure; the experimental outcomes of the different unions are illustrated in the bottom row. When (in the left column) an obese ob/ob mouse (dark gray) was joined parabiotically to a normal control mouse (medium gray), the ob/ob mouse reduced its food intake and dieted down, suggesting that the normal-weight control animal was producing a lipostatic signal (now known to be leptin) that the ob/ob mouse could detect, but not produce. When (in the middle column) a fat db/db mouse (light gray) was

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intake and body weight while simultaneously increasing energy expenditure. The second class of arcuate neurons, intermingled with the first, has reciprocal effects. This second class also releases GABA, but produces and secretes the neuropeptides neuropeptide Y (NPY) and agouti gene-related transcript (AgRP). The peptides are endogenous antagonists of the MC3R and MC4R, thereby blocking the activation of the melanocortin receptors by ␣-MSH and other products of the POMC/ CART neurons. NPY and AgRP release with its blockade

ob

db

joined to a control mouse (medium gray), the control mouse reduced its food intake below normal and dieted down, suggesting that the obese db/ db mouse was producing high levels of a lipostatic signal (leptin, as it turns out), which it did not detect but which the normal control mouse interpreted as excess adiposity. When (in the right-hand column) an obese ob/ob mouse (dark gray) was parabiosed with a similarly obese db/db mouse (light gray), the ob/ob mouse reduced its intake and dieted down while the db/db mouse remained fat, suggesting again that the ob/ob mouse was sensitive to, or had the receptor for, a circulating lipostatic factor produced by the db/db mouse. The db/db mouse of the pair remained obese, consistent with the conclusion that this animal lacked the receptor for the lipostatic hormone. From “Genetics of food intake, body weight and obesity,” By R. Bowen(2001). Web publication: http://www.vivo.colostate .edu/hbooks/pathphys/digestion/pregastric/fatgenes.html. Reprinted with permission. As the Coleman experiment illustrates, the parabiosis method can yield compelling analyses of humoral factors (see also Martin, White, & Hulsey, 1991, for a review of additional demonstrations). Variants of the surgical protocol can also be employed to particular effect. In one such variant, it is possible to cross not only the blood supplies of parabiotic twins, but also segments of their gastrointestinal tracts. Koopmans, McDonald, and DiGirolamo (1997), for example, have done a number of experiments with parabiotic “intestines-crossed” rats in which food ingested and then partially digested by one animal can be diverted from its proximal intestines into the distal intestines of its parabiotic partner (and vice versa).

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Second-order and downstream neurons

Neuron Y1r

Mc4r

Food intake

Energy expenditure

Food intake

Food intake

Arcuate nucleus

Ghsr

Agrp/ Npy

Y1r First-order neurons

Pomc/ Cart

Mc3r Third ventricle

Lepr ⫹

Mc3r

Lepr ⫺

⫹ Pancreas

Ghrelin Insulin, leptin Insulin Stomach Leptin

Adipose tissue

of the MC3/4R system leads to increased food intake and weight gain as well a corresponding energy conservation. The orexigenic NPY/AgRP neurons of the arcuate nucleus also project onto local anorexigenic POMC/CART neurons, where their GABA release effectively inhibits the anorexigenic neurons. With their somata in the median eminence and with the tuberal region’s fenestrated capillaries and weak blood-brain barrier, the reciprocally organized or push-pull orexigenic NPY/AgRP neurons and the anorexigenic POMC/CART neurons are viewed as first-order neurons that transduce circulating hormonal and humoral signals reflecting energy balance conditions. The neurons express receptors not only for leptin, but also for ghrelin and insulin and numerous other metabolic hormones. Thus, the neuropeptidergic arcuate neurons are situated to transduce and integrate hormones that reflect the energy regulation at the level of the fat pad (e.g., leptin), stomach (e.g., ghrelin), and pancreas (e.g., insulin). The first-order arcuate NPY/AgRP and POMC/CART neurons project to second-order melanocortin system neurons distributed within paraventricular nucleus (PVN) and perifornical and lateral hypothalamic regions of the

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Figure 34.8 Hypothalamic circuitry controlling food intake and energy balance, as delineated with molecular biological and other modern neuroscience tools. Note: The model includes two set of neurons in the arcuate nucleus—Agrp/Npy and Pomc/Cart neurons— that are regulated by circulating anabolic and catabolic hormones. Ghsr ⫽ Growth hormone secretagogue receptor; Lepr ⫽ Leptin receptor; Mc3r/Mc4r ⫽ Melanocortin 3/4 receptor; Y1r ⫽ Neuropeptide Y1 receptor. From “Genetic Approaches to Studying Energy Balance: Perception and Integration,” by G. S. Barsh and M. W. Schwartz, 2002, Nature Reviews: Genetics, 3, pp. 592. Reprinted with permission.

hypothalamus. In the case of the lateral hypothalamus, two subpopulations of neurons have been implicated in feeding. One group expresses the neuropeptide hypocretin (or orexin); the other group expresses melanin-concentrating hormone (MCH). Both subpopulations have wide projection fields throughout the brain, suggesting that they modulate arousal, motivation, emotion, and motor systems. In terms of their projections and effects, the two subpopulations seem to operate independently, perhaps coordinating different responses that synergize in complementary responses appropriate to energy balance status. The ventromedial nucleus of the hypothalamus also receives inputs from, and reciprocally projects to, both NPY/AgRP and POMC/CART arcuate neurons. In their strategic location to monitor signals of metabolic status and with their demonstrated effects on feeding and body weight, the arcuate nucleus neurons and the hypothalamic neurons of the melanocortin system have become the subject of impressive and intense research efforts. Neurons of the local hypothalamic circuitry bind not only leptin, insulin, and ghrelin, they also bind many, if not most, hormones that impact catabolic or anabolic processes. A partial list includes glucocorticoids, estrogen,

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Hypothalamic Circuits of Ingestion and Their Signals Characterized with Molecular Biology

prolactin, and interleukins. In addition, neurons within the circuitry, through their intracellular utilization, also appear to monitor circulating fuels including glucose and fatty acids. The intracellular signaling cascades initiated by leptin and ghrelin receptor binding in the hypothalamic neurons have been particularly thoroughly delineated, because of the strategic position for transducing the many signals reflecting energy status. It is appropriate to consider these observations on hypothalamic circuitry and the signals involved in the context of other observations, some already surveyed and others discussed next. Like the dramatic symptoms elicited from the hypothalamus by lesions during the early feeding center analyses, the striking feeding effects that can be elicited by genetic and molecular manipulations (gene knockouts, peptide infusions, etc.) at a first look seem to substantiate the early feeding center description of hypothalamic function. Rather than seeing the hypothalamus as merely one important station among others in a multiplicity of complex circuits operating cooperatively to organize energy balance, it is tempting to return to the idea that it is the critical, executive node in the neural feeding apparatus. As mentioned, a number of reports have suggested that there is a “renaissance” of the hypothalamic feeding model (Elmquist et al., 1999; King, 2006). Similarly, many of the schematic summaries of feeding emphasize the hypothalamic circuitry while they reduce the contributions of the rest of the nervous system to a few vectors or arrows (e.g., see schematics in Figures 34.1, 34.3, and 34.8). Although there is a widespread reluctance to speak in terms of “center,” some analyses appear to circumvent the negative connotations of the term not by moving to a noncenter analysis but by stripping terms such as “circuit” or “network” of much of their meaning and using them as circumlocutions, in effect, for centers. Such views are at risk of being myopic. As discussed earlier, many of the receptors manipulated and discussed in terms of their hypothalamic expression are actually broadly distributed throughout the nervous system, many of the manipulations (e.g., peptide infusions) directed at the hypothalamus are not limited to the hypothalamus, and many of the feeding or energy balance effects that have been ascribed to the hypothalamus can be elicited from the caudal brain stem when infusions or other manipulations of the peptide systems are confined to the medulla. In this regard, it should be noted that a number of syntheses in which the hypothalamic circuitry is more explicitly integrated into or with the caudal brain stem and other CNS circuitries implicated in feeding have been suggested (e.g., Berthoud, 2002, 2004; Broberger & Hokfelt, 2001; Williams et al., 2001). The risk of short-sightedness is also underscored by the recent realization that the hypothalamic mechanisms are not highly stable hardwired circuits, but

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rather neural pathways capable of synaptic plasticity and reorganization in response to different demands (Horvath & Diano, 2004). Another methodological footnote is appropriate here: Gene knockouts and mutations are fundamentally lesions. They are molecular lesions, but they are lesions just as surely as are ablative lesions and space-occupying lesions. Though they are often interpreted—as are other types of lesions—as revealing the function of the disabled or destroyed element, or in this case protein product, the effects that these molecular lesions cause may, of course, result from any of the convoluted, potentially distorted, and compensatory adjustments they occasion (Glassman, 1978). Knocking out or mutating, say the ob gene for leptin, does not illustrate by any simple subtractive logic the normal function of leptin. Instead, it illustrates how the organism is able to develop, adapt, compensate, and adjust in the absence of that gene. There have been repeated reminders that inferences from mutation effects back to normal function can be problematic. Loss-of-function lesions of the gene for leptin lead to dramatic obesity in rodents and humans, and this observation was initially used to support the conclusion that leptin operates as a critical negative feedback signal to inhibit excess positive energy balance, obesity, and overconsumption. Ironically, though, leptin administration to obese rodents or humans typically produces relatively subtle—or no—effects on the positive energy balance conditions (except for, of course, those few rodents and humans with loss-of-function mutations of the ob gene). And conversely, there are many other examples where a given neuropeptide or gene product has one effect when administered to intact individuals, but loss of the gene product produces very different and asymmetrical effects. NPY, for example, administered into the hypothalamus produces dramatic feeding, an effect that led to the conclusion that the peptide is a transmitter coding for feeding, but loss-of-function mutations or knockouts of the NPY gene have marginal to no effects on feeding (Qian et al., 2002). Or, for a final example, the orexigenic gut peptide hormone ghrelin is secreted by the stomach in a pattern that tracks hunger (by increasing) and satiety (by decreasing), and administration of the peptide elicits food intake, yet molecular “lesions” that eliminate ghrelin have little to no effect on feeding (Sun, Ahmed, & Smith, 2003). Finally, the risk of a myopia or a tunnel vision can be appreciated by comparison with other neural systems. The successes of modern molecular techniques in unraveling the neural connectivities and neurochemistries of the hypothalamic sites implicated in feeding have been dramatic. Nonetheless, most of the recent scrutiny of the circuitry of feeding focuses on first-order arcuate neurons that detect

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circulating hormones and metabolites and second-order hypothalamic neurons (the paraventricular, ventromedial, dorsomedial, etc. neurons) and treats them as an integrative system, or as a simple system—a simulacrum standing in for all the extended neural machinery of feeding. All the rest of the nervous system and other peripheral signals tend to be subsumed into schematic input and output vectors (see, for example, Figure 34.8). Clearly, though, treating a two- or three-neuron chain of afferents as an executive site responsible for all the integration and analysis of the body’s energy economy is unrealistic. Relegating the rest of the neural trafficking to flowchart vectors begs questions of how feeding is orchestrated by the nervous system. It is hard to imagine anyone attempting to explain visual perception or visually guided behavior in terms of only a simplified circuitry consisting of retinal amacrine and bipolar cells, with the rest of the visual system reduced to schematic arrows.

NEUROIMAGING IDENTIFIES DISTRIBUTED CORTICAL AND DIENCEPHALIC SITES PARTICIPATING IN INGESTIVE BEHAVIOR The recent revolution in noninvasive neuroimaging techniques provides yet another perspective on the neural circuitry of feeding. As subjects—typically human subjects in this case—are presented with food cues or feeding opportunities while they are under either fasting or sated conditions, patterns of neural activity can be assessed. Within-subject comparisons can be made of the different neural signatures that characterize hunger and satiety by comparing the dynamic differences that occur when the subjects’ fasted trials are compared with their sated trials. Alternative, between-subject comparisons (obese vs. normal-weight subjects, anorexics vs. controls, etc.) can also be made. With the high temporal resolution permitted by current scanning techniques, the patterns of brain activation can be examined essentially in real time. Deprivation that presumably makes subjects hungry generally increases regional cerebral blood flow and hence regional activity in a variety of limbic, paralimbic, and cortical sites. Details of the pattern of activation vary to some extent between subject populations and between laboratories, but activation is commonly observed in the orbitofrontal and anterior cingulate cortex, as well as in visceral sensory cortex, including most particularly insular and piriform cortex (Tataranni et al., 1999; Wang et al., 2004). Notably, activity in the orbitofrontal cortex tends to correlate particularly well with self-reports and ratings of hunger (Wang et al., 2004). Additionally, during fasting, increased activation is also often seen in the amygdala, hippocampus, and parahippocampal regions, the striatum, and the

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cerebellum, among other sites (Arana et al., 2003; Morris & Dolan, 2001; Tataranni et al., 1999). Increases in activation are also occasionally, but not always, seen in the region of the hypothalamus as well (Tataranni et al., 1999). Repletion or satiety or, even more operationally, the consumption of a test meal or nutrient load tends to be associated with increases in activation in prefrontal cortex and the inferior parietal lobe. Neuroimaging experiments also commonly assay the CNS response to external stimuli such as to taste stimuli or to food-related stimuli under different conditions such as fasted or fed state. Food stimuli routinely activate the insula and neighboring superior temporal cortex as well as the orbitofrontal cortex. Amygdaloid, temporal lobe, and parahippocampal lobe activity appears to be particularly sensitive to stimulus properties, including the attractiveness or salience of food stimuli (Morris & Dolan, 2001; Wang et al., 2004). Scanning methods have also been employed to evaluate how brain activity varies as a function of internal visceral and hormonal signals. Visceral inputs such as gastric stimulation also tend to activate the insula, amygdala, and hippocampus (Wang et al., 2006). As another means of appreciating the neural mechanisms of feeding and disturbances in these mechanisms that must cause and/or reflect common eating disorders, neuroimaging studies have also begun to characterize how patterns of regional blood flow vary between normal-weight and overweight populations or between health control populations and those with anorexia or bulimia or other feeding disorders (Kaye et al., 2005; Liu & Gold, 2003). Whereas most other neuroscience techniques have focused—or been focused—on the hypothalamus and the caudal brain stem, the functional scanning literature implicates limbic regions of the diencephalon and telencephalon in hunger, satiety, and food selection, many of these same limbic regions are also implicated in a variety of emotional and motivational behaviors (see Chapters 36 and 38). The pattern suggests that the processing associated with feeding shares common circuitry with other motivated behavior— that, in essence, feeding programs run on the same processors as other functional motivational systems, not on a dedicated feeding processor. Something of an apparent paradox in the scanning literature is the fact that the hypothalamus appears to have a much less conspicuous and prominent pattern of activation than might be inferred from the lesion and hormone binding analyses on the structure. Several factors—some methodological, but some perhaps substantive—may explain the apparent paradox. Such a lack of a hypothalamic signature may, in some cases, merely reflect the limits of spatial resolution of current scanning protocols and equipment. Furthermore, as Liu, Gao, Liu, and Fox (2000) have

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suggested, activation patterns within the distributed network that seems to organize ingestive behavior may have complex phasic temporal and spatial patterns, and only analyses that examine the spatial and temporal parcellations will be able to capture all the local transients. Alternatively, the lack of a conspicuous involvement may also reflect the possibilities that the hypothalamus is more heavily involved in longer-term regulatory adjustments of energy balance that eventually modulate feeding, and less involved in the real-time organization of feeding behavior. Finally, scanning experiments may be, perhaps very correctly, suggesting that the hypothalamic role in hunger and satiety has historically been blown out of proportion in respect to the roles of the various limbic, cortical, diencephalic, mesencephalic, and rhombencephalic circuits that recent research has implicated in ingestive behavior. In summary, sensitive neuroimaging techniques that have recently become available, have begun to delineate a picture of the neural substrate of ingestion that is quite different from that which was concentrated on the hypothalamus. Scanning work describes dynamic and distributed patterns of activation more adequately characterized by Sherrington’s “enchanted loom” weaving “dissolving pattern(s),” always “meaningful,” never “abiding,” than by executive centers in the hypothalamus. SUMMARY Behavioral neuroscience has yet to produce, as measured by its unsatisfactory record in predicting effective therapies for eating disorders, a completely coherent account of food intake. The different methods employed in the neurosciences generate distinctly different, sometimes even conflicting, views of the neural basis of food intake. These disparate views serve as reminders that, while our models affect our choice of methods, our methods also shape our models. Since the era of the early experimental formulations that accounted for feeding and energy balance in terms of hypothalamic centers, neuroscience has discovered a much more extensive and distributed network of sites participating in feeding. This network or visceral neuroaxis includes a multiplicity of CNS sites from the caudal brain stem to the frontal cortex. This visceral network also includes the enteric nervous system or “little brain” in the gut, and the autonomic efferents and visceral afferents linking the enteric network to the CNS, all extensively interconnected by multiple pathways. In addition, a diverse battery of gut-brain and adipocyte hormones, paracrine factors, neurocrine factors, cytokines, and other signals modulate or set the gains on neurons throughout the entire span of the visceral neuroaxis. Experimental protocols that employ limited environments, paradigms with tight experimental control, and techniques

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biased toward localization outcomes (e.g., lesions, focal stimulation) have tended to support the inference that the brain is organized with compartmentalized centers specialized for the control of feeding or body weight. And this center paradigm has provided a convenient, accessible, and simplifying model of ingestive behavior. Though experimentally and conceptually tractable, the model appears, in some cases, to beg the questions it purports to answer and, in other cases, to be inaccurate and invalid. In contrast, tests that employ more complex environmental situations or stimuli, experimental paradigms designed to provide subjects with more opportunities or options, and techniques adapted to characterizing distributed networks (e.g., nervous-system-wide mapping of receptors or neuronal connections, functional scanning techniques) have tended to support the conclusion that feeding and body weight regulation are organized by an extensive network of decentralized sites throughout the central—as well as peripheral and enteric—nervous systems, with substantial interconnections and parallel architectures. Additionally, these open-architecture techniques challenge the idea that the brain “wetware” can be compartmentalized with any one area dedicated to, or specialized for, a single type of behavior such as feeding. In the immediate future, a major—perhaps the major— goal for the neuroscience of feeding behavior may be to better reconcile and synthesize the multiple dissimilar views of the neural circuitry of ingestion suggested by the disparate techniques now in use. To achieve this end, it will be particularly useful to weigh the influences—and recognize the biases—of the different methodologies on both the data collected and the interpretations generated.

REFERENCES Aboitiz, F. (1996). Does bigger mean better? Evolutionary determinants of brain size and structure. Brain, Behavior and Evolution, 47, 225–245. Anand, B. K., & Brobeck, J. R. (1951). Localization of a “feeding center” in the hypothalamus of the rat. Proceedings of the Society of Experimental Biology and Medicine, 77, 323–324. Arana, F. S., Parkinson, J. A., Hinton, E., Holland, A. J., Owen, A. M., & Roberts, A. C. (2003). Dissociable contributions of the human amygdala and orbitofrontal cortex to incentive motivation and goal selection. Journal of Neuroscience, 23, 9632–9638. Araujo, I. E., Gutierrez, R., Oliveira-Maia, A. J., Pereira, A., Jr., Nicolelis, M. A. L., & Simon, S. A. (2006). Neural ensemble coding of satiety states. Neuron, 51, 483–494. Badman, M. K., & Flier, J. S. (2007). The adipocyte as an active participant in energy balance and metabolism. Gastroenterology, 132, 2103–2115. Barsh, G. S., & Schwartz, M. W. (2002). Genetic approaches to studying energy balance: Perception and integration. Nature Reviews: Genetics, 3, 589–600.

8/19/09 3:36:49 PM

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Hunger

Bernard, R. T. F., & Nurton, J. (1993). Ecological correlates of relative brain size in some South-African rodents. South African Journal of Zoology, 28, 95–98. Berthoud, H.-R. (2002). Multiple neural systems controlling food intake and body weight. Neuroscience and Biobehavioral Reviews, 26, 393–428. Berthoud, H.-R. (2004). Mind versus metabolism in the control of food intake and energy balance. Physiology and Behavior, 81, 781–793. Bjorklund, A., Hokfelt, T., & Owman, C. (Eds.). (1988). Handbook of chemical neuroanatomy: Vol. 6. The peripheral nervous system. Amsterdam: Elsevier. Bjorntorp, P. (2001). Thrifty genes and human obesity: Are we chasing ghosts? Lancet, 358, 1006–1008. Blessing, W. W. (1997). The lower brainstem and bodily homeostasis. New York: Oxford University Press. Blundell, J. E. (1980). Hunger, appetite and satiety: Constructs in search of identities. In M. Turner (Ed.), Nutrition and lifestyles (pp. 21–42). London: Applied Science. Bowen, R. (2001). Genetics of food intake, body weight and obesity. Retrieved (April 23, 2008) from www.vivo.colostate.edu/hbooks/ pathphys/digestion/pregastric/fatgenes.html. Brobeck, J. R. (1957). Neural control of hunger, appetite, and satiety. Yale Journal of Biology and Medicine, 29, 566–574. Broberger, C., & Hokfelt, T. (2001). Hypothalamic and vagal neuropeptide circuitries regulating food intake. Physiology and Behavior, 74, 669–682. Coleman, D. L. (1973). Effects of parabiosis of obese with diabetes and normal mice. Diabetologia, 9, 294–298. Coleman, D. L. (1979, February 16). Obesity genes: Beneficial effects in heterozygous mice. Science, 203, 663–665. Coleman, D. L., & Hummel, K. P. (1969). Effects of parabiosis of normal with genetically diabetic mice. American Journal of Physiology, 217, 1298–1304. Collier, G., & Johnson, D. F. (2004). The paradox of satiation. Physiology and Behavior, 82, 149–153. Corbit, J. D., & Stellar, E. (1964). Palatability, food intake, and obesity in normal and hyperphagic rats. Journal of Comparative and Physiological Psychology, 58, 63–67.

Harris, M., & Ross, E. B. (Eds.). (1987). Food and evolution: Toward a theory of human food habits. Philadelphia: Temple University Press. Harvey, J., & Ashford, M. L. J. (2003). Leptin in the CNS: Much more than a satiety signal. Neuropharmacology, 44, 845–854. Healy, S. D., & Rowe, C. (2007). A critique of comparative studies of brain size. Proceedings of the Royal Society, B, 274, 453–464. Hetherington, A. W., & Ranson, S. W. (1942). The relation of various hypothalamic lesions to adiposity in the rat. Journal of Comparative Neurology, 76, 475–499. Hill, J. M., Lesniak, M. A., Pert, C. B., & Roth, J. (1986). Autoradiographic localization of insulin receptors in rat brain: Prominence in olfactory and limbic areas. Neuroscience, 17, 1127–1138. Hirvonen, M. D., & Keesey, R. E. (1996). Chronically altered body protein levels following lateral hypothalamic lesions in rats. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 270, R738–R743. Hoebel, B. G., & Teitelbaum, P. (1966). Weight regulation in normal and hypothalamic hyperphagic rats. Journal of Comparative and Physiological Psychology, 61, 189–193. Holst, M.-C., Kelly, J. B., & Powley, T. L. (1997). Vagal preganglionic projections to the enteric nervous system characterized with PHA-L. Journal of Comparative Neurology, 381, 81–100. Holstege, G., Bandler, R., & Saper, C. B. (Eds.). (1996). The emotional motor system. Progress in Brain Research, 107, (pp. 1–627). Amsterdam: Elsevier Press. Horvath, T. L., & Diano, S. (2004). The floating blueprint of hypothalamic feeding circuits. Nature Reviews: Neuroscience, 5, 662–667. Iwaniuk, A. N., & Hurd, P. L. (2005). The evolution of cerebrotypes in birds. Brain, Behavior and Evolution, 65, 215–230. Janig, W. (2006). The integrative action of the autonomic nervous system: Neurobiology of homeostasis. Cambridge, MA: Cambridge University Press. Kar, S., Chabot, J. G., & Quirion, R. (1993). Quantitative autoradiographic localization of [I-125] insulin-like growth factor-I, I[I-125] insulin-like growth factor-II, and [I-125] insulin-receptor binding sites in developing and adult rat brain. Journal of Comparative Neurology, 333, 375–397.

Cummings, D. E., & Overduin, J. (2007). Gastrointestinal regulation of food intake. Journal of Clinical Investigation, 117, 13–23.

Kaye, W. H., Frank, G. K., Bailer, U. F., Henry, S. E., Meltzer, C. C., Price, J. C., et al. (2005). Serotonin alterations in anorexia and bulimia nervosa: New insights from imaging studies. Physiology and Behavior, 85, 73–81.

Eaton, S. B., Eaton, S. B. III, & Konner, M. J. (1999). Paleolithic nutrition revisited. In W. R. Trevathan, E. O. Smith, & J. J. McKenna (Eds.), Evolutionary medicine (pp. 313–332). New York: Oxford University Press.

Keesey, R. E., Powley, T. L., & Kemnitz, J. W. (1976). Prolonging lateral hypothalamic anorexia by tube-feeding. Physiology and Behavior, 17, 367–371.

Elmquist, J. K., Elias, C. F., & Saper, C. B. (1999). From lesions to leptin: Hypothalamic control of food intake and body weight. Neuron, 22, 221–232.

Kennedy, G. C. (1953). The role of depot fat in the hypothalamic control of food intake in the rat. Proceedings of the Royal Society, B., 140, 578–592.

Friedman, J. M., & Halaas, J. L. (1998, October 22). Leptin and the regulation of body weight in mammals. Nature, 395, 763–770.

King, B. M. (2006). The rise, fall, and resurrection of the ventromedial hypothalamus in the regulation of feeding behavior and body weight. Physiology and Behavior, 87, 221–244.

Funahashi, H., Yada, T., Suzuki, R., & Shioda, S. (2003). Distribution, function, and properties of leptin receptors in the brain. International Review of Cytology, 224, 1–27.

King, J. M., & Cox, V. C. (1973). The effects of estrogens on food intake and body weight following vengtromedial hypothalamic lesions. Physiological Psychology, 1, 261–264.

Garcia, J., Hankins, W. G., & Coil, J. D. (1977). Koalas, men, and other conditioned gastronomes. In N. W. Milgram, L. Krames, & T. M. Alloway (Eds.), Food aversion learning (pp. 196–218). New York: Plenum Press.

Kishi, T., Aschkenasi, C. J., Lee, C. E., Mountjoy, K. G., Saper, C. B., & Elmquist, J. K. (2003). Expression of melanocortin 4 receptor mRNA in the central nervous system of the rat. Journal of Comparative Neurology, 457, 213–235.

Glassman, R. B. (1978). The logic of the lesion experiment and its role in the neural sciences. In S. Finger (Ed.), Recovery from brain damage: Research and theory (pp. 3–31). New York: Plenum Press. Grill, H. J., & Kaplan, J. M. (1990). Caudal brainstem participates in the distributed neural control of feeding. In E. M. Stricker (Ed.), Handbook of behavioral neurobiology: Vol. 10, Neurobiology of Food and Fluid Intake, (pp. 125–149). New York: Plenum Press. Grill, H. J., & Norgren, R. (1978, July 21). Chronically decerebrate rats demonstrate satiation but not bait shyness. Science, 201, 267–269.

c34.indd Sec7:678

Koopmans, H. S., McDonald, T. J., & DiGirolamo, M. (1997). Morphological and metabolic changes associated with large differences in daily food intake in crossed-intestines rats. Physiology and Behavior, 62, 129–136. Lefebvre, L., Reader, S. M., & Sol, D. (2004). Brains, innovations and evolution in birds and primates. Brain, Behavior and Evolution, 63, 233–246. Liu, Y., Gao, J. H., Liu, H. L., & Fox, P. T. (2000, June 29). The temporal responses of the brain after eating revealed by functional MRI. Nature, 405, 1058–1062.

8/19/09 3:36:49 PM

References 679 Liu, Y., & Gold, M. S. (2003). Human functional magnetic resonance imaging of eating and satiety in eating disorders and obesity. Psychiatric Annals, 33,127–132.

Sun, Y., Ahmed, S., & Smith, R. G. (2003). Deletion of ghrelin impairs neither growth nor appetite. Molecular and Cellular Biology, 23, 7973–7981.

Marshall, J. F., Richardson, J. S., & Teitelbaum, P. (1974). Nigrostriatal bundle damage and the lateral hypothalamic syndrome. Journal of Comparative and Physiological Psychology, 87, 808–830.

Swanson, L. W. (2000). Cerebral hemisphere regulation of motivated behavior. Brain Research, 886, 113–164.

Marshall, J. F., Turner, B. H., & Teitelbaum, P. (1971, October 29). Sensory neglect produced by lateral hypothalamic damage. Science, 174, 523–525. Martin, R. J., White, B. D., & Hulsey, M. G. (1991). The regulation of body weight. American Scientist, 79, 528–541. Marx, J. (2003, February 7). Cellular warriors at the battle of the bulge. Science, 299, 846–849. Mayer, J. (1953). Glucostatic mechanisms of regulation of food intake. New England Journal of Medicine, 249, 13–16. Morris, J. S., & Dolan, R. J. (2001). Involvement of human amygdala and orbitofrontal cortex in hunger-enhanced memory for food stimuli. Journal of Neuroscience, 21, 5304–5310. Neel, J. V. (1962). Diabetes mellitus: A “thrifty” genotype rendered detrimental by “progress”? American Journal of Human Genetics, 14, 353–362. Nicolakakis, N., & Lefebvre, L. (2000). Forebrain size and innovation rate in European birds: Feeding, nesting and confounding variables. Behaviour, 137, 1415–1429. Pollan, M. (2006). The omnivore’s dilemma. New York: Penguin Press. Powell, K. (2007, May 31). The two faces of fat. Nature, 447, 525–527.

Tataranni, P. A., Gautier, J.-F., Chen, K., Uecker, A., Bandy, D., Salbe, A. D., et al. (1999). Neuroanatomical correlates of hunger and satiation in humans using positron emission tomography. Proceedings of the National Academy of Sciences, USA, 96, 4569–4574. Teitlebaum, P. (1955). Sensory control of hypothalamic hyperphagia. Journal of Comparative and Physiological Psychology, 48, 156–163. Teitelbaum, P., & Epstein, A. N. (1962). The lateral hypothalamic syndrome: Recovery of feeding and drinking after lateral hypothalamic lesions. Psychological Review, 69, 74–90. Thomas, P. R. (Ed.). (1995). Weighing the options: Criteria for evaluating weight-management programs. Washington, DC: National Academy Press. Timmermans, S., Lefebvre, L., Boire, D., & Basu, P. (2000). Relative size of the hyperstriatum ventrale is the best predictor of feeding innovation rate in birds. Brain, Behavior, and Evolution, 56, 196–203.

Powley, T. L. (1977). The ventromedial hypothalamic syndrome, satiety, and a cephalic phase hypothesis. Psychological Review, 84, 89–126.

Ungerstedt, U. (1970). Is interruption of the nigro-striatal dopamine system producing the “lateral hypothalamus syndrome”? Acta Physiologica Scandinavia, 80, A35–A36.

Powley, T. L., & Keesey, R. E. (1970). Relationship of body weight to the lateral hypothalamic syndrome. Journal of Comparative and Physiological Psychology, 70, 25–36.

van den Pol, A. N. (2003). Weighing the role of hypothalamic feeding centers. Neuron, 40, 1059–1061.

Powley, T. L., & Phillips, R. J. (2002). Musings on the wanderer: What’s new in our understanding of vago-vagal reflexes? Pt. I. Morphology and topography of vagal afferents innervating the GI tract. American Journal of Physiology, 283, G1217–G1225. Qian, S., Chen, H., Weingarth, D., Trumbauer, M. E., Novi, D. E., Guan, X., et al. (2002). Neither agouti-related protein nor neuropeptide Y is critically required for the regulation of energy homeostasis in mice. Molecular and Cellular Biology, 22, 5027–5035. Rehfeld, J. F. (1998). The new biology of gastrointestinal hormones. Physiological Reviews, 78, 1087–1108. Rolls, E. T. (2005). Taste, olfactory, and food texture processing in the brain, and the control of food intake. Physiology and Behavior, 85 45–56. Rozin, P. (1976). The selection of foods by rats, humans, and other animals. In J. Rosenblatt, R. A. Hide, C. Beer, & E. Shaw (Eds.), Advances in the study of behavior, Volume 6 (pp. 21–76). New York: Academic Press.

Walgren, M. C., & Powley, T. L. (1985). Effects of intragastric hyperalimentation on pair-fed rats with ventromedial hypothalamic lesions. American Journal of Physiology, 248, R172–R180. Wang, G.-J., Volkow, N. D., Telang, F., Jayne, M., Ma, J., Rao, M., et al. (2004). Exposure to appetitive food stimuli markedly activates the human brain. NeuroImage, 21, 1790–1797. Wang, G.-J., Yang, J., Volkow, N. D., Telang, F., Ma, Y., Zhu, W., et al. (2006). Gastric stimulation in obese subjects activates the hippocampus and other regions involved in brain reward circuitry. Proceedings of the National Academy of the Sciences, USA, 103, 15641–15645. Williams, G., Bing, C., Cai, X. J., Harrold, J. A., King, P. J., & Liu, X. H. (2001). The hypothalamus and the control of energy homeostasis: Different circuits, different purposes. Physiology and Behavior, 74, 683–701. Williams, G. Cai, X. J., Elliott, J. C., & Harrold, J. A. (2004). Anabolic neuropeptides. Physiology & Behavior, 81, 211–222.

Saper, C. B., Chou, T. C., & Elmquist, J. K. (2002). The need to feed: Homeostatic and hedonic control of eating. Neuron, 36, 199–211.

Woods, S. C. (1991). The eating paradox: How we tolerate food. Psychological Review, 98, 488–505.

Sawchenko, P. E. (1998). Toward a new neurobiology of energy balance, appetite, and obesity: The anatomist weighs in. Journal of Comparative Neurology, 402, 435–441.

Woods, S. C., Schwartz, M. W., Baskin, D. G., & Seeley, R. J. (2000). Food intake and the regulation of body weight. Annual Review of Psychology, 51, 255–277.

Schwartz, M. W., Woods, S. C., Porte, D., Jr., Seeley, R. J., & Baskin, D. G. (2000, April 6). Central nervous system control of food intake. Nature, 404, 661–671.

Zaborszky, L., Wouterlood, F. G., & Lanciego, J. L. (Eds.). (2006). Neuroanatomical tract-tracing 3: Molecules, neurons, and systems. New York: Springer.

Schwartz, M. W., Woods, S. C., Seeley, R. J., Barsh, G. S., Baskin, D. G., & Leibel, R. L. (2003). Is the energy homeostasis system inherently biased toward weight gain? Diabetes, 52, 232–238.

Zarbin, M. A., Innis, R. B., Wamsley, J. K., Snyder, S. H., & Kuhar, M. J. (1983). Autoradiographic localization of cholecystokinin receptors in rodent brain. Journal of Neuroscience, 3, 877–906.

Scott, T. R., Yan, J., & Rolls, E. T. (1995). Brain mechanisms of satiety and taste in macaques. Neurobiology, 3, 281–292.

Zhang, Y., Proenca, R., Maffei, M., Barone, M., Leopold, L., & Friedman, J. M. (1994, December 1). Positional cloning of the mouse obese gene and its human homologue. Nature, 372, 425–432.

Sherrington, C.S. (1906). The integrative action of the nervous system. New York: Charles Scribner ’s Sons. Stellar, E. (1954). The physiology of motivation. Psychological Review, 61, 5–22.

c34.indd Sec7:679

Tartaglia, L. A., Dembski, M., Weng, X., Deng, N., Culpepper, J., Devos, R., et al. (1995). Identification and expression cloning of a leptin receptor, OB-R. Cell, 83, 1263–1271.

Zigman, J. M., Jones, J. E., Lee, C. E., Saper, C. B., & Elmquist, J. K. (2006). Expression of ghrelin receptor mRNA in the rat and mouse brain. Journal of Comparative Neurology, 494, 528–548.

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Chapter 35

Thirst MICHAEL J. MCKINLEY

THE NATURE OF THIRST

cardiovascular system and for thermoregulation. Obligatory losses of water from the body occur continually from the respiratory and gastrointestinal tracts, kidney, and skin. To replace these losses, some water is ingested in the form of food and some obtained from metabolic reactions, however, the drinking of aqueous fluid is the main source for replacing fluid deficits. While much of fluid intake is of a social, habitual, and prandial nature, rather than a response to thirst, if these sources of fluid are inadequate, thirst provides the fail-safe system to ensure that fluid deficits are replaced. No matter how effectively the kidney can concentrate urine and reduce water lost therein, this mechanism does not replace the obligatory fluid deficits mentioned. Analogous to the hunger for air ensuring sufficient oxygen intake and survival in the short term, thirst is a homeostatic emotion essential for survival in the longer term (Cannon, 1919).

A Homeostatic Emotion Thirst is an impelling urge to drink water or aqueous fluids. As a private, subjective state, thirst is difficult to define. Yet few who read this chapter have not experienced thirst. It can be classified with the urge to inhale air, the desire for sleep, feeling hot or cold, pain, hunger, fatigue, full bladder or bowel, and nausea as essential motivating emotions arising from interoceptive signals that lead subsequently to appropriate behavior to correct bodily deficits or surfeits, thereby restoring normal physiological set points. Thirst is an essential homeostatic emotion (Craig, 2003). Some have defined thirst as a disposition to drink (Booth, 1991), but such a definition includes the motivation to drink resulting from habit, advice, ritual, or social, cultural, and psychotic drives. Such dispositions to drink almost certainly do not reflect the same motivational emotion that arises from bodily dehydration and they should be differentiated from the homeostatic emotion of thirst that is the subject of this chapter. An essential feature of thirst is a degree of discomfort or craving, so that as thirst intensifies, it becomes more distressing, tormenting, and ultimately agonizing and overwhelming. Fortunately, most people will not experience thirst of this severity. It is not surprising that as fluid deficits increase, thirst and the motivation to ingest water increase concomitantly. Water is by far the most abundant molecule in the body, being 60% of its weight but approximately 98% of all the molecules in the body. Adequate intracellular water is essential for maintaining intracellular concentrations of dissolved enzymes, substrates, and ions that allow maximal functioning of cellular activity. Adequate extracellular water is essential for maintaining the integrity of the

Concepts of Thirst The development of ideas on how thirst is generated has been described in some detail by Fitzsimons (1979). At the beginning of the twentieth century, Mayer (1900) proposed that thirst was a sensation that arose essentially as a result of the osmotic pressure of the body fluids and tissues increasing. In attempting to define thirst, Cannon (1919) proposed a somatosensory explanation, attributing dryness in the pharynx and mouth as the essential feature of thirst. However, if dry mouth and throat are experimentally contrived by pharmacological means, subjects are not rendered thirsty, arguing against this explanation. Thus, the idea of thirst as a specific somatic sensation in the mouth and throat has lost favor. Following the demonstration that water drinking could be evoked by chemical or electrical stimulation of the hypothalamus (Andersson, 1953;

The author was supported by an Australian NHMRC Fellowship (ID 454369), and grants from the NHMRC (Project Grant ID 350437), Australian Research Council, the Robert J. Jr. & Helen C. Kleberg Foundation, and the G. Harold and Leila Y. Mathers Charitable Foundation. Thanks to Dr. Robin McAllen for comments and Julianna McKinley for artwork. 680

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Figure 35.1 (Figure C. 34 in color section) A guide to the location within the brain of regions implicated in the generation and regulation of thirst. Note: Top left panel: specific regions are projected onto a longitudinal magnetic resonance (MR) image of the midline of the human brain. The other panels show several of these regions in transverse MR images of

Andersson & McCann, 1955; Greer, 1955) and hypothalamic lesions caused adipsia (Teitelbaum & Epstein, 1962), a hypothalamic thirst center became a popular theme, although the idea of a brain center mediating thirst had been advocated earlier by Nothnagel (1881) and Mayer (1900). In the past half century, identification of several relevant brain regions, hormonal stimuli, and neural pathways linking sensor and integrative regions have been elucidated. Nowadays, the concept that thirst is an emotion generated centrally by the integration within the brain stem, hypothalamus, and preoptic area of neural, osmotic, and hormonal signals, transmitted via multiple neural pathways to produce neural outputs that are distributed to cortical effector sites, holds sway. A guide to the cerebral locations of a number of brain regions implicated in the generation and regulation of thirst is provided in Figure 35.1. REGULATORY THIRST The Dual Depletion Theory Reduction in the volume of either the intracellular or extracellular fluid generates an urge to ingest water. Satisfaction of this urge by drinking fluid can replenish both of these fluid compartments. When a mammal is deprived of water

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the same brain at three rostro-caudal levels (1,2,3) that are indicated by the white vertical lines in panel A. AC = Anterior cingulate cortex; INS = Insula; LH = Lateral hypothalamic area; LP = Lateral preoptic area; MP = Median preoptic nucleus; NTS = Nucleus of the solitary tract; OF = Orbito-frontal cortex; OV = Organum vasculosum of the lamina terminalis; P = Parabrachial nucleus; R = Midbrain raphé (dorsal and medial nuclei); S = Septal region; SFO = Subfornical organ.

and becomes dehydrated, water is usually lost from both intracellular and extracellular compartments. Separate bodily sensors detect changes in intracellular and extracellular volumes, and signals from these sensors subsequently initiate homeostatic responses that include thirst. Therefore, a dual depletion mechanism to drive thirst mechanisms in dehydrated animals has been advanced. The sensor and signaling mechanisms that respond to such “dual depletion” are discussed in the following section. Osmoregulatory Thirst and Intracellular Dehydration Under normal physiological conditions, the osmolality (i.e., total concentration of dissolved solutes in a liquid) of plasma is maintained within a narrow range in most mammals (280 to 290 mosmol/kg). Plasma osmolality will increase if ingested solutes such as sodium chloride are absorbed into the bloodstream, or if evaporative dehydration occurs. In both conditions, as the effective osmotic pressure of the circulating and interstitial fluids (i.e., the extracellular fluid within tissues) increases, water will move by osmosis from the interior of cells to the extracellular space, causing depletion of the intracellular fluid.

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682 Thirst

Plasma Sodium (mEq/L)

290

Thirst Score

146

Plasma Osmolality (mOsm/kg.H2O)

There is considerable evidence to show that depletion of the intracellular compartment is associated with thirst, and that specific brain sensors (osmoreceptors) detect intracellular dehydration and initiate compensatory mechanisms such as water drinking, vasopressin secretion, and natriuresis, all of which act to reduce the hypertonicity (McKinley et al., 1987a; Verney, 1947; Wolf, 1950). Investigation of the thirst-stimulating effects of intravenous infusions of hypertonic solutions led to the concept of osmoregulatory thirst. Intravenous infusion of concentrated solutions of sodium chloride, sucrose, fructose, or mannitol increase the tonicity of plasma and stimulate water drinking in species as diverse as dogs, rats, goats, sheep, iguanas, pigs, and pigeons (Fitzsimons, 1979). Because it is not possible to truly know the subjective perception experienced by animals that lead them to drink water, it is assumed that such drinking is the result of a thirst being generated in these species. However, in the case of human studies, thirst ratings can be obtained from experimental subjects, and the results are consistent with the animal investigations. Increasing the effective osmotic pressure of plasma by means of intravenous infusion of hypertonic sodium chloride (Figure 35.2), sorbitol, or mannitol stimulates thirst in humans (Wolf, 1950; Zerbe & Robertson, 1983). Not all types of systemically infused hyperosmolar solutions stimulate drinking or strong thirst. When concentrated solutions of urea, glycerol, glucose, or isomannide are administered systemically, thirst ratings or the volume of water drunk are considerably less than those observed with infusion of equivalent amounts of hypertonic

PET Scans

144 142 140

Figure 35.2 Increase of plasma sodium concentration, plasma osmolality, and thirst rating during an intravenous infusion of 0.51 mol/l saline for 25 or 50 minutes in 10 healthy adult human subjects, then subsequently after rinsing the mouth with water, and drinking water to satiate thirst.

288 286 284 282 7 6 5 4 3 2 1 0 Control

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saline, sorbitol, fructose, mannitol, or sucrose solutions (Fitzsimons, 1963; Gilman, 1937; Holmes & Gregerson, 1950; McKinley, Denton, & Weisinger, 1978; Olsson, 1972; Zerbe & Roberston, 1983). The solutes that are effective dipsogenic agents are those that do not readily permeate into cells (sodium chloride, sucrose, fructose, mannitol) so that an osmotic gradient is established across the semi-permeable cell membranes. As a consequence of this gradient, movement of water out of cells by osmosis results in cellular dehydration. The movement of smaller solutes such as urea and glycerol into cells via specific urea and glycerol channels and glucose via a glucose transporter is relatively rapid, so that an osmotic gradient from outside to inside of the cell is not maintained, and significant cellular dehydration does not occur. Therefore, thirst is stimulated acutely by increases in the effective osmotic pressure (the tonicity) of plasma, a condition that causes cellular dehydration. As expressed initially by Gilman (1937), “The logical conclusion to draw from the above results is that cellular dehydration rather than an increase in cellular osmotic pressure per se is the stimulus of true thirst.” Olsson (1972) also showed that infusions of hypertonic sodium chloride or fructose into the carotid artery were far more effective than equivalent infusions into the jugular vein, indicating that the osmosensors were probably located in the brain. Contemporaneous with the investigations of Gilman, Wolf, and others on osmoregulatory thirst was the discovery of osmoreceptors that regulate the secretion of the antidiuretic hormone, more commonly termed vasopressin (Verney, 1947). Cellular dehydration

End of Infusion

Max. Wet Drink Drink Drink Thirst Mouth ⫹3 ⫹14 ⫹60 min min min

Note: The subjects were asked to rate thirst on a scale of 0 to 10 with 0 being no thirst, and 10 the thirstiest they had ever experienced. These data were obtained while subjects underwent positron emission topography scans indicated by the arrowheads. Asterisks at points indicate significant difference from control, and those between points indicate significant difference between those two observations. From “The Correlation of Regional Blood Flow (rCBF) and Plasma Sodium Concentration during Genesis and Satiation of Thirst,” by D. A. Denton et al., 1999, Proceedings of the National Academy of Sciences, USA, 96, p. 2533, Fig. 1. Reprinted with permission.

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Regulatory Thirst

was shown to stimulate vasopressin release from the posterior pituitary, and in addition, the location of the relevant osmoreceptors was shown to be within the hypothalamic region of the brain (Jewell & Verney, 1957). Renal water retention under the regulation of vasopressin is the complementary arm of body fluid homeostasis, in that although fluid losses from the body are not restored by vasopressin action on the kidney, further obligatory fluid losses in urine are minimized. The integrated neural and endocrine regulation of body fluids is summarized in Figure 35.3. Location of Osmoreceptors for Thirst Following the discovery of a cerebral location for osmoreceptors that regulate vasopressin secretion (Verney, 1947), interest focused on the hypothalamus as a probable site of osmoreceptors that drive thirst. Consistent with this idea was the observation that injection of a small amount of hypertonic sodium chloride into the hypothalamus in the region of the mammillothalamic tract stimulated copious water drinking in water-replete goats (Andersson, 1953). Relative to the physiological concentration of sodium

chloride in the extracellular fluid of the brain (0.15 M), the high concentrations of sodium chloride that were injected in these experiments may have nonspecifically stimulated neurons or fibers subserving drinking, or spread the stimulus to an adjacent brain region. Therefore, Andersson was conservative in the interpretation of these experiments, having reservations that the injection regions were the sites of thirst osmoreceptors. Subsequently, investigators in Sweden (Andersson, Leksell, & Lishajko, 1975; Rundgren & Fyhrquist, 1978) showed that ablation of a region rostral to these hypothalamic sites, the anterior wall of the third ventricle, resulted in adipsia in goats. As well, they observed that infusions of hypertonic saline but not hypertonic saccharide solutions into the third cerebral ventricle stimulated drinking. As a result, they proposed that cerebral sodium receptors located in the anterior wall of the third ventricle of the brain mediated osmoregulatory thirst, rather than hypothalamic osmoreceptors. However, injection of hypertonic sucrose into the third ventricle does stimulate drinking (in sheep) if the sucrose is prepared in an artificial cerebrospinal fluid (CSF), although the response was less than that with hypertonic saline injection

Glomerular filtration (kidney)

Atrial natriuretic peptide (released by the heart in response to increased blood volume)

683

Increased urine Na & water excretion

Thirst (brain)

Water intake

Vasopressin release (brain/post. pituitary)

Reduced urinary water loss

Osmoreceptor stimulation

Na depletion, low kidney Na Angiotensinogen (circulating protein from the liver) Enzyme action

Sym

path

etic

R per educe fus d ion rena pre l ssu re

Reduced arterial pressure, blood volume & central venous pressure

nerv

es Vasoconstriction (arterioles/veins)

Renal symp n.

Increased arterial pressure

Tubular Na & water reabsorption (kidney)

Renin release (kidney)

Blood pressure, volume & osmolality normalised

Reduced urinary Na losses

Angiotensin I (inactive circulating decapeptide)

Aldosterone secretion (adrenal cortex)

ACE (lungs) Salt appetite (brain)

Angiotensin II (circulating octapeptide)

Denotes stimulatory influence

Restored body NaCl

Denotes inhibitory influence

Figure 35.3 Diagrammatic summary of the main hormonal regulators of body fluid homeostasis in mammals. Note: ACE = Angiotensin converting enzyme.

c35.indd Sec1:683

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684 Thirst

Osmoreceptors in the Lamina Terminalis To test the hypothesis that brain sodium receptors are responsible for thirst (Andersson, 1978), studies were made in conscious sheep of the effects of infusions of various hypertonic solutions into the carotid artery on water intake and on CSF sodium concentration (McKinley et al., 1978). Infusions of hyperosmolar sodium chloride, sucrose, or urea into the carotid arterial blood supply to the brain all increased CSF Na concentration, even though plasma sodium concentration increased only with hypertonic saline. The increased CSF sodium levels occurring with all three solutes was attributed to the exclusion of urea as well as sodium chloride and sucrose from the brain interstitium by the blood-brain barrier (Oldendorf, 1971), creating an osmotic gradient, resulting in osmotic movement of water from brain interstitium to the bloodstream. The increased CSF sodium concentration was indicative that the brain had been osmotically dehydrated by all three infusions, but only two of the infusions rapidly stimulated drinking—hypertonic saline and sucrose. Hyperosmolar urea was much less effective as a dipsogen. While these results did not support a role for sodium sensors in osmoregulatory thirst, there was a paradox. Why do only hypertonic sucrose and sodium chloride stimulate drinking, while all three solutions dehydrate the brain? Therefore, it was proposed that at least some osmoreceptors must be located in regions of the brain devoid of a blood-brain barrier—specifically circumventricular organs such as the organum vasculosum of the lamina terminalis (OVLT) or subfornical organ (see Figure 35.1 for locations) which lack a blood-brain barrier. Within these sites, it should be possible for osmoreceptors to distinguish the different solutes (McKinley et al., 1978). Subsequent studies in sheep (McKinley et al., 1982) and dogs (Thrasher, Keil, & Ramsay, 1982b) showed that ablation of the OVLT reduced drinking responses to systemic infusion of hypertonic saline, consistent with the presence of thirst osmoreceptors in the OVLT. Complete ablation of the OVLT did not totally abolish osmoregulatory drinking. Injection of hypertonic 0.2 M sodium chloride into the subfornical stimulates drinking (Camargo, Menani, Saad, & Saad, 1984), and ablation of the subfornical organ in the rat and sheep (but not dog) has been shown to reduce

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osmotically stimulated drinking also (Hosutt, Rowland, & Stricker, 1978; Lind, Thunhorst, & Johnson, 1984; Thrasher, Simpson, & Ramsay, 1982). Combined ablation of the subfornical organ and OVLT severely reduces osmoregulatory drinking (Figure 35.4) in sheep, but does not abolish it (McKinley, Mathai, Pennington, Rundgren, & Vivas, 1999). Only total or near total ablation of the lamina terminalis (i.e., subfornical organ, median preoptic nucleus, and OVLT) prevents drinking responses to acute intravenous infusion of hypertonic saline in sheep (Figure 35.4), and it was suggested that there may be considerable redundancy within the lamina terminalis for osmoreceptor function (McKinley et al., 1999). It is unlikely that the other sensory circumventricular organ, the area postrema (located in the hindbrain immediately dorsal to the nucleus of the solitary tract; see Figure 35.1), is a site of thirst osmoreceptors, because ablation of this structure had no inhibitory effect on drinking in response to intravenous hypertonic saline in sheep (Slavin & McKinley, 1989, unpublished observations). However, ablation of the area postrema does alter hypothalamic and drinking responses to intragastric or ingested hypertonic saline (Carlson, Collister, & Osborn, 1998; Curtis, Huang, Sved, Verbalis, & Stricker, 1999) suggesting it may relay signals from visceral osmoreceptors that could influence thirst.

1,500

Water Intake (ml)

(McKinley, Blaine, & Denton, 1974). Thus, while central osmoreceptors were indicated by this result, a specific effect of sodium chloride was also evident. Observations that ablation of the medial preoptic region including lamina terminalis or anteroventral third wall of the third ventricle (AV3V) region disrupted osmoregulatory thirst in rats (Black, 1976; Johnson & Buggy, 1978) were consistent with the evidence in goats of a role of the anterior wall of the third ventricle in osmoregulatory thirst.

1,000

500

0

SFO OVLT MnPO SFO/ SFO/ SFO/ OVLT/ LT DMS VL n⫽5 n⫽4 n⫽7 OVLT dMnPO MnPO MnPO n⫽7 n⫽6 n⫽4 n⫽6 n⫽4 n⫽4 n⫽4 Site of Lesion

Figure 35.4 Effect of ablation of different parts of the lamina terminalis, alone or in combination, on water drinking of sheep in response to intravenous infusion of hypertonic 4 mol/l saline at 1.3 ml/min that increased plasma osmolality by 12 mosmol/kg over 30 minutes. Note: Open bars show the drinking responses prelesion and the filled bars the postlesion responses. DB = Diagonal band; dMnPO = Dorsal median preoptic nucleus; LT = Total lamina terminalis; MS = Medial septum; OVLT = Organum vasculosum of the lamina terminalis; T = Septal triangularis nucleus; vMnPO = Ventral median preoptic nucleus; SFO = Subfornical organ; VL = Lateral to MnPO. From Figure 35.6, “The Effect of Individual or Combined Ablation of the Nuclear Groups of the Lamina Terminalis on Water Drinking in Sheep,” by M. J. McKinley, M. L. Mathai, G. L. Pennington, Rundgren M., and L. Vivas, 1999, American Journal of Physiology, 276, p. R678. Reprinted with permission.

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Regulatory Thirst

In regard to the other anatomical component of the lamina terminalis, the median preoptic nucleus (see Figure 35.1), which is behind the blood-brain barrier, ablation of this nucleus by either electrolytic or neurotoxin (ibotenic acid) techniques severely disrupts acute osmoregulatory water drinking (Cunningham et al., 1991; Mangiapane, Thrasher, Keil, Simpson, & Ganong, 1983; McKinley et al., 1999). While the median preoptic nucleus receives neural input from both the subfornical organ and OVLT (Miselis, 1981; Saper & Levisohn, 1983), and this may be the cause of the reduced osmoregulatory drinking when it is ablated, it seems likely that the median preoptic nucleus is also a sensor region for hypertonicity. This is because ablation of both OVLT and subfornical organ in combination do not completely abolish osmoregulatory drinking, and the residual response is abolished if the median preoptic nucleus (see Figure 35.1) is ablated as well (McKinley et al., 1999). Neurons within the median preoptic nucleus are responsive to directly applied hypertonic saline in vitro, although they are inhibited by hypertonicity and excited by hypotonicity (Travis & Johnson, 1993). Studies of c-fos expression, show median preoptic neurons are activated in vivo by systemic infusion of hypertonic saline when the subfornical organ and OVLT have been ablated (Hochstenbach & Ciriello, 1996). There is also reason to believe that some osmoreceptors exist on the brain side of the blood-brain barrier. For example, osmoreceptors may reside in the median preoptic nucleus, and these sensors could explain why intracarotid infusions of urea cause a dipsogenic response of much slower onset and much lesser magnitude, than the responses to hypertonic saline, sucrose, or fructose mentioned previously. Intracarotid infusion of hypertonic glucose is invariably ineffective as a dipsogen (McKinley et al., 1978; Olsson, 1972), infusions of hypertonic urea increase the sodium chloride concentration, and therefore the effective osmotic pressure, of the brain extracellular fluid (except in the circumventricular organs) whereas infusions of hypertonic glucose do not because it is rapidly transported across the blood-brain barrier as well as into cells (McKinley et al., 1978). An osmoreceptor (or sodium sensor) for thirst in the median preoptic nucleus should respond to hypertonic urea but not to hypertonic glucose infusion, while osmoreceptors in the subfornical organ and OVLT would not respond to either. This arrangement would explain the moderate drinking response to urea but the lack of response to glucose. Immunohistochemical detection of Fos, a protein that influences gene transcription in the nucleus of the cell, and identifies neurons that have been activated in response to a particular stimulus (Sagar, Sharp, & Curran, 1988), has allowed histological identification of the neurons within

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685

the lamina terminalis that respond to systemic hypertonicity. This method shows that intravenously infused solutions of hypertonic saline or sucrose activate neurons throughout the lamina terminalis of the rat (Oldfield, Bicknell, McAllen, Weisinger, & McKinley, 1991); so too does dehydration resulting from water deprivation for 24 to 48 hours (McKinley, Hards, & Oldfield, 1994). However, in the rat, osmotically stimulated neurons are concentrated particularly in the dorsal cap of the OVLT, the periphery of the subfornical organ (Figure 35.5), and throughout the median preoptic nucleus (McKinley et al., 1994). Intense Fos immunoreactivity induced in mouse OVLT in response to an intraperitoneal injection of hypertonic saline can be seen in Figure 35.6. These results are consistent with earlier electrophysiological studies that all parts of the lamina terminalis are responsive to systemically infused hypertonic stimuli (Gutman, Ciriello, & Mogenson, 1988; McAllen, Pennington, & McKinley, 1990; Vivas, Chiaraviglio, & Carrer, 1990). Functional brain imaging studies in human subjects infused systemically with hypertonic saline also show the lamina terminalis (Figure 35.7) to be activated by hypertonicity (Egan et al., 2003). In contrast to the effects on dipsogenic responses to acute hypertonicity, drinking following water deprivation for 48 hours was not inhibited by ablation of considerable parts of the lamina terminalis; it was reduced but not abolished by complete destruction of the lamina terminalis (McKinley et al., 1999). These observations show that other brain regions may have a role in osmoregulatory thirst. They also indicate that the mechanism of the acute thirst response to hypertonicity, largely under the control of the lamina terminalis, may be different to the mechanism regulating thirst in response to long-term hypertonicity. Lateral Preoptic Area Another region that has been implicated as a site of thirst osmoreceptors is the lateral preoptic region (see panel 2 of Figure 35.1). Injections of hypertonic saline or sucrose, but not urea, into the lateral preoptic area stimulates drinking in rats and rabbits, while ablation of the lateral preoptic area disrupts osmoregulatory drinking in a 4-hour test following intraperitoneal injection of hypertonic saline. Drinking following 24-hour water deprivation was not affected by lateral preoptic lesions (Blass & Epstein, 1971; Peck & Novin, 1971). More detailed mapping studies using the microinjection technique showed that osmotically stimulated drinking sites in the preoptic region were more widespread than initially observed (Peck & Blass, 1975). Osmosensitive neurons have been detected in the lateral preoptic area (Malmo & Mundl, 1975). However, osmoregulatory thirst is delayed rather than blocked by ablation of the lateral preoptic area; as well, appropriate drinking

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686 Thirst

Figure 35.5 Activated neurons in coronal sections of the subfornical organ of rats as shown by Fos-immunoreactivity (black dots) induced by intravenous infusion of hypertonic saline (A), intravenous infusion of angiotensin II (B), intravenous infusion of relaxin (C), and control infusion of isotonic 0.15 mol/l saline.

Note: Magnification bar = 100 µm. Infusion of hypertonic saline or relaxin activated neurons mainly in the periphery of the subfornical organ, while angiotensin II stimulated neurons throughout this region.

Hepatic or Gastrointestinal Osmoreceptors

OVLT

OC

Figure 35.6 Activated neurons, shown by intense Fos-immunreactivity (black dots), in the OVLT of a mouse brain 2 hours after an intraperitoneal injection of hypertonic saline (0.8 mol/l). Note: The arrow in the inset indicates the site of the OVLT in the coronal section of the mouse brain. OC = Optic chiasma; OVLT = Organum vasculosum of the lamina terminalis.

occurs in lateral preoptic-lesioned rats in response to intravenous infusion of hypertonic saline, increased dietary sodium chloride intake, or water deprivation (Coburn & Stricker, 1978). It is proposed that neurons within the lateral preoptic area and the lamina terminalis may interact in the regulation of thirst (Camargo et al., 1984), but the relationship of putative osmoreceptors in the lateral preoptic area with those in the lamina terminalis remains to be elucidated.

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Osmoreceptors in the hepatic portal vein or liver have been shown to influence vasopressin secretion and urine output (Baertschi & Vallet, 1981; Haberich, 1971). In regard to thirst, infusion of water but not saline into the portal vein inhibits water intake of dehydrated rats (Kobashi & Adachi, 1992). As well, water drinking by rats administered intragastric loads of hypertonic saline occurs before any measurable change in systemic plasma osmolality, suggesting that gastrointestinal or hepato-portal osmoreceptors may regulate thirst (Kraly, Kim, Dunham, & Tribuzio, 1995). Further support for this idea comes from studies in which intragastric hypertonic saline loads caused a potentiation of water drinking in response to dehydration or intravenous hypertonic saline infusion (Stricker, Callahan, Huang, & Sved, 2002). These investigators suggested that signals from central osmoreceptors interact with those from peripheral osmoreceptors, depending on the animal’s hydration state; the peripheral sensors providing early signaling of ingested fluid before any change in the tonicity of the general circulation occurs. Signals from portal or gastrointestinal osmoreceptors may be relayed via the area postrema (Curtis et al., 1999; Stricker et al., 2002). There is evidence that osmoreceptors within the lamina terminalis influence these signals (Freece, Van Bebber, Zierath, & Fitts, 2005).

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Hypovolemic Thirst and Extracellular Fluid Depletion 687

OrG AC Ins STG

LT

Cb

MC

AC

Cb

Figure 35.7 (Figure C. 35 in color section) Functional magnetic resonance imaging (BOLD signal) sections of a conscious subject experiencing maximum thirst resulting from intravenous infusion of hypertonic saline. Note: Activations (light gray regions): ACC = Anterior cingulate cortex; Cb = Cerebellum; Ins = Insula; LT = Lamina terminalis; MC = Orbital gyrus, mid cingulate region, posterior part; OrG = Orbital gyrus; STG = Superior temporal gyrus. From Figure 35.3, “Neural Correlates of the Emergence of Consciousness of Thirst,” by G. Egan et al., 2003, Proceedings of the National Academy of Sciences, USA, 100, p. 15245. Reprinted with permission.

TRPV Channels and Osmosensory Transduction Identification of the molecular characteristics of thirst osmoreceptors has been advanced recently with the discovery that ion channels of the transient receptor potential vanilloid (TRPV) class may play a role in transducing osmosensory function. Both TRPV1 and TRPV4 channels have been implicated in osmoregulatory transduction because they are located within osmosensory neurons of the lamina terminalis, and deletion of genes encoding these TRPV channels moderately reduces osmoregulatory drinking in mice (Ciura & Bourque, 2006; Liedtke, 2007). HYPOVOLEMIC THIRST AND EXTRACELLULAR FLUID DEPLETION Thirst and drinking of fluids can also result from depletion of the extracellular fluid without any intracellular

c35.indd Sec11:687

dehydration. Loss of fluid from the extracellular compartment (hypovolemia) may occur naturally under physiological or pathophysiological conditions that include hemorrhage, vomiting, diarrhea, burns, or sweating. If there is sufficient loss of extracellular fluid, thirst results that drives fluid intake (Fitzsimons, 1961). In the laboratory, several strategies have been employed in experimental animals to deplete the extracellular fluid without any apparent depletion of the intracellular compartment. These include hemorrhage, subcutaneous injection of colloid (e.g., polyethylene glycol) causing sequestration of extracellular fluid under the skin, diuretic treatment, peritoneal dialysis, diuretics, hemofiltration or loss of saliva from a parotid fistula (Abraham et al., 1976; Anderson & Houpt, 1990; Fitzsimons, 1961; Rabe, 1975; Stricker, 1966; Zimmerman, Blaine, & Stricker, 1981). These procedures are associated with increased water intake, although hemorrhage is an inconsistent dipsogenic stimulus (Fitzsimons, 1979; Wolf, 1958). The extracellular compartment is comprised of fluid within the circulation (plasma) and the interstices of tissues (interstitial fluid). When the extracellular compartment is depleted, the volume of fluid within the circulation falls, particularly on the venous side of the heart, resulting in reduced central venous pressure and reduced venous return to the heart. Experimental procedures (e.g., constriction of the vena cava) that mimic the changes in pressures that occur within the great veins returning blood to the heart during hypovolemic conditions also stimulate thirst and water intake (Thrasher, Keenan, & Ramsay, 1999). Sodium Appetite If there is depletion of the extracellular fluid without a concomitant increase of extracellular concentration of sodium chloride, a deficit in whole body sodium chloride as well as water occurs. Therefore, while ingestion of water may restore the volume of fluid lost, unless sodium chloride is also ingested, restoration of both volume and ionic concentrations of extracellular fluid will not result. Thus, it is not surprising that extracellular hypovolemia is also associated with the development of an appetite that is specific for sodium salts. Sodium appetite is much slower in onset than thirst, developing over several hours following the loss of extracellular fluid (Fitzsimons, 1979). Endocrine signaling mechanisms play a major role in the generation of sodium appetite during conditions of hypovolemia. Blood concentrations of both angiotensin II and aldosterone increase and may act synergistically within the brain to generate an appetite for salt (Figure 35.3). It is beyond the scope of this chapter to review the physiological mechanisms subserving sodium appetite and more details can be found in number of excellent reviews and monographs (Denton, 1982; Fitzsimons, 1979; Johnson & Thunhorst, 1997).

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688 Thirst

Additivity of Hyperosmotic and Hypovolemic Dipsogenic Signals As stated earlier, in a condition of dehydration, water is withdrawn from both intracellular and extracellular compartments of the body. Signals from both osmoreceptors and volume sensors contribute to the resultant thirst. While osmotic signals account for the majority of the dipsogenic response to dehydration, volume signals make a significant contribution (Ramsay, Rolls, & Wood, 1977). Further, studies in which simultaneous delivery of an osmotic load with a hypovolemic stimulus resulted in water intake that was the sum of that attributable to each independent stimulus show the probable additivity of hypovolemic and osmotic stimuli mediating dehydrational thirst (Blass & Fitzsimons, 1970; Fitzsimons, 1979). Sensors and Afferent Signaling of Hypovolemic Thirst With only a few exceptions, studies of the afferent signaling mechanisms mediating water drinking in response to hypovolemia have been confined to rats and dogs. The experimental model of hypovolemia that has been studied in the dog is constriction or obstruction of the inferior vena cava (caval ligation) to reduce venous return to the heart, lowering central venous pressure and arterial pressure. In the rat, subcutaneous sequestration of extracellular fluid under the skin has been utilized as a means of producing systemic hypovolemia. Neural Signaling Hypovolemic thirst results from both hormonal and neural signals being transmitted to the brain. These signals may act either singly, or in combination to generate thirst. Stretch receptors in the heart and blood vessels provide the neural signals that relate information to the CNS on the degree of filling and pressures within the circulation. These afferent signals, which are stimulated by increases in pressure and reduced when pressure decreases, are carried largely by the vagus and glossopharyngeal nerves and terminate in the nucleus of the solitary tract in the medulla oblongata (Dampney, 1994; see Figure 35.1, panel A). From there, polysynaptic neural pathways, that involve both excitatory and inhibitory synapses, relay signals to other sites that control a number of functions that include the baroreceptor reflex, vasopressin secretion, and thirst. It is possible that neural relays via the A1 cell group in the caudal ventrolateral medulla, and/or other hindbrain and midbrain sites send signals to neurons in the lamina terminalis to influence drinking (Johnson, Cunningham, & Thunhorst, 1996). However, the participation of these pathways in hypovolemic thirst remains to be proven.

c35.indd Sec11:688

Angiotensin-Mediated Hormonal Signaling When blood volume, arterial pressure and central venous pressure fall, circulating concentrations of angiotensin II increase (see Figure 35.3). The initial step in the generation of angiotensin II, the effector peptide of the reninangiotensin system, is the release of the proteolytic enzyme renin from the kidney. The signals that drive renin secretion in hypovolemic states are increased renal sympathetic nerve activity, reduced renal perfusion pressure, and altered sodium load at the macula densa of the distal tubule of the kidney. Once released into the bloodstream, renin catalyzes the formation of the decapeptide molecule angiotensin I in the circulation by cleaving it from a large (40,000 Dalton) plasma protein, angiotensinogen that is synthesized in the liver. Angiotensin converting enzyme (ACE) mainly in the lung, but also in other tissues, then splits off two more amino acids from the carboxyl terminus of angiotensin I causing the formation of the biologically active octapeptide angiotensin II (see Montani & Van Vliet, 2004, for a review). In regard to the humoral signals for hypovolemic thirst, evidence in both rat and dog favors a significant role for the renin-angiotensin system in thirst associated with depletion of the extracellular fluid. There are compelling data favoring a role for circulating angiotensin II, at least in combination with neural signals, in the genesis of hypovolemic thirst. First, removal by bilateral nephrectomy of the source of renin, the enzyme needed for generation of angiotensin I, reduces drinking in response to caval ligation in rats (Fitzsimons, 1969). Second, peripheral administrations of inhibitors of angiotensin converting enzyme (ACE inhibitors) or angiotensin receptor antagonists reduce drinking responses to caval ligation in dog or rat (Fitzsimons & Elfont, 1982; Fitzsimons & Moore-Gillon, 1980; Thrasher, Keil, & Ramsay, 1982). Third, the blood levels of angiotensin II that are reached following subcutaneous injection of polyethylene glycol (a large colloidal molecule) or caval ligation are above the threshold plasma concentrations of angiotensin II for drinking achieved by intravenous infusion of angiotensin II (Johnson & Thunhorst, 1997). Fourth, ablation of the subfornical organ, the site of angiotensin receptors mediating the dipsogenic action of this octapeptide, inhibits water intake in response to hypovolemia (Lind et al., 1984; Stratford & Wirtshafter, 2000). Intrathoracic Receptors Signaling Hypovolemia It is also clear that other signaling mechanisms besides the renin-angiotensin system play an important role in hypovolemic thirst. Data from three studies of the dipsogenic effect of caval ligation in the dog are particularly relevant to this point. First, significant drinking in response to caval ligation

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Hormonal Influences on Thirst

is still evident in dogs in which angiotensin receptors have been totally blocked pharmacologically (Thrasher, Simpson, et al., 1982). Second, the drinking response to caval ligation was reduced by approximately half when the heart was denervated so that putative low pressure atrial stretch receptors no longer send signals to the brain. Denervation of the high pressure baroreceptors in the aortic arch and carotid sinus also substantially reduced such drinking while combined cardiac and arterial baroreceptor denervation totally abolished the dipsogenic effect of caval ligation, despite high circulating angiotensin II levels being maintained (Quillen, Keil, & Reid, 1990). These data indicate that signals from baroreceptors in the heart and carotid sinus and aortic arch have an important role in mediating hypovolemic thirst in the dog. More recently, Thrasher et al. (1999) performed a series of intrathoracic vascular ligations of the inferior vena cava (IVC), the pulmonary artery, or the ascending aorta in conscious dogs so that blood pressure at the arterial baroreceptors fell by 25 mm Hg in each case. As expected, constriction of the IVC reduced left atrial, right atrial, and mean arterial pressure and stimulated drinking. So too did constriction of the pulmonary artery that reduced left atrial and arterial pressure, but increased right atrial pressure. However, constriction of the ascending aorta that also reduced arterial pressure and right atrial pressure, but increased left atrial pressure, did not stimulate thirst in the dogs. These results show either that loading left atrial receptors can override signals from other baroreceptors, or it is possible that unloading of low-pressure stretch receptors in the left atrium of the heart in combination with unloading of high-pressure arterial baroreceptors is the cause of hypovolemic thirst in the dog (Thrasher et al., 1999). In sheep, a crushing injury to the left atrial appendage caused hypovolemia-induced water intake to be depressed, evidence supporting a role for left atrial receptors mediating hypovolemic thirst in this species (Zimmerman et al., 1981). There are stretch receptors in the ventricles of the heart and coronary arteries as well as the atria that could also have role in mediating hypovolemic thirst; investigations have yet to be made in this regard. In rats, the right atrium appears to play an important sensor role for hypovolemic thirst. Since nephrectomized, polyethylene glycol-treated (i.e., hypovolemic) rats that cannot increase blood angiotensin II levels still increase water intake, nonangiotensin signaling must be involved (Fitzsimons, 1961). Inflation of a balloon at the junction of the superior vena cava and the right atrium abolished drinking responses to intraperitoneal polyethylene glycol, reduced dehydration-induced drinking, or the spontaneous overnight water intake, but had no effect on the drinking response to intravenous infusion of hypertonic saline (Kaufman, 1984). An interesting aspect of this study was that atrial stretch reduced the volume of water drunk in

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response to 24 hours of water-deprivation by 30%, which is the proportion of water intake of dehydrated rats that Ramsay et al. (1977) attributed to the reduced extracellular volume, the rest being osmotically stimulated. Alternatively, drinking in response to subcutaneous injection of polyethylene glycol was totally blocked, consistent with this stimulus being a pure hypovolemia. Kaufman (1984) proposed that a direct nervous input from the right atrium to the central nervous system mediated hypovolemic drinking. However, it is also possible that release of atrial natriuretic peptide from the heart contributes to the inhibition of hypovolemic thirst by atrial balloon inflation. HORMONAL INFLUENCES ON THIRST Angiotensin II Following the discovery of a renal dipsogen that appeared to be renin (Fitzsimons, 1969), rapid progress was made in identifying the thirst-stimulating properties of angiotensin II, and the evidence for its role as a dipsogenic hormone has been detailed previously. Systemic administration of components of the renin-angiotensin system—renin, angiotensin I, or angiotensin II—stimulates water drinking in many mammals and reptiles (Fitzsimons, 1979). In some species (e.g., sheep, humans), the blood levels of infused angiotensin II needed to stimulate drinking were found to be high relative to the levels observed physiologically during hypovolemia (Abraham, Baker, Blaine, Denton, & McKinley, 1975; Phillips, Rolls, Ledingham, Morton, & Forsling, 1985). However, it is likely that in these species, the dipsogenic effect of angiotensin II that would occur normally during intravenous infusion of this octapeptide is offset by the simultaneous rise in blood pressure that inhibits thirst by stimulation of arterial baroreceptors (Evered, 1992; Klingbeil, Brooks, Quillen, & Reid, 1991). Because arterial pressure does not increase during hypovolemia, such an inhibitory influence on the dipsogenic action of angiotensin II is not a consideration in this condition and lower circulating levels of the peptide should induce thirst. Site in the Brain of the Dipsogenic Action of Angiotensin II Hydrophilic peptide molecules like angiotensin II do not normally gain rapid passage into the brain interstitium from the bloodstream because of the blood-brain barrier. Following the discovery of the dipsogenic action of angiotensin II, the question soon arose as to how a polar molecule like angiotensin II could act on the brain to stimulate thirst if did not cross the blood-brain barrier. Experiments in the rat showed that angiotensin II from

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the bloodstream acted on neurons located in the subfornical organ to stimulate drinking behavior (Simpson & Routtenberg, 1973). This circumventricular organ lacks a normal blood-brain barrier (Wislocki & Leduc, 1952) and neurons within it express high concentrations of angiotensin AT1 receptors in all species studied (Allen et al., 2000) including humans (McKinley, Allen, Clevers, Paxinos, & Mendelsohn, 1987). Angiotensin II, directly applied or from blood, stimulates action potentials in subfornical neurons (Felix & Schlegel, 1978; Gutman et al., 1988) and circulating angiotensin II activates neurons throughout the subfornical organ (Figure 35.5B) as indicated by expression of the proto-oncogene c-fos (McKinley, Badoer, & Oldfield, 1992). For water drinking, the rat subfornical organ is exquisitively sensitive to minute quantities of directly injected angiotensin II, while ablation of the subfornical organ prevents drinking in response to intravenously infused angiotensin II and some hypovolemic stimuli (Simpson, Epstein, & Camardo, 1978). Paradoxical Potentiation of Thirst by Angiotensin Converting Enzyme Inhibitors An interesting aspect of angiotensin action on the subfornical organ is the very high concentrations of angiotensin converting enzyme (ACE) present there (Brownfield, Reid, Ganten, & Ganong, 1982). These high concentrations of ACE allow angiotensin I originating from the systemic circulation to be converted to angiotensin II locally within the subfornical organ. This probably explains why lower doses of ACE inhibitors, such as captopril, that block peripheral generation of angiotensin II, not only do not block drinking responses, but actually potentiate them (Lehr, Goldman, & Casner, 1973). This paradox arises because although peripheral ACE blockade reduces circulating angiotensin II levels, the concentration of blood-borne angiotensin I increases dramatically. This angiotensin I can then be converted to angiotensin II locally in the subfornical organ to stimulate thirst, because the doses of ACE inhibitors used may not be sufficient to block the high concentrations of ACE in the subfornical organ. In line with this interpretation, administration of a much higher concentration of ACE inhibitor directly into the subfornical organ, blocks drinking responses (Thunhorst, Fitts, & Simpson, 1989).

mammary duct development), relaxin can stimulate water drinking and the secretion of vasopressin. Intravenous infusion of relaxin (Sinnayah, Burns, Wade, Weisinger, & McKinley, 1999) or direct injection into the brain ventricles (Thornton & Fitzsimons, 1995) stimulates water drinking by rats of either sex. Administration of relaxin-neutralizing antibodies to pregnant rats reduced water intake during the second half of pregnancy in these animals, indicating a likely role for relaxin in their fluid intake (Zhao, Malmgren, Shanks, & Sherwood, 1985). Blood concentrations of angiotensin II as well as relaxin increase during pregnancy and a synergy between circulating angiotensin II and relaxin to stimulate water drinking in rats has been demonstrated (Sinnayah et al., 1999). Circulating relaxin also stimulates vasopressin secretion (Parry, Poterski, & Summerlee, 1994) and in combination with its dipsogenic action, relaxin would be expected to promote a positive fluid balance. Indeed, pregnancy is characterized by a reduction in plasma osmolality in many mammals, and it has been suggested that a resetting of the osmostat occurs (Durr, Stamotsos, & Lindheimer, 1981). It seems likely that this so-called resetting of the osmostat is due in part to the dipsogenic action of relaxin to maintain water intake in pregnancy despite the hyponatremia and hypotonicity of body fluids. Site in the Brain of the Dipsogenic Action of Relaxin The relaxin receptor (LGR-7) is present at relatively high concentrations in several regions of the brain that include the subfornical organ and OVLT, sites devoid of a bloodbrain barrier and accessible to circulating relaxin (Osheroff & Phillips, 1991). The dipsogenic action of relaxin is almost certainly initiated via a group of relaxin-sensitive neurons in the periphery of the subfornical organ because (a) relaxin acts directly on neurons of the isolated subfornical organ in vitro to increase the frequency of action potentials; (b) intravenous infusion of relaxin activates subfornical organ neurons as indicated by the increased expression of c-fos in a subgroup of neurons within its periphery (Figure 35.5C); and (c) ablation of the subfornical organ (but not the OVLT) abolishes drinking in response to systemically infused relaxin (Sunn et al., 2002). The efferent neural pathways from the subfornical organ mediating this relaxin-induced drinking are unknown.

Relaxin Source of Relaxin and Effects on Fluid Balance

Atrial Natriuretic Peptide

Relaxin is a peptide hormone that is synthesized in the corpus lutea of the ovary and secreted into the systemic circulation during most of pregnancy in many mammals. In addition to its actions on reproductive tissues (e.g., inhibition of uterine contractions, ripening of the cervix, and

Atrial natriuretic peptide (ANP) is one of three closely related natriuretic peptides released from cardiac myocytes in conditions of increased extracellular fluid volume (see Figure 35.3). As befits its release during states of fluid loading, ANP inhibits water drinking. ANP exerts inhibitory

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actions on angiotensin-related drinking and dehydrationinduced drinking in rats (Antunes-Rodrigues, McCann, Rogers, & Sampson, 1985) and inhibits osmoregulatory thirst in humans (Burrell, Lambert, & Bayliss, 1991). The actions of ANP on thirst are probably due to a direct inhibitory action on neurons of the subfornical organ because ANP, directly applied to subfornical neurons, inhibits their firing rate and excitatory response to angiotensin II (Hattori, Kasai, Uesugi, Kawata, & Yamashita, 1988); and direct injection of ANP into the subfornical organ of rats reduces water intake in response to water deprivation or angiotensin (Ehrlich & Fitts, 1990).

estrogen does not seem to affect osmotically stimulated drinking (Findlay et al., 1979).

Estrogen

Hormones Associated with Feeding

Ovarian steroid hormones such as estrodiol probably have a physiological role in regulating thirst in females. Day-today water drinking changes during the course of the estrus cycle in animals, and these alterations can be abolished by oophorectomy (Findlay, Fitzsimons, & Kucharczyk, 1979; Michell, 1979). If estrodiol benzoate is implanted into ovariectomized female rats, water intake falls, as it does in intact female rats at estrus when blood levels of endogenous estrogen increase (Findlay et al., 1979). Estrogen (but not progesterone) treatment in ovariectomized rats causes a reduction in water drinking elicited by angiotensin II administered peripherally or centrally, but does not affect osmoregulatory drinking (Findlay et al., 1979; Fregly, 1980; Kisley, Sakai, Ma, & Fluharty, 1999). A likely explanation of the estrogen-induced reduction in angiotensin-related drinking is a down regulation of angiotensin AT1 receptors in the subfornical organ. AT1 receptors are co-located on many neurons that also express estrogen receptors in the periphery of the subfornical organ of ovariectomized rats. The expression of AT1 receptors on these neurons is greatly reduced following estrogen administration for 5 days (Rosas-Arellano, Solano-Flores, & Ciriello, 1999). It has been shown also that estrogen treatment reduces the angiotensin responsiveness of subfornical neurons from ovariectomized rats (Tanaka, Miyakubo, Okamura, Sakamaki, & Hayashi, 2001). Water intake in response to injection of angiotensin II directly into the subfornical was attenuated in estrogen- treated rats, whereas these rats drank normally in response to injections of angiotensin II into the median preoptic nucleus. The authors propose that estrogen depresses the activity of angiotensin-responsive neurons in the subfornical organ projecting to the median preoptic nucleus (Tanaka, Miyakubo, Fujisawa, & Nomura, 2003). The estrogen receptor ER-α is expressed in osmoresponsive neurons in the periphery of the subfornical organ and dorsal cap of the OVLT, and this expression is greatly increased by hypertonicity resulting from water deprivation (Somponpun, Johnson, Beltz, & Sladek, 2004), however

Much of the normal day-to-day water drinking of mammals is closely associated with feeding, and it is possible that hormones from the gastrointestinal region may influence thirst and water intake (Kraly, 1991). Amylin is a hormone secreted from pancreatic islet beta cells following the intake of food. When administered peripherally, it stimulates water drinking in rats. Amylin causes excitation of neurons in the subfornical organ in vitro, and it has been suggested that it is a dipsogenic hormone that acts via the subfornical organ to stimulate prandial drinking (Riediger, Rauch, & Schmid, 1999). Amylin also stimulates renin secretion from the kidney (Wookey, Cao, & Cooper, 1998) and its dipsogenic action could also be mediated in part via increased angiotensin II levels in the circulation. Obestatin, a peptide from the gastrointestinal tract, is a posttranslational variant of the ghrelin gene. It inhibits drinking following feeding or centrally administered angiotensin II in rats. Obestatin depresses the activity of subfornical organs in vitro, suggesting that its antidipsogenic action may be mediated via an action on this circumventricular organ (Samson, White, Price, & Ferguson, 2007). Its physiological significance as an antidipsogenic hormone requires further evaluation.

Other Hormones Vasopressin Systemically infused vasopressin has been observed to increase the osmotic responsiveness of dogs to drink (Szczepanska-Sadowska, Sobocinska, & Sodowski, 1982). Despite the obvious association of vasopressin secretion and thirst, there is little if any evidence in other species to suggest that vasopressin is a dipsogenic hormone.

INTEGRATIVE BRAIN REGIONS RELAYING THIRST SIGNALS Neural signals from peripheral and central sensors are relayed and integrated within the central nervous system to generate the conscious emotion of thirst. Major neural pathways are summarized in Figure 35.8. Nucleus of the Soltary Tract and Area Postrema Vagal and glossopharyngeal afferent nerves transmitting sensory signals from visceral sensors that include baroreceptors, stretch receptors in the gastrointestinal tract, and

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Circulating Hormones Angiotensin II Relaxin ANP THIRST

SFO

MnPO OVLT

R LH

LPBN NTS/AP CVLM

Systemic hypertonicity Arterial baroreceptors Cardiac receptors GI tract, liver

Figure 35.8 A diagram of major neural pathways (excitatory or inhibitory) linking sensors in the lamina terminalis for osmoreception and circulating hormones, regions of the medulla that receive afferent neural input from arterial baroreceptors, the heart, gastrointestinal tract, and liver, with integrative regions in the midbrain and hypothalamus.

Note: The influence of ANP (interrupted arrow) on the subfornical organ is inhibitory. ANP = Atrial natriuretic peptide; AP = Area postrema; CVLM = Caudal ventrolateral medulla; GI = Gastrointestinal; LH = Lateral hypothalamic area; LPBN = Lateral parabrachial nucleus; MnPO = Median preoptic nucleus; NTS = Nucleus of the solitary tract; OVLT = Organum vasculosum of the lamina terminalis; R = Midbrain raphe; SFO = Subfornical organ.

taste receptors terminate within the nucleus of the soltary tract (NTS) and area postrema (Contreras, Beckstead, & Norgren, 1982) and may influence thirst. Combined ablation of the area postrema and adjacent NTS increased water intake in response to angiotensin-related dipsogenic stimuli in rats (Edwards & Ritter, 1982; Ohman & Johnson, 1989). This effect was less if the lesion was restricted more to the area postrema, and greater if a larger proportion of the NTS was ablated (T. Wang & Edwards, 1997). It is proposed that signals from the viscera, that have an influence on thirst, are relayed via the NTS to more rostral brain regions via the lateral parabrachial nucleus and ventrolateral medulla (Johnson & Thunhorst, 1997). Ad libitum water drinking, or that resulting from hypovolemia, was not affected by lesions of the NTS designed to destroy neural input from intrathoracic baroreceptors, and it has been suggested that neural input from ascending spinal pathways transmitting signals from renal sensors could be influencing thirst in these animals (Schreihofer et al., 1999).

the NTS relaying afferent nerve signals from the viscera (Herbert, Moga, & Saper, 1991). In turn, neurons within the LPBN project efferent nerve fibers to regions known to influence thirst such as the median preoptic nucleus and subfornical organ in the lamina terminalis and the lateral hypothalamic area (Herbert, et al., 1991; Saper & Levisohn, 1983). Ablation of the LPBN, by either electrolysis or injection of neurotoxin, enhanced water intake in response to angiotensin-mediated dipsogenic stimuli, but did not change day-to-day water intake, or drinking responses to systemic hypertonicity or polyethylene glycol-induced hypovolemia (Edwards & Johnson, 1991; Ohman & Johnson, 1989). Therefore, neurons within the LPBN do not seem to influence ad libitum water drinking, but may relay signals that inhibit water drinking associated with angiotensin’s dipsogenic action, possibly preventing excessive water intake in response to angiotensin. It is possible that the LPBN restricts water drinking by engaging neural mechanisms mediating satiety. Microinjection of the GABA agonist muscimol into the LPBN to inhibit its neural activity in water-sated rats causes a small but significant increase in drinking, consistent with the idea that LPBN neurons play a role in thirst satiety. Another interesting aspect of LPBN function in relation to thirst mechanisms is the apparent switching of a thirst

Lateral Parabrachial Nucleus The lateral parabrachial nucleus (LPBN) in the dorsolateral midbrain is the major efferent target of neurons within

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to an appetite for salt when serotonergic antagonists are injected into the LPBN of rats administered various dipsogenic stimuli (Menani, Columbari, Beltz, Thunhorst, & Johnson, 1998). Midbrain Raphé Nuclei Located in the dorsal midline of the midbrain (see Figure 35.1, panel A), the dorsal and median raphé nuclei probably relay neural signals that exert inhibitory serotonergic influences on thirst mechanisms. Ablation of the dorsal raphé nucleus causes increased water intake in response to dehydration or an angiotensin II–mediated stimulus (isoproterenol treatment), and also changes the sodium/water preference of rats resulting in large increases in both salt and water intake (Olivares, Costa-e-Sousa, & CavalcanteLimal 2003). In regard to the median raphé nucleus, ablation of serotonergic neurons therein resulted in a gradual increase in water intake of rats (Barofsky, Grier, & Pradhan, 1980), while acute inhibition of neurons within this brain region by microinjection of muscimol (a drug that inhibits neurons by acting at GABA receptors) into it caused a rapid drinking response in normally hydrated rats. Inhibitory neural pathways from median raphé to the subfornical organ and/or lateral hypothalamic area may mediate its inhibitory influence on thirst because ablation of either of these regions disrupts the dipsogenic effect of injections of muscimol into the median raphé nucleus (Stratford & Wirtshafter, 2000). Zona Incerta Located ventral to the thalamus, the zona incerta has been implicated in thirst mechanisms because its ablation disrupts drinking responses. However, results are inconsistent as to the type of drinking that is affected by ablation of the zona incerta. Grossman (1984) reported that osmoregulatory but not hypovolemic drinking was inhibited by lesions in the rostral zona incerta, whereas Evered and Mogenson (1976) observed that water intake in response to hypertonicity or hypovolemia was normal in rats with zona incerta lesions, but secondary, nonhomeostatic drinking was impaired. The zona incerta is connected to many brain regions, including the subfornical organ, lateral hypothalamic area, and several thalamic sites (Miselis, Weiss, & Shapiro, 1987; Ricardo, 1981) and is well positioned to relay neural signals for thirst. Lateral Hypothalamic Area As mentioned in an earlier section, stimulation of the lateral hypothalamus (LH) stimulates drinking (Andersson &

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McCann, 1955; Greer, 1955), while ablation of the LH caused severe hypodipsia and hypophagia (Teitelbaum & Epstein, 1962). The disruption of ascending catecholaminergic fibers of passage in the medial forebrain bundle passing through the LH was considered a crucial factor in the cause of adipsia and aphagia of the LH syndrome (Ungerstedt, 1971). However, later investigations in rats, in which the excitotoxin kainic acid was used to ablate LH neurons but leave fibers of passage intact, revealed that osmoregulatory, hypovolemic, and angiotensin-stimulated water-drinking responses were severely disrupted, but drinking following water deprivation was not (Stricker, Swerdloff, & Zigmond, 1978; Winn, Tarbuck, & Dunnett, 1984). The LH receives a strong afferent neural input from the lamina terminalis, LPBN, and midbrain raphe (Berk & Finkelstein, 1981; Herbert et al., 1991; Miselis et al., 1987) and has numerous efferent connections to thalamic and cortical regions. It is possible that it could relay thirst related signals to these cortical regions from sensors in the lamina terminalis. Median Preoptic Nucleus The median preoptic nucleus, located in the lamina terminalis between the subfornical organ and OVLT, has direct neural links with many brain regions that have been implicated in the control of body fluid homeostasis. These include a rich reciprocal neural connectivity with the OVLT and subfornical organ, neural input from the LPBN, ventrolateral medulla, midbrain raphé, and hypothalamic paraventricular nucleus (Saper & Levisohn, 1983; Zardetto-Smith & Johnson, 1995). As well, its strong efferent links to the lateral preoptic and lateral hypothalamic areas, parastrial nucleus, supraoptic nucleus, magno- and parvocellular parts of the hypothalamic paraventricular nucleus, midbrain and medullary raphé, periaqueductal grey, bed nucleus of the stria terminalis, and amygdala (Gu & Simerly, 1997) emphasize the potential of this nucleus for an integrative role in the regulation of thirst. Evidence that is consistent with an integrative role of the median preoptic nucleus in thirst is the severe disruption of osmoregulatory, angiotensin-stimulated or hypovolemic drinking responses caused by ablation of this nucleus by either neurotoxin or electrolytic methods (Cunningham et al., 1991; Johnson et al., 1996; Mangiapane et al., 1983; McKinley et al., 1999). Neurons within the median preoptic nucleus are activated when animals are dehydrated, infused systemically with hypertonic saline or angiotensin II, or intracerebroventricularly with angiotensin II or relaxin, which are all dipsogenic stimuli (Herbert, Forsling, Howes, Stacey, & Shiers, 1992;

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McAllen et al., 1990; McKinley et al., 1992, 1994, 1997). Severing the neural connections between the subfornical organ and median preoptic nucleus disrupts drinking in response to systemically administered angiotensin II, as does cutting the efferent neural output from the median preoptic nucleus (Eng & Miselis, 1981; Lind & Johnson, 1982). The high concentration of angiotensin AT1 receptors in the median preoptic nucleus (Allen et al., 2000) that would not be directly accessed by circulating angiotensin II (because of the blood-brain barrrier), indicate that it probably receives afferent angiotensinergic input. It has been proposed that angiotensin-senstive neurons in the subfornical organ that are stimulated by blood-borne angiotensin relay neural signals out of the lamina terminalis via an angiotensergic synapse in the median preoptic nucleus (Johnson et al., 1996). Septal Nuclei Harvey and Hunt (1965) showed initially that ablation of the septum (see Figure 35.1, panel 2 for location) could cause large, prolonged (over months) increases in daily fluid intake in rats (termed septal hyperdipsia), suggesting that this region may relay inhibitory neural signals related to thirst. Polydipsia resulting from septal lesions persisted in rats with the ureter ligated to prevent urine loss, demonstrating that a primary polydipsia occurs with septal lesions (Blass & Hanson, 1970). In rats, day-to-day drinking increases, and angiotensin-stimulated but not osmoregulatory drinking is potentiated (Blass, Nussbaum, & Hanson, 1974). In sheep with septal lesions, neither angiotensin- or osmotically stimulated drinking is potentiated, but daily water intake may more than double. Such water drinking continues during the day when plasma osmolality has decreased below the normal set point (Smardencas & McKinley, 1994). It is possible that normal inhibitory influences of hypotonicity or hypovolemia on thirst may be relayed via the septum, being disrupted when the septal region is ablated. There is also evidence of a relay via the nucleus of the diagonal band from the hindbrain that is involved in hypovolemic thirst (Sullivan et al., 2003).

EFFECTOR REGIONS FOR THIRST IN THE CEREBRAL CORTEX Unlike vasopressin secretion, where the neuroendocrine motor output from neurons in the supraoptic and paraventricular nucleus is well defined, the effector sites in the brain that generate the emotion of thirst remain clouded in uncertainty. Thirst demands a behavioral response— fluid ingestion. As a function of the conscious brain, the

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emotion of thirst has been assumed to be generated by the cerebral cortex. Cortical Stimulation In a survey of the cerebral cortex of conscious monkeys, Robinson and Mishkin (1966) electrically stimulated many cortical loci and obtained drinking responses at several sites. These included the substantia innominata, putamen, substantia nigra, preoptic region, lateral hypothalamus, and ventral tegmentum, but the region that most reliably yielded drinking behavior when stimulated was the anterior cingulate cortex. In the classical studies of Penfield and colleagues, many different superficial sites in the cerebral cortex of conscious human surgical patients were electrically stimulated, and their subjective responses recorded. The subjects, with scalp and skull locally anesthetized, although reporting many somatic and visceral sensations, rarely mention the induction of thirst during these stimulations (Penfield & Faulk, 1955; Penfield & Rasmussen, 1950). However, in two epileptic subjects, thirst or a need for water was associated with stimulation of the superior temporal gyrus (Penfield & Jasper, 1954). Brain Imaging Studies Functional brain imaging studies in human subjects infused intravenously with hypertonic saline to induce thirst have also provided information in this regard. Both positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) that reflect changes in regional blood flow in the brain, show that the anterior cingulate region (see Figure 35.1, panels A and 1 for location) is activated in subjects made thirsty by this procedure (Figure 35.7), and that satiation of thirst by drinking, quickly led to this activation being extinguished (Denton et al., 1999; Egan et al., 2003). Activation of the anterior cingulate region was also correlated with thirst in another group of subjects (de Araujo, Kringelbach, Rolls, & McGlone, 2003). Activations in the posterior cingulate, parahippocampal gyrus, insular cortex (Figure 35.7), precentral gyrus, orbital gyrus, superior temporal gyrus (Figure 35.7), anterior perforated substance, and regions within the cerebellum also correlated with thirst scores. While the activity of several brain regions correlated with the thirst score, such correlations do not allow the precise function of these regions to be specified from these imaging experiments. The superior temporal region was implicated in thirst by Penfield and Jasper (1954). Three of the regions mentioned—,the anterior cingulate, insular and orbito-frontal cortices—have been implicated in the generation of homeostatic emotions (Craig, 2002).

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Neurochemistry of Thirst

Anterior Cingulate Cortex The brain imaging investigations in human subjects, and electrical stimulation studies in monkeys mentioned, consistently link the anterior cingulate cortex with thirst mechanisms. The anterior cingulate region has been shown to be activated by sensory (e.g., pain, temperature), emotional (e.g., depression), cognitive (e.g., mental arithmetic), autonomic and reward based stimuli (Bush, Luu & Posner, 2000; Craig, 2002; Critchley, Corfield, Chandler, Mathias, & Dolan, 2000; Gehring & Taylor, 2004; Mayberg et al., 1999). It appears to be activated when an adverse condition occurs and a decision regarding a response strategy needs to be made (Gehring & Taylor, 2004). A characteristic of humans and rats that have undergone destruction of the anterior cingulate region is an apparent apathy, or lack of concern to rectify an adverse condition (Eslinger & Damasio, 1985; Johansen, Fields, & Manning, 2001). Although there appears to have been no investigation of thirst in patients who have undergone surgical cingulotomy, reports of dehydration or disordered fluid homeostasis in such patients are not readily found. It is possible that the role of the anterior cingulate is to provide the motivational or impelling aspect of the emotion of thirst that will result in the drinking of water. Insular Cortex The insular cortex (see Figure 35.1, panels 2 and 3) receives visceral afferent sensory input via synapses in the NTS, parabrachial nucleus, and thalamus (Saper, 2002). The parasympathetic afferents carry neural signals to the anterior insular cortex from many different sensors that include baroreceptors and receptors in the gastrointestinal tract that are known to influence thirst. In the schema proposed by Craig (2003), the role of the insula in the generation of specific homeostatic emotions, such as thirst, is to give specificity to the emotion in regard to the homeostatic perturbation—the subjective feeling of a person’s homeostatic state. Orbito-Frontal Cortex de Araujo et al. (2003) utilized fMRI to show that water in the mouth activated part of the orbito-frontal cortex of thirsty subjects, but not water-replete subjects. The location of this region is shown in Figure 35.1 (panels A and 1). They interpreted this result as an indication that the orbitofrontal cortex provided a hedonic component to the behavioral response of drinking. Water in the mouth is pleasant if the subject is thirsty, but less so if the subject is not thirsty. Craig (2002) concluded that as the anterior cingulate and insular cortices are connected to the orbito-frontal cortex, sensory signals relating to the homeostatic state of the individual will reach this region and be interpreted there as to their pleasantness or reward value. In this schema, the

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cortical generation of the emotion of thirst would involve activations of neurons within the anterior cingulate cortex for motivational intensity, the insula for homeostatic specificity, and the orbito-frontal region for reward.

NEUROCHEMISTRY OF THIRST Neurotransmitters Glutamate Glutamatergic neural pathways are likely to have an important role in mediating thirst. Intracerebroventricular injection of the NMDA receptor antagonist MK801 blocks drinking responses stimulated by angiotensin II, intragastric hypertonic saline or water deprivation for 22 hours. Increased c-fos expression in the median preoptic nucleus, but not the subfornical organ, in response to angiotensin II and water deprivation was reduced by the MK801 (Xu & Herbert, 1998; Xu, Lane, Zhu, & Herbert, 1997), suggesting that a glutamatergic input to the median preoptic nucleus mediates both osmoregulatory and angiotensin-stimulated thirst. The vesicular glutamate transporter vGlut2, a marker of glutamatergic neurons, is expressed in the median preoptic nucleus, periphery of the subfornical organ, and dorsal cap of the OVLT, consistent with excitatory glutamatergic output from each of these regions of the lamina terminalis (Grob, Trottier, Drolet, & Mouginot, 2003). The non-NMDA receptor antagonist drug CNQX stimulates drinking in the rat (Xu & Johnson, 1998), suggesting that a glutamatergic pathway also drives an inhibitory input to thirst. Acetylcholine Acetylcholine has long been considered to have a role as a neurotransmitter in the neural circuitry subserving thirst. Microinjection of acetylcholine or the cholinergic agonist carbachol into several regions of the brain that include the LH, the preoptic and septal regions, hypothalamic paraventricular nucleus, and the subfornical organ stimulates water intake in rats (Fisher & Levitt, 1967; Grossman, 1960; Mangiapane & Simpson, 1983; Swanson & Sharpe, 1973). Systemic administration of the cholinergic muscarinic receptor blocking drug atropine sulphate, that has passage across the blood-brain barrier, inhibits but does not abolish water drinking in response to hypertonicity resulting from either intraperitoneal injection of hypertonic saline or water deprivation, hypovolemia resulting from polyethylene glycol injection, or day to water drinking in normal and lactating rats (Blass & Chapman, 1971; Fitzsimons & Setler, 1975; Speth, Smith, & Grove, 2002). Whether acetylcholine has a role in thirst in species other than the rat has yet to be proven.

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Regarding the dipsogenic action of carbachol on the subfornical organ of the rat (Mangiapane & Simpson, 1983), neurons within the outer annulus of the subfornical organ receive a strong cholinergic innervation (Xu, Pekarek, Ge, & Yao, 2001). This cholinergic input originates from the medial septal nucleus and diagonal band, exerting direct excitatory actions on subfornical neurons via the M3 muscarinic receptor subtype (Honda et al., 2003). Acetylcholine could also affect thirst by a presynaptic action to reduce GABAergic influences on the subfornical organ (Xu, Honda, Ono, & Inenaga, 2001). Dopamine Interest in possible dopaminergic involvement in thirst mechanisms has resulted from observations that polydipsia that is often observed in psychotic states is modified by neuroleptic drugs (Canuso & Goldman, 1996). Injection of dopamine intracerebroventricularly at relatively large dosage (Setler, 1973) or peripheral dopaminergic agonists (pergolide, bromocriptine) that enter the brain (Fregly & Rowland, 1988; Zabik, Sprague, & Odio, 1993) will either stimulate or augment water drinking in rats, although another dopaminergic agonist, quinpirole hydrochloride, inhibits a number of dipsogenic responses (Fregly & Rowland, 1986). Systemic administration of dopaminergic D2 receptor blocking drugs such as haloperidol, pimozide, or spiperone inhibit water intake in response to several different dipsogenic stimuli in rats (Fitzsimons & Setler, 1975; Fregly & Rowland, 1986, 1988; Zabik et al., 1993). However, while these data show that dopamine is probably influential in neural pathways of thirst, drawing any firm conclusions in regard to its exact role and locus of action as a transmitter in thirst circuitry is fraught with difficulty. This is because of the multiplicity of neural systems (e.g., sensory-motor, reward, neuroendocrine pathways) they influence and the lack of receptor specificity of most dopaminergic antagonists. Noradrenaline Noradrenergic pathways may influence thirst at more than one level of organization. Peripheral administration of clonidine, the presynaptically acting ·2-adrenoceptor agonist has a strong inhibitory action on drinking responses to osmoregulatory and hypovolemic dipsogenic stimuli in the rat. These actions are blocked by the α2-antagonist yohimbine, and this drug also augments angiotensininduced thirst (Fregly, Kelleher, & Greenleaf, 1981; Fregly & Rowland, 1986). As stimulation of ·2-adrenoceptors reduces the presynaptic release of noradrenaline at nerve terminals, these data suggest an important function of noradrenaline release within neural pathways of thirst, that may be regulated by adrenergic α2-autoreceptors. In

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regard to central sites of noradrenaline action, depletion of catecholamines within the ventral lamina terminalis region by injections of the neurotoxin 6-hydroxydopamine therein disrupts angiotensin-stimulated drinking, and this effect is due to loss of noradrenergic rather than dopaminergic neurons (Bellin, Landas, & Johnson, 1988; Cunningham & Johnson, 1989, 1991). The origin of the noradrenergic input to the median preoptic nucleus is likely to be the A1 group in the caudal ventrolateral medulla (Kawano & Masuko, 1999), and it is proposed that this noradrenergic input combines an angiotensinergic pathway to drive thirst responses (Johnson & Thunhorst, 1997). There is also evidence of an ascending noradrenergic input from the A2 group in the nucleus of the solitary tract to the subfornical organ that could influence thirst (Tanaka, Hayashi, Shimamune, & Nomura, 1997). Serotonin Pharmacological studies of serotonergic agonist and antagonists injected into the CNS show that drinking responses can be influenced by these agents. They are particularly effective when injected into the lateral parabrachial nucleus in the midbrain where the nonselective serotonin-blocking drug methysergide causes enhanced angiotensin-induced water drinking, while injection there of serotonin agonists depress water drinking (Menani & Johnson, 1995). Gamma Amino Butyric Acid Inhibitory neural pathways influencing thirst presumably have an important role in satiety and baroreceptor inhibition of thirst, and gamma amino butyric acid (GABA) neurotransmission is likely to mediate these inhibitory influences. GABAergic mechanisms appear to play an important role within the lamina terminalis. The median preoptic nucleus receives GABAergic input from the subfornical organ as well as other brain regions. GABA may act presynaptically via GABAB receptors, and at postsynaptic sites through GABAA receptors in the median preoptic nucleus (Kolaj, Bai, & Renaud, 2004). GABAergic neurons are present in the median preptic nucleus (Grob et al., 2003), providing a significant output from it to vasopressin-containing neurons in the supraoptic nucleus (Nissen & Renaud, 1994), and possibly also to regions that influence thirst. Nitric Oxide Nitrous oxide (NO) may have a role as an inhibitory neurotransmitter or locally released influence on thirst. The enzyme that facilitates its production, neuronal nitric oxide synthase, is found in high concentration within the thirstmediating regions of the subfornical organ, OVLT, and median preoptic nucleus (Jurzak, Schmid, & Gerstberger,

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Neurochemistry of Thirst

1994). Water intake in response to angiotensin II or water deprivation is inhibited by central administration of l-arginine from which NO is synthesized, and this effect is blocked by inhibitors of nitric oxide synthase (Calapai & Caputi, 1996). Direct application of nitroprusside, an NO donor, to angiotensin-sensitive subfornical organ neurons in vitro depresses their electrical activity (Rauch, Schmid, DeVente, & Simon, 1997), showing it likely that NO exerts its action on thirst by inhibiting angiotensin-sensitive neurons in the lamina terminalis. On the other hand, it has also been shown that centrally administered inhibitors of neuronal nitric oxide synthase also cause an inhibition of angiotensin drinking in the rat (Kadekaro & Summy-Long, 2000), which is not consistent with the theory that NO inhibits thirst. However, results from experiments employing centrally administered nitric oxide synthase inhibitors should be viewed with caution because reduction in fluid intakes caused by central administration of these drugs could be secondary to other effects such as hyperthermia (Mathai, Arnold, Febbraio, & McKinley, 2004) and increased arterial pressure (Kadekaro & Summy-Long, 2000) that also result from NOS inhibition.

Neuropeptides A number of neuropeptides have been reported to influence water drinking when injected into the brain (Table 35.1). However, the physiological role in thirst of most of these peptides is still unclear. Angiotensin II is by far the most widely studied of the dipsogenic neuropeptides.

TABLE 35.1

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697

Angiotensin II In addition to its function as the circulating effector hormone of the peripheral renin-angiotensin system, the octapetide angiotensin II is generated within the brain independently of the peripheral renin-angiotensin system. All components of a brain renin-angiotensin system (peptides, enzymes and receptors) exist within the brain. While astrocytes are the main site for synthesis in the brain of the precursor peptide angiotensinogen (Lynch, Hawelu-Johnson, & Guyenet, 1987), angiotensin peptides are probably generated within neurons. Angiotensin receptors, both AT1 and AT2 subtypes, are located on neurons in many brain regions associated with body fluid and cardiovascular homeostasis (Allen et al., 2000; Lenkei, Palkovits, Corvol, & Llorens-Cortes, 1997). Except for those receptors in the circumventricular organs that have been described in an earlier section of this chapter, these receptor sites are within the blood barrier. Therefore, they are not influenced directly by blood-borne angiotensin II, but are likely to have brain-generated angiotensin as their endogenous ligand. One of the most powerful of all experimental dipsogenic procedures, and one of the most investigated, is the injection of angiotensin II into the ventricular system or specific regions of the brain, whether the subject be rat, dog, goat, sheep, cow, or monkey (Fitzsimons, 1998). This effect is mediated largely by AT1 receptors in the region of the anteroventral wall of the third ventricle (AV3V region), and not the subfornical organ, the site that transduces the drinking response to circulating angiotensin II (Buggy & Johnson, 1978). Neurons within the median

Neuropeptides that influence water intake when injected into the cerebral ventricles or specific brain regions.

Neuropeptide

Effect on Water Intake

Reference

Adrenomedullin

Inhibition

Murphy & Samson (1995)

Angiotensin II

Stimulation

Fitzsimons (1998)

Angiotensin III

Stimulation

Wilson et al. (2005)

Appelin

Stimulation

Taheri et al. (2002)

Atrial natriuretic peptide

Inhibition

Antunes-Rodrigues et al. (1985)

Brain natriuretic peptide

Inhibition

Itoh et al. (1988)

Corticotropin-releasing hormone

Inhibition

Van Gaalen et al. (2002)

Dermorphin

Inhibition

De Caro (1986)

β-endorphin

Inhibition

Summy-Long et al. (1981)

Leu-enkephalin

Inhibition

Summy-Long et al. (1981)

Met-enkephalin

Inhibition

Summy-Long et al. (1981)

Endothelin

Inhibition

Samson et al. (1991)

Galanin

Inhibition

Brewer et al. (2005)

Melanin-concentrating hormone

Stimulation

Clegg et al. (2002)

Orexin A

Stimulation

Kunii et al. (1999)

Substance P

Inhibition (mammals)

De Caro (1986)

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preoptic nucleus, which is situated within the AV3V region, are activated by intracerebroventricular angiotensin II (Herbert et al., 1992), and direct injection of angiotensin II into the median preoptic nucleus of the rat causes water drinking (O’Neill & Brody, 1987). Most likely, intracerebroventricularly injected angiotensin II acts on the median preoptic nucleus to stimulate drinking and mimics the effect of synaptically released angiotensin II at this site. Angiotensin III, the heptapeptide formed by degradation of angiotensin II is also dipsogenic when administered centrally (Wright, Morseth, Abhold, & Harding, 1985) and may act also at central angiotensinergic receptors for thirst. In regard to central angiotensinergic relays mediating thirst, an angiotensergic synapse within the median preoptic nucleus relaying signals to it from angiotensin II stimulated neurons in the subfornical organ has been proposed (Johnson et al., 1996). Pharmacological blockade of AT1 receptors in the brain by losartan inhibits water drinking in the rat in response to intracerebroventricular infusion of hypertonic saline in several species, suggesting that a central angiotensinergic relay mediates osmoregulatory drinking (Blair-West et al., 1994). However, recent observations that osmoregulatory drinking is entirely intact in genetically modified mice totally lacking angiotensin peptides due to deletion of the gene encoding angiotensinogen are not in agreement with this suggestion (McKinley, Alexiou, et al., 2006).

SATIATION OF THIRST Satiation of thirst, at least initially, is more than just an absence of thirst. The act of drinking by the thirsty person is pleasurable, and this response appears to be more than just the mere removal of the tormenting aspects of thirst. It is likely that extinguishing thirst involves the activation of dopaminergic reward pathways in the brain (Ettenberg & Camp, 1986). The Swedish explorer Sven Hedin (1865–1952) gives an account (cited by Wolf, 1958) of the exhilaration and joy that came from finding and ingesting water when he was extremely thirsty during a harsh journey through the arid Taklamakan desert of western China. He relates “I stood on the brink of a little pool of water—beautiful water! … I took the tin box out of my pocket and filled it, and drank. How sweet that water tasted! Nobody can conceive it who has not been within an ace of dying of thirst. I lifted the tin to my lips, calmly, slowly, deliberately, and drank, drank, drank, time after time. How delicious! What exquisite pleasure! The noblest wine pressed out of the grape, the divinest nectar ever made, was never half so sweet.” Thirst satiety provides the signal that prevents excessive hydration, and this satiety usually occurs before systemic

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absorption of ingested water. The consequences of overhydration—hyponatremia and cerebral oedema—can be as lethal as severe dehydration. Thus, this satiating mechanism is a crucial homeostatic emotion that contributes to accurate repletion of body fluids without overhydration following a period of dehydration. Voluntary Dehydration The speed at which water is drunk and thirst quenched following a period of water deprivation varies considerably across mammals. Some species (e.g., dog, camel, sheep, goats, deer) when dehydrated, replace fluid deficits immediately on gaining access to drinking water. Others (e.g., rats, humans, horse) replace their fluid deficit more slowly and may take several hours to restore fluid balance (Adolph, 1950). In this latter group, after an initial drinking bout that is not sufficient to replace all the fluid lost, but sufficient to cause temporary satiety or loss of thirst, intermittent drinking bouts occur over a few hours until rehydration is complete. This phenomenon has been termed “voluntary dehydration.” The gradual replenishment of a fluid deficit may be protective against rapidly occurring hyponatremia and cerebral oedema. Scientific investigation of the signals that bring about thirst satiety goes back to Claude Bernard who studied the effects of “sham drinking” in dehydrated dogs with an esophageal fistula. This phenomenon has been investigated in several species (dog, rat, sheep, monkey, man) and it is clear that animals drink considerably more than their fluid deficit if the water imbibed immediately leaves the body through an oesophageal or gastric fistula (Bott, Denton, & Weller, 1965; Towbin, 1949; Wood, Maddison, Rolls, Rolls, & Gibbs, 1980). Indeed, animals with an open fistula will continue to drink until they appear fatigued from the effort. Therefore, the act of drinking and swallowing water per se is an insufficient stimulus to satiate thirst. Yet, if a quantity of water equivalent to a dehydrational deficit is placed by tube directly in the stomach without it touching the mouth, pharynx, and esophagus, thirst will not be relieved for some time (Figaro & Mack, 1997; Thrasher, Nistal-Herrera, Keil, & Ramsay, 1981). However, an essential aspect of the initial thirst-satiating mechanism is that fluid remains in the stomach after it has been ingested (Blass & Hall, 1976; Gibbs, Rolls, & Rolls, 1986). Therefore, a combination of oropharyngeal, esophageal, and gastric signals, in appropriate temporal sequence, appear to be necessary for thirst satiety to be achieved in the short term. The amount of water ingested during the initial rehydrating bout does not depend on its temperature or composition—water, isotonic saline, or isotonic glucose are

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Physiological and Pathophysiological Conditions Influencing Thirst

similarly ingested (Appelgren, Thrasher, Keil, & Ramsay, 1991; Hoffman, DenBleyker, Smith, & Stricker, 2005). These data suggest that mechanical distention signals from the throat, esophagus, and stomach mediate the initial stop signal for drinking. Capsaicin treatment, which damages vagal afferent nerves from the gut in rats, leads to lack of thirst satiation initially (Curtis & Stricker, 1997), suggesting that these neural signals are carried via this nerve to the brain. These preabsorptive signals from the gastrointestinal tract also influence other homeostatic responses; they rapidly suppress vasopressin release and stimulate sweating in humans and panting in animals when they rehydrate (Appelgren et al., 1991; Baker & Turlejska, 1989; Figaro & Mack, 1997; Takamata, Mack, Gillen, Jozsi, & Nadel, 1995; Thrasher et al., 1981). Thereafter, absorption of ingested water in the duodenum also provides a satiating signal for thirst (Gibbs et al., 1986) that could be vagally mediated because selective hepatic vagotomy causes dehydrated rats to overdrink (Smith & Jerome, 1983). In the longer term, absorption of fluid into the systemic circulation and reduction of plasma osmolality makes a minor contribution to satiety (Wood et al., 1980).

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699

treatment with human chorionic gonadotrophin reduces the thirst threshold in mothers, and have suggested that this hormone has a signaling role in resetting the osmoreceptor. Relaxin may have a similar role in rats (Weisinger, Burns, Eddie, & Wintour, 1993). Lactation

PHYSIOLOGICAL AND PATHOPHYSIOLOGICAL CONDITIONS INFLUENCING THIRST

Lactation involves loss of fluid by mothers in the form of milk. This fluid loss needs to be replaced if dehydration is to be prevented and adequate milk supply maintained. There are many anecdotal reports of thirst being experienced by nursing mothers, and the daily water intake of animals with multiple offspring such as rats and rabbits increases considerably during lactation (Denton, et al., 1977; Richter & Barelare, 1938). Inhibition of the increased daily water intake in lactating rats by a centrally administered angiotensin antagonist suggests a role for brain angiotensinergic pathways in lactation-induced thirst (Speth et al., 2002). James, Irons, Holmes, Drewett, and Bayliss (1995) observed that thirst and water intake increased during suckling periods in 10 nursing mothers, and the increased thirst corresponded with oxytocin secretion and milk letdown. No change in plasma osmolality or vasopressin levels occurred. These investigators suggested that suckling may send afferent nerve signals to the hypothalamus that generate thirst as well as oxytocin secretion.

Pregnancy

Age

Pregnancy is a condition that alters body fluid balance. Plasma osmolality falls by approximately 10 mosmol/kg within 5 to 10 weeks following conception; the reduced plasma osmolality is maintained throughout pregnancy (Davison, Gilmore, Durr, Robertson, & Lindheimer, 1984). Plasma osmolality also falls during pregnancy in rats, yet vasopressin continues to be secreted, as it does in pregnant women, in the face of the plasma hypotonicity. It is proposed that the osmostat for vasopressin secretion is reset to a lower level (Davison et al., 1984; Durr et al., 1981). Despite plasma osmolality falling, animals increase daily water intake during pregnancy (Denton, McKinley, Nelson, & Weisinger, 1977; Richter & Barelare, 1938), which suggests that the osmoreceptor for thirst is also reset during pregnancy. The observation that pregnant homozygous Brattleboro rats, which are devoid of vasopressin, double daily water intake and lower plasma osmolality from 310 to 292 mosmol/kg, is consistent with a resetting of the thirst osmostat in these animals. The threshold osmolality for thirst in pregnant women is 10 mosmol/kg lower than it is preconception or postpartum (Davison, Shiells, Phillips, & Lindheimer, 1988). The same authors have shown also that

As animals and humans age, thirst may become impaired. As a consequence, they drink less fluid in response to being dehydrated, and this impaired thirst renders them liable to the deleterious effects of dehydration such as heat stroke in hot weather. The reasons for the waning of thirst with age are unclear. Some but not all investigators report that thirst ratings and amount of water drunk in response to a purely osmotic stimulus (e.g., intravenous infusion of hypertonic saline) are depressed in elderly subjects (Kenney & Chiu, 2001; Phillips et al., 1984). More consistent are observations that thirst and fluid intake following a period of water deprivation are less intense in elderly subjects in comparison to young adults (Kenney & Chiu, 2001). Animal models of the influence of aging on thirst have been described recently. Brown-Norway and Munich Wistar (but not Sprague Dawley or Fischer 344) strains of rat exhibit progressively impaired drinking responses to water deprivation and hypertonicity as they age, while hypovolemic thirst was not reduced until advanced age. The dipsogenic reponse to angiotensin II, however, was not diminished with age (McKinley, Denton, et al., 2006;

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Thunhorst & Johnson, 2003). Several influences on thirst are changed with age (Ferrari, Radaelli, & Centola, 2003; Kenney & Chiu, 2001), and could contribute to age-impaired thirst. First, cardiovascular reflexes arising from arterial baroreceptors and cardiopulmonary receptors are known to be reduced in elderly humans and this may depress thirst in response to hypovolemia; second, elevated plasma concentrations of atrial natriuretic peptide have been observed in aged humans and rats; third, the reninangiotensin system is depressed in the aged, and this may influence thirst mechanisms in older subjects. In a recent study (Farrell et al., in press), young adults and elderly human subjects were infused intravenously with hypertonic saline, then allowed to satiate their thirst while undergoing PET imaging of the brain. Both groups reported similar thirst ratings as a result of the hypertonic stimulus, but the elderly only drank half the volume of water to quench their thirst and reduce activity in the cingulate cortex compared to the younger group. The results suggest that mechanisms of thirst satiety change with age; this may contribute to the susceptibility of the elderly to become dehydrated. An increase in core body temperature induces evaporative cooling responses of sweating and panting (depending on the species) that result in loss of fluid from the body. If thirst is a response to increased core temperature, resulting fluid intake could be an anticipatory mechanism to prevent dehydration. While most water intake that occurs with hyperthermia is secondary to dehydration (Barney & Folkerts, 1995; Hainsworth, Stricker, & Epstein, 1968), there is evidence that direct thermal stimulation of the preoptic-hypothalamic region of the brain can stimulate water drinking (Andersson & Larsson, 1961). An increase of 0.8oC in core temperature potentiated the thirst and water ingested by human subjects in response to intravenous hypertonic saline (Takamata, Mack, Stachenfeld, & Nadel, 1995), suggesting that there is a physiological effect of increased body temperature to enhance thirst. Thirst in Some Disease States Diabetes Insipidus The water intake of patients with diabetes insipidus is enormous, often exceeding 12 litres per day (Blotner, 1951). It is driven by thirst that arises from continual dehydration caused by excessive loss of dilute urine due to lack of vasopressin (cranial diabetes insipidus) or insensitivity of the kidney to its action (nephrogenic diabetes insipidus). Administration of pitressin or the vasopressin analogue dDAVP ameliorates the diuresis of central diabetes insipidus and fluid intake correspondingly decreases. The sensitivity of the thirst mechanism to hypertonicity is normal

c35.indd Sec5:700

in patients with diabetes insipidus (Thompson & Bayliss, 1987). Indeed, normal thirst and subsequent fluid intake are essential for preventing lethal dehydration in untreated diabetes insipidus. Diabetes Mellitus Untreated diabetes mellitus (type 1 or 2) is characterized by strong thirst, often the first indication of its onset. Excessive urine output in the form of a glycosuria also occurs due to failure of the kidney to reabsorb the high filtered glucose load that results from lack of insulin, or insensitivity of tissues to insulin. Dehydration of both intracellular and extracellular compartments may result, but because high blood glucose levels do not usually stimulate thirst, diabetic thirst has been considered more likely to be the result of extracellular fluid loss (Fitzsimons, 1998). A recent study of water intake in rats with diabetes mellitus (induced by treatment with the drug streptozotocin) showed that systemic administration of an angiotensin antagonist had only a small inhibitory effect on their water drinking. Increased expression of c-fos was observed in osmoregulatory regions of the lamina terminalis: the dorsal cap of the OVLT, periphery of subfornical organ, and median preoptic nucleus. Moreover, while intravenous infusion of hypertonic glucose is not normally dipsogenic, it does stimulate drinking in diabetic rats, suggesting that diabetic thirst involves osmoreceptor stimulation (McKinley, Burns, Oldfield, Sunigawa, & Weisinger, 2004). Intracellular dehydration of the osmoreceptor could occur in diabetic rats because the osmoreceptor ’s glucose transporter is saturated by high plasma glucose concentrations, and/or it is normally insulin sensitive. If so, when insulin is absent or ineffective, hypertonic glucose would be excluded from the osmoreceptor thereby creating an osmotic gradient and cellular dehydration. Heart Failure When the left ventricle of the heart is damaged and fails to adequately perfuse the tissues of the body with blood, compensatory autonomic and neuroendocrine mechanisms are engaged. These include increased activity of sympathetic nerves and the renin-angiotensin-aldosterone system, and vasopressin release. Congestive heart failure is characterized by excessive fluid retention and hyponatremia, that together with increased sympathetic- and angiotensinmediated vasoconstriction, deleteriously increase the load on the failing heart (Kalra, Anker, & Coats, 2001; Packer, 1992). Fluid intake is maintained in the face of low plasma osmolality, high blood levels of ANP, and expanded extracellular fluid volume. Studies of thirst in such patients have not been undertaken, but it has been shown in rats with heart failure caused experimentally by coronary

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Summary

artery ligation, that water intake in response to dehydration is excessive (De Smet, Menadue, Oliver, & Phillips, 2003). The authors suggested that increased sensitivity of the osmoreceptor, increased blood angiotensin II levels, or changed baroreceptor stimulation could be driving the increased thirst in these animals. Fever There are reports that administration of the pyrogenic agent lipopolysaccharide (a molecule derived from bacterial cell walls) stimulates drinking behavior, particularly in the early phase of fever (Szczepanska-Sadowska, Sobocinska, & Kozlowski, 1979; Wang & Evered, 1993). However, once lipopolysaccharide-induced fever has peaked and stabilized, thirst is depressed (Nava, Calapai, De Sarro, & Caputi, 1996; Szczepanska-Sadowska et al., 1979). The inhibition of thirst that occurs following pyrogen administration can be dissociated from the febrile response in that repeated treatment with pyrogen can result in tolerance to fever, but not to the antidipsogenic action of lipopolysaccharide (Nava & Carta, 2000). While a centrally administered interleukin-1 antagonist blocked pyrogen-induced fever, it did not block its antidipsogenic effect (Nava et al., 1996). Central administration of the nitric oxide oxide synthase inhibitor L-NAME, while normally inhibitory to thirst, has been shown to reverse the inhibition of dehydration-induced thirst caused by fever in rats (Raghavendra, Agrewala, & Kulkarni, 1999), suggesting that an inhibitory influence of NO on the neural circuitry of thirst results from the administration of a pyrogen.

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701

mechanism operates normally around a lower set point of plasma osmolality. The cause of the alteration in thirst threshold plasma osmolality in SIADH is unknown. Hemodialysis Many patients with end stage renal failure undergoing hemodialysis report strong thirst, water drinking, weight gain, and extracellular fluid volume expansion between periods of dialysis. There is evidence that high plasma angiotensin II levels contribute to this thirst because it has been eliminated by bilateral nephrectomy (Rogers & Kurtzman, 1973) and reduced by treatment with an angiotensin converting enzyme inhibitor (Oldenburg, MacDonald, & Shelley, 1988). Increase of plasma sodium and urea concentrations during the interdialytic period, and the concentration of sodium in the dialysis fluid may also be dipsogenic factors in these patients (Giovannetti et al., 1994). Schizophrenia Compulsive water drinking is observed in 10% to 20% of schizophrenic subjects. Excessive water drinking and fluid retention can lead to lethal hyponatremia in these patients (Illowski & Kirch, 1988). Whether the compulsion to drink water is the result of altered thirst is debatable (Goldman, Robertson, Luchin, & Hedeker, 1996). However, it has been reported that thirst ratings in compulsive water drinkers in response to infusion of hypertonic saline are higher than normal, and remained elevated following drinking episodes, unlike control subjects in which drinking quickly reduced the thirst rating (Thompson, Edwards, & Bayliss, 1991).

Syndrome of Inappropriate Antidiuretic Hormone Secretion

SUMMARY

The syndrome of inappropriate antidiuretic hormone secretion (SIADH) is characterized by hyponatremia, normovolemia, and abnormally high plasma vasopressin concentrations relative to the low plasma osmolality. This condition is associated with various diseases such as lung cancer, pancreatic cancer, and bronchiectasis and is side effect of some therapeutic drugs (Robertson, 1989; Smith, Moore, Tormey, Bayliss, & Thompson, 2004). While vasopressin secretion is excessive relative to plasma osmolality (approximately 270 mosmol/kg) in SIADH, the maintenance of normal fluid intake and thirst by SIADH patients is also inappropriate. Smith et al. (2004) studied thirst in eight patients with SIADH of varying etiology, and consistently observed that the osmotic threshold for thirst was reduced from 288 in normal control subjects to 270 mosmol/kg in SIADH. The intensity of thirst increased from these different thresholds similarly in both groups, and the drinking of water immediately and normally suppressed thirst in both groups. Therefore, in SIADH, the thirst

This chapter focused on the physiological mechanisms that regulate the emotion of thirst. However, in regard to the physiological and psychological mechanisms regulating the amount of water ingested by humans and animals, thirst is but one of a number of interacting factors that determine fluid intake. In some instances, thirst may have no influence at all on the amount of water ingested. A striking and tragic example of this in recent times is the slavish adherence to advice of some athletes to drink excessive amounts of water prior to endurance sporting events. Fluid intake in the face of overhydration, and therefore lack of any thirst, has caused severe hyponatremia (low plasma sodium concentration), cerebral edema, and rapid death in some cases (Noakes et al., 2005; Verbalis, Goldsmith, Greenberg, Schrier, & Sterns, 2007). The popular belief that it is necessary to ingest eight glasses of water per day, regardless of physical activity or ambient temperature, is another example, albeit less dangerous, of water intake

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702 Thirst

based not on thirst, but on advice that appears to lack supporting empirical evidence (Valtin, 2002). In many human societies, water is ingested most often in the form of beverages. While thirst may drive the intake of beverages in certain circumstances, most often such intake is determined by reward aspects of the ingested fluid. Taste, odor, and temperature of ingested beverages can be reinforcers of beverage ingestion, as can the pharmacological properties of alcohol- and caffeinecontaining drinks and the social consequences of drinking behavior (Booth, 1991). Learning also probably plays an important role in determining regulatory (homeostatic) and nonregulatory drinking behavor. Complex neural pathways involving the orbito-frontal cortex and amygdala (McDannald, Saddoris, Gallagher, & Holland, 2005; Rolls, 2000) as well as mesolimbic dopaminergic and opiate reward system (Kelley & Berridge, 2002; Wise, 2002) may drive a large part of the fluid intake of sedentary humans. Nevertheless, a major proportion of the world’s population still resides in tropical and subtropical climates and participates in relatively intense physical activity in the course of earning a living or recreational pursuits. It is in conditions where nonregulatory intake of fluid as a beverage fails to deliver sufficient water to maintain adequate fluid balance, that the emotion of thirst provides the fail-safe signal to ingest fluid and restore a water deficit. The remarkable constancy of plasma osmolality that occurs throughout life, and across many mammalian species, attests to the effectiveness of the neural, endocrine and behavioral mechanisms (Figure 35.3) that regulate body fluid homeostasis. Thirst has a pivotal role in these mechanisms.

Andersson, B. (1953). The effect of injections of hypertonic NaCl solutions into different parts of the hypothalamus of goats. Acta Physiologica Scandinavica, 81, 188–201. Andersson, B. (1978). Regulation of water intake. Physiological Reviews, 58, 582–683. Andersson, B., & Larsson, S. (1961). Influence of local temperature changes in the preoptic area and rostral hypothalamus on the regulation of food and water intake. Acta Physiologica Scandinavica, 52, 75–89. Andersson, B., & McCann, S. M. (1955). A further study of polydipsia evoked by hypothalamic stimulation. Acta Physiologica Scandinavica, 33, 333–346. Andersson, B., Leksell, L. G., & Lishajko, F. (1975). Perturbations in fluid balance induced by medially placed forebrain lesions. Brain Research, 99, 261–275. Antunes-Rodrigues, J., McCann, S. M., Rogers, L. C., & Sampson, W. K. (1985). Atrial natriuretic factor inhibits dehydration and angiotensin II-induced water intake in the conscious unrestrained rat. Proceedings of the National Academy of Sciences, USA, 82, 8720–8723. Appelgren, B. H., Thrasher, T. N., Keil, L. C., & Ramsay, L. C. (1991). Mechanism of drinking: Induced inhibition of vasopressin secretion in dehydrated dogs. American Journal of Physiology, 261, R1226–R1233. Baertschi, A. J., & Vallet, P. G. (1981). Osmosensitivity of the hepatic portal vein area and vasopressin release in rats. Journal of Physiology, 215, 217–230. Baker, M. A., & Turlejska, E. (1989). Thermal panting in dehydrated dogs: Effects of plasma volume expansion and drinking. Pflugers Archive, 413, 511–515. Barney, C. C., & Folkerts, M. M. (1995). Thermal dehydration-induced thirst in rats: Role of body temperature. American Journal of Physiology, 269, R557–R564. Barofsky, A. L., Grier, H. C., & Pradhan, T. K. (1980). Evidence for regulation of water intake by median raphe serotoninergic neurons. Physiology and Behavior, 24, 951–955. Bellin, S. I., Landas, S. K., & Johnson, A. K. (1988). Selective catecholamine depletion of structures along the ventral lamina terminalis: Effects on experimentally induced drinking and pressor responses. Brain Research, 456, 9–16. Berk, M. L., & Finkelstein, J. A. (1981). Afferent projections to the preoptic and hypothalamic regions of the rat brain. Neuroscience, 6, 1601–1624.

REFERENCES Abraham, S. F., Baker, R. M., Blaine, E. H., Denton, D. A., & McKinley, M. J. (1975). Water drinking induced in sheep by angiotensin: A physiological or pharmacological effect? Journal of Comparative and Physiological Psychology, 88, 503–518. Abraham, S. F., Coghlan, J. P., Denton, D. A., McDougall, J. G., Mouw, D. R., & Scoggins, B. A. (1976). Increased water drinking induced by sodium depletion in sheep. Quarterly Journal of Experimental Physiology, 61, 185–192. Adolph, E. F. (1950). Thirst and its inhibition in the stomach. American Journal of Physiology, 161, 374–386. Allen, A. M., Oldfield, B. J., Giles, M. E., Paxinos, G., McKinley, M. J., & Mendelsohn, F. A. O. (2000). Localization of angiotensin receptors in the nervous system. In R. Quirion, A. Bjorklund, & T. Hokfelt (Eds.), Handbook of chemical neuroanatomy: Peptide Receptors, Pt. I (Vol. 16, pp. 79–124). Amsterdam: Elsevier Science. Anderson, C. R., & Houpt, T. R. (1990). Hypertonic and hypovolemic stimulation of thirst in pigs. American Journal of Physiology, 258, R149–R154.

c35.indd Sec6:702

Black, S. L. (1976). Preoptic hypernatremic syndrome and the regulation of water balance in the rat. Physiology and Behavior, 17, 473–482. Blair-West, J. R., Burns, P., Denton, D. A., Ferraro, T., McBurnie, M. I., Tarjan, E., et al. (1994). Thirst induced by increasing brain sodium concentration is mediated by brain angiotensin. Brain Research, 637, 335–338. Blass, E. M., & Chapman, H. W. (1971). An evaluation of cholinergic mechanisms to thirst. Physiology and Behavior, 7, 679–686. Blass, E. M., & Epstein, A. N. (1971). A lateral preoptic osmosensitive zone for thirst in the rat. Journal of Comparative and Physiological Psychology, 76, 378–394. Blass, E. M., & Fitzsimons, J. T. (1970). Additivity of effect and interaction of a cellular and an extracellular stimulus of drinking. Journal of Comparative and Physiological Psychology, 70, 200–205. Blass, E. M., & Hall, W. G. (1976). Drinking termination: Interactions among hydrational, orogastric, and behavioural controls in rats. Psychological Review, 83, 356–374. Blass, E. M., & Hanson, D. G. (1970). Primary hyperdipsia in the rat following septal lesions. Journal of Comparative and Physiological Psychology, 70, 87–93.

8/17/09 3:06:15 PM

References 703 Blass, E. M., Nusssbaum, A. I., & Hanson, D. G. (1974). Septal hyperdipsia: Specific enhancement of drinking to angiotensin in rats. Journal of Comparative and Physiological Psychology, 87, 422–439. Blotner, H. (1951). Diabetes insipidus. New York: Oxford University Press. Booth, D. (1991). Influences on human fluid consumption. In D. J. Ramsay & D. A. Booth (Eds.), Thirst: Physiological and psychological aspects (pp. 53–73). London: Springer Verlag. Bott, E., Denton, D. A., & Weller, S. (1965). Water drinking in sheep with oesophageal fistulae. Journal of Physiology, 176, 323–336. Brewer, A., Langel, U., & Robinson, J. K. (2005). Intracerebroventricular administration of galanin decreases free water intake and operant water reinforcer efficacy in water-restricted rats. Neuropeptides, 39, 117–124. Brownfield, M. S., Reid, I. A., Ganten, D., & Ganong, W. F. (1982). Differential distribution of immunoreactive angiotensin and angiotensinconverting enzyme in rat brain. Neuroscience, 7, 1759–1769.

Cunningham, J. T., & Johnson, A. K. (1991). The effects of central norepinephrine infusions on drinking behaviour induced by angiotensin after 6-hydroxydopamine injections into the anteroventral region of the third ventricle (AV3V). Brain Research, 558, 112–116. Cunningham, J. T., Sullivan, M. J., Edwards, G. L., Farinpour, R., Beltz, T., & Johnson, A. K. (1991). Dissociation of experimentally induced drinking behavior by ibotenate injection into the median preoptic nucleus. Brain Research, 554, 153–158. Curtis, K. S., Huang, W., Sved, A. F., Verbalis, J. G., & Stricker, E. M. (1999). Impaired osmoregulatory responses in rats with area postrema lesions. American Journal of Physiology, 277, R209–R219. Curtis, K., & Stricker, E. M. (1997). Enhanced fluid intake by rats after capsaicin treatment. American Journal of Physiology, 272, R704–R709.

Buggy, J. W., & Johnson, A. K. (1978). Angiotensin induced thirst: Effects of third ventricular obstruction and periventricular ablation. Brain Research, 149, 117–128.

Dampney, R. A. (1994). Functional organization of central pathways regulating the cardiovascular system. Physiological Reviews, 74, 323–364.

Burrell, L. M., Lambert, H. J., & Bayliss, P. H. (1991). Effect of atrial natriuetic peptide on thirst and arginine vasopressin release in humans. American Journal of Physiology, 260, R475–R479.

Davison, J. M., Gilmore, E. A., Durr, J., Robertson, G. L., & Lindheimer, M. D. (1984). Altered osmotic thresholds for vasopressin secretion and thirst in human pregnancy. American Journal of Physiology, 247, F105–F109.

Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Science, 4, 215–222. Calapai, G., & Caputi, A. P. (1996). Nitric oxide and drinking behaviour. Regulatory Peptides, 66, 117–121.

Davison, J. M., Shiells, E. A., Phillips, P. R., & Lindheimer, M. D. (1988). Serial release of vasopressin and thirst in human pregnancy: Role of human gonadotrophin in the osmoregulatory changes of gestation. Journal of Clinical Investigation, 81, 798–806.

Camargo, L. A. A., Menani, J. V., Saad, W. A., & Saad, W. A. (1984). Interaction between areas of the central nervous system in the control of water intake and arterial pressure in rats. Journal of Physiology, 350, 1–8.

de Araujo, I. E. T., Kringelbach, M. L., Rolls, E. T., & McGlone, F. (2003). Human cortical reponses to water in the mouth and the effects of thirst. Journal of Neurophysiology, 90, 1865–1876.

Cannon, W. B. (1919). The physiological basis of thirst. Proceedings of the Royal Society, B, 90, 283–301.

De Caro, G. (1986). Effects of peptides of the gut-brain-skin-triangle on drinking behaviour ofv rats and birds. In G. de Caro, A. N. Epstein, & M. Massi (Eds.), The physiology of thirst and sodium appetite (pp. 213–226). New York: Plenum Press.

Canuso, C. M., & Goldman, M. B. (1996). Does minimizing neuroleptic dosage influence hyponatremia. Psychiatry Research, 63, 227–229. Carlson, S. H., Collister, J. P., & Osborn, J. W. (1998). The area postrema modulates hypothalamic fos responses to intragastric hypertonic saline in conscious rats. American Journal of Physiology, 275, R1921–R1927. Ciura, S., & Bourque, C. W. (2006). Transient receptor vanilloid I is required for intrinsic osmoreceptionin organum vasculosum lamina terminalis neurons for normal thirst responses to systemic hyperosmolalit. Journal of Neuroscience, 26, 9069–9075. Clegg, D. J., Air, E. L., Benoit, S. C., Sakai, R. S., Seeley, R. J., & Woods, S. C. (2003). Intraventricular melanin-concentrating hormone stimulates water intake independently of food intake. American Journal of Physiology, 284, R494–R499. Coburn, P. C., & Stricker, E. M. (1978). Osmoregulatory thirst after lateral preoptic lesions. Journal of Comparative and Physiological Psychology, 92, 350–361. Contreras, R. J., Beckstead, R. M., & Norgren, R. (1982). The central projections of the trigeminal, facial, glossopharyngeal and vagus nerves: An autoradiographic study in the rat. Journal of the Autonomic Nervous System, 6, 303–322. Craig, A. D. (2002). How do you feel? Interoception: The sense of the physiological condition of the body. Nature Reviews Neuroscience, 3, 655–666.

c35.indd Sec7:703

Cunningham, J. T., & Johnson, A. K. (1989). Decreased norepinephrine in the ventral lamina terminalis region is associated with angiotensin II drinking response deficits following local 6-hydroxydopamine injections. Brain Research, 480, 65–71.

Denton, D. A. (1982). The hunger for salt. Berlin: Springer. Denton, D. A., McKinley, M. J., Nelson, J. F., & Weisinger, R. S. (1977). Pregnancy, lactation and hormone induced mineral appetite. In Y. Katsuki, M. Sato, S. Takagi, & Y. Oomura (Eds.), Food intake and chemical senses (pp. 247–262). Tokyo: Japan Scientific Societies Press. Denton, D. A., Shade, R., Zamarippa, F., Egan G., Blair-West, J., McKinley, M., et al. (1999). The correlation of regional blood flow (rCBF) and plasma sodium concentration during genesis and satiation of thirst. Proceedings of the National Academy of Sciences, USA, 96, 2532–2537. De Smet, H. R., Menadue, M. F., Oliver, J. R., & Phillips, P. A. (2003). Increased thirst and vasopressin secretion after myocardial infarction in rats. American Journal of Physiology, 285, R1203–R1211. Durr, J. A., Stamotsos, B., & Lindheimer, M. D. (1981). Osmoregulation durng pregnancy in the rat: Evidence of the resetting of the threshold for vasopressin secretion during gestation. Journal of Clinical Investigation, 68, 337–346. Edwards, G. L., & Johnson, A. K. (1991). Enhanced drinking after excitotoxic lesions of the parabrachial nucleus in the rat. American Journal of Physiology, 261, R1039–R1044.

Craig, A. D. (2003). A new view of pain as a homeostatic emotion. Trends in Neurosciences, 26, 303–307.

Edwards, G. L., & Ritter, R. C. (1982). Area postrema lesions increase drinking to angiotensin and extracellular dehydration. Physiology and Behavior, 29, 943–947.

Critchley, H. D., Corfield, D. R., Chandler, M. P., Mathias, C. J., & Dolan, R. J. (2000). Cerebral correlates of autonomic cardiovascular arousal: A functional neuroimaging investigation in humans. Journal of Physiology, 523, 259–270.

Egan, G., Silk, T., Zammaripa, F., Williams, J., Federico, P., Cunnington, R., et al. (2003). Neural correlates of the emergence of consciousness of thirst. Proceedings of the National Academy of Sciences, USA, 100, 15241–15246.

8/17/09 3:06:15 PM

704 Thirst Ehrlich, K. J., & Fitts, D. A. (1990). Atrial natriuretic peptide in the subfornical organ reduces drinking induced by angiotensin or in response to water deprivation. Behavioral Neuroscience, 104, 365–372.

Fregly, M. J. (1980). Effect of chronic treatment with estrogen on the dipsogenic response of rats to angiotensin. Pharmacology Biochemistry and Behavior, 12, 131–136.

Eng, R., & Miselis, R. R. (1981). Ploydipsia and abolition of angiotensininduced drinking after transections of subfornical organ efferent projections in the rat. Brain Research, 225, 200–206.

Fregly, M. J., Kelleher, D. L., & Greenleaf, J. E. (1981). Antidipsogenic effect of clonidine on angiotensin II-, hypertonic saline-, pilocarpine-, and dehydration-induced water intakes. Brain Research Bulletin, 7, 661–664.

Eslinger, P. J., & Damasio, A. R. (1985). Severe disturbance of higher cognition after bilateral frontal lobe ablation: Patient EVR. Neurology, 35, 1731–1741. Ettenberg, A., & Camp, C. H. (1986). A partial reinforcement extinction effect in water-reinforced rats intermittently treated with haloperidol. Pharmacology, Biochemistry and Behavior, 25, 1231–1235. Evered, M. D. (1992). Investigating the role of angiotensin II in thirst: Interactions between arterial pressure and the control of drinking. Canadian Journal of Physiology and Pharmacology, 70, 791–797. Evered, M. D., & Mogenson, G. J. (1976). Regulatory and secondary water intake in rats with lesions of the zonz incerta. American Journal of Physiology, 230, 1049–1057. Farrell, M. J., Zamarripa, F., Shade, R., Phillips, P. A., McKinley, M., Fox, P. T., et al. (in press). Effect of ageing on regional cerebral blood flow responses associated with osmotic thirst and its satiation by water drinking: A PET study. Proceedings of the National Academy of Sciences, USA.

Fregly, M. J., & Rowland, N. E. (1986). Role for ·2 adrenoceptors in experimentally-induced drinking in rats. In G. de Caro, A. N. Epstein, & M. Massi (Eds.), The physiology of thirst and sodium appetite (pp. 509–519). New York: Plenum Press. Fregly, M. J., & Rowland, N. E. (1988). Augmentation of isoproterenolinduced drinking by acute treatment with certain dopaminergic agonists. Physiology and Behavior, 44, 473–481. Gehring, W. J., & Taylor, S. F. (2004). When the going gets tough, the cingulate gets going. Nature Neuroscience, 7, 1285–1287. Gibbs, J., Rolls, B. J., & Rolls, E. T. (1986). Preabsorptive and postabsorptive factors in termination of drinking in the rhesus monkey. In G. de Caro, A. N. Epstein, & M. Massi (Eds.), The physiology of thirst and sodium appetite (pp. 287–294). New York: Plenum Press. Gilman, A. (1937). The relation between blood osmotic pressure, fluid distribution and voluntary water intake. American Journal of Physiology, 120, 323–328.

Felix, D., & Schlegel, W. (1978). Angiotensin receptive neurons in the subfornical organ: Structure activity relations. Brain Research, 149, 107–116.

Giovannetti, S., Barsotti, G., Cupista, A., Morelli, E., Agostini, B., Posella, L., et al. (1994). Dipsogenic factors operating in chronic uremics on maintenance hemodialysis. Nephron, 66, 413–420.

Ferrari, A. U., Radaelli, A., & Centola, M. (2003). Aging and the cardovascular system. Journal of Applied Physiology, 95, 2591–2597.

Goldman, M. B., Robertson, G. L., Luchin, D. J., & Hedeker, D. (1996). The influence of polydipsia on water excretion in hyponatremic, polydipsic, schizophrenic patients. Journal of Clinical and Endocrinology and Metabolism, 81, 1465–1470.

Figaro, M. K., & Mack, G. W. (1997). Regulation of fluid intake in dehydrated humans: Role of oropharyngeal stimulation. American Journal of Physiology, 272, R1740–R1746. Findlay, A. L. R., Fitzsimons, J. T., & Kucharczyk, J. (1979). Dependence of spontaneous and angiotensin-induced drinking in the rat upon the oestrous cycle and ovarian hormones. Journal of Endocrinology, 82, 215–225. Fisher, A. N., & Levitt, R. A. (1967). Drinking induced by carbachol: Thirst circuit or ventricular modification? Science, 157, 839–841. Fitzsimons, J. T. (1961). Drinking by rats depleted of body fluid without increase in osmotic pressure. Journal of Physiology, 159, 297–309. Fitzsimons, J. T. (1963). The effects of slow infusions of hypertonic solutions on drinking and drinking thresholds in rats. Journal of Physiology, 167, 344–354. Fitzsimons, J. T. (1969). The role of a renal thirst factor in drinking induced by extracellular stimuli. Journal of Physiology, 201, 349–368. Fitzsimons, J. T. (1979). The physiology of thirst and sodium appetite. Cambridge: Cambridge University Press. Fitzsimons, J. T. (1998). Angiotensin, thirst and sodium appetite. Physiological Reviews, 78, 583–686. Fitzsimons, J. T., & Elfont, R. M. (1982). Angiotensin does contribute to drinking induced by caval ligation in rat. American Journal of Physiology, 243, R558–R562. Fitzsimons, J. T., & Moore-Gillon, M. J. (1980). Drinking and antidiuresis in response to reductions in venous return in the dog: Neural and endocrine mechanisms. Journal of Physiology, 308, 403–416. Fitzsimons, J. T., & Setler, P. (1975). The relative importance of central nervous catecholaminergic and cholinergic mechanisms in drinking in response to angiotenein and other thirst stimuli. Journal of Physiology, 250, 613–631. Freece, J. A., Van Bebber, J. E., Zierath, D. K., & Fitts, D. A. (2005). Subfornical organ disconnection alters Fos expression in the lamina terminalis, supraoptic nucleus, and area postrema after intragastric hypertonic NaCl. American Journal of Physiology, 288, R947–R955.

c35.indd Sec7:704

Greer, M. A. (1955). Suggestive evidence of a primary “drinking center.” Proceedings of the Society of Experimental Biology, 89, 59–62. Grob, M., Trottier, J.-F., Drolet, G., & Mouginot, D. (2003). Characterization of the neurochemical content of neuronal populations of the lamina terminalis activated by acute hydromineral challenge. Neuroscience, 122, 247–257. Grossman, S. P. (1960, July 29). Eating or drinking elicited by direct adrenergic or cholinergic stimulation of the lateral hypothalamus. Science, 132, 301–302. Grossman, S. P. (1984). A reassessment of the brain mechanisms that control thirst. Neuroscience Biobehavioral Review, 8, 95–104. Gu, G. B., & Simerly, R. B. (1997). Projections of the sexually dimorphic anteroventral periventricular nucleus in the female rat. Journal of Comparative Neurology, 384, 142–164. Gutman, M. B., Ciriello, J., & Mogenson, G. J. (1988). Effects of plasma angiotensin: Pt. II. Hypernatremia on subfornical organ neurons. American Journal of Physiology, 254, R746–R754. Haberich, F. J. (1971). Osmoreceptors in the portal circulation and their significance for the regulation of water balance. Triangle, 10, 123–130. Hainsworth, F. R., Stricker, E. M., & Epstein, A. N. (1968). Water metabolism of rats in the heat. American Journal of Physiology, 214, 983–989. Harvey, J. A., & Hunt, H. F. (1965). Effect of septal lesions on thirst in the rat as indicated by water consumption and operant responding for water reward. Journal of Comparative and Physiological Psychology, 59, 49–56. Hattori, Y., Kasai, M., Uesugi, S., Kawata, M., & Yamashita, H. (1988). Atrial natriuretic polypeptide depresses angiotensin II-induced excitation of neurons in the rat subfornical organ in vitro. Brain Research, 443, 355–359. Herbert, H., Moga, M. M., & Saper, C. B. (1991). Connections of the parabrachial nucleus with the nucleus of the solitary tract and medullary

8/17/09 3:06:16 PM

References 705 reticular formation in the rat. Journal of Comparative Neurology, 293, 540–580.

Kaufman, S. (1984). Role of right atrial receptors in the control of drinking in the rat. Journal of Physiology, 349, 389–396.

Herbert, J., Forsling, M. L., Howes, S. R., Stacey, P. M., & Shiers, H. M. (1992). Regional expression of c-fos antigen in the basal forebrain following intraventricular infusions of angiotensin and its modulation by drinking either water or saline. Neuroscience, 51, 857–882.

Kawano, H., & Masuko, S. (1999). Synaptic contacts between nerve terminals originating from the ventrolateral medullary catecholaminergic area and median preoptic neurons projecting to the paraventricular hypothalamic nucleus. Brain Research, 817, 110–116.

Hochstenbach, S. L., & Ciriello, J. (1996). Effect of lesions of forebrain circumventricular organs on c-fos expression in the central nervous system to plasma hypernatremia. Brain Research, 713, 17–28.

Kelley, A. E., & Berridge, K. C. (2002). The neuroscience of natural rewards: Relevance to addictive drugs. Journal of Neuroscience, 22, 3306–3311.

Hoffman, M. L., DenBleyker, M., Smith, J. C., & Stricker, E. M. (2005). Inhibition of thirst when dehydrated rats drink water or saline. American Journal of Physiology, 290, R1199–R1207. Holmes, J. H., & Gregerson, M. I. (1950). Observations on drinking induced by hypertonic solutions. American Journal of Physiology, 162, 326–337. Honda, E., Ono, K., Toyono, T., Kawano, H., Masuko, S., & Inenaga, K. (2003). Activation of muscarinic receptors in rat subfornical organ neurones. Journal of Neuroendocrinology, 15, 770–777. Hosutt, J. A., Rowland, N., & Stricker, E. M. (1978). Impaired drinking responses of rats with lesions of the subfornical organ. Journal of Comparative and Physiological Psychology, 95, 104–113. Illowski, B. P., & Kirch, D. G. (1988). Polydipsia and hyponatremia in psychiatric patients. American Journal of Psychiatry, 145, 675–683. Itoh, H., Nakao, K., Yamada, T., Shirakami, G., Kangawa, K., Minamino, N., et al. (1988). Antidipsogenic activity of a novel peptide, “brain natriuretic peptide” in rats. European Journal of Pharmacology, 150, 193–196. James, R. J. A., Irons, D. W., Holmes, C., Drewett, R. F., & Bayliss, P. H. (1995). Thirst induced by a suckling episode during breast feeding and its relation with plasma vasopressin, oxytocin and osmoregulation. Clinical Endocrinology, 43, 277–282. Jewell, P. A., & Verney, E. B. (1957). An experimental attempt to determine the site of the neurohypohysial osmoreceptors in the dog. Philosophical Transactions of the Royal Society. Series B, Biological Sciences, 240, 197–324. Johansen, J. P., Fields, H. L., & Manning, B. H. (2001). The affective component of pan in rodents: Direct evidence for a contribution of the anterior cingulate cortex. Proceedings of the National Academy of Sciences, USA, 98, 8077–8082. Johnson, A. K., & Buggy, J. (1978). Periventricular preoptic-hypothalamus is vital for thirst and normal water economy. American Journal of Physiology, 23, R122–R129.

Kisley, L. R., Sakai, R. R., Ma, L. Y., & Fluharty, S. J. (1999). Ovarian steroid regulation of angiotensin II-induced water intake in the rat. American Journal of Physiology, 276, R90–R96. Klingbeil, C. K., Brooks, V. L., Quillen, E. W., & Reid, I. A. (1991). Effect of baroreceptor denervation on stimulation of drinking by angiotensin II in conscious dogs. American Journal of Physiology, 260, E333–E337. Kobashi, M., & Adachi, A. (1992). Effect of hepatic portal infusion of water on water intake by water-deprived rats. Physiology and Behavior, 52, 885–888. Kolaj, M., Bai, D., & Renaud, L. P. (2004). GABAB receptor modification of rapid inhibitory and excitatory neurotransmission from the subfornical organ and other afferents tomedian preoptic nucleus neurons. Journal of Neurophysiology, 92, 111–122. Kraly, F. S. (1991). Effects of eating on drinking. In D. J. Ramsay & D. A. Booth (Eds.), Thirst: Physiological and psychological aspects (pp. 296–312). London: Springer Verlag. Kraly, F. S., Kim, Y. M., Dunham, L. M., & Tribuzio, R. A. (1995). Drinking after intragastric NaCl without increase in systemic plasma osmolality in rats. American Journal of Physiology, 276, R1085–R1092. Kunii, K., Yamanaka, A., Nambu, T., Matsuzaki, I., Goto, K., & Sakurai, T. (1999). Orexins/hypocretins regulate drinking behaviour. Brain Research, 842, 256–261. Lehr, D., Goldman, H. W., & Casner, P. (1973, December 7). Renin angiotensin role in thirst: Paradoxical enhancement of drinking by angiotensin converting enzyme inhibitor. Science, 182, 1031–1034. Lenkei, Z., Palkovits, M., Corvol, P., & Llorens-Cortes, C. (1997). Expression of angiotensin type-1 (AT1) and type-2 (AT2) receptor mRNAs in the adult rat brain: A functional neuroanatomical review. Frontiers in Neuroendocrinology, 18, 383–439. Liedtke, W. (2007). Role of TRPV ions channels in sensory transduction of osmotic stimuli in mammals. Experimental Physiology, 92, 507–512. Lind, R. W., & Johnson, A. K. (1982). Subfornical organ-median preoptic connections and drinking and pressor responses to angiotensin II. Journal of Neuroscience, 2, 1043–1051.

Johnson, A. K., Cunningham, J. T., & Thunhorst, R. L. (1996). Integrative role of the lamina terminalis in the regulation of cardiovascular and body fluid homeostasis. Clinical and Experimental Pharmacology and Physiology, 23, 183–191.

Lind, R. W., Thunhorst, R. L., & Johnson, A. K. (1984). The subfornical organ and the integration of multiple factors in thirst. Physiology and Behavior, 32, 69–74.

Johnson, A. K., & Thunhorst, R. L. (1997). The neuroendocrinoloy of thirst and salt appetite: Visceral sensory signals and mechanisms of central integration. Frontiers in Neuroendocrinology, 18, 292–353.

Lynch, K. R., Hawelu-Johnson, C. L., & Guyenet, P. G. (1987). Localization of brain angiotensinogen mRNA by hybridization histochemistry. Brain Research, 388, 149–158.

Jurzak, M., Schmid, H., & Gerstberger R. (1994). NADPH-diaphorase staining and NO-synthase immunoreactivity in circumventricular organs of the rat brain. In K. Pleschka & R. Gerstberger (Eds.), Integrative and cellular aspects of autonomic functions: Temperature and osmoregulation (pp. 451–459). Paris: John Libbey Eurotext.

Malmo, R. B., & Mundl, W. J. (1975). Osmosensitive neurons in the rat’s preoptic area: Medial-lateral comparison. Journal of Comparative and Physiological Psychology, 88, 161–175.

Kadekaro, M., & Summy-Long, J. (2000). Centrally produced nitric oxide and the regulation of body fluid and blood pressure homeostasis. Clinical and Experimental Pharmacology and Physiology, 27, 450–459. Kalra, P. R., Anker, S. D., & Coats, A. J. S. (2001). Water and sodium regulation in chronic heart failure: The role of natriuretic peptides and vasopressin. Cardiovascular Research, 51, 495–509.

c35.indd Sec7:705

Kenney, W. L., & Chiu, P. (2001). Influence of age on thirst and fluid intake. Medical Science Sports Exercise, 33, 1524–1532.

Mangiapane, M. L., & Simpson, J. B. (1983). Drinking and pressor responses after acetylcholine injection into the subfornical organ. American Journal of Physiology, 24, R508–R513. Mangiapane, M. L., Thrasher, T. N., Keil, L. C., Simpson, J. B., & Ganong, F. (1983). Deficits in drinking and vasopressin secretion after lesions of the nucleus medianus. Neuroendocrinology, 37, 73–77. Mathai, M. L., Arnold, I., Febbraio, M. A., & McKinley, M. J. (2004). Central blockade of nitric oxide synthesis induces hyperthermia that is prevented by indomethacin in rats. Journal of Thermal Biology, 29, 401–405.

8/17/09 3:06:16 PM

706 Thirst Mayberg, H. S., Liotti, M., Brannan, S. K., McGinnis, S., Mahurin, R. K., Jerabik, P. A., et al. (1999). Reciprocal limbic-cortical function and negative mood: Converging PET signals in depression and normal sadness. American Journal of Psychiatry, 156, 675–682. Mayer, A. (1900). Variations de la tension osmotique du sang chez les animaux prives de liquids. Comptes Rendus de la Société Biologie (Paris), 52, 153–155. McAllen, R. M., Pennington, G. L., & McKinley, M. J. (1990). Osmoresponsive units in sheep median preoptic nucleus. American Journal of Physiology, 259, R593–R600. McDannald, M. A., Saddoris, M. P., Gallagher, M, & Holland, P. C. (2005). Lesions of orbitofrontal cortex impair rats’ differential outcome expectancy learning but not conditioned stimulus-potentiated feeding. Journal of Neuroscience, 25, 4628–4632. McKinley, M. J., Alexiou, T., Boon, W. M., Campbell, D. J., Denton, D. A., Dinicolantonio, R., et al. (2006). Osmoregulatory thirst in mice lacking angiotensin. Procedings of the Australian Neuroscience Society, 17, 45. McKinley, M. J., Allen, A. M., Clevers, J., Paxinos, G., & Mendelsohn, F. A. O. (1987). Angiotensin receptor binding in human hypothalamus: Autoradiographic localization. Brain Research, 420, 375–379. McKinley, M. J., Badoer, E., & Oldfield, B. J. (1992). Intravenous angiotensin II induces Fos-immunoreactivity in circumventricular organs of the lamina terminalis. Brain Research, 594, 295–300.

Michell, A. R. (1979). Water and electrolyte excretion during the oestrous cycle in sheep. Quarterly Journal of Experimental Physiology, 64, 79–88. Miselis, R. R. (1981). The efferent projections of the subfornical organ of the rat: A circumventricular organ within a neural network subserving water balance. Brain Research, 230, 1–23. Miselis, R. R., Weiss, M., & Shapiro, R. F. (1987). Modulation of the visceral neuraxis. In P. M. Gross (Ed.), Circumventricular organs and body fluids (Vol. III, pp. 143–162). Boca Raton: CRC Press. Montani, J.-P., & Van Vliet, B. N. (2004). General physiology and pathophysiology of the renin-angiotensin system. In T. Unger & B. A. Scholkens (Eds.), Handbook of experimental pharmacology (Vol. 163, Angiotensin Vol. I, pp. 3–30). Berlin: Springer. Murphy, T., & Samson, W. K. (1995). The novel vasoactive hormone, adrenomedullin, inhibits water drinking in the rat. Endocrinology, 136, 2459–2463. Nava, F., Calapai, G., De Sarro, A., & Caputi, A. P. (1996). Interleukin-1 receptor antagonist does not reverse lipopolysaccharide-induced inhibition of water inake in rat. European Journal of Pharmacology, 309, 223–227. Nava, F., & Carta, G. (2000). Repeated lipopolysaccharide administration produces tolerance to anorexia and fever but not to inhibition of thirst in rat. International Journal of Immunopharmacology, 22, 943–953.

McKinley, M. J., Blaine, E. H., & Denton, D. A. (1974). Brain osmoreceptors, cerebrospinal fluid electrolyte composition and thirst. Brain Research, 70, 532–537.

Nissen, R., & Renaud, L. P. (1994). GABA receptor mediation of median preoptic nucleus-evoked inhibition of supraoptic neurosecretory neurones in rat. Journal of Physiology, 479, 207–216.

McKinley, M. J., Burns, P., Colvill, L. M., Oldfield, B. J., Wade, J. D., Weisinger, R. S., et al. (1997). Distribution of Fos immunoreactivity in the lamina terminalis and hypothalamus induced by centrally administered relaxin in conscious rats. Journal of Neuroendocrinology, 9, 431–438.

Noakes, T. D. (2003). Overconsumption of fluids by athletes. British Medical Journal, 327, 113–114.

McKinley, M. J., Burns, P., Oldfield, B. J., Sunigawa, K., & Weisinger, R. S. (2004). Diabetic thirst: Osmoreceptor stimulation by hyperglycemia in streptozotocin-induced diabetic rats. Appetite, 42, 384. McKinley, M. J., Denton, D. A., Coghlan, J. P., Harvey, R. B., McDougall, J. G., Rundgren, M., et al. (1987). Cerebral osmoregulation of renal sodium excretion: A response analogous to thirst and vasopressin release. Canadian Journal of Physiology and Pharmacology, 65, 1724–1729. McKinley, M. J., Denton, D. A., Leksell, L. G., Mouw, D. R., Scoggins, B. A., Smith, M. H., et al. (1982). Osmoregulatory thirst in sheep is disrupted by ablation of the anterior wall of the optic recess. Brain Research, 236, 210–215. McKinley, M. J., Denton, D. A., Thomas, C., Woods, R., & Mathai, M. L. (2006). Differential effects of aging on fluid intake in response to hypovolemia, hypertonicity and hormonal stimuli in hypertonicity and hormonal stimuli in Munich Wistar rats. Proceedings of the National Academy of Sciences, USA, 103, 3450–3455.

Nothnagel, H. (1881). Durst und polydipsia. Archiv fur pathologische Anatomie und Physiologie, 86, 435–437. Ohman, L. E., & Johnson, A. K. (1989). Brain stem mechanisms and the inhibition of angiotensin-induced drinking. American Journal of Physiology, 256, R264–R269. Oldenburg, B., MacDonald, G. J., & Shelley, S. (1988). Controlled trial of enalapril inpatients with chronic fluid overload undergoing dialysis. British Medical Journal, 296, 1089–1091. Oldendorf, W. H. (1971). Brain uptake of radiolabeled amino acids, amines, and hexoses after arterial injection. American Journal of Physiology, 221, 1629–1639. Oldfield, B. J., Bicknell, R. J., McAllen, R. M., Weisinger, R. S., & McKinley, M. J. (1991). Intravenous hypertonic saline induces Fos immunoreactivity in neurons throughout the lamina terminalis. Brain Research, 561, 151–156. Olivares, E. L., Costa-e-Sousa, R. H., & Cavalcante-Lima, H. R. (2003). Effect of electrolytic lesions of the dorsal raphe nucleus on water intake and sodium appetite. Brazilian Journal of Medical Biological Research, 36, 1709–1716.

McKinley, M. J., Denton, D. A., & Weisinger, R. S. (1978). Sensors for thirst and antidiuresis: Osmoreceptors or CSF sodium detectors. Brain Research, 141, 89–103.

Olsson, K. (1972). Dipsogenic effects of intracaotid infusions of various hypertosmolar solutions. Acta Physiologica Scandinavica, 85, 517–522.

McKinley, M. J., Hards, D. K., & Oldfield, B. J. (1994). Identification of neural pathways activated in dehydrated rats by means of Fos-immunohistochemistry and neural tracing. Brain Research, 653, 305–314.

O’Neill, T. P., & Brody, M. J. (1987). Role for the median preoptic nucleus in centrally evoked pressor responses. American Journal of Physiology, 252, R1165–R1172.

McKinley, M. J., Mathai, M. L., Pennington, G. L., Rundgren M., & Vivas, L. (1999). The effect of individual or combined ablation of the nuclear groups of the lamina terminalis on water drinking in sheep. American Journal of Physiology, 276, R673–R683.

Osheroff, P. L., & Phillips, H. S. (1991). Autoradiographic localization of relaxin binding sites in rat brain. Proceedings of the National Academy of Sciences, USA, 88, 6413–6417.

Menani, J. V., Columbari, D. S. A., Beltz, T. G., Thunhorst, R. L., & Johnson, A. K. (1998). Salt appetite: Interaction of forebrain angiotensinergic and hindbrain serotonergic mechanisms. Brain Research, 801, 29–35. Menani, J. V., & Johnson, A. K. (1995). Lateral parabrachial serotonergic mechanisms: Angiotensin-induced pressor and drinking responses. American Journal of Physiology, 269, R1044–R1049.

c35.indd Sec7:706

Packer, M. (1992). The neurohumoral hypothesis: A theory to explain the mechanism of disease progression in heart failure. Journal of the American College of Cardiology, 20, 248–252. Parry, L. J., Poterski, R. S., & Summerlee, A. J. (1994). Effects of relaxin on blood pressure and the release of vasopressin and oxytocin in anesthetised rats during pregnancy and lactation. Biology of Reproduction, 50, 622–628.

8/17/09 3:06:17 PM

References 707 Peck, J. W., & Blass, E. M. (1975). Localization of thirst and antidiuretic osmoreceptors by intracranial injections in rats. American Journal of Physiology, 228, 1501–1509.

Samson, W. K., Skala, K., Huang, F. L. S., Gluntz, S., Alexander, B., & Gomez-Sanchez, C. E. (1991). Central nervous system of endothelin-3 to inhibit water drinking in the rat. Brain Research, 539, 347–351.

Peck, J. W., & Novin, D. (1971). Evidence that osmoreceptors mediating drinking are in the lateral preoptic area. Journal of Comparative and Physiological Psychology, 74, 134–147.

Samson, W. K., White, M. M., Price, C., & Ferguson, A. V. (2007). Obestatin acts in the brain to inhibit thirst. American Journal of Physiology, 292, R637–R643.

Penfield, W., & Faulk, M. E. (1955). The insula: Further observations on its function. Brain, 78, 445–470.

Saper, C. B. (2002). The central autonomic nervous system: Conscious visceral perception and autonomic pattern generation. Annual Review of Neuroscience, 25, 433–469.

Penfield, W., & Jasper, H. (1954). Epilepsy and the functional anatomy of the human brain. Boston: Little, Brown. Penfield, W., & Rasmussen, T. (1950). The cerebral cortex of man. New York: Macmillan. Phillips, P. A., Rolls, B. J., Ledingham, J. G. G., Forsling, M. L., Morton, J. J., Crowe, M. J., et al. (1984). Reduced thirst after water deprivation in healthy elderly men. New England Journal of Medicine, 311, 753–759. Phillips, P. A., Rolls, B. J., Ledingham, J. G. G., Morton, J. J., & Forsling, M. L. (1985). Angiotensin-induced thirst and vasopressin release in man. Clinical Science, 68, 669–674. Quillen, E.W., Keil, L. C., & Reid, I. A. (1990). Effects of baroreceptor denervation on endocrine and drinking responses to caval constriction in dogs. American Journal of Physiology, 259, R618–R626. Rabe, E. F. (1975). Relationship between absolute body deficits and fluid intake in the rat. Journal of Comparative and Physiological Psychology, 89, 468–477. Raghavendra, V., Agrewala, J. N., & Kulkarni, S. K. (1999). Role of centrally administered melatonin and inhibitors of COX, and NOS in LPS-induced hyperthermia and adipsia. Prostaglandins Leukotrienes Essential Fatty Acids, 60, 249–253. Ramsay, D. J., Rolls, B. J., & Wood, R. J. (1977). Body fluid changes which influence drinking in the water deprived rat. Journal of Physiology, 266, 453–469. Rauch, M., Schmid, H., DeVente, J., & Simon, E. (1997). Electrophysiological and immunocytochemical evidence for a cGMP-mediated inhibition of subfornical organ neurons by nitric oxide. Journal of Neuroscience, 17, 363–371. Ricardo, J. A. (1981). Efferent connections of the subthalamic region in the rat: Pt. II. The zona incerta. Brain Research, 214, 43–60. Richter, C. P., & Barelare, B. (1938). Nutritional requirements of pregnant and lactating rats studied by self-selection method. Endocrinology, 23, 15–24. Riediger, T., Rauch, M., & Schmid, H. A. (1999). Actions of amylin on subfornical organ neurons and on drinking behavior in rats. American Journal of Physiology, 276, R514–R521. Robertson, G. L. (1989). Syndrome of inappropriate antidiuresis. New England Journal of Medicine, 321, 538–539. Robinson, B. W., & Mishkin, M. (1966). Alimentary responses to forebrain stimulation in monkeys. Experimental Brain Research, 4, 330–366. Rogers, P. W., & Kurtzman, N. A. (1973). Renal failure, uncontrollable thirst, and hyperreninaemia: Cessation of thirst with bilateral nephrectomy. Journal of the American Medical Association, 225, 1236–1238. Rolls, E. T. (2000). The orbitofrontal cortex and rewaed. Cerebral Cortex, 10, 284–294.

c35.indd Sec7:707

Saper, C. B., & Levisohn, D. (1983). Afferent connections of the median preoptic nucleus in the rat: Anatomical evidence for a cardiovascular integrative mechanism in the anteroventral third ventricular (AV3V) region. Brain Research, 288, 21–31. Schreihofer, A. M., Anderson, B. K., Schiltz, J. C., Xu, L., Sved, A. F., & Stricker, E. M. (1999). Thirst and salt appetite elicited by hypovolemia in rats with chronic lesions of the nucleus of the solitary tract. American Journal of Physiology, 276, R251–R258. Setler, P. (1973). The role of catecholamines in thirst. In A. N. Epstein, H. R. Kissileff, & E. Stellar (Eds.), The neuropsychology of thirst: New findings and advances in concepts (pp. 279–291). Washington, DC: Winston. Simpson, J. B., Epstein, A. N., & Camardo, J. S. (1978). Localization of receptors for the dipsogenic action of angiotensin II in the subfornical organ of the rat. Journal of Comparative and Physiological Psychology, 92, 581–608. Simpson, J. B., & Routtenberg, A. (1973). Subfornical organ: Site of dipsogenic action of angiotensin II. Science, 181, 1172–1175. Sinnayah, P., Burns, P., Wade, J., Weisinger, R. S., & McKinley, M. J. (1999). Water drinking in rats resulting from intravenous relaxin and its modification by other dipsogenic factors. Endocrinology, 140, 5082–5086. Slavin, A., & McKinley, M. J. (1989). [The physiology and anatomy of the area postrema of the sheep]Unpublished observations. Smardencas, A, & McKinley, M. J. (1994). unpublished observations Smith, D., Moore, K., Tormey, W., Bayliss, P. H., & Thompson, C. J. (2004). Downward resetting of the osmotic threshold for thirst in patients with SIADH. American Journal of Physiology, 287, E1019–E1023. Smith, G. P., & Jerome, C. (1983). Effects on total and selective vagotomies on water intake in rats. Journal of the Autonomic Nervous System, 9, 259–271. Somponpun, S. J., Johnson, A. K., Beltz, T., & Sladek, C. D. (2004). Estrogen receptor-alpha expression in osmosensitive elements of the lamina terminalis: Regulation by hypertonicity. American Journal of Physiology, 287, R661–R669. Speth, R. C., Smith, M. S., & Grove, K. L. (2002). Brain angiotensinergic mediation of enhanced water consumption in lactating rats. American Journal of Physiology, 282, R695–R701. Stratford, T. R., & Wirtshafter, D. (2000). Forebrain lesions differentially affect drinking eleicited by dipsogenic challenges and injections of muscimol into the median raphe nucleus. Behavioral Neuroscience, 114, 760–771. Stricker, E. M. (1966). Extracellular fluid volume and thirst. American Journal of Physiology, 211, 232–238.

Rosas-Arellano, M. P., Solano-Flores, L. P., & Ciriello, J. (1999). Co-localization of estrogen and angiotensin receptors within subfornical organ neurons. Brain Research, 837, 254–262.

Stricker, E. M., Callahan, J. B., Huang, W., & Sved, A. F. (2002). Early osmoregulatory stimulation of neurohypohysial hormone secretion and thirst after gastric NaCl loads. American Journal of Physiology, 282, R1710–R1717.

Rundgren, M., & Fyhrquist, F. (1978). A study of permanent adipsia induced by medial forebrain lesions. Acta Physiologica Scandinavica, 103, 463–467.

Stricker, E. M., Swerdloff, A. F., & Zigmond, M. J. (1978). Intrahypothalamic injections of kainic acid produce feeding and drinking deficits in rat. Brain Research, 158, 470–473.

Sagar, S. M., Sharp, F. R., & Curran, T. (1988, June 3). Expression of c-fos protein in brain: Metabolic mapping at the cellular level. Science, 240, 1328–1331.

Sullivan, M. J., Cunningham, J. T., Mazzella, D., Allen, A. M., Nissen, R., & Renaud, L. P. (2003). Lesions of the diagonal band of Broca enhance drinking in the rat. Journal of Neuroendocrinology, 15, 907–915.

8/17/09 3:06:17 PM

708 Thirst Summy-Long, J. Y., Keil, L. C., Deen, K., Rosella, L., & Severs, W. B. (1981). Endogenous opioid peptide inhibition of the central action of angiotensin. Journal of Pharmacology and Experimental Therapeutics, 217, 619–629. Sunn, N., Egli, M., Burazin, T., Burns, P., Colvill, L., Davern, P., et al. (2002). Circulating relaxin acts on the subfornical organ to stimulate water drinking in the rat. Proceedings of the National Academy of Sciences, USA, 99, 1701–1706.

induced drinking and vasopressin secretion in the dog. Endocrinology, 110, 1837–1839. Thrasher, T. N., Nistal-Herrera, J. F., Keil, L. C., & Ramsay, D. J. (1981). Satiety and inhibition of vasopressin secretion after drinking in dehydrated dogs. American Journal of Physiology, 240, E394–E401. Thrasher, T. N., Simpson, J. B., & Ramsay, D. J. (1982). Lesions of the subfornical organ block angiotensin-induced drinking in the dog. Neuroendocrinology, 35, 68–72.

Swanson, L. W., & Sharpe, L. G. (1973). Centrally induced drinking: Comparison of angiotensin II- and carbachol-sensitive sites in rats. American Journal of Physiology, 225, 566–573.

Thunhorst, R. L., Fitts, D. A., & Simpson, J. B. (1989). Angiotensin-converting enzyme in subfornical organ mediates captopril- induced drinking. Behavioral Neuroscience, 103, 1302–1310.

Szczepanska-Sadowska, E., Sobocinska, J., & Kozlowski, S. (1979). Thirst and renal excretion of water and electrolytes during pyrogen fever in dogs. Archives of the International Physiologie Biochimie, 87, 673–686.

Thunhorst, R. L., & Johnson, A. K. (2003). Thirst and salt appetite responses in young and old brown Norway rats. American Journal of Physiology, 284, R417–R327.

Szczepanska-Sadowska, E., Sobocinska, J., & Sadowski, S. (1982). Central dipsogenic effect of vasopressin. American Journal of Physiology, 242, R372–R379. Taheri, S., Murphy, K., Cohen, M., Sujkovic, E., Kennedt, A., Dhillo, W., et al. (2002). The effects of centrally administered apelin-13 on food intake, water intake and pituitary hormone release in rats. Biochemical Biophysical Research Communication, 291, 1208–1212. Takamata, A., Mack, G. W., Gillen, C. M., Jozsi, A. C., & Nadel, E. R. (1995). Osmoregulatory modulation of thermal sweating in humans: Reflex effects of drinking. American Journal of Physiology, 268, R414–R442. Takamata, A., Mack, G. W., Stachenfeld, N. S., & Nadel, E. R. (1995). Body temperature modification of osmotically induced vasopressin secretion and thirst in humans. American Journal of Physiology, 269, R874–R880. Tanaka, J., Hayashi, Y., Shimamune, S., Hori, K., & Nomura, H. (1997). Subfornical efferents enhance extracellular noradrenaline concentration in the median preoptic nucleus area of rats. Neuroscience Letters, 230, 171–174. Tanaka, J., Miyakubo, H., Fujisawa, S., & Nomura, M. (2003). Reduced dipsogenic response to angiotensin II activation of subfornical organ projections to the median preoptic nucleus in estrogen-treated rats. Experimental Neurology, 179, 83–89. Tanaka, J., Miyakubo, H., Okamura, T., Sakamaki, K., & Hayashi, Y. (2001). Estrogen decreases the responsiveness of subfornical organ neurons projecting to the hypothalamic paraventricular nucleus to angiotensin II in female rats. Neuroscience Letters, 307, 155–158.

Towbin, E. J. (1949). Gastrc distention as a factor in the satiation of thirstin esophagostomized dogs. American Journal of Physiology, 159, 533–541. Travis, K. A., & Johnson, A. K. (1993). In vitro sensitivity of median preoptic neurons to angiotensin II, osmotic pressure, and temperature. American Journal of Physiology, 264, R1200–R1205. Ungerstedt, U. (1971). Adipsia and aphagia after 6-hydroxydopamine induced degeneration of the nigro-striatal dopamine system. Acta Physiologica Scandinavica, 367, 95–122. Valtin, H. (2002). “Drink at least eight glasses of water a day.” Really? Is there scientific evidence for “8 ⫻ 8”? American Journal of Physiology, 283, R993–R1004. van Gaalen, M. M., Stenzel-Poore, M. P., Holsboer, F., & Steckler, T. (2002). Effects of transgenic overproduction of CRH on anxiety-like behaviour. European Journal of Neuroscience, 15, 2007–2015. Verbalis, J., Goldsmith, S. R., Greenberg, A., Schrier, R. W., & Sterns, R. H. (2007). Hyponatremia treatment guidelines: Expert panel recommendations. American Journal of Medicine, 120, S1–S21. Verney, E. B. (1947). The antidiuretic hormone and factors which determine its release. Proceedings of the Royal Society, B, 135, 25–106. Vivas, L., Chiaraviglio, E., & Carrer, H. F. (1990). Rat organum vasculosum laminae terminalis in vitro: Responses to changes in sodium concentration. Brain Research, 519, 294–300. Wang, K., & Evered, M. D. (1993). Endotoxin stimulates drinking in rat without changing dehydrational signals controlling thirst. American Journal of Physiology, 265, R1043–R1051. Wang, T., & Edwards, G. L. (1997). Differential effects of dorsomedial medulla lesion size on ingestive behavior in rats. American Journal of Physiology, 273, R1299–R1308.

Teitelbaum, P., & Epstein, A. N. (1962). The lateral hypothalamic syndrome: Recovery of feeding and drinking after lateral hypothalamic lesions. Psychological Review, 69, 74–90.

Weisinger, R. S., Burns, P., Eddie, L. W., & Wintour, E. M. (1993). Relaxin alters the plasma osmolality-arginine vasopressin relationship in the rat. Journal of Endocrinology, 137, 505–510.

Thompson, C. J., & Bayliss, P. H. (1987). Thirst in diabetes insipidus: Clinical relevance of quantitative assessment. Quarterly Journal of Medicine, 65, 853–862.

Wilson, W. L., Roques, B. P., Llorens-Cortes, C., Speth, R. C., Harding, J. W., & Wright, J. W. (2005). Roles of brain angiotensins II and III in thirst and sodium appetite. Brain Research, 1060, 108–117.

Thompson, C. J., Edwards, C. R., & Bayliss, P. H. (1991). Osmotic and non-osmotic regulation of thirst and vasopressin secretion in patients with compulsive water drinking. Clinical Endocrinology, 35, 221–228.

Winn, P., Tarbuck, A., & Dunnett, S. B. (1984). Ibotenic acid lesions of the lateral hypothalamus: Comparison with the electrolytic syndrome. Neuroscience, 12, 225–240.

Thornton, S. N., & Fitzsimons, J. T. (1995). The effects of centrally administered porcine relaxin on drinking behaviour in male and female rats. Journal of Neuroendocrinology, 7, 165–170.

Wise, R. A. (2002). Brain reward circuitry: Insights from unsensed incentives. Neuron, 36, 229–240.

Thrasher, T. N., Keenan, C. R., & Ramsay, D. (1999). Cardiovascular afferent signals and drinking in response to hypotension in dogs. American Journal of Physiology, 277, R795–R801. Thrasher, T. N., Keil, L. C., & Ramsay, D. J. (1982a). Hemodynamic, hormonal, and drinking responses to reduced venous return in the dog. American Journal of Physiology, 243, R354–R362. Thrasher, T. N., Keil, L. C., & Ramsay, D. (1982b). Lesions of the organum vasculosum of the lamina terminalis (OVLT) attenuate osmotically-

c35.indd Sec7:708

Wislocki, G. B., & Leduc, E. H. (1952). Vital staining of the hematoencephalic barrier by silver nitrate and trypan blue, and cytological comparisons of the neurohypophysis, pineal body, area postrema, intercolumnar tubercle and supraoptic crest. Journal of Comparative Neurology, 96, 371–414. Wolf, A. V. (1950). Osmometric analysis of thirst in man and dog. American Journal of Physiology, 161, 75–86. Wolf, A. V. (1958). Thirst: Physiology of the urge to drink and problems of water lack. Springfield, IL: Charles C Thomas.

8/17/09 3:06:17 PM

References 709 Wood, R. J., Maddison, S., Rolls, E. T., Rolls, B. J., & Gibbs, J. (1980). Drinking in rhesus monkeys: Roles of presystemic and systemic factors in control of drinking. Journal of Comparative and Physiological Psychology, 94, 1135–1148.

Xu, Z., Lane, J. M., Zhu, B., & Herbert, J. (1997). Dizocilpine maleate an N-methyl-D-aspartate antagonist inhibits dipsogenic reponses and C-Fos expression induced by intracerebroventricular infusion of angiotensin Pt. II. Neuroscience, 78, 203–214.

Wookey, P. J., Cao, Z. & Cooper, M.E. (1998). Interaction of the renal amylin and renin-angiotensin system in animal models of diabetes and hypertension. Mineral and Electrolyte Metabolism, 24, 389–399.

Zabik, J. E., Sprague, J. E., & Odio, M. (1993). Interactive dopaminergic and noradrenergic systems in the regulation of thirst in the rat. Physiology and Behavior, 54, 29–33.

Wright, J. W., Morseth, S. L., Abhold, R. H., & Harding, J. W. (1985). Pressor action and dipsogenicity induced by angiotensin: Pts. II & III in rats. American Journal of Physiology, 249, R514–R521.

Zardetto-Smith, A. M., & Johnson, A. K. (1995). Chemical topography of efferent projections from the median preoptic nucleus to pontine monoaminergic groups in the rat. Neuroscience Letters, 199, 215–219.

Xu, D. S. H., Honda, E., Ono, K., & Inenaga, K. (2001). Muscarinic modulation of GABAergic transmission to neurons in the rat subfornical organ. American Journal of Physiology, 280, R1657–R1664. Xu, J., Pekarek, E., Ge, J., & Yao, J. (2001). Functional relationship between subfornical organ cholinergic stimulation and cellular activation in the hypothalamus and AV3V region. Brain Research, 922, 191–200. Xu, Z., & Herbert, J. (1998). Effects of intracerebroventricular dizocilpine (MK801) on dehydration-induced dipsogenic responses, plasma vasopressin and c-Fos expression in the rat forebrain. Brain Research, 784, 91–99. Xu, Z., & Johnson, A. K. (1998). Non-NMDA receptor antagonist-induced drinking in rat. Brain Research, 808, 124–127.

c35.indd Sec7:709

Zerbe, R. L., & Robertson, G. L. (1983). Osmoregulation of thirst and vasopressin secretion in human subjects: Effect of various solutes. American Journal of Physiology, 244, E607–E614. Zhao, S., Malmgren, C. H., Shanks, R. D., & Sherwood, O. D. (1985). Monclonal antibodies specific for rat relaxin: Pt. VII. Passive immunization with monoclonal antibodies throughout the second half of pregnancy reduces water consumption in rats. Endocrinology, 136, 1892–1897. Zimmerman, M. B., Blaine, E. H., & Stricker, E. M. (1981, January 30). Water intake in hypovolemic sheep: Effects of crushing the left atrial appendage. Science, 211, 489–491.

8/17/09 3:06:18 PM

Chapter 36

Central Theories of Motivation and Emotion NEIL McNAUGHTON AND PHILIP J. CORR

and negative reinforcement, ignoring the specific nature of the reinforcer. Further, it is variation in the sensitivities of the systems that control positive and negative affect generally that appears to make the greatest contribution to human personality and to the risk of psychopathology—areas of human psychology where we clearly see the importance, or at least the prominence, of emotion and motivation. In this chapter, we present emotion as a cluster of reactions, including motivation, that are linked to specific classes of affordances (the aspects of an object or situation that make certain actions available) of stimuli in the world—where both the nature of the external stimulus and the animal’s internal state combine to determine the precise affordance at any particular point in time. In the process, it will be necessary to consider neural plasticity resulting from:

The concept of emotion has aroused extreme theoretical positions: from Skinner ’s (1953) denouncement of it as a muddle-minded causal fiction to the view that it is fundamental to the whole of psychology (Panksepp, 1998). Although it is more than 120 years since William James (1884) asked, “What is an emotion?” the question proved so difficult to answer that for a long period the word emotion virtually disappeared from psychology textbooks and even from more specialized books on learning or cognition. For those with a strongly behaviorist perspective, there might seem to be no reason to regret this; nor, indeed, to concern yourself with theories, central or otherwise, of emotion and motivation. For those focusing on cognitive processes also, motivation and emotion may seem peripheral. However, we believe that behavioral observations can best be integrated, and cognitive processes best understood, if we see behavior as the result of activation of one or more of a set of distinct hierarchically organized systems in the brain, where each system has evolved under pressure from a different specific class of adaptive requirements. Critically, we believe we can identify the resultant emotion, and associated motivation, with the general adaptive function that defines a class of behaviors even when the specific behaviors produced differ across occasions or species. By this route, we can achieve theoretical integration along the phylogenetic scale. The emotion systems controlling such behaviors, and their interaction with cognitive processes, such as working memory, have now become the subject of intense and detailed study (LeDoux, 1993).

• Simple association: Where no specialized reinforcer is required to generate plasticity and where behavior undergoes relatively little modification but engages in stimulus substitution; • Stimulus-reinforcer pairing: Where the result will often be observationally classical conditioning, but where response to the conditional stimulus may not be the same as those to the unconditional stimulus, and where the result can also be observationally instrumental conditioning; and • Stimulus-response-reinforcer pairings: Where the result will be observationally instrumental conditioning.

These adaptation-specific (emotional) systems are also connected with two general systems that control approach and avoidance motivations, respectively—as well as a third system that resolves conflicts between these motivations. In this context, motivation is an ambiguous term. A motivation (e.g., thirst) is specific and distinct from other motivations (e.g., hunger). But the specificity is most obvious in terms of elicited behavior and, when we talk about motivation rather than emotion, we are most often thinking of it in terms of general approach and avoidance tendencies, or positive

Particularly in this latter case, learning itself is initially associated with strong emotional reactions but well-learned responding need not be. Thus, there is a strong link between emotion and motivation (with the latter apparently embedded in the former). But, emotional reactions have many semi- or actually independent parts and so, at the limit, all that may apparently be left is a motivation. The relation between motivation and emotion, as linguistic terms, may be murky but, as we shall see, the phenomenology, and the use of the terms, can be anchored through central (neurally based) theories. 710

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Value of Central (Neurally Based) Theories

VALUE OF CENTRAL (NEURALLY BASED) THEORIES Recently, rather than being the topic that cannot be named, emotion (often without any definition of the term) has become a focus of study of a wide variety of phenomena in behavioral neuroscience. But there is still no consensus as to what an emotion is (and, as we shall see, the term may not refer to any single coherent internal entity). Motivation is also not clearly defined. The root of both words implies that the construct being referred to is something that produces movement—and yet most psychologists contrast emotion with motivation. Despite this, it is difficult to think of motivationally significant stimuli that are not characterized by the capacity to elicit emotion. In this chapter, we hope to show that a neuroscientific approach can clarify the nature of emotion, motivation, drive, and related constructs in ways that, if not impossible for a purely behaviorist approach, are at least very difficult if all that is measured is behavior. The focus of this chapter on central theories of motivation and emotion is to a large extent predicated on taking a neuroscientific approach. Behaviorally-based theories of, for example, a central motivational state have been proposed in the past (Bindra, 1969). However, the dissection of the parts of which emotional and motivational reactions are composed and the linking of those parts into coherent, predictive theory is very difficult with purely behavioral methodologies. By contrast, a neuroscientist can, often literally, dissect classes of behavior and their control systems. They can also do so without first defining, or even proving the existence of, the higher order entity that they are dissecting. If a particular drug or brain lesion changes one set of behaviors, but not another, then clearly these sets represent different functional classes. That said, proper behavioral analysis will also then be required to determine the functional nature of the classes that have been so separated. Neurally grounded theories of emotion and motivation have the key advantage, then, that they are anchored in specific anatomically identifiable systems. Their accounts do not depend on the superficial characters of behaviors and, indeed, can treat superficially quite different behaviors in different species as homologous. Neural homology and evolutionary (functional) homology, therefore, go hand in hand. When one is discerned, the other can usually be discovered—and vice versa. Evolutionary (and thus psychological function) become, then, things that must be extracted from the nature of known neural systems. With this approach, the definition of a psychological construct should map to a specific aspect of a coherent neural and functional system. In some cases, achieving this mapping

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requires elimination of an older psychological term and creation of a new one. However, neural analysis cannot proceed by itself. While it can anchor and dissect constructs derived from the experimental analysis of behavior and from ethological analysis, the brain is so complex that, without preliminary behavioral analysis, functional systems cannot easily be identified. Neural analysis of circuits that show lateral inhibition, for example, allows explanation of a wide range of sensory illusions—including those where the presence of lateral inhibition in the relevant circuits is inferred rather than directly measured. However, one could not have easily predicted any of these illusions (or any aspect of our experience of “normal” perception) from the simple observation of lateral inhibition at the neural level. So, central theories of emotion and motivation are the result of continuous interaction between behavioral and neuroscientific approaches. The neuroscientist provides anchors and mechanisms for genuine central (nervous system) theories of motivation and emotion; but, when these theories are properly developed, they are also central theories from a more psychological perspective. The patterns of activity in their higher order neural elements are central cognitive and emotional states. The behavioral neuroscientist, then, can integrate behavioral observations in terms of higher order internal states (something that all but the most radical behaviourist would see as desirable), but does so in terms of direct measures of those central states and so avoids the problems (which drove the development of the radical behaviourist philosophy) of inferring specific complex central states solely from patterns of behavior or, worse, introspection. Perhaps the most important feature of central theories of motivation and emotion for higher-level psychological analysis is one that is usually implicit rather than explicit at the level of neuroscientific analysis. Central motivationalemotional states need to be viewed at the neural level as complex compounds. This is true in two senses. On the one hand, they are complexes of emotional reactions and motivation: Initial elicitation of emotional reactions also generates motivation; but, particularly with well-learned responses, motivation can drive behavior in the absence of major emotional reactions. On the other hand, an emotion can be the result of parallel independent processes rather than of output from a single central control system. It will be seen, later, that current central theories share a tendency to see the critical elements of neural/cognitive processing as “goals.” Neither purely cognitive nor purely emotional/ motivational attributes are given primacy; and simple stimulus-response reactions are rejected. The key drivers of behavior are seen as cognitive-emotional compounds (Hinde, 1998).

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ROAD MAP TO CENTRAL THEORIES OF EMOTION AND MOTIVATION We start with the esoteric and microscopic. We look at the bits and pieces from which evolution has formed emotions and motivations. We then move to the general, the basic reinforcement systems through which the stimuli that elicit highly specific “fixed action patterns” can, through learning, shape general, flexible, emotion-independent behavior. We then compare and contrast some current central theories of emotion and motivation that amalgamate these specific and general aspects of behavioral control. Finally, we indulge ourselves—and hopefully show that our previous dry, didactic analysis has significant mundane applications—by looking at some possibly unexpected implications of current central theories.

EMOTION, MOTIVATION, AND EVOLUTION The behavioral neuroscientist thinks in terms of specific neural networks that deliver, often complex, patterns of behavior in response to appropriate environmental circumstance. Such networks cannot appear in evolution or development fully formed. They must result from progressive, incremental changes. In evolution, these changes occur as the result of random mutations interacting with selection pressures. As mentioned earlier, we would equate a specific emotion with the nature of the consistent selection pressure (functional requirement) that has driven the evolution of a set of reactions. But this means that the underlying control of behaviors (and other, e.g., autonomic, reactions) need not map simply to their superficial organization. Evolution and “Rules of Thumb” as a Problem for Behavioral Analysis The selection pressure driving evolution can be understood in terms of models such as those of optimal foraging theory. These are theoretical analyses that determine the behavioral rules required to maximize such things as the amount of food that an animal can obtain given specific starting assumptions about the environmental constraints (McNamara & Houston, 1980). It should be noted that these analyses are not predictions as to the rules that an animal will use, but define the boundary conditions toward which an animal should evolve if there is sufficient mutation and if selection of advantageous mutations is not blocked in some way. The important concept here is that the animal can use rules of thumb (ROT) of a relatively simple sort to achieve behavior, under normal ecological conditions, that

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approaches optimality—but where, in phylogenetically unusual conditions, responding may be suboptimal. For example, the parasite Nemeritis canescens “allocates its searching time in relation to host density approximately as predicted by an optimal foraging model [but] the decision rule used by Nemeritis . . . is a simple mechanism based on habituation to host scent—a far cry from the Lagrange multipliers and Newton’s iterative approximations used by the theorist to solve the problem” (Krebs, Stephens, & Sutherland, 1983, p. 188). ROT originate because, in the absence of any adaptive behavior, any mutation that results in any increase in adaptive value, however limited, will be selected. A later mutation can then provide a further increase in adaptive value—and so on. The result is that emotional control mechanisms may involve both serial and parallel ROT. In some cases, specific ROT may produce conflicting responses to the same stimulus (freezing and escape when faced with a threat, for example). These present no problem for behavior analysis as the distinct behaviors can be analyzed separately. In other cases, specific ROT may not conflict but may nonetheless fulfill quite different functions (increased blood-clotting factor is only required if escape is not successful). Again, because the responses are different, they can be identified as such and analyzed separately. The critical problem for behavior analysis is that in some cases multiple ROT can deliver essentially the same superficial behavior. They then provide the appearance, but not actuality, of a single generalized pattern of adaptive responding resulting from the application of a single, higher order, functional rule. This is exemplified by the partial reinforcement extinction effect. The Partial Reinforcement Extinction Effect and Serial ROT The partial reinforcement extinction effect (PREE) is a greater persistence of responding in extinction after prior training on partial (intermittent) reinforcement than after prior training with continuous (consistent) reinforcement. It is one of the more reliable phenomena in behavioral analysis. McNamara and Houston (1980) analyzed the general problem of how long to persist when responses no longer yield rewards. They looked at the specific case (which occurs with extinction of any positively reinforced response) of a number of initial responses that are rewarded with some probability p that are followed by a number of later responses that deliver no reward. The response is assumed to have some cost (e.g., loss of energy in making the response). Absolute optimality (which cannot be achieved in the real world without precognition) is to cease responding as soon as reward is no longer available. The theoretical

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Emotion, Motivation, and Evolution

optimality problem is, then, to determine the rule that defines the point when an animal should decide that reward has actually become unavailable rather than the alternative possibility that it is faced with an unusually long run of nonrewarded responses in a sequence with average probability p. The precise answer to this question depends on the cost of responding and the value of p. Under realistic conditions, the value of p is not known and so it must be estimated from the pattern of rewards. Further p—and reward value and even cost value—are likely to vary from response to response. This presents a highly complex set of adaptive requirements. However, it turns out that “regardless of the exact [values of these parameters], the optimal policy for this sort of problem involves persisting for far more trials in the face of failure if [the original] p [of reward] is low. This provides an explanation of the PREE in terms of optimality theory” (McNamara & Houston, 1980, p. 687). The explanation of the PREE by optimality theory is not a mechanistic explanation. It is, rather, a description of the general functional requirements that provide a background against which any mechanism that results in persistent responding will be selected. It is not a prediction as to how an animal will actually solve the problem. Further, it does not give us any insight into what ROT the animal uses; whether more than one ROT is required; or even whether extinction and resistance to extinction are derived from the same ROT. This is where attempts to determine the central mechanisms underlying the PREE provide some surprising answers. Behavioral analysis of the PREE suggested that it could depend on simple associative effects (Sutherland, 1966), including those based on conditioning to the after-effects of reward and nonreward (Capaldi, 1967) or, alternatively, could involve more emotionally mediated effects resulting from the generation, by nonreward, of frustration (Amsel, 1992). Consistent with the idea that independent ROT can control apparently similar behavior under different conditions, the PREE is differentially sensitive to drugs. With short inter-trials intervals (when associative explanations appear to explain the behavioral phenomena best) the PREE is not sensitive to anxiolytic drugs; whereas at long inter-trial intervals (when frustration appears to explain the behavioral phenomena best) the PREE can be essentially eliminated by anxiolytic drugs (Feldon, Guillamon, Gray, De Wit, & McNaughton, 1979; Ziff & Capaldi, 1971). However, if we ask about the psychological nature of the neural systems specifically affected by these drugs, we discover some interesting properties of the processes involved. Emotional explanations of the PREE have often focused on counterconditioning—the reduction in negative affective value when negative stimuli are paired with positive

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ones. Anxiolytic drugs do not reduce counterconditioning (McNaughton & Gray, 1983). The drugs appear, instead, to reduce a nonassociative “toughening up” process (McNaughton, 1989b, chap. 7). Further, although the drugs affect both extinction (which could be viewed as dependent on conditioned frustration) and the PREE (which could be viewed as dependent on toughening up to the experience of conditioned frustration) in ways that could seem to depend simply on changes in sensitivity to the emotional experience of conditioned frustration, it turns out that extinction and the PREE depend on quite distinct neural systems and are, in a sense, unrelated to each other (Gray & McNaughton, 2000, appendix 9, table 1). Extinction in continuously reinforced rats is retarded by fiber-sparing lesions of the hippocampus proper, which do not reduce the PREE. Conversely, extinction in continuously reinforced rats is unaffected by lesion of the pathway connecting the subiculum of the hippocampus to the nucleus accumbens but these same lesions abolish the PREE. Thus, both extinction and the PREE each appear to depend on a number of mechanisms (each one based on a particular ROT) and, in at least some cases, the mechanisms delivering extinction are quite distinct from those delivering the PREE. We thus have evidence for a variety of parallel ROT delivering adaptive extinction responding under a variety of situational circumstances (in particular, varying schedules of reward and reward omission). Separation Anxiety and Parallel ROT In one sense, the idea of parallel ROT—that is parallel systems concurrently activated—seems trivial. Autonomic and skeletal reactions, for example, must have evolved separately and are certainly represented in separate parts of the brain once we get “below” command centers such as the periaqueductal grey (Bandler, Keay, Floyd, & Price, 2000; Bandler, Price, & Keay, 2000). However, this issue is only trivial if a single command center controls both aspects of output. At least in the case of separation anxiety, this is not the case. Separation anxiety is clearly identifiable, both by the means of producing it (removal of the primary caregiver, usually the mother) and by its characteristic pattern of autonomic and behavioral changes. It can be seen, in much the same form, in human children and the young of other mammals, such as rats, dogs, and primates. When the “reaction is beyond that expected for the child’s developmental level,” it becomes Separation Anxiety Disorder (American Psychiatric Association, 1987). The behavioral and autonomic components of this emotion give the appearance of joint outputs from a single command center—and, if either output were missing, the

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result would not be what is generally recognized as separation anxiety. However, it has been shown that, in rats, the behavioral reactions (locomotion, grooming, defecation, and urination elicited by a novel environment) can be eliminated by the presence of a nonlactating foster mother, whereas the autonomic reaction (a reduction in heart rate) can be eliminated by regular feeding with milk—but not, in either case, vice versa (Hofer, 1972). Thus, the two effector aspects of the one emotion can be doubly dissociated in the laboratory. It appears that rather than available stimuli each activating a single cognitive center (detecting, say, threat in general), it is possible that each recognizable aspect of an emotion could result from a different aspect of the available stimulus input (Figure 36.1). Each emotion could consist of multiple parallel ROT. As with serial ROT, this does not create a problem for our naming of the phenomena. Separation anxiety remains a nameable set of entities that are coherent under normal ecological circumstances and our analysis does not require any change in the everyday use of the term. But, for scientific purposes, we must view the term S1 S2 S3 S4 S5 S6 S7 S8 or S9

S1 S2 S3 S4 S5 S6 S7 S8 S9

THREAT

Respiration (+) Muscle energy (+) Blood clotting (+) Freeze Gesture Heart rate Flight

Respiration (+) Blood clotting (+) Freeze Gesture Heart rate Muscle energy (+) Flight-A Flight-B

Figure 36.1 The extremes of the possible neural relations that could have evolved to control responses to threat. Note: The top half of the figure shows the functional relations linking stimuli (S1–S9) to responses where the stimuli are either regular predictors of threat (S1–S7) or where different stimuli are predictive of threat at different times (S8, S9). It can also be viewed as a representation of the simplest view of emotional states, namely that all stimuli activate a single neural representation of threat and this in turn activates the separate response systems. The bottom half of the figure shows, in its most extreme form, the opposite type of neural organization suggested by Hofer ’s experiments (see text). Here, each response system is under its own private stimulus control. Some stimuli (S2) may have not acquired control over any response system and some stimuli (S8, S9) may have acquired control over a particular response (flight) but only under some circumstances (-A, -B). Redrawn from “Anxiety: One label for many processes.” New Zealand Journal of Psychology, 18, Figure 1, p53 by McNaughton, 1989a.

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as grounded in a particular class of evolutionarily recurring situations (loss of parents) that give rise to a consistent set of adaptive requirements and so a usually consistent effector pattern (behavioral and autonomic) that constitutes a fairly consistent distributed central state—but without the need for a single command center or any other internal link between the components. Evolution, ROT and Functional Definitions of Emotional Systems If parts of a functional system can be independent, whether as a result of serial or parallel ROT, how can we understand or define the system—or even refer to it as a system at all? Rather than being a major problem, inverting this question allows us not only a convenient way to refer to, and to distinguish among, central emotional and motivational systems but also as well as a means of dealing with the fact that these systems involve multiple hierarchically organized layers: [This] approach to [emotion] stems from analysis of its possible functional significance. This approach is based on the premise that important and pervasive human action tendencies, particularly those which occur across a wide range of cultures and specific learning situations, are very likely to have their origin in the functionally significant behavior patterns of nonhuman animals. . . . This approach, working through the characteristic behavior patterns seen in response to important ecological demands (e.g., feeding, reproduction, defense) when animals are given the rather wide range of behavioral choices typical of most natural habitats, is called ethoexperimental analysis. It involves a view that the functional significance of behavior attributed to anxiety (or other emotions) needs to be taken into account; and that this functional significance reflects the dynamics of that behavior in interaction with the ecological systems in which the species has evolved, implying that these dynamics . . . can be determined far more efficiently when the behavior is studied under conditions typical of life for the particular species. (R. J. Blanchard & Blanchard, 1990b, p. 125)

Detailed ethological analysis of defensive responses obtained under experimentally controlled conditions by the Blanchards has demonstrated a categorical separation of a set of reactions that can be grouped together under the rubric of “fear” from a quite distinct “anxiety” set (R. J. Blanchard & Blanchard, 1988; R. J. Blanchard & Blanchard, 1989, 1990a, 1990b; R. J. Blanchard, Griebel, Henrie, & Blanchard, 1997). The Blanchards elicited their set of “fear” behaviors with a predator. These behaviors, originally all linked solely through ethology, turn out to be sensitive to drugs that are panicolytic but not to those that are only anxiolytic

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Emotion, Motivation, and Evolution

(R. J. Blanchard et al., 1997). The Blanchards elicited their set of “anxiety” behaviors (especially risk assessment; see Chapter 49) with stimuli that only suggested the potential presence of a predator. These behaviors, again originally all linked solely through ethology, turn out to be sensitive to anxiolytic drugs. The Blanchards’ detailed analysis, and its pharmacological validation, provides a basis for coherent conceptualization of a vast animal literature. For example, their analysis of fear predicts the well-demonstrated insensitivity to anxiolytic drugs of active avoidance in a wide variety of species and of phobia in humans (Sartory, MacDonald, & Gray, 1990). Because of the detailed effects of anxiolytic drugs on operant and other behavior (Gray, 1977), we have argued (Gray & McNaughton, 2000; McNaughton & Corr, 2004) that the key factor distinguishing fear and anxiety is one of “defensive direction.” Fear is that set of reactions that have evolved to allow the animal to leave a dangerous situation (predator escape; operant active avoidance); anxiety is that set of reactions that have evolved to allow the animal to enter a dangerous situation (e.g., cautious “risk assessment” approach behavior) or to withhold entrance (passive avoidance). Evolution, ROT and Hierarchical Organization With the PREE, we simply accepted the fact that, where there is a single high-level general rule for optimal behavior, there may be multiple ROT that deliver the appropriate behavior under different circumstances. However, when the functional requirement is something as general as “escape,” different circumstances may not only require different ROT to produce essentially the same behavior pattern under those different circumstances but also require noticeably different behavior patterns to achieve the result. Here we can link the evolution of serial ROT to the hierarchical organization of emotional systems. At the perceptual level, there are both “quick and dirty” as well as “slow and sophisticated” sensory mechanisms for detecting predators (LeDoux, 1994). There are also simpler and more complex behaviors that can be generated depending on the time available for execution (and other constraints). We can see all these mechanisms as parallel ROT that have evolved to improve survival in the face of threat, each new one filling a gap left by existing mechanisms. But these ROT have not evolved entirely independently of each other. First, simpler mechanisms will have evolved before more complex ones, providing a substrate for the development of the more complex and also providing a partial solution to the global problem that leaves a gap in adaptive advantage that later ROT must fill. Second, it makes no sense to have available a slow and sophisticated

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strategy for, say, escape if an evolutionarily older panic reaction takes command of the motor apparatus. When it is activated, a higher and slower mechanism must be capable of inhibiting inconvenient aspects of the lower and faster mechanisms. The result, with defensive behavior, has been the evolution of a hierarchically ordered series of defensive reactions (each appropriate to a particular “defensive distance,” see the discussion that follows) that, in turn, map to lower and higher levels of the nervous systems, respectively. While behaviorally and neurally complex, all these reactions fulfill the same basic function and so can all be seen as part of a single “fear system.” The Blanchards developed the concept of defensive distance as part and parcel of their analysis of the differences between fear and anxiety, mentioned earlier. Operationally, with the most basic defensive reactions, it can be viewed as the literal distance between the subject and a predator. It is a dimension controlling the type of defensive behavior observed—that is, specific behaviors appear consistently at particular distances. In the case of defensive avoidance, the smallest defensive distances result in explosive attack, intermediate defensive distances result in freezing and flight, and very great defensive distances (i.e., absence of the predator) result in normal nondefensive behavior. However, defensive distance is not related directly to distance per se. It operationalizes an internal cognitive construct of intensity of perceived threat. For a particular individual in a particular situation, defensive distance equates with real distance. But, in a more dangerous situation, a greater real distance will be required to achieve the same defensive distance. Likewise, in the same situation, but with a braver individual, a smaller real distance will be required to achieve the same defensive distance. This concept can resolve otherwise unexpected findings in, for example, behavioral pharmacology. It is tempting for those who focus on behavior as the thing to be studied in itself, as opposed to being a sign of states within the organism, to expect particular pharmacological interventions to affect specific behaviors in a consistent way. That this is not the case is shown by the effects of anti-anxiety drugs on risk assessment behavior. If perceived intensity of threat is high (small defensive distance), an undrugged rat is likely to remain still. Under these conditions, an anxiolytic drug will increase risk assessment (this will increase approach to the source of threat). But, if perceived threat is medium, an undrugged rat is likely to engage in risk assessment behavior. Under these conditions, an anxiolytic drug will decrease risk assessment (which again increases approach to the source of threat as it releases normal appetitive behavior). Thus, the drug does not alter specific observable risk assessment behaviors consistently but instead produces

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changes in behavior that depend on the animal’s initial state and are consistent with a pharmacological increase in defensive distance (R. J. Blanchard & Blanchard, 1990; R. J. Blanchard, Blanchard, Tom, & Rodgers, 1990). This leaves us with a picture of ROT (in this case various levels of defense reaction) that have accumulated hierarchically. Their evolution has been accompanied not only by mechanisms controlling which ROT control behavior at any particular moment in time but also by mechanisms that can adjust which level of the system is selected by any particular external stimulus configuration (or rather the cognitions engendered by the stimuli). In the case of the defense system, the hierarchical levels of responding can be mapped to levels of the nervous system

Defensive Distance

Defensive Avoidance Prefrontal Ventral Stream

Posterior Cingulate

OCD1 Surface Obsession

Septo-HippoCampal System

Generalized Amxiety Cognition/Aversion

⫹ Amygdala

PhobiaArousal

Generalized Amxiety Arousal/Startle

⫹ Medial Hypothalamus

Agoraphobia Cognition/Rumination

⫹

PhobiaAvoid

⫹ Amygdala

Social Anxiety Complex Cognition

⫹

⫹ Amygdala

Defensive Approach Prefrontal Dorsal Stream

OCD2 Deep Obsession

⫹ Anterior Cingulate

and, at least, some of the overall control mechanisms identified. This is shown in Figure 36.2. The precise details contained in the figure are not important for our current argument and are dealt with in detail elsewhere (Gray & McNaughton, 2000; McNaughton & Corr, 2004; see Chapter 36) and are also briefly summarized in the section on specific central theories that follows. The important point is that a central theory of emotion, such as this, can treat different classes of behavior as, in one sense, discrete—each controlled by a particular different part of the brain—but at the same time can show that these different classes contribute to a more generalized functional system with control of the different parts that is at least sometimes integrated.

⫹ Medial Hypothalamus

Phobia Escape

Focussed Anxiety Risk Assessment

⫹ Periaqueductal Gray

Periaqueductal Gray

Panic Explode/Freeze

⫺

5HT NA

Figure 36.2 The two-dimensional defense system. Note: The two columns of structures represent subsystems controlling defensive avoidance and defensive approach, respectively. Each subsystem is divided, from top to bottom, into a number of hierarchical levels, both with respect to neural level (and cytoarchitectonic complexity) and to functional level (i.e., defensive distance—small at the bottom, large at the top). Each level is associated with specific classes of normal behavior and so, also, symptom and syndrome of abnormal behavior. Each level is interconnected with adjacent levels (vertical arrows shown) and also with higher and lower levels (connections not shown) and these connections allow integrated control of the whole subsystem. The subsystems are also connected with each other (horizontal arrows shown) allowing for control of behavior to pass between one and the other. Superimposed on the levels

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“Anticipatory Panic”Defensive Quiescence

BDZ

of each system is input from monoamines systems. The monoamines modulate activity, essentially altering defensive distance generally, and so which level of a subsystem will be in control of behavior at any particular point in time. Endogenous hormones binding to the benzodiazepine receptor (BDZ) can similar alter defensive distance but only in relation to structures in the defensive approach subsystem and to a lesser extent at the highest and lowest levels of the system than at the middle levels (as indicated by the width of the stippled oval as it intersects a structure). NA 5 Noradrenaline; 5HT 5 Serotonin. For details see “A Two-Dimensional Neuropsychology of Defense: Fear/Anxiety and Defensive Distance,” by McNaughton and Corr, 2004, Neuroscience and Biobehavioral Reviews, 28, pp. 285–305. Adapted with permission from Figure 3, p. 293.

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Emotion, Motivation, and Learning 717

EMOTION, MOTIVATION, AND LEARNING Emotional systems have multiple parts that are several and distinct. Each involves a particular proximal form of appetitive or aversive behavior. But emotional stimuli are also reinforcing and, here, there is a surprising functional unity. Before proceeding to a consideration of the link between motivation and emotion, it will be helpful to clarify what modern neuroscience can tell us about the central mechanisms of reinforcement. Much analysis of emotion and motivation in the experimental literature has used learned responses because of their analytical simplicity. This can create problems when we attempt to link emotional concepts developed via ethological analysis with theories of learning and motivation developed via the experimental analysis of behavior. Association versus Classical Conditioning versus Instrumental Conditioning at the Neural Level The dominant paradigm for the study of synaptic processes underlying learning and memory is long-term potentiation (LTP), a phenomenon discovered by Bliss and Lomo (Bliss, Gardner-Medwin, & Lomo, 1973; Bliss & Lomo, 1973). Although LTP is usually studied electrophysiologically by high-frequency stimulation of a single neural pathway, its molecular mechanisms can clearly support strengthening of a single synapse that is driven by the coincidence of a previously weak input at that synapse with the firing of the cell produced by a strong input. The key aspect of this strengthening (which at most junctions depends on a specific receptor, the NMDA receptor) is that it is associative. Only currently active synapses (essentially acting as CS⫹) are strengthened and other inputs to the same target cell that are not active (CS⫺) are not strengthened. This strengthening appears, ultimately, to involve structural changes in the synapses and not merely depend on modification of biochemical pathways (see Chapter 27). LTP has attracted particular attention because it conforms very tightly to the requirements for memory formation postulated of cortical neural processes by Hebb (1949). Hebb’s rule (as it has come to be known) can be summarized as “cells that fire together wire together” and was postulated simply on the basis of psychological findings with no evidence for a matching real neural process until the discovery of LTP. An important point to note is that Hebb’s original example discussed the linking of two stimuli within the visual cortex. His postulated mechanism was, therefore, purely associative, requiring no additional reinforcer to strengthen the connection. A light paired with a light

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would become associated via connections within the visual cortex, as could a light with a tone—given the existence of “silent” connections between visual and auditory areas (Figure 36.3). Thus Hebbian learning is best exemplified by what is normally known as “sensory preconditioning.” (“Sensory preconditioning” is essentially a misnomer based on the, false, assumptions that learning requires a reinforcer and that without a change in behavior conditioning has not occurred.) The typical sensory preconditioning experiment can be confusing because it requires a reinforcer in order to demonstrate learning that did not itself depend on one. (With humans, we can omit the reinforcer by asking people to report their knowledge verbally.) The typical phases of a sensory preconditioning experiment are: Phase 1: Stimulus A (a light) is paired with stimulus B (a tone) in a series of classical (Pavlovian) conditioning-like trials. Neither A nor B produces any observable response, before or after the conditioninglike trials. Phase 2: Stimulus B (the tone) is next paired with a food in a series of conditioning trials. Initially, the subject salivates when the food is presented; after a number of trials, they salivate when B is presented. Phase 3: Stimulus A is now presented to the subject without any previous pairing of A with food. In experiments of this type it is usually found that the subject will salivate when A is presented. Yet, A has never been paired with food. The conclusion from these results has to be that, during Phase 1, an association was formed between A and B. In Hebb’s version of events, there initially exists a weak connection between a cell assembly activated by A and a cell assembly activated by B. When A is presented close in time to B, its weak synapses on the cell assembly encoding B will be activated at the same time that the cell assembly fires and so the connection will be strengthened. On later presentation of A, this connection activates (at least partially) the B cell assembly—and so produces, although perhaps weakly, the neural effects of the presentation of B (Figure 36.3A). In Phase 2, stimulus B acquires observable consequences. These consequences are therefore likely to follow from the subsequent activation of the A assembly even in the absence of direct input by the B stimulus to the B assembly. This effect of A is demonstrated in Phase 3. The purely associative process of long-term potentiation can also explain “classical conditioning” involving

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B: LTP – Stimulus Substitution

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Figure 36.3 Different ways in which neural plasticity can result in associative learning. Note: A: Long-term potentiation (LTP) resulting in sensory preconditioning. Pairing of a neutral stimulus A with a second neutral stimulus B strengthens the connection between the representation of A and B such that presentation of A activates the representation of B when B is not physically present. B: As in A but with the second neutral stimulus (B) substituted by a reinforcer. The unconditioned response (UR) to ! undergoes Pavlovian stimulus substitution with the result that it, or some component of it, appears as the conditioned response (CR) when A is later presented alone. C: Activity dependent facilitation (ADF) as a basis for reinforced classical conditioning. Pairing of a neutral stimulus with a reinforcer results in strengthening of the connection of A with the neural

Pavlovian stimulus substitution without the need to invoke a specific reinforcement process (Figure 36.3B). If B is a motivationally significant stimulus prior to pairing with A, then activation of its stimulus representation by A will result in the same responses to A as previously occurred to B. This is like sensory preconditioning but with the link between B and an observable response having been established previously by evolution rather than later by an experimenter. In the case of tone-shock conditioning, the specific synaptic junction generating the conditioned fear reaction has been identified as a monosynaptic connection

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representation of a response (R), independent of whether the response is currently activated. The result is classical conditioning that can produce a response that was not elicited by the unconditioned stimulus and so need not involve stimulus substitution. D: Dopamine-dependent-LTP (DA왘LTP) as a basis for reinforcement of instrumental responding. A low baseline emission of an operant response R is supported by the presence of an eliciting stimulus B. A conditional stimulus A is paired with the delivery of reinforcement (!) when the response is emitted as a UR. This strengthens the connection between the neural representation of A and the neural center controlling the emission of the response. This results in the response being emitted as a conditioned response when A is presented in future.

between the thalamus (containing the tone representation) and the amygdala (which is activated by the shock and generates the unconditioned response). Injection of an NMDA antagonist into the amygdala blocks LTP and so acquisition of the conditioned response but has no effect if injected once conditioning is complete (LeDoux, 1994; for a more detailed analysis of fear conditioning see Chapter 39; for a comparison of the neural circuits involved in fear conditioning and eyeblink conditioning see Chapter 26). Simple LTP-based association can also explain what appears to be instrumental conditioning but is in fact

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disguised classical conditioning with stimulus substitution. Pigeons are typically conditioned to peck keys that are lit prior to delivery of the reward. Under these conditions, autoshaping occurs. The pigeon comes to peck the key, essentially because its lit state predicts reward and not because the pecking is instrumentally reinforced. This is shown by two pieces of evidence. First, autoshaping with a superimposed instrumental omission contingency (which pits classical autoshaping against instrumental omission of reward if the pigeon pecks the key) results in behavior cycling between pecking and not pecking. The attractiveness of the lit key overrides any instrumental learning that pecking cancels reward; and the cyclical loss of responding can be attributed to extinction of the classical contingency rather than any effect of the instrumental one. Second, the nature of the key peck is determined by the reinforcer. The pigeon, effectively “drinks” a key paired with water and “eats” a key paired with food (Jenkins & Moore, 1973). With so much possible with simple LTP-dependent association and its resultant stimulus substitution, we might be inclined to abandon the idea of reinforcement altogether. However, neuroscience provides at least two cases where true reinforcement mechanisms can be invoked. The first reinforcement mechanism has been demonstrated in classical conditioning in the sea slug Aplysia californica. This animal is so simple that specific neurons can be identified and named reliably from animal to animal and be shown to control the same responses in each individual. This has allowed detailed analysis of the entire neural circuit involved in conditioning (Chapter 27; Kandel & Hawkins, 1992). Shock to the tail activates a single neuron that can release transmitter presynaptically onto the terminals connecting sensory neurons with a motor neuron that controls gill withdrawal. Pairing of a light touch to the mantle of Aplysia with a shock to the tail can then strengthen the connection between a sensory neuron that detects the touch and the motor neuron—a process referred to as activity dependent facilitation (ADF). The activity dependence of ADF results in a conditioned withdrawal of the gill to subsequent touching of the mantle (the CS⫹), but not of other sensory inputs, for example, a touch to the siphon. As with LTP, ADF is truly associative in that previous CS⫺ can be conditioned if they are later paired with the shock. An important feature of ADF is that, in contrast to LTP, it allows true reinforcement in the sense of production of a new response that is not elicited by the unconditional stimulus (e.g., freezing to a CS for a shock, in contrast to the movement and vocalization normally produced by the shock). The second reinforcement mechanism combines features of both standard LTP and ADF (Figure 36.3D). Like LTP, it requires the coincidence of the release of transmitter from

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the presynaptic neuron with the firing of the postsynaptic cell. However, in addition, LTP only occurs if dopamine is released presynaptically as a result of activation of the brain’s “reward system” (Reynolds, Hyland, & Wickens, 2001). Notably the postsynaptic cell controls responding rather than encoding a stimulus. Its initial activation (on which responding and so reward-delivery are dependent) results from the presence in the environment of appropriate eliciting stimuli (unless the response can be spontaneously generated). As discussed next, this allows responses to continue to be produced on some occasions even when reinforcement conditions are changed or when the reinforcer is devalued. That is, a response can be habitual and its cessation will depend on active extinction as a result of negative reinforcement rather than simply fading away in the absence of significant events. (The phasic release of dopamine, relating to reinforcement and tonic release that can be identified with hedonic changes appear to activate different networks; and dopamine may not underlie all rewarding effects, see Chapter 40). From Emotion to Motivation Our argument, so far, is that specific ROT (controlled by specific neural mechanisms) have evolved in a not entirely piecemeal fashion so that, in at least some cases, they become organized into functional systems. In the case of defensive avoidance, we have a hierarchically organized system, each part of which can generate appropriate defensive behavior (e.g., freezing, aggression, escape, avoidance) within a specific range of environmental circumstances. A large part of the theoretical structure of Figure 36.2 is devoted to an account of a fairly large number of particular situation-typical behaviors, which we group together not because of their specific form but because they share the same general function: removing the animal from danger. Aversive stimuli—both natural stimuli, such as a cat, and artificial stimuli, such as presentation of a shock, as well as the omission of expected rewards (frustrative nonreward)—all tend to have similar eliciting properties. Presentation of a cat elicits autonomic arousal, freezing, or attack, if defensive distance is short, and escape where this is possible. Much the same pattern is produced by both presentation of shock and frustrative nonreward: autonomic arousal, attack if there is a conspecific close by to attack, and escape if this is available (Gray, 1987, chap. 10). More general avoidance behavior is appropriate not only for a wide range of dangers, in the sense of things that can cause pain, but also for other stimuli that are merely disgusting, or even simply of no current interest. Fear conditioning, learned escape, and learned avoidance of the simplest sort can all be viewed, in this context, as

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the result of simple Pavlovian stimulus substitution. Pure associative conditioning results in a previously neutral stimulus becoming a signal for an upcoming noxious event and resulting in the class of defensive response appropriate to the level of threat signaled. Whether the unconditioned stimulus is the presentation of a natural or artificial punishment or the removal of a natural or artificial reward, we can view avoidance behavior in general as resulting from activity in what has been known as the fight-flight system (Gray, 1987) but is probably better called the fight-flight-freeze system (FFFS; Gray & McNaughton, 2000). It is at this point that we must distinguish between two quite distinct ways in which the words fear and conditioning can be combined. In the first conjunction of fear and conditioning, fear conditioning, a neutral stimulus is paired with a shock and responses such as freezing are conditioned. Critically, the shock is inescapable and so the conditioned form of the previously unconditioned fear responses remains even after many trials. This conditioning is purely associative, as with the learning of a light-tone pairing of the type evidenced in experiments on sensory preconditioning. It is dependent simply on the coincidence of the two critical stimuli that then become associated via the process of long term potentiation (Fanselow & LeDoux, 1999). The stimulus we often refer to as the reinforcer is necessary if a response of some type is to be observed but the learned association can be formed even with neutral stimuli and so does not depend on reinforcement in the strict Pavlovian meaning of the term. In the second conjunction of fear and conditioning, conditioning of avoidance by fear (with, for example, a lever press as the avoidance response), something quite different happens. In the initial phases of training, there is both a high level of autonomic arousal and the release of the stress hormone corticosterone (Brady, 1975a, 1975b). However, once avoidance is well established, all these signs of emotional reaction disappear and the only obvious difference in behavior, as compared with behavior observed before training, is that the avoidance response is reliably produced. This leaves us in the apparently odd situation of maintaining that although an avoidance response is being made (as a result of the motivation of fear) the animal is not afraid (in the sense of showing emotional reactions). The commonsense view is that there is no reason for the animal to be afraid because it knows the avoidance response will prevent it from receiving a shock. There are two levels at which we need to take this idea seriously. The more trivial level at which a learned avoidance response is not driven by fear is that well-learned responses are, in a very real sense, habits. Even with positive reinforcers that are physically present on every trial (such as food for

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a hungry animal), sufficiently long training results in the animal continuing to respond even when the reinforcer is devalued. The “rewarded response” is made but the reward itself is not consumed (Dickinson, 1980). With successful avoidance responses the reinforcer is never present and so responding can be even more resistant to extinction. The same is true of the conditioned suppression of behavior by anxiety. After extended training, the suppression becomes insensitive to anxiolytic drugs (McNaughton, 1985). The deeper level at which a learned avoidance response is not driven by fear rests in the fact that, unlike fear conditioning, it is not the presentation of shock that “reinforces” learning: rather it is the omission of shock. Continued responding is driven by relief, not fear. This is not mere semantic quibbling. In the same way that omission of reward has the same reinforcing properties (and many of the same eliciting properties) as the presentation of punishment—the “fear = frustration hypothesis” (Gray, 1987)—omission of punishment has the same reinforcing properties as the presentation of reward—the “hope ⫽ relief hypothesis” (Gray, 1987). As we shall see, below, we can attribute the learning of new responses to the release of dopamine and, consistent with this, dopamine is involved in avoidance conditioning (Sokolowski, McCullough, & Salamone, 1994; Stark, Bischof, & Scheich, 1999). Omission of punishment is, thus, truly rewarding. Here we should notice an asymmetry in the types of released behaviors associated with approach reactions compared to those associated with avoidance. Avoidance involves, in general, a hierachically organized set of released action patterns that do not vary much with the specific eliciting stimulus and that vary with “defensive distance”; approach involves, in general, released action patterns only in contact with the eliciting stimulus and then produces stimulus-specific responses. (Avoidance also involves stimulus-specific responses with contacting stimuli: for example attack of a predator is replaced with defensive burying of a shock probe—but these are not as many or various as the stimulus-specific responses produced by contact with appetitive stimuli. Likewise, there is little difference in principle between an appetitive-conditioned jaw movement response and an aversive-conditioned eyeblink response.) The specific behaviors observed in the context of active avoidance (when the animal moves away from a localized aversive stimulus) are suprisingly general and depend much more on defensive distance than on the specific nature of the aversive stimulus. Thus, both punishment and frustration will generally increase aggressive responses within and between many species, including humans (Renfrew & Hutchinson, 1983) and, in humans, will even increase aggressive responses directed at completely innocent inanimate objects (Kelly & Hake, 1970). Defensive behaviors,

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then, give the appearance of output from a single, fairly homogenous, system—with specific released, as opposed to learned, behaviors varying mainly with the defensive distance. By contrast, the proximal behaviors required to consummate the approach to an appetitive stimulus are entirely stimulus specific. We eat food and mount a sexual partner, but not vice versa. There has not been reported, however, a hierarchical series of standard behaviors required for approach that varies with “appetitive distance.” It may simply be that there has been a lack of appropriate ethological analysis of such approach behavior. However, the behaviors required to approach an appetitive stimulus (other than simple locomotion) are unique to each situation and driven by the specifics of the situation rather than the nature of the appetitive stimulus. Indeed, the most obvious fundamental requirement is the learning of whatever new and, in evolutionary terms, completely arbitrary responses are required to achieve the goal. There are, then, no emotiongeneral innate reactions that characterize a specific appetitive distance. This not to say that there is no dimension of appetitive distance. Appetitive goals produce a systematic, distance-related, effect. But the evidence is that variation in distance between an organism and an appetitive goal drives the quantity or intensity of behavior, but not its quality. The intensity with which approach behavior is executed increases the closer an animal is to the goal, as if there is a “goal gradient” (Hull, 1952)—but this is as true of lever pressing on a fixed interval schedule in an operant chamber as of running in runway on a continuous reinforcement schedule. We have, therefore, two fundamental systems: one that controls the avoidance of specific stimuli (including reward omission) and one that controls approach to specific stimuli (including safety). Each of these is linked to systems that determine the specific aversive (e.g., defensive burying) or appetitive (e.g., eating) behavior that will be released by contact with a motivationally significant stimulus. But each is also more fundamentally a generic system devoted to avoidance or approach, respectively. Because of the asymmetry in functional requirements noted previously, the avoidance system has been named in terms of some common discrete elicited behaviors (fight, flight, freeze); while the approach system has been named generically the Behavioral Approach System (Gray, 1982) or Behavioral Activation System (Smits & Boeck, 2006)— with the abbreviation, BAS, designating the same appetitive neural system in both cases. Here we come to the nub of the relationship between the central control of emotion and that of motivation. To a first approximation, when we talk about emotion, we are talking about the elicitation of particular patterns of internal

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(autonomic) and external (skeletal) behavior; when we talk about motivation, we are talking about the production of generalized approach or avoidance tendencies. Motivation, in this sense, cannot exist without emotion—at least in the initial phases of learning. But, in stable environments, with habitual responses reliably delivering appropriate appetitive stimuli or apparently successfully avoiding aversive stimuli, emotional reactions are minimized. We need to clear up a common misconception: There can be a tendency to link aversive stimuli and avoidance to emotion and to see them as distinct from appetitive stimuli and an approach that just involves motivation. This tendency results from the fact that the usual way to study aversive stimuli in the laboratory is to deliver electric shock (which requires no prior deprivation of some need for it to be effective); while the usual way to study appetitive stimuli is to deliver food to a hungry animal or water to a thirsty animal. It is common to see the eliciting stimulus of shock as creating the motivational state that drives behavior in the aversive case but to see deprivation, rather than the appetitive stimulus, as driving the motivational state in the appetitive case. Positive motivation does not, however, require a state of deprivation of some basic need. Female rats can often appear relatively passive during copulation—albeit showing receptive behavior linked to the phase of their ovarian cycle. However, not only does their receptive phase involve permitting the male to mount, it turns out that it involves more active tendencies when appropriate. Male (rats) normally pause for a while after intromissions, and for a longer time after intromissions that culminate in ejaculation. . . . Bermant (1961a, 1961b) provided female rats with a lever they could press to produce a male rat. After a mount (regardless of whether it resulted in intromission) the male was removed. The females quickly pressed the lever after the male was removed following a mount (without intromission), paused a bit more after an intromission (without ejaculation), and waited the longest time before summoning a male rat after ejaculation. Thus it appears that male and female rats prefer the same frequency of sexual contact. (Carlson, 1980, p. 333)

Here the reaction of the female rat to the male (albeit approach) is essentially the same class of reaction as that of a rat to a cat (albeit avoidance). The availability of the motivationally significant stimulus—and interactions with it—drives the behavior. One could argue that there is a background level of preparedness on the part of the female rat driven by the ovarian cycle—but there are also variations in fearfulness within rats from time to time and between rats, and the same is true for humans—especially with sexual receptivitiy.

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Even with hunger, it should be noted that the normal experience of hunger is linked as much or more to the availability of palatable food, or some other external or temporal cue for eating, than it is related to tissue need or level of deprivation (Pinel, 1997). For example, if a rat is provided for some time with six meals a day that are spaced irregularly but signaled by a buzzer and light stimulus and is then placed on free food so that it is satiated, presentation of the buzzer and light will elicit eating of as much as 20% of their daily food intake (Weingarten, 1983). (The total amount eaten over a day was not changed as later free feeding adjusted for the extra meal.) Likewise, if we see hunger as an essentially emotional rather than homeostatic state, we can understand its links with emotional disorder: the life-threatening reductions in weight that can occur in anorexia nervosa and the health-threatening increases in weight that can occur in depression. Likewise, simple rewarded responding can depend, like simple fear conditioning, on stimulus substitution. As we noted earlier, in experiments with autoshaped responses, pigeons produce stereotyped responses that show they are effectively drinking the key when they are thirsty and eating the key when they are hungry (Jenkins & Moore, 1973). Further, if the autoshaping schedule (which pairs a lit key with the reward) has added to it an instrumental omission contingency (so that pecking cancels food), the pigeon goes through cycles of responding and nonresponding corresponding to the simple associative contingencies in the situation, unlike a rat that ceases responding and reacts to the reinforcement contingencies (see Millenson & Leslie, 1979).

SOME CURRENT CENTRAL THEORIES OF MOTIVATION AND EMOTION There are many specific hypotheses currently being advanced by neuroscientists in relation to detailed aspects of the control of specific emotional reactions, motivational control systems, and learning and memory. For the behavioral scientist wanting to enter this field (which can appear like a minefield of novel jargon and mind-boggling detail), it is probably most important to note that the many detailed issues can be dealt with one at a time. You can focus on the detail that pertains only to the current issue. In essence, one is dealing with the neural specifics of particular ROT. Provided one has been warned about the capacity of ROT to be nested both in serial and parallel, it is not difficult to accept the bits of the jigsaw piecemeal and leave integration until sufficient bits have been obtained to make the overall puzzle worth solving. The most important thing is to not believe that the solution to the puzzle is obvious

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and to wait for a sufficient number of the pieces to become available. Partly because they deal with the neural instantiation of ROT, neuroscientists seldom integrate their findings on emotion and motivation into grand overall theoretical schemes. They do use global, apparently integrative, concepts. But these concepts are usually taken directly from behavior analysis and so subsume ROT within what are effectively clusters (such as the PREE and instrumental learning) based on overall evolutionary function. This may give the impression that they are ascribing to ROT a specific source of integrated control but, as we have seen, this need not be the case. Instead, the use value of this approach is to gather together phenomena that may have some, albeit loose, integrated control—or that may have the appearance of control as an emergent property of the interaction of multiple ROT. There are, nonetheless, neuroscientifically grounded theories that attempt to provide more wholistic, integrated perspectives. In this section, we briefly describe some of these and show how the architecture of each maps to the basic ideas we have presented above. Gray and McNaughton We have based a number of the concepts we have already presented on one such theory—the idea, originally proposed by Jeffrey Gray (1982), that behavior is primarily controlled by a Fight-Flight-Freeze System (FFFS) and a Behavioral Approach System (BAS) with, linked to these, and controlling conflict between approach and avoidance, a Behavioral Inhibition System (BIS). This theory has clear links with the idea of multiple ROT, especially in its more recent development (Gray & McNaughton, 2000; McNaughton & Corr, 2004). Multiple ROT are instantiated in the mixture of levels and streams of Figure 36.2, which shows the FFFS and BIS, and in the matching levels of the separate stream of structures controlling the BAS (not shown). It also has at its core the idea that, in general, approach and avoidance behavior are each controlled in fundamentally the same way independent of the specific source of motivation for that approach or avoidance. Rolls This latter perspective is presented in perhaps an even stronger way by Edmund Rolls (1990, 2000) in his general theory of the control of emotion and motivation by the brain. He sees evolution as starting with simple ROT in the form of taxes that attract simple animals (including those with no nervous system) toward items that promote

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survival and reproduction and that drive them away from items with the opposite consequences. He argues that: brains are designed around reward- and punishment-evaluation systems, because this is how genes can build a complex system that will produce appropriate but flexible behavior to increase fitness. . . . If arbitrary responses are to be made by the animals, rather than just preprogrammed movements such as tropisms and taxes, [is] there any alternative to such a reward/punishment based systems in this evolution by natural selection situation? It is not clear that there is, if the genes are efficiently to control behavior. The argument is that genes can specify actions that will increase fitness if they specify the goals for action. It would be very difficult for them in general to specify in advance the particular response to be made to each of a myriad of different stimuli. . . . Outputs of the reward and punishment system must be treated by the action systems as being the goals for action. (Rolls, 2000, pp. 190, 183, 191).

Rolls could, at first blush, appear to be taking an excessively binary view. He states, for example, that “emotions can usefully be defined as states elicited by rewards and punishments, including changes in rewards and punishments” (Rolls, 2000, p. 178). He also argues that “the amygdala and orbitofrontal cortex . . . [are] of great importance for emotions, in that they are involved, respectively in the elicitation of learned emotional responses and in the correction or adjustment of these emotional responses as the reinforcing value of the environmental stimuli alters” (Rolls, 1990, p. 161). This perspective seems to force all emotion into either a reward or a punishment box with variation in behavior simply being the results of the learning of arbitrary responses. However, on closer inspection of the details of Rolls’ theory, it is clear that he allows not only for multiple ROT in terms of elements of behavior but also in terms of the separation of, for example, autonomic from behavioral aspects of emotional response. In his view, there are three major, neurally separate, classes of output available for any emotion: There are autonomic and endocrine outputs that optimize the state of the animal for particular types of action; there are implicit behavioral responses; and there are explicit behavioral responses. Implicit behavioral responses are controlled “via brain systems that have been present . . . for millions of years and can operate without conscious control. These systems include the amygdala and, particularly well developed in primates, the orbitofrontal cortex. They provide information about the possible goals for action based on their decoding of primary reinforcers taking into account the current motivational state, and on their decoding of whether stimuli have been associated by previous learning with reinforcement.” This clearly encompasses a wide range of emotion-specific and innately elicited responses. The control of explicit behavioral responses, by contrast,

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“involves a computation with many ‘if . . . then’ statements, to implement a plan to obtain a reward or to avoid a punisher.” Here the behavior controlled is clearly general in its form and largely based on strategies for simple approach or avoidance. He locates the highest levels of this control in the dorsolateral prefrontal cortex—where they are strongly related to the processing of shortterm (or “active”) memory. Despite Rolls’ somewhat different perspective compared to Gray and McNaughton, he is like them in seeing orbitofrontal cortex as, in essence, coding “what” a stimulus is. “What” here has the sense of what specific class of reinforcer such as food, drink, or sex it is that the stimulus represents—and compounds “sensory integration, emotional processing, and hedonic experience” (see Chapter 41). Dorsolateral frontal cortex, by contrast, codes “where” a stimulus is. Thus both theories see a distinction between a reactive and excitatory orbital system and a prospective and inhibitory dorsolateral system. Critically, in the context of ROT, Rolls (2000) warns that “these three systems do not necessarily act as an integrated whole. Indeed, insofar as the implicit system may be for immediate goals, and the explicit system is computationally appropriate for deferred longer terms goal, they will not always indicate the same action. Similarly, the autonomic system does not use entirely the same neural systems . . . and will not always be an excellent guide to the emotional state of the animal, which the above argument in any case indicates is not unitary” (pp. 188–189). There is a strong link between emotion and motivation for Rolls, in both their more innate and more conditioned forms. While starting from the position that “emotions can usefully be considered as states produced by reinforcing stimuli” (Rolls, 1990), he sees the particular value of those states as involving elicitation of autonomic and hormonal responses and, in learning experiments, in the production of various conditioned emotional responses. Emotion, viewed in this light, provides a basis for the facilitation of memory storage and for the immediate elicitation of flexible responding when conditions change. The blocking of a learned response by new circumstances leaves intact the conditioned emotional response, which then provides the basis for the development of new behavior. Background autonomic and hormonal reactions provide the basis for the storage of such strategies as then prove successful. Ledoux For many years, Joe Ledoux has been developing a theory of fear, and consequentially, anxiety that is more limited in terms of the emotions analyzed but potentially deeper in the picture it presents of the details of the emotional systems. This can be seen as dovetailing to some extent with both the theoretical positions we have described so far.

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While Gray and McNaughton focus on hierarchy in terms of the specific elicited behaviors associated with specific defensive distances, Ledoux can be thought of as focusing more on hierarchies of stimulus analysis that are to some extent also selected by defensive distance. He has contrasted “quick and dirty” threat detection systems (operating via the thalamus) with slower and more sophisticated ones operating through sensory cortex (Ledoux, 1994) and more recently (Ledoux, 2002) has laid emphasis on the even slower, and potentially more sophisticated, mechanisms that reside in frontal cortex and are linked to working memory and that form of planning that we can call “worry.” At first, his theory appears to be at total variance with that of Gray and McNaughton. However, when we look at the neural details, we discover that the discrepancy is not great; and we demonstrate a major advantage of a central/neural approach to emotion and motivation. The details of the theories are linked to neural reality very tightly and this allows one to resolve, relatively easily, issues that depend much more on arbitrary linguistic definitions than scientific facts. Ledoux (2002) argues to some extent that anxiety is really fear but represented differently in consciousness. Thus: anxiety, in my view, is a cognitive state in which working memory is monopolized by fretful, worrying thoughts. The difference between an ordinary state of mind (of working memory) and an anxious one is that, in the latter case, systems involved in emotional processing, such as the amygdala, have detected a threatening situation, and are influencing what working memory attends to and processes. This in turn will affect the manner in which executive functions select information from other cortical networks and from memory systems and make decisions about the course of action to take. . . . I believe that the hippocampus is involved in anxiety not because it processes threat, as Gray suggests, but instead because it supplies working memory with information about stimulus relations in the current environmental context, and about past relations stored in explicit memory. . . . When the organism, through working memory, conceives that it is facing a threatening situation and is uncertain about what is going to happen or about the best course of action to take, anxiety occurs. (p. 288)

Ledoux’s very influential theory of the neural processing of fear was essentially incorporated into Gray’s (1982) original, essentially amygdala-free, theory in its revision by Gray and McNaughton (2000). So, as far as fear goes, there is essentially general agreement among the theories of Ledoux, Gray and McNaughton, and Rolls. It is in dealing with anxiety that he sees the Gray and McNaughton theory as underemphasizing working memory and worry, “in my opinion, it still gives the septum and hippocampus too prominent a role, at the expense of the amygdala and prefrontal cortex” (p. 288).

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In resolving the differences, let us first note that Gray and McNaughton’s theory is anchored primarily in the effects of anxiolytic drugs. The link between anxiolytic action and effects on hippocampal electrical activity have been ever more firmly established (McNaughton, Kocsis, & Hajós, 2007). However, as Gray and McNaughton noted in their introduction: “psychosurgery”—lesions of the cingulate or prefrontal cortex—has been used as a treatment with some degree of success. So these cortical areas could well mediate extreme (Marks, Birley, & Gelder, 1966) or complex forms of anxiety, especially . . . in the case of obsessive-compulsive disorder (Rapoport, 1989). (Gray & McNaughton, 2000, p. 5)

Gray and McNaughton (2000) have a theory of “anxiolytic-sensitive anxiety” that necessarily separates this from the processes of anxiety (or fear or obsession) that are controlled by frontal cortex. What of their view of frontal and cingulate cortex—on which Ledoux focuses: We view them . . . as being hierarchically organized areas which deal (in their successively “higher” layers) with progressively higher levels of anticipation of action. . . . In the same way, then, that we distinguished the role of the hippocampus (in resolving concurrent goal-goal conflict) from the role of the defense system and other motor systems in resolving motor program conflicts without goal conflict, so we must distinguish its role from that of prefrontal and cingulate cortex. In our view these cortical areas are involved, quite independently of the hippocampus, in the resolution (i.e., ordering) of conflicts between successive sub-goals in a task. In the case of prefrontal cortex this amounts to saying that it is concerned with plans more than goals as such. However, where (as is common in certain types of working memory task) there is concurrent goal conflict within such a task, both the septo-hippocampal system and the prefrontal cortex are likely to be involved. (p. 5)

This view is not far from that expressed by Ledoux 2 years later, if we do not try and force the word “anxiety” to mean the same thing in the two cases. Gray and McNaughton focus on approach-avoidance conflict; something that can occur as a result of the apposition of two classes of innate releasing stimulus, with no requirement for learning or working memory. Ledoux focuses on “worry,” the maintaining of a perception of threat in working memory (with no necessary requirement for anything other than pure avoidance). The two theories are talking about different processes in different structures— and Gray and McNaughton have much the same view of the operations of frontal cortex and of the amygdala as Ledoux. Both theories agree that “the amygdala and hippocampus normally cooperate in the intact brain to store

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While it is not a full theory of emotion, mention should also be made here of Damasio’s somatic marker hypothesis (Damasio, 1995, 1996). This is a partial theory of how emotion or motivation can interact with cognition. It is

intended to be an account of only one of several ways that affect can influence decision making and focuses primarily on the operation of the ventromedial prefrontal cortex, to the exclusion of other frontal areas. It is of interest here for two reasons: First, its view of emotional influence is different from the theories we have discussed so far. Second, its view of somatic phenomena is broader ranging. Damasio’s theory (Damasio 1995, 1996; for a critical review, see Dunn, Dalgleish, & Lawrence, 2006; also see Chapter 38) originated in an attempt to account for the effects of ventromedial prefrontal damage. His patients showed severe impairments in decision making and in social choices but have intact IQ, learning, and retention of knowledge (including social knowledge) and skills, logical analysis, and language skills. They also perform normally on the Wisconsin Card Sorting Test that is normally affected by frontal damage. The abnormal decision making and social choices of these patients were accompanied by abnormalities in emotion and feeling and Damasio postulated that these emotional changes were the cause of the abnormal decision making. “The somatic marker hypothesis proposes that ‘somatic marker’ biasing signals from the body are represented and regulated in the emotion circuitry of the brain . . . to help regulate decision-making in situations of complexity and uncertainty” (Dunn et al., 2006, p. 240). The presence of what is, in effect, a somatic image called up by a situation constrains decision making and limits the amount of processing required of cognitive mechanisms either by explicitly labeling a scenario as negative or positive; or implicitly biasing decision mechanisms in a positive or negative direction. The somatic marker hypothesis differs from the other theories we have discussed in that it keeps the encoding of emotion (or strictly soma, see discussion that follows) distinct from the encoding of the information on which cognitive processes act, even at the prefrontal level. That is, emotional information can supplant, or bias, the processing of other types of information and is only integrated with them by altering their processing. The other theories, by and large, operate in terms of goals—compounds of cognitive (situational) and affective (affordance) information. It remains to be seen (Dunn et al., 2006) how far a somatic marker system in the ventromedial prefrontal cortex can be distinguished from some specific aspect of goal processing and how far it is qualitatively distinct from the other classes of processing that the hypothesis allows occur in other areas of frontal cortex. The somatic marker hypothesis is also broader ranging than conventional postive/negative valence approaches. Here it departs both from the other theories and from more conventional behaviorist perspectives. Damasio (1996) holds:

* We thank Rama Ganesan for bringing this literature to our notice.

that the results of emotion are primarily represented in the brain in the form of transient changes in the activity patterns

different components of the fear learning experience” (see Chapter 39). There is perhaps one area where discrepency may remain and where further experiment (or theoretical analysis) may be required to integrate the theories. Gray and McNaughton see the personality factor of neuroticism as being linked to frontal cortex, and as predisposing to both fear (threat avoidance) and anxiety (threat approach) disorders. Although they do not explicitly do so, they should link this personality factor to worry. For them worry is something that, if excessive, can lead to both pathological fear and pathological anxiety. These two states would appear to not only be conflated in Ledoux’s analysis but also to be consequences not causes. Ledoux sees threat, detected in the amygdala, as infecting working memory processes and resulting in worry. There is evidence that worry is not directly aligned with anxiety as measured by standard anxiety scales (Meyer, Miller, Metzger, & Borkovec, 1999) and that worry can result in intrusive negative thoughts (Borkovec, Robinson, Pruzinsky, & DePree, 1983).* This suggests that, provided we use the words “worry” and “anxiety” with sufficiently restricted definitions, we can see Ledoux’s theory as being more focused on a cause of pathological anxiety (and fear and depression), and their etiology, and Gray and McNaughton’s as providing a view of state fear and state anxiety that encompasses both normal and pathological examples of these emotions but distinguishes between them. Many of the differences between these three theories of central emotional and motivational states are more apparent (through variations in the use and meanings of words) than real. Critically, when what each theorist says of the mechanisms and psychological constructs associated with a particular neural structure is compared with the others, their fundamental message is very similar. They all believe that central states are fundamental to emotion and motivation, either in its normal or pathological form. We would also agree with Ledoux (2002) when he states, “I don’t study behavior to understand behavior so much as to understand how processes in the brain work” (p. 209). To this we, personally, would add the coda that we want to understand the processes in the brain because these anchor our understanding of the workings of the mind. Damasio

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of somato-sensory structures. I designated the emotional changes under the umbrella term ‘somatic state’. Note that by somatic I refer to the musculoskeletal, visceral and internal milieu components of the soma rather than just to the musculoskeletal aspect; and note also that a somatic signal or process, although related to structures which represent the body and its states does not need to originate in the body in every instance. (p. 1412, italics added for emphasis)

Thus, somatic markers are not the simple assignation of valence or even of specific motivation to a stimulus. They are the perception or recall of a quite specific and detailed somatic image. There is no question that we can encode such images, and rehearse in our “mind’s eye” the somatic experience of, say, a competition dive. However, to see this image as the basis of a background biasing of a cognitive decision about whether to make a particular bet in Damasio’s paradigm task, the Iowa Gambling Task, is a radical departure from most other views of decision making and goal processing.

Central Theories of Emotion and Motivation—Some Broad Conclusions The details, perspectives, and specific assignment of functions to structures by the theories we have considered differ. However, they all share a picture of the control of behavior by multiple serial and parallel ROT by hierarchically organized systems in the brain. They thus account for (without producing a complete explanation of) the apparent theoretical impenetrability of emotion. No two emotions need be constructed or controlled in the same way as each other. No single emotion need have a unitary control. Rather, an emotion, as normally identified, may be an emergent structure deriving much of its superficial unity from the evolutionary path that has shaped the various component reactions. That said, the adaptive requirements facing, for example, the autonomic nervous system are sufficiently similar across the different emotions that at the general, as opposed to specific, level they can be seen to have many common features. Critically, neural analysis can determine the similarities and differences in the control of both superficially similar and superficially different reactions. The theories also share a common picture of a variety of emotions being linked to two broad classes of general behavioral tendency: approach and avoidance. These have their origin, as emphasized by Rolls, in the fundamental properties of taxes—which are defined in terms of their being the result of the simplest stimuli generating, in the simplest way, either approach or avoidance—these ideas follow from Gray’s early articulation of the same basic

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principles. Thus, while affective stimuli will define specific goals (and, with the possible exception of Damasio, the theories all see behavior as goal directed), a behavior such as a lever press can result in food, delivery of a mate, safety from shock, or a variety of other specific results—but in all cases (including relief from nonpunishment) it is reinforced in the same basic way, by the release of dopamine. The control of distal behavior, then, depends on systems fundamentally devoted to approach, in general, and avoidance, in general. To these basic systems, Gray and McNaughton add an additional system that resolves conflict between the approach and avoidance systems—but their view of the basic approach and avoidance systems is essentially similar to that of Rolls and their view of the basic control of avoidance is much the same as that of Ledoux.

FUTURE DIRECTIONS So far, it might be thought that our analysis has not produced much of an advance, from a behaviorist perspective, beyond confirming the unsurprising conclusions that different stimuli elicit different proximal behaviors, and that behavioral plasticity can be understood in terms of positive and negative reinforcement. However, there are a number of points where neural analysis provides specific departures from any simple form of these conclusions and where it leads, in extreme cases, to unexpected conclusions. Beyond the Basics—The Potential for Unexpected Conclusions Perhaps the most important conclusion that neural analysis allows is that what is paradigmatically conditioning does not necessarily require explicit reinforcement. As we noted, sensory preconditioning and Pavlovian fear conditioning both involve the same basic form of stimulus-stimulus association in which simple long-term potentiation is all that is required for the strengthening of connections. The specific site of this potentiation, for fear conditioning, has been identified as the input from the thalamus (which encodes the conditional stimulus) to the amygdala (which generates the unconditioned, and then conditioned, responses). We have also argued that this purely associative, nonreinforced, type of learning underlies what often appears to be instrumental learning in cases, such as a pigeon pressing a lit key, where the behavior is autoshaping in disguise—although it has not yet been proved that this involves long-term potentiation. Following on from this conclusion is the fact that true reinforcement in the classic sense intended by Pavlov, while strengthening neural connections, need not reinforce

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Future Directions

a previously occurring response. This provides a simple explanation of the fact that, for example, the conditioned response to a stimulus that predicts shock (usually freezing) is not simply the unconditioned response to the shock (vocalization, movement) moved forward in time. Indeed, while there will not be a perfect match between dependence on association rather than reinforcement and the occurrence of stimulus substitution, the neural data suggest that association rather than reinforcement should be suspected whenever the conditioned response (including those that are superficially the result of instrumental conditioning) can be accounted for by stimulus substitution. A related issue, with instrumental reinforcement, is the demonstration that punishers release dopamine. The broad two-dimensional affective model we presented is, admittedly, derived originally from learning theoretic analysis (Gray, 1975). In this analysis, the omission of expected, or termination of, punishment is functionally equivalent to the presentation of rewarding stimuli; and in a symmetrical manner, the omission of expected, or termination of, reward is functionally equivalent to the presentation of punishment. But the demonstration of a link between punishment and dopamine, and of the role of dopamine in controlling instrumental reinforcement (Reynolds et al., 2001), has two important consequences for this model. First, it means we can be sure that, at a mechanistic level, the effects of reward and punishment omission are identical—they both change behavior by releasing dopamine. It is not the case that they happen to coincidentally produce the same superficial effects on behavior through independent mechanisms. Second, we can link both normal reward and normal punishment omission directly to explanations of addiction—where all addictive drugs (and some addictivelike behavior) have been shown to support continued behavior by the release of dopamine (but see also Chapter 40). We use this fact to provide potential explanations of some behaviors that might not be expected from the perspective of a simple reinforcement theory. A final point we need to consider before moving on to some specific scenarios is the nature of the interaction between reward and punishment—where we again need to take into account the tendency of evolution to select multiple ROT rather than producing integrated control systems. In terms of simple decision making, for example, reward and punishment systems suppress each other. However, with respect to arousal, and so sometimes the vigor of production of responses, they can summate (Gray & Smith, 1969). These are quite distinct computations and, in terms of the effect of anxiolytic drugs on approach-avoidance conflict, can be shown to be processed in quite different parts of the brain. The inhibitory effect of punishment on rewarded behavior is mediated via the hippocampus,

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while the excitatory effect of punishment on reward-elicited arousal is mediated via the amygdala and not, in either case, vice versa (Gray & McNaughton, 2000). As a result, the addition of negative reinforcement can increase the levels of behavior generated by a positive reinforcer (e.g., in behavioral contrast). More peripheral theories of emotion and motivation would struggle to account for such findings. In the sections that follow, we speculatively consider the possible insight that these features of the reward and punishment systems can offer into some of the more perplexing behaviors shown by human beings. (For a higher level view of apparently irrational behavior patterns, see Chapter 37.) Relief of Nonpunishment: Gambling We have already considered the complex mechanisms underlying the partial reinforcement extinction effect— where we argued that the phenomena are generally adaptive in that they conform to optimal foraging analyses. Here we consider cases of pathological gambling where persistence in the face of intermittent reinforcement is, in optimal foraging terms, maladaptive. According to standard behavioral accounts, pathological gambling should not develop very easily and should extinguish fast. That is, engaging in a behavior that provides a high ratio of punishment to reward should led to avoidance behavior, which of course it does in the majority of the population. However, in a significant minority of people, pathological gambling behavior develops. That is, the behavior entails high monetary losses leading to personal, family, and societal problems. We could attempt to explain this maladaptive behavior using standard learning theory. There is intermittent positive reinforcement, and the ratio and pattern of reward to response are carefully chosen to produce robust conditioning and maximum resistance to extinction. To some extent, this can explain gambling. But it seems not to be a sufficient explanation of normal gambling far less its pathological form. First, in animals simply subjected to intermittent schedules, as we noted earlier, the level of behavior conforms approximately to optimality—with over-responding being present only while information about a new reward density is being gathered. Second, quite apart from the local preponderance of negative reinforcement for the behavior, there is usually additional negative reinforcement in terms of the effect on other aspects of life, and this should produce robust avoidance behavior. Third, there is the brute fact that the majority of people who engage in recreational gambling do not develop pathological gambling behavior. These facts suggest that we must look elsewhere for

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a sufficient explanation of this form of counterproductive behavior. One alternative theory is to assume that people prone to pathological gambling have biased cognitions (e.g., “The more I lose, the more chance of have of winning”). We may suppose that such biases are important in maintaining pathological gambling, but such explanations are high on description but low on powers of explanation, and specifically fail to reveal why such cognitive biases exists, let alone how they relate to reinforcement sensitivity (which we know is important in gambling behavior). Nor do they explain the intensity of the behavior. A possibility suggested by our current analysis is that pathological gambling develops as a as a form of selfdefeating dopamine-mediated approach behavior. On this view, punishment summates with the expectation of rare, large rewards, to create a high level of arousal. It thus energies and invigorates behavior. Even if the schedule of reinforcement were net positive for the player (as it can be with games such as “21”) it involves a background of fairly steady punishment, in the form of loss of the stake and reward omission. This means that when a reward occurs its effect is supercharged by the positive effects of relieving nonpunishment. The resultant physiological arousal acts in the same way as a drug, such as amphetamine, to create an emotional high that produces rapid and resistant conditioning (e.g., to the paraphilia of the gambling context). These emotional “highs”, that are predicted by the higherdensity of punishments, can become associated with it and so, through counterconditioning, reduce its negative reinforcing value (which is weak at the level of the individual response). The overall picture, as with chemical addiction, is an overriding of background negative stimuli by occasional powerful stimulation of the dopamine system. As yet these behavioral processes, and the apparently paradoxical fact that punishment in gambling seems to maintain pathological gambling itself, does not make much sense in traditional Skinnerian terms, but it finds a natural explanation within the context of the known properties of the dopamine system—and with the low level of genuine “pleasure” in those addicted to drugs. Reward-Punishment Mutual Inhibition: Romantic Partner Abuse A similar process to that seen in pathological gambling may also operate in romantic partners who suffer long-term abuse but who are reluctant to escape their abusing partner (i.e., are reluctant to engage in FFFS-mediated avoidance of the threat stimulus). Putting aside other relevant factors involved in such situations (e.g., children and financial dependence), some abused partners (both males as well

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as females—here the forms of abuse may differ) repeatedly fail to leave their partners who, on the one hand, they openly declare are abusing them, but, on the other hand, find it difficult to break away (even where there do not exist an financial, or other, objective reason, for doing so). Partner abuse should be expected to activate the FFFS (as well as the BIS due to the likelihood of conflict) leading to punishment-mediated behaviors (in this case fear, tension, attempts to avoid/escape abuse). When the abusive partner reconciles, the abused partner will not only experience an absence of punishment (itself a good thing in terms of reduced FFFS activity), but also a strong boost to the BAS in the form of release of suppression of the reward system by the punishment (FFFS/BIS) system. As in the case of gambling (see above), relief of nonpunishment processes may also be assumed to operate. This release, and the subsequent rebound effects, would be expected to lead to a heightened BAS activity and an emotional high, which would stamp in, via conditioning, behaviors immediately preceding it, namely the partner ’s reconciliation behavior and associated stimuli—Konorski (1967) made a similar claim about the rebound effects in romantic “making-up” behavior. Once again, the FFFS/BIS-induced arousal would serve further to augment the rebound of the BAS, increasing the subjective intensity of the positive emotional high. (Rebound effects are also suggested by anti-anxiety drugs that are traded illegally for the highs they produce in some people.) There is a further theoretical twist that would make an additional contribution to this BAS-mediated emotional high and resulting approach behavior (e.g., making up). The mutual inhibition of the reward and punishment systems would mean that the previous negative emotion and behavior associated with the punishment system would now itself be suppressed, making the abused partner, emotionally speaking, to forget (or, at least, attenuate the strength of) the previous punishment delivered by the partner. Thus, we may predict that one of the major factors contributing to the continuation of abusive relationships is that the abused partner has a strong mutual inhibition between their reward and punishment systems, rendering a supercharged BAS input from the abusive partner ’s reconciliation behavior. It might be the case that the abusive partner learns how to manipulate the emotions of the abused partner, and this would contribute to the cycle of abuse.

SUMMARY In our discussion of central states and theories of emotion and motivation, we ranged freely from the exotic, but fairly

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References 729

well established, theories of the partial reinforcement extinction effect (PREE) to the prosaic, but not clearly understood, behavior of pathological gambling and romantic partner relations. We attempted to show that neural analysis can, and has, generated quite distinct theories that not only have the advantage of being tied to neural and pharmacological reality (and so are less subject to the whims of verbal definition) but also have the advantage of throwing into strong relief some of the less obvious properties of emotional and motivational systems. These properties derive from the fact the emotion and motivation involve multiple serial and parallel ROT, each of which has evolved separately but nonetheless regularly co-occurs with and is often seamlessly integrated with others. The existence of multiple ROT itself creates an environment in which higher order control mechanisms can evolve. The addition of later, complex, ROT to sets of simpler ones has also tended to produce hierarchical structures with the quickest, dirtiest, and phylogenetically earliests mechanisms located at lower levels of the neuraxis and progressively slower and more sophisticated mechanism located at progressively higher levels. We considered a number of current central theories of emotion and motivation. These differ in detail and even in their use of terms. But they can all be seen as sharing a fundamentally Hebbian (purely associative, as opposed to reinforced) view of basic memory processes; a picture of two fundamental reinforcement systems—with dopaminergic systems reinforcing specific responses whether these produce reward or relieving nonpunishment; a distinction between ventral (“what”) and dorsal (“where”) processing streams; a view that behavior results from neural processing of goals (stimulus/response or, better, occasion/affordance compounds); and a view of prefrontal cortex as holding potential or intended goals in mind (i.e., in “working” or “active” memory). The take-home message is that emotion and motivation are intertwined and each is multifaceted. This is often blindingly obvious at the neural level—but still goes against the grain of our normal use of emotional terms. As we have seen, what is meant by “anxiety” can differ even among neurally driven theorists—making it unclear how far disagreements are about real facts or arbitrary definitions. What is needed, then, is recursive processing of neural and behavioral information. When the resultant “psychological” constructs are also firmly tied down to particular neural instantiations then we will be in a position to say that we truly understand the resultant structure of the behaviors emitted by the organism—and will be on the way to understanding our own minds from an objective standpoint.

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REFERENCES American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., rev.). Washington, DC: Author. Amsel, A. (1992). Frustration theory: An analysis of dispositional learning and memory. Cambridge: Cambridge University Press. Bandler, R., Keay, K. A., Floyd, N., & Price, J. (2000). Central circuits mediating patterned autonomic activity during active vs. passive emotional coping. Brain Research Bulletin, 53(1), 95–104. Bandler, R., Price, J. L., & Keay, K. A. (2000). Brain mediation of active and passive emotional coping. Progress in Brain Research, 122, 331–347. Bindra, D. (1969). A unified interpretation of emotion and motivation. Annals of the New York Academy of Sciences, 159, 1071–1083. Blanchard, D. C., & Blanchard, R. J. (1988). Ethoexperimental approaches to the biology of emotion. Annual Review of Psychology, 39, 43–68. Blanchard, D. C., & Blanchard, R. J. (1990). Effects of ethanol, benzodiazepines and serotonin compounds on ethopharmacological models of anxiety. In N. McNaughton & G. Andrews (Eds.), Anxiety (pp. 188–199). Dunedin: Otago University Press. Blanchard, D. C., Blanchard, R. J., Tom, P., & Rodgers, R. J. (1990). Diazepam changes risk assessment in an anxiety/defense test battery. Psychopharmacology (Berl), 101, 511–518. Blanchard, R. J., & Blanchard, D. C. (1989). Antipredator defensive behaviors in a visible burrow system. Journal of Comparative Psychology, 103(1), 70–82. Blanchard, R. J., & Blanchard, D. C. (1990a). Anti-predator defense as models of animal fear and anxiety. In P. F. Brain, S. Parmigiani, R. J. Blanchard, & D. Mainardi (Eds.), Fear and defence (pp. 89–108). Chur: Harwood. Blanchard, R. J., & Blanchard, D. C. (1990b). An ethoexperimental analysis of defense, fear and anxiety. In N. McNaughton & G. Andrews (Eds.), Anxiety (pp. 124–133). Dunedin: Otago University Press. Blanchard, R. J., Griebel, G., Henrie, J. A., & Blanchard, D. C. (1997). Differentiation of anxiolytic and panicolytic drugs by effects on rat and mouse defense test batteries. Neuroscience and Biobehavioral Reviews, 21(6), 783–789. Bliss, T. V. P., Gardner-Medwin, A. R., & Lomo, T. (1973). Synaptic plasticity in the hippocampus. In G. B. Ansell & P. B. Bradley (Eds.), Macromolecules and behaviour (pp. 193–203). London: Macmillan. Bliss, T. V. P., & Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaethsetised rabbit following stimulation of the perforant path. Journal of Physiology (Lond), 232, 331–356. Borkovec, T. D., Robinson, E., Pruzinsky, T., & DePree, J. A. (1983). Preliminary exploration of worry: Some characteristics and processes. Behaviour Research and Therapy, 21(1), 9–16. Brady, J. V. (1975a). Conditioning and emotion. In L. Levi (Ed.), Emotions: Their parameters and measurement (pp. 309–340). New York: Raven Press. Brady, J. V. (1975b). Toward a behavioural biology of emotion. In L. Levi (Ed.), Emotions: Their parameters and measurement (pp. 17–46). New York: Raven Press. Capaldi, E. J. (1967). A sequential hypothesis of instrumental learning. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation (pp. 67–156). New York: Academic Press. Carlson, N. R. (1980). Physiology of behavior. Boston: Allyn & Bacon. Damasio, A. R. (1995). On some functions of the human prefrontal cortex. Annals of the New York Academy of Sciences, 769, 241–251. Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 351, 1413–1420.

8/19/09 4:13:15 PM

730

Central Theories of Motivation and Emotion

Dickinson, A. (1980). Contemporary animal learning theory. Cambridge: Cambridge University Press. Dunn, B. D., Dalgleish, T., & Lawrence, A. D. (2006). The somatic marker hypothesis: A critical evaluation. Neuroscience and Biobehavioral Reviews, 30(2), 239–271. Fanselow, M. S., & LeDoux, J. E. (1999). Why we think plasticity underlying pavlovian fear conditioning occurs in the basolateral amygdala. Neuron, 23(2), 229–232. Feldon, J., Guillamon, A., Gray, J. A., De Wit, H., & McNaughton, N. (1979). Sodium amylobarbitone and responses to nonreward. Quarterly Journal of Experimental Psychology, 31, 19–50. Gray, J. A. (1975). Elements of a two-process theory of learning. London: Academic Press. Gray, J. A. (1977). Drug effects on fear and frustration: Possible limbic site of action of minor tranquilizers. In L. L. Iversen, S. D. Iversen, & S. H. Snyder (Eds.), Handbook of psychopharmacology, drugs, neurotransmitters and behaviour (Vol. 8, pp. 433–529). New York: Plenum Press. Gray, J. A. (1982). The neuropsychology of anxiety: An enquiry in to the functions of the septo-hippocampal system. Oxford: Oxford University Press. Gray, J. A. (1987). The psychology of fear and stress. London: Cambridge University Press. Gray, J. A., & McNaughton, N. (2000). The neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system. Oxford: Oxford University Press. Gray, J. A., & Smith, P. T. (1969). An arousal-decision model for partial reinforcement and discrimination learning. In R. Gilbert & N. S. Sutherland (Eds.), Animal discrimination learning (pp. 243–272). London: Academic Press. Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley-Interscience. Hinde, R. A. (1998). Animal behaviour. New York: McGraw-Hill. Hofer, M. A. (1972). Physiological and behavioural processes in early maternal deprivation. In R. Porter & J. Knight (Eds.), Physiology, emotion and psychosomatic illness (pp. 175–186). Ciba symposium No. 8 (new series). Elsevier. Hull, C. L. (1952). A behavior system. Yale University Press. James, W. (1884). What is an emotion? Mind, 9, 188–205. Jenkins, H. M., & Moore, B. R. (1973). The form of the auto-shaped response with food or water reinforcers. Journal of the Experimental Analysis of Behavior, 20, 163–181. Kandel, E. R., & Hawkins, R. D. (1992). The biological basis of learning and individuality. Scientific American, 267, 79–86. Kelly, J. F., & Hake, D. F. (1970). An extinction-induced increase in an aggressive response with humans. Journal of the Experimental Analysis of Behavior, 14, 153–164. Konorski, J. (1967). Integrative activity of the brain: An interdisciplinary approach. Chicago: University of Chicago Press. Krebs, J. R., Stephens, D. W., & Sutherland, W. J. (1983). Perspectives in optimal foraging. In G. A. Clark & A. H. Brush (Eds.), Perspectives in ornithology (pp. 165–221). Cambridge: Cambridge University Press.

McNamara, J., & Houston, A. (1980). The application of statistical decision theory to animal behaviour. Journal of Theoretical Biology, 85, 673–690. McNaughton, N. (1985). Chlordiazepoxide and successive discrimination: Different effects on acquisition and performance. Pharmacology, Biochemistry and Behavior, 23, 487–494. McNaughton, N. (1989a). Anxiety: One label for many processes. New Zealand Journal of Psychology, 18, 51–59. McNaughton, N. (1989b). Biology and emotion. Cambridge: Cambridge University Press. McNaughton, N., & Corr, P. J. (2004). A two-dimensional neuropsychology of defense: Fear/anxiety and defensive distance. Neuroscience and Biobehavioral Reviews, 28, 285–305. McNaughton, N., & Gray, J. A. (1983). Pavlovian counterconditioning is unchanged by chlordiazepoxide or by septal lesions. Quarterly Journal of Experimental Psychology, 35B, 221–233. McNaughton, N., Kocsis, B., & Hajós, M. (2007). Elicited hippocampal theta rhythm: A screen for anxiolytic and pro-cognitive drugs through changes in hippocampal function? Behavioural Pharmacology, 18, 329–346. Meyer, T. J., Miller, M. L., Metzger, R. L., & Borkovec, T. D. (1999). Development and validation of the Penn state worry questionnaire. Behaviour Research and Therapy, 28, 487–495. Millenson, J. R., & Leslie, J. C. (1979). Principles of behavior analysis. New York: MacMillan. Panksepp, J. (1998). Affective neuroscience: The foundations of human and animal emotions. New York: Oxford University Press. Pinel, J. P. J. (1997). Biopsychology. Boston: Allyn & Bacon. Renfrew, J. W., & Hutchinson, R. R. (1983). Motivation The motivation of aggression. In E. Satinoff & P. Teitelbaum (Eds.) Handbook of Behavioural Neurobiology, (Vol. 6, pp. 511–541). Plenum Press. Reynolds, J. N. J., Hyland, B. I., & Wickens, J. R. (2001, August 23). A cellular mechanism of reward-related learning. Nature, 413(6851), 67–70. Rolls, E. T. (1990). A theory of emotion, and its application to understanding the neural basis of emotion. Cognition and Emotion, 4, 161–190. Rolls, E. T. (2000). On the brain and emotion. Behavioral and Brain Sciences, 23(2), 219–228. Sartory, G., MacDonald, R., & Gray, J. A. (1990). Effects of diazepam on approach, self-reported fear and psychophysological responses in snake phobics. Behaviour Research and Therapy, 28(4), 273–282. Skinner, B. F. (1953). Science and human behavior. New York: Macmillan. Smits, D. J. M., & Boeck, P. D. (2006). From BIS/BAS to the big five. European Journal of Personality, 20, 255–270. Sokolowski, J. D., McCullough, L. D., & Salamone, J. D. (1994). Effects of dopamine depletions in the medial prefrontal cortex on active avoidance and escape in the rat. Brain Research, 651, 293–299. Stark, H., Bischof, A., & Scheich, H. (1999). Increase of extracellular dopamine in prefrontal cortex of gerbils during acquisition of the avoidance strategy in the shuttle-box. Neuroscience Letters, 264(1–3), 77–80. Sutherland, N. S. (1966). Partial reinforcement and the breadth of learning. Journal of Experimental Psychology, 18, 289–301.

LeDoux, J. E. (1993). Emotional memory systems in the brain. Behavioural Brain Research, 58, 69–79.

Weingarten, H. P. (1983). Conditioned cues elicit feeding in sated rats: A role for learning in meal initiation. Science, 220(4595), 431–433.

LeDoux, J. E. (1994). Emotion, memory and the brain. Scientific American, 270, 50–59.

Ziff, D. R., & Capaldi, E. J. (1971). Amytal and the small trial partial reinforcement effect: Stimulus properties of early trial nonrewards. Journal of Experimental Child Psychology, 87, 263–269.

LeDoux, J. E. (2002). Synaptic self. Harmondsworth: Viking Penguin.

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Chapter 37

The Affective Neuroscience of Emotion: Automatic Activation, Interoception, and Emotion Regulation ANDREAS OLSSON AND ARNE ÖHMAN

Emotions sometimes appear mysterious. They can appear unmistakably clear, yet at the same time elusive. In our daily lives we often define the basic goals of human striving in terms of emotion: We yearn for happiness and do our best to avoid misery. But making the distinction between positive and negative emotions is not as simple as saying that we seek the former and shun the latter. Peace Corps workers, parachute jumpers, and snake handlers might seek situations that most of us fear. Likewise, we may indulge in behaviors such as passionate love, overeating, drinking too much, and substance abuse even when it brings misery. At times we may simultaneously experience two conflicting emotions about another person, pulling us in opposite directions, and after a separation we may find that our current emotions are not as abyssal as we forecasted them to be some time ago. In many of the most common psychological disorders—depression, anxiety disorders, and phobias—individuals find that their emotional lives defy reason and rational thought. Still, for most of us, life without emotion would not be worth living. Conflicts are not only abundant in our everyday experience of emotions. The landscape of scientific theories aspiring to describe and explain emotion is also riddled with conflicts. However, these often opposing theories of emotion have over the past century inspired various research paradigms in the behavioral and neurosciences that have contributed importantly to what we know about emotions today. In this chapter we revisit a selection of the most influential

approaches to emotion to discuss findings that are enlightening in terms of the link between behavior and its neural bases. A greater appreciation of the link between emotional behavior and its biological bases is critical for a more complete understanding of emotion and its conflicting nature.

UNDERSTANDING EMOTION THROUGH THE BRAIN–BEHAVIOR LINK The Neuroscience of Emotion Psychological research on emotion basically is a multivariate enterprise seeking to develop theories that relate emotion-provoking circumstances to verbal reports of emotion, psychophysiological data, and behavioral responses. An important contribution of this research is that it has made sophisticated methodologies available for manipulating and measuring emotion in the psychological laboratory (see Coan & Allen, 2007). Neuroscience offers unique prospects for a deeper understanding of emotion by revealing the neural underpinnings of the relationships revealed in the psychological laboratory. Imaging Emotion Animal research over the past century laid the foundation for understanding some of the basic neural mechanisms of emotion. However, the recent development of techniques to image the healthy human brain in vivo has provided an unprecedented opportunity to directly study the neural elements of emotional responses and experiences. Through imaging, the workings of the emotional brain can now be observed independently of people’s introspective accounts of their emotional states. Together with our already quite sophisticated knowledge of the peripheral psychophysiology of emotional responses, descriptions of neural processes are helping us to understand the specific mechanisms

The authors are also affiliated with the NIHM Center for the Study of Emotion and Attention at the University of Florida–Gainesville and the NOS-HS Center of Excellence on Cognitive Control. Arne Öhman is the recipient of a grant for Long-Term Support of Leading Investigators from the Swedish Science Research Council. Address for both authors: Section of Psychology, Department of Clinical Neuroscience, Karolinska University Hospital, Solna. 731

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732 The Affective Neuroscience of Emotion: Automatic Activation, Interoception, and Emotion Regulation

underlying emotions. In turn, mechanistic accounts provide a biological grounding that can be used to constrain alternative models and theories of emotion. Moreover, knowledge about which psychological processes involve similar and different neurobiological processes allows us to use neurobiology to carve the nature of emotion at its joints. For example, brain imaging research has confirmed that negative attitudes to members belonging to a racial group other than one’s own has an immediate, nonconscious emotional component, which is modulated by prefrontal influences (Cunningham, Johnson, Gatenby, Gore, & Banaji, 2004; Phelps et al., 2000). The rapidly growing body of knowledge about the workings of the human brain has made it possible to compare and integrate this knowledge with what we know about the brain– behavior link in other animals. Comparisons across species make it possible to use experimental paradigms in nonhuman animals, which would not be ethically feasible in humans, to learn more about human emotions. As a result, evolutionary theory has gained increasing influence as a unifying theory in psychology, solidifying its place within the realm of biological sciences. This development has facilitated scientific accounts of emotional behavior comprising both its proximate mechanisms and evolutionary functions (Damasio, 1994; LeDoux, 1996; Öhman, 1986; Rolls, 1999). Limitations of Imaging It should be remembered that neuroimaging has inherent limitations because of its correlational nature. For example, it can tell us which brain areas are involved in an emotional response, but it remains silent on what neural processes are necessary for a specific response, which is a critical step in inferring causality. To learn about the causal links between brain functions and behavior we need observations of the effects on behavior when the functioning of well-defined functional neural circuitry is impaired. To this end, systematic studies of patients with localized brain damage have proven invaluable. Lately a new technique, transcranial magnetic stimulation, has gained popularity. This technique has been developed to induce temporary and completely reversible brain lesions in healthy volunteers through the application of strong magnetic fields to specific cortical areas. Although there are several limitations to this technique, such as its being applicable only to cortical regions, it has been successfully used to examine the causal link between brain and behavior in humans. Nevertheless, to overcome the limitations associated with experimentation in humans, the study of other animals will remain imperative in our quest for a full understanding of emotion. Implicit and Explicit Measures of Emotion As stated earlier, neural activation, whether central or peripheral, can be assessed independently of the individual’s verbal

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accounts of emotional experiences. Measures of emotional responses that are nonverbal in nature are often referred to as “implicit,” in contrast to “explicit,” verbal accounts. Whereas implicit measures assess responses of which the individual often lacks introspective awareness, explicit measures tap responses dependent of linguistic processes, such as reflective reasoning. Because of their relative insensitivity to demand characteristics, implicit responses are critical in crossspecies comparisons of emotion. Particularly informative are implicit responses that can be dissociated from the emoter’s explicit reports. There are good reasons for assuming that these responses provide a window into the workings of phylogenetically older mechanisms, drawing on brain systems that are partially independent from more recently evolved systems that are responsible for linguistic processing (LeDoux, 1996; Öhman, 1986; Öhman & Wiens, 2003). Although our current understanding of implicit emotional responses relies on recent technological advancements, the interest in different, separable nuances of emotional responses is ancient.

TWO EMOTIONAL MOVEMENTS More than 2,000 years ago Greek philosophers belonging to the Stoic tradition proposed an interesting separation between what they called the first and second “movements” of an emotion (Oatley, 2004). This would turn out to be prophetic about an important distinction in modern emotion research: automatic versus controlled emotional processing, or primary versus secondary appraisal. The first movement is rapid and reflexive, such as when we freeze when we are confronted with a snake or a fearful other whose fear expression might be informative about an imminent danger (Figure 37.1). The second movement is what we make of this instinctive response and how we appraise the current situation: Is the snake poisonous? Why is the other expressing fear? How does this fit into the surrounding context (Figure 37.1)? What are his or her intentions? This evaluative or reflective process depends mainly on voluntary mental activity. Reflecting about the properties of a stimulus is likely to change our emotional experience without our intending to do so. However, reflection can also be intentionally used to change our emotional responses. For example, you might want to curb the empathic response you feel for a terrified child who is about to receive an injection (Figure 37.1)—especially if you are the doctor who has to administer the injection with acute precision. An emotional response can be down-regulated in various ways, such as by shifting one’s attention to something less evocative or by reinterpreting the meaning of the situation. For the purpose of this chapter, and to maintain the Stoic terminology, we have tentatively called this intentional regulation the “third movement.”

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Two Emotional Movements External stimuli attended

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The temporal flow of emotion

1st movement

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Figure 37.1 The temporal flow of emotion. Note. This figure illustrates how an emotional situation (a fearful child anticipating an injection) gives rise to the unfolding of a series of emotional responses in a perceiver. The temporal flow of these responses and a selection of the associated functional neuroanatomy is divided into three hypothetical stages, approximating the first (A), second (B), and third (C) emotional movements. (A) Initially, the fearful face of the child is encountered, giving rise to a reflexive orienting response, which mobilizes the perceiver to search of the environment for potential threats that could have caused the child’s fear expression. The attentional focus on the fearful face produces heart rate deceleration and an increased arousal response (SCR). These primary responses are predominantly mediated by a subcortical neural circuitry centered on the amygdala, which affects behavior through hypothalamic and brain stem nuclei. (B) As additional information becomes available to the perceiver, the situation is appraised in terms of its context and memories of similar situations. For example, facilitated by an automatically triggered mirroring of the child’s emotional responses, the reflective attribution of pain might produce an empathic response in the perceiver, which leads to an increase in physiological arousal. Apart from the subcortical circuitry described (A), this process is likely to draw on both regions involved in the retrieval of episodic memories, such as the hippocampus, sensory, and frontal regions responsible for the empathic experience of and reflection about the child’s emotional state. (C) The empathic response can be intentionally down-regulated through reappraisal of the situation. For example, reminding oneself of the long-term benefits to the child from receiving a life-saving vaccination or attending to his or her soothing parent might mitigate the perceiver’s emotional response. This regulatory process can be facilitated by selectively attending information that confirms one’s reappraisal of the situation. Emotion regulation has been shown to involve prefrontal influences on the subcortical circuitry described in A. As illustrated by the arrows at the bottom of the figure, the reflective appraisals of the situation described in B and C are likely to affect the initial response to new stimuli through top-down modulation of primary appraisals, supported by bidirectional neural connections. ACC = Anterior cingulate cortex; AI = Anterior insula; LPFC = Lateral prefrontal cortex; MPFC = Medial prefrontal cortex; PFC = Prefrontal cortex; SCR = Skin conductance response.

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By making the second movement the essence of emotion, the Stoics changed the meaning of emotion from an automatic and involuntary response to something individuals could consciously control and take responsibility for. The distinction between the first and second movements is reminiscent of today’s accounts of dual processing of emotion that comes in many guises. Common to all of them is the suggestion that two distinct kinds of processes, resembling the two movements, are responsible for different aspects of emotional responses. Recent neuroscientific research has substantiated dual processing by describing two general types of processes, engaging partially separated but, under normal circumstances, interacting neural systems. The first system comprises reflexive, automatic responses that are implicitly expressed; the second comprises more reflective processes that can be affected by voluntary control.

The First Movement of Emotion Automatic Appraisal In 1980 Robert Zajonc and colleagues proposed a conceptualization of the first movement of emotion in terms of an automatic affective response that was primary to, and independent of, cognitive responses to the stimulus (KunstWilson & Zajonc, 1980). Succinctly summarized, Zajonc claimed that we are sure of what we like, even though we may not know why we like it, or even what it is that we like. This proposal was backed up by an extensive series of studies examining the liking of stimuli as a function of repeated exposure (see Zajonc, 2004, for a concise summary). For example, Kunst-Wilson and Zajonc presented Chinese ideograms at durations as short as a few milliseconds to make them unrecognizable. Even though the data confirmed their unrecognizability, the participants liked previously shown (but nonseen) more than new ideograms. The Neural Bases of Automatic Appraisal LeDoux (1996) suggested that the automatic affective response described by Zajonc (1980) was related to the very fast brain activation he and his colleagues had demonstrated through a direct route between sensory thalamic nuclei and the amygdala, a conglomerate of subnuclei bilaterally located in the medial temporal lobes, which has long been considered a central substrate of emotion (Klüver & Bucy, 1939; Weiskrantz, 1956). It has been argued that this neural system evolved to cope with rapidly emerging dangers, quickly relaying crude representations of potentially threatening stimuli to the amygdala, bypassing the cortex (Adolphs, 2001; de Gelder, Snyder, Greve, Gerard, & Hadjikhani, 2004; LeDoux, 1996, 2000; Morris, Öhman, & Dolan, 1999; Öhman & Mineka, 2001; Whalen et al., 1998).

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734 The Affective Neuroscience of Emotion: Automatic Activation, Interoception, and Emotion Regulation

In an extensive research program, LeDoux (1996, 2000) and others (see Davis & Whalen, 2001) have demonstrated that the amygdala is the hub of this automatic and phylogenetically ancient neural circuitry centered on subcortical regions, controlling fear responses to threatening stimuli. The lateral nucleus of the amygdala receives input from the external world via the direct thalamic route and via the classical sensory pathways through sensory thalamic nuclei and the primary and secondary sensory cortices. This information is forwarded to the central nucleus, which has efferent connections to areas involved in emotional expression, such as the hypothalamus (sympathetic branch of the autonomic nervous system), and midbrain and brain stem nuclei related to fear behavior (periaqueductal gray), defensive reflexes (such as startle), and facial expression (facial nucleus). Although involved in many emotionally relevant processes, the primary function of this network is to quickly enhance attention to evaluate a potential threat by means of ramping up activation in a distributed network of functional brain systems (Davis & Whalen, 2001). It is also critical to the acquisition, retention, and expression of basic emotional forms of learning (Phelps & LeDoux, 2005) and certain social functions (Adolphs, 2001; Adolphs et al., 2005). Consistent with the role of this neural system in assembling perceptual and attentional resources and to prepare for action, the processing of emotionally significant stimuli that is known to involve the amygdala has been reported to influence early visual and attentional processing (Anderson & Phelps, 2001; Morris, Friston, et al., 1998; Phelps, Ling, & Carrasco, 2006; Vuilleumier, Richardson, Armony, Driver, & Dolan, 2004; see also Volume II, Section V, Chapter 11 of this handbook [D'Stanley, Ferneyhough & Phelps]) and action representations (de Gelder et al., 2004). However, the precise mechanisms underlying the amygdala’s influence on a widely distributed network of cortical areas have not yet been specified. These issues are currently attracting much research and debate (McGaugh, 2004; Phelps, 2006). Data Supporting the Concept of Automatic Appraisal Supporting the concept of a first automatic stage in emotion activation are both behavioral and neuroimaging indicators of immediate nonconscious emotional responses to unseen images presented very briefly and effectively masked from conscious perception by an immediate masking picture. For example, participants fearful of snakes and spiders responded with enhanced skin conductance responses (SCRs) to pictorial representations of the feared animals even when pictures of the animals were unseen (masked; Öhman & Soares, 1994). These behavioral data have been supported by brain imaging studies showing amygdala activations to masked snakes and spiders in fearful individuals

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(Carlsson et al., 2004; Figure 37.2), to masked fearful faces (Whalen et al., 1998), and to aversively conditioned masked angry faces (Morris, Öhman, & Dolan, 1998). Furthermore, two studies have reported reliable amygdala activation to suppressed pictures of fearful faces in a binocular rivalry paradigm (Pasley, Mayes, & Schultz, 2004; Williams, Morris, McGlone, Abbott, & Mattingley, 2004), thus providing converging evidence that the amygdala can be activated by “unseen” visual stimuli. There is also suggestive evidence that visual information may take a direct route to the amygdala, bypassing the cortex. Morris, Öhman, & Dolan (1999) examined the neural connectivity between the amygdala and other brain regions when the amygdala was activated by masked stimuli. They reported that such activation could be reliably predicted from activation of subcortical way stations in the visual systems, such as the superior colliculus and the right pulvinar nucleus of the thalamus, but not from any cortical regions. The superior colliculus and the pulvinar are both involved in attentional processes, the superior colliculus P-unseen vs. N-unseen (A)

F-unseen vs. N-unseen (B) 3

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Figure 37.2 Amygdala activations to masked and nonmasked pictures of snakes and spiders in individuals fearful of snakes or spiders. Note. Upper panels (A–B) show coronal views (y = ⫺4) of activation maps for unseen (masked) phobic stimuli (e.g., picture of a snake) contrasted with an unseen neutral stimulus (a picture of a mushroom) (P-unseen versus N-unseen) and a fear-relevant but nonfeared (e.g., a spider) stimulus (F-unseen versus N-unseen). Note the left-lateralized amygdala activations in both conditions. Lower panels (C–D) show contrasts between seen (nonmasked) phobic and neutral stimuli (P-seen versus N-seen, y = ⫺8) and between phobic and fear-relevant but nonfeared stimuli (P-seen versus F-seen, y = ⫺4). Note the bilateral amygdala activations seen in both contrasts. From “Fear and the Amygdala: Manipulation of Awareness Generates Differential Cerebral Responses to Phobic and Fear-Relevant (but Nonfeared) Stimuli,” by Carlsson et al., 2004, Emotion, 4, p. 345. Reprinted with permission.

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Two Emotional Movements

in eye movement control and shifts of attention and the pulvinar in monitoring attentional salience. Liddell et al. (2005) examined the effect of masked fearful versus masked neutral faces on anatomically defined regions of interest. Confirming the connectivity data reported by Morris, Öhman, et al. (1999), they found reliable activation to masked fearful faces in the left superior colliculi, the left pulvinar, and the bilateral amygdala. In addition they found activation in the locus coeruleus and the anterior cingulate cortex. The visual resolution that can be achieved by this route is likely to be quite restricted. Vuilleumier, Armony, Driver, and Dolan (2003) suggested that it operates primarily on gross, low-frequency information. Accordingly, they filtered the spatial frequency of pictures of faces to produce facial stimuli that retained only high- or low-frequency spatial information. Their results showed that amygdala responses were larger for low-frequency faces provided that they showed expressions of fear. Moreover, these researchers demonstrated activation of the pulvinar and superior colliculus by low-frequency but not high-frequency fearful faces. Thus these results suggest that there is a distinct superior colliculus-pulvinar pathway to the amygdala that operates primarily on low-frequency information. Appraising Reward Whereas the amygdala has been primarily implicated in the processing of aversive information (although evidence for its involvement in appetitive processing is accumulating; see, e.g., Murray, 2007; Sabatinelli, Bradley, Fitzsimmons, & Lang, 2005), the striatum has been singled out as critical for the automatic processing of appetitive stimuli and the generation of implicit positive emotional responses. Research has shown that the striatum can be engaged in a variety of different, but often related, tasks, such as the processing of reward stimuli (Cromwell & Schultz, 2003; Knutson, Adams, Fong, & Hommer, 2001), error-driven learning (O’Doherty et al., 2004; Schultz, 2002), novelty seeking (Bevins et al., 2002), and the initiation of instrumental behaviors (Rolls, 1999). With regard to appetitive emotional stimuli, Berridge and Winkielman (2003) showed that perceptually masked pictures of happy faces facilitated fluid consumption in thirsty participants who remained completely unaware of the emotional faces and any relationship between them and the drinking task. Angry faces had the opposite effect. Pessiglione et al. (2007) demonstrated that the striatum was critical in mediating motivation to perform in a task reinforced by monetary incentives. They provided masked or nonmasked information to their participants about how much money they could earn by exerting maximal handgrip force. Even when pictures of expected monetary

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gain (a pound or a penny) were blocked from awareness by masking stimuli, activation in the ventral striatum was larger and the force exerted higher when the potential gain was large rather than small. Thus, whereas the amygdala can be nonconsciously activated to mediate fear, the striatum can be nonconsciously activated to mediate reward and positive emotion. There is converging evidence, therefore, that rapid, automatic processes operate both in aversive and appetitive circumstances to result in immediate and unconscious emotional activation. At this point a caveat is in place. Although we have focused on two core functional regions (the amygdala and the striatum), both aversive and reward processes normally draw on a widely distributed network of neural systems. This is discussed in greater detail later. Automatic Appraisal in Social Contexts Designed by Evolution Evolutionary scientists commonly assume that the pressure of complex social organization catalyzed the rapid enlargement of the human brain during the past million years. Robinson Crusoe, as Nicholas Humphrey once remarked, illustrates this model of human evolution: The real challenge for Crusoe was not to survive alone on the island but to survive the relationship with his man, Friday (Humphrey, 1983). Compared to the physical environment, our social milieu is more complex, less predictable, and, importantly, more responsive to our behavior. These characteristics, especially the benefits associated with social reciprocity and the dangers linked to interpersonal aggression, are thought to have driven the evolution of emotional responses to social cues. In light of this, it is not surprising that emotional and social tasks to a large extent recruit overlapping regions of the brain, engaging both subcortical and more posterior regions of the brain that traditionally have been ascribed a role in more reflexive emotional functions, as well as more anterior-medial cortices supporting reflective emotional functions (Ochsner et al., 2004a; Olsson & Ochsner, 2008). Indeed, there are both ontogenetic and phylogenetic reasons for why emotional and social processes should be intimately intertwined at all levels of explanation: the behavioral, the experiential, and the neural. Next we consider emotions in the social domain to illustrate the unfolding of the two movements and the dynamic interplay between them. Automatic Appraisal of Social Threat Our social environment is the source of our happiest moments, but also our greatest fears, and the emotional value of social stimuli can change within fractions of a second. Indeed, the swift alternation of facial expressions in a

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736 The Affective Neuroscience of Emotion: Automatic Activation, Interoception, and Emotion Regulation

conspecific, signaling a shift from benevolent to adversary intentions, illustrates rapidly occurring changes that may have fatal implications for the individual if not adaptively responded to immediately. Under these circumstances it is critical to make a rapid evaluation of emotionally relevant cues around us, as well as of the current social context (Figure 37.1). As discussed earlier, the amygdala has been shown to play a role in the automatic appraisal of biologically relevant cues, such as fearful and angry faces (Morris et al., 1998). Adding importantly to our knowledge about the role of the amygdala in social emotions, a study on braindamaged patients suggested that the amygdala not only is sensitive to fear and other expressions, but may also contribute to the generation of actions favoring the detection of fear (Adolphs et al., 2005). This study reported that the inability to recognize fear expressions in a patient with bilateral amygdala lesions was due to the patient’s inability to make spontaneous use of information in the eye region, a region that is particularly diagnostic for fear expressions. These results remind us about the importance of complementing imaging work with research on patients with specific brain damages. Naturally our social environment contains significant cues other than emotional expressions, which can trigger a fast emotional response in the perceiver. Based on the significance of the sex and group belonging of others, signaling a potential mate, friend, or foe, it is reasonable that these categories are evaluated quickly. Indeed, research has shown that these categories are coded instantly, giving rise to a reflexive response. For example, there are now numerous demonstrations that unknown racial outgroup members, that is, individuals not belonging to one’s own racial group, can elicit a rapid threat response associated with the amygdala (Cunningham et al., 2004; Phelps et al., 2000). Pointing to the possibility of a hard-wired disposition to develop xenophobic responses, when associated with something aversive, male outgroup members elicit a stronger implicit fear response that is more persistent than that produced by male ingroup members. Interestingly, this effect has been demonstrated to be independent of explicit attitudes as well as previous exposure to the racial outgroup, with the exception of having outgroup dating experience, which abolishes the effect (Navarrete et al., 2009; Olsson, Ebert, Banaji, & Phelps, 2005).

reliable. For example, a recent study shows that viewing times as short as 100 ms allow a person to form an impression about a target person’s likeability, trustworthiness, competence, and aggressiveness that are highly correlated with impressions made in the absence of time constraints (Willis & Todorov, 2006). This resonates well with previous imaging (Winston, Strange, O’Doherty, & Dolan, 2002) and neuropsychological (Adolphs, 2001) studies reporting that the amygdala is involved in automatic evaluation of trustworthiness. That some of these immediate evaluations can have real and important behavioral implications outside the laboratory is evidenced by the fact that impression of competence formed about a face within 1 second has been shown to predict U.S. congressional elections better than chance (Todorov, Mandisodza, Goren, & Hall, 2005).

Automatic Appraisal of Traits

To predict the potential threat or reward value of another individual, information about his or her emotional state and intentions is critical. To this end people might benefit from an experiential sharing of the other ’s emotional state. Apart form enhancing the predictability of others’ behavior, this might also facilitate our social interaction.

People’s evaluations of each other often involve more complex judgments that are detached from the physical features of the target individual. Research has shown that even thin temporal slices of visual information can result in social evaluations that are quite sophisticated and surprisingly

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Automatic Appraisal of Social Rewards Because danger in an evolutionary perspective was defined by agile, hungry predators and attacking conspecifics, with potentially deadly consequences, there was a high premium on responding quickly to these kinds of stimuli. Missing out on positive resources such as mates and meals, on the other hand, typically involved a missed opportunity rather than the end of a genetic lineage. In this perspective, it is hardly surprising that “negative information weighs more heavily on the brain” (Ito, Larsen, Smith, & Cacioppo, 1998, p. 887). Naturally, social stimuli can also trigger rapid reward-related emotional responses, although the heavy research bias on studying aversive responses appears to suggest something else. For example, attractive faces are instantly responded to even when the perceiver ’s attentional resources are consumed by some other explicit task. These emotional responses are tracked by the striatum (Childress et al., 2008; Cloutier, Heatherton, Whalen, & Kelley, 2008). Not only facial displays are instantly registered. Other physical traits signaling fertility and biological aptness, such as symmetry and body proportions, are also registered by the striatum even when the perceiver is lacking awareness of these cues (Cornwell et al., 2004; Schützwohl, 2006). Research has shown that unseen social rewards (erotica) can also affect our behavior (Jiang, Costello, Fang, Huang, & He, 2006). Mirroring Emotions

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Two Emotional Movements

Supporting these assumptions, it has been shown that humans spontaneously imitate emotional facial expressions. Experimenters using masking stimuli have even observed unconscious imitation responses (as assessed by electrophysiological measurements of facial muscles) to both angry and happy faces (Dimberg, Thunberg, & Elmehed, 2000). Without knowing it, we respond with minuscule facial gestures, which adds emotional color to our social interactions. In addition, social psychologists have demonstrated that whether we feel relaxed or uncomfortable with some people is affected by nonconscious emotional cues (Chartrand & Bargh, 1999). Mirror Neurons The discovery of so-called mirror neurons in the motor cortex that represent both one’s own and the corresponding actions in others has led to the suggestion that these reflexive responses play a key role in the understanding of actions and intentions (Gallese, Keysers, & Rizzolatti, 2004; Iacoboni & Dapretto, 2006). Inspired by this development, it has been proposed that shared representations of one’s own and others’ emotional experiences provide the reflexive basis for emotional empathy. This logic has guided subsequent studies of the direct experience and observation of pain or emotion showing activation of overlapping neural systems, mainly two cortical regions receiving ascending viscerosensory inputs: the anterior insula (AI) and the mid portion of the anterior cingulate cortex (ACC; Carr, Iacoboni, Dubeau, Mazziotta, & Lenzi, 2003; Decety & Jackson, 2007; Gallese et al., 2004; Iacoboni & Dapretto, 2006; Morrison, Lloyd, di Pellegrino, & Roberts, 2004; Singer, et al., 2004; Zaki, Ochsner, Hanelin, Wager, & Mackey, 2007). The AI is thought to support affective experience in part by providing awareness of these body state inputs (Craig, 2002; Critchley, Wiens, Rotshtein, Öhman, & Dolan, 2004), and the ACC is believed to code affective attributes of pain, such as perceived unpleasantness as opposed to sensory-discriminative properties, such as location and intensity (Eisenberger, Lieberman, & Williams, 2003; Hutchison, Davis, Lozano, Tasker, & Dostrovsky, 1999; Wager et al., 2004), and motivate appropriate behavior via projections to brain regions supporting motor and autonomic output (Critchley et al., 2004). The engagement of the AI, ACC, and other regions supporting the automatic sharing of, and hence an experiential understanding of, the intentions behind others’ emotional behaviors may in turn provide a substrate for the empathic responses underlying prosocial behaviors (Decety & Jackson, 2007; Lamm, Batson, & Decety, 2007; Singer et al., 2004). Of course, emotion understanding isn’t always so simple. Nonverbal emotional cues are often ambiguous, and additional information is needed to constrain attributions

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about someone’s emotional state and intentions. Our prior experience with that person and knowledge about antecedent events and the wider surrounding context are important sources of information (Figure 37.1). For example, wide-open eyes could mean someone is either afraid or surprised—triggering amygdala activity accordingly— depending on our knowledge of what has just happened to him or her (Kim, Somerville, Johnstone, Alexander, & Whalen, 2003). Similarly, activation of shared representations—and presumably empathic responses—may be blocked if one perceives another to be a past or potential competitor (Batson, Thompson, & Chen, 2002; Singer et al., 2006) or the situation makes an emotional response inappropriate (Figure 37.1). Indeed, our understanding of the context and our expectations about the future (e.g., others’ behavior) are important determinants in our experience of a situation, which brings us to the next step of emotional processing. The Second Movement of Emotion Secondary Appraisal Automatic emotional activation provides input to the next stage of emotional processing, which we have called the second movement of emotion (Oatley, 2004). This is a more sluggish activity, which is responsive to reflection and explicit mental processes. It depends on more flexible neural mechanisms, primarily drawing on regions in sensory and prefrontal cortices as well as regions responsible for episodic memories, such as the hippocampus (Tulving, 2002). Of course, in our daily lives, which for most people is predominantly spent outside the laboratory, distant from experimenters’ surreptitious attempts to artificially tease apart different kinds of emotional processes, there is a constant interplay between rapid, reflexive processes on the one hand and slower reflective processes on the other. Providing the basis for this cross-talk, the amygdala and the striatum are reciprocally connected with cortical brain regions with a more recent evolutionary past. Constructing Emotions The second movement is the driving force behind the fact that people tend to construct their emotions. Depending on our previous experiences, we may respond in vastly different ways to the same emotional stimulus (Figure 37.1). Following this, some researchers have argued that the perceived meaning of the situation is the central determinant of the emotional response. And emotional meaning, it is claimed, results from an appraisal process (Scherer, Schorr, & Johnstone, 2001). To distinguish this from the automatic processes that provide an immediate evaluation of a stimulus, these appraisal processes should be called secondary appraisal.

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738 The Affective Neuroscience of Emotion: Automatic Activation, Interoception, and Emotion Regulation

Input from Automatic to Secondary Appraisal There are different kinds of input from automatic to secondary appraisal. A major part concerns outputs from preliminary sensory processing. For example, these outputs may segregate the perceptual field into central figures and background, provide preliminary identification of the figures, and command attention to them for further processing. More important in the present context, the automatic processes of the amygdala and the striatum provide emotional coloring of the stimulus even before it is identified. The primary result of this evaluation is an estimate of the “goodness” and the “badness” of the stimulus, relating to a behavioral posture of approach or avoidance (e.g., Lang, Bradley, & Cuthbert, 1998). Interoceptive Input to Emotion What is the medium for conveying the emotional tone between the automatic and secondary appraisal stages? Conceptualizing the interface between the two processes in this way invites resurrecting the basic idea of the James-Lange theory of emotion: Feedback from the emotional response is a central stimulus source for emotion (and emotional experience). Concisely put, we do not cry because we feel sad; we feel sad because we cry. This idea has been in disrepute for close to a century after Cannon’s (1927) devastating critique. Cannon’s basic argument was that the physiological activation seen in intense emotion is too crude and too slow to account for the richness and nuance of emotional experience. Indeed, Cannon himself demonstrated that the patterns of physiological responses observed in anger and fear are indistinguishable and that it takes several seconds (and sometimes even tens of seconds) for the autonomic response to reach its maximum after an emotional provocation. Given what we know today, this critique is not as damaging as once perceived. Emotional information may reach the amygdala and start activating the bodily response within some 10 ms after it reaches sensory receptors and before it reaches the adequate cortical areas for identification. Feedback from facial responses is highly patterned and available within a few hundred milliseconds. Furthermore, facial feedback remains available even after surgery that blocks information from the body from reaching the brain. This may help explain why animals with such surgery (and humans with spinal chord damage that blocks feedback from the body) still appear to have emotion (thus rebutting another of Cannon’s [1927] critiques of the James-Lange theory). Feedback from the slow autonomic responses may not have to await the full-blown peripheral responses but may start coming in as soon as the relevant brain nuclei are activated. As Damasio (1999) pointed out, actual bodily

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feedback may not always be necessary, because there may be “as-if body loops” that provide central simulations of previously experienced “real” emotional body loops in compressed time scales. Thus the brain may have quite specific information from the body early enough to make it a factor in shaping emotional experience and behavior. Indeed, Damasio and coworkers (2000) showed that simple instructions to recall different emotional episodes activated distinct patterns of activity in brain structures that regulate and represent bodily states. Such patterns provide “neural maps” that differ among emotions. According to Damasio, feelings can be understood as mental images arising from changes in such neural maps representing bodily activations brought about by emotional stimuli. He further speculated that these neural maps may be stored in the anterior insula, which is located in the convoluted cortex between the temporal and frontal lobes and which contains topographical maps of the viscera (see also Craig, 2002, 2004). Critchley et al. (2004) provided experimental data consistent with these conjectures when participants “listened for their heartbeats.” Participants listened to a sequence of tones that were either directly elicited by their heartbeats or were delayed by half a second from the heartbeats, which perceptually disconnected them from the heart. Their task was to decide whether the tones were synchronous with or independent from their heartbeats. In a control task, the participants listened for a tone that was fainter than the others. Compared to the control task, listening for the heartbeats produced activations in the insula, somatomotor, and cingulated cortex (Figure 37.3). Activity in the anterior insula predicted accuracy of heartbeat perception, and self-rated anxiety was related both to insula activation and to accurate heartbeat detection. Gray matter volume in the insula was correlated with heartbeat perception and with self-reported awareness of bodily changes. These data provide a quite compelling case for giving the insula a central role in perception and awareness of bodily changes (Craig, 2004). In addition, there are masking studies suggesting that the anterior insula is one of the brain areas that is exclusively correlated with conscious recognition of emotional stimuli (Critchley, Mathias, & Dolan, 2002). The exact role of bodily input to emotional processing remains to be determined. Because automatic appraisal decides that a stimulus is relevant for well-being, the associated bodily activation provides a critical signal that something should be done about the situation. In most cases, the reason for this activation should be readily appreciated from available contextual information, which also will support appropriate action. However, as we have seen, conscious perception of the eliciting stimulus is not necessary, which means that there might be instances when an emotional arousal is activated in the absence of a readily

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Two Emotional Movements

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Figure 37.3 Functional neural correlates of interoceptive sensitivity. Note. A: Activation of the right anterior insula/opercular area is illustrated as a contrast between activities in the heartbeat auditory detection tasks. The anatomical location is mapped on orthogonal sections of a template brain, with coordinates in mm from anterior commissure. B: Activity within right insular/opercular cortex during interoceptive trials is plotted against interoceptive accuracy (relative to exteroceptive accuracy, to control for nonspecific detection difficulty in the noisy scanning environment). The Pearson correlation coefficient (R) is given in the plot. C: Subject scores on the Hamilton Anxiety Scale are plotted against relative interoceptive

awareness to illustrate the correlation in these subjects between sensitivity to bodily responses and subjective emotional experience, particularly of negative emotions. D: Activity in right anterior insula/opercular activity during interoception also correlated with anxiety score, suggesting that emotional feeling states are supported by explicit interoceptive representations within the right insula cortex. From “Neural Systems Supporting Interoceptive Awareness,” by Critchley et al., 2004, Nature Neuroscience, 7, p. 192. Reprinted with permission.

available explanation. Such unexplained arousal is a powerful motivation to search for explanations (Schachter & Singer, 1962), which will affect how the situation is interpreted. Thus, there may be spillover effects that influence the way the situation is emotionally interpreted. For example, running up stairs triggers an activation of the cardiovascular system. However, the emotional ramification of this activation is very different if we do it for exercise, to meet a lover waiting at the top of the stairs, or to escape a man chasing us with an axe. Although we discuss appraisal processes in terms of explicit mental activity, they need not be conscious. Although originally conscious activities, appraisals, particularly immediate ones, may become automatized to work outside of awareness (Lieberman, 2007).

emotional responses. People use conscious reflection to understand both their own and others’ emotional states. As discussed earlier, interoception provides a key component in reflecting about one’s own emotional state. Because we have already elaborated interoceptive input to the understanding of one’s own emotions was quite extensively, we focus next on reflection about others’ emotions.

Secondary Appraisal in Social Contexts The range of reflective emotional processes is, if anything, both larger and more complex than the first movement of

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Appraising Others’ Emotions The ability to understand another individual’s emotional states is essential for virtually all aspects of social behavior and is likely to depend on both the reflexive emotional empathic responses discussed here and the attribution of mental states. Indeed, emotion understanding by definition requires a causal attribution about the intentions behind an action. As we have seen, people understand others’ emotions partly through the operation of rapid reflexive processes, such as the automatic appraisal of facial expressions and the sharing of emotional states. However, when

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Figure 37.4

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740 The Affective Neuroscience of Emotion: Automatic Activation, Interoception, and Emotion Regulation

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Functional activation during an observational fear-learning task.

A: A coronal view of activation in the right anterior insula, AI (–28, 15, –4)a when observing the pain response of a learning model to a shock. The adjacent graph shows that the magnitude of this activation predicts the strength of the conditioned response (indexed by the SCR) at a later time to a cue associated with the pain of the learning model. B: A sagittal view of activation in the (1) MPFC (1,46,24)a and (2) the ACC (3,27,32)a during the observation of the pain response of a learning model to a

shock. As in A, adjacent graphs display the positive relationship between the magnitude of activation during observation and the subsequent conditioned response. From “The Role of Social Cognition in Emotion,” by Olsson and Ochsner, 2008, Trends in Cognitive Sciences, 12, pp. 65–71. Reprinted with permission. a x, y, z coordinates for local maxima in Talairach space.

stimulus-driven processing of information is not sufficient, more reflective mental state attributions may be needed to understand another individual’s emotional state. These controlled attributions allow us to actively take other people’s perspectives and make judgments about their emotions or diagnostic elements of their emotional dispositions, thereby changing empathic responding (Batson et al., 2002) and recruitment of the anterior insula and the ACC (Lamm et al., 2007). These reflective processes have been shown to depend on a network of regions, including the right temporal parietal junction and dorsal regions of the medial prefrontal cortex (MPFC), including Brodmann area 10 (BA 10; Mitchell, Macrae, & Banaji, 2006; Ochsner et al., 2004a; Saxe, Moran, Scholz, & Gabrieli, 2006). Interestingly, a recent meta-analysis singles out BA 10, which is especially developed in humans, as particularly sensitive to tasks involving both emotions and mental state attributions (Gilbert et al., 2006). Taken together, these lines of work suggest that if the ACC and AI support direct experiential awareness of intentional states, the MPFC network might support the metacognitive reflective awareness of these experiences. The function of appropriately attributing emotional states to others is not limited to understanding, and thus responding to, their emotional expressions in the present moment, but additionally helps us to learn about the events causing others’ emotional responses. Previous behavioral research across primates has suggested that learning through observation draws on overlapping neural processes as classical conditioning (Mineka & Cook, 1993; Olsson & Phelps, 2004). Indeed, these findings were corroborated in

an imaging study demonstrating that overlapping regions of the amygdala, AI, and ACC were active during both observational learning and subsequent expression of fear responses (Olsson, Nearing, & Phelps, 2007). In contrast, the dorsal MPFC was active only during observation of another ’s distress. Importantly, the magnitude of the conditioned response was predicted by activity in the AI, ACC, and dorsal MPFC, suggesting that shared representations supporting experiential understanding of emotion, as well as regions supporting reflective mental state attributions, together support social-emotional learning (Figure 37.4).

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REGULATING OUR EMOTIONAL RESPONSES: A “THIRD MOVEMENT”? An important role of reflective emotional processes is to regulate one’s own emotional responses. Once an emotional response has arisen, there are several ways it can be affected through top-down control (Ochsner & Gross, 2005). It has been argued that primitive regulatory processes that do not need voluntary effort, such as the extinction of a conditioned response, involve the down-regulation of amygdala activity by means of ventral and medial regions of the prefrontal cortex (PFC) across species (Quirk, Garcia, & Gonzalez-Lima, 2006). In contrast, depending on the strategy, voluntary up or down-regulation of amygdalabased emotions have been shown to draw on more dorsal medial and lateral regions, which have been implicated in executive control (Kalisch et al., 2005; Ochsner & Gross, 2005; Ochsner et al., 2004b).

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Summary and Conclusions

The most basic way of intentionally regulating one’s emotional response is to divert one’s attention away from the emotion-provoking stimuli. However, the flexibility of the human mind allows people to reappraise, or reinterpret, the situation in light of other knowledge. For example, if an emotion is evoked by another individual’s emotional expression, one strategy is to reinterpret the situational meaning of the other ’s intentions or feelings, as when thinking positively or negatively about the dispositions (“He is hearty [or weak]”) and future emotions (“Receiving the injection will inflict pain [or make him healthy]”; Figure 37.1). Interestingly, recent work suggests that simply making an attribution about the feelings of another person can have the unintended consequence of disrupting amygdala-mediated negative evaluations of him or her (Harris, Todorov, & Fiske, 2005; Lieberman, 2007). One reason for this may be that the attribution of emotional states can direct attention to the nonthreatening intentions (e.g., thinking about his or her food preferences) of a social target, thereby disambiguating the individual as a potential source of threat. This regulatory strategy can modulate amygdala activity through primarily ventrolateral PFC regions used to select from memory information that helps interpret another ’s feelings (Ochsner et al., 2004a). It is suggested that similar areas inhibit the enhanced amygdala response of nonprejudiced White participants exposed to masked Black faces (indicating an implicit racial bias) when the masking interval was extended to allow conscious recognition (Amodio, Devine, & Harmon-Jones, 2008). The Power of Language Humans are prone to retrospective justifications. As dramatically stated by V. S. Ramachandran (2004, p. 1), “Your conscious life . . . is nothing but an elaborate posthoc rationalization of things you really do for other reasons.” Famous examples of this process were inspired by Leon Festinger ’s (1957) theory of cognitive dissonance, which stated that humans seek balance and consistency in their belief systems. As a consequence, we are motivated to restore balance when there are conflicts between beliefs or between belief and action. For example, when persuaded by shrewd social psychologists to publicly express a view that was inconsistent with their beliefs, research participants were more likely to actually change their beliefs if paid a small rather than a large sum of money. With a big reward, participants could explain away the dissonant action as “I only did it for the money,” whereas with a trivial reward, justifying the action was more likely to require a change in conviction (Festinger & Carlsmith, 1959). Similar processes may be at work in emotion. Even though specific stimuli automatically activate emotions, this

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automatic response often merely sets the constructive mind to work. We feel pressed to understand and to justify our emotions (“The man was so scary, what could I do but try to escape?” or “As adorable as she was, I just fell helplessly in love”), and we retrospectively manipulate emotion to justify our action (“I hit him because he made me so mad” or “I certainly must be madly in love to act this stupidly”). This was illustrated in a choice experiment in which subjects had to choose the most attractive face from two presented alternatives. Unbeknown to the subjects, a card trick made them believe that they had chosen the face they actually had not chosen. Not only did the surreptitious manipulation go unnoticed, but the vast majority of subjects also provided quite elaborated motivations for what they thought were their choices (Johansson, Hall, Sikstrom, & Olsson, 2005). Largely based on his research on split-brain patients, who have had their two cerebral hemispheres surgically disconnected from each other as a treatment for epilepsy, Michael Gazzaniga (1998, 2000) argued that the pressure to justify one’s actions reflects the operation of “an interpreter system” housed in the left frontal cerebral hemisphere. According to this view, the brain automatically takes care of most of the exigencies raised by the interaction of person and environment. The fundamental interpretive component of the human mind comes in late to make sense of the unfolding scenario mindlessly managed by the brain, to fit it into one’s worldview and self-image, and to keep constructing the narrative that we take to be our lives. Unlike all other creatures, humans can, by their access to language, keep a running commentary on their lives. As a consequence, we are prone to mixing up the commentary and the commented-on events in our memories, which may explain the unreliability of our memories (Loftus, 2005). But the commentary is not merely epiphenomenal activity. Rather, it gives consistency to the world and to our actions in it, and it helps us to cope with new situations by timeproven (and largely culturally and socially determined) formulas. In doing its work, the interpreter tries hard to be rational. Indeed, Gazzaniga claims that the interpreter is behind the human adoration of reason. Indeed, this mechanism might also explain the way emotions sometimes appear disconnected from our rational thinking.

SUMMARY AND CONCLUSIONS Our aim in this chapter has been to provide a selective overview of research that illuminates the link between emotional behavior and its neural substrate. To this end we have drawn on various streams of work within the realms of affective and cognitive neuroscience. Providing a rough framework for this endeavor, we have revisited the ancient

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742 The Affective Neuroscience of Emotion: Automatic Activation, Interoception, and Emotion Regulation

division of emotion into two separable movements. To this division, which was originally proposed by the Stoics more than 2,000 years ago (Oatley, 2004), we have added another stage that we have called the third movement. Recent behavioral, psychophysiological, and neural data have validated these distinctions. The first movement describes the initial surge of rapid, automatic, often implicit emotional responses to a stimulus or event, whereas the second movement captures the slower unfolding of a more nuanced repertoire of emotional responses that are dependent on conscious appraisals. Unlike the initial response, these secondary appraisals are shaped by situational factors, our explicit memories, and reflective reasoning. The amygdala has been identified as a core region in neural circuitry responsible for the automatic appraisal of emotionally relevant, especially potentially threatening stimuli. Providing further rapid information and connecting us with our social environment, another network of cortical regions, including the so-called mirror neurons system and the AI, is believed to support rapid mimicking of others’ emotional expressions and experiential sharing of their emotions. These neural systems supporting the first emotional movement contribute important input to a more widely distributed neural network underlying the second emotional movement. For example, the AI provides interoceptive information about one’s own emotional state, and together with prefrontal regions, such as the ACC and medial region of the PFC, it provides the basis for reflections about both one’s own and others’ emotional states. Thanks to the reciprocal connectivity between these more recently evolved prefrontal circuits and evolutionarily older circuits supporting automatic appraisal, emotional responses can not only be spontaneously regulated, but can be shaped by volitional strategies. The reinterpretation of the emotional value of a situation is one such strategy that has received much attention lately and has been shown to involve medial and lateral prefrontal regions. In a way, this prefrontally driven regulation extends the ancient Stoic description of emotions by adding a third movement to the two existing ones. A third movement closes the circle and provides for a loop of neural activation that can be continuously calibrated to produce the currently most adaptive emotional response. In humans language brings about a uniquely flexible tool kit for up or down-regulation of emotional responses. Indeed, the flexibility of symbols can sometimes play tricks on us, such as when our verbal reports of emotions or reflective reasoning stand in conflict with our own emotional experience or behavior. Although much work remains before we can fully understand the nature of emotion, this chapter

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has shown that we have made good progress so far. With the rapid development of techniques to map the functional activity of the human brain, it will continue to be imperative to link these data with their physiological and behavioral correlates. Only this way will emotion—as we known it— appear less mysterious.

REFERENCES Adolphs, R. (2001). The neurobiology of social cognition. Current Opinion in Neurobiology, 11, 231–239. Adolphs, R., Gosselin, F., Buchanan, T. W., Tranel, D., Schyns, P., & Damasio, A. R. (2005, January 20). A mechanism for impaired fear recognition after amygdala damage. Nature, 433, 68–72. Amodio, D. M., Devine, P. G., & Harmon-Jones, E. (2008). Individual differences in the regulation of intergroup bias: The role of conflict monitoring and neural signals for control. Journal of Personality and Social Psychology, 94, 60–74. Anderson, A. K., & Phelps, E. A. (2001, May 17). Lesions of the human amygdala impair enhanced perception of emotionally salient events. Nature, 411, 305–309. Batson, C. D., Thompson, E. R., & Chen, H. (2002). Moral hypocrisy: Addressing some alternatives. Journal of Personality and Social Psychology, 83, 330–339. Berridge, K. C., & Winkielman, P. (2003). What is an unconscious emotion? (The case for unconscious “liking”). Cognition and Emotion, 17, 181–211. Bevins, R. A., Besheer, J., Palmatier, M. I., Jensen, H. C., Pickett, K. S., & Eurek, S. (2002). Novel-object place conditioning: Behavioral and dopaminergic processes in expression of novelty reward. Behavioral Brain Research, 129, 41–50. Cannon, W. (1927). The James-Lange theory of emotions: A critical examination and an alternative theory. American Journal of Psychology, 39, 106–124. Carlsson, K., Petersson, K. M., Lundqvist, D., Karlsson, A., Ingvar, M., & Öhman, A. (2004). Fear and the amygdala: Manipulation of awareness generates differential cerebral responses to phobic and fear-relevant (but nonfeared) stimuli. Emotion, 4, 340–353. Carr, L., Iacoboni, M., Dubeau, M. C., Mazziotta, J. C., & Lenzi, G. L. (2003). Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas. Proceedings of the National Academy of Sciences, USA, 100, 5497–5502. Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: The perception-behavior link and social interaction. Journal of Personality and Social Psychology, 76, 893–910. Childress, A. R., Ehrman, R. N., Wang, Z., Li, Y., Sciortino, N., Hakun, J., et al. (2008). Prelude to passion: Limbic activation by “unseen” drug and sexual cues. PLoS ONE, 3, 1506. Cloutier, J., Heatherton, T. F., Whalen, P. J., & Kelley, W. M. (2008). Are attractive people rewarding? Sex differences in the neural substrates of facial attractiveness. Journal of Cognitive Neuroscience, 20, 941–951. Coan, J. A., & Allen, J. J. B. (Eds.). (2007). Handbook of emotion elicitation and assessment. New York: Oxford University Press. Cornwell, R. E., Boothroyd, L., Burt, D. M., Feinberg, D. R., Jones, B. C., Little, A. C., et al. (2004). Concordant preferences for opposite-sex signals? Human pheromones and facial characteristics. Proceedings of the Royal Society: Biological Science, 271, 635–640. Craig, A. D. (2002). How do you feel? Interoception: The sense of the physiological condition of the body. Nature Reviews, 3, 655–666. Craig, A. D. (2004). Human feelings: Why are some more aware than others? Trends in Cognitive Science, 8, 239–241. Critchley, H., Mathias, C., & Dolan, R. J. (2002). Fear conditioning in humans: The influence of awareness and autonomic arousal on functional neuroanatomy. Neuron, 33, 653–663.

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References Critchley, H. D., Wiens, S., Rotshtein, P., Öhman, A., & Dolan, R. J. (2004). Neural systems supporting interoceptive awareness. Nature Neuroscience, 7, 189–195. Cromwell, H. C., & Schultz, W. (2003). Effects of expectations for different reward magnitudes on neuronal activity in primate striatum. Journal of Neurophysiology, 89, 2823–2838. Cunningham, W. A., Johnson, M. K., Gatenby, J. C., Gore, J. C., & Banaji, M. R. (2003). Neural components of social evaluation. Journal of Personality and Social Psychology, 85, 639–649. Cunningham, W. A., Johnson, M. K., Raye, C. L., Gatenby, J., Gore, J. C., & Banaji, M. R. (2004). Separable neural components in the processing of Black and White faces. Psychological Science, 15, 806–313. Damasio, A. (1994). Descartes’ error: Emotion, reason, and the human brain. New York: Avon Books. Damasio, A. (1999). The feeling of what happens: Body and emotion in the making of consciousness. New York: Harcourt Brace. Damasio, A. R., Grabowski, T. J., Bechara, A., Damasio, H., Ponto, L. L., Parvizi, J., Hichwa, R. D. (2000). Subcortical and cortical brain activity during the feeling of self-generated emotions. Nature Neuroscience, 3, 1049–1056. Davis, M., & Whalen, P. J. (2001). The amygdala: Vigilance and emotion. Molecular Psychiatry, 6, 13–34. Decety, J., & Jackson, P. L. (2007). The functional architecture of human empathy. Behavioral and Cognitive Neuroscience Reviews, 3, 71–100. de Gelder, B., Snyder, J., Greve, D., Gerard, G., & Hadjikhani, N. (2004). Fear fosters flight: A mechanism for fear contagion when perceiving emotion expressed by a whole body. Proceedings of the National Academy of Sciences, USA, 101, 16701–16706. Dimberg, U., Thunberg, M., & Elmehed, K. (2000). Unconscious facial reactions to emotional facial expressions. Psychological Science, 11, 86–89. Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003, October 10). Does rejection hurt? An FMRI study of social exclusion. Science, 302, 290–292. Festinger, L. (1957). A theory of cognitive dissonance. Evanston, IL: Row, Peterson. Festinger, L., & Carlsmith, C. (1959). Cognitive consequences of forced compliance. Journal of Abnormal and Social Psychology, 58, 203–210. Gallese, V., Keysers, C., & Rizzolatti, G. (2004). A unifying view of the basis of social cognition. Trends in Cognitive Science, 8, 396–403. Gazzaniga, M. (1998). The mind’s past. Berkeley: University of California Press. Gazzaniga, M. (2000). Cerebral specialization and interhemispheric communication: Does the corpus callosum enable the human condition? Brain, 123, 1293–1326. Gilbert, S. J., Spengler, S., Simons, J. S., Steele, J. D., Lawrie, S. M., Frith, C. D., Burgess, P. W. (2006). Functional specialization within rostral prefrontal cortex (area 10): A meta-analysis. Journal of Cognitive Neuroscience, 18, 932–948. Harris, L. T., Todorov, A., & Fiske, S. T. (2005). Attributions on the brain: Neuro-imaging dispositional inferences, beyond theory of mind. NeuroImage, 28, 763–769. Humphrey, N. (1983). Consciousness regained: Chapters in the development of mind. Oxford University Press. Hutchison, W. D., Davis, K. D., Lozano, A. M., Tasker, R. R., & Dostrovsky, J. O. (1999). Pain-related neurons in the human cingulate cortex. Nature Neuroscience, 2, 403–405. Iacoboni, M., & Dapretto, M. (2006). The mirror neuron system and the consequences of its dysfunction. Nature Reviews Neuroscience, 7, 942–951. Ito, T. A., Larsen, J. T., Smith, N. K., & Cacioppo, J. T. (1998). Negative information weighs more heavily on the brain: The negativity bias in evaluative categorizations. Journal of Personality and Social Psychology, 75, 887–900. Jiang, Y., Costello, P., Fang, F., Huang, M., & He, S. A. (2006). A genderand sexual orientation-dependent spatial attentional effect of invisible images. Proceedings of the National Academy of Science, USA, 103, 1748–1752.

c37.indd Sec3:743

743

Johansson, P., Hall, L., Sikstrom, S., & Olsson, A. (2005, October 7). Failure to detect mismatches between intention and outcome in a simple decision task. Science, 310, 116–119. Kalisch, R., Wiech, K., Critchley, H. D., Seymour, B., O’Doherty, J. P., Oakley, D. A., Allen, P., & Dolan, R. J. (2005). Anxiety reduction through detachment: Subjective, physiological, and neural effects. Journal of Cognitive Neuroscience, 17, 874–883. Kim, H., Somerville, L. H., Johnstone, T., Alexander, A. L., & Whalen, P. J. (2003). Inverse amygdala and medial prefrontal cortex responses to surprised faces. NeuroReport, 14, 2317–2322. Klüver, H., & Bucy, P. (1939). Preliminary analysis of functioning of the temporal lobes in monkeys. Archives of Neurological Psychiatry, 42, 979–1000. Knutson, B., Adams, C. M., Fong, G. W., & Hommer, D. (2001). Anticipation of increasing monetary reward selectively recruits nucleus accumbens. Journal of Neuroscience, 21, 1–5. Kunst-Wilson, W. R., & Zajonc, R. B. (1980, February 1). Affective discrimination of stimuli that cannot be recognized. Science, 207, 557–558. Lamm, C., Batson, C. D., & Decety, J. (2007). The neural substrate of human empathy: Effects of perspective-taking and cognitive appraisal. Journal of Cognitive Neuroscience, 19, 42–58. Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1998). Emotion, motivation, and anxiety: Brain mechanisms and psychophysiology. Biological Psychiatry, 44, 1248–1263. LeDoux, J. E. (1996). The emotional brain. New York: Simon & Schuster. LeDoux, J. E. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 23, 155–184. Liddell, B. J., Brown, K. J., Kemp, A. H., Barton, M. J., Das, P., Peduto, A., et al. (2005). A direct brainstem-amygdala-cortical “alarm” system for subliminal signals of fear. Neuroimage, 24, 235–243. Lieberman, M. D. (2007). Social cognitive neuroscience: A review of core processes. Annual Review of Psychology, 58, 259–289. Loftus, E. (2005). Planting misinformation in the human mind: A 30-year investigation of the malleability of memory. Learning and Memory, 12, 361–366. McGaugh, J. L. (2004). The amygdala modulates the consolidation of memories of emotionally arousing experiences. Annual Review of Neuroscience, 27, 1–28. Mineka, S., & Cook M. (1993). Mechanisms involved in the observational conditioning of fear. Journal of Experimental Psychology, 122, 23–38. Mitchell, J. P., Macrae, C. N., & Banaji, M. R. (2006). Dissociable medial prefrontal contributions to judgments of similar and dissimilar others. Neuron, 50, 655–663. Morris, J. S., Friston, K. J., Buchel, C., Frith, C. D., Young, A. W., Calder, A. J., et al. (1998). A neuromodulatory role for the human amygdala in processing emotional facial expressions. Brain, 121, 47–57. Morris, J. S., Öhman, A., & Dolan, R. J. (1998, June 4). Conscious and unconscious emotional learning in the amygdala. Nature, 393, 467–470. Morris, J. S., Öhman, A., & Dolan, R. J. (1999). A subcortical pathway to the right amygdala mediating “unseen” fear. Proceedings of the National Academy of Sciences, USA, 96, 1680–1685. Morrison, I., Lloyd D., di Pellegrino, G., & Roberts, N. (2004). Vicarious responses to pain in anterior cingulate cortex: Is empathy a multisensory issue? Cognitive, Affective and Behavioral Neuroscience, 4, 270–278. Murray, E. A. (2007). The amygdala, reward and emotion. Trends in Cognitive Sciences, 11, 489–497. Navarrete, C. D., Olsson, A., Ho, A., Mendes, W., Thomsen, L., & Sidanius, J. (2009). The roles of race and gender in the persistence of learned fear. Psychological Science, 20, 155–158. Oatley, K. (2004). Emotions: A brief history. Toronto: University of Toronto Press. Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotion. Trends in Cognitive Sciences, 9, 242–249. Ochsner, K. N., Ray, R. D., Cooper, J. C., Robertson, E. R., Chopra, S., Gabrieli, J. D., et al. (2004a). For better or for worse: Neural systems supporting the cognitive down- and up-regulation of negative emotion. NeuroImage, 23, 483–499.

8/17/09 3:07:10 PM

744 The Affective Neuroscience of Emotion: Automatic Activation, Interoception, and Emotion Regulation Ochsner, K. N., Ray, R. D., Cooper, J. C., Robertson, E. R., Chopra, S., Gabrieli, J. D., et al. (2004b). Reflecting upon feelings: An fMRI study of neural systems supporting the attribution of emotion to self and other. Journal of Cognitive Neuroscience, 16, 1746–1772. O’Doherty, J., Dayan, P., Schultz, J., Deichmann, R., Friston, K., & Dolan, R. J. (2004, April 16). Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science, 304, 452–454. Öhman, A. (1986). Face the beast and fear the face: Animal and social fears as prototypes for evolutionary analyses of emotion. Psychophysiology, 23, 123–145. Öhman, A., & Mineka, S. (2001). Fears, phobias, and preparedness: Toward an evolved module of fear and fear learning. Psychological Review, 108, 483–522. Öhman A., & Soares, J. J. (1994). “Unconscious anxiety”: Phobic responses to masked stimuli. Journal of Abnormal Psychology, 103, 231–240. Öhman, A., & Soares, J. J. F. (1998). Emotional conditioning to masked stimuli: Expectancies for aversive outcomes following non-recognized fear-relevant stimuli. Journal of Experimental Psychology: General, 127, 69–82. Öhman, A., & Wiens, S. (2003). On the automaticity of autonomic responses in emotion: An evolutionary perspective. In R. J. Davidson, K. Scherer, & H. H. Goldsmith (Eds.), Handbook of affective sciences (pp. 256–275). New York: Oxford University Press. Olsson, A., Ebert, J. P., Banaji, M. R., & Phelps, E. A. (2005, July 29). The role of social groups in the persistence of learned fear. Science, 309, 785–787. Olsson, A., Nearing, K. I., & Phelps, E. A. (2007). Learning fears by observing others: The neural systems of social fear transmission. Social Cognitive and Affective Neuroscience, 2, 3–11. Olsson, A., & Ochsner, K. N. (2008). The role of social cognition in emotion. Trends in Cognitive Sciences, 12, 65–71. Olsson, A., & Phelps, E. A. (2004). Learned fear of “unseen” faces after Pavlovian, observational, and instructed fear. Psychological Science, 15, 822–828. Pasley, B. N., Mayes, L. C., & Schultz, R. T. (2004). Subcortical discrimination of unperceived objects during binocular rivalry. Neuron, 42, 163–172. Pessiglione, M., Schmidt, L., Draganski, B., Kalisch, R., Lau, H., Dolan, R. J., et al. (2007, May 11). How the brain translates money into force: A neuroimaging study of subliminal motivation. Science, 316, 904–906. Phelps, E. A. (2006). Emotion and cognition: Insights from studies of the human amygdala. Annual Review of Psychology, 24, 27–53. Phelps, E. A., & LeDoux, J. E. (2005). Contributions of the amygdala to emotion processing: From animal models to human behavior. Neuron, 48, 175–187. Phelps, E. A., Ling, S., & Carrasco, M. (2006). Emotion facilitates perception and potentiates the perceptual benefit of attention. Psychological Science, 17, 292–299. Phelps, E. A., O’Connor, K. J., Cunningham, W. A., Funayma, E. S., Gatenby, J. C., Gore, J. C., et al. (2000). Performance on indirect measures of race evaluation predicts amygdala activity. Journal of Cognitive Neuroscience, 12, 1–10. Quirk, G. J., Garcia, R., & Gonzalez-Lima, F. (2006). Prefrontal mechanisms in extinction of conditioned fear. Biological Psychiatry, 60, 337–643. Ramachandran, V. S. (2004). A brief tour of human consciousness. New York: Pi Press. Rolls, E. T. (1999). The brain and emotion. New York: Oxford University Press. Sabatinelli, D., Bradley, M. M., Fitzsimmons, J. R., & Lang, P. J. (2005). Parallel amygdala and inferotemporal activation reflect emotional intensity and fear relevance. NeuroImage, 24, 1265–1270.

c37.indd Sec3:744

Saxe, R., Moran, J. M., Scholz, J. K., & Gabrieli, J. D. E. (2006). Overlapping and non-overlapping brain regions for theory of mind and self reflection in individual subjects. Social Cognitive Affective Neuroscience, 1, 229–234. Schachter, S., & Singer, J. E. (1962). Cognitive, social, and physiological determinants of emotional state. Psychology Review, 69, 379–399. Scherer, K. R., Schorr, A., & Johnstone, T. (Eds.). (2001). Appraisal processes in emotion: Theory, methods, and research. New York: Oxford University Press. Schultz, W. (2002). Getting formal with dopamine and reward. Neuron, 36, 241–263. Schützwohl, A. (2006). Judging female figures: A new methodological approach to male attractiveness judgments of female waist-to-hip ratio. Biological Psychology, 71, 223–229. Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R. J., & Frith, C. D. (2004, February 20). Empathy for pain involves the affective but not sensory components of pain. Science, 303, 1157–1162. Singer, T., Seymour, B., O’Doherty, J., Stephan, K. E., Dolan, R. J., & Frith, C. D. (2006, January 26). Empathic neural responses are modulated by the perceived fairness of others. Nature, 439, 466–469. Todorov, A., Mandisodza, A. N., Goren, A., & Hall, C. C. (2005, June 10). Inferences of competence from faces predict election outcomes. Science, 308, 1623–1626. Tulving, E. (2002). Episodic memory: From mind to brain. Annual Review of Psychology, 53, 1–25. Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2003). Distinct spatial frequency sensitivities for processing faces and emotional expressions. Nature Neuroscience, 6, 624–631. Vuilleumier, P., Richardson, M. P., Armony, J. L., Driver, J., & Dolan, R. J. (2004). Distant influences of amygdala lesion on visual cortical activation during emotional face processing. Nature Neuroscience, 7, 1271–1278. Wager, T. D., Rilling, J. K., Smith, E. E., Sokolik, A., Casey, K. L., Davidson, R. J., Kosslyn, S. M., Rose, R. M, Cohen, J. D. (2004, February 20). Placebo-induced changes in fMRI in the anticipation and experience of pain. Science, 303, 1162–1167. Weiskrantz, L. (1956). Behavioral changes associated with ablation of the amygdaloid complex in monkeys. Journal of Comparative Physiological Psychology, 49, 381–391. Whalen, P. J., Rauch, S. L., Etcoff, N. L., McInerney, S. C., Lee, M. B., & Jenike, M. A. (1998). Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge. Journal of Neuroscience, 18, 411–418. Williams, M. A., Morris, A. P., McGlone, F., Abbott, D. F., & Mattingley, J. B. (2004). Amygdala responses to fearful and happy facial expressions under conditions of binocular suppression. Journal of Neuroscience, 24, 2898–2904. Willis, J., & Todorov, A. (2006). First impressions: Making up your mind after a 100-ms exposure to a face. Psychological Science, 17, 592–598. Winston, J. S., Strange, B. A., O’Doherty, J., & Dolan, R. J. (2002). Automatic and intentional brain responses during evaluation of trustworthiness of faces. Nature Neuroscience, 5, 277–283. Zajonc, R. B. (2004). Exposure effects: An unmediated phenomenon. In A. S. R. Manstead, N. Frijda, & A. Fischer (Eds.), Feelings and emotions, (pp. 194–203). Amsterdam Symposium: Cambridge: Cambridge University Press. Zajonc, R. B. (1980). Feeling and thinking: Preferences need no inferences. American Psychologist, 1980, 35, 151–175. Zaki, J., Ochsner, K. N., Hanelin, H., Wager, T. D., & Mackey, S. (2007). Different circuits for different pain: Patterns of functional connectivity reveal distinct networks for processing pain in self and others. Social Neuroscience, 2, 276–291.

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Chapter 38

The Somatovisceral Components of Emotions and Their Role in Decision Making: Specific Attention to the Ventromedial Prefrontal Cortex ANTOINE BECHARA AND NASIR NAQVI

the same. However, philosophers argued that emotions are not just bodily sensations; the two have different objects. Body sensations are about awareness of the internal state of the body. Emotional feelings are directed toward objects in the external world. Neuroscientific evidence based on functional magnetic resonance imaging (fMRI) tend to provide important validation of the theoretical view of James-Lange that neural systems supporting the perception of body states provide a fundamental ingredient for the subjective experience of emotions. This is consistent with contemporary neuroscientific views (e.g., see Craig, 2002), which suggest that the anterior insular cortex, especially on the right side of the brain, plays an important role in the mapping of bodily states and their translation into emotional feelings. The view of A. R. Damasio (1999, 2003) is consistent with this notion, but it suggests further that emotional feelings are not just about the body, but they are also about things in the world. In other words, sensing changes in the body requires neural systems, in which the anterior insular cortex is a critical substrate. However, the feelings that accompany emotions require additional brain regions. In Damasio’s view, feelings arise in conscious awareness through the representation of bodily changes in relation to the object or event that incited the bodily changes. This second-order mapping of the relationship between organism and object occurs in brain regions that can integrate information about the body with information about the world. Such regions include the anterior cingulate cortex (Figure 38.1), especially its dorsal part. According to A. R. Damasio (1994, 1999, 2003), there is an important distinction between emotions and feelings. Emotions are a collection of changes in body and brain states triggered by a dedicated brain system that responds

The orbital and mesial prefrontal cortices have been implicated in a range of affective processes, including hedonic and anticipatory responses to reward and punishment, subjective states of desire, and basic as well as social emotions. Changes in the visceral state may be considered a form of anticipation of the bodily impact of objects and events in the world. Visceral responses to biologically relevant stimuli allow an organism to maximize the survival value of situations that may impact the state of the internal milieu. These include events that promote homeostasis, such as an opportunity to feed or engage in social interaction, as well as events that disrupt homeostasis, such as a physical threat or a signal of social rejection. Visceral responses are just one component of a broader emotional response system that also includes changes in the endocrine and skeletomotor systems, as well as changes within the brain that alter the perceptual processing of biologically relevant stimuli (A. R. Damasio, 1994). William James (1884) initially proposed that visceral responses to biologically relevant stimuli are a necessary component of the subjective experience of emotion. More specifically, suppose you saw the person you love bringing you flowers. The encounter may cause your heart to race, your skin to flush, and your facial muscles to contract with a happy expression. The encounter may also be accompanied by some body sensations, such as hearing your heartbeat and sensing “butterflies” in your stomach. However, there is also another kind of sensation: the emotional feeling of love, ecstasy, and elation directed toward your loved one. Since James’s initial proposal, neuroscientists and philosophers have debated whether these two sensations are fundamentally the same. The psychological view of James-Lange (James, 1884) implied that the two were 745

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746 The Somatovisceral Components of Emotions and Their Role in Decision Making

The World

The Brain Anterior cingulate cortex

Sensory systems

Insular cortex

Orbitofrontal cortex Amygdala

Emotionally competent stimulus

Brain Stem/ hypothalamus

Visceral motor

Visceral sensory

The Body

The viscera

Autonomic responses

Figure 38.1 Information related to the emotionally competent object is represented in one or more of the brain’s sensory processing systems. Note: This information, which can be derived from the environment or recalled from memory, is made available to the amygdala and the orbitofrontal cortex, which are trigger sites for emotion. The emotion execution sites include the hypothalamus, the basal forebrain, and nuclei in the

to the content of one’s perceptions of a particular entity or event. The responses toward the body proper enacted in a body state involve physiological modifications that range from changes that are hidden from an external observer (e.g., changes in heart rate, smooth muscle contraction, endocrine release) to changes that are perceptible to an external observer (e.g., skin color, body posture, facial expression). The signals generated by these changes toward the brain itself produce changes that are mostly perceptible to the individual in whom they were enacted, which then provide the essential ingredients for what is ultimately perceived as a feeling. Thus, emotions are what an outside observer can see, or at least can measure through neuroscientific tools. Feelings are what the individual senses or subjectively experiences. An emotion begins with a stimulus (imagined or perceived), such as a snake, a speaking engagement, or the person you are in love with. In neural terms, images related to the emotional stimulus are represented in one or more of the brain’s sensory processing systems. Regardless of how short this presentation is, signals related to the

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brain stem tegmentum. Only the visceral response is represented, although emotion comprises endocrine and somatomotor responses as well. Visceral sensations reach the anterior insular cortex by passing through the brain stem. Feelings result from the re-representation of changes in the viscera in relation to the object or event that incited them. The anterior cingulate cortex is a site where this second-order map is realized.

presence of that stimulus are made available to a number of emotion-triggering sites elsewhere in the brain. Two of these emotion-triggering sites are the amygdala and the orbitofrontal cortex (Figure 38.1). Evidence suggests that there may be some difference in the way the amygdala and the orbitofrontal cortex process emotional information: The amygdala is more engaged in the triggering of emotions when the emotional stimulus is present in the environment; the orbitofrontal cortex is more important when the emotional stimulus is recalled from memory (Bechara, Damasio, & Damasio, 2003). To create an emotional state, the activity in triggering sites must be propagated to execution sites by means of neural connections. The emotion execution sites are visceral motor structures that include the hypothalamus, the basal forebrain, and some nuclei in the brain stem tegmentum (Figure 38.1). Feelings result from neural patterns that represent changes in the body’s response to an emotional object. Signals from body states are relayed back to the brain, and representations of these body states are formed at the level of

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Visceral Functions of the vmPFC 747

visceral sensory nuclei in the brain stem. Representations of these body signals also form at the level of the insular cortex and lateral somatosensory cortex (Figure 38.1). It is most likely that the reception of body signals at the level of the brain stem does not give rise to conscious feeling as we know it, but the reception of these signals at the level of the cortex does so. The anterior insular cortex plays a special role in mapping visceral states and in bringing interoceptive signals to conscious perception. It is less clear whether the anterior insular cortex also plays a special role in translating the visceral states into subjective feeling and self-awareness. In A. R. Damasio’s (1999) view, feelings arise in conscious awareness through the representation of bodily changes in relation to the emotional object (present or recalled) that incited the bodily changes. A first-order mapping of self is supported by structures in the brain stem, insular cortex, and somatosensory cortex. However, additional regions, such as the anterior cingulate cortex, are required for a second-order mapping of the relationship between organism and emotional object, and the integration of information about the body with information about the world. According to the somatic marker hypothesis (A. R. Damasio, 1994), the sensory mapping of visceral responses not only contributes to feelings, but is also important for the execution of highly complex, goal-oriented behavior. In this view, visceral responses function to mark potential choices as advantageous or disadvantageous. This process aids in decision making in which there is a need to weigh positive and negative outcomes that may not be predicted decisively through cold rationality alone. Both the Jamesian view and the somatic marker hypothesis hold that the brain contains a system that translates the sensory properties of external stimuli into changes in the visceral state that reflect their biological relevance. We propose that this is the essential function of the ventromedial prefrontal cortex (vmPFC), a function that ties control of the visceral state to decision making and affect. In this chapter we review evidence that the vmPFC plays a role in eliciting visceral responses that are related to the value of objects and events in the world. We start by discussing anatomical and physiological evidence that the vmPFC can both influence the state of the viscera and also register changes in the viscera elicited by biologically relevant stimuli. We then review the results of lesion studies in humans showing that the vmPFC is necessary for eliciting visceral responses to certain forms of emotional stimuli. Finally, we review evidence supporting the somatic marker hypothesis, showing that the visceral responses that are controlled by the vmPFC play a role in guiding decision making in the face of uncertain reward and punishment.

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It is important to clarify up front that although the Jamesian and the Damasio views have insinuated that different emotions and bodily states (or somatic states) are characterized by a unique signature of visceral responses (see Rainville et al., 2006, for an example), the fact remains that the preponderance of evidence does not seem to support this depiction (for reviews, see, e.g., Cacioppo, Berntson, Larsen, Poehlmann, & Ito, 2000; Cacioppo, Klein, Berntson, & Hatfield, 1993). However, the cumulative evidence suggesting that there are no physiological response profiles that differentiate discrete emotions is not necessarily fatal to the concept of the somatic marker hypothesis, as interoception from visceral responses is still likely to be playing a role (see the discussion of the somatovisceral afference model of emotion, SAME, which was first proposed in 1992 to explain precisely how the undifferentiated visceral responses might produce immediate, discrete, and indubitable emotions; Cacioppo et al., 1993, 2000). Thus, somatic markers can be viewed as becoming differentiated at the level of the central nervous system and not necessarily in the periphery, although peripheral visceral input still plays a key role.

VISCERAL FUNCTIONS OF THE vmPFC The terms ventromedial prefrontal cortex and orbitofrontal cortex (OFC) are often used interchangeably in the literature, even though they do not refer to identical regions. For this reason it is necessary to clarify exactly what we mean when we use these terms. The OFC is the entire cortex occupying the ventral surface of the frontal lobe, dorsal to the orbital plate of the frontal bone. We have used the term vmPFC to designate a region that encompasses medial portions of the OFC along with ventral portions of the medial prefrontal cortex. The vmPFC is an anatomical designation that has arisen because lesions that occur in the basal portions of the anterior fossa, which include meningiomas of the cribiriform plate and falx cerebri, and aneurysms of the anterior communicating and anterior cerebral arteries, frequently lead to damage in this area (Figure 38.2). Often this damage is bilateral. With respect to the cytoarchitectonic fields identified in the human orbitofrontal and medial prefrontal cortices by Price and colleagues (Ongur & Price, 2000), the vmPFC comprises Brodmann area (BA) 14 and medial portions of BA 11 and 13 on the orbital surface and BA 25 and 32 and caudal portions of BA 10 on the mesial surface. The vmPFC excludes lateral portions of the OFC, namely BA 47/12, as well as more dorsal and posterior regions of BA 24 and 32 of the medial prefrontal cortex. The vmPFC is thus a relatively large and heterogeneous area.

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748 The Somatovisceral Components of Emotions and Their Role in Decision Making (A)

(B)

Figure 38.2 ( Figure C. 36 in color section) A: The orbitofrontal cortex. B: The location of the vmPFC as defined in our lesion studies and in this chapter. Note: (A) On the sagittal section (left), the medial sector of the orbitofrontal cortex is depicted on the area of the brain highlighted in yellow. On the coronal slice (right), both the medial and lateral areas of the orbitofrontal cortex are depicted on the area of the brain highlighted in yellow. (B) A map showing areas of the brain that are damaged in patients who show impairments in visceral response and decision making. The colors reflect the number of subjects with damage in a given voxel. The region of greatest overlap is the vmPFC. Note the involvement of medial wall and medial orbitofrontal areas and the relative absence of involvement of the lateral orbitofrontal areas.

Viewing the vmPFC as a single region may blur the distinction between functions subserved by the OFC on the one hand and the ventral portion of the medial prefrontal cortex on the other. Recent evidence in both rodents (Chudasama & Robbins, 2003) and nonhuman primates (Pears, Parkinson, Hopewell, Everitt, & Roberts, 2003) suggests that these regions subserve distinct motivational and learning functions. Thus, lesions of the vmPFC in humans may disrupt more than one process. This is important to keep in mind when inconsistencies arise between animal studies and human studies. These differences are also important when comparing human lesion studies, which tend to examine the functions of relatively large regions, and functional imaging studies, which reveal more focused patterns of activity. The vmPFC encompasses different regions that have been identified in functional imaging studies. The vmPFC includes the medial prefrontal area identified as being deactivated by a broad range of cognitive tasks that require focused attention, reflecting a high level of resting activity that is suspended during goal-directed behavior (Raichle et al., 2001). Other investigators have argued that the

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vmPFC encompasses the medial orbitofrontal area, in which activity is consistently related to the “reward value” of hedonically positive stimuli (Kringelbach & Rolls, 2004). In addition, the vmPFC includes the subgenual cingulate cortex (Figure 38.2), an area that has been implicated through functional imaging studies in the pathogenesis of mood disorders (Drevets et al., 1997). It remains to be seen whether these seemingly distinct functions reflect the operation of a single brain area that can be called the vmPFC, or instead are due to separate mental processes mediated by three functionally distinct areas encompassed by the vmPFC. There are two points that need to be considered when interpreting functional imaging studies of the vmPFC. First, as with all cognitive functions, activation or deactivation may show that this region is engaged by a particular function, but does not mean that it is necessary for the function to be performed. Thus, even though activity in the vmPFC can be shown to correlate with the reward value of hedonically positive stimuli, it still remains to be seen whether lesions in the human vmPFC disrupt the subjective experience of, for example, pleasure. Second, in fMRI studies the vmPFC undergoes significant BOLD signal dropout due to its location near an air–tissue interface. For this reason, the failure to detect activation or deactivation of the vmPFC using fMRI should not be taken as evidence that the vmPFC is not involved in the function under investigation, unless special procedures have been implemented to overcome signal dropout. For example, an fMRI study of decision making (Fukui, Murai, Fukuyama, Hayashi, & Hanakawa, 2005) using a task (the Iowa Gambling Task) on which subjects with vmPFC damage are impaired did not show activation of the vmPFC. An earlier positron emission tomography (PET) study (Ernst et al., 2002) using the same task did find activation in the vmPFC. Although one may cite differences in the experimental conditions or data analysis to explain this discrepancy, perhaps the most parsimonious explanation is that the Fukui et al. study did not use procedures to counteract BOLD signal dropout in the vmPFC, which was not an issue in the Ernst et al. study. This points to the larger possibility that the vmPFC is involved in a broader set of functions than would be indicated by fMRI studies alone. Nauta (1971) and then Neafsey (1990) proposed that the ventral prefrontal cortex represents a distinct visceromotor output region. Price and colleagues (Ongur & Price, 2000) later refined this concept, synthesizing the previous anatomic literature on the prefrontal cortex with their own anatomical studies in macaques. In their conception, the ventral prefrontal cortex (a region they term “the orbitomedial prefrontal cortex”) is composed of functionally

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Visceral Functions of the vmPFC 749

distinct orbital and medial networks. The orbital network is essentially a sensory input area that receives afferents from late sensory cortices for vision, audition, olfaction, taste, and visceral sensation and has reciprocal connections with the dorsolateral prefrontal cortex. The medial network is essentially a visceromotor output area that sends projections to subcortical structures that are involved in emotional and motivational processes, such as the amygdala and the nucleus accumbens, as well as regions of the brain stem and hypothalamus that directly govern the state of the viscera. In between the orbital and medial networks lies a transitional zone that is interconnected with both the orbital and medial networks that may function to transfer information between those networks. According to this scheme, the vmPFC, as we define it, corresponds largely to the medial and intermediate networks, areas that are largely concerned with translating highly processed sensory inputs into visceromotor output. Much of the early evidence for the role of the vmPFC in the control of visceral functions came from studies examining the cardiorespiratory effects of stimulation in this area in cats and macaques, but further support for the role of the vmPFC in visceral motor functions also comes from lesion studies. Early studies in humans (Luria, Pribram, & Homskaya, 1964) examined the effects of relatively large lesions of the frontal lobes on visceral functions. Our own laboratory has performed studies examining the effects of lesions in an array of cortical areas on visceral responses (Tranel & Damasio, 1994). These studies demonstrated that a number of cortical regions, including the vmPFC but also the anterior cingulate cortex and the right inferior parietal cortex, are necessary for the generation of visceral responses to sensory stimuli. These studies also showed that the role of the vmPFC in governing visceral responses is specific to stimuli with emotional or social content. More recent evidence for the visceral functions of the vmPFC comes from functional imaging studies. One study showed that neural activity in the vmPFC covaries with visceral responses, this time with skin conductance response during anticipation and receipt of monetary rewards (Critchley, Elliott, Mathias, & Dolan, 2000). In this study, skin conductance responses were modeled as discrete events, which allowed for the correlation with brain activity both preceding and following the responses. Using this approach it was possible to show that activity in the vmPFC was related to both the generation of skin conductance responses as well as the afferent mapping of skin conductance responses, indicating both visceral motor and visceral sensory functions for the vmPFC. Using fMRI it has also been shown that activity in vmPFC is correlated with skin conductance response across a variety of cognitive

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states, including the resting state (Nagai, Critchley, Featherstone, Trimble, & Dolan, 2004; Patterson, Ungerleider, & Bandettini, 2002). These studies suggest that the visceral functions of the vmPFC are not specific to emotional stimuli, contrary to the results of the lesion studies described earlier. However, just because activity in the vmPFC is related to visceral responses in a given context does not mean that it is necessary for the generation of visceral responses in that context. Both the somatic marker hypothesis and the Jamesian view of emotion place importance on the sensory representations of the visceral responses that are elicited by biologically relevant stimuli. An important question, therefore, regards how visceral responses, once generated by the vmPFC and deployed in the body, are mapped in the brain. Visceral sensations are represented at multiple levels of the neuraxis, including the spinal cord, brain stem, hypothalamus, thalamus, and cortex (Craig, 2002). Each of these stages of visceral representation may have a specific role to play in affective and executive processes. The sensory representation of the viscera within the insular cortex, in particular the right anterior insular cortex, has been proposed to play a special role in conscious emotional feelings (Craig, 2002; Damasio et al., 2000). The right insular cortex, along with the right somatosensory cortices, have also been proposed by A. R. Damasio (A. R. Damasio, 1994) to be a component of the somatic marker network for decision making. The anterior (agranular) insular cortex projects to a number of areas involved in emotion and motivation, including the amygdala and the nucleus accumbens. The anterior insular cortex also projects to the vmPFC, both via the orbital network and through direct projections to medial network areas (Flynn, Benson, & Ardila, 1999). In addition, recent anatomical evidence suggests that the right anterior insular cortex has evolved special functions in higher primates (Craig, 2002), consistent with a role in conscious feelings. Indeed, the right anterior insular cortex has been shown to be active during a number of subjective feeling states (Critchley, Wiens, Rotshtein, Ohman, & Dolan, 2004; A. R. Damasio et al., 2000; Lane, Reiman, Ahern, Schwartz, & Davidson, 1997). Thus, the visceral sensory representation within the right anterior insular cortex may play a role in the feelings that accompany decision making, such as hunches and gut feelings that may guide decision making in the face of uncertainty. According to the somatic marker hypothesis (Bechara & Damasio, 2005), visceral sensory signals can also influence decision making by acting on brain stem nuclei forascending neurotransmitter systems, including dopaminergic, serotonergic, and noradrenergic systems. These neurotransmitter

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750 The Somatovisceral Components of Emotions and Their Role in Decision Making

systems exert widespread influence on the function of the prefrontal cortex, including the dorsolateral, medial, and orbital prefrontal cortices, and on subcortical structures, including the amygdala and the ventral and dorsal striata. Through these projections, ascending neurotransmitter systems play a role in multiple attentional, executive, and motivational processes (Berridge & Robinson, 1998; Rahman, Sahakian, Cardinal, Rogers, & Robbins, 2001). There is evidence for direct visceral sensory inputs to these nuclei and that visceral states can influence neurotransmitter release from these nuclei (Berntson, Sarter, & Cacioppo, 2003). The somatic marker hypothesis holds that visceral states, via their influence on ascending neurotransmitter systems, can influence decision making both by promoting the maintenance of specific goals in working memory and by biasing of behavior toward these goals. In this framework, brain stem neurotransmitter nuclei may also be engaged by “as if” loops. Here, areas such as the vmPFC, instead of triggering visceral responses in the body that feed back to brain stem neurotransmitter nuclei, facilitate neurotransmitter release via direct brain stem projections that bypass the body. This triggers neurotransmitter release as if a visceral response had been expressed in the body. As discussed earlier, functional imaging evidence (Critchley et al., 2000) indicates that the vmPFC, in addition to its role in the generation of visceral responses, plays a role in the sensory representation of the visceral state. The vmPFC may receive visceral sensory information from the insular cortex or via ascending neurotransmitter systems. One function of the visceral sensory inputs to the vmPFC may be to represent the visceral responses that are themselves induced within the vmPFC, amygdala, or other areas that trigger visceral responses to biologically relevant stimuli. This function may allow the vmPFC to compare the sensory representations of the visceral state with an efferent copy of the visceral response evoked by a biologically relevant stimulus. Differences between these two inputs may signal that a goal has been achieved; that is, a consummatory event has occurred that has altered the state of the viscera. The vmPFC has available to it information regarding the sensory consequences of innately pleasurable consummatory behaviors that impinge directly on the viscera, such as feeding, as well as innately aversive bodily states that result from actual or potential tissue damage (nociception). For example, it has been shown that activity in the vmPFC is correlated with the subjective pleasantness of stimuli such as taste (Kringelbach & Rolls, 2004), the oral sensations elicited by water (Denton et al., 1999a, 1999b), and pleasant touch (Rolls, 2000). In addition, activity in the vmPFC is correlated with subjective ratings of the intensity of thermal pain (Craig, 2002). All of these

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stimuli have direct homeostatic relevance and are signaled through a distinct sensory channel that includes the insular cortex (Craig, 2002). This suggests that the vmPFC represents the hedonic value of the visceral sensory signals generated by innately rewarding or punishing consummatory behavior (so-called primary reinforcers; Kringelbach & Rolls, 2004). These representations may be important for the feelings of pleasure and pain that accompany good and bad outcomes. However, there is a lack of human lesion evidence that indicates that the vmPFC mediates the subjective hedonic impact of interoceptive stimuli. Another possibility is that interoceptive or visceral signals within the vmPFC may be important for learning to associate the hedonic consequences of behavior with the particular courses of action that precede them. This is supported by the findings from functional imaging studies that the vmPFC is activated when feedback indicating a correct choice is signaled specifically via an interoceptive route (Hurliman, Nagode, & Pardo, 2005). In this way, interoceptive signals generated by primary reinforcers may form the basis for representations of more abstract rewards and punishments that are elicited within the vmPFC.

DECISION-MAKING FUNCTIONS OF THE vmPFC Our laboratory’s interest in the functions of the vmPFC was fueled by observations in neurological patients that lesions in this area led to profound impairments in personality and real-life decision-making capabilities. One of the first and most famous cases of the so-called frontal lobe syndrome was the patient Phineas Gage, a railroad construction worker who survived an explosion that blasted an iron tamping bar through the front of his head (Harlow, 1848). Before the accident Gage was a man of normal intelligence, energetic and persistent in executing his plans of operation. He was responsible, sociable, and popular among peers and friends. After the accident his medical recovery was remarkable. He survived the accident with normal intelligence, memory, speech, sensation, and movement. However, his behavior changed completely. He became irresponsible, untrustworthy, and impatient of restraint or advice when it conflicted with his desires. Using modern neuroimaging techniques, H. Damasio and colleagues (H. Damasio, Grabowski, Frank, Galburda, & Damasio, 1994 ) have reconstituted the accident by relying on measurements taken from Gage’s skull. The key finding of this neuroimaging study was that the most likely placement of Gage’s lesion included the vmPFC region, bilaterally.

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Decision-Making Functions of the vmPFC 751

The case of Phineas Gage paved the way for the notion that the frontal lobes were linked to social conduct, judgment, decision making, and personality. A number of instances similar to Gage’s case have since appeared in the literature (Ackerly & Benton, 1948; Brickner, 1932; Welt, 1888). Interestingly, these cases received little attention for many years. Over the years, we have studied numerous patients with this type of lesion. Such patients with damage to the vmPFC develop severe impairments in personal and social decision making, in spite of otherwise largely preserved intellectual abilities. These patients had normal intelligence and creativity before their brain damage. After the damage they begin to have difficulties planning their workday and future and difficulties in choosing friends, partners, and activities. The actions they elect to pursue often lead to losses of diverse order, for example, financial losses, losses in social standing, losses of family and friends. The choices they make are no longer advantageous and are remarkably different from the kinds of choices they were known to make in the premorbid period. These patients often decide against their best interests. They are unable to learn from previous mistakes, as reflected by repeated engagement in decisions that lead to negative consequences. In striking contrast to this real-life decisionmaking impairment, problem-solving abilities in laboratory settings remain largely normal. As noted, the patients have normal intellect, as measured by a variety of conventional neuropsychological tests (Bechara, Damasio, Tranel, & Anderson, 1998; A. R. Damasio, Tranel, & Damasio, 1990; Eslinger & Damasio, 1985; Saver & Damasio, 1991). The Genesis of the Somatic Marker Hypothesis When we first observed the real-life decision-making deficits of patients with vmPFC damage, a good deal of evidence for the visceral motor functions of the vmPFC, described earlier, had already accumulated. The question then arose as to whether the decision-making deficits caused by vmPFC damage were related to its visceromotor functions. Nauta (1971) had by then proposed that the guidance of behavior by the frontal lobes was linked to the interoceptive and visceromotor functions of this area. Specifically, he proposed that the prefrontal cortex, broadly defined, functioned to compare the affective responses evoked by the various choices for behavior and to select the option that “passed censure by an interoceptive sensorium.” (p. 172). According to Nauta, the “interoceptive agnosia” suffered by patients with frontal lobe damage could explain their impairments in real life, as well as their poor performance on various tests of executive function, including the Wisconsin card sort task. This model was

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meant to explain the function of the prefrontal cortex as a whole. Furthermore, it was meant as a broad explanation of executive function deficits, not of a specific deficit in decision making within the social and personal domains. However, the deficits of patients with damage in the vmPFC were limited to the personal and social domains; patients with focal vmPFC damage showed marked impairments in their real-life personal and social functioning but had intact intelligence. Indeed, these patients performed normally on standard laboratory tests of executive function such as the Wisconsin card sort task. This background helped to shape a more specific formulation, deemed the “somatic marker hypothesis” (A. R. Damasio, 1994; A. R. Damasio, Tranel, & A. R. Damasio, 1991). According to this hypothesis, patients with damage in the vmPFC make poor decisions in part because they are unable to elicit somatic (visceral) responses that mark the consequences of their actions as positive or negative. In this framework, the vmPFC functions to elicit visceral responses that reflect the anticipated value of the choices. Though this function is specific to the vmPFC, it draws on information about the external world that is represented in multiple higher order sensory cortices. Furthermore, this function is limited to specific types of decision making, in particular those situations where the meaning of events is implied and the consequences of behavior are uncertain. These are situations, such as social interactions and decisions about one’s personal and financial life, where the consequences of behavior have emotional value; that is, they can be experienced as subjective feelings and can also increase or decrease the likelihood of similar behavior in the future (they are rewarding or punishing). Furthermore, these are situations where the rules of behavior are not explicit but yet require some form of mental deliberation in real time in order to navigate them successfully. This form of reasoning is distinct from reasoning that does not require the weighing of positive and negative consequences, or in which the outcomes of decisions are known with a high degree of certainty. In addition to explaining the specificity of the impairments in patients with vmPFC damage, the somatic marker framework leads to testable hypotheses about the kinds of information represented within the vmPFC and the relationship of this information to the state of the viscera. Lesions of the vmPFC Impair Visceral Responses to Emotional Stimuli One of the first empirical tests of the somatic marker hypothesis came from studies examining the effects of vmPFC lesions on the visceral response to complex visual

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752 The Somatovisceral Components of Emotions and Their Role in Decision Making

stimuli (A. R. Damasio et al., 1990). Though it was known that the vmPFC played a role in the elicitation of visceral responses, the precise behavioral context in which visceral functions were engaged by the vmPFC was not known. In other words, it was possible that the vmPFC functioned to elicit visceral responses to all forms of stimuli, or only certain types of stimuli, such as those that, like social signals, possess emotional value that is often largely implicit. According to the somatic marker hypothesis, the visceral responses that were mediated by the vmPFC were especially related to the implied meaning of social stimuli. To address this hypothesis, one study examined the visceral responses of a group of patients with damage in the vmPFC, all of whom showed a pattern of behavior that reflected an inability to make advantageous decisions in the personal and social realms despite intact intellectual functioning. The subjects were shown a series of affectively charged pictures, including pictures of mutilations, disasters, and sexual images, along with a series of neutral pictures. The patients were tested in two conditions, one in which they watched the stimuli passively and another in which they were asked to describe the pictures in terms of their emotional content. After each stimulus, the skin conductance response (SCR), an index of sympathetic arousal, was measured. In addition, SCRs to orienting stimuli, including loud noises and deep breaths, were measured. The responses of patients with vmPFC damage were compared to the responses of patients with damage to regions outside the vmPFC as well as the responses of neurologically intact comparison subjects. It was found that patients with vmPFC damage were significantly impaired in their visceral responses to emotional pictures when required to view them passively, compared to both neurologically intact and brain-damaged comparison subjects. However, when required to comment on the content of the pictures, the visceral responses of the vmPFC patients to the emotional pictures were largely intact. In addition, the vmPFC patients showed intact SCRs to orienting stimuli. These results indicate that the vmPFC plays a role in the elicitation of visceral responses to biologically relevant stimuli. This impairment is specific to stimuli for which emotional meaning must be decoded through cognitive processes and does not extend to stimuli that elicit visceral responses because they are innately aversive or arousing (e.g., a loud noise) or to stimuli that are physiological elicitors of visceral responses (e.g., a deep breath). The results also imply that the vmPFC mediates the visceral response to emotional stimuli when the evaluation of these stimuli does not require verbal mediation, that is, when it is implicit.

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Lesions in the vmPFC Lead to Impairments in the Iowa Gambling Task Once it was known that patients with vmPFC damage were abnormal both in their capacity to make decisions and in their ability to respond viscerally to the emotional meaning of certain stimuli, it still remained to be shown that these two abnormalities were linked. Up to this point, the behavioral abnormalities of these patients, which were striking in real life, had largely eluded conventional neuropsychological and laboratory tests. Thus, it was important to develop a laboratory test that simulated the real-life decisions in which patients with vmPFC damage failed. This test factored in reward and punishment, as well as the uncertainty and risk that accompany many real-life decisions. In addition, this test required participants to reason and deliberate the outcome of choices in real time. The Iowa Gambling Task The Iowa Gambling Task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994; Bechara, Tranel, & Damasio, 2000) uses four decks of cards, named A, B, C, and D. The goal in the task is to maximize profit on a loan of play money. Subjects are required to make a series of 100 card selections. However, they are not told ahead of time how many card selections they are going to make. Subjects can select one card at a time from any deck they choose, and they are free to switch from any deck to another at any time and as often as they wish. However, the subject’s decision to select from one deck versus another is largely influenced by various schedules of immediate reward and future punishment. These schedules are preprogrammed and known to the examiner but not to the subject, and they entail the following principles. Every time the subject selects a card from deck A or deck B, the subject gets $100. Every time the subject selects a card from deck C or deck D, the subject gets $50. However, in each of the four decks, subjects encounter unpredictable punishments (money loss). The punishment is set to be higher in the high-paying decks A and B and lower in the low-paying decks C and D. For example, if one picks 10 cards from deck A, one would earn $1,000. However, in those 10 card picks, five unpredictable punishments would be encountered, ranging from $150 to $350, bringing a total cost of $1,250. Deck B is similar: Every 10 cards picked from deck B would earn $1,000; however, these 10 card picks would encounter one high punishment of $1,250. On the other hand, every 10 cards from deck C or D earn only $500, but they cost only $250 in punishment. Hence, decks A and B are disadvantageous because they cost more in the long run; that is, one loses $250 every 10 cards. Decks C and D are advantageous because they

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Decision-Making Functions of the vmPFC 753

result in an overall gain in the long run; that is, one wins $250 every 10 cards. We investigated the performance of normal controls and patients with vmPFC lesions on this task. Normal subjects avoided the bad decks A and B and preferred the good decks C and D. In sharp contrast, the vmPFC patients did not avoid the bad decks A and B; indeed, they preferred those decks (Figure 38.3). From these results we suggested that the patients’ performance profile is comparable to their real-life inability to decide advantageously. This is especially true in personal and social matters, a domain for which, in life, as in the task, an exact calculation of the future outcomes is not possible and choices must be based on hunches and gut feelings. Lesions in the vmPFC Disrupt Visceral Responses during the Iowa Gambling Task In light of the finding that the IGT is an instrument that detects the decision-making impairment of vmPFC patients in the laboratory, we went on to address the next question: whether the impairment is linked to a failure in somatic signaling (Bechara, Tranel, Damasio, & Damasio, 1996). To address this question, we added a physiological measure to the IGT. The goal was to assess somatic state activation while subjects were making decisions during performance of the task. We studied two groups: normal subjects and vmPFC patients. We had them perform the IGT while we recorded their electrodermal activity (SCRs). As the body begins to change after a thought, and as a given somatic state begins to be enacted, the autonomic nervous system begins to increase the activity in the skin’s sweat glands. Although this sweating activity is relatively small and not observable by the naked eye, it can be amplified and recorded by a polygraph as a wave. The amplitude of this wave can be measured and thus provide an indirect measure of the somatic state experienced by the subject.

vmPFC Patients

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Both normal subjects and vmPFC patients generated SCRs after they had picked a card and were told that they won or lost money. The most important difference, however, was that normal subjects, as they became experienced with the task, began to generate SCRs prior to the selection of any cards, that is, during the time they were pondering which deck to choose. These anticipatory SCRs were more pronounced before picking a card from the risky decks A and B when compared to the safe decks C and D. In other words, these anticipatory SCRs were like gut feelings that warned the subject against picking from the bad decks. Patients with vmPFC damage failed to generate such SCRs before picking a card. This failure to generate anticipatory SCRs before picking cards from the bad decks correlates with their failure to avoid these bad decks and choose advantageously in this task (Figure 38.4). These results provide strong support for the notion that decision making is guided by emotional signals (gut feelings) that are generated in anticipation of future events. An important question regards the information content of visceral responses that are elicited by the vmPFC. If somatic markers are to be useful in guiding decision-making processes involving uncertain reward and punishment, then they should provide information about both the valence of an anticipated outcome (e.g., whether a choice will result in winning or losing money) and the magnitude of the anticipated outcome (e.g., how much money will be won or lost). Our results using the IGT show that the vmPFC triggers anticipatory visceral responses to both the advantageous and the disadvantageous decks. These responses are larger for disadvantageous decks than for advantageous decks, though they are still deployed for the advantageous decks. Further experiments from our laboratory (Bechara, Dolan, & Hindes, 2002) and others (Tomb, Hauser, Deldin, & Caramazza, 2002) have shown that when the reward-punishment contingencies are reversed, with the disadvantageous decks paying out

Figure 38.3 Card selection on the Iowa Gambling Task as a function of group (normal control, vmPFC), deck type (disadvantageous versus advantageous), and trial block. Note: Normal control subjects shifted their selection of cards to the advantageous decks. The vmPFC prefrontal patients did not make a reliable shift and opted for the disadvantageous decks.

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754 The Somatovisceral Components of Emotions and Their Role in Decision Making (A)

Control Subjects

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Figure 38.4 Magnitudes of anticipatory SCRs as a function of group (control [A] versus vmPFC [B]), deck, and card position within each deck.

Note: Control subjects gradually began to generate high-amplitude SCRs to the disadvantageous decks. The vmPFC patients failed to do so.

a lower quantity of reward rather than doling out a higher punishment, the SCRs are now greater to the advantageous decks than to the disadvantageous decks. This suggests that SCR is not merely an index of the potential “badness” of choices. Rather, SCR can index the magnitude of both the anticipated negative and the anticipated positive outcomes of a choice. It seems, however, that SCR does not differentiate the anticipated valence of the outcomes. This is consistent with work by others (Lang, Bradley, Cuthbert, & Patrick, 1993) showing that SCR does not differentiate the hedonic valence of emotional stimuli but does index the magnitude of the arousal that they elicit. This would mean that some other signal is required in order to assess the valence of the anticipated outcome. Although our laboratory (Rainville et al., 2006) has provided preliminary evidence that cardiovascular responses, such as changes in heart rate, can provide information that distinguishes between positive and negative emotional states, the fact remains that the preponderance of evidence speaks to the lack of such a distinction at the peripheral visceral level (Cacioppo et al., 1993, 2000). Although it is possible that such signals can combine with those reflected in the SCR to provide information about both the perceived valence and the perceived magnitude of the future outcome of a choice, there is a strong likelihood that this discrimination is not achieved until the signals reach the central nervous system. Indeed, the somatovisceral afference model of emotion does provide an explanation for how undifferentiated visceral responses might produce distinguishable emotions (Cacioppo et al., 1993, 2000). Perhaps somatic markers operate in a fashion that is consistent with that model. Future experiments may examine at what level of the brain the visceral signals reflecting different channels

of autonomic outflow become differentiated in such a manner to exert influence on decision making.

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Visceral Responses That Signal the Correct Strategy Do Not Need to Be Conscious According to the somatic marker hypothesis, the vmPFC mediates an implicit representation of the anticipated value of choices that is distinct from an explicit awareness of the correct strategy. To test this idea, we performed a study (Bechara, Damasio, Tranel, & Damasio, 1997) in which we examined the development of SCRs over time in relation to subjects’ knowledge of the advantageous strategy in the IGT. In this study the IGT was administered as before, but this time the task was interrupted at regular intervals and the subjects were asked to describe their knowledge about what was going on in the task and their feelings about the task. Normal subjects began to choose preferentially from the advantageous decks before they were able to report why these decks were preferred. They then began to form hunches about the correct strategy, which corresponded to their choosing more from the advantageous decks than from the disadvantageous decks. Finally, some subjects reached a conceptual stage where they possessed explicit knowledge about the correct strategy (i.e., to choose from decks C and D because, although they pay less, they result in less punishment). As before, normal subjects developed SCRs preceding their choices that were larger for the disadvantageous decks than for the advantageous decks. This time it was also found that the SCR discrimination between advantageous and disadvantageous decks preceded the development of conceptual knowledge of the correct strategy. In fact, the SCR discrimination between

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Decision-Making Functions of the vmPFC 755

advantageous and disadvantageous decks even preceded the development of hunches about the correct strategy. In contrast to the normal subjects, subjects with damage in the vmPFC failed to switch from the disadvantageous decks to the advantageous decks, as in the previous study. In addition, subjects in this group again failed to develop anticipatory responses that discriminated between the disadvantageous and advantageous decks. Furthermore, patients with vmPFC damage never developed hunches about the correct strategy. Together, these results suggest that anticipatory visceral responses that are governed by the vmPFC precede emergence of advantageous choice behavior, which itself precedes explicit knowledge of the advantageous strategy. This further suggests that signals generated by the vmPFC, reflected in visceral states, may function as a nonconscious bias toward the advantageous strategy. More recently, other investigators have questioned whether it is necessary to invoke visceral responses as constituting nonconscious biasing signals (Maia & McClelland, 2004). By using more detailed questions to probe subjects’ awareness of the attributes of each of the decks in the IGT, this study showed that subjects possess explicit knowledge of the advantageous strategy at an earlier stage in the task than was shown in the Bechara et al. (1997) study. Furthermore, the Maia and McClelland study found that subjects began to make advantageous choices at around the same time that they reported knowledge of the correct strategy. Based on these findings, it was argued that nonconscious somatic marker processes are not required in order to explain how decision making occurs. A response to this study has been published elsewhere (Bechara, Damasio, Tranel, & Damasio, 2005), along with a rebuttal by Maia and McClelland (2005). Two points bear discussion here. First, because this study did not measure visceral responses and did not examine the effects of brain damage, it does not disprove the hypothesis that somatic markers mediated by the vmPFC play a role in decision making; it only shows that conscious awareness of the correct strategy occurs at around the same time as advantageous decision making. Second, both the Bechara et al. (1997) study and the Maia and McClelland (2005) study found that some subjects continue to make disadvantageous choices despite being able to report the correct strategy. This pattern bears an uncanny resemblance to the way subjects with lesions in the vmPFC are able to report the correct strategies for personal and social decision making, despite their severe deficits in the actual execution of personal and social behavior in real life. Indeed, this clinical observation provided the initial impetus to hypothesize a role for covert biasing processes in decision making in the first place. This indicates that,

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in both the IGT and in real life, conscious knowledge of the correct strategy may not be enough to guide advantageous decision making. Thus, some process that operates independently of conscious knowledge of the correct strategy (i.e., somatic markers) must be invoked to explain fully how individuals make advantageous decisions. Indeed, it seems likely that this process can sometimes bias behavior that goes against what a person consciously thinks to be the correct strategy. That nonconscious biasing processes may not precede conscious knowledge in time is potentially an important finding, but it does not provide a basis for rejection of the fundamental role of somatic markers as nonconscious biases of behavior. The Decision-Making Functions of the vmPFC Are Different from the Decision-Making Functions of the Amygdala Like patients with damage to the vmPFC, patients with bilateral damage to the amygdala also demonstrate impairments in their ability to make advantageous choices in their personal and social lives (A. R. Damasio, 1994). The amygdala, like the vmPFC, has been strongly implicated in emotional and motivational processes (Cardinal, Parkinson, Hall, & Everitt, 2002; LeDoux, 1996). There is much in common between the amygdala and the vmPFC in terms of cortical and subcortical connectivity. In particular, the amygdala receives information from higher order sensory cortices for vision, olfaction, audition, and visceral sensation (Amaral, Price, Pitkanen, & Carmichael, 1992) and sends output to subcortical sites that regulate the state of the viscera, including nuclei of the brain stem and hypothalamus. Thus, like the vmPFC, the amygdala is positioned to receive multiple sensory inputs pertaining to biologically relevant stimuli and to trigger changes in the visceral state. This suggests that the amygdala plays a role similar to that of the vmPFC in decision making. However, there are important differences between the amygdala and the vmPFC in terms of their visceral and decision-making functions. One source of evidence regarding these distinct roles comes from an experiment in which we administered the IGT to a group of subjects with bilateral amygdala damage (Bechara, Damasio, Damasio, & Lee, 1999). Their performance on this task, along with their SCRs, were compared to a group of subjects with vmPFC damage and a group of neurologically intact subjects. Similar to subjects with vmPFC damage, subjects with damage to the amygdala performed poorly on the IGT, failing to shift toward choosing more frequently from the advantageous decks. When examining the SCRs, however, there were different

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756 The Somatovisceral Components of Emotions and Their Role in Decision Making

patterns of deficit in amygdala-lesioned subjects versus vmPFC-lesioned subjects. As discussed earlier, patients with vmPFC damage fail to deploy anticipatory visceral responses before their choices, responses that, in normal subjects, are larger before choosing from the disadvantageous decks than before choosing from the advantageous decks. As also noted previously, vmPFC-lesioned subjects continue to have normal SCRs in response to receiving reward and punishment. In contrast, patients with amygdala damage fail to deploy SCRs during both the anticipatory period and in response to receiving rewards and punishments. These data are shown in Figure 38.5. This suggests that the decision-making deficit in patients with amygdala damage is due to an inability to respond viscerally to rewards and punishments. This is different from the deficit in patients with vmPFC damage, who possess an inability to viscerally anticipate uncertain rewards and punishments but who are normal in their ability to respond viscerally to rewards and punishments once they are received.

Amygdala (Nⴝ5)

Normal Control (Nⴝ13)

(A)

To examine further the distinction between the affectivevisceral functions of the amygdala and the vmPFC, the same subjects underwent a Pavlovian conditioning paradigm (Bechara et al., 1999). Here it was found that patients with bilateral amygdala damage failed to acquire conditioned SCRs. In contrast, patients with vmPFC damage were not different from neurologically intact subjects in their ability to produce conditioned SCRs. Both the amygdala and the vmPFC patients were normal in their SCRs in response to the unconditioned stimulus. This indicates that the amygdala, but not the vmPFC, is required for the acquisition of Pavlovian conditioning. In other words, the visceral responses to stimuli that acquire hedonic value through simple associative learning processes are not mediated by the vmPFC but are mediated by the amygdala. This parallels the dissociation between the amygdala and the vmPFC with respect to the visceral response to reward and punishment in the IGT. The distinction between the affective-visceral functions of the amygdala and the vmPFC may be conceptualized in

Ventromedial Prefrontal (VMF) (Nⴝ5)

Total # of Cards Selected from Decks

Disadvantageous decks (A&B) Advantageous decks (C&D) 20

20

20

15

15

15

10

10

10

5

5

5

0

0 1–20 21–40 41–60 61–80 81–100

0 1–20 21– 40 41– 60 61–80 81–100

1–20 21–40 41– 60 61–80 81–100

Order of Card Selection from the 1st to the 100th Trial

(C) 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00

Advantageous decks (C&D) Disadvantageous decks (A&B)

Normal (n=13)

Amygdala (n=5)

VMF (n=5)

Figure 38.5 The visceral functions of the vmPFC are different from those of the amygdala. Note: A: Behavioral performance on the IGT. Both vmPFC- and amygdala-lesioned subjects fail to switch to choosing preferentially from the advantageous decks. B: Both vmPFC and amygdala-lesioned subjects fail to produce anticipatory SCRs. C: vmPFC-lesioned subjects produce normal SCRs in response to reward and punishment, but amygdala lesioned subjects fail to produce SCRs in response to reward and punishment.

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Area under the Curve of SCRs (␮S/sec) after Receiving a Reward or Punishment

Area under the Curve of SCRs (␮S/sec) Prior to the Selection of a Card

(B)

Advantageous decks (C&D) Disadvantageous decks (A&B) 0.24 0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00

Reward Punishment Reward Punishment Reward Punishment Normal (n= 13)

Amygdala (n=5)

VMF (n=5)

Error bars represent the standard error of the mean. Not depicted are results showing that vmPFC-lesioned subjects produce normal SCRs to a classically conditioned stimulus, whereas amygdala-lesioned subjects fail to produce classically conditioned SCRs. From “Different Contributions of the Human Amygdala and Ventromedial Prefrontal Cortex to Decision-Making,” by A. Bechara, H. Damasio, A. R. Damasio, and G. P. Lee, 1999, Journal of Neuroscience, 19, pp. 5473–5481. Reprinted with permission.

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Summary

terms of the demands of processing biologically relevant stimuli on attention and working memory. In this view, the amygdala triggers visceral responses to stimuli whose biological significance can be decoded in relatively automatic fashion, such as winning or losing money, conditioned stimuli that reliably predict aversive and pleasurable events in the future, and stimuli with innate biological significance, such as the sight of spiders and snakes and facial expressions of fear. We refer to this class of stimuli as “primary inducers.” The visceral responses to primary inducers can be elicited quickly and without thought or complex attention. The vmPFC, in contrast, triggers visceral responses to stimuli whose biological significance must be decoded through a deliberative process. We refer to this class of stimuli as “secondary inducers.” This includes thoughts of future loss or gain, particularly when loss or gain is uncertain, as well as the recollection of pleasant and unpleasant events from the past. Secondary inducers, which may not be present within the sensory field, must be brought to mind to elicit a visceral response. Thus, vmPFC functions are related to attention and working memory and also to the recall of episodic memories.

SUMMARY The Somatic Maker Hypothesis Somatic Markers as Executive Processes According to the somatic marker hypothesis (A. R. Damasio, 1994), the visceral response elicited during decision making, both during the contemplation of the future outcome of a choice and after the outcome of a choice has been signaled, aid in guiding decisions toward advantageous choices and away from disadvantageous choices. The process that is assessed by the IGT is ultimately a learning process, one in which knowledge of the correct strategy evolves over time. In this view, visceral responses to receiving reward and punishment, which are mediated by the amygdala, contribute to the encoding of the predictive value of the sensory cues and actions that preceded reward and punishment. Over time, through this encoding, subjects learn the association between a given choice and its outcome. This learning may precede explicit awareness of the contingencies between specific choices and their outcome. This learning is expressed by the vmPFC, which evokes learned representations of the predictive value of a choice in the period before a choice is made, when the outcomes of various choices are weighed against each other as they are held in mind. The representation of predictive value is based on the visceral response that is triggered within the vmPFC, an emotional response that marks the value of options for behavior based on past experience.

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757

Within the somatic marker framework, then, the vmPFC functions as a system that holds the affective-visceral properties of objects in mind during the planning and organization of behavior that is directed toward courses of action that are in the overall best interests of the organism. This function falls into the broader executive role of the prefrontal cortex, of which the vmPFC is a part. This role is supported by connections between the vmPFC and higher order sensory cortices, as well as connections between the vmPFC and the dorsolateral prefrontal cortex (both of which are mediated by the orbital network). The connections with higher order sensory cortices provide a route for highly processed information about the sensory properties of biologically relevant stimuli to reach the vmPFC. The connections with the dorsolateral prefrontal cortex link the functions of the vmPFC to executive processes that guide attention and prioritize action, allowing the vmPFC to serve as a buffer for the maintenance of information pertaining to the homeostatic value of goal objects (i.e., predictive value). Thus, the vmPFC is not involved in regulating global working memory processes, as indicated by the finding that damage to the vmPFC does not disrupt performance on broad tasks of working memory (Bechara et al., 1998). However, vmPFC function does require intact working memory processes, as indicated by the finding that damage in regions of the prefrontal cortex that play a global role in working memory impairs performance on the IGT (Bechara, 2004; Clark, Cools, & Robbins, 2004). Some tasks that call on representations of predictive reward value but that do not require this information to be held in working memory may also engage the vmPFC. For example, one study has shown that damage to the vmPFC disrupts both reversal learning and IGT performance (Fellows & Farah, 2005). In contrast, damage to the dorsolateral prefrontal cortex impairs performance on the IGT but does not impair reversal learning. An important caveat in the comparison of this study with studies from our laboratory is that the Fellows and Farah study examined damage in posterior regions of vmPFC that also impinged on basal forebrain structures, such as the nucleus accumbens. Our studies, in contrast, have found that lesions restricted to more anterior regions of the vmPFC that do not include the basal forebrain can alter performance on the IGT (Bechara et al., 1998). Thus, it is possible that the reversal learning deficits found in the Fellows and Farah study are attributable to damage in the basal forebrain rather than to damage in the vmPFC. Notwithstanding this, it is possible that both reversal learning and the IGT require an ability to register that the predictive reward value of a stimulus has changed, as well as an ability to inhibit a previously rewarded response. However, unlike the IGT, reversal learning does not require that information about the predictive

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758 The Somatovisceral Components of Emotions and Their Role in Decision Making

reward value of a stimulus be held in working memory. Thus, the vmPFC may be engaged by processes that invoke representations of predictive reward value as well as by processes that require inhibition of a previously rewarded response. Such processes, which may themselves rely on somatic markers, could operate independently of working memory under certain experimental situations, such as reversal learning. In real life, however, where decision making usually requires holding representations of predictive reward value in mind over a delay, they are likely to work in concert with working memory processes. The Role of Feedback of Somatic Markers in Decision Making According to the somatic marker hypothesis, the afferent feedback of visceral responses is an important component of the decision-making process. In other words, the visceral responses during the contemplation of choices are necessary for biasing behavior in the advantageous direction, as well as for gut feelings and hunches related to choices. The question arises, then, as to whether the visceral responses induced by the vmPFC are actually necessary for decision making or are merely an epiphenomenal bodily reflection of the operation of certain mental processes. One way to address this question is to directly manipulate the sensory feedback of the visceral state during performance of the IGT. A number of studies have attempted to do this. For example, one study has examined how cervical transection of the spinal cord affects performance on the IGT (North & O’Carroll, 2001). This study found no effect of the manipulation on performance on the IGT. Because the spinal cord carries somatosensory and interoceptive information from the body to the brain (Craig, 2002), this may be taken as evidence that the sensory feedback of bodily states does not contribute to decision making. However, a great deal of the information about visceral states is conveyed to the central nervous system via the vagus nerve, which is spared by spinal transection. If visceral states play a special role in signaling homeostatic processes, which we believe they do, then it is not surprising that spinal transection has a limited effect on decision making. Another study (Heims, Critchley, Dolan, Mathias, & Cipolotti, 2004) examined more specifically the role of visceral states in decision making. This study showed that patients with pure autonomic failure, a peripheral nervous disorder that broadly disrupts the ability to deploy visceral responses, do not demonstrate impaired performance on the IGT. This can also be taken as evidence that visceral responses are not necessary for decision making. However, this study did not actually measure visceral responses during the IGT, so it is possible that subjects still produced some form of visceral response during the

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task. Also, it is possible that, because pure autonomic failure develops slowly and manifests later in life, significant neural reorganization may take place in subjects with this disease, altering the normal mechanism of decision making. Yet another study (Martin, Denburg, Tranel, Granner, & Bechara, 2004) showed that electrical stimulation of the vagus nerve during the IGT, which largely affects visceralafferent signaling, can actually improve performance. This can be taken as evidence that visceral states do play a role in decision making. However, this study was limited by the fact that most of the subjects suffered long-standing epilepsy, and many of them had lower than normal decisionmaking ability to begin with. Functional imaging studies have provided circumstantial evidence of the role of visceral states in decision making. As discussed earlier, the insular cortex is a visceral sensory region that has been hypothesized to play a role in decision making by mapping the visceral responses that are induced by the vmPFC and the amygdala. A number of studies (Craig, 2002) have shown that activity in the insular cortex is correlated with changes in the visceral state. The insular cortex is also activated by decision-making tasks that involve uncertain reward and punishment and an evaluation of emotional information. For example, a PET study (Ernst et al., 2002) has shown that performance of the IGT, in addition to activating the vmPFC, also activates the insular cortex. Moreover, this study found that activity in the insular cortex was correlated with performance on the IGT. The insular cortex has also been shown using fMRI to be activated during other decision-making tasks that involve uncertain reward and punishment (Critchley, Mathias, & Dolan, 2001). In addition, one study (Sanfey, Hastie, Colvin, & Grafman, 2003) found that the insular cortex is activated by the evaluation of the fairness of offers of money. Here activity in the insular cortex was shown to be correlated with the tendency to reject unfair offers. Although these studies did not examine visceral responses directly, they show that the insular cortex, an area that has previously been established as a visceral sensory representation area, is engaged during decision making, particularly when the decisions require an evaluation of emotional consequences that are uncertain. Thus, on balance, the evidence seems to favor the role of visceral states in decision making; however, more definitive evidence is required to establish exactly how and under what circumstances visceral states contribute to decision making. Though certain forms of decision making may engage somatic marker processes, it may be that not all forms of decision making require the elicitation and sensory mapping of visceral states. Indeed, the somatic marker hypothesis maintains that, under some conditions, as-if representations of the visceral state, mediated by direct

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References 759

connections between the vmPFC and brain stem neurotransmitter nuclei, may be sufficient to guide decision making in the advantageous direction. Also, decisions that do not require the weighing of rewarding or punishing consequences or in which the outcome is relatively certain may not engage somatic marker processes at all. Why Somatic Markers? A strictly computational approach to decision making may not require that the brain represent signals that are expressed within the body in order to compute the anticipated value of options for behavior (Maia & McClelland, 2004, 2005; Rolls, 1999). It is important to keep in mind, however, that human brains differ from computers in many ways, not the least of which is a concern for the promotion of survival through regulation of the internal milieu—which is the regulation of bodily processes. All nervous systems contain representations of basic bodily processes, such as those that regulate energy demands, reproduction, fluid balance, temperature, and the response to sickness and injury. Survival requires precise control over the state of these processes in order to maintain them within the narrow range that is compatible with life (i.e., homeostasis). The autonomic nervous system functions to make relatively rapid adjustments in the visceral state that maximize the survival value of events in the world that have the potential to impact homeostasis. It can be argued that visceral responses operate merely as reflexes, acting independently of higher order cognitive processes. Indeed, visceral reflexes that are implemented at the level of the spinal cord and brain stem do provide some benefit after the fact for reacting to events that challenge homeostasis. However, it is more advantageous to be able to predict the impact of events on the internal milieu before they occur. To do this the brain must connect sensory and motor representations of the viscera with processes that govern perception, learning, memory, and goal-directed behavior. It is clear, based on a multitude of studies, many of which are reported in this volume, that the vmPFC plays a role in a number of cognitive processes. It is also clear that the vmPFC plays a role in the control and mapping of visceral states. The most parsimonious explanation would seem to be that the cognitive processes that are mediated by the vmPFC and the visceral functions mediated by this area are somehow linked. According to the somatic marker hypothesis, the integration of visceral states into higher cognitive functions, such as decision making, is the function of the vmPFC. This function has expanded in evolution, allowing for the planning of behaviors that are executed further into the future and for which the outcomes of behavior in terms of rewards and punishments are more abstract. In humans, as

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well as in nonhuman primates and rodents, the vmPFC is involved in the planning of behaviors related to the most immediate and basic needs, such as food, water, and sex. In humans the vmPFC also plays a role in guiding behaviors for which choosing advantageously requires a deliberate concern for one’s long-term well-being as well as knowledge of cultural norms and expectations. In this way the vmPFC may function to link highly evolved human faculties, such as moral behavior, altruism, financial reasoning, creativity, and a sense of purpose in one’s work life and social relationships, to the basic mechanisms that govern survival and the maintenance of homeostasis.

REFERENCES Ackerly, S. S., & Benton, A. L. (1948). Report of a case of bilateral frontal lobe defect. Proceedings of the Association for Research in Nervous and Mental Disease (Baltimore), 27, 479–504. Amaral, D. G., Price, J. L., Pitkanen, A., & Carmichael, S. T. (1992). Anatomical organization of the primate amygdaloid complex. In J. P. Aggleton (Ed.), The amygdala: Neurobiological aspects of emotion, memory, and mental dysfunction (pp. 1–66). New York: Wiley-Liss. Bechara, A. (2004). The role of emotion in decision-making: Evidence from neurological patients with orbitofrontal damage. Brain and Cognition, 55, 30–40. Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52, 336–372. Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7–15. Bechara, A., Damasio, H., & Damasio, A. R. (2003). The role of the amygdala in decision-making. In P. Shinnick-Gallagher, A. Pitkanen, A. Shekhar, & L. Cahill (Eds.), The amygdala in brain function: Basic and clinical approaches (Vol. 985, pp. 356–369). New York: Annals of the New York Academy of Sciences. Bechara, A., Damasio, H., Damasio, A. R., & Lee, G. P. (1999). Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making. Journal of Neuroscience, 19, 5473–5481. Bechara, A., Damasio, H., Tranel, D., & Anderson, S. W. (1998). Dissociation of working memory from decision making within the human prefrontal cortex. Journal of Neuroscience, 18, 428–437. Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997, February 28). Deciding advantageously before knowing the advantageous strategy. Science, 275, 1293–1295. Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (2005). The Iowa Gambling Task (IGT) and the somatic marker hypothesis (SMH): Some questions and answers. Trends in Cognitive Sciences, 9, 159–162. Bechara, A., Dolan, S., & Hindes, A. (2002). Decision-making and addiction: Pt. II. Myopia for the future or hypersensitivity to reward? Neuropsychologia, 40, 1690–1705. Bechara, A., Tranel, D., & Damasio, H. (2000). Characterization of the decision-making impairment of patients with bilateral lesions of the ventromedial prefrontal cortex. Brain, 123, 2189–2202. Bechara, A., Tranel, D., Damasio, H., & Damasio, A. R. (1996). Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex, 6, 215–225.

8/17/09 3:07:33 PM

760 The Somatovisceral Components of Emotions and Their Role in Decision Making Berntson, G. G., Sarter, M., & Cacioppo, J. T. (2003). Ascending visceral regulation of cortical affective information processing. European Journal of Neuroscience, 18, 2103–2109. Berridge, K. C., & Robinson, T. E. (1998). What is the role of dopamine in reward: Hedonic impact, reward learning, or incentive salience? Brain Research Reviews, 28, 309–369. Brickner, R. M. (1932). An interpretation of frontal lobe function based upon the study of a case of partial bilateral frontal lobectomy: Localization of function in the cerebral cortex. Proceedings of the Association for Research in Nervous and Mental Disease (Baltimore), 13, 259–351. Cacioppo, J., Berntson, G., Larsen, J., Poehlmann, K., & Ito, T. (2000). The psychophysiology of emotion. In M. Lewis & J. Haviland-Jones (Eds.), The handbook of emotion (2nd ed., pp. 173–191). New York: Guilford Press. Cacioppo, J., Klein, D. J., Berntson, G. G., & Hatfield, E. (1993). The psychophysiology of emotion. In M. Lewis & J. M. Haviland (Eds.), The handbook of emotion (pp. 119–148). New York: Guilford Press. Cardinal, R. N., Parkinson, J. A., Hall, J., & Everitt, B. J. (2002). Emotion and motivation: The role of the amygdala, ventral striatum and prefrontal cortex. Neuroscience and Biobehavioral Reviews, 26, 321–352. Chudasama, Y., & Robbins, T. W. (2003). Dissociable contributions of the orbitofrontal and infralimbic cortex to Pavlovian autoshaping and discrimination reversal learning: Further evidence for the functional heterogeneity of the rodent frontal cortex. Journal of Neuroscience, 23, 8771–8780. Clark, L., Cools, R., & Robbins, T. (2004). The neuropsychology of ventral prefrontal cortex: Decision-making and reversal learning. Brain and Cognition, 55, 41–53. Craig, A. D. (2002). How do you feel? Interoception: The sense of the physiological condition of the body. Nature Reviews: Neuroscience, 3, 655–666. Critchley, H., Elliott, R., Mathias, C. J., & Dolan, R. J. (2000). Neural activity relating to generation and representation of galvanic skin conductance responses: A functional magnetic resonance imaging study. Journal of Neuroscience, 20, 3033–3040. Critchley, H., Mathias, C., & Dolan, R. (2001). Neuroanatomical basis for first- and second-order representations of bodily states. Nature Neuroscience, 4, 207–212.

Denton, D., Shade, R., Zamarippa, F., Egan, G., Blair-West, J., McKinley, M., et al. (1999a). Correlation of regional cerebral blood flow and change of plasma sodium concentration during genesis and satiation of thirst. Proceedings of the National Academy of Sciences, USA, 96, 2532–2537. Denton, D., Shade, R., Zamarippa, F., Egan, G., Blair-West, J., McKinley, M., et al. (1999b). Neuroimaging of genesis and satiation of thirst and an interoceptor-driven theory of origins of primary consciousness. Proceedings of the National Academy of Sciences, USA, 96, 5304–5309. Drevets, W. C., Price, J. L., Simpson, J. R., Todd, R. D., Reich, T., Vannier, M., et al. (1997, April 24). Subgenual prefrontal cortex abnormalities in mood disorders. Nature, 386, 824–827. Ernst, M., Bolla, K., Moratidis, M., Contoreggi, C. S., Matochick, J. A., Kurian, V., et al. (2002). Decision-making in a risk taking task. Neuropsychopharmacology, 26, 682–691. Eslinger, P. J., & Damasio, A. R. (1985). Severe disturbance of higher cognition after bilateral frontal lobe ablation: Patient evr. Neurology, 35, 1731–1741. Fellows, L. K., & Farah, M. J. (2005). Different underlying impairments in decision making following ventromedial and dorsolateral frontal lobe damage in humans. Cerebral Cortex, 15, 58–63. Flynn, F. G., Benson, D. F., & Ardila, A. (1999). Anatomy of the insula: Functional and clinical correlates. Aphasiology, 13(1), 55–78. Fukui, H., Murai, T., Fukuyama, H., Hayashi, T., & Hanakawa, T. (2005). Functional activity related to risk anticipation during performance of the Iowa Gambling Task. NeuroImage, 24, 253–259. Harlow, J. M. (1848). Passage of an iron bar through the head. Boston Medical and Surgical Journal, 39, 389–393. Heims, H. C., Critchley, H. D., Dolan, R., Mathias, C. J., & Cipolotti, L. (2004). Social and motivational functioning is not critically dependent on feedback of autonomic responses: Neuropsychological evidence from patients with pure autonomic failure. Neuropsychologia, 42, 1979–1988. Hurliman, E., Nagode, J. C., & Pardo, J. V. (2005). Double dissociation of exteroceptive and interoceptive feedback systems in the orbital and ventromedial prefrontal cortex of humans. Journal of Neuroscience, 25, 4641–4648. James, W. (1884). What is an emotion? Mind, 9, 188–205.

Critchley, H., Wiens, S., Rotshtein, P., Ohman, A., & Dolan, R. (2004). Neural systems supporting interoceptive awareness. Nature Neuroscience, 7, 189–195.

Kringelbach, M. L., & Rolls, E. T. (2004). The functional neuroanatomy of the human orbitofrontal cortex: Evidence from neuroimaging and neuropsychology. Progress in Neurobiology, 72(5), 341–372.

Damasio, A. R. (1994). Descartes’ error: Emotion, reason, and the human brain. New York: Grosset/Putnam.

Lane, R., Reiman, E., Ahern, G., Schwartz, G., & Davidson, R. (1997). Neuroanatomical correlates of happiness, sadness, and disgust. American Journal of Psychiatry, 154, 926–933.

Damasio, A. R. (1999). The feeling of what happens: Body and emotion in the making of consciousness. New York: Harcourt. Damasio, A. R. (2003). Looking for Spinoza: Joy, sorrow, and the feeling brain. New York: Harcourt. Damasio, A. R., Grabowski, T. G., Bechara, A., Damasio, H., Ponto, L. L. B., Parvizi, J., et al. (2000). Subcortical and cortical brain activity during the feeling of self-generated emotions. Nature Neuroscience, 3, 1049–1056.

Lang, P. J., Bradley, M. M., Cuthbert, B. N., & Patrick, C. J. (1993). Emotion and psychopathology: A startle probe analysis. In L. J. Chapman, J. P. Chapman, & D. C. Fowles (Eds.), Experimental personality and psychopathology research (Vol. 16, pp. 163–199). New York: Spring Publishing. LeDoux, J. (1996). The emotional brain: The mysterious underpinnings of emotional life. New York: Simon & Schuster.

Damasio, A. R., Tranel, D., & Damasio, H. (1990). Individuals with sociopathic behavior caused by frontal damage fail to respond autonomically to social stimuli. Behavioral Brain Research, 41, 81–94.

Luria, A. R., Pribram, K. H., & Homskaya, E. D. (1964). An experimental analysis of the behavioral disturbance produced by a left frontal arachnoidal endothelioma (meningioma). Neuropsychologia, 2, 257–280.

Damasio, A. R., Tranel, D., & Damasio, H. (1991). Somatic markers and the guidance of behavior: Theory and preliminary testing. In H. S. Levin, H. M. Eisenberg, & A. L. Benton (Eds.), Frontal lobe function and dysfunction (pp. 217–229). New York: Oxford University Press.

Maia, T. V., & McClelland, J. L. (2004). A reexamination of the evidence for the somatic marker hypothesis: What participants really know in the Iowa Gambling Task. Proceedings of the National Academy of Sciences, USA, 101, 16075–16080.

Damasio, H., Grabowski, T., Frank, R., Galburda, A. M., & Damasio, A. R. (1994, May 20). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. Science, 264, 1102–1104.

Maia, T. V., & McClelland, J. L. (2005). The somatic marker hypothesis: Still many questions but no answers: Response to Bechara et al. Trends in Cognitive Sciences, 9, 162–164.

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References 761 Martin, C., Denburg, N., Tranel, D., Granner, M., & Bechara, A. (2004). The effects of vagal nerve stimulation on decision-making. Cortex, 40, 1–8. Nagai, Y., Critchley, H. D., Featherstone, E., Trimble, M. R., & Dolan, R. J. (2004). Activity in ventromedial prefrontal cortex covaries with sympathetic skin conductance level: A physiological account of a “default mode” of brain function. NeuroImage, 22, 243–251. Nauta, W. J. H. (1971). The problem of the frontal lobes: A reinterpretation. Journal of Psychiatric Research, 8, 167–187. Neafsey, E. J. (1990). Prefrontal cortical control of the autonomic nervous system: Anatomical and physiological observations. In H. B. M. Uylings, C. G. Van Eden, J. P. C. De Bruin, M. A. Corner, & M. G. P. Feenstra (Eds.), Progress in brain research (Vol. 85, pp. 147–166). New York: Elsevier. North, N. T., & O’Carroll, R. E. (2001). Decision making in patients with spinal cord damage: Afferent feedback and the somatic marker hypothesis. Neuropsychologia, 39, 521–524. Ongur, D., & Price, J. L. (2000). The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebral Cortex, 10, 206–219. Patterson, J. C., Ungerleider, L. G., & Bandettini, P. A. (2002). Task-independent functional brain activity correlation with skin conductance changes: An fMRI study. NeuroImage, 17, 1797–1806. Pears, A., Parkinson, J. A., Hopewell, L., Everitt, B. J., & Roberts, A. C. (2003). Lesions of the orbitofrontal but not medial prefrontal cortex disrupt conditioned reinforcement in primates. Journal of Neuroscience, 23, 11189–11201.

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Rahman, S., Sahakian, B. J., Cardinal, R. N., Rogers, R. D., & Robbins, T. W. (2001). Decision making and neuropsychiatry. Trends in Cognitive Sciences, 6, 271–277. Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, USA, 98, 676–682. Rainville, P., Bechara, A., Naqvi, N., Virasith, A., Bilodeau, M., & Damasio, A. R. (2006). Basic emotions are associated with distinct patterns of cardiorespiratory activity. International Journal of Psychophysiology, 61, 5–18. Rolls, E. T. (1999). The brain and emotion. Oxford: Oxford University Press. Rolls, E. T. (2000). The orbitofrontal cortex and reward. Cerebral Cortex, 10, 284–294. Sanfey, A., Hastie, R., Colvin, M., & Grafman, J. (2003). Phineas Gage: Decision-making and the human prefrontal cortex. Neuropsychologia, 41, 1218–1229. Saver, J. L., & Damasio, A. R. (1991). Preserved access and processing of social knowledge in a patient with acquired sociopathy due to ventromedial frontal damage. Neuropsychologia, 29, 1241–1249. Tomb, I., Hauser, M., Deldin, P., & Caramazza, A. (2002). Do somatic markers mediate decisions on the gambling task? Nature Neuroscience, 5, 1103–1104. Tranel, D., & Damasio, H. (1994). Neuroanatomical correlates of electrodermal skin conductance responses. Psychophysiology, 31, 427–438. Welt, L. (1888). Uber charaktervaranderungen des menschen infoldge von lasionen des stirnhirns. Dutsch Archives of Klinical Medicine, 42, 339–390.

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Chapter 39

Neural Basis of Fear Conditioning DAVID E. A. BUSH, GLENN E. SCHAFE, AND JOSEPH E. LEDOUX

Emotion affects behavior in numerous ways. Emotion increases arousal, triggers hormonal stress responses, and alters motivation. Emotion can also alter how well something is learned and the strength of memory storage. But it is important to keep in mind that for these things to occur there needs to be a mechanism that assesses whether a given stimulus should trigger an emotional response in the first place. The problem is not so difficult for biologically significant unconditioned stimuli, such as an electric shock or the taste of a sweet food, because these stimuli are already hardwired to produce emotional responses. However, in everyday life we have emotional responses to all sorts of stimuli that are not innately aversive or appetitive. This is important because organisms need to respond to stimuli that predict dangerous or desirable events. Emotional stimuli thus serve as guides to direct behavior in adaptive directions. Novel stimuli acquire emotional significance through emotional learning. Emotional learning shares many characteristics with other forms of learning, especially at cellular and molecular levels, but there are also important differences. These differences exist essentially because of the distinct brain circuitry that underlies the interpretation and encoding of emotional information: the inputs, outputs, and processing resources in between. Fear conditioning is the best studied example of emotional learning. In this chapter we therefore focus on our current understanding of fear conditioning. However, it is important to keep in mind that fear is just one example of emotion. Emotional learning can also involve stimuli that predict positive or appetitive emotional properties. As we will see, the amygdala has emerged as a structure that is crucial for fear learning, but the role of the amygdala in appetitive emotional memories is still not well understood.

Although studies using appetitive conditioning in animals and humans find involvement of the amygdala, other studies that have examined humans with amygdala damage have found a selective role of the amygdala in negative emotion (Berntson, Bechara, Damasio, Tranel, & Cacioppo, 2007). For an in-depth survey of aspects of emotional learning related to positive or appetitive conditioning, see reviews by Balleine and Dickinson (1998), Cardinal, Parkinson, Hall, and Everitt (2002), and Holland and Gallagher (2004). AN OVERVIEW OF FEAR CONDITIONING In fear conditioning, the subject, typically a rat, is placed in an experimental chamber and given paired presentations of an innocuous conditioned stimulus (CS), such as a tone, together with an aversive unconditioned stimulus (US), such as a brief footshock. The CS does not elicit defensive behavior before fear conditioning, but after even a single CS-US pairing the animal begins to exhibit a range of conditioned responses (CRs), both to the CS and to the context (i.e., the conditioning chamber) in which conditioning occurs. In rats these responses include freezing or immobility (a species-typical behavioral response that makes a rodent less easily detected by predators), autonomic and endocrine responses (such as changes in heart rate and blood pressure, defecation, and increased levels of circulating stress hormones), and the potentiation of reflexes, such as the acoustic startle response (Blanchard & Blanchard, 1969; Davis, Walker, & Lee, 1997; Kapp, Frysinger, Gallagher, & Haselton, 1979; LeDoux, Iwata, Cicchetti, & Reis, 1988; Roozendaal, Koolhaas, & Bohus, 1991; Smith, Astley, Devito, Stein, & Walsh, 1980). Thus, as the result of a simple associative pairing, the CS comes to elicit many of the same defensive responses that are elicited by naturally aversive or threatening stimuli (see Figure 39.1). Similar responses occur in other mammals, including humans, allowing the fear conditioning procedure to be used to compare brain mechanisms across species.

This work was supported in part by National Institutes of Health grants MH 46516, MH 00956, MH 39774, and MH 11902, and a grant from the W. M. Keck Foundation to New York University. 762

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The Neural Circuitry of Fear Conditioning (A) Conditioned Stimulus (CS) (Tone or light) Unconditioned Stimulus (US) (Foot shock)

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Figure 39.1 Pavlovian fear conditioning. Note: Fear conditioning involves the presentation of an initially innocuous stimulus, such as a tone (conditioned stimulus; CS), that is paired or associated with a noxious stimulus, such as a brief electric shock to the feet (unconditioned stimulus; US). Before conditioning, the CS elicits little response from the animal. After conditioning, the CS elicits a wide range of behavioral and physiological responses, including freezing, that are characteristically elicited by naturally aversive or threatening stimuli.

THE NEURAL CIRCUITRY OF FEAR CONDITIONING The fear conditioning paradigm has enabled researchers to be systematic about studying the way that emotional stimuli are processed in the brain, and this research has implicated the amygdala as a region that is crucial for assessing fear CS inputs, and then coordinating outputs to brain regions that mediate fear responses. In the sections that follow we review how auditory CS information reaches the amygdala, how this information then flows through different amygdala subregions, and how amygdala outputs coordinate fear responses. Input Pathways to the Amygdala The neural circuitry underlying Pavlovian fear conditioning, particularly auditory fear conditioning, has been well characterized (Figure 39.2). Cells in the lateral amygdala (LA) receive excitatory, glutamatergic projections (Farb, Aoki, Milner, Kaneko, & LeDoux, 1992; LeDoux & Farb, 1991) from areas of the auditory thalamus, including the medial division of the thalamic medial geniculate body (MGm) and the posterior intralaminar nucleus (PIN), and also from the auditory cortex (area TE3; Bordi & LeDoux, 1992; Doron & LeDoux, 1999; LeDoux, Farb, & Romanski, 1991; LeDoux, Ruggerio, & Reis, 1985; McDonald, 1998; Romanski & LeDoux, 1993). Thus, there are two general routes: a direct subcortical route from the auditory thalamus (MGm/PIN) and a more indirect route that continues from the thalamus to the cortex before descending to the amygdala from cortical regions (e.g., TE3). Thalamic and cortical inputs to the

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LA, both capable of mediating fear learning (Romanski & LeDoux, 1992b), are believed to carry different types of information to the LA. The thalamic route (often called the “low road”) is believed to be critical for rapidly transmitting crude aspects of the CS to the LA, whereas the cortical route (known as the “high road”) is believed to carry highly refined information to the amygdala (LeDoux, 2000). Interestingly, whereas pretraining lesions of MGm/PIN impair auditory fear conditioning (LeDoux, Iwata, Pearl, & Reis, 1986; LeDoux, Sakaguchi, & Reis, 1984), similar lesions of the auditory cortex do not (LeDoux et al., 1984; Romanski & LeDoux, 1992a). Thus, the thalamic pathway between the MGm/PIN and the LA appears to be particularly important for auditory fear conditioning. This is not to say that the cortical input to the LA is not involved. Electrophysiological responses of cells in the auditory cortex are modified during fear conditioning (Edeline, Pham, & Weinberger, 1993), and posttraining lesions of the insular cortex attenuate auditory fear conditioning (Brunzell & Kim, 2001), suggesting that cortical inputs to the LA contribute to fear memory in the intact brain. Indeed, when conditioning depends on the ability of the animal to make fine discriminations between different auditory CSs, or when the CS is a complex auditory cue, such as an ultrasonic vocalization, then cortical regions appear to be crucial (Jarrell, Gentile, Romanski, McCabe, & Schneiderman, 1987; Lindquist & Brown, 2004). Recent evidence suggests that thalamic and cortical inputs terminate on different dendritic sites and have different

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Figure 39.2 Anatomy of the fear system. Note: A: Auditory fear conditioning involves the transmission of CS sensory information from areas of the auditory thalamus and cortex to the lateral amygdala (LA), where it can converge with incoming somatosensory information from the foot shock US. It is in the LA that alterations in synaptic transmission are thought to encode key aspects of the learning. B: During fear expression the LA engages the central nucleus of the amygdala (CE), which projects widely to many areas of the forebrain and brain stem that control the expression of fear CRs, including freezing, hypothalamic-pituitary-adrenal axis activation, and alterations in cardiovascular activity.

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cellular properties (Humeau & Lüthi, 2007). Nevertheless, the inputs from MGm/PIN and TE3 converge onto single cells in the LA (Li, Stutzmann, & LeDoux, 1996), and these same cells are also responsive to the footshock US (Romanski, Clugnet, Bordi, & LeDoux, 1993). Thus, individual cells in the LA are well suited to integrate information about the tone and shock during fear conditioning, which highlights the LA as a likely locus of the cellular events underlying fear acquisition. Consistent with this, behavioral studies have demonstrated that acquisition of auditory fear conditioning is disrupted both by conventional electrolytic or neurotoxic lesions of the LA and by reversible inactivation of LA cellular activity (Campeau & Davis, 1995; Helmstetter & Bellgowan, 1994; Kim, Rison, & Fanselow, 1993; LeDoux, Cicchetti, Xagoraris, & Romanski, 1990; Muller, Corodimas, Fridel, & LeDoux, 1997; Wilensky, Schafe, & LeDoux, 2000). Output Pathways from the Amygdala Although the LA is important for fear acquisition, its connections with other amygdaloid nuclei (Paré, Smith, & Paré, 1995; Pitkänen, Savander, & LeDoux, 1997), including the basal nucleus (B) and the central nucleus (CE), are essential for fear expression. During retrieval or expression of a fear memory, activation of the LA is thought to control CE activity through activation of B and/or the GABAergic intercalated cell masses situated along the lateral CE border (Paré, Quirk, & LeDoux, 2004). Auditory fear conditioning is disrupted by damage confined only to the LA and CE (Amorapanth, LeDoux, & Nader, 2000; Nader, Majidishad, Amorapanth, & LeDoux, 2001), suggesting that communication between the LA and the CE is sufficient to mediate fear conditioning. The connectivity of the CE with downstream brain regions is consistent with the traditional view that it serves as a principal output nucleus of the fear learning system. The CE projects to areas of the forebrain, the hypothalamus, and the brain stem, regions that control behavioral, endocrine, and autonomic CRs associated with fear learning (Davis, 1997; Davis et al., 1997; Kapp, Frysinger, Gallagher, & Haselton, 1979; LeDoux et al., 1988; Roozendaal et al., 1991). Projections from the CE to the midbrain periaqueductal gray, for example, have been shown to be particularly important for mediating behavioral and endocrine responses such as freezing and hypoalgesia (De Oca, DeCola, Maren, & Fanselow, 1998; Helmstetter & LandeiraFernandez, 1990; Helmstetter & Tershner, 1994; LeDoux et al., 1988), and projections to the lateral hypothalamus have been implicated in the control of conditioned cardiovascular responses (Iwata, LeDoux, & Reis, 1986; LeDoux et al., 1988). Importantly, whereas lesions of these individual areas can selectively impair expression of individual

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CRs, damage to the CE interferes with the expression of all fear CRs (LeDoux, 2000). Thus, the CE is typically thought of as the principal output nucleus of the fear system that acts to orchestrate the collection of hardwired, and typically species-specific responses that underlie defensive behavior. Pretraining electrolytic lesions of the B, unlike lesions of the LA and the CE, do not disrupt fear conditioning, which suggests that the B is not essential for fear conditioning (Amorapanth et al., 2000). However, one study observed deficits in auditory and contextual fear conditioning when pretraining lesions were localized to the anterior, but not posterior, divisions of the B (Goosens & Maren, 2001). Thus, although not essential, projections from the LA to the B may be important under some circumstances. In support of this, if B lesions occur after fear conditioning rather than before, fear memory expression is impaired (Anglada-Figueroa & Quirk, 2005), which indicates that the B participates in fear memory when it is intact at the time of conditioning. Interestingly, the B is also important for mediating more complex responses to fear stimuli, such as the performance of instrumental responses that actively avoid or escape a threatening stimulus (Amorapanth et al., 2000). We will return to this topic in a later section. AMYGDALA SYNAPTIC PLASTICITY AND FEAR CONDITIONING Synaptic plasticity is believed to be a neural mechanism that underlies learning, as was discussed in detail in Chapter 1. Considerable evidence shows that synapses in the LA are plastic, which could enable the LA to store memories for fear conditioning by altering connections between converging CS and US inputs. Unlike most other brain regions, synaptic plasticity in the amygdala has been directly related to learning. Consequently, this research has facilitated research not only on fear and emotion, but also on learning and memory. Synaptic Plasticity in the Lateral Amygdala Induced by Fear Conditioning Individual cells in the LA alter their response properties after a CS and US are paired during fear conditioning. The LA cells that are only weakly responsive to auditory input prior to conditioning will respond vigorously to the same input after fear conditioning (Goosens, Hobin, & Maren, 2003; Goosens & Maren, 2004; Maren, 2000; Quirk, Armony, & LeDoux, 1997; Quirk, Repa, & LeDoux, 1995). Thus, as a consequence of the training, a change occurs in the response of LA cells to the auditory CS, which is consistent with the view that neural plasticity in the LA encodes key aspects of fear learning and memory storage (Blair, Schafe, Bauer, Rodrigues, & LeDoux, 2001; Fanselow & LeDoux, 1999;

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Amygdala Synaptic Plasticity and Fear Conditioning

Maren, 1999; Quirk, Armony, Repa, Li, & LeDoux, 1997; for review, see Maren & Quirk, 2004). Interestingly, single-unit studies have suggested that there are at least two populations of LA cells that undergo plastic changes during fear conditioning in unique ways (Repa et al., 2001). The first is a more dorsal population (near the border of the caudate/putamen) that shows enhanced firing to the CS in the initial stages of training and testing and is sensitive to fear extinction (see Figure 39.3; Repa et al., 2001). These so-called transiently plastic cells exhibit short-latency changes (within 10 to 15 ms after tone onset). These short latencies are consistent with a rapid, monosynaptic thalamic input. The second population of LA cells occupies a more ventral position. In contrast to the transiently plastic cells, the more ventral cells exhibit enhanced firing to the CS throughout training and testing and do not appear to be sensitive to

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extinction. Further, these long-term plastic cells exhibit longer latencies (within 30 to 40 ms after tone onset), which indicates a polysynaptic pathway. Thus, it has been hypothesized that a dorsal-to-ventral network of neurons within the LA is responsible for triggering and storing fear memories, respectively (Medina, Repa, & LeDoux, 2002; Radwanska, Nikolaev, Knapska, & Kaczmarek, 2002; Repa et al., 2001). Long-Term Potentiation as a Mechanism for Lateral Amygdala Synaptic Plasticity Underlying Fear Conditioning The change in the responsiveness of LA cells during fear conditioning suggests that alterations in excitatory transmission between LA synapses might be critical for fear conditioning. Many of the recent studies that have examined

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enhanced firing throughout training and extinction. D: Transiently plastic cells are generally found in the dorsal tip of the lateral amygdala (LAd), where they may serve to trigger the initial stages of memory formation. Long-term plastic cells, on the other hand, are found in the ventral regions of the LAd and may be important for long-term, extinction-resistant memory storage. From “Two Different Lateral Amygdala Cell Populations Contribute to the Initiation and Storage of Memory,” by Repa et al., 2001, Nature Neuroscience, 4, pp. 724–731. Adapted with permission.

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the biochemical basis of fear conditioning have drawn on a larger literature that has focused on the biochemical events that underlie long-term potentiation (LTP), an activity-dependent form of synaptic plasticity that was initially discovered in the hippocampus (Bliss & Lømo, 1973). Importantly, LTP has also been demonstrated, both in vivo and in vitro, in each of the major auditory input pathways to the LA, including the thalamic and cortical auditory pathways (Chapman, Kairiss, Keenan, & Brown, 1990; Clugnet & LeDoux, 1989; Huang & Kandel, 1998; Rogan & LeDoux, 1995; Weisskopf, Bauer, & LeDoux, 1999; Weisskopf & LeDoux, 1999). This includes tetanus-induced LTP, which appears to

depend on activation of the glutamatergic NMDA receptor (Bauer, Schafe, & LeDoux, 2002; Huang & Kandel, 1998), and also associative LTP, which is induced following pairing of subthreshold presynaptic auditory inputs with postsynaptic depolarizations of LA cells (Bauer et al., 2002; Huang & Kandel, 1998; Weisskopf et al., 1999). Unlike LTP induced by a tetanus, associative LTP in the LA is dependent on L-type voltage-gated calcium channels (VGCCs; Bauer et al., 2002; Humeau & Lüthi, 2007; Weisskopf et al., 1999). A number of findings have converged to support the hypothesis that fear conditioning is mediated by an associative LTP-like process in the LA (see Figure 39.4). First,

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Figure 39.4 LTP in the LA. Note: A: (top) LTP is induced in the LA following high-frequency electrical stimulation of the MGm/PIN. The trace represents a stimulation-evoked field potential in the LA before and after LTP induction. (bottom) Following artificial LTP induction, processing of naturalistic auditory stimuli is also enhanced in the LA. The trace represents an auditory-evoked field potential in the LA before and after LTP induction. B: (top) Fear conditioning leads to electrophysiological changes in the LA in a manner similar to LTP. The figure represents a percentage change in the slope of the auditory-evoked field potential in the LA before, during, and after conditioning in both paired and unpaired rats. (bottom) Freezing behavior across training and

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testing periods. Note that both paired and unpaired groups show equivalent freezing behavior during training, but only the paired group shows an enhanced neural response. C: Associative LTP is induced in the amygdala slice by pairing trains of presynaptic stimulation of fibers coming from the auditory thalamus with depolarization of LA cells. Stimulation of fibers coming from cortical areas serves as a control for input specificity. D: LTP induced by pairing as measured by the change in the slope of the excitatory postsynaptic potential (EPSP) over time. In this case, the thalamic pathway received paired stimulation, whereas the cortical pathway received unpaired stimulation (i.e., trains and depolarizations, but in a noncontingent manner). The black bar represents the duration of the pairing.

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Beyond the Simple Fear Conditioning Circuit

LTP induction at thalamic inputs to the LA has been shown to enhance auditory processing, and thus natural information flow within the LA (Rogan & LeDoux, 1995). Second, fear conditioning has been shown to lead to electrophysiological changes in the LA in a manner that is very similar to those observed following artificial LTP induction, and these changes persist over days (McKernan & ShinnickGallagher, 1997; Rogan, Staubli, & LeDoux, 1997). Third, associative LTP in the LA has been shown to be sensitive to the same contingencies as fear conditioning. That is, LTP is strong when presynaptic trains precede the onset of postsynaptic depolarizations 100% of the time. However, LTP is much weaker if noncontingent depolarizations of the postsynaptic LA cell are interleaved within the same number of contiguous pairings (Bauer, LeDoux, & Nader, 2001). Thus, the LTP-induced change in synaptic efficacy within the LA depends on the contingency between preand postsynaptic activity rather than simply on temporal contiguity. Importantly, it is contingency, rather than temporal pairing, that is known to be critical for associative learning, including fear conditioning (Rescorla, 1968). Fourth, fear conditioning and LTP induction have been characterized by a common pharmacological and biochemical substrate. Fear conditioning, for example, has been shown to be impaired by pharmacological blockade of both NMDA receptors (Kim, DeCola, Landeira-Fernandez, & Fanselow, 1991; Miserendino, Sananes, Melia, & Davis, 1990; Rodrigues, Schafe, & LeDoux, 2001) and L-type VGCCs (Bauer et al., 2002) in the amygdala. Training-induced elevations in Ca2⫹ through both NMDA and L-type VGCCs in the LA appear to set in motion a process that is essential for both synaptic plasticity and fear memory formation, and this process appears to share essential features with that underlying LTP in the hippocampus and in other systems. Recent studies, for example, have demonstrated the involvement of Ca2⫹-regulated intracellular signaling cascades, including protein kinase A (PKA) and the mitogen-activated protein kinase (MAPK) in synaptic plasticity in fear memory consolidation. Each of these signaling cascades is thought to promote long-term synaptic plasticity and memory formation, in part, by activating transcription factors in the nucleus, including the cyclic adenosine monophosphate (cAMP)-response element binding (CREB) protein. In turn, CREB and cAMPresponse element (CRE)-mediated transcription is thought to promote the long-term structural and functional changes underlying memory formation. Many of these recent studies have used molecular genetic methods in which the molecules of interest have been manipulated in knockout or transgenic mouse lines (Abel et al., 1997; Bourtchuladze et al., 1994; Brambilla et al., 1997). Other recent studies have used pharmacological or viral transfection methods to

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examine the involvement of these molecules specifically in the amygdala. For example, recent studies have shown that infusions of drugs into the LA that specifically block RNA or protein synthesis or PKA activity impair the formation of fear memories (Bailey, Kim, Sun, Thompson, & Helmstetter, 1999; Schafe & LeDoux, 2000). Further, extracellular signal-regulated kinase (ERK)/MAPK is activated in the LA following fear conditioning, and pharmacological blockade of this activation via localized infusions of ERK/ MAPK inhibitors impairs fear conditioning (Schafe et al., 2000). Another recent study has shown that overexpression of the transcription factor CREB in the LA facilitates formation of fear memories (Josselyn et al., 2001). Thus, as shown in Figure 39.5 (Rodrigues, Schafe, & LeDoux, 2004; Huang, Martin, & Kandel, 2000), similar biochemical signaling pathways and molecular events that are involved in amygdala LTP are also necessary for fear conditioning. Most studies have emphasized the role of postsynaptic processes in fear conditioning. However, recent studies suggest that LTP at LA synapses may involve pre- as well as postsynaptic mechanisms (Apergis-Schoute, Debiec, Doyère, LeDoux, & Schafe, 2005; Humeau, Shaban, Bissière, & Lüthi, 2003; Schafe et al., 2005).

BEYOND THE SIMPLE FEAR CONDITIONING CIRCUIT Fear conditioning to a discrete cue is probably the simplest form of emotional learning, but most emotions are far more complex. Stimuli that trigger fear responses can involve much more than a pure tone. Also, regardless of the trigger stimulus, emotions can lead to a wide range of responses beyond the initial fear reaction, including active responses that help the animal cope with the stimulus, as well as other mechanisms that help to reduce the intensity of fear reactions. In the sections that follow we first examine neural circuitry that is thought to underlie the processing of more complex stimuli, and then consider how established fear memories can be modified and diminished. Contextual Fear Conditioning In a typical auditory fear conditioning experiment, the animal learns to fear not only the footshock-paired tone, but also the context in which conditioning occurs. Contextual fear is also learned when footshock stimuli are presented in the absence of a discrete CS. With contextual fear conditioning, fear to the context is later measured by returning the rat to the conditioning chamber on the test day and measuring the CR, including freezing behavior (Blanchard, Dielman, & Blanchard, 1968; Fanselow, 1980).

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Figure 39.5 Molecular pathways underlying fear conditioning. Note: A: Illustration of the molecular pathways within cells of the LA that are needed for the acquisition and consolidation of fear conditioning and also for LA LTP. Both fear conditioning and LTP involve the release of glutamate and Ca2⫹ influx through either NMDA receptors or L-type VGCCs. The increase in intracellular Ca2⫹ leads to the activation of protein kinases, such as PKA and ERK/MAPK. Once activated, these kinases can translocate to the nucleus, where they activate transcription factors such as CREB. The activation of CREB by PKA and ERK/MAPK promotes CRE-mediated gene transcription and the synthesis of new proteins. From Figure 2, page 85 in “Molecular mechanisms underlying emotional learning and memory in the lateral amygdala,” by Rodrigues et al., 2004, Neuron, 44, pp. 75–91. Adapted with permission. B: Disruption of these molecular pathways in the LA interferes with fear memory formation. In these studies, rats received intra-amygdala infusions of anisomycin (a protein synthesis inhibitor; B-Top), Rp-cAMPS (a PKA inhibitor; B-Middle), or U0126 (a MEK inhibitor, which is an

In comparison to auditory fear conditioning, much less is known about the neural systems underlying contextual fear. Substrates of contextual fear have been identified primarily through the use of lesion methods, and, as in auditory fear conditioning, the amygdala appears to play an essential role. For example, lesions of the amygdala, including the LA and B, have been shown to disrupt

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upstream regulator of ERK/MAPK activation; B-Bottom) at or around the time of training and were assayed for both short-term memory (1 to 4 hours later) and long-term memory (24 hours later) of auditory fear conditioning. In each figure vehicle-treated rats are represented by the gray bars, and drug-treated animals are represented by the black bars. *p ⬍ .05 relative to vehicle controls. C: Amygdala LTP has been shown to require the same biochemical processes. In these studies amygdala slices were treated with either anisomycin (C-Top), KT5720 (a PKA inhibitor; C-Middle), or PD098059 (a MEK inhibitor; C-Bottom) prior to and during tetanus of the thalamic pathway. In each experiment field recordings were obtained from the LA and expressed across time as a percentage of baseline. From “Both Protein Kinase A and Mitogen-Activated Protein Kinase Are Required in the Amygdala for the Macromolecular SynthesisDependent Late Phase of Long-Term Potentiation,” by Huang et al., 2000, Journal of Neuroscience, 20, pp. 6317–6325. Copyright 2000 by the Society for Neuroscience. Reprinted with permission.

both the acquisition and the expression of contextual fear conditioning (Kim et al., 1993; Maren, 1998; Phillips & LeDoux, 1992). Reversible inactivation, usually achieved by microinjecting muscimol or tetrodotoxin into the LA, has similar effects (Muller et al., 1997). Contextual fear conditioning is also impaired by infusion of NMDA receptor antagonists, RNA and protein synthesis inhibitors,

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Beyond the Simple Fear Conditioning Circuit

and inhibitors of PKA into the amygdala (Bailey et al., 1999; Goosens, Holt, & Maren, 2000; Huff & Rudy, 2004; Kim et al., 1991; Rodrigues et al., 2001; but see Walker, Paschall, & Davis, 2005). Collectively these findings suggest that essential aspects of the memory are encoded and stored in the amygdala. At this time, however, there is little evidence that allows us to distinguish between the involvement of different amygdala subnuclei in contextual fear, although recent lesion evidence suggests that the LA and anterior B, but not the posterior regions of the B, are critical (Goosens & Maren, 2001). The CE is, of course, also essential for the expression of contextual fear, as it is for auditory fear conditioning (Goosens & Maren, 2001). However, although lesions of the CE disrupt both cued and contextual fear, lesion studies suggest that other brain regions, including the bed nucleus of the stria terminalis, appear to be required only for the expression of contextual (not cued) fear (Sullivan et al., 2004). The hippocampus has also been implicated in contextual fear conditioning, although its exact role has been difficult to define. A number of studies have shown that electrolytic and neurotoxic lesions of the hippocampus disrupt contextual, but not auditory, fear conditioning (see Figure 39.6, Kim & Fanselow, 1992; Kim et al., 1993; Maren, Aharonov, & Fanselow, 1997; Phillips & LeDoux, 1992). However, only lesions given shortly after training disrupt contextual fear conditioning (Frankland, Cestari,

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Filipkowski, McDonald, & Silva, 1998). If rats are given hippocampal lesions 28 days after training, there is no memory impairment (Kim & Fanselow, 1992). This “retrograde gradient” of recall suggests that hippocampal-dependent memories are gradually transferred over time to other regions of the brain for permanent storage, an idea that is consistent with the findings of hippocampal-dependent episodic memory research in humans (Milner, Squire, & Kandel, 1998). It is clear, however, that the hippocampus undergoes plastic changes during fear conditioning, some of which may be necessary for memory formation of contextual fear. For example, intrahippocampal infusion of the NMDA receptor antagonist APV impairs contextual fear conditioning (Stiedl, Birkenfeld, Palve, & Spiess, 2000; Young, Bohenek, & Fanselow, 1994). Also, fear conditioning to a context, but not to an auditory CS, is impaired in mice that lack the NR1 subunit of the NMDA receptor exclusively in area CA1 of the hippocampus (Rampon et al., 2000). Fear conditioning also leads to increases in the activation of Calmodulin-dependent Protein Kinase II (CaMKII), PKC, ERK/MAPK, and CRE-mediated gene expression in the hippocampus (Atkins, Selcher, Petraitis, Trzaskos, & Sweatt, 1998; Hall, Thomas, & Everitt, 2000; Impey et al., 1998). These findings add support to the notion that NMDA receptor-dependent plastic changes in the hippocampus, in addition to the amygdala, are required for

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Figure 39.6 Hippocampal-dependent contextual fear. Note: Contextual fear conditioning requires the dorsal hippocampus, but only for a limited time. A: Experimental protocol from Kim and Fanselow (1992), where rats were trained with tone-shock pairings and then given lesions of the dorsal hippocampus either 1, 7, 14, or 28 days later. B: Contextual memory was impaired when lesions were given 1 day after training, but not if given 28 days after training. C: Auditory fear conditioning was not affected by hippocampal lesions. In each panel, the lesioned rats are represented by the black circles. From “ModalitySpecific Retrograde Amnesia of Fear,” by J. J. Kim, and M. S. Fanselow, Science, 256, May 1, 1992, p. 676. Copyright 1992 by the American

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Association for the Advancement of Science (AAAS). Reprinted with permission. D: A model of the neural system underlying contextual fear conditioning. The hippocampus (1) is necessary for forming an initial representation of the context and for providing that information as a CS to the amygdala (2) during fear conditioning. In the amygdala, the contextual CS can converge with the footshock US, and it is here that the memory of contextual fear is thought to be formed. Over time, however, the contextual memory formed by the hippocampus is transferred to the cortex (3) for permanent storage. At this point, the hippocampus is not necessary to retrieve the memory.

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contextual fear conditioning. However, it should be emphasized that the exact contribution of these plastic changes to contextual fear conditioning remains unclear. Most of these studies cannot distinguish between a role for NMDA receptor-mediated plasticity in fear memory formation and in the formation of contextual representations that then serve as the fear CS (Rudy, Huff, & Matus-Amat, 2004). Further, regulation of intracellular signaling cascades in the hippocampus by fear conditioning, though potentially indicative of some type of memory storage, does not necessarily indicate that these changes are related to the acquisition of fear memories. They may be related to episodic memories of the training experience that are acquired at the same time as fearful memories (LeDoux, 2000; and see later discussion). Indeed, a number of studies have shown that hippocampal cells undergo plastic changes during and after fear conditioning (Doyère et al., 1995; Moita, Rosis, Zhou, LeDoux, & Blair, 2003), including auditory fear conditioning, which is spared following hippocampal lesions (Kim & Fanselow, 1992). Although auditory fear conditioning can be learned independently of the hippocampus, it has recently been shown that hippocampal involvement is recruited with weaker fear conditioning protocols that involve low footshock intensity and few training trials (Quinn, Wied, Ma, Tinsley, & Fanselow, 2008). Thus, the amygdala and hippocampus normally cooperate in the intact brain to store different components of the fear learning experience. The amygdala independently stores the direct association between the cue and the footshock, and the hippocampus stores more general features of the conditioning episode that can contribute to the fear memory. Altering Established Fear Memories Thus far we have focused on how a fear memory is formed. But what happens after a fear memory has been retrieved? Two paradigms have been used to examine how fear memories change with retrieval: reconsolidation and extinction. Reconsolidation Blockade The traditional way of thinking about memory formation is that memories are laid down by a time-dependent process, called consolidation, that stabilizes the neuronal representation of the memory trace. Newly acquired memories, for example, are thought to be inherently unstable, acquiring stability only over time as RNA and protein synthesisdependent processes kick in. According to this view, after the memory has been consolidated retrieval simply involves going back and reactivating the original trace. However, over the years a number of studies have challenged this linear notion of memory formation and retrieval. In these studies, manipulations that are known to disrupt memory

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consolidation when given around the time of initial learning have also been found to disrupt the integrity of an established memory when given around the time of memory retrieval (see Sara, 2000). These findings suggest that the retrieval process renders a memory susceptible to disruption, similar to the susceptibility that exists prior to the consolidation of a newly formed memory. Recent studies using the fear conditioning paradigm have rekindled interest in this phenomenon. For example, infusion of the protein synthesis inhibitor anisomycin into the amygdala immediately after retrieval of an auditory fear memory was shown to impair memory retrieval on subsequent tests (Nader, Schafe, & LeDoux, 2000). This effect was clearly dependent on retrieval of the memory because no subsequent memory deficit was observed if the CS exposure was omitted. Further, the anisomycin-induced memory deficit was observed not only when the initial CS exposure was given shortly after training (i.e., 1 day), but also when the CS exposure was given 14 days after initial training, suggesting that the effect could not be attributable to disruption of late phases of protein synthesis necessary for consolidation. Thus, following active retrieval of a previously consolidated fear memory, that memory appears to undergo a second stabilization process (socalled reconsolidation) that requires protein synthesis in the amygdala. There is much that remains unknown about reconsolidation, but recent work has extended these findings by showing that amygdala CREB activation is also required for reconsolidation, because transient overexpression of a dominant negative isoform of CREB at the time of memory retrieval disrupts memory for both auditory and contextual fear conditioning (Kida et al., 2002), suggesting that a nuclear event is involved. Recent studies have demonstrated a similar role for activation of the ERK/MAPK pathway in the LA. Interestingly, postretrieval blockade of ERK/MAPK signaling in the LA not only disrupts fear memory consolidation, but also reverses the fear retrievalinduced potentiation of LA field potentials that accompanies the fear response (Doyère, Debiec, Monfils, Schafe, & LeDoux, 2007). In essence, reconsolidation blockade is associated not only with disruption of the fear memory, but also with disruption of the potentiated electrophysiological response that is associated with the retrieved memory. The therapeutic implications of this ability to disrupt fear memory reconsolidation still need to be explored. Of course, protein synthesis inhibition, or even disruption of ERK/MAPK and CREB signaling, is not likely to be useful for reconsolidation-based fear reduction manipulations in the clinic. Therefore, based on reconsolidation experiments in rats with other kinds of memories (Sara, 2000), it has recently been shown that beta-adrenergic receptor antagonists (e.g., propranolol), which are already

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Beyond the Simple Fear Conditioning Circuit

used in humans for other purposes, can also disrupt fear memory reconsolidation (Debiec & LeDoux, 2004). This suggests that beta blockers, given in conjunction with the retrieval of traumatic memories, might be able to reduce the potency of fear-related pathologies, such as Posttraumatic Stress Disorder (Debiec & LeDoux, 2006).

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Early studies showed that selective lesions of the ventral mPFC retard the extinction of fear to an auditory CS, while having no effect on initial fear acquisition (Morgan & LeDoux, 1995; Morgan, Romanski, & LeDoux, 1993; but see Gewirtz, Falls, & Davis, 1997). Further, neurons in the mPFC alter their response properties as a result of extinction (Garcia, Vouimba, Baudry, & Thompson, 1999; Herry, Vouimba, & Garcia, 1999). However, recent work suggests that the role of the ventral mPFC in extinction is complex. Briefly, the mPFC may not be necessary for the initial acquisition of fear extinction, but rather in the posttraining storage of information needed later to rapidly retrieve extinction learning under appropriate circumstances (see Figure 39.7; Quirk, Russo, Barron, & Lebron, 2000; Milad & Quirk, 2002). For example, rats with mPFC lesions are able to extinguish within a session but show impaired extinction retrieval when tested in a later session (Quirk et al., 2000). Further, neurons in the mPFC fire strongly to a tone CS after behavioral extinction has occurred, and artificial stimulation of the mPFC that resembles responding in an extinguished rat is sufficient to inhibit behavioral expression of fear in nonextinguished rats (Milad & Quirk, 2002). Consistent with this, blockade of mPFC NMDA receptors shortly after extinction

Extinction Extinction is a more traditional way of decreasing the potency of established fear memories. Extinction is a process whereby repeated presentations of the CS in the absence of the US lead to a weakening of the expression of conditioned responding. Unlike reconsolidation blockade, which is thought to disrupt the original memory trace, extinction involves the formation of a new inhibitory memory (i.e., a CS-No US trace) that competes with the original trace for control over behavior. Extinction of conditioned fear has been well documented in the behavioral literature, but we know comparatively little about its neurobiological substrate. However, research over the past 2 decades has implicated a circuit that involves complex interactions among the amygdala, the ventral medial prefrontal cortex (mPFC), and the hippocampus in fear extinction learning and retrieval.

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Neuroscience. Adapted with permission. B: Single cells in the mPFC are generally unresponsive to tones during training and extinction (Day 1), but signal vigorously during long-term recall of extinction (Day 2; 24 hours later). C: Direct stimulation of the mPFC during the early phases of extinction (Day 2) results in a dramatic reduction in fear, which is longlasting (Day 3; 24 hours later). In each figure the stimulated animals are represented by the black squares. From “Neurons in Medial Prefrontal Cortex Signal Memory for Fear Extinction,” by M. R. Milad and G. J. Quirk, November 7, 2002, Nature, 420, pp. 70–74. Copyright 2002 by Macmillan Publishers Ltd. Adapted with permission.

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Figure 39.8 The amygdala and fear extinction. Note: Extinction of fear-potentiated startle (FPS) can be impaired or facilitated by pharmacological manipulations of the amygdala. A: Extinction of FPS is impaired in a dose-dependent manner following infusion of AP5, an NMDA receptor antagonist, into the amygdala. White bars represent preextinction startle baselines; black bars represent the amount of startle potentiation after an extinction session in each group. Note that with increasing doses there is less extinction. From “Extinction of Fear-Potentiated Startle: Blockade by Infusion of an NMDA Antagonist into the Amygdala,” by W. A. Falls, M. J. Miserendino, and M. Davis, 1992, Journal of Neuroscience, 12, pp. 854–863. Copyright 1992 by the Society for Neuroscience. Adapted with permission. B: Extinction of FPS can be facilitated by infusion of a partial agonist of the NMDA receptor in the amygdala. Rats that were given intra-amygdala infusions of D-cycloserine (DCS; DCS/saline), a partial agonist of the glycine recognition site of the NMDA receptor, had facilitated extinction relative to controls (saline/saline). This effect could be reversed by HA966 (DCS/HA966), an antagonist of the glycine recognition site that has no effect on extinction itself (saline/HA966). In each group white bars represent preextinction startle baselines, and black bars represent the amount of startle potentiation after drug treatment and an extinction session. From “Facilitation of Conditioned Fear Extinction by Systemic Administration or Intra-Amygdala Infusions of D-Cycloserine as Assessed with FearPotentiated Startle in Rats,” by D. L. Walker, K. J. Ressler, K.-T. Lu, and M. Davis, 2002, Journal of Neuroscience, 22, pp. 2343–2351. Copyright 2002 by the Society for Neuroscience. Adapted with permission. C: Intra-amygdala

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training disrupts the subsequent retrieval of the extinction memory (Burgos-Robles, Vidal-Gonzalez, Santini, & Quirk, 2007), suggesting that cells in the mPFC store features of the fear extinction experience after training is complete. Importantly, extinction is known to be context-specific. That is, if a fear-conditioned rat is given fear extinction training in one context (Context A), the ability to inhibit fear is apparently linked to that context because renewal of the fear response occurs if the rat is presented with the CS outside of the extinction context (Bouton & Ricker, 1994). This fact, together with the finding that fully extinguished memories are capable of reinstating upon presentation of the US (Rescorla & Heth, 1975), has led to the long-held view that extinction does not result in the erasure of the original memory trace but is instead a new kind of learning that serves to inhibit expression of the old memory (Pavlov, 1927). Not surprisingly, recent studies have indicated that the hippocampus plays an important role in the contextual modulation of fear extinction. Maren and colleagues (Hobin, Goosens, & Maren, 2003), for example, have shown that training-induced neurophysiological responses in the LA readily extinguish within a fear extinction session, but that this neural representation of extinction, like the behavior itself, is specific to the context in which extinction has taken place. Further, functional inactivation of the hippocampus using the GABA-A agonist muscimol can impair the contextspecific expression of fear extinction (Corcoran & Maren, 2001). The requirements for both hippocampal and mPFC activity in extinction suggest that connections from the hippocampus to the mPFC are important for encoding contextual constraints on fear extinction learning. Beyond this, these findings have led researchers to propose a broad circuit model for fear extinction that involves projections from the hippocampus to the mPFC, and from the mPFC to the amygdala. The hippocampal-mPFC connection is needed to appropriately contextualize extinction, and the mPFCamygdala connection is needed to express extinction by inhibiting fear outputs from the fear circuitry of the amygdala (LA/B–intercalated cell masses–CE) that was discussed earlier in this chapter (Corcoran & Quirk, 2007; Hobin et al., 2003; Maren & Quirk, 2004; Paré et al., 2004; Quirk & Mueller, 2008; Sotres-Bayon, Bush, & LeDoux, 2004).

infusion of a MAP kinase inhibitor (PD98095) blocks extinction of FPS. Rats were infused with PD98095 before the 1st extinction session. The 2nd extinction session was given drug-free. Note the absence of extinction on the 1st session. In each group, black bars represent preextinction startle baselines, and white bars represent the amount of startle potentiation after an extinction session. From “Mitogen-Activated Protein Kinase Cascade in the Basolateral Nucleus of Amygdala Is Involved in Extinction of Fear-Potentiated Startle,” by K. T. Lu, D. L. Walker, and M. Davis, 2001, Journal of Neuroscience, 21, RC162. Copyright 2001 by the Society for Neuroscience. Adapted with permission.

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Beyond the Simple Fear Conditioning Circuit

These advances in our understanding of hippocampal and mPFC control over extinction retrieval have been important steps for the field. But questions about the neural mechanisms that underlie the actual formation of the extinction memory are not explained by this model. Insights into this problem come from a number of studies that have implicated the amygdala as an essential site of plasticity for the acquisition of fear extinction (Figure 39.8; Falls, Miserendino, & Davis, 1992; Lu, Walker, & Davis, 2001; Walker, Ressler, Lu, & Davis, 2002). Infusions of NMDA receptor antagonists or ERK/MAPK inhibitors into the amygdala have been shown to impair fear extinction (Davis, 2002; Falls et al., 1992; Lu et al., 2001). Conversely, both systemic and intra-amygdala infusions of partial agonists of the NMDA receptor facilitate fear extinction (Walker et al., 2002). These experiments suggest that some type of activity-dependent synaptic plasticity must take place in the amygdala during extinction learning, as it does during initial learning. In fact, unlike the mPFC, the amygdala appears to be necessary for the acquisition of fear extinction because blockade of NR2B-containing NMDA receptors in the LA prevents rats from the fear extinction learning that occurs across trials within a single extinction training session (Sotres-Bayon, Bush, & LeDoux, 2007). In contrast, disruption of BDNF-TrkB signaling with viral vector-mediated amygdala expression of dominant-negative TrkB was found to disrupt the consolidation, but not the acquisition, of fear extinction, suggesting that BDNF participates in the consolidation of the extinction memory within the intrinsic circuitry of the amygdala (Chhatwal, StanekRattiner, Davis, & Ressler, 2006). When considered together with the systems-level circuit discussed earlier, these findings suggest that fear extinction is first encoded in the amygdala during extinction training, and subsequently the amygdala trains the hippocampal-mPFC circuit so that the extinction memory can be later retrieved under contextually appropriate circumstances. The mechanisms for this systems-level consolidation process are not yet understood but may involve an amygdala-driven rehearsal of the extinction training experience via reciprocal connections from the amygdala to the hippocampus and mPFC. Fear-Motivated Instrumental Learning Reconsolidation blockade and extinction both represent mechanisms for diminishing the intensity of fear memories. Active coping is yet another mechanism for reducing the behavioral and emotional impact of fear. Pavlovian fear conditioning is useful for learning to detect a dangerous object or situation, but animals must also be able to use this information to guide ongoing behavior that is instrumental in avoiding that danger. Successful avoidance, made possible by Pavlovian associations that provide advance

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warning of danger, is therefore a potentially positive (and behaviorally reinforcing) outcome following CS exposure. In experimental situations this type of learning can be modeled by requiring the animal to make a response (i.e., move away, press a bar, turn a wheel) that will allow it to avoid presentation of a shock or danger signal, a form of learning known as “active avoidance.” In other experimental situations, the animal can be required to learn not to respond, known as “passive avoidance.” Both of these are examples of instrumental conditioning, and the amygdala, cooperating with other brain regions, plays a vital role in each. Previously, we mentioned that only the LA and CE were critical for Pavlovian fear conditioning. However, we have recently begun to appreciate the significance of projections from the LA to the basal nucleus of the amygdala (B, as defined earlier). Studies that employ fear learning tasks that require rats to learn both classical and instrumental components have begun to develop our knowledge of how emotional information can be used to motivate goaldirected responses (Amorapanth et al., 2000; Killcross, Robbins, & Everitt, 1997). Amorapanth et al., for example, first trained rats to associate a tone with footshock (the Pavlovian component). Next, rats learned to move from one side of a 2-compartment box to the other to avoid presentation of the tone (the instrumental component), a so-called escape-from-fear task. Findings showed that whereas lesions of the LA impaired both types of learning, lesions of the CE impair only the Pavlovian component (i.e., the tone-shock association). Conversely, lesions of the B impaired only the instrumental component (learning to move to the second compartment). Thus, different outputs of the LA appear to mediate Pavlovian and instrumental behaviors elicited by a fear-arousing stimulus (Amorapanth et al., 2000). It is important to note, however, that these findings do not indicate that the B is a site of motor control or a locus of memory storage for instrumental learning. Rather, the B likely guides fear-related behavior and reinforcement learning via its projections to nearby striatal regions that are known to be necessary for instrumental learning and reward processes (Everitt, Cador, & Robbins, 1989; Everitt et al., 1999; Robbins, Cador, Taylor, & Everitt, 1989). Our knowledge of how the amygdala transfers emotional information to brain regions involved in motivation and instrumental learning is still in its infancy. Research that addresses this issue is needed to unite these two related but sparsely integrated disciplines within behavioral neuroscience. Modulation of Explicit Memory by Fear Arousal Pavlovian fear conditioning is an implicit form of learning and memory. However, during most emotional experiences, including fear conditioning, explicit or conscious

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memories are also formed (LeDoux, 1996). These occur through the operation of the medial temporal lobe memory system involving the hippocampus and related cortical areas (Eichenbaum, 2000; Milner et al., 1998). The role of the hippocampus in the explicit memory of an emotional experience is much the same as its role in other kinds of experiences, with one important exception. During fearful or emotionally arousing experiences, the amygdala activates neuromodulatory systems in the brain and hormonal systems in the body via its projections to the hypothalamus, which can drive the hypothalamic-pituitary-adrenal (HPA) axis. Neurohormones released by these systems can, in turn, feed back to modulate the function of forebrain structures such as the hippocampus and serve to enhance the storage of the memory in these regions (McGaugh, 2000). The primary support for this model comes from studies of inhibitory avoidance learning, a type of passive avoidance learning, briefly introduced in the preceding section, whereby the animal must learn to not enter a chamber in which it previously received a shock. In this paradigm, various pharmacological manipulations of the amygdala that affect neurotransmitter or neurohormonal systems modulate the strength of the memory. For example, immediate posttraining blockade of intra-amygdala noradrenergic or glucocorticoid receptors impairs retention of inhibitory avoidance, whereas facilitation of these systems in the amygdala enhances acquisition and memory storage (McGaugh, 2000; McGaugh et al., 1993). The exact subnuclei in the amygdala that are critical for memory modulation remain unknown, as do the areas of the brain where these amygdala projections influence memory storage. Candidate areas include the hippocampus and entorhinal and parietal cortices (Izquierdo et al., 1997). Indeed, it would be interesting to know whether the changes in unit activity, or the activation of intracellular signaling cascades, in the hippocampus during and after fear conditioning, as discussed earlier, might be related to formation of such explicit memories, and how regulation of these signals depends on the integrity of the amygdala and its neuromodulators. Interestingly, it has been shown that stimulation of the B can modulate the persistence of LTP in the hippocampus (Frey, BergadoRosado, Seidenbecher, Pape, & Frey, 2001), which provides a potential mechanism whereby the amygdala can modulate hippocampal-dependent memories (Roozendaal, Barsegyan, & Lee, 2008; Roozendaal, Okuda, de Quervain, & McGaugh, 2006).

FEAR CONDITIONING IN HUMANS Studies of fear conditioning in humans have corroborated the findings from fear conditioning research in rodents.

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In general, advances in our understanding of the brain’s contributions to human emotions have come from two broad categories of neuropsychology research: studies in patients with damage to localized brain regions and studies that involve brain imaging in healthy subjects. The former provide evidence for a causal link between loss of function and region-specific damage, but the extent of brain damage cannot be easily controlled. The latter provide greater spatial and temporal precision. Neuropsychology of Fear in Brain-Lesioned Patients One of the most important insights gained from studies in brain-lesioned patients is that emotional learning and conscious awareness are dissociable phenomena. Patients with damage to the medial temporal lobe region, including the amygdala, show deficits in the ability to acquire conditioned fear responses, even when conscious awareness of the fear conditioning experience is intact. Conversely, patients with selective damage to the hippocampus and related areas of the medial temporal lobe show the opposite pattern: impaired declarative memory for the fear conditioning experience but intact implicit emotional responses to the CS (Bechara et al., 1995; Hamann, Monarch, & Goldstein, 2002; LaBar, LeDoux, Spencer, & Phelps, 1995). Hippocampal damage also appears to remove contextual constraints on fear extinction (LaBar & Phelps, 2005). In addition to the hippocampus, damage to the ventral mPFC also produces fear extinction deficits (Bechara, Damasio, Damasio, & Anderson, 1994; Davidson, Putnam, & Larson, 2000; Rolls, Hornak, Wade, & McGrath, 1994), which corresponds to deficits observed in rats with ventral mPFC lesions (Lebron, Milad, & Quirk, 2004; Morgan & LeDoux, 1995; Morgan et al., 1993; Morgan, Schulkin, & LeDoux, 2003; Quirk et al., 2000; Sierra-Mercado, Corcoran, Lebron-Milad, & Quirk, 2006). Functional Brain Imaging of Fear in Healthy Subjects Functional imaging during fear conditioning of healthy volunteers consistently reveals increased amygdala activation during fear conditioning and early phases of extinction (Buchel, Dolan, Armony, & Friston, 1999; Buchel, Morris, Dolan, & Friston, 1998; Cheng, Knight, Smith, Stein, & Helmstetter, 2003; Knight, Cheng, Smith, Stein, & Helmstetter, 2004; LaBar, Gatenby, Gore, LeDoux, & Phelps, 1998; Phelps, Delgado, Nearing, & LeDoux, 2004). In fact, individual differences in fear have been found to correlate with the degree of amygdala activity (Cheng et al., 2003; Furmark, Fischer, Wik, Larsson, & Fredrikson, 1997; LaBar

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Summary

et al., 1998). Interestingly, the strongest amygdala activation is observed during the early phase of conditioning (Buchel et al., 1998; LaBar et al., 1998), which is reminiscent of the transiently plastic cells observed in the dorsal regions of the LA in the rat (Repa et al., 2001). In addition to learning about danger from direct contact with an unconditioned stimulus, humans also learn in indirect ways. This is illustrated by a paradigm called “instructed fear,” whereby the subjects are told that one stimulus may be paired with a shock, but the subjects never receive a shock. Nevertheless, the CS leads to amygdala activation (Phelps et al., 2001), and damage to the amygdala disrupts the expression of the CS-elicited autonomic responses (Funayama, Grillon, Davis, & Phelps, 2001). Another way that fear is learned indirectly is by observation; that is, subjects who observe others being conditioned also develop conditioned responses to the CS. Such a CS then leads to amygdala activation (Olsson & Phelps, 2007). Fear conditioning leads to CS-induced amygdala activation even when subjects are unaware of the CS due to subliminal presentation techniques (Morris, Ohman, & Dolan, 1999). Similarly, a subliminal CS elicits amygdala activation after observational learning but not after instructed fear conditioning (Olsson & Phelps, 2004). Examples of fMRI imaging of amygdala activity after fear conditioning, instructed fear, and observational fear are shown in Figure 39.9 (courtesy of Elizabeth A. Phelps).

Clinical Implications The close correspondence between the brain regions involved in rodent and human fear conditioning suggests that insights gained from animal studies can be applied to the clinical setting. Exposure therapy is procedurally similar to fear extinction training and is currently the most effective method for treating anxiety disorders, especially phobias. However, similar to the postextinction renewal of fear seen in rat studies, when the CS is presented outside the extinction training context, patients often experience relapse of fear symptoms when they leave the therapeutic setting (Rodriguez, Craske, Mineka, & Hladek, 1999). Because animal studies conducted by Davis and colleagues have shown that partial NMDA agonists can facilitate fear extinction (Walker et al., 2002), the same group was able to use a similar pharmacological treatment and successfully enhance the clinical efficacy of exposure therapy in human patients (Ressler et al., 2002). This kind of translational research is becoming an increasingly important emphasis in emotion research.

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(A)

(B)

(C)

R

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Figure 39.9 Fear-induced amygdala activation in humans. Note: CS presentations to humans cause similar increases in amygdala activation after A: fear conditioning, in which subjects are given paired presentations of the CS and US, B: instructed fear, in which subjects are instructed about the CS-US association but do not directly experience the association, and C: observational fear learning, in which subjects observe someone else undergoing fear conditioning. Figure shows structural MRI of the human brain. Figure courtesy of Elizabeth A. Phelps.

SUMMARY In just over 2 decades we have seen a remarkable resurgence of interest in emotion research. Advances in brain research have been systematically combined with the fear conditioning paradigm, which has enabled us to trace how stimuli are attributed with fear-eliciting properties through their temporal association with innately aversive events. The amygdala has emerged as a crucial site of convergence for CS and US input pathways, and we now have knowledge of the cellular and molecular events that are needed to encode and store fear memories. This has led to important discoveries about the different ways and mechanisms through which an established fear memory can be modified, including reconsolidation, extinction, and the learning of active coping responses. These discoveries, in turn,

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Neural Basis of Fear Conditioning ing stimulation of the perforant path. Journal of Physiology, 232, 331–356.

have provided empirical data that indicate a separation of the brain mechanisms that mediate emotion and conscious awareness, as well as improved understanding of how these dissociable processes interact. Finally, these insights have begun to suggest new methods that can be introduced into the clinical setting.

Bordi, F., & LeDoux, J. E. (1992). Sensory tuning beyond the sensory system: An initial analysis of auditory properties of neurons in the lateral amygdaloid nucleus and overlying areas of the striatum. Journal of Neuroscience, 12, 2493–2503.

REFERENCES

Bouton, M. E., & Ricker, S. T. (1994). Renewal of extinguished responding in a second context. Animal Learning and Behavior, 22, 317–324.

Abel, T., Nguyen, P. V., Barad, M., Deuel, T. A., Kandel, E. R., & Bourtchouladze, R. (1997). Genetic demonstration of a role for PKA in the late phase of LTP, and in hippocampus-based long-term memory. Cell, 88, 615–626. Amorapanth, P., LeDoux, J. E., & Nader, K. (2000). Different lateral amygdala outputs mediate reactions and actions elicited by a fear-arousing stimulus. Nature Neuroscience, 3, 74–79. Anglada-Figueroa, D., & Quirk, G. J. (2005). Lesions of the basal amygdala block expression of conditioned fear but not extinction. Journal of Neuroscience, 25, 9680–9685. Apergis-Schoute, A. M., Debiec, J., Doyère, V., LeDoux, J. E., & Schafe, G. E. (2005). Auditory fear conditioning and long-term potentiation in the lateral amygdala require ERK/MAP kinase signaling in the auditory thalamus: A role for presynaptic plasticity in the fear system. Journal of Neuroscience, 25, 5730–5739. Atkins, C. M., Selcher, J. C., Petraitis, J. J., Trzaskos, J. M., & Sweatt, J. D. (1998). The MAPK cascade is required for mammalian associative learning. Nature Neuroscience, 1, 602–609.

Bourtchuladze, R., Frenguelli, B., Blendy, J., Cioffi, D., Schutz, G., & Silva, A. J. (1994). Deficient long-term memory in mice with a targeted mutation of the cAMP-responsive element-binding protein. Cell, 79, 59–68.

Brambilla, R., Gnesutta, N., Minichiello, L., White, G., Roylance, A. J., Herron, C. E., et al. (1997, November 20). A role for the ras signaling pathway in synaptic transmission and long-term memory. Nature, 390, 281–286. Brunzell, D. H., & Kim, J. J. (2001). Fear conditioning to tone, but not to context, is attenuated by lesions of the insular cortex and posterior extension of the intralaminar complex in rats. Behavioral Neuroscience, 115, 365–375. Buchel, C., Dolan, R. J., Armony, J. L., & Friston, K. J. (1999). Amygdalahippocampal involvement in human aversive trace conditioning revealed through event-related functional magnetic resonance imaging. Journal of Neuroscience, 19, 10869–10876. Buchel, C., Morris, J., Dolan, R. J., & Friston, K. J. (1998). Brain systems mediating aversive conditioning: An event-related fMRI study. Neuron, 20, 947–957. Burgos-Robles, A., Vidal-Gonzalez, I., Santini, E., & Quirk, G. J. (2007). Consolidation of fear extinction requires NMDA receptor-dependent bursting in the ventromedial prefrontal cortex. Neuron, 53, 871–880.

Bailey, D. J., Kim, J. J., Sun, W., Thompson, R. F., & Helmstetter, F. J. (1999). Acquisition of fear conditioning in rats requires the synthesis of mRNA in the amygdala. Behavioral Neuroscience, 113, 276–282.

Campeau, S., & Davis, M. (1995). Involvement of the central nucleus and basolateral complex of the amygdala in fear conditioning measured with fear-potentiated startle in rats trained concurrently with auditory and visual conditioned stimuli. Journal of Neuroscience, 15, 2301–2311.

Balleine, B. W., & Dickinson, A. (1998). Goal-directed instrumental action: Contingency and incentive learning and their cortical substrates. Neuropharmacology, 37(4–5), 407–419.

Cardinal, R. N., Parkinson, J. A., Hall, J., & Everitt, B. J. (2002). Emotion and motivation: The role of the amygdala, ventral striatum, and prefrontal cortex. Neuroscience and Biobehavioral Reviews, 26, 321–352.

Bauer, E. P., LeDoux, J. E., & Nader, K. (2001). Fear conditioning and LTP in the lateral amygdala are sensitive to the same stimulus contingencies. Nature Neuroscience, 4, 687–688.

Chapman, P. F., Kairiss, E. W., Keenan, C. L., & Brown, T. H. (1990). Longterm synaptic potentiation in the amygdala. Synapse, 6, 271–278.

Bauer, E. P., Schafe, G. E., & LeDoux, J. E. (2002). NMDA receptors and L-type voltage-gated calcium channels contribute to long-term potentiation and different components of fear memory formation in the lateral amygdala. Journal of Neuroscience, 22, 5239–5249. Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1/3), 7–15. Bechara, A., Tranel, D., Damasio, H., Adolphs, R., Rockland, C., & Damasio, A. R. (1995, August 25). Double dissociation of conditioning and declarative knowledge relative to the amygdala and hippocampus in humans. Science, 269, 1115–1118. Berntson, G. G., Bechara, A., Damasio, H., Tranel, D., & Cacioppo, J. T. (2007). Amygdala contribution to selective dimensions of emotion. Social Cognitive and Affective Neuroscience, 2, 123–129. Blair, H. T., Schafe, G. E., Bauer, E. P., Rodrigues, S. M., & LeDoux, J. E. (2001). Synaptic plasticity in the lateral amygdala: A cellular hypothesis of fear conditioning. Learning and Memory, 8, 229–242.

Cheng, D. T., Knight, D. C., Smith, C. N., Stein, E. A., & Helmstetter, F. J. (2003). Functional MRI of human amygdala activity during Pavlovian fear conditioning: Stimulus processing versus response expression. Behavioral Neuroscience, 117, 3–10. Chhatwal, J. P., Stanek-Rattiner, L., Davis, M., & Ressler, K. J. (2006). Amygdala BDNF signaling is required for consolidation but not encoding of extinction. Nature Neuroscience, 9, 870–872. Clugnet, M. C., & LeDoux, J. E. (1989). Synaptic plasticity in fear conditioning circuits: Induction of LTP in the lateral nucleus of the amygdala by stimulation of the medial geniculate body. Journal of Neuroscience, 10, 2818–2824. Corcoran, K. A., & Maren, S. (2001). Hippocampal inactivation disrupts contextual retrieval of fear memory after extinction. Journal of Neuroscience, 21, 1720–1726. Corcoran, K. A., & Quirk, G. J. (2007). Recalling safety: Cooperative functions of the ventromedial prefrontal cortex and the hippocampus in extinction. CNS Spectrums, 12(3), 200–206.

Blanchard, R. J., & Blanchard, D. C. (1969). Crouching as an index of fear. Journal of Comparative Physiological Psychology, 67, 370–375.

Davidson, R. J., Putnam, K. M., & Larson, C. L. (2000). Dysfunction in the neural circuitry of emotion regulation: A possible prelude to violence. Science, 289, 591–594.

Blanchard, R. J., Dielman, T. E., & Blanchard, D. C. (1968). Postshock crouching: Familiarity with the shock situation. Psychonomic Science, 10, 371–372.

Davis, M. (1997). Neurobiology of fear responses: The role of the amygdala. Journal of Neuropsychiatry and Clinical Neurosciences, 9, 382–402.

Bliss, T. V. P., & Lømo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit follow-

c39.indd Sec4:776

Davis, M. (2002). Role of NMDA receptors and MAP kinase in the amygdala in extinction of fear: Clinical implications for exposure therapy. European Journal of Neuroscience, 16, 395–398.

8/17/09 3:07:59 PM

References 777 Davis, M., Walker, D. L., & Lee, Y. (1997). Roles of the amygdala and bed nucleus of the stria terminalis in fear and anxiety measured with the acoustic startle reflex: Possible relevance to PTSD. Annals of the New York Academy of Sciences, 821, 305–331. Debiec, J., & LeDoux, J. E. (2004). Disruption of reconsolidation but not consolidation of auditory fear conditioning by noradrenergic blockade in the amygdala. Neuroscience, 129, 267–272. Debiec, J., & LeDoux, J. E. (2006). Noradrenergic signaling in the amygdala contributes to the reconsolidation of fear memory: Treatment implications for PTSD. Annals of the New York Academy of Sciences, 1071, 521–524.

Furmark, T., Fischer, H., Wik, G., Larsson, M., & Fredrikson, M. (1997). The amygdala and individual differences in human fear conditioning. NeuroReport, 8, 3957–3960. Garcia, R., Vouimba, R. M., Baudry, M., & Thompson, R. F. (1999, November 18). The amygdala modulates prefrontal cortex activity relative to conditioned fear. Nature, 402, 294–296. Gewirtz, J. C., Falls, W. A., & Davis, M. (1997). Normal conditioned inhibition and extinction of freezing and fear-potentiated startle following electrolytic lesions of medial prefrontal cortex in rats. Behavioral Neuroscience, 111, 712–726.

De Oca, B. M., DeCola, J. P., Maren, S., & Fanselow, M. S. (1998). Distinct regions of the periaqueductal gray are involved in the acquisition and expression of defensive responses. Journal of Neuroscience, 18, 3426–3432.

Goosens, K. A., Hobin, J. A., & Maren, S. (2003). Auditory-evoked spike firing in the lateral amygdala and Pavlovian fear conditioning: Mnemonic code or fear bias? Neuron, 40, 1013–1022.

Doron, N. N., & LeDoux, J. E. (1999). Organization of projections to the lateral amygdala from auditory and visual areas of the thalamus in the rat. Journal of Comparative Neurology, 412, 383–409.

Goosens, K. A., Holt, W., & Maren, S. (2000). A role for amygdaloid, PKA and PKC in the acquisition of long-term conditional fear memories in rats. Behavioural Brain Research, 114(1–2), 145–152.

Doyère, V., Debiec, J., Monfils, M. H., Schafe, G. E., & LeDoux, J. E. (2007). Synapse-specific reconsolidation of distinct fear memories in the lateral amygdala. Nature Neuroscience, 10, 414–416.

Goosens, K. A., & Maren, S. (2001). Contextual and auditory fear conditioning are mediated by the lateral, basal, and central amygdaloid nuclei in rats. Learning and Memory, 8, 148–155.

Doyère, V., Redini-Del Negro, C., Dutrieux, G., Le Floch, G., Davis, S., & Laroche, S. (1995). Potentiation or depression of synaptic efficacy in the dentate gyrus is determined by the relationship between the conditioned and unconditioned stimulus in a classical conditioning paradigm in rats. Behavioural Brain Research, 70, 15–29.

Goosens, K. A., & Maren, S. (2004). NMDA receptors are essential for the acquisition, but not expression, of conditional fear and associative spike firing in the lateral amygdala. European Journal of Neuroscience, 20, 537–548.

Edeline, J.-M., Pham, P., & Weinberger, N. M. (1993). Rapid development of learning-induced receptive field plasticity in the auditory cortex. Behavioral Neuroscience, 107, 539–551.

Hall, J., Thomas, K. L., & Everitt, B. J. (2000). Rapid and selective induction of BDNF expression in the hippocampus during contextual learning. Nature Neuroscience, 3, 533–535.

Eichenbaum, H. (2000). A cortical-hippocampal system for declarative memory. Nature Reviews: Neuroscience, 1, 41–50.

Hamann, S., Monarch, E. S., & Goldstein, F. C. (2002). Impaired fear conditioning in Alzheimer ’s disease. Neuropsychologia, 40, 1187–1195.

Everitt, B. J., Cador, M., & Robbins, T. W. (1989). Interactions between the amygdala and ventral striatum in stimulus-reward associations: Studies using a second-order schedule of sexual reinforcement. Neuroscience, 30, 63–75.

Helmstetter, F. J., & Bellgowan, P. S. (1994). Effects of muscimol applied to the basolateral amygdala on acquisition and expression of contextual fear conditioning in rats. Behavioral Neuroscience, 108, 1005–1009.

Everitt, B. J., Parkinson, J. A., Olmstead, M. C., Arroyo, M., Robledo, P., & Robbins, T. W. (1999). Associative processes in addiction and reward: The role of amygdala-ventral striatal subsystems. Annals of the New York Academy of Sciences, 877, 412–438.

Helmstetter, F. J., & Landeira-Fernandez, J. (1990). Conditional hypoalgesia is attenuated by naltrexone applied to the periaqueductal gray. Brain Research, 537, 88–92.

Falls, W. A., Miserendino, M. J., & Davis, M. (1992). Extinction of fearpotentiated startle: Blockade by infusion of an NMDA antagonist into the amygdala. Journal of Neuroscience, 12, 854–863.

Helmstetter, F. J., & Tershner, S. A. (1994). Lesions of the periaqueductal gray and rostral ventromedial medulla disrupt antinociceptive but not cardiovascular aversive conditional responses. Journal of Neuroscience, 14, 7099–7108.

Fanselow, M. S. (1980). Conditional and unconditional components of postshock freezing. Pavlovian Journal of Biological Science, 15, 177–182.

Herry, C., Vouimba, R. M., & Garcia, R. (1999). Plasticity in the mediodorsal thalamo-prefrontal cortical transmission in behaving mice. Journal of Neurophysiology, 82, 2827–2832.

Fanselow, M. S., & LeDoux, J. E. (1999). Why we think plasticity underlying Pavlovian fear conditioning occurs in the basolateral amygdala. Neuron, 23, 229–232.

Hobin, J. A., Goosens, K. A., & Maren, S. (2003). Context-dependent neuronal activity in the lateral amygdala represents fear memories after extinction. Journal of Neuroscience, 23, 8410–8416.

Farb, C. R., Aoki, C., Milner, T., Kaneko, T., & LeDoux, J. E. (1992). Glutamate immunoreactive terminals in the lateral amygdaloid nucleus: A possible substrate for emotional memory. Behavioural Brain Research, 593, 145–158.

Holland, P. C., & Gallagher, M. (2004). Amygdala-frontal interactions and reward expectancy. Current Opinion in Neurobiology, 4, 148–155.

Frankland, P. W., Cestari, V., Filipkowski, R. K., McDonald, R. J., & Silva, A. J. (1998). The dorsal hippocampus is essential for context discrimination but not for contextual conditioning. Behavioral Neuroscience, 112, 863–874. Frey, S., Bergado-Rosado, J., Seidenbecher, T., Pape, H. C., & Frey, J. U. (2001). Reinforcement of early long-term potentiation (earlyLTP) in dentate gyrus by stimulation of the basolateral amygdala: Heterosynaptic induction mechanisms of late-LTP. Journal of Neuroscience, 21, 3697–3703. Funayama, E. S., Grillon, C., Davis, M., & Phelps, E. A. (2001). A double dissociation in the affective modulation of startle in humans: Effects

c39.indd Sec5:777

of unilateral temporal lobectomy. Journal of Cognitive Neuroscience, 13, 721–729.

Huang, Y. Y., & Kandel, E. R. (1998). Postsynaptic induction and PKAdependent expression of LTP in the lateral amygdala. Neuron, 21, 169–178. Huang, Y. Y., Martin, K. C., & Kandel, E. R. (2000). Both protein kinase A and mitogen-activated protein kinase are required in the amygdala for the macromolecular synthesis-dependent late phase of long-term potentiation. Journal of Neuroscience, 20, 6317–6325. Huff, N. C., & Rudy, J. W. (2004). The amygdala modulates hippocampusdependent context memory formation and stores cue-shock associations. Neuroscience, 118, 53–62. Humeau, Y., & Lüthi, A. (2007). Dendritic calcium spikes induce bi-directional synaptic plasticity in the lateral amygdala. Neuropharmacology, 52, 234–243.

8/17/09 3:08:00 PM

778

Neural Basis of Fear Conditioning

Humeau, Y., Shaban, H., Bissière, S., & Lüthi, A. (2003, December 18). Presynaptic induction of heterosynaptic associative plasticity in the mammalian brain. Nature, 426, 841–845.

LeDoux, J. E., & Farb, C. R. (1991). Neurons of the acoustic thalamus that project to the amygdala contain glutamate. Neuroscience Letters, 134, 145–149.

Impey, S., Smith, D. M., Obrietan, K., Donahue, R., Wade, C., & Storm, D. R. (1998). Stimulation of cAMP response element (CRE)-mediated transcription during contextual learning. Nature Neuroscience, 1, 595–601.

LeDoux, J. E., Farb, C. R., & Romanski, L. M. (1991). Overlapping projections to the amygdala and striatum from auditory processing areas of the thalamus and cortex. Neuroscience Letters, 134, 139–144.

Iwata, J., LeDoux, J. E., & Reis, D. J. (1986). Destruction of intrinsic neurons in the lateral hypothalamus disrupts the classical conditioning of autonomic but not behavioral emotional responses in the rat. Behavioural Brain Research, 368, 161–166.

LeDoux, J. E., Iwata, J., Cicchetti, P., & Reis, D. J. (1988). Different projections of the central amygdaloid nucleus mediate autonomic and behavioral correlates of conditioned fear. Journal of Neuroscience, 8, 2517–2529.

Izquierdo, I., Quillfeldt, J. A., Zanatta, M. S., Quevedo, J., Schaeffer, E., Schmitz, P. K., et al. (1997). Sequential role of hippocampus and amygdala, entorhinal cortex and parietal cortex in formation and retrieval of memory for inhibitory avoidance in rats. European Journal of Neuroscience, 9, 786–793. Jarrell, T. W., Gentile, C. G., Romanski, L. M., McCabe, P. M., & Schneiderman, N. (1987). Involvement of cortical and thalamic auditory regions in retention of differential bradycardia conditioning to acoustic conditioned stimuli in rabbits. Brain Research, 412, 285–294. Josselyn, S. A., Shi, C., Carlezon, W. A., Jr., Neve, R. L., Nestler, E. J., & Davis, M. (2001). Long-term memory is facilitated by cAMP responseelement binding protein overexpression in the amygdala. Journal of Neuroscience, 21, 2404–2412. Kapp, B. S., Frysinger, R. C., Gallagher, M., & Haselton, J. R. (1979). Amygdala central nucleus lesions: Effect on heart rate conditioning in the rabbit. Physiology and Behavior, 23, 1109–1117. Kida, S., Josselyn, S. A., de Oritz, S. P., Kogan, J. H., Chevere, I., Masushige, S., et al. (2002). CREB required for the stability of new and reactivated fear memories. Nature Neuroscience, 5, 348–355. Killcross, S., Robbins, T. W., & Everitt, B. J. (1997, July 24). Different types of fear-conditioned behaviour mediated by separate nuclei within amygdala. Nature, 388, 377–380. Kim, J. J., DeCola, J. P., Landeira-Fernandez, J., & Fanselow, M. S. (1991). N-Methy-D-Aspartate receptor antagonist APV blocks acquisition but not expression of fear conditioning. Behavioral Neuroscience, 105, 126–133. Kim, J. J., & Fanselow, M. S. (1992, May 1). Modality-specific retrograde amnesia of fear. Science, 256, 675–677. Kim, J. J., Rison, R. A., & Fanselow, M. S. (1993). Effects of amygdala, hippocampus, and periaqueductal gray lesions on short- and long-term contextual fear. Behavioral Neuroscience, 107, 1–6. Knight, D. C., Cheng, D. T., Smith, C. N., Stein, E. A., & Helmstetter, F. J. (2004). Neural substrates mediating human delay and trace fear conditioning. Journal of Neuroscience, 24, 218–228. LaBar, K. S., Gatenby, J. C., Gore, J. C., LeDoux, J. E., & Phelps, E. A. (1998). Human amygdala activation during conditioned fear acquisition and extinction: A mixed-trial fMRI study. Neuron, 20, 937–945. LaBar, K. S., LeDoux, J. E., Spencer, D. D., & Phelps, E. A. (1995). Impaired fear conditioning following unilateral temporal lobectomy in humans. Journal of Neuroscience, 15, 6846–6855. LaBar, K. S., & Phelps, E. A. (2005). Reinstatement of conditioned fear in humans is context dependent and impaired in amnesia. Behavioral Neuroscience, 119, 677–686. Lebron, K., Milad, M. R., & Quirk, G. J. (2004). Delayed recall of fear extinction in rats with lesions of ventral medial prefrontal cortex. Learning and Memory, 11, 544–548. LeDoux, J. E. (1996). The emotional brain. New York: Simon & Schuster. LeDoux, J. E. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 23, 155–184. LeDoux, J. E., Cicchetti, P., Xagoraris, A., & Romanski, L. M. (1990). The lateral amygdaloid nucleus: Sensory interface of the amygdala in fear conditioning. Journal of Neuroscience, 10, 1062–1069.

c39.indd Sec5:778

LeDoux, J. E., Iwata, J., Pearl, D., & Reis, D. J. (1986). Disruption of auditory but not visual learning by destruction of intrinsic neurons in the rat medial geniculate body. Behavioural Brain Research, 371, 395–399. LeDoux, J. E., Ruggerio, D. A., & Reis, D. J. (1985). Projections to the subcortical forebrain from anatomically defined regions of the medial geniculate body in the rat. Journal of Comparative Neurology, 242, 182–213. LeDoux, J. E., Sakaguchi, A., & Reis, D. J. (1984). Subcortical efferent projections of the medial geniculate nucleus mediate emotional responses conditioned by acoustic stimuli. Journal of Neuroscience, 4, 683–698. Li, X. F., Stutzmann, G. E., & LeDoux, J. E. (1996). Convergent but temporally separated inputs to lateral amygdala neurons from the auditory thalamus and auditory cortex use different postsynaptic receptors: In vivo intracellular and extracellular recordings in fear conditioning pathways. Learning and Memory, 3(2/3), 229–242. Lindquist, D. H., & Brown, T. H. (2004). Temporal encoding in fear conditioning revealed through associative reflex facilitation. Behavioral Neuroscience, 118, 395–402. Lu, K. T., Walker, D. L., & Davis, M. (2001). Mitogen-activated protein kinase cascade in the basolateral nucleus of amygdala is involved in extinction of fear-potentiated startle. Journal of Neuroscience, 21, RC162. Maren, S. (1998). Overtraining does not mitigate contextual fear conditioning deficits produced by neurotoxic lesions of the basolateral amygdala. Journal of Neuroscience, 18, 3088–3097. Maren, S. (1999). Long-term potentiation in the amygdala: A mechanism for emotional learning and memory. Trends in Neuroscience, 22, 561–567. Maren, S. (2000). Auditory fear conditioning increases CS-elicited spike firing in lateral amygdala neurons even after extensive overtraining. European Journal of Neuroscience, 12, 4047–4054. Maren, S., Aharonov, G., & Fanselow, M. S. (1997). Neurotoxic lesions of the dorsal hippocampus and Pavlovian fear conditioning in rats. Behavioural Brain Research, 88, 261–274. Maren, S., & Quirk, G. J. (2004). Neuronal signaling of fear memory. Nature Reviews: Neuroscience, 5, 844–852. McDonald, A. J. (1998). Cortical pathways to the mammalian amygdala. Progress in Neurobiology, 55, 257–332. McGaugh, J. L. (2000, January 14). Memory: A century of consolidation. Science, 287, 248–251. McGaugh, J. L., Introini-Collison, I. B., Cahill, L. F., Castellano, C., Dalmaz, C., Parent, M. B., et al. (1993). Neuromodulatory systems and memory storage: Role of the amygdala. Behavioural Brain Research, 58, 81–90. McKernan, M. G., & Shinnick-Gallagher, P. (1997, December 11). Fear conditioning induces a lasting potentiation of synaptic currents in vitro. Nature, 390, 607–611. Medina, J. F., Repa, J. C., & LeDoux, J. E. (2002). Parallels between cerebellum- and amygdala-dependent conditioning. Nature Reviews: Neuroscience, 3, 122–131. Milad, M. R., & Quirk, G. J. (2002, November 7). Neurons in medial prefrontal cortex signal memory for fear extinction. Nature, 420, 70–74. Milner, B., Squire, L. R., & Kandel, E. R. (1998). Cognitive neuroscience and the study of memory. Neuron, 20, 445–468.

8/17/09 3:08:00 PM

References 779 Miserendino, M. J. D., Sananes, C. B., Melia, K. R., & Davis, M. (1990, June 21). Blocking of acquisition but not expression of conditioned fear-potentiated startle by NMDA antagonists in the amygdala. Nature, 345, 716–718. Moita, M. A., Rosis, S., Zhou, Y., LeDoux, J. E., & Blair, H. T. (2003). Hippocampal place cells acquire location-specific responses to the conditioned stimulus during auditory fear conditioning. Neuron, 37, 485–497. Morgan, M. A., & LeDoux, J. E. (1995). Differential contribution of dorsal and ventral medial prefrontal cortex to the acquisition and extinction of conditioned fear in rats. Behavioral Neuroscience, 109, 681–688. Morgan, M. A., Romanski, L. M., & LeDoux, J. E. (1993). Extinction of emotional learning: Contribution of medial prefrontal cortex. Neuroscience Letters, 163, 109–113. Morgan, M. A., Schulkin, J., & LeDoux, J. E. (2003). Ventral medial prefrontal cortex and emotional perseveration: The memory for prior extinction training. Behavioural Brain Research, 146(1/2), 121–130. Morris, J. S., Ohman, A., & Dolan, R. J. (1999). A subcortical pathway to the right amygdala mediating “unseen” fear. Proceedings of the National Academy of Sciences, USA, 96, 1680–1685. Muller, J., Corodimas, K. P., Fridel, Z., & LeDoux, J. E. (1997). Functional inactivation of the lateral and basal nuclei of the amygdala by muscimol infusion prevents fear conditioning to an explicit conditioned stimulus and to contextual stimuli. Behavioral Neuroscience, 111, 683–691. Nader, K., Majidishad, P., Amorapanth, P., & LeDoux, J. E. (2001). Damage to the lateral and central, but not other, amygdaloid nuclei prevents the acquisition of auditory fear conditioning. Learning and Memory, 8, 156–163. Nader, K., Schafe, G. E., & LeDoux, J. E. (2000, August 17). Fear memories require protein synthesis in the amygdala for reconsolidation after retrieval. Nature, 406, 722–726. Olsson, A., & Phelps, E. A. (2004). Learned fear of “unseen” faces after Pavlovian, observational, and instructed fear. Psychological Sciences, 15, 822–828. Olsson, A., & Phelps, E. A. (2007). Social learning of fear. Nature Neuroscience, 10, 1095–1102. Paré, D., Quirk, G. J., & LeDoux, J. E. (2004). New vistas on amygdala networks in conditioned fear. Journal of Neurophysiology, 92, 1–9. Paré, D., Smith, Y., & Paré, J. F. (1995). Intra-amygdaloid projections of the basolateral and basomedial nuclei in the cat: Phaseolus vulgarisleucoagglutinin anterograde tracing at the light and electron microscopic level. Neuroscience, 69, 567–583.

Quirk, G. J., Armony, J. L., Repa, J. C., Li, X.-F., & LeDoux, J. E. (1997). Emotional memory: A search for sites of plasticity. Cold Spring Harbor Symposia on Biology, 61, 247–257. Quirk, G. J., & Mueller, D. (2008). Neural mechanisms of extinction learning and retrieval. Neuropsychopharmacology, 33, 56–72. Quirk, G. J., Repa, C., & LeDoux, J. E. (1995). Fear conditioning enhances short-latency auditory responses of lateral amygdala neurons: Parallel recordings in the freely behaving rat. Neuron, 15, 1029–1039. Quirk, G. J., Russo, G. K., Barron, J. L., & Lebron, K. (2000). The role of ventromedial prefrontal cortex in the recovery of extinguished fear. Journal of Neuroscience, 20, 6225–6231. Radwanska, K., Nikolaev, E., Knapska, E., & Kaczmarek, L. (2002). Differential response of two subdivisions of lateral amygdala to aversive conditioning as revealed by c-Fos and P-ERK mapping. NeuroReport, 13, 2241–2246. Rampon, C., Tang, Y. P., Goodhouse, J., Shimizu, E., Kyin, M., & Tsien, J. Z. (2000). Enrichment induces structural changes and recovery from nonspatial memory deficits in CA1 NMDAR1-knockout mice. Nature Neuroscience, 3, 238–244. Repa, J. C., Muller, J., Apergis, J., Desrochers, T. M., Zhou, Y., & LeDoux, J. E. (2001). Two different lateral amygdala cell populations contribute to the initiation and storage of memory. Nature Neuroscience, 4, 724–731. Rescorla, R. A. (1968). Probability of shock in the presence and absence of CS in fear conditioning. Journal of Comparative Physiological Psychology, 66, 1–5. Rescorla, R. A., & Heth, C. D. (1975). Reinstatement of fear to an extinguished conditioned stimulus. Journal of Experimental Psychology: Animal Behavior Processes, 1(1), 88–96. Ressler, K. J., Rothbaum, B. O., Tannenbaum, L., Anderson, P., Graap, K., Zimand, E., et al. (2002). Cognitive enhancers as adjuncts to psychotherapy: Use of D-cycloserine in phobic individuals to facilitate extinction of fear. Archives of General Psychiatry, 61, 1136–1144. Robbins, T. W., Cador, M., Taylor, J. R., & Everitt, B. J. (1989). Limbicstriatal interactions in reward-related processes. Neuroscience Biobehavioral Reviews, 13, 155–162. Rodrigues, S. M., Schafe, G. E., & LeDoux, J. E. (2001). Intraamygdala blockade of the NR2B subunit of the NMDA receptor disrupts the acquisition but not the expression of fear conditioning. Journal of Neuroscience, 21, 6889–6896.

Pavlov, I. P. (1927). Conditioned reflexes. London: Oxford University Press.

Rodrigues, S. M., Schafe, G. E., & LeDoux, J. E. (2004). Molecular mechanisms underlying emotional learning and memory in the amygdala. Neuron, 44, 75–91.

Phelps, E. A., Delgado, M. R., Nearing, K. I., & LeDoux, J. E. (2004). Extinction learning in humans: Role of the amygdala and vmPFC. Neuron, 43, 897–905.

Rodriguez, B. I., Craske, M. G., Mineka, S., & Hladek, D. (1999). Contextspecificity of relapse: Effects of therapist and environmental context on return of fear. Behaviour Research and Therapy, 37, 845–862.

Phelps, E. A., O’Connor, K. J., Gatenby, J. C., Gore, J. C., Grillon, C., & Davis, M. (2001). Activation of the left amygdala to a cognitive representation of fear. Nature Neuroscience, 4, 437–441.

Rogan, M., & LeDoux, J. E. (1995). LTP is accompanied by commensurate enhancement of auditory-evoked responses in a fear conditioning circuit. Neuron, 15, 127–136.

Phillips, R. G., & LeDoux, J. E. (1992). Differential contribution of amygdala and hippocampus to cued and contextual fear conditioning. Behavioral Neuroscience, 106, 274–285.

Rogan, M., Staubli, U., & LeDoux, J. E. (1997, December 11). Fear conditioning induces associative long-term potentiation in the amygdala. Nature, 390, 604–607.

Pitkänen, A., Savander, V., & LeDoux, J. E. (1997). Organization of intraamygdaloid circuitries in the rat: An emerging framework for understanding functions of the amygdala. Trends in Neuroscience, 20, 517–523.

Rolls, E. T., Hornak, J., Wade, D., & McGrath, J. (1994). Emotion-related learning in patients with social and emotional changes associated with frontal lobe damage. Journal of Neurology, Neurosurgery, and Psychiatry, 57, 1518–1524.

Quinn, J. J., Wied, H. M., Ma, Q. D., Tinsley, M. R., & Fanselow, M. S. (2008). Dorsal hippocampus involvement in delay fear conditioning depends upon the strength of the tone-footshock association. Hippocampus, 18, 640–654.

c39.indd Sec5:779

Quirk, G. J., Armony, J. L., & LeDoux, J. E. (1997). Fear conditioning enhances different temporal components of tone-evoked spike trains in auditory cortex and lateral amygdala. Neuron, 19, 613–624.

Romanski, L. M., Clugnet, M. C., Bordi, F., & LeDoux, J. E. (1993). Somatosensory and auditory convergence in the lateral nucleus of the amygdala. Behavioral Neuroscience, 107, 444–450.

8/17/09 3:08:01 PM

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Neural Basis of Fear Conditioning

Romanski, L. M., & LeDoux, J. E. (1992a). Bilateral destruction of neocortical and perirhinal projection targets of the acoustic thalamus does not disrupt auditory fear conditioning. Neuroscience Letters, 142, 228–232.

Smith, O. A., Astley, C. A., Devito, J. L., Stein, J. M., & Walsh, R. E. (1980). Functional analysis of hypothalamic control of the cardiovascular responses accompanying emotional behavior. Federation Proceedings, 39, 2487–2494.

Romanski, L. M., & LeDoux, J. E. (1992b). Equipotentiality of thalamoamygdala and thalamo-cortico-amygdala circuits in auditory fear conditioning. Journal of Neuroscience, 12, 4501–4509.

Sotres-Bayon, F., Bush, D. E. A., & LeDoux, J. E. (2004). Emotional perseveration: An update on prefrontal-amygdala interactions in fear extinction. Learning and Memory, 11, 525–535.

Romanski, L. M., & LeDoux, J. E. (1993). Information cascade from primary auditory cortex to the amygdala: Corticocortical and corticoamygdaloid projections of temporal cortex in the rat. Cerebral Cortex, 3, 515–532.

Sotres-Bayon, F., Bush, D. E. A., & LeDoux, J. E. (2007). Acquisition of fear extinction requires activation of NR2B-containing NMDA receptors in the lateral amygdala. Neuropsychopharmacology, 32, 1929–1940.

Roozendaal, B., Barsegyan, A., & Lee, S. (2008). Adrenal stress hormones, amygdala activation, and memory for emotionally arousing experiences. Progressive Behavioural Brain Research, 167, 79–97. Roozendaal, B., Koolhaas, J. M., & Bohus, B. (1991). Attenuated cardiovascular, neuroendocrine, and behavioral responses after a single footshock in central amygdaloid lesioned male rats. Physiology and Behavior, 50, 771–775. Roozendaal, B., Okuda, S., de Quervain, D. J., & McGaugh, J. L. (2006). Glucocorticoids interact with emotion-induced noradrenergic activation in influencing different memory functions. Neuroscience, 138, 901–910. Rudy, J. W., Huff, N. C., & Matus-Amat, P. (2004). Understanding contextual fear conditioning: Insights from a two-process model. Neuroscience and Biobehavioral Reviews, 28, 675–685. Sara, S. J. (2000). Retrieval and reconsolidation: Toward a neurobiology of remembering. Learning and Memory, 7, 73–84.

Stiedl, O., Birkenfeld, K., Palve, M., & Spiess, J. (2000). Impairment of conditioned contextual fear of C57BL/6J mice by intracerebral injections of the NMDA receptor antagonist APV. Behavioural Brain Research, 116, 157–168. Sullivan, G. M., Apergis, J., Bush, D. E. A., Johnson, L. R., Hou, M., & LeDoux, J. E. (2004). Lesions in the bed nucleus of the stria terminalis disrupt corticosterone and freezing responses elicited by a contextual but not by a specific cue-conditioned fear stimulus. Neuroscience, 128, 7–14. Walker, D. L., Paschall, G. Y., & Davis, M. (2005). Glutamate receptor antagonist infusions into the basolateral and medial amygdala reveal differential contributions to olfactory vs. context fear conditioning and expression. Learning and Memory, 12, 120–129. Walker, D. L., Ressler, K. J., Lu, K.-T., & Davis, M. (2002). Facilitation of conditioned fear extinction by systemic administration or intra-amygdala infusions of D-cycloserine as assessed with fear-potentiated startle in rats. Journal of Neuroscience, 22, 2343–2351.

Schafe, G. E., Atkins, C. M., Swank, M. W., Bauer, E. P., Sweatt, J. D., & LeDoux, J. E. (2000). Activation of ERK/MAP kinase in the amygdala is required for memory consolidation of Pavlovian fear conditioning. Journal of Neuroscience, 20, 8177–8187.

Weisskopf, M. G., Bauer, E. P., & LeDoux, J. E. (1999). L-type voltagegated calcium channels mediate NMDA-independent associative long-term potentiation at thalamic input synapses to the amygdala. Journal of Neuroscience, 19, 10512–10519.

Schafe, G. E., Bauer, E. P., Rosis, S., Farb, C. R., Rodrigues, S. M., & LeDoux, J. E. (2005). Memory consolidation of Pavlovian fear conditioning requires nitric oxide signaling in the lateral amygdala. European Journal of Neuroscience, 22, 201–211.

Weisskopf, M. G., & LeDoux, J. E. (1999). Distinct populations of NMDA receptors at subcortical and cortical inputs to principal cells of the lateral amygdala. Journal of Neurophysiology, 81, 930–934.

Schafe, G. E., & LeDoux, J. E. (2000). Memory consolidation of auditory Pavlovian fear conditioning requires protein synthesis and protein kinase A in the amygdala. Journal of Neuroscience, 20, RC96.

Wilensky, A. E., Schafe, G. E., & LeDoux, J. E. (2000). The amygdala modulates memory consolidation of fear-motivated inhibitory avoidance learning but not classical fear conditioning. Journal of Neuroscience, 20, 7059–7066.

Sierra-Mercado, D., Jr., Corcoran, K. A., Lebron-Milad, K., & Quirk, G. J. (2006). Inactivation of the ventromedial prefrontal cortex reduces expression of conditioned fear and impairs subsequent recall of extinction. European Journal of Neuroscience, 24, 1751–1758.

Young, S. L., Bohenek, D. L., & Fanselow, M. S. (1994). NMDA processes mediate anterograde amnesia of contextual fear conditioning induced by hippocampal damage: Immunization against amnesia by context preexposure. Behavioral Neuroscience, 108, 19–29.

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Chapter 40

Neural Basis of Pleasure and Reward CLIFFORD M. KNAPP AND CONAN KORNETSKY

Rewarding stimuli all share the properties of engendering approach behaviors and of being able to act as unconditioned stimuli. Natural rewards include food, water, and copulation, all of which are closely linked to the survival of a species. Food and water will serve as motivators of animals to expend energy in tasks such as lever pressing. Similar behaviors are produced by certain artificial rewarding stimuli that include pharmacological agents such as cocaine and heroin and the electrical stimulation of select brain areas. Human experience indicates that interaction with rewarding stimuli is frequently associated with the experiencing of both short-lived pleasurable sensations and longer lasting periods of elevated mood. We can infer from the behavior of animals that they may also experience the hedonic effects of rewarding stimuli. This inference is strengthened by evidence that many of the systems that have been implicated in the experience of pleasure in humans are similar to their counterparts in other mammals. One example of this arises from the finding that the electrical stimulation of what were characterized as “septal” areas in humans in early experiments produce reports of pleasurable responses (Bishop, Elder, & Heath, 1963), while the delivery of brain stimulation to comparable regions in the rat is rewarding enough to maintain sustained responding for this stimulation (J. Olds & Milner, 1954). What had been called septal areas have now been identified as regions linked to the functioning of mesocorticolimbic systems. These systems have been implicated in the regulation of reward-related behavior. Within these mesocorticolimbic systems the ventral tegmental area (VTA) sends neuronal projections to other mesocorticolimbic structures, most notably the nucleus accumbens and the prefrontal cortical (see Figure 40.1; Emson & Koob, 1978; Hasue & Shammah-Lagnado, 2002). These projections are from cells that contain the neurotransmitter dopamine. When released from these cells, dopamine may interact with five types of receptors,

Prefrontal Cortex

Nucleus Accumbens

Ventral Pallidum

Amygdala

Ventral Tegmental Area

Figure 40.1 Simplified schematic of projections of dopaminergic neurons (arrows) from the ventral tegmental area to target structures within the mesocorticolimbic system.

which are divided into two basic classes. One class includes the dopamine D1-like receptors (the D1 and D5 receptors), and the second consists of D2-like receptors (the D2, D3, and D4 receptors). Several approaches are used to measure the hedonic effects of rewarding stimuli in human subjects. Such measures have been extensively developed in the field of drug abuse studies. Subjects may be asked to place a mark on visual analogue scales (i.e., Likert scales) to indicate to what degree they like a drug or experience a high after receiving a drug (Fischman & Foltin, 1991). Questionnaires have been developed based on the responses of drug users to series of questions that allow for the measure of different aspects of the subjective effects of drugs. The Addiction Research Center Inventory is one such questionnaire that allows rating of euphoric responses to drugs using the Morphine Benzedrine Group scale (ARCI-MBG) of the inventory (Haertzen, Hill, & Belleville, 1963).

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The likelihood that a drug will be abused is related to the ability of the drug to produce elevations in subjective scales of drug liking and euphoric effects as assessed using scales such as the ARCI-MBG. All of the commonly abused drugs, including amphetamine, heroin, and morphine, produce such elevations in recovering addict populations (Preston & Jasinski, 1991). Although these findings indicate that the hedonic effects of abused drugs play a role in the development of drug dependence, the determination of the precise role of these effects in this process remains a challenge. The study of the hedonic effects of stimuli in animals requires less direct approaches than are used for human subjects because hedonic value of any stimulus in an animal is based on inference. Measuring response rates for rewards delivered under schedules of reinforcement has been one approach to assessing the extent of reward produced by these stimuli. The progressive ratio schedule, which allows the determination of a break point, that is, the point of maximal response to obtain a reward (Roberts, Loh, & Vickers, 1989), is an example of such a schedule. Rates of response remain an indirect measure of reward value because responding may be driven in some cases by negative reinforcement, such as withdrawal symptoms, or may be depressed by conditions that involve impairment of motor function. In the conditioned place-preference approach, the amount of time that an animal will spend in an area that has been paired with a rewarding stimulus is regarded as another measure of the reward produced by a stimulus. Several factors, however, other than the degree of reward produced by a stimulus can influence conditioned place preference. These include factors that regulate learning, memory, and responsiveness to conditioned cues. Measures of level of currents that will maintain responding for brain stimulation offer a more direct means of examining the effects of rewarding stimuli on brain reward systems than do other behavioral measures. This is because responding for brain stimulation reward (BSR) involves responses to direct activation of the brain’s reward systems. Sensitivity to brain stimulation reward, as reflected in the lowering of current thresholds for brain stimulation reward responding, is increased by a variety of commonly abused drugs, such as alcohol, cocaine, and morphine (Kornetsky, 2004), that have hedonic effects in humans. An example of the effects of three of these drugs—heroin, methamphetamine, and nicotine—on brain stimulation reward thresholds is shown in Figure 40.2. Sensitivity to rewarding brain stimulation is measured by determining thresholds for some level of responding for this stimulation. One method of determining thresholds involves determining the current intensity or frequency at which animals will exhibit a half-maximal response for

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MAMP Heroin Nicotine

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⫺1 ⫺2 ⫺3 ⫺4 0.00

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0.50 Dose

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Figure 40.2 The effects of heroin, methamphetamine (MAMP), and nicotine on brain stimulation reward thresholds as a function of dose. Note: Current thresholds, determined by the rate-independent method, are expressed as standardized z-scores using performance on saline as the baseline condition. Consequently, a value of zero represents the threshold obtained when saline was administered. A z-score of 2 or greater represents a significant change from the saline condition, with p  .05. Note that at the highest dose tested heroin raised the reward threshold, indicating what is often a common U-shaped dose-response.

rewarding stimulation. An alternative method of determining brain stimulation thresholds involves the use of classic psychophysics to generate a rate-independent threshold (Kornetsky, 2004). This approach involves the presentation of different current intensities during discrete trials; the threshold is taken to be the current intensity at which responding is maintained 50% of the time for a certain level of stimulation. The rate-independent method for assessing BSR thresholds is less influenced by the effects of drugs on motor behaviors and thus in some circumstances may more accurately reflect the effects of drugs on reward systems than do rate-dependent methods (Markou & Koob, 1992). Fundamental questions remain unanswered with respect to the neural basis of the hedonic effects of rewarding stimuli. It is not clear to what extent there is overlap in the neuronal networks that produce the hedonic effects associated with the wide variety of types of rewarding stimuli. For example, how similar are the neuronal networks that mediate the hedonic effects of natural rewards such as food compared to those of drugs, or those involved in the hedonic actions of psychomotor stimulants such as cocaine compared to other classes of drugs such as the opioids? Studies using multiple electrodes to detect the firing of individual neurons in the nucleus accumbens, a central structure in the mesolimbic system, indicate that distinct neuron populations encode information about cocainerelated reward compared to natural rewards such as food and water (Carelli & Wondolowski, 2003; Deadwyler,

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Hayashizaki, Cheer, & Hampson, 2004). Distinct patterns of discharge have been observed in the nucleus accumbens for the time preceding response for a reward and the period after the reward delivery (Deadwyler et al., 2004). The actual response of the brain to rewarding stimuli most likely involves extensively distributed networks of neurons that consist of, at least, thousands of cells. It is not clear, then, that studies in which the activity of only a few cells is monitored can provide a comprehensive picture of the changes in neuronal activity that occur following exposure to rewarding stimuli. One problem that makes it difficult to identify the neuronal networks involved in the production of the hedonic effects of rewarding stimuli is that many rewarding stimuli have a diverse range of actions other than activation of reward processes. Drugs such as cocaine, for example, in addition to their rewarding effects, can produce anxiety, enhance locomotor activity, and increase arousal levels. One approach to dealing with this problem is to examine the effects of brain stimulation on changes in regional brain activity. The advantages of this approach are, first, that brain stimulation reward can be delivered to discrete brain regions and, second, that the stimulation is presumed to activate regions directly involved in the production of rewarding effects. Changes in neuronal activity result in alterations of glucose metabolism that can be monitored using 2-[14C]deoxyglucose autoradiography. This technique offers the advantage of allowing identification of changes of neuronal activity throughout the brain. Rewarding brain stimulation delivered to the medial forebrain bundle at the level of the lateral hypothalamus resulted in increases in metabolic activity in several discrete brain regions (Porrino, Huston-Lyons, Bain, Sokoloff, & Kornetsky, 1990). These included the nucleus accumbens, olfactory tubercle, lateral septum, medial prefrontal cortex, and VTA. The olfactory tubercle appears to be functionally linked to the nucleus accumbens (Ikemoto, 2007). Overall, then, these findings implicate the mesocorticolimbic systems as being activated by rewarding brain stimulation. One limitation of the 2-[14C]deoxyglucose method is that it does not provide spatial resolution down to the cellular level. This limitation is not associated with techniques in which the product of the immediate early gene c-Fos is measured using immunohistochemical techniques. Increased Fos levels are indicative of enhanced neuronal activity. The number of Fos-positive cells has been found to be greater in several brain regions in animals receiving brain stimulation reward delivered to the medial forebrain bundle, the ventral pallidum, and the medial prefrontal cortex. Fos-like immunoreactivity is increased by brain stimulation delivered to the medial forebrain bundle in many of the structures that were also found to be increased

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in regional glucose metabolism studies. These structures included the nucleus accumbens shell, medial prefrontal cortex, VTA, and lateral septum (Hunt & McGregor, 1998). Other structures in which Fos-like immunoreactivity increased were the locus coeruleus, bed nucleus of the stria terminalis, and central nucleus of the amygdala. The ventral pallidum, which receives projections from the nucleus accumbens, will also support responding for brain stimulation reward (Panagis et al., 1997). Self-stimulation of this structure has been found to produce elevations in Fos-like immunoreactivity in the medial prefrontal cortex, nucleus accumbens, and posterior lateral hypothalamus (Panagis et al., 1997). A slightly different pattern of increases in Foslike immunoreactivity has been seen as a consequence of self-stimulation of the medial prefrontal cortex. These increases were found to be located in the prelimbic cortex, cingulate cortex, nucleus accumbens, lateral hypothalamus, amygdala, and anterior portion of the VTA (Arvanitogiannis, Tzschentke, Riscaldino, Wise, & Shizgal, 2000). An alternative approach to establishing which brain regions are involved in the production of rewarding effects is to identify discrete regions of the brain into which animals will micro-inject pharmacologically active agents. In an early study, for example, it was shown that rats will self-administer heroin directly into the nucleus accumbens, establishing this structure as important in the production of the rewarding effects of -opioid receptor agonists (M. E. Olds, 1982). Another example of the infusion mapping approach includes findings that rats also will selfadminister amphetamine into the nucleus accumbens (McBride, Murphy, & Ikemoto, 1999), particularly the medial shell of this structure (Ikemoto, Qin, & Liu, 2005). Amphetamine administration by rats into the nucleus accumbens is antagonized by the concurrent infusion of either dopamine D1 or D2 receptor antagonists, suggesting that dopamine receptors mediate the rewarding effects of amphetamine. Rats will also self-administer high concentrations of cocaine into the shell of the nucleus accumbens (Ikemoto et al., 2005). The ventral olfactory tubercle will also support cocaine self-administration (Ikemoto, 2003). Both cocaine and morphine increase the rate of glucose utilization in the olfactory tubercle (Kornetsky, HustonLyons, & Porrino, 1991) in rats responding for brain stimulation reward. This suggests that structure may play an important role in reward processes, at least in rodents. Functional magnetic resonance imaging (fMRI) is another approach used to identify areas in human subjects that may play a role in mediating the hedonic effects of rewarding stimuli. Functional MRI can provide information concerning changes in regional blood flow in the brain that may reflect changes in neuronal activity. For example, the presentation of pleasant images of erotic and romantic

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interactions between couples were found to produce increases in activity in the nucleus accumbens and medial prefrontal cortex of healthy subjects (Sabatinelli, Bradley, Lang, Costa, & Versace, 2007). In contrast, unpleasant but arousing and neutral pictures failed to produce this effect. When a single dose (0.6 mg/kg) of cocaine was administered to cocaine-dependent subjects, fMRI signals increased in the nucleus accumbens, putamen, ventral tegmentum, cingulate prefrontal, and temporal cortices, and several other regions (Breiter et al., 1997). Subjects in this study first experienced sensations of “rush” and “high” that were then followed by feeling of “craving” and “low.” During the repeated self-administration of cocaine by subjects dependent on this drug, self-ratings on the intensity of the drug-induced high were found to correlate with decreased activity in several brain regions, including the nucleus accumbens, frontal cortical areas, and the anterior cingulate (Risinger et al., 2005). These subjects received doses of cocaine of 20 mg/70 kg up to six times. Functional MRI signals were found to be increased in smokers following the intravenous injection of nicotine in the nucleus accumbens, amygdala, cingulate, and frontal lobes (Stein et al., 1998). These changes occurred in association with feelings of “rush” and “high” and a sustained feeling of pleasantness. When administered to healthy volunteers, morphine produced a different pattern of changes in regional brain activity (Becerra, Harter, Gonzalez, & Borsook, 2006). A low dose of morphine that produced mild euphoria in subjects increased activity in the nucleus accumbens, the hippocampus, the hypothalamus, the orbitofrontal cortex, and the putamen. Morphine administration also resulted in the decreased activation of several cortical structures, including the dorso-lateral prefrontal cortex, the temporal lobe, and the anterior cingulate. Overall, the results of these mapping approaches demonstrate that mesocorticolimbic structures play a key role in the processing of rewarding stimuli. While dopamine in the mesocorticolimbic systems has been regarded as a key neurotransmitter in the processing of rewarding stimuli, clearly many other systems are involved because activity within brain neuronal networks is always the product of the interaction of a wide variety of neurotransmitters. In addition to dopaminergic receptors, cholinergic, GABAergic, glutamatergic, opioid, and serotonergic systems have been implicated in regulating the actions of rewarding stimuli. In this chapter we consider the putative roles of these receptor systems in regulating reward processes. Much of our emphasis is on the actions of brain stimulation reward and drugs of abuse on brain receptor systems. Other reviews are available that have a greater focus on the neural basis of the rewarding effects of food (see, e.g., Kelley, Baldo, Pratt, & Will, 2005; Rolls, 2006).

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MESOLIMBIC DOPAMINE AND REWARD The nucleus accumbens may play a key role in the processing of rewarding stimuli. This structure receives information concerning rewarding stimuli from both cortical and limbic structures and appears to integrate this information to regulate reward-related behaviors. The nucleus accumbens receives dense dopaminergic innervation from VTA afferents (see Figure 40.1). It also receives glutamatergic afferents that originate from the prefrontal cortex, amygdala, hippocampus, and thalamus (see Figure 40.3; Groenewegen, Wright, Beijer, & Voorn, 1999). Dopaminergic and glutamatergic systems interact to regulate activity within the nucleus accumbens by influencing a network of medium spiny neurons located in this structure (West, Floresco, Charara, Rosenkranz, & Grace, 2003). This network processes incoming information and sends out afferent projections that release the inhibitory neurotransmitter gamma amino butyric acid (GABA) to the ventral pallidum and the VTA. The ventral pallidum provides feedback from the nucleus accumbens to cortical areas via the mediodorsal thalamus (O’Donnell, Lavín, Enquist, Grace, & Card, 1997). Dopaminergic neurons in the mesocorticolimbic systems may serve a number of functions. One may involve the processing of reward-predictive signals. Midbrain dopaminergic neurons exhibit phasic (i.e., short-duration) activation following the presentation of stimuli that predict the availability of a reward (Schultz, 2007). A second possible and related function of dopaminergic neurons is to elicit drug-seeking behavior. Animals trained to self-administer drugs will stop responding if saline is substituted for the drug. Exposure to a priming dose of the drug will reinstate responding. Cocaine actions result, in part, from the blockade of the reuptake of dopamine, norepinephrine, and serotonin by binding to monoamine transporter proteins. Evidence of the involvement of dopamine in reinstatement of responding for cocaine

Thalamus Prefrontal Cortex Nucleus Accumbens

Ventral Pallidum

Hippocampus Amygdala

Figure 40.3 Simplified schematic of glutamatergic neuron projections to the nucleus accumbens from cortical and limbic structures (arrows). Note: Dashed lines indicate nonglutamatergic connections between structures.

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Mesolimbic Dopamine and Reward 785

includes the finding that responding on a lever previously associated with cocaine administration is reinstated by the administration of dopamine transporter (DAT) but not norepinephrine transporter (NET) or serotonin transporter (SERT) protein inhibitors (Schmidt & Pierce, 2006). Similar effects are produced by the infusion of selective dopamine D1 and D2 receptor agonists into the shell of the nucleus accumbens (Schmidt, Anderson, & Pierce, 2006). A third function of mesocorticolimbic dopaminergic neurons may be the modulation and possibly the direct activation of reward systems resulting in the production of hedonic effects. An increase in the extracellular concentrations of dopamine in the nucleus accumbens has been observed following exposure to a wide variety of rewarding stimuli (see Table 40.1). These elevations occur over time spans of minutes to tens of minutes that may produce changes in the tonic activity of neurons in the accumbens. The administration of amphetamine, cocaine, and morphine has been found to produce more pronounced increases in dopamine extracellular concentrations in the shell as compared to the core of the nucleus accumbens (Pontieri, Tanda, & Di Chiara, 1995). The chronic self-administration of cocaine (Lecca, Cacciapaglia, Valentini, Acquas, & Di Chiara, 2007), heroin (Lecca, Valentini, Cacciapaglia, Acquas, & Di Chiara, 2007), or nicotine (Lecca et al., 2006) preferentially increased extracellular dopamine levels in the shell as compared to the core of the nucleus accumbens. Clinical studies implicate drug-induced elevation in ventral striatal dopamine levels in the production of drugrelated hedonic effects. Dopamine release in the human brain can be assessed by measuring the displacement of highly selective dopamine ligands produced by the administration of indirect dopamine agonists such as amphetamine

table 40.1 Example of rewards that produce sustained elevations in nucleus accumbens extracellular dopamine concentrations during consummatory or self-administration periods

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Reward

Class

Reference

Food

Natural

Martel & Fantino (1996)

Sucrose

Natural

Hajnal et al. (2004)

Copulation

Natural

Fiorino et al. (1997)

Brain stimulation

Artificial stimulant

Hernandez et al. (2006)

Amphetamine

Psychomotor stimulant

Ranaldi et al. (1999)

Cocaine

Psychomotor stimulant

Bradberry et al. (2000)

Nicotine

Nicotinic agonist

Lecca et al. (2006)

Heroin

Opioid agonist

Lecca et al. (2007)

Ethanol

Positive GABAA receptor modulator

Doyon et al. (2003)

or cocaine that increase extracellular dopamine levels. Such displacement can be detected by labeling drugs with high affinities for a particular dopamine receptor subtype with a radioactive isotope such as carbon 11 (11C). The concentration of this radio-labeled drug in the brain can be measured using positron emission tomography (PET). Displacement of the dopamine D2 receptor selective agent raclopride in the striatum is produced by the intravenous administration of the psychomotor stimulant methylphenidate to healthy subjects (Volkow et al., 1999). The magnitude of this displacement was found to correlate with increased ratings of sensations of “high” and “rush.” Similarly, amphetamineinduced displacement of raclopride in the ventral striatum was found to correlate with increases in ratings of euphoric feelings in healthy subjects (Drevets et al., 2001; Martinez et al., 2003; Oswald et al., 2005). Reductions in the binding of raclopride in the ventral striatum have also been found to correlate with hedonic responses to nicotine delivered in cigarette smoke (Barrett, Boileau, Okker, Pihl, & Dagher, 2004). The administration of a variety of abused drugs and selective dopamine and opioid agonists has been found to lower thresholds for rewarding brain stimulation delivered to the medial forebrain bundle (see Table 40.2). This suggests that agents that either promote dopamine release into the nucleus accumbens or directly stimulate dopamine receptors act to enhance the sensitivity of the brain to rewarding stimuli. This link is supported by the observations that the infusion of amphetamine directly into the nucleus accumbens results in a lowering of brain stimulation reward thresholds (Knapp, Lee, Foye, Ciraulo, & Kornetsky, 2001) and in enhanced rates of responding for brain stimulation reward (Broekkamp, Pijnenburg, Cools, & Van Rossum, 1975). Perhaps the clearest evidence that dopaminergic receptor stimulation results in rewarding effects is that administration of either the direct dopamine D2 receptor agonist bromocriptine (Knapp & Kornetsky, 1994; Steiner, Katz, & Carroll, 1980) or the selective DAT inhibitor GBR 12909 (Baldo, Jain, Veraldi, Koob, & Markou, 1999; MaldonadoIrizarry, Stellar, & Kelley, 1994) enhances the effects of rewarding brain stimulation. Also, the selective DAT inhibitor GBR 12783 will produce conditioned placed preference (Le Pen, Duterte-Boucher, & Costentin, 1996). Finally, rats and monkeys (Wise, Murray, & Bozarth, 1990; Woolverton, Goldberg, & Ginos, 1984) will self-administer bromocriptine, and rhesus monkeys will respond for GBR 12909 (Stafford, LeSage, Rice, & Glowa, 2001; Wojnicki & Glowa, 1996), which is also consistent with these agents having rewarding actions. The administration of dopamine receptor antagonists would be expected to attenuate the effects of rewarding

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table 40.2 Drugs that produce lowering of brain stimulation reward thresholds Drug

Class

Reference

Amphetamine

Psychomotor stimulant

Kornetsky & Esposito (1979)

Cocaine

Psychomotor stimulant

Gill et al. (2004)

MDMA

Psychomotor stimulant

Hubner et al. (1988)

Bromocriptine

D2 receptor agonist

Knapp & Kornetsky (1994)

GBR 12909

Selective DAT inhibitor

Baldo et al. (1999)

Buprenorphine

Opioid agonist

Hubner & Kornetsky (1988)

Heroin

Opioid agonist

Hubner & Kornetsky (1992)

Morphine

Opioid agonist

Esposito & Kornetsky (1977)

DAMGO

Mu-receptor opioid agonist

Duvauchelle, Fleming, & Kornetsky (1997)

DPDPE

Delta-receptor opioid agonist

Duvauchelle et al. (1997)

Nicotine

Nicotinic agonist

Huston-Lyons, Sarkar, & Kornetsky (1993)

Ethanol

Positive GABAA modulator

Kornetsky, Moolten, & Bain (1991)

brain stimulation if dopamine receptor systems acted to positively modulate these effects. Experimental results concerning the actions of dopamine receptor antagonist administration on responding for brain stimulation reward may be influenced by the nonspecific effects of these agents on cognitive and motor performance. Treatment with the dopamine D2 selective antagonist pimozide, however, was found to increase reward threshold levels at doses that did not influence attentional processes (Bird & Kornetsky, 1990). The administration of the dopamine receptor antagonist haloperidol elevated reward thresholds for BSR at doses that did not influence a measure of motor performance, that is, latency of response (Esposito, Faulkner, & Kornetsky, 1979). Similar effects were produced by the administration of the selective dopamine D1 receptor antagonist SCH 23390. It has not, however, always been possible to separate blockade of rewarding stimulation from impairment of motor effects. Administration of the D2 selective antagonist raclopride, for example, was shown to increase response latencies as it elevated brain stimulation reward thresholds (Baldo, Jain, Veraldi, Koob, & Markou, 1999). The infusion of dopamine antagonists into discrete brain areas would be expected to produce less nonselective disruption of responding for rewarding brain stimulation than does systemic administration of these drugs. Microinjection

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of the dopamine antagonist cis-flupenthixol into the nucleus accumbens attenuated the effects of brain stimulation reward (Stellar & Corbett, 1989). Infusion of the dopamine D1 receptor antagonist SCH 23390 into the nucleus accumbens decreased rates of responding for rewarding brain stimulation, but administration of the D2 receptor selective antagonist raclopride did not produce a significant effect (Cheer et al., 2007). Interestingly, microinjection of SCH 23390 into the nucleus accumbens elevated brain stimulation thresholds; in contrast, thresholds were lowered when this drug was administered into the prefrontal cortex (Duvauchelle, Fleming, & Kornetsky, 1998). Pharmacological agents that block dopamine receptors can be considered to have only relative selectivity for the different dopamine receptor subtypes. Attempts have been made to address this problem by using animals in which the genes that express the proteins needed to form a particular type of receptor have been deleted or rendered inactive. This typically involves the use of transgenic mice. A transgenic mouse is one that carries a foreign gene that has been inserted into its genome. The foreign gene is constructed using recombinant DNA. In knockout mice the replacement gene (or null gene) is nonfunctional. In homozygous mice, who receive the null gene from both parents, the expression of a specific protein may be completely absent. In heterozygotic animals, in which the null gene is received from only one parent, the level expression of the protein is greatly reduced. Wild-type animals have parents that are both nontransgenic. Selective deletion of specific dopamine receptor proteins in knockout mice allows for the assessment of brain stimulation reward in animals in whom the expression of specific dopamine receptor subtypes has been blocked. One group of researchers found that thresholds for brain stimulation were elevated in dopamine D1 receptor knockout mice compared to thresholds obtained for wild-type mice (Tran et al., 2005). This finding implicates dopamine D1 receptors in the regulation of responses to brain stimulation reward. In contrast, this same group of investigators reported that responding for brain stimulation reward was unaltered in dopamine D2 receptor knockout mice compared to wild-type animals (Tran et al., 2002). This suggests that dopamine D2 receptors do not play an essential role in the maintenance of baseline levels of responding for brain stimulation. Other researchers, however, have found that significantly higher levels of current intensity are needed to maintain responding for brain stimulation to obtain stimulation in D2 receptor knockout mice than was required for wild-type mice (Elmer et al., 2005). Knockout mice have been used to examine the role played by dopamine receptor systems in mediating the

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GABA and Reward 787

rewarding effects of drugs of abuse. Dopamine D2 receptor knockout mice will self-administer cocaine (Caine et al., 2002). The intake of cocaine by these animals is higher at high doses of cocaine than for wild-type mice. These results indicate that dopamine D2 receptors may not be essential for mediating the rewarding effects of cocaine. Cocaine-induced conditioned place preference was not found to differ significantly in either dopamine D1 receptor or dopamine D3 receptor knockout mice compared to that seen in wild-type animals (Karasinska, George, Cheng, & O’Dowd, 2005). Dopamine D1 receptor knockout mice, however, failed to reliably self-administer cocaine (Caine et al., 2007). Overall these studies tentatively suggest that the dopamine D2 receptor may not be essential for the induction of cocaine’s rewarding actions, whereas D1 receptors might play a critical role in these actions. If the rewarding effects of cocaine are related to increases in extracellular levels of dopamine in the brain, then the DAT protein would be expected to be the site of action at which this drug would bind to produce these increases. However, DAT knockout mice will self-administer cocaine (Rocha et al., 1998). Cocaine administration also has been found to induce conditioned place preference in DAT knockout mice (Sora et al., 1998). These results are not consistent with a role for DAT in mediating the rewarding effects of cocaine. There is evidence, however, that the regulation by neurotransmitters of reward system function is altered in DAT knockout mice. Most notably, nisoxetine, a NET inhibitor, and fluoxetine, a SERT inhibitor, both produce conditioned place preference in DAT knockout mice, although they do not have similar actions in wild-type animals (Hall et al., 2002). In an attempt to circumvent the problems associated with neuronal development in animals that never express DAT, one group of researchers has developed a strain of DAT knockin mice in which DAT that has a low affinity for cocaine is expressed (R. Chen et al., 2006). The DAT expressed in these animals remains functional with respect to the transport of dopamine, but it does not interact with cocaine. In the DAT knockin mice cocaine administration failed to produce conditioned place preference. This result supports the idea that DAT is involved in mediating the rewarding effects of cocaine in animals in which this transporter protein remains functional. In clinical studies the role of dopamine receptors in mediating the hedonic effects of commonly abused stimulants has been assessed by examining how these effects are modified by the administration of dopamine receptor antagonists. In one study, an injection of the dopamine antagonist haloperidol had no effect on the sensation of “rush” produced by intravenous cocaine, but ratings of “good” feelings and “high” were significantly diminished (Sherer,

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Kumor, & Jaffe, 1989) by administration of this drug. In another study, haloperidol administration blocked euphoria induced by the stimulant methylphenidate in manicdepressive subjects (Wald, Ebstein, & Belmaker, 1978). Ratings of “high” and “good effects” produced by cocaine were reduced by concurrent treatment with the dopamine D1 antagonist ecopipam (Romach et al., 1999). Administration of the atypical neuroleptic clozapine to cocaine-dependent individuals reduced cocaine-induced increases in feelings of “high” and “rush” (Farren et al., 2000). Haloperidol administration also was found to decrease the euphoric and stimulant effects of ethanol in social drinkers, suggesting that dopamine antagonists can block the hedonic effects of abuse drugs that are not stimulants (Enggasser & de Wit, 2001). Not all findings are consistent with the notion that the administration of dopamine antagonists will block the hedonic effects of drugs of abuse. Amphetamine-induced euphoria was not blocked by administration of the dopamine D2 antagonist pimozide (Brauer & DeWit, 1997). Administration of either haloperidol or the atypical neuroleptic risperidone did not alter the subjective response of healthy volunteers to methamphetamine (Wachtel, Ortengren, & de Wit, 2002). Findings from a few studies suggest that dopamine receptor antagonists may not block the hedonic effects of nicotine. The positive subjective effects produced by nicotine have not been reduced by the administration of either ecopipam (Chausmer, Smith, Kelly, & Griffiths, 2003) or haloperidol (Walker, Mahoney, Ilivitsky, & Knott, 2001). Whether differences in factors such as drug doses used can explain the discrepancies among studies concerning the effects of dopamine antagonists on drug-induced hedonic effects remains to be determined. GABA AND REWARD GABA may act on both GABAA and GABAB receptors within the brain to regulate the effects of rewarding stimuli. The ventral pallidum is a subcortical structure that receives GABAergic input from the nucleus accumbens. This structure sends inhibitory GABAergic projections into the VTA (Wu, Hrycyshyn, & Brudzynski, 1996). GABA released within the VTA may act on GABAA receptors located on interneurons in the VTA. This may result in an inhibition of release of GABA from these interneurons. GABA released from interneurons in the VTA interacts with GABAB receptors located on dopamine neurons to inhibit the activity of dopaminergic neurons (Kalivas, Duffy, & Eberhardt, 1990). Thus, activation of GABAA neurons within the VTA may lead to a disinhibition of dopaminergic activity by indirectly preventing the stimulation of GABAB receptors located on dopamine neurons.

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Neural Basis of Pleasure and Reward

There also appear to be extensive interactions between dopaminergic and GABAergic systems within the structures that regulate reward in the nucleus accumbens (Geldwert et al., 2006). Coinfusion of either bicuculline, a GABAA receptor antagonist, or the GABAB antagonist phaclofen with the selective DAT inhibitor GBR 12909 leads to increases in dopamine concentrations within the nucleus accumbens significantly above those seen with GBR 12909 alone (Rahman & McBride, 2002). These results suggest that the GABA receptors regulate the release of dopamine within the nucleus accumbens. Rats will self-administer the GABAA receptor agonist muscimol into the VTA, suggesting that stimulation of GABAA receptors in this structure produces rewarding effects (Ikemoto, Murphy, & McBride, 1998). The finding that muscimol injection into the VTA can result in conditioned place preference is consistent with this idea (Laviolette & van der Kooy, 2001). Place preference resulting from intra-VTA muscimol administration was blocked by treatment with the dopamine antagonist -flupenthixol (Laviolette & van der Kooy, 2001), indicating that muscimolrewarding actions may be linked to dopaminergic-related reward processes. The infusion of muscimol into the rostral portion of the VTA has been found to increase break points for cocaine delivered under a progressive ratio schedule (D. Y. Lee et al., 2007). This is consistent with muscimol activating the same reward pathways as does cocaine. This activation may result from stimulation of GABAA receptors located on interneurons in the VTA that, in turn, leads to inhibitory effects that block the release of GABA from these neurons (Kalivas et al., 1990). GABA is metabolized in the brain by the enzyme GABA transaminase. Inhibition of this enzyme can be produced by administration of the GABA transaminase inhibitor vigabatrin, resulting in marked elevations in brain GABA levels. Treatment with vigabatrin results in the blockade of increases in nucleus accumbens dopamine levels that are produced by the administration of methamphetamine, heroin, or ethanol (Gerasimov et al., 1999). Vigabatrin, then, would be expected to block the rewarding effects of many commonly abused drugs by suppressing the increased release of dopamine that would otherwise occur when they are administered. This drug has been found to decrease the self-administration of cocaine, ethanol, and morphine (Buckett, 1981; Stromberg, Mackler, Volpicelli, O’Brien, & Dewey, 2001) and block heroin-induced place preference (Paul, Dewey, Gardner, Brodie, & Ashby, 2001). Break points for cocaine self-administration are decreased by concurrent treatment with vigabatrin, indicating a reduction in cocaine’s rewarding effects (Kushner, Dewey, & Kornetsky, 1999). In addition, the administration of vigabatrin

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blocks cocaine-induced lowering of brain stimulation reward thresholds (Kushner, Dewey, & Kornetsky, 1997). Vigabatrin administration also decreases responding for food, which has led some investigators to question the specificity of its effects (Barrett, Negus, Mello, & Caine, 2005). Both the GABAB receptor agonist baclofen and the positive modulator of GABAB activity GS39783 will attenuate cocaine-induced enhancement of animals’ sensitivity to brain stimulation reward (Slattery, Markou, Froestl, & Cryan, 2005). When administered alone at a higher dose baclofen will significantly elevate brain stimulation reward thresholds. These results are in accord with the view that GABAB receptors may act to inhibit the rewarding effects of both brain stimulation and cocaine. This may be related to inhibition of mesolimbic dopamine release produced by the activation of GABAB receptors located on dopaminergic neurons. Cocaine self-administration is inhibited by the systemic administration of the GABAA agonist muscimol or the GABAB receptor agonist baclofen (Barrett et al., 2005). These findings suggest that both the GABAA and GABAB receptors may have inhibitory actions on the rewarding effects of cocaine. This finding also indicates that systemically administered muscimol may have actions distinct from those caused by the infusion of this drug into the VTA. It should also be noted that baclofen and muscimol decreased cocaine self-administration only at doses that also decreased food-maintained responding, which raises the question of whether these GABA agonists when administered systemically selectively act on cocaine-induced reward (Barrett et al., 2005). These agents also either may have nonselective effects on lever pressing or may alter the rewarding effects of food (Barrett et al., 2005). Both barbiturates and benzodiazepine sedative-hypnotics act at the GABAA receptor complex to enhance the effects of GABA on this complex. Clinical studies indicate that the administration of barbiturates, including pentobarbital (Carter, Richards, Mintzer, & Griffiths, 2006) and butabarbital (Zawertailo, Busto, Kaplan, Greenblatt, & Sellers, 2003), produce elevations in ratings of drug liking and other measures of pleasant drug effects in subjects with a history of recreational sedative use. Similar effects are seen following the administration of benzodiazepines to sedative users (Carter et al., 2006; Zawertailo et al., 2003), abstinent alcoholics (Ciraulo et al., 1997), and children of alcoholics (Ciraulo, Barnhill, Ciraulo, Greenblatt, & Shader, 1989; Ciraulo et al., 1996). Thus, sedative-hypnotics that enhance the activity of GABAA receptors produce rewarding actions in human subjects. Treatment with pentobarbital, however, did not result in alterations in brain stimulation reward thresholds (Kornetsky, 2004). When administered

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Opioid Systems and Reward 789

systemically the benzodiazepines diazepam (Invernizzi, Pozzi, & Samanin, 1991) and midazolam (Finlay, Damsma, & Fibiger, 1992) both decreased dopamine concentrations in the nucleus accumbens. These results suggest that the hedonic effects of barbiturates and benzodiazepines may result from dopamine-independent processes.

OPIOID SYSTEMS AND REWARD There are three types of opioid receptors: the , , and , receptors (De Vries & Shippenberg, 2002). Most commonly used opioid analgesics act at the -receptor, producing elevation of mood and euphoria in many individuals. A few of the available opioid analgesics act by stimulating -receptors, but these drugs may sometimes produce unpleasant (dysphoric) feelings. Endogenous opioids known as the enkephalins activate -receptors. The administration of opioid drugs with -opioid receptor agonist effects can result in lowering brain stimulation reward thresholds (see Table 40.2). In contrast, the administration of the selective opioid -receptor agonist U-69593 may elevate brain stimulation reward thresholds (Todtenkopf, Marcus, Portoghese, & Carlezon, 2004). The -receptor agonist ethylketocyclazocine, on the other hand, has been found to have no effect on these thresholds (Unterwald, Sasson, & Kornetsky, 1987). Several findings indicate that changes in dopaminergic activity can influence the effects of -opioid receptor agonists on brain stimulation thresholds, suggesting that this neurotransmitter may be a mediator of the effects of these agents on reward systems. Injection of morphine into the VTA enhances the effects of brain stimulation reward (Broekkamp & Phillips, 1979). This action may result from morphine-induced increases in the firing of dopaminergic cells within the VTA (Gysling & Wang, 1983; Kiyatkin & Rebec, 1997). Opioid-induced increases in VTA dopamine cell firing may account for the increase in dopamine efflux in the nucleus accumbens that occurs during the administration of either morphine (Pontieri et al., 1995) or heroin (Lecca et al., 2007). The increase in firing produced by opioid administration may result from the suppression of GABA release from interneurons that act to inhibit dopaminergic neuronal activity in the VTA (Bergevin, Girardot, Bourque, & Trudeau, 2002; Kalivas et al., 1990; Klitenick, DeWitte, & Kalivas, 1992). Evidence of this inhibitory effect includes the finding that the firing rate of GABA neurons in the VTA is reduced following the self-administration of heroin (Steffensen et al., 2006). The infusion of vigabatrin into either the VTA or ventral pallidum resulted in the suppression of heroin self-administration (Xi & Stein, 2000).

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Systemic administration of the GABAB receptor antagonist 2-OH-saclofen antagonized the inhibitory effects of vigabatrin administered into the VTA on heroin selfadministration (Xi & Stein, 2000). This finding suggests that the activation of GABAB receptors in the VTA may antagonize the rewarding effects of opioid agonists. Evidence of the involvement of the dopaminergic systems in the interaction between opioids and brain stimulation reward includes the finding that low doses of apomorphine, which may act presynaptically to block dopamine release, blocks morphine-induced lowering of brain stimulation reward thresholds (Knapp & Kornetsky, 1996). Also, the systemic administration of the nonselective dopamine antagonist cis-flupenthixol blocked the stimulation reward threshold lowering effects produced by the microinjection into the accumbens of either the -opioid receptor agonist DAMGO or the -opioid agonist DPDPE (Duvauchelle, Fleming, & Kornetsky, 1997). Finally, in dopamine D2 receptor knockout mice, morphine administration produces an elevation as opposed to a decrease in frequency thresholds for rewarding brain stimulation, suggesting that the rewarding effects of morphine do not occur in these animals (Elmer et al., 2005). In a complementary self-administration study, dopamine D2 receptor knockout mice did not show greater responses for morphine than they did for saline (Elmer et al., 2002). This result is consistent with the notion that dopamine D2 receptor systems are needed for the production of the rewarding actions of opioids. There are some findings that do not support the idea that dopamine is needed for the production of the rewarding effects of opioids. Heroin self-administration has been found to persist in animals in which dopamine terminals in the nucleus accumbens have been destroyed using 6-hydroxydopamine (Pettit, Ettenberg, Bloom, & Koob, 1984). Injection of the dopamine antagonist -flupenthixol into the nucleus accumbens failed to block morphineinduced conditioned place preference in opioid-naive rats (Laviolette, Nader, & van der Kooy, 2002). Morphineinduced conditioned place preference also is produced in dopamine-deficient mutant mice (Hnasko, Sotak, & Palmiter, 2005). At present it is hard to reconcile the inconsistent evidence concerning the role of dopamine in the production of the rewarding effects of opioids. One likely possibility is that opioid-induced reward may be mediated by both dopamine-dependent and dopamine-independent mechanisms. Several studies implicate CB1 cannabinoid receptor systems in the regulation of the rewarding effects of opioids. Administration of the CB1 receptor antagonist SR1451716A produces a decrease in the break points for heroin delivered under a progressive ratio schedule

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Neural Basis of Pleasure and Reward

(Caillé & Parsons, 2003; De Vries, Homberg, Binnekade, Raasø, & Schoffelmeer, 2003). The administration of this antagonist also blocked morphine-induced place preference (Mas-Nieto et al., 2001; Navarro et al., 2001). Other evidence of involvement of CB1 receptors in modulating the rewarding effects of opioids includes findings that morphine self-administration is not seen in CB1 receptor knockout mice (Cossu et al., 2001). Acquisition of morphine-induced place preference may be blocked in these animals (Martin, Ledent, Parmentier, Maldonado, & Valverde, 2000), although this has not been a consistent finding (Rice, Gordon, & Gifford, 2002). The lack of rewarding effects of morphine in CB1 receptor knockout mice may be related to a reduction of morphine-induced accumbens dopamine release in these animals (Mascia et al., 1999). The threshold lowering of several psychomotor stimulants, including cocaine (Bain & Kornetsky, 1987), d-amphetamine (Esposito, Perry, & Kornetsky, 1980), amfonelic acid (Knapp & Kornetsky, 1989), and 3,4-methlenedioxymethamphetamine (Hubner, Bird, Rassnick, & Kornetsky, 1988), are attenuated by the concurrent administration of high doses of the opioid antagonist naloxone. Treatment with naloxone will significantly decrease response rates for the self-administration of cocaine (Kiyatkin & Brown, 2003). When microinjected into the ventral pallidum, naloxone also blocks cocaine-induced place preference (Skoubis & Maidment, 2003). These results suggest that endogenous opioid peptides may play a role in the modulation of the rewarding actions of psychomotor stimulants. The -opioid receptor has been implicated as the opioid receptor subtype that regulates drug-induced reward. Evidence of this includes the finding that intracerebroventricular infusion of the selective -receptor antagonist CTAP prevents the development of cocaine-induced conditioned place preference (Schroeder et al., 2007). Selective deletion of the OPRM1 (i.e., the -opioid receptor) gene from mice results in the failure of cocaine to produce conditioned place preference (Becker et al., 2002; Hall, Goeb, Li, Sora, & Uhl, 2004). The finding that ethanol reward and ethanol-induced place preference are attenuated in OPRM1 knockout mice suggests that this opioid receptor is also involved in mediating the rewarding effects of alcohol (Hall, Sora, & Uhl, 2001). Several studies have examined the interaction between the opioid antagonist naltrexone and commonly abused drugs on the subjective response of human subjects to these agents. Treatment with naltrexone decreased ratings of “high” but not ratings of “like the drug” in healthy volunteers challenged with a dose of amphetamine (JayaramLindström, Wennberg, Hurd, & Franck, 2004). Ratings

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of cocaine’s “good effects” were decreased by naltrexone administration in crack cocaine users (Sofuoglu et al., 2003). In contrast, in one study with subjects with a history of cocaine and heroin use, naltrexone treatment had no significant effect on ratings of either cocaine-associated “high,” “good effects,” or “liking” (Walsh, Sullivan, Preston, Garner, & Bigelow, 1996). The administration of opioid antagonists may attenuate the hedonic effects of rewarding substances other than cocaine, including those of food. Administration of naltrexone reduced ratings of the pleasantness of food (M. R. Yeomans & Gray, 1996, 1997). The euphoria-inducing effects of nicotine gum in smokers were blocked by pretreatment with naltrexone (Knott & Fisher, 2007). Treatment with naltrexone reduced ratings by alcoholic subjects of ethanol-induced “high” (Volpicelli, Watson, King, Sherman, & O’Brien, 1995) and by heavy drinkers of ratings of alcohol “liking” (McCaul, Wand, Eissenberg, Rohde, & Cheskin, 2000). In alcoholic individuals the initial stimulatory effects of ethanol are decreased by the administration of either naltrexone or the opioid antagonist nalmefene (Drobes, Anton, Thomas, & Voronin, 2004).

SEROTONIN AND NOREPINEPHRINE AND REWARD At least 14 subtypes of receptors have been identified as mediating the effects of serotonin (Hoyer, Hannon, & Martin, 2002). Many of these are located within the brain, including the 5-HT1A, 5-HT1B, 5-HT2A, 5-HT2B, 5-HT2C, and 5-HT3 subtypes. This extensive diversity of serotonin receptor subtypes in the brain has complicated the task of characterizing the role of serotonin receptors in the regulation of reward system activity. The nonselective stimulation of serotonin receptors produced by the administration of the SERT inhibitor fluoxetine has been found to reduce the sensitivity of rats to rewarding brain stimulation (Harrison & Markou, 2001; K. Lee & Kornetsky, 1998). Treatment with fluoxetine has been found to decrease the positive effects of intravenous cocaine administration on mood (Walsh, Preston, Sullivan, Fromme, & Bigelow, 1994). In the squirrel monkey administration of the SERT inhibitor alaproclate both reduces cocaine self-administration and blocks cocaineinduced elevation in extracellular dopamine concentrations in the nucleus accumbens (Czoty, Ginsburg, & Howell, 2002). These results suggest that the increase in serotonin levels produced by acute SERT inhibitor administration may attenuate the rewarding effects of both brain stimulation reward and cocaine. The actions of alaproclate suggest that SERT inhibitors can block cocaine-induced increases in extracellular dopamine levels within the nucleus accumbens,

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Serotonin and Norepinephrine and Reward 791

an effect that would explain how they block the effects of rewarding stimuli. This explanation conflicts, however, with evidence that fluoxetine administration enhances cocaineinduced elevations in nucleus accumbens dopamine in the rat (Bubar, McMahon, De Deurwaerdere, Spampinato, & Cunningham, 2003). The 5-HT1 serotonin receptors appear to regulate the reward processes that are activated by rewarding brain stimulation. In one study 5-HT1A and 5-HT1/B agonists elevated brain stimulation reward thresholds (see Table 40.3). When microinjected in the median raphe or when administered in low doses the 5-HT1A agonist 8-OH-DPAT produced a lowering of reward thresholds. Both reward threshold lowering and elevating effects of 8-OH-DPAT are blocked by the administration of the selective 5-HT1A antagonist p-MMPI. It has been suggested that the threshold-lowering effect of 8-OH-DPAT results from 5-HT1A autoreceptor-mediated reductions in serotonin release from median raphe neurons (Harrison & Markou, 2001). The 5-HT1B receptor gene can be incorporated into a viral DNA that can then enter a neuron to increase the expression of that gene in the transfected neuronal cell. Viral-mediated gene transfer–induced increases in 5-HT1B receptor expression in accumbens neurons enhanced the sensitivity of rats to the place-preference-inducing actions of cocaine (Barot, Ferguson, & Neumaier, 2007). Administration of 5-HT1B agonists may increase the break point for self-administered cocaine (see Table 40.3). These results suggest that the stimulation of 5-HT1B receptors may enhance the rewarding actions of cocaine. Elevation in extracellular dopamine levels in the accumbens produced

table 40.3

by cocaine administration is enhanced by the infusion of the 5-HT1B agonist CP 93,129 (O’Dell & Parsons, 2004). Infusion of this agonist into the nucleus accumbens increased extracellular dopamine in this structure (Yan & Yan, 2001). Serotonin 5-HT1B-mediated enhancement of cocaine’s rewarding actions, then, may be due to facilitation of dopamine release by this receptor. The various subtypes of 5-HT2 receptors may differ in how they modulate the actions of rewarding stimuli. Administration of the 5-HT2A receptor antagonist M100907 may not alter brain stimulation thresholds or cocaine selfadministration responding (see Table 40.3). These results suggest that 5-HT2A receptors are not involved in the modulation of the rewarding effects of either brain stimulation or cocaine. In contrast, the 5-HT2C receptors appear to regulate the rewarding effects of many commonly abused drugs and of food. The administration of 5-HT2C agonists reduces self-administration responding for a variety of rewarding substances (see Table 40.3), suggesting that activation of these receptors decreases the effects of rewarding stimuli. This idea is supported by evidence showing that mutant mice with deletions of the protein for 5-HT2C receptors exhibited higher break points for cocaine than did wildtype mice (Rocha et al., 2002). This may have occurred because cocaine-induced increases in extracellular dopamine levels were greater in these mutant mice compared to wild-type animals. Consistent with the idea that 5-HT2C receptors attenuate the rewarding actions of cocaine is evidence that treatment with the 5-HT2C antagonists may produce a leftward shift in the dose-response curve for cocaine self-administration. These agents enhance

Effects of systemically administered serotonin receptor subtype selective drugs on rewarding stimuli

Receptor

Drug

Class

Effect

Reference

5-HT1A 5-HT1A/B 5-HT1B 5-HT2A 5-HT2A 5-HT2C 5-HT2C 5-HT2C 5-HT2C 5-HT2C 5-HT3 5-HT3 5-HT3 5-HT3 5-HT3 5-HT3

8-OH-DPAT Ru 24969 CP 94,253 M100907 M100907 SB242,084 R0 60-0175 R0 60-0175 R0 60-0175 R0 60-0175 Y-25130 Ondansetron Ondansetron Ondansetron GBR 38032F MDL 72222

Agonist Agonist Agonist Antagonist Antagonist Antagonist Agonist Agonist Agonist Agonist Antagonist Antagonist Antagonist Antagonist Antagonist Antagonist

↑ BSR ↑ BSR ↑ BP Cocaine SA 0 BSR Threshold 0 Cocaine SA ↑ Cocaine SA ↓ Cocaine SA ↓ Nicotine SA ↓ Ethanol SA ↓ Food SA 0 BSR 0 BSR 0 Cocaine SA ↓ Cocaine BP 0 Cocaine SA ↓ Cocaine SA

Harrison & Markou (2001) Harrison, Parsons, Koob, & Markou (1999) Parsons, Weiss, & Koob (1998) Benaliouad, Kapur, & Rompré (2007) Fletcher, Grottick, & Higgins (2002) Fletcher et al. (2002) Grottick, Fletcher, & Higgins (2000) Grottick, Corrigall, & Higgins (2001) Tomkins et al. (2002) Grottick et al. (2000) Kelley & Hodge (2003) Herberg, De Belleroche, Rose, & Montgomery (1992) Lane et al. (1992) Davidson, Lee, Xiong, & Ellinwood (2002) Peltier & Schenk (1991) Kankaanpää, Meririnne, & Seppälä (2002)

↑ BP SA  Increase in break point for self-administration; ↓ BP SA  Decrease in breakpoint for self-administration; 0 BSR  No change in sensitivity to brain stimulation ↑ reward; BSR  Increase in sensitivity to brain stimulation reward; 0 SA  No change in self-administration; ↑ SA  Increased self-administration; ↓ SA  Decreased self-administration; 8-O-DPAT  8-hydroxy-2-(di-n-propyl-amino) tetralin hydrobromide.

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792

Neural Basis of Pleasure and Reward

cocaine-induced elevations in extracellular dopamine in the nucleus accumbens (Navailles, De Deurwaerdere, Porras, & Spampinato, 2004). The 5-HT3 antagonists have not been found to alter the effects of rewarding brain stimulation. Treatment with the 5-HT3 antagonist Y-25130, however, attenuated the threshold-lowering effects of cocaine (S. P. Kelley & Hodge, 2003). Co-administration of the serotonin 5-HT3 antagonist ICS 205,930 blocks the self-administration of cocaine into the posterior portion of the VTA (Rodd et al., 2005). Results concerning the effects of systemically administered 5-HT3 antagonists on cocaine self-administration, however, are inconsistent (see Table 40.3). The 5-HT3 antagonist ondansetron reverses both cocaine- and amphetamineinduced increases in extracellular dopamine levels in the nucleus accumbens (Kankaanpää, Lillsunde, Ruotsalainen, Ahtee, & Seppâlä, 1996). In contrast, MDL 72222, a different 5-HT3 antagonist, failed to have similar effects on cocaine’s action on accumbens dopamine levels (De Deurwaerdère, Moison, Navailles, Porras, & Spampinato, 2005). Thus, some findings indicate that the stimulation of 5-HT3 receptors may somehow facilitate the rewarding effects of psychomotor stimulants, possibly through the modulation of mesolimbic dopamine levels. Other results, though, have failed to support this view. Evidence that noradrenergic systems are involved in the regulation of the actions of rewarding stimuli is inconsistent. Recently it has been shown that the noradrenergic 1 receptor antagonist terazosin, when infused into the locus coeruleus, produces a rightward shift in rate-frequency curves for brain stimulation reward responding (Y. Lin, de Vaca, Carr, & Stone, 2007). As mentioned earlier, nisoxetine administration will induce conditioned place preference in mice with DAT deletions (Hall et al., 2002). Findings implicating noradrenergic receptors in the regulation of drug-induced reward is provided by observation of drug effects in mutant mice deficient in the 1b-noradrenergic receptor (Drouin et al., 2002). These mice failed to exhibit morphine-induced conditioned place preference or a preference for orally administered cocaine. In mice lacking the gene for beta-hydroxylase, the enzyme needed to synthesize norepinephrine, the administration of either cocaine or morphine fails to induce conditioned place preference (Jasmin, Narasaiah, & Tien, 2006). Other evidence of the noradrenergic system’s involvement in the production of cocaine’s rewarding actions includes the finding that dose-response curves for cocaine self-administration may be shifted to the right in NET knockout mice compared to wild-type mice (Rocha, 2003). In contrast to the studies just discussed, there are results suggesting that noradrenergic receptor systems are not involved in facilitating the effects of rewarding stimuli. This

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includes evidence that the administration of the selective NET inhibitor nisoxetine did not alter brain stimulation reward thresholds in the rat (Izenwasser & Kornetsky, 1989). Also, monkeys will not self-administer nisoxetine (Woolverton, 1987). Treatment with the 1 noradrenergic receptor antagonist prazosin has not been shown to significantly influence patterns of cocaine self-administration by monkeys (Woolverton, 1987). Sensitivity to the conditionedplace-preference-inducing effects of cocaine was found to be enhanced in NET knockout mice (Xu et al., 2000).

MUSCARINIC AND NICOTINIC RECEPTORS AND REWARD The neurotransmitter acetylcholine stimulates both muscarinic and nicotinic types of receptors. Different types of muscarinic receptors exist, including the M1 and M2 types. Nicotinic receptors are composed of different subunits, such as the 7 subunit. Both muscarinic and nicotinic receptors have been found to modulate the actions of rewarding stimuli. The stimulation of acetylcholine receptors within the VTA may result in rewarding effects. Rats will self-administer the nonselective cholinergic agonist carbachol directly into the VTA (Ikemoto & Wise, 2002). They will also administer the acetylcholinesterase inhibitor neostigmine, which increases intracellular levels of acetylcholine into the posterior region of the VTA. The selfadministration of carbachol into the VTA is blocked by treatment with either scopolamine, a muscarinic receptor antagonist, or dihydro--erythroidine, a nicotinic receptor antagonist. These results provide evidence that both muscarinic and nicotinic receptors in the VTA are implicated in mediating the rewarding actions of cholinergic agonists in the VTA. The finding that the administration of the muscarinic receptor antagonists atropine and scopolamine results in an elevation of brain stimulation reward thresholds supports the idea that muscarinic receptors are involved in regulating the effects of rewarding stimulation (Kornetsky, 2004). Further evidence of this is supplied by the finding that infusion of the muscarinic M5 receptor oligonucleotide antisense into the VTA resulted in elevation of brain stimulation reward thresholds (J. S. Yeomans et al., 2000). Findings that nicotine receptors also may regulate the actions of rewarding stimulation include results showing that brain stimulation reward thresholds are lowered by the administration of nicotine (Huston-Lyons, Sarkar, & Kornetsky, 1993; Ivanová & Greenshaw, 1997). The nicotinic receptor antagonist dihydro--erythroidine blocks nicotine-induced lowering of brain stimulation reward thresholds (Harrison, Gasparini, & Markou, 2002). The

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Muscarinic and Nicotinic Receptors and Reward 793

threshold-lowering effects of nicotine are potentiated by the concurrent administration of either amphetamine or morphine (Huston-Lyons et al., 1993). These effects of nicotine are blocked by the co-administration of the dopamine D2 receptor antagonist pimozide (Huston-Lyons et al., 1993), the nonselective dopamine antagonist haloperidol (Ivanová & Greenshaw, 1997), and the dopamine D1 receptor antagonist SCH 23390 (Harrison et al., 2002). These findings suggest that nicotine interacts with brain stimulation reward in a manner similar to many other commonly abused drugs. The systemic administration of nicotine produces an increase in dopamine release in the nucleus accumbens (Imperato, Mulas, & Di Chiara, 1986). This action may be explained by the observation that the intravenous administration of nicotine results in short-lasting inhibition of dopamine neurons in the VTA, which is followed by increased rates of firing (Erhardt, Schwieler, & Engberg, 2002). Mice will self-administer nicotine into the VTA, indicating that this structure may be an important site for the mediation of the rewarding effects of this compound (David, Besson, Changeux, Granon, & Cazala, 2006). The excitatory effects of nicotine may be related to an increase of glutamate release within the VTA. The release of glutamate may lead to the activation of NMDA receptors (Chergui et al., 1993). Presynaptic nicotine receptors that contain the 7 nicotinic receptor subunit appear to regulate the release of glutamate within the VTA. Microinjection of the 7 nicotine receptor subunit antagonist methyllycaconitine blocks both nicotine- and food-induced dopamine release in the nucleus accumbens (Schilström, Svensson, Svensson, & Nomikos, 1998). Methyllycaconitine administration into the VTA will attenuate the threshold-lowering effects of nicotine, suggesting that the 7 nicotine receptor subunit is involved in mediating the effects of nicotine on reward pathways (Panagis, Kastellakis, Spyraki, & Nomikos, 2000). Infusion of this antagonist into the VTA has also been demonstrated to block cocaine-induced lowering of brain stimulation reward thresholds (Panagis et al., 2000). This finding suggests that 7 nicotine-containing receptors may also regulate the rewarding actions of psychomotor stimulants. The VTA receives input from the laterodorsal and pedunculopontine tegmental nuclei in the rostral midbrain. These structures contain cholinergic neurons that project to the VTA (Oakman, Faris, Kerr, Cozzari, & Hartman, 1995). The pedunculopontine tegmental nucleus itself receives projections from the lateral hypothalamus (Semba & Fibiger, 1992). Self-stimulation of the lateral hypothalamus leads to enhanced release of acetylcholine in both the pedunculopontine tegmental nucleus and the VTA (Chen, Nakamura, Kawamura, Takahashi, & Nakahara, 2006;

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Rada, Mark, Yeomans, & Hoebel, 2000). Acetylcholine levels in the VTA are also elevated in food- or waterdeprived animals after they start to eat or drink, respectively (Rada et al., 2000). Microinjection of the muscarinic antagonist scopolamine into the pedunculopontine tegmental nucleus will lower thresholds for rewarding stimulation, possibly by blocking inhibition of presynaptic neurotransmitter release. The nicotine receptor antagonist mecamylamine, when infused into the VTA, does not significantly alter brain stimulation reward thresholds. Nicotine-induced lowering thresholds for rewarding brain stimulation, however, are blocked by infusion of mecamylamine into the pedunculopontine tegmental nucleus (Chen et al., 2006). Acetylcholine receptors in the nucleus accumbens may regulate extracellular dopamine levels within the nucleus accumbens. Cocaine-induced increases in nucleus accumbens dopamine levels in mice are decreased by infusion of mecamylamine into this structure (Zanetti, Picciotto, & Zoli, 2007). Increases in extracellular dopamine concentrations induced by the local infusion of the selective DAT inhibitor GBR 12909 within the nucleus accumbens are attenuated by the administration of either scopolamine or mecamylamine (Rahman & McBride, 2002). These findings are consistent with the involvement of both nicotinic and muscarinic receptors in the regulation of nucleus accumbens dopamine concentrations acting via an inhibitory feedback mechanism. The involvement of muscarinic receptors in regulating dopamine levels within the nucleus accumbens may partly explain why the selfadministration of cocaine is attenuated by the concurrent administration of scopolamine (Ranaldi & Woolverton, 2002). Both the M1 and M5 muscarinic receptor subtypes have been implicated in the regulation of drug-induced reward. Sensitivity to either cocaine or morphine in the production of conditioned place preference is attenuated in muscarinic M1 receptor knockout mice (Carrigan & Dykstra, 2007). Administration of the selective M1 receptor antagonist pirenzepine has been shown to antagonize the induction of conditioned place preference produced by the administration of either cocaine or morphine (Carrigan & Dykstra, 2007). Deletion of the M5 muscarinic receptor from mice resulted in a marked reduction in either cocaine- (Fink-Jensen et al., 2003) or morphine-induced conditioned place preference (Basile et al., 2002). Break points for low but not high doses of selfadministered cocaine are lower for M5 knockout mice than they are for wild-type mice, suggesting that cocaine’s rewarding actions are altered in the absence of the M5 receptor but that they are still retained at higher doses of this drug (Thomsen et al., 2005).

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794

Neural Basis of Pleasure and Reward

GLUTAMATE AND REWARD Glutamate acts at the ionotropic glutamate receptors that include the N-methyl-D-aspartate (NMDA), -amino-3hydroxy-5-methylisoxazole-4-propionic acid (AMPA), and kainate receptors. Glutamate also interacts with several types of metabotropic receptors (mGluR) that influence intraneuronal activity via second messenger systems, such as the cyclic adenosine monophosphate (cAMP) signaling pathway. As discussed earlier, glutamatergic neurons project from limbic and cortical areas to the nucleus accumbens. The VTA also receives glutamatergic inputs from the medial prefrontal cortex and the pedunculopontine nucleus (Meltzer, Christoffersen, & Serpa, 1997; Sesack & Pickel, 1992). Given that glutamatergic neurons project into mesolimbic areas, it would be expected that glutamate receptor systems are involved in the regulation of the effects of rewarding stimuli. The systemic administration of NMDA receptor antagonists can result in a lowering of brain stimulation reward thresholds (see Table 40.4). Microinjections of phencyclidine or other NMDA receptor antagonists, including dizocilpine and CPP [3-(( ) 2-carboxypiperazin-4-yl) propyl-1-phosphonate], into the nucleus accumbens produce a decrease in frequency thresholds for rewarding brain stimulation (Carlezon & Wise, 1996; Clements & Greenshaw, 2005). These findings indicate that the NMDA receptors may have an inhibitory influence on rewarding stimulation. Intracerebroventricular infusion of the competitive NMDA receptor antagonist LY235959 increases break points for self-administered cocaine (Allen, Uban, Atwood, Albeck, & Yamamoto, 2007). This finding indicates that NMDA receptors may also have an inhibitory influence on the rewarding actions of cocaine. Infusion of either of the AMPA/kainate antagonists CNQX or NBQX did not significantly alter the rewarding

table 40.4

effects of brain stimulation delivered to the VTA (Choi, Clements, & Greenshaw, 2005). When administered in combination with the D2/3 dopamine receptor agonist 7-OH-DPAT, these agents produced an elevation in frequency thresholds. Other evidence of the role of AMPA receptors in regulating the actions of rewarding stimulation includes the finding that this stimulation alters the levels of expression of different AMPA receptor subunits. These subunits, which combine to form AMPA receptors, include the GluR1 and GluR2 subunits. Increasing the expression of the GluR2 AMPA receptor subunit in the shell of the nucleus accumbens using viral vectors results in a decrease in brain stimulation reward thresholds (Todtenkopf et al., 2006). Opposite effects are produced by the enhanced expression of the GluR1 subunit in the accumbens shell (Todtenkopf et al., 2006). Group I metabotropic receptors (mGluR1 and mGluR5) stimulate the second messenger phospholipase C and activate phosphoinositide hydrolysis (Kew & Kemp, 2005). Null mutant mice lacking mGluR5 receptors will not selfadminister cocaine (Chiamulera et al., 2001). Cocaine and nicotine self-administration and break points for cocaine, nicotine, and food administered under progressive ratio schedules are decreased by treatment with the mGluR5 antagonist MPEP, [2-methyl-6-(phenylethynyl)-pyridine] (see Table 40.4). When administered into the ventricles of the brain, this antagonist also blocks the development of morphine-induced conditioned place preference (Aoki, Nirata, Shibasaki, & Suzuki, 2004). These findings suggest that mGluR5 receptors contribute to the rewarding effects of many abused drugs. The administration of a mGluR5 antagonist produces elevations in brain stimulation reward thresholds, which again implicates mGluR5 receptors in the production of the hedonic effects of rewarding stimuli. Administration of MPEP attenuates increases in sensitivity to rewarding brain stimulation that are produced by

Effects of systemically administered glutamate receptor agents on rewarding stimuli

Receptor

Drug

Class

Effect

Reference

NMDA mGluR5 mGluR5 mGluR5 mGluR5 mGluR5 mGluR5 mGluR2/3

PCP MPEP MPEP MPEP MPEP MPEP MPEP LY379268

Antagonist Antagonist Antagonist Antagonist Antagonist Antagonist Antagonist Agonist

↑ BSR ↓ Cocaine SA ↓ BP Cocaine SA ↓ BP Nicotine SA ↓ BP Food SA ↓ BSR ↓ Nicotine SA ↓ Nicotine SA

Kornetsky & Esposito (1979) Kenny, Boutrel, Gasparini, Koob, & Markou (2005) Paterson & Markou (2005) Paterson & Markou (2005) Paterson & Markou (2005) Harrison, Gasparini, & Markou (2002) Liechti & Markou (2007) Liechti, Lhuillier, Kaupmann, & Markou (2007)

↑ BP SA  Increase in break point for self-administration; ↓ BP  Decrease in breakpoint for self-administration; 0 BSR  No change in sensitivity to brain stimulation reward; ↑ BSR  Increase in sensitivity to brain stimulation reward; ↓ BSR  Decreased sensitivity to brain stimulation reward; MPEP  2-methyl-6-(phenylethynyl)-pyridine; PCP  Phencyclidine; ↑ SA  Increased self-administration; ↓ SA  Decreased self-administration.

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Signal Transduction Pathways and Reward 795

cocaine administration (Kenny, Boutrel, Gasparini, Koob, & Markou, 2005). Group II metabotropic receptors (mGluR2 and mGluR3) inhibit the formation of the second messenger cAMP (Kew & Kemp, 2005). Release of dopamine and glutamate within the nucleus accumbens (Greenslade & Mitchell, 2004; Xi, Baker, Shen, Carson, & Kalivas, 2002) is regulated by mGluR2/3 receptors, and so it would be expected that these receptors can modulate the effects of rewarding stimuli. The self-administration of nicotine, but not food, is decreased by mGluR2/3 agonist administration. This suggests that the stimulation of Group II metabotropic glutamate receptors can attenuate the rewarding actions of nicotine. The role these receptors play in regulating the rewarding effects of psychomotor stimulants is, at present, less clear. Antagonism of Group II receptors produced by the injection of the antagonist (2 S)--ethylglutamic acid resulted in the blockade of amphetamine-induced conditioned place preference (Gerjikov & Beninger, 2006). Stimulation of these receptors with the mGluR2/3 agonist LY379268 was found to decrease cocaine self-administration, but only when this agent was administered at higher doses (Baptista, MartinFardon, & Weiss, 2004). Exactly why both blockade and stimulation of Group II receptors appear to decrease the rewarding effects of psychomotor stimulants remains to be elucidated.

SIGNAL TRANSDUCTION PATHWAYS AND REWARD The D1 receptor agonists produce increases in intracellular levels of the second messenger cAMP. In addition to dopamine D1 receptors, cAMP acts as a second messenger for a variety of other types of receptors, including adenosine A2, serotonin 5-HT4, and GABAB receptors. Activation of cAMP-dependent protein kinase (PKA) produced by the stimulation of dopamine D1 receptors can lead to phosphorylation of a wide variety of proteins that regulate cellular function. The catabolism of cAMP is catalyzed by Type IV phosphodiesterase enzymes. Administration of the Type IV phosphodiesterase inhibitor rolipram into the nucleus accumbens resulted in a lowering of brain stimulation reward thresholds (Knapp et al., 2001). This suggests that acute elevations of cAMP levels in the nucleus accumbens may produce rewarding effects. When rolipram infused into accumbens and in combination with systemically administered cocaine was administered, brain stimulation reward thresholds were lowered in what was at least an additive fashion. Infusion of the PKA inhibitor Rp-cAMPS into the nucleus accumbens produces a blockade of amphetamine-induced conditioned place preference

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(Beninger, Nakonechny, & Savina, 2003; Gerdjikov, Giles, Swain, & Beninger, 2007). This would suggest that the activation of PKA contributes to the rewarding effects of amphetamine. In seeming contradiction to the studies just discussed, the results of other studies indicate that the activation of cAMP pathways may have an inhibitory action on reward processes, whereas inhibition of these pathways has the opposite effect. Infusion of the PKA activating agent Sp-cAMPS also results in a reduction of amphetamineinduced place preference (Beninger et al., 2003). Other investigators have found that infusion of Sp-cAMPS into the nucleus accumbens shifts to the right dose-response curves for self-administered cocaine (Self et al., 1998). The PKA inhibitor Rp-cAMPS was shown to shift to the left cocaine self-administration dose-response curves (Self et al., 1998). This result would suggest that activation of cAMP systems attenuates the rewarding effects of cocaine. The systemic administration of rolipram has been found to attenuate both cocaine- and morphine-induced place preference (Thompson, Sachs, Kantak, & Cherry, 2004), a finding consistent with the idea that stimulation of PKA can block the rewarding effects of either cocaine or opioids. At present it is not clear why different manipulations of cAMP pathways have very different effects on rewarding stimuli. Factors that might explain these discrepancies include the differences in the time course of the effects of the agents used to activate or inhibit PKA activity, the existence of several isoforms of PKA, and the diverse nature of the function of cAMP pathways in different cell types. Calcium-dependent protein kinase (PKC) is another enzyme that may play an important role in signal transduction pathways that regulate the actions of rewarding stimuli. The concurrent administration of the PKC inhibitor NPC 1537 blocks conditioned place preference produced by the infusion of amphetamine into the nucleus accumbens (Aujla & Beninger, 2003). Intracerebroventricular infusion of the PKC inhibitor calphostin C has been found to dose-dependently block morphine-induced conditioned place preference (Narita, Aoki, Ozaki, Yajima, & Suzuki, 2001). Calcium-dependent protein kinase consists of a series of isoenzymes. Of these, the epsilon and gamma isoforms have been implicated in regulating the rewarding effects of drugs of abuse. In mice, morphine-induced conditioned place preference results in an increase in the limbic forebrain of the gamma isoform of PKC (Aoki et al., 2004). The morphine-induced increase in this isoform of PKC is antagonized by the administration of the mGluR5 antagonist MPEP (Aoki et al., 2004). The administration of this antagonist also blocks morphine-induced place preference. Genetic deletion of the gamma isoform of PKC

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Neural Basis of Pleasure and Reward

in mice results in an increase in ethanol consumption (P. M. Newton & Ron, 2007). In contrast, mice deficient in the epsilon form of PKC showed reduced intake of selfadministered ethanol compared to wild-type mice (Olive, Mehmert, Messing, & Hodge, 2000). The PKC epsilondeficient mice fail to show an increase in nucleus accumbens dopamine concentrations after the administration of ethanol. This may be an important factor as to why these mice exhibit patterns of low ethanol intake. The cAMP response element binding protein (CREB) is a transcription factor that is activated by several signal-transduction pathways, including cAMP signaling pathways. CREB is activated by its transformation into phospho-CREB. Phospho-CREB regulates gene expression via interactions with CREs (cAMP response elements that regulate transcription). Exposure to amphetamine, cocaine, or morphine can produce activation of CREB (Konradi, Cole, Heckers, & Hyman, 1994; Walters, Kuo, & Blendy, 2003). This effect is important because CREB may be involved in regulating the rewarding effects of both the opioids and the psychomotor stimulants. Down regulation of CREB-regulated gene transcription by the infusion of CREB antisense into the nucleus accumbens produces a downward shift in cocaine self-administration, suggesting a reduction in the rewarding effects of this drug (Choi, Whisler, Graham, & Self, 2006). Mutant mice lacking the alpha and delta isoforms of CREB show diminished sensitivity to low-dose morphine in conditioned placepreference experiments (Walters & Blendy, 2001). These animals, in comparison to wild-type mice, displayed a positive place-preference response to high-dose morphine (Walters, Godfrey, Li, & Blendy, 2005). This suggests that the loss of the alpha and delta isoforms may produce a rightward shift in the dose-response curve for morphineinduced conditioned place preference. While CREB has been implicated in the production of the rewarding effects of cocaine and morphine, this protein has been shown to have different, regionally specific actions that are dependent on the level of expression of CREB in a particular region. Increased expression of CREB in the shell of the nucleus accumbens produced by the injection of a viral vector has reduced the sensitivity of rats to the rewarding effects of cocaine (Carlezon et al., 1998). In contrast, increased expression of CREB in the lateral hypothalamus greatly increases the sensitivity of animals to the rewarding effects of morphine in place-preference experiments (Olson, Green, Neve, & Nestler, 2007). Viral-mediated expression of CREB in this structure also enhanced food consumption. In the rostral portion of the VTA, increased CREB expression induced by a viral vector increased sensitivity to the rewarding effects of either cocaine or morphine (Olson et al., 2005).

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A different effect resulted from the injection of a CREBenhancing viral vector into the caudal portion of the VTA, with low doses of either cocaine or morphine producing conditioned place aversion.

ADDICTION AND CHRONIC EXPOSURE TO REWARDING STIMULI The major theories concerning the development of drug addiction differ in basic assumptions concerning how the hedonic effects of drugs of abuse change following periods of chronic drug exposure. The hedonic theory of drug addiction posits that the hedonic effects of drugs act as a persistent factor in both drug abuse and dependence, and so involves the assumption that drugs continue to produce rewarding effects in drug-using populations (Kornetsky & Bain, 1987). The incentive-salience theory of addiction (Robinson & Berridge, 2003) sees a diminished role for the hedonic effects of drugs in addiction, with a greater role played by salient conditioned stimuli. Thus, this theory emphasizes the development of tolerance to the hedonic effects of drugs as drug dependence progresses. The hedonic dysregulation theory of drug addiction holds that there are decreases in the basal activity of reward processes as a consequence of chronic drug use and an accompanying onset of negative emotions, such as anhedonia and depression (Le Moal & Koob, 2007). Drugs are then used by dependent individuals to counter this dysregulation of reward processes. The results of numerous studies indicate that the rewarding effects of drugs remain essentially unaltered when they are administered chronically, at least under certain schedules of drug administration (see Table 40.5). The repeated daily administration of morphine over a period of several weeks did not result in a loss in the ability of this agent to produce lowering of reward threshold levels (Esposito & Kornetsky, 1977). This occurred despite the development of physical dependence in the animals that were treated with morphine. The findings of some investigations, however, suggest that the chronic administration of psychomotor stimulants produces either sensitization or tolerance to the effects of these drugs on brain stimulation reward. Overall it appears that whether the chronic administration of psychomotor stimulants produces the development of sensitization or tolerance or no effect at all in the sensitivity of animals to brain stimulation reward may be related to the history of drug exposure of the animal. Data from human drug users indicate that many continue to experience the hedonic effects of drugs. For subjects with a history of heavy cocaine use or cocaine dependence, subjective measures of drug “liking” and drug

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Addiction and Chronic Exposure to Rewarding Stimuli table 40.5

Effects of chronic administration of several drugs of abuse on sensitivity to the effects of rewarding brain stimulation

Effect

Drug

Schedule

Reference

None

Morphine

Daily

Esposito & Kornetsky (1977)

GBR 12909

Every other day

Melnick, Maldonado-Vlaar, Stellar, & Trzcinska (2001)

Cocaine

Daily

Frank, Manderscheid, Panicker, Williams, & Kokoris (1992)

Cocaine

3 times daily or every other day

Bauco & Wise (1997)

Phencyclidine

Intermittent

Carlezon & Wise (1993)

Nicotine

Daily

Bauco & Wise (1994)

Sensitization

Amphetamine

Once every 5 days

Lin, Koob, & Markou (2000)

Amphetamine

Twice daily for 5 days

Kokkinidis & Zacharko (1980)

Tolerance

Cocaine

Multiple times daily

Frank, Manderscheid, Panicker, Williams, & Kokoris (1992)

Amphetamine

Increasing dose or high dose twice daily

Kokkinidis & Zacharko (1980); Leith & Barrett (1976)

Cocaine/methylphenidate

Postamphetamine

Leith & Barrett (1981)

“high” become elevated during cocaine self-administration sessions (Foltin & Fischman, 1992; Lynch et al., 2006). Similarly, in heroin-dependent individuals this drug produces dose-dependent increases in measures of euphoria, “good” drug effects, and “high” during periods of heroin self-administration testing (Comer, Collins, MacArthur, & Fischman, 1999; Comer et al., 1998). Evidence that individuals with a history of dependence on psychomotor stimulants or opioids remain able to derive pleasure from these drugs provides allowance for the theory that hedonic drug effects are a major factor underlying the development and continuation of drug abuse. Although drug users remain sensitive to the hedonic effects of drugs of abuse there is evidence that this sensitivity can decrease during periods of sustained drug use. During testing in a “binge” cocaine self-administration session, subjects with a history of cocaine use developed acute tolerance to several of the subjective effects produced by this drug (Ward, Haney, Fischman, & Foltin, 1997). In cocaine users subjective feelings of “high” and euphoria were found to be diminished as concentrations of cocaine remained at sustained levels in the plasma (Foltin & Fischman, 1991). This finding is also consistent with the development of acute tolerance. Animal studies of the effects of prolonged exposure to drugs of abuse on brain stimulation reward threshold show changes in baseline sensitivities to this stimulation. The repeated daily administration of amphetamine was found to result in an elevation in brain stimulation reward threshold levels at times subsequent to lowering of thresholds produced by this agent (D. Lin, Koob, & Markou, 2000). This effect may be related to alteration in reward system function following acute drug withdrawal. Similar elevations in stimulation threshold levels have been observed during acute withdrawal from ethanol (Schulteis, Markou, Cole, & Koob, 1995) and heroin (Kenny, Chen, Kitamura,

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797

Markou, & Koob, 2006). Brain stimulation reward thresholds have become elevated above baseline levels at 2 hours after the self-administration of 40 and 80 injections (0.25 mg/injection) of cocaine, but not after 10 or 20 self-administered injections of this agent (Kenny, Polis, Koob, & Markou, 2003). These threshold elevations persist for 24 to 48 hours after the administration of higher doses of cocaine. Following the administration of 40 or fewer injections of cocaine, the threshold-lowering effects of this drug were still evident 15 minutes after the end of self-administration sessions. Rats allowed to self-administer cocaine or heroin during short intervals of 1 to 3 hours show stable levels of intake of this drug. In contrast, cocaine intake has been found to escalate in rats given access to this agent for extended intervals of 6 hours or more (Ahmed & Koob, 1998). Baseline brain stimulation reward thresholds remain stable in rats given access to cocaine or heroin for only short intervals. Longterm access to either cocaine or heroin results in an elevation in threshold levels (Ahmed, Kenny, Koob, & Markou, 2002; Ahmed, Walker, & Koob, 2000). This effect persists for several days when animals first given prolonged access to cocaine are then allowed access to this stimulant for only short intervals. Acute cocaine administration resulted in a decrease in brain stimulation reward thresholds from elevated levels observed in rats given prolonged access to cocaine (Ahmed et al., 2002). This may indicate that the rewarding actions of cocaine may remain in animals in which prolonged exposure to cocaine has decreased basal levels of sensitivity to brain stimulation reward. It has been suggested that prolonged exposure to many drugs of abuse results in persistent deficits in the regulation of brain reward function. This deficit is postulated to contribute to the compulsive use of drugs and to be related to decreased activity in mesolimbic dopaminergic systems. Evidence of reduced dopamine neurotransmission has been

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demonstrated in clinical studies in some drug-using populations. Cocaine-dependent individuals show a marked reduction in amphetamine-induced dopamine release in the ventral striatum and the anterior cingulate, assessed using PET imaging (Martinez et al., 2007). This has also been seen in the ventral striatum of alcohol-dependent subjects (Martinez et al., 2005). Deficits in brain reward function are postulated to be related to the occurrence of anhedonia and related depressive symptoms in drug-using populations. It has been suggested that the administration of abusable drugs may reverse anhedonia that occurs during drug withdrawal, and that this may be a contributing factor to the development of addiction. Consistent with this hypothesis, smokers with high scores on ratings of anhedonia, compared to subjects with low scores, had greatly enhanced increases in the levels of positive affect produced by pleasurable memories and music when challenged with nicotine (Cook, Spring, & McChargue, 2007). In related findings, high levels of apathy or depressive symptoms in cocaine-dependent subjects were associated with the experience of a greater “high” after cocaine administration than were low levels of apathy or depression (T. F. Newton, Kalechstein, De La Garza, Cutting, & Ling, 2005; Uslaner, Kalechstein, Richter, Ling, & Newton, 1999). Negative emotions such as clinically significant depression, however, have yet to be identified as occurring in a substantial number of individuals who are drug-dependent (Darke & Ross, 1997; Gawin & Kleber, 1986; Grant et al., 2004). Serious questions therefore remain as to what role the negative affective states play in the development of addictive disorders.

addition to resulting from modulation of mesocorticolimbic dopaminergic activity, might also produce reward through dopamine-independent mechanisms. Indeed, some authors have argued that the hedonic effects of palatable foods are dopamine-independent and involve primarily opioid-related mechanisms (A. E. Kelley et al., 2005). Of critical importance to our understanding of how disorders that involve the compulsive use of rewarding substances develop is knowledge concerning how the hedonic effects associated with such use change over time. Individuals with histories of use of sedative agents, psychomotor stimulants, and opioids report experiencing the hedonic effects of these drugs under laboratory situations. This suggests that the mood-elevating and pleasurable actions of abused drugs could continue to exert some influence on the behavior of individuals with prolonged histories of drug use. This does not, however, mean that factors such as conditioned stimuli associated with drug use or negative mood states observed after intense periods of drug use are never involved in the development of addictive disorders. Determining how these factors interact to regulate drug self-administration may require more sophisticated modeling of patterns of use in drug-dependent individuals.

SUMMARY

Ahmed, S. H., Walker, J. R., & Koob, G. F. (2000). Persistent increase in the motivation to take heroin in rats with a history of drug escalation. NeuroPsychopharmacology, 22, 413–421.

The evidence presently available indicates that sustained elevations of dopamine levels within the nucleus accumbens are associated with the production of hedonic effects by a variety of rewarding stimuli, ranging from natural stimuli such as sucrose to artificial rewards such as heroin. Changes in the phasic activity of dopaminergic neurons, in contrast, have been linked to behaviors associated with functions such as prediction of reward. The sustained elevations in mesolimbic dopamine levels that result from exposure to rewarding stimuli may activate different networks that are involved in the production of hedonic effects. Dopaminergic receptor systems, however, constitute only one element of the network of neurons that support pleasurable experiences. It is clear that the hedonic effects of rewarding stimuli reflect the interactions of a large variety of neurotransmitters. The existing evidence suggests that the rewarding effects of opioid agonists and GABA agonists, in

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REFERENCES Ahmed, S. H., Kenny, P. J., Koob, G. F., & Markou, A. (2002). Neurobiological evidence for hedonic allostasis associated with escalating cocaine use. Nature Neuroscience, 5, 625–626. Ahmed, S. H., & Koob, G. F. (1998, October 9). Transition from moderate to excessive drug intake: Change in hedonic set point. Science, 282, 298–300.

Allen, R. M., Uban, K. A., Atwood, E. M., Albeck, D. S., & Yamamoto, D. J. (2007). Continuous intracerebroventricular infusion of the competitive NMDA receptor antagonist, LY235959, facilitates escalation of cocaine self-administration and increases break point for cocaine in Sprague-Dawley rats. Pharmacology Biochemistry and Behavior, 88, 82–88. Aoki, T., Nirata, M., Shibasaki, M., & Suzuki, T. (2004). Metabotropic glutamate receptor 5 localized in the limbic forebrain is critical for the development of morphine-induced rewarding effect in mice. European Journal of Neuroscience, 20, 1633–1638. Arvanitogiannis, A., Tzschentke, T. M., Riscaldino, L., Wise, R. A., & Shizgal, P. (2000). Fos expression following self-stimulation of the medial prefrontal cortex. Behavioral Brain Research, 107, 123–132. Aujla, H., & Beninger, R. J. (2003). Intra-accumbens protein kinase C inhibitor NPC 15437 blocks amphetamine-produced conditioned place preference in rats. Behavioral Brain Research, 147, 41–48. Bain, G. T., & Kornetsky, C. (1987). Naloxone attenuation of the effect of cocaine on rewarding brain stimulation. Life Sciences, 40, 1119–1125. Baldo, B. A., Jain, K., Veraldi, L., Koob, G. F., & Markou, A. (1999). A dopamine D1 agonist elevates self-stimulation thresholds: Comparison

8/17/09 3:08:43 PM

References 799 to other dopamine-selective drugs. Pharmacology Biochemistry and Behavior, 62, 659–672. Baptista, M. A., Martin-Fardon, R., & Weiss, F. (2004). Preferential effects of the metabotropic glutamate 2/3 receptor agonist LY379268 on conditioned reinstatement versus primary reinforcement: Comparison between cocaine and a potent conventional reinforcer. Journal of Neuroscience, 24, 4723–4727. Barot, S. K., Ferguson, S. M., & Neumaier, J. F. (2007). 5-HT(1B) receptors in nucleus accumbens efferents enhance both rewarding and aversive effects of cocaine. European Journal of Neuroscience, 25, 3125–3131. Barrett, A. C., Negus, S. S., Mello, N. K., & Caine, S. B. (2005). Effect of GABA agonists and GABA-A receptor modulators on cocaine- and food-maintained responding and cocaine discrimination in rats. Journal of Pharmacology and Experimental Therapeutics, 315, 858–871. Barrett, S. P., Boileau, I., Okker, J., Pihl, R. O., & Dagher, A. (2004). The hedonic response to cigarette smoking is proportional to dopamine release in the human striatum as measured by positron emission tomography and [11C]raclopride. Synapse, 54, 65–71. Basile, A. S., Fedorova, I., Zapata, A., Liu, X., Shippenberg, T., Duttaroy, A., et al. (2002). Deletion of the M5 muscarinic acetylcholine receptor attenuates morphine reinforcement and withdrawal but not morphine analgesia. Proceedings of the National Academy of Sciences, USA, 99, 11452–11457. Bauco, P., & Wise, R. A. (1994). Potentiation of lateral hypothalamic and midline mesencephalic brain stimulation reinforcement by nicotine: Examination of repeated treatment. Journal of Pharmacology and Experimental Therapeutics, 271, 294–301. Bauco, P., & Wise, R. A. (1997). Synergistic effects of cocaine with lateral hypothalamic brain stimulation reward: Lack of tolerance or sensitization. Journal of Pharmacology and Experimental Therapeutics, 283, 1160–1167. Becerra, L., Harter, K., Gonzalez, R. G., & Borsook, D. (2006). Functional magnetic resonance imaging measures of the effects of morphine on central nervous system circuitry in opioid-naive healthy volunteers. Anesthesia and Analgesia, 103, 208–216. Becker, A., Grecksch, G., Kraus, J., Loh, H. H., Schroeder, H., & Höllt, V. (2002). Rewarding effects of ethanol and cocaine in mu opioid receptor-deficient mice. Naunyn Schmiedebergs Archives of Pharmacology, 365, 296–302.

Broekkamp, C. L., & Phillips, A. G. (1979). Facilitation of self-stimulation behavior following intracerebral microinjections of opioids into the ventral tegmental area. Pharmacology Biochemistry and Behavior, 11, 289–295. Broekkamp, C. L., Pijnenburg, A. J., Cools, A. R., & Van Rossum, J. M. (1975). The effect of microinjections of amphetamine into the neostriatum and the nucleus accumbens on self-stimulation behaviour. Psychopharmacologia, 28, 179–183. Bubar, M. J., McMahon, L. R., DeDeurwaerdere, P., Spampinato, U., & Cunningham, K. A. (2003). Selective serotonin reuptake inhibitors enhance cocaine-induced locomotor activity and dopamine release in the nucleus accumbens. Neuropharmacology, 44, 342–353. Buckett, W. R. (1981). The influence of a GABA transaminase inhibitor, gamma-vinyl GABA, on voluntary morphine consumption in the rat. Psychopharmacology, 75, 214–216. Caillé, S., & Parsons, L. H. (2003). SR141716A reduces the reinforcing properties of heroin but not heroin-induced increases in nucleus accumbens dopamine in rats. European Journal of Neuroscience, 18, 3145–3149. Caine, S. B., Negus, S. S., Mello, N. K., Patel, S., Bristow, L., Kulagowski, J., et al. (2002). Role of dopamine D2-like receptors in cocaine selfadministration: Studies with D2 receptor mutant mice and novel D2 receptor antagonists. Journal of Neuroscience, 22, 2977–2988. Caine, S. B., Thomsen, M., Gabriel, K. I., Berkowitz, J. S., Gold, L. H., Koob, G. F., et al. (2007). Lack of self-administration of cocaine in dopamine D1 receptor knock-out mice. Journal of Neuroscience, 27, 13140–13150. Carelli, R. M., & Wondolowski, J. (2003). Selective encoding of cocaine versus natural rewards by nucleus accumbens neurons is not related to chronic drug exposure. Journal of Neuroscience, 23, 11214–11223. Carlezon, W. A., Jr., Thome, J., Olson, V. G., Lane-Ladd, S. B., Brodkin, E. S., Hiroi, N., et al. (1998, December 18). Regulation of cocaine reward by CREB. Science, 282, 2272–2275. Carlezon, W. A., Jr., & Wise, R. A. (1993). Phencyclidine-induced potentiation of brain stimulation reward: Acute effects are not altered by repeated administration. Psychopharmacology, 111, 402–408.

Benaliouad, F., Kapur, S., & Rompré, P. P. (2007). Blockade of 5HT2a receptors reduces haloperidol-induced attenuation of reward. NeuroPsychopharmacology, 32, 51–61.

Carlezon, W. A., Jr., & Wise, R. A. (1996). Microinjections of phencyclidine (PCP) and related drugs into nucleus accumbens shell potentiate medial forebrain bundle brain stimulation reward. Psychopharmacology, 128, 413–420.

Beninger, R. J., Nakonechny, P. L., & Savina, I. (2003). CAMP-dependent protein kinase and reward-related learning: Intra-accumbens RpcAMPS blocks amphetamine-produced place conditioning in rats. Psychopharmacology, 170, 23–32.

Carrigan, K. A., & Dykstra, L. A. (2007). Behavioral effects of morphine and cocaine in M1 muscarinic acetylcholine receptor-deficient mice. Psychopharmacology, 191, 985–993.

Bergevin, A., Girardot, D., Bourque, M. K., & Trudeau, L. E. (2002). Presynaptic muopioid receptors regulate a late step of the secretory process in rat ventral tegmental area GABAergic neurons. Neuropharmacology, 42, 1065–1078. Bird, M., & Kornetsky C. (1990). Dissociation of the attentional and motivational effects of pimozide on the threshold for rewarding brain stimulation. NeuroPsychopharmacology, 3, 33–40. Bishop, M. P., Elder, S. T., & Heath, R. G. (1963, April 26). Intracranial self-stimulation in man. Science, 140, 394–396. Bradberry, C. W., Barrett-Larimore, R. L., Jatlow, P., & Rubino, S. R. (2000). Impact of self-administered cocaine and cocaine cues on extracellular dopamine in mesolimbic and sensorimotor striatum in rhesus monkeys. Journal of Neuroscience, 20, 3874–3883. Brauer, L. H., & DeWit, H. (1997). High dose pimozide does not block amphetamine-induced euphoria in normal volunteers. Pharmacology Biochemistry and Behavior, 56, 265–272.

c40.indd 799

Breiter, H. C., Gollub, R. L., Weisskoff, R. M., Kennedy, D. N., Makris, N., Berke, J. D., et al. (1997). Acute effects of cocaine on human brain activity and emotion. Neuron, 19, 591–611.

Carter, L. P., Richards, B. D., Mintzer, M. Z., & Griffiths, R. R. (2006). Relative abuse liability of GHB in humans: A comparison of psychomotor, subjective, and cognitive effects of supratherapeutic doses of triazolam, pentobarbital, and GHB. NeuroPsychopharmacology, 31, 2537–2551. Chausmer, A. L., Smith, B. J., Kelly, R. Y., & Griffiths, R. R. (2003). Cocaine-like subjective effects of nicotine are not blocked by the D1 selective antagonist ecopipam (SCH 39166). Behavioral Pharmacology, 14, 111–120. Cheer, J. F., Aragona, B. J., Heien, M. L., Seipel, A. T., Carelli, R. M., & Wightman, R. M. (2007). Coordinated accumbal dopamine release and neural activity drive goal-directed behavior. Neuron, 54, 237–244. Chen, J., Nakamura, M., Kawamura, T., Takahashi, T., & Nakahara, D. (2006). Roles of pedunculopontine tegmental cholinergic receptors in brain stimulation reward in the rat. Psychopharmacology, 184, 514–522. Chen, R., Tilley, M. R., Wei, H., Zhou, F., Zhou, F. M., Ching, S., et al. (2006). Abolished cocaine reward in mice with a cocaine-insensitive dopamine

8/17/09 3:08:43 PM

800

Neural Basis of Pleasure and Reward

transporter. Proceedings of the National Academy of Sciences, USA, 103, 9333–9338.

the nucleus accumbens. Neuroscience and Biobehavioral Review, 27, 703–711.

Chergui, K., Charléty, P. J., Akaoka, H., Saunier, C. F., Brunet, J. L., Buda, M., et al. (1993). Tonic activation of NMDA receptors causes spontaneous burst discharge of rat midbrain dopamine neurons in vivo. European Journal of Neuroscience, 5, 137–144.

De Deurwaerdère, P., Moison, D., Navailles, S., Porras, G., & Spampinato, U. (2005). Regionally and functionally distinct serotonin3 receptors control in vivo dopamine outflow in the rat nucleus accumbens. Journal of Neurochemistry, 94, 140–149.

Chiamulera, C., Epping-Jordan, M. P., Zocchi, A., Marcon, C., Cottiny, C., Tacconi, S., et al. (2001). Reinforcing and locomotor stimulant effects of cocaine are absent in mGluR5 null mutant mice. Nature Neuroscience, 4, 873–874.

De Vries, T. J., Homberg, J. R., Binnekade, R., Raasø, H., & Schoffelmeer, A. N. (2003). Cannabinoid modulation of the reinforcing and motivational properties of heroin and heroin-associated cues in rats. Psychopharmacology, 168, 164–169.

Choi, K. H., Clements, R. L., & Greenshaw, A. J. (2005). Simultaneous AMPA/kainate receptor blockade and dopamine D(2/3) receptor stimulation in the nucleus accumbens decreases brain stimulation reward in rats. Behavioral Brain Research, 158, 79–88.

De Vries, T. J., & Shippenberg, T. S. (2002). Neural systems underlying opiate addiction. Journal of Neuroscience, 22, 3321–3325.

Choi, K. H., Whisler, K., Graham, D. L., & Self, D. W. (2006). Antisenseinduced reduction in nucleus accumbens cyclic AMP response element binding protein attenuates cocaine reinforcement. Neuroscience, 137, 373–383. Ciraulo, D. A., Barnhill, J. G., Ciraulo, A. M., Greenblatt, D. J., & Shader, R. I. (1989). Parental alcoholism as a risk factor in benzodiazepine abuse: A pilot study. American Journal of Psychiatry, 146, 1333–1335. Ciraulo, D. A., Barnhill, J. F., Ciraulo, A. M., Sarid-Segal, O., Knapp, C., Greenblatt, D. J., et al. (1997). Alterations in pharmacodynamics of anxiolytics in abstinent alcoholic men: Subjective responses, abuse liability, and electroencephalographic effects of alprazolam, diazepam, and buspirone. Journal of Clinical Pharmacology, 37, 64–73. Ciraulo, D. A., Sarid-Segal, O., Knapp, C., Ciraulo, A. M., Greenblatt, D. J., & Shader, R. I. (1996). Liability to alprazolam abuse in daughters of alcoholics. American Journal of Psychiatry, 153, 956–958. Clements, R. L., & Greenshaw, A. J. (2005). Facilitation of brain stimulation reward by MK-801 (dizocilpine) may be independent of D2-like dopamine receptor stimulation in rats. Psychopharmacology, 182, 65–74. Comer, S. D., Collins, E. D., MacArthur, R. B., & Fischman, M. W. (1999). Comparison of intravenous and intranasal heroin self-administration by morphine-maintained humans. Psychopharmacology, 143, 327–338. Comer, S. D., Collins, E. D., Wilson, S. T., Donovan, M. R., Foltin, R. W., & Fischman, M. W. (1998). Effects of an alternative reinforcer on intravenous heroin self-administration by humans. European Journal of Pharmacology, 345, 13–26. Cook, J. W., Spring, B., & McChargue, D. (2007). Influence of nicotine on positive affect in anhedonic smokers. Psychopharmacology, 192, 87–95. Cossu, G., Ledent, C., Fattore, L., Imperato, A., Böhme, G. A., Parmentier, M., et al. (2001). Cannabinoid CB1 receptor knockout mice fail to selfadminister morphine but not other drugs of abuse. Behavioral Brain Research, 118, 61–65. Czoty, P. W., Ginsburg, B. C., & Howell, L. L. (2002). Serotonergic attenuation of the reinforcing and neurochemical effects of cocaine in squirrel monkeys. Journal of Pharmacology and Experimental Therapeutics, 300, 831–837. Darke, S., & Ross, J. (1997). Polydrug dependence and psychiatric comorbidity among heroin injectors. Drug and Alcohol Dependence, 48, 135–141. David, V., Besson, M., Changeux, J. P., Granon, S., & Cazala, P. (2006). Reinforcing effects of nicotine microinjections into the ventral tegmental area of mice: Dependence on cholinergic nicotinic and dopaminergic D1 receptors. Neuropharmacology, 50, 1030–1040. Davidson, C., Lee, T. H., Xiong, Z., & Ellinwood, E. H. (2002). Ondansetron given in the acute withdrawal from a repeated cocaine sensitization dosing regimen reverses the expression of sensitization and inhibits self-administration. Neuropsychopharmacology, 27, 542–553. Deadwyler, S. A., Hayashizaki, S., Cheer, J., & Hampson, R. E. (2004). Reward, memory and substance abuse: Functional neuronal circuits in

c40.indd 800

Doyon, W. M., York, J. L., Diaz, L. M., Samson, H. H., Czachowski, C. L. & Gonzales, R. A. (2003). Dopamine activity in the nucleus accumbens during consummatory phases of oral ethanol self administration. Alcoholism Clinical and Experimental Research, 27, 1573–1582. Drevets, W. C., Gautier, C., Price, J. C., Kupfer, D. J., Kinahan, P. E., Grace, A. A., et al. (2001). Amphetamine-induced dopamine release in human ventral striatum correlates with euphoria. Biological Psychiatry, 49, 81–96. Drobes, D. J., Anton, R. F., Thomas, S. E., & Voronin, K. (2004). Effects of naltrexone and nalmefene on subjective response to alcohol among non-treatment-seeking alcoholics and social drinkers. Alcoholism Clinical and Experimental Research, 28, 1362–1370. Drouin, C., Darracq, L., Trovero, F., Blanc, G., Glowinski, J., Cotecchia, S., et al. (2002). Alpha1b-adrenergic receptors control locomotor and rewarding effects of psychostimulants and opiates. Journal of Neuroscience, 22, 2873–2884. Duvauchelle, C. L., Fleming, S. M., & Kornetsky, C. (1997). DAMGO and DPDPE facilitation of brain stimulation reward thresholds is blocked by the dopamine antagonist cis-flupenthixol. Neuropharmacology, 36, 1109–1114. Duvauchelle, C. L., Fleming, S. M., & Kornetsky, C. (1998). Prefrontal cortex infusions of SCH 23390 cause immediate and delayed effects on ventral tegmental area stimulation reward. Brain Research, 811, 57–62. Elmer, G. I., Pieper, J. O., Levy, J., Rubinstein, M., Low, M. J., Grandy, D. K., et al. (2005). Brain stimulation and morphine reward deficits in dopamine D2 receptor-deficient mice. Psychopharmacology, 182, 33–44. Elmer, G. I., Pieper, J. O., Rubinstein, M., Low, M. J., Grandy, D. K., & Wise, R. A. (2002). Failure of intravenous morphine to serve as an effective instrumental reinforcer in dopamine D2 receptor knock-out mice. Journal of Neuroscience, 22, RC224 1–6. Emson, P. C., & Koob, G. F. (1978). The origin and distribution of dopamine-containing afferents to the rat frontal cortex. Brain Research, 142, 249–267. Enggasser, J. L., & de Wit, H. (2001). Haloperidol reduces stimulant and reinforcing effects of ethanol in social drinkers. Alcoholism Clinical and Experimental Research, 25, 1448–1456. Erhardt, S., Schwieler, L., & Engberg, G. (2002). Excitatory and inhibitory responses of dopamine neurons in the ventral tegmental area to nicotine. Synapse, 43, 227–237. Esposito, R., Faulkner, W., & Kornetsky, C. (1979). Specific modulation of brain stimulation reward by haloperidol. Pharmacology Biochemistry and Behavior, 10, 937–940. Esposito, R., & Kornetsky C. (1977, January 14). Morphine lowering of self-stimulation thresholds: Lack of tolerance with long-term administration. Science, 195, 189–191. Esposito, R., Perry, W., & Kornetsky, C. (1980). Effects of d-amphetamine and naloxone on brain stimulation reward. Psychopharmacology, 69, 187–191. Farren, C. K., Hameedi, F. A., Rosen, M. A., Woods, S., Jatlow, P., & Kosten, T. R. (2000). Significant interaction between clozapine and cocaine in cocaine addicts. Drug and Alcohol Dependence, 59, 153–163.

8/17/09 3:08:43 PM

References 801 Fink- Jensen, A., Fedorova, I., Wörtwein, G., Woldbye, D. P., Rasmussen, T., Thomsen, M., et al. (2003). Role for M5 muscarinic acetylcholine receptors in cocaine addiction. Journal of Neuroscience Research, 74, 91–96. Finlay, J. M., Damsma, G., & Fibiger, H. C. (1992). Benzodiazepineinduced decreases in extracellular concentrations of dopamine in the nucleus accumbens after acute and repeated administration. Psychopharmacology, 106, 202–208. Fiorino, D. F., Coury, A., & Phillips, A. G. (1997). Dynamic changes in nucleus accumbens dopamine efflux during the Coolidge effect in male rats. Journal of Neuroscience, 17, 4849–4855. Fischman, M. W., & Foltin, R. W. (1991). Utility of subjective-effects measurements in assessing abuse liability of drugs in humans. British Journal of Addiction, 86, 1563–1570. Fletcher, P. J., Grottick, A. J., & Higgins, G. A. (2002). Differential effects of the 5-HT(2A) receptor antagonist M100907 and the 5-HT(2C) receptor antagonist SB242084 on cocaine-induced locomotor activity, cocaine self-administration and cocaine-induced reinstatement of responding. NeuroPsychopharmacology, 27, 576–586. Foltin, R. W., & Fischman, M. W. (1991). Smoked and intravenous cocaine in humans: Acute tolerance, cardiovascular and subjective effects. Journal of Pharmacology and Experimental Therapeutics, 257, 247–261. Foltin, R. W., & Fischman, M. W. (1992). The cardiovascular and subjective effects of intravenous cocaine and morphine combinations in humans. Journal of Pharmacology and Experimental Therapeutics, 261 623–632.

Grottick, A. J., Corrigall, W. A., & Higgins, G. A. (2001). Activation of 5-HT(2C) receptors reduces the locomotor and rewarding effects of nicotine. Psychopharmacology, 157, 292–298. Grottick, A. J., Fletcher, P. J., & Higgins, G. A. (2000). Studies to investigate the role of 5-HT(2C) receptors on cocaine- and food-maintained behavior. Journal of Pharmacology and Experimental Therapeutics, 295, 1183–1191. Gysling, K., & Wang, R. Y. (1983). Morphine-induced activation of A10 dopamine neurons in the rat. Brain Research, 277, 119–127. Haertzen, C. A., Hill, H. E., & Belleville, R. E. (1963). Development of the Addiction Research Center Inventory (ARCI): Selection of items that are sensitive to the effects of various drugs. Psychopharmacologia, 4, 155–166. Hajnal, A., Smith, G. P., & Norgren, R. (2004). Oral sucrose stimulation increases accumbens dopamine in the rat. American Journal Physiology Regulatory Integrative and Comparative Physiology, 286, R31–R37. Hall, F. S., Goeb, M., Li, X, Sora, I., & Uhl, G. (2004). Mu-opioid receptor knockout mice display reduced cocaine conditioned place preference but enhanced sensitization of cocaine-induced locomotion. Brain Research Molecular Brain Research, 121, 123–130. Hall, F. S., Li, X. F., Sora, I., Xu, F., Caron, M., Lesch, K. P., et al. (2002). Cocaine mechanisms: Enhanced cocaine, fluoxetine and nisoxetine place preferences following monoamine transporter deletions. Neuroscience, 115, 153–161.

Frank, R. A., Manderscheid, P. Z., Panicker, S., Williams, H. P., & Kokoris, D. (1992). Cocaine euphoria, dysphoria, and tolerance assessed using drug-induced changes in brain-stimulation reward. Pharmacology Biochemistry and Behavior, 42, 771–719.

Hall, F. S., Sora, I., & Uhl, G. R. (2001). Ethanol consumption and reward are decreased in mu-opiate receptor knockout mice. Psychopharmacology, 154, 43–49.

Gawin, F. H., & Kleber, H. D. (1986). Abstinence symptomatology and psychiatric diagosis in cocaine abusers. Clinical observations. Archives of General Psychiatry, 43, 107–113.

Harrison, A. A., Gasparini, F., & Markou, A. (2002). Nicotine potentiation of brain stimulation reward reversed by DH beta, E and SCH 23390, but not by eticlopride, LY 314582 or MPEP in rats. Psychopharmacology, 160, 56–66.

Geldwert, D., Norris, J. M., Feldman, I., Schulman, J. J., Joyce, M. P., & Rayport S. (2006). Dopamine presynaptically and heterogenously modulates nucleus accumbens medium-spiny neuron GABA synapses in vitro. BMC Neuroscience, 7, 53. Gerasimov, M. R., Ashby, C. R. Jr., Gardner, E. L., Mills, M. J., Brodie, J. D., & Dewey, S. L. (1999). Gamma-vinyl GABA inhibits methamphetamine, heroin, or ethanol-induced increases in nucleus accumbens dopamine. Synapse, 34, 11–19. Gerdjikov, T. V., & Beninger, R. J. (2006). Place preference induced by nucleus accumbens amphetamine is impaired by local blockade of Group II metabotropic glutamate receptors in rats. BMC Neuroscience, 7, 43. Gerdjikov, T. V., Giles, A. C., Swain, S. N., & Beninger, R. J. (2007). Nucleus accumbens PKA inhibition blocks acquisition but enhances expression of amphetamine-produced conditioned activity in rats. Psychopharmacology, 190, 65–72. Gill, B. M., Knapp, C. M., & Kornetsky, C. (2004). The effects of cocaine on the rate independent brain stimulation reward threshold in the mouse. Pharmacology Biochemistry and Behavior, 79, 165–170. Grant, B. F., Stinson, F. S., Dawson, D. A., Chou, S. P., Dufour, M. C., Compton, W., et al. (2004). Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Archives of General Psychiatry, 61, 807–816. Greenslade, R. G., & Mitchell, S. N. (2004). Selective action of (-)-2oxa-4-aminobicyclo[3.1.0]hexane-4, 6-dicarboxylate (LY379268), a group II metabotropic glutamate receptor agonist, on basal and phencyclidine-induced dopamine release in the nucleus accumbens shell. Neuropharmacology, 47, 1–8.

c40.indd 801

Groenewegen, H. J., Wright, C. I., Beijer, A. V., & Voorn, P. (1999). Convergence and segregation of ventral striatal inputs and outputs. Annals of the New York Academy of Sciences, 877, 49–63.

Harrison, A. A., & Markou, A. (2001). Serotonergic manipulations both potentiate and reduce brain stimulation reward in rats: Involvement of serotonin-1A receptors. Journal of Pharmacology and Experimental Therapeutics, 297, 316–325. Harrison, A. A., Parsons, L. H., Koob, G. F., & Markou, A. (1999). RU 24969, a 5-HT1A/1B agonist, elevates brain stimulation reward thresholds: An effect reversed by GR 127935, a 5-HT1B/1D antagonist. Psychopharmacology, 141, 242–250. Hasue, R. H., & Shammah-Lagnado, S. J. (2002). Origin of the dopaminergic innervation of the central extended amygdala and accumbens shell: A combined retrograde tracing and immunohistochemical study in the rat. Journal of Comparative Neurology, 454, 15–33. Herberg, L. J., De Belleroche, J. S., Rose, I. C., & Montgomery, A. M. (1992). Effect of the 5-HT3 receptor antagonist ondansetron on hypothalamic self-stimulation in rats and its interaction with the CCK analogue caerulein. Neuroscience Letters, 140, 16–18. Hernandez, G., Hamdani, S., Rajabi, H., Conover, K., Stewart, J., Arvanitogiannis, A., et al. (2006). Prolonged rewarding stimulation of the rat medial forebrain bundle: Neurochemical and behavioral consequences. Behavioral Neuroscience, 120, 888–904. Hnasko, T. S., Sotak, B. N., & Palmiter, R. D. (2005, December 8). Morphine reward in dopamine-deficient mice. Nature, 438, 854–857. Hoyer, D., Hannon, J. P., & Martin, G. R. (2002). Molecular, pharmacological and functional diversity of 5-HT receptors. Pharmacology Biochemistry and Behavior, 71, 533–554. Hubner, C. B., Bird, M., Rassnick, S., & Kornetsky, C. (1988). The threshold lowering effects of MDMA (ecstasy) on brain-stimulation reward. Psychopharmacology, 95, 49–51.

8/17/09 3:08:44 PM

802

Neural Basis of Pleasure and Reward

Hubner, C. B., & Kornetsky, C. (1988). The reinforcing properties of the mixed agonist-antagonist buprenorphine as assessed by brain-stimulation reward. Pharmacology Biochemistry and Behavior, 30, 195–197. Hubner, C. B., & Kornetsky, C. (1992). Heroin, 6-acetylmorphine and morphine effects on threshold for rewarding and aversive brain stimulation. Journal of Pharmacology and Experimental Therapeutics, 260, 562–567. Hunt, G. E., & McGregor, I. S. (1998). Rewarding brain stimulation induces only sparse Fos-like immunoreactivity in dopaminergic neurons. Neuroscience, 83, 501–515. Huston- Lyons, D., Sarkar, M., & Kornetsky, C. (1993). Nicotine and brainstimulation reward: Interactions with morphine, amphetamine and pimozide. Pharmacology Biochemistry and Behavior, 46, 453–457. Ikemoto, S. (2003). Involvement of the olfactory tubercle in cocaine reward: Intracranial self-administration studies. Journal of Neuroscience, 23, 9305–9311. Ikemoto, S. (2007). Dopamine reward circuitry: Two projection systems from the ventral midbrain to the nucleus accumbens-olfactory tubercle complex. Brain Research Reviews, 56, 27–78. Ikemoto, S., Murphy, J. M., & McBride, W. J. (1998). Regional differences within the rat ventral tegmental area for muscimol self-infusions. Pharmacology Biochemistry and Behavior, 61, 87–92. Ikemoto, S., Qin, M., & Liu, Z. H. (2005). The functional divide for primary reinforcement of D-amphetamine lies between the medial and lateral ventral striatum: Is the division of the accumbens core, shell, and olfactory tubercle valid? Journal of Neuroscience, 25, 5061–5065. Ikemoto, S., & Wise, R. A. (2002). Rewarding effects of the cholinergic agents carbachol and neostigmine in the posterior ventral tegmental area. Journal of Neuroscience, 22, 9895–9904. Imperato, A., Mulas, A., & Di Chiara, G. (1986). Nicotine preferentially stimulates dopamine release in the limbic system of freely moving rats. European Journal of Pharmacology, 132, 337–338. Invernizzi, R., Pozzi, L., & Samanin, R. (1991). Release of dopamine is reduced by diazepam more in the nucleus accumbens than in the caudate nucleus of conscious rats. Neuropharmacology, 30, 575–578. Ivanová, S., & Greenshaw, A. J. (1997). Nicotine-induced decreases in VTA electrical self-stimulation thresholds: Blockade by haloperidol and mecamylamine but not scopolamine or ondansetron. Psychopharmacology, 134, 187–192. Izenwasser, S., & Kornetsky, C. (1989). The effect of amfonelic acid or nisoxetine in combination with morphine on brain-stimulation reward. Pharmacology Biochemistry and Behavior, 32, 983–986. Jasmin, L., Narasaiah, M., & Tien, D. (2006). Noradrenaline is necessary for the hedonic properties of addictive drugs. Vascular Pharmacology, 45, 243–250. Jayaram-Lindström, N., Wennberg, P., Hurd, Y. L., & Franck, J. (2004). Effects of naltrexone on the subjective response to amphetamine in healthy volunteers. Journal of Clinical Psychopharmacology, 24, 665–669. Kalivas, P. W., Duffy, P., & Eberhardt, H. (1990). Modulation of A10 dopamine neurons by gamma-aminobutyric acid agonists. Journal of Pharmacology and Experimental Therapeutics, 253, 858–866. Kankaanpää, A., Lillsunde, P., Ruotsalainen, M., Ahtee, L., & Seppâlä, T. (1996). 5-HT3 receptor antagonist MDL 72222 dose-dependently attenuates cocaine- and amphetamine-induced elevations of extracellular dopamine in the nucleus accumbens and the dorsal striatum. Pharmacology and Toxicology, 78, 317–321.

and CREB phosphorylation. European Journal of Neuroscience, 22, 1741–1750. Kelley, A. E., Baldo, B. A., Pratt, W. E., & Will, M. J. (2005). Corticostriatalhypothalamic circuitry and food motivation: Integration of energy, action and reward. Physiology and Behavior, 86, 773–795. Kelley, S. P., & Hodge, C. W. (2003). The 5-HT3 antagonist Y-25130 blocks cocaine-induced lowering of ICSS reward thresholds in the rat. Pharmacology Biochemistry and Behavior, 74, 297–302. Kenny, P. J., Boutrel, B., Gasparini, F., Koob, G. F., & Markou, A. (2005). Metabotropic glutamate 5 receptor blockade may attenuate cocaine self-administration by decreasing brain reward function in rats. Psychopharmacology, 179, 247–254. Kenny, P. J., Chen, S. A., Kitamura, O., Markou, A., & Koob, G. F. (2006). Conditioned withdrawal drives heroin consumption and decreases reward sensitivity. Journal of Neuroscience, 26, 5894–5900. Kenny, P. J., Polis, I., Koob, G. F., & Markou, A. (2003). Low dose cocaine self-administration transiently increases but high dose cocaine persistently decreases brain reward function in rats. European Journal of Neuroscience, 17, 191–195. Kew, J. N., & Kemp, J. A. (2005). Ionotropic and metabotropic glutamate receptor structure and pharmacology. Psychopharmacology, 179, 4–29. Kiyatkin, E. A., & Brown, P. L. (2003). Naloxone depresses cocaine self-administration and delays its initiation on the following day. NeuroReport, 14, 251–255. Kiyatkin, E. A., & Rebec, G. V. (1997). Activity of presumed dopamine neurons in the ventral tegmental area during heroin self-administration. NeuroReport, 8, 2581–2585. Klitenick, M. A., DeWitte, P., & Kalivas, P. W. (1992). Regulation of somatodendritic dopamine release in the ventral tegmental area by opioids and GABA: An in vivo microdialysis study. Journal of Neuroscience, 12, 2623–2632. Knapp, C. M., & Kornetsky, C. (1989). The effects of amfonelic acid alone and in combination with naloxone on brain-stimulation reward. Pharmacology Biochemistry and Behavior, 32, 977–982. Knapp, C. M., & Kornetsky, C. (1994). Bromocriptine, a D2 receptor agonist, lowers the threshold for rewarding brain stimulation. Pharmacology Biochemistry and Behavior, 49, 901–904. Knapp, C. M., & Kornetsky, C. (1996). Low-dose apomorphine attenuates morphine-induced enhancement of brain stimulation reward. Pharmacology Biochemistry and Behavior, 55, 87–91. Knapp, C. M., Lee, K., Foye, M., Ciraulo, D. A., & Kornetsky, C. (2001). Additive effects of intra-accumbens infusion of the cAMP-specific phosphodiesterase inhibitor, rolipram and cocaine on brain stimulation reward. Life Sciences, 69, 1673–1682. Knott, V. J., & Fisher, D. J. (2007). Naltrexone alteration of the nicotineinduced EEG and mood activation response in tobacco-deprived cigarette smokers. Experimental and Clinical Psychopharmacology, 15, 368–381. Kokkinidis, L., & Zacharko, R. M. (1980). Enhanced lateral hypothalamic self-stimulation responding after chronic exposure to amphetamine. Behavioral Neural Biology, 29, 493–497. Konradi, C., Cole, R. L., Heckers, S., & Hyman, S. E. (1994). Amphetamine regulates gene expression in rat striatum via transcription factor CREB. Journal of Neuroscience, 14, 5623–5634. Kornetsky, C. (2004). Brain-stimulation reward, morphine-induced oral stereotypy, and sensitization: Implications for abuse. Neuroscience and Biobehavioral Reviews, 27, 777–786.

Kankaanpää, A., Meririnne, E., & Seppälä, T. (2002). 5-HT3 receptor antagonist MDL 72222 attenuates cocaine- and mazindol-, but not methylphenidate-induced neurochemical and behavioral effects in the rat. Psychopharmacology, 159, 341–350.

Kornetsky, C., & Bain, G. (1987). Neuronal bases for hedonic effects of cocaine and opiates. In S. Fisher, A. Raskin, & E. H. Uhlenhuth (Eds.), Cocaine: Clinical and biobehavioral aspects (pp. 66–82). New York: Oxford University Press.

Karasinska, J. M., George, S. R., Cheng, R., & O’Dowd, B. F. (2005). Deletion of dopamine D1 and D3 receptors differentially affects spontaneous behaviour and cocaine-induced locomotor activity, reward

Kornetsky, C., & Esposito, R. U. (1979). Euphorigenic drugs: Effects on the reward pathways of the brain. Federation Proceedings, 38, 2473–2476.

c40.indd 802

8/17/09 3:08:44 PM

References 803 Kornetsky, C., Huston-Lyons, D., & Porrino, L. J. (1991). The role of the olfactory tubercle in the effects of cocaine, morphine and brainstimulation reward. Brain Research, 541, 75–81. Kornetsky, C., Moolten, M., & Bain, G. (1991). Ethanol and rewarding brain stimulation. In R. E. Meyer, M. J. Lewis, S. M. Paul, & G. F. Koob (Eds.), Neuropharmacology of ethanol: New approaches (pp. 179–199). Boston: Birkhäuser. Kushner, S. A., Dewey, S. L., & Kornetsky, C. (1997). Gamma-vinyl GABA attenuates cocaine-induced lowering of brain stimulation reward thresholds. Psychopharmacology, 133, 383–388. Kushner, S. A., Dewey, S. L., & Kornetsky, C. (1999). The irreversible gamma-aminobutyric acid (GABA) transaminase inhibitor gammavinyl-GABA blocks cocaine self-administration in rats. Journal of Pharmacology and Experimental Therapeutics, 290, 797–802. Lane, J. D., Pickering, C. L., Hooper, M. L., Fagan, K., Tyers, M. B., & Emmett-Oglesby, M. W. (1992). Failure of ondansetron to block the discriminative or reinforcing stimulus effects of cocaine in the rat. Drug and Alcohol Dependence, 30, 151–162. Laviolette, S. R., Nader, K., & van der Kooy, D. (2002). Motivational state determines the functional role of the mesolimbic dopamine system in the mediation of opiate reward processes. Behavioral Brain Research, 129, 17–29. Laviolette, S. R., & van der Kooy, D. (2001). GABAA receptors signal bidirectional reward transmission from the ventral tegmental area to the tegmental pedunculopontine nucleus as a function of opiate state. European Journal of Neuroscience, 20, 2179–2187. Lecca, D., Cacciapaglia, F., Valentini, V., Acquas, E., & Di Chiara, G. (2007). Differential neurochemical and behavioral adaptation to cocaine after response contingent and noncontingent exposure in the rat. Psychopharmacology, 191, 653–667. Lecca, D., Cacciapaglia, F., Valentini, V., Gronli, J., Spiga, S., & Di Chiara, G. (2006). Preferential increase of extracellular dopamine in the rat nucleus accumbens shell as compared to that in the core during acquisition and maintenance of intravenous nicotine self-administration. Psychopharmacology, 184, 435–446. Lecca, D., Valentini, V., Cacciapaglia, F., Acquas, E., & Di Chiara, G. (2007). Reciprocal effects of response contingent and noncontingent intravenous heroin on in vivo nucleus accumbens shell versus core dopamine in the rat: A repeated sampling microdialysis study. Psychopharmacology, 194, 103–116. Lee, D. Y., Guttilla, M., Fung, K. D., McFeron, S., Yan, J., & Ranaldi, R. (2007). Rostral-caudal differences in the effects of intra-VTA muscimol on cocaine self-administration. Pharmacology Biochemistry and Behavior, 86, 542–549. Lee, K., & Kornetsky, C. (1998). Acute and chronic fluoxetine treatment decreases the sensitivity of rats to rewarding brain stimulation. Pharmacology Biochemistry and Behavior, 60, 539–544. Leith, N. J., & Barrett, R. J. (1976). Amphetamine and the reward system: evidence for tolerance and post-drug depression. Pharmacologia, 46, 19–25. Leith, N. J., & Barrett, R. J. (1981). Self-stimulation and amphetamine: Tolerance to d and l isomers and cross tolerance to cocaine and methylphenidate. Psychopharmacology, 74, 23–28. Le Moal, M., & Koob, G. F. (2007). Drug addiction: Pathways to the disease and pathophysiological perspectives. European Neuropsychopharmacology, 17, 377–393. Le Pen, G., Duterte-Boucher, D., & Costentin, J. (1996). Place conditioning with cocaine and the dopamine uptake inhibitor GBR12783. NeuroReport, 7, 2839–2842. Liechti, M. E., Lhuillier, L., Kaupmann, K., & Markou, A. (2007). Metabotropic glutamate 2/3 receptors in the ventral tegmental area and the nucleus accumbens shell are involved in behaviors relating to nicotine dependence. Journal of Neuroscience, 27, 9077–9085.

c40.indd 803

Liechti, M. E., & Markou, A. (2007). Interactive effects of the mGlu5 receptor antagonist MPEP and the mGlu2/3 receptor antagonist LY341495 on nicotine self-administration and reward deficits associated with nicotine withdrawal in rats. European Journal of Pharmacology, 554, 164–174. Lin, D., Koob, G. F., & Markou, A. (2000). Time-dependent alterations in ICSS thresholds associated with repeated amphetamine administrations. Pharmacology Biochemistry and Behavior, 65, 407–417. Lin, Y., de Vaca, S. C., Carr, K. D., & Stone, E. A. (2007). Role of alpha(1)adrenoceptors of the locus coeruleus in self-stimulation of the medial forebrain bundle. NeuroPsychopharmacology, 32, 835–841. Lynch, W. J., Sughondhabirom, A., Pittman, B., Gueorguieva, R., Kalayasiri, R., Joshua, D., et al. (2006). A paradigm to investigate the regulation of cocaine self-administration in human cocaine users: A randomized trial. Psychopharmacology, 185, 306–314. Maldonado-Irizarry, C. S., Stellar, J. R., & Kelley, A. E. (1994). Effects of cocaine and GBR-12909 on brain stimulation reward. Pharmacology Biochemistry and Behavior, 48, 915–920. Markou, A., & Koob, G. F. (1992). Construct validity of a self-stimulation threshold paradigm: Effects of reward and performance manipulations. Physiology and Behavior, 51, 111–119. Martel, P., & Fantino, M. (1996). Mesolimbic dopaminergic system activity as a function of food reward: A microdialysis study. Pharmacology Biochemistry and Behavior, 53, 221–226. Martin, M., Ledent, C., Parmentier, M., Maldonado, R., & Valverde, O. (2000). Cocaine, but not morphine, induces conditioned place preference and sensitization to locomotor responses in CB1 knockout mice. European Journal of Neuroscience, 12, 4038–4046. Martinez, D., Gil, R., Slifstein, M., Hwang, D. R., Huang, Y., Perez, A., et al. (2005). Alcohol dependence is associated with blunted dopamine transmission in the ventral striatum. Biological Psychiatry, 58, 779–786. Martinez, D., Narendran, R., Foltin, R. W., Slifstein, M., Hwang, D. R., Broft, A., et al. (2007). Amphetamine-induced dopamine release: Markedly blunted in cocaine dependence and predictive of the choice to self-administer cocaine. American Journal of Psychiatry, 164, 622–629. Martinez, D., Slifstein, M., Broft, A., Mawlawi, O., Hwang, D. R., Huang, Y., et al. (2003). Imaging human mesolimbic dopamine transmission with positron emission tomography: Pt. II. Amphetamine -induced dopamine release in the functional subdivisions of the striatum. Journal of Cerebral Blood Flow Metabolism, 23, 285–300. Mascia, M. S., Obinu, M. C., Ledent, C., Parmentier, M., Böhme, G. A., Imperato, A., et al. (1999). Lack of morphine-induced dopamine release in the nucleus accumbens of cannabinoid CB(1) receptor knockout mice. European Journal of Pharmacology, 383, R1–R2. Mas-Nieto, M., Pommier, B., Tzavara, E. T., Caneparo, A., Da Nascimento, S., Le Fur, G., et al. (2001). Reduction of opioid dependence by the CB(1) antagonist SR141716A in mice: Evaluation of the interest in pharmacotherapy of opioid addiction. British Journal of Pharmacology, 132, 1809–1816. McBride, W. J., Murphy, J. M., & Ikemoto, S. (1999). Localization of brain reinforcement mechanisms: Intracranial self-administration and intracranial place-conditioning studies. Behavioral Brain Research, 101, 129–152. McCaul, M. E., Wand, G. S., Eissenberg, T., Rohde, C. A., & Cheskin, L. J. (2000). Naltrexone alters subjective and psychomotor responses to alcohol in heavy drinking subjects. Neuropsychopharmacology, 22, 480–492. Melnick, S. M., Maldonado-Vlaar, C. S., Stellar, J. R., & Trzcinska, M. (2001). Effects of repeated GBR 12909 administration on brain stimulation reward. European Journal of Pharmacology, 419, 199–205. Meltzer, L. T., Christoffersen, C. L., & Serpa, K. (1997). Modulation of dopamine neuronal activity by glutamate receptor subtypes. Neuroscience Biobehavioral Reviews, 21, 511–518.

8/17/09 3:08:45 PM

804

Neural Basis of Pleasure and Reward

Narita, M., Aoki, T., Ozaki, S., Yajima, Y., & Suzuki, T. (2001). Involvement of protein kinase C gamma isoform in morphine-induced reinforcing effects. Neuroscience, 103, 309–314. Navailles, S., De Deurwaerdere, P., Porras, G., & Spampinato, U. (2004). In vivo evidence that 5-HT2C receptor antagonist but not agonist modulates cocaine-induced dopamine outflow in the rat nucleus accumbens and striatum. NeuroPsychopharmacology, 29, 319–326.

Paul, M., Dewey, S. L., Gardner, E. L., Brodie, J. D., & Ashby, C. R. Jr., (2001). Gamma-vinyl GABA (GVG) blocks expression of the conditioned place preference response to heroin in rats. Synapse, 41, 219–220. Peltier, R., & Schenk, S. (1991). GR38032F, a serotonin 5-HT3 antagonist, fails to alter cocaine self-administration in rats. Pharmacology Biochemistry and Behavior, 39, 133–136.

Navarro, M., Carrera, M. R., Fratta, W., Valverde, O., Cossu, G., Fattore, L., et al. (2001). Functional interaction between opioid and cannabinoid receptors in drug self-administration. Journal of Neuroscience, 21, 5344–5350.

Pettit, H. O., Ettenberg, A., Bloom, F. E., & Koob, G. F. (1984). Destruction of dopamine in the nucleus accumbens selectively attenuates cocaine but not heroin self-administration in rats. Psychopharmacology, 84, 167–173.

Newton, P. M., & Ron, D. (2007). Protein kinase C and alcohol addiction. Pharmacological Research, 55, 570–577. Newton, T. F., Kalechstein, A. D., De La Garza, R. Jr., Cutting, D. J., & Ling, W. (2005). Apathy predicts hedonic but not craving response to cocaine. Pharmacology Biochemistry and Behavior, 82, 236–240.

Pontieri, F. E., Tanda, G., & Di Chiara, G. (1995). Intravenous cocaine, morphine, and amphetamine preferentially increase extracellular dopamine in the “shell” as compared with the “core” of the rat nucleus accumbens. Proceedings of the National Academy of Sciences, USA, 92, 12304–12308.

Oakman, S. A., Faris, P. L., Kerr, P. E., Cozzari, C., & Hartman, B. K. (1995). Distribution of pontomesencephalic cholinergic neurons projecting to substantia nigra differs significantly from those projecting to ventral tegmental area. Journal of Neuroscience, 15, 5859–5869.

Porrino, L. J., Huston-Lyons, D., Bain, G., Sokoloff, L., & Kornetsky, C. (1990). The distribution of changes in local cerebral energy metabolism associated with brain stimulation reward to the medial forebrain bundle of the rat. Brain Research, 511, 1–6.

O’Dell, L. E., & Parsons, L. H. (2004). Serotonin1B receptors in the ventral tegmental area modulate cocaine-induced increases in nucleus accumbens dopamine levels. Journal of Pharmacology and Experimental Therapeutics, 311, 711–719.

Preston, K. L., & Jasinski, D. R. (1991). Abuse liability studies of opioid agonist-antagonists in humans. Drug and Alcohol Dependence, 28, 49–82.

O’Donnell, P., Lavín, A, Enquist, L. W., Grace, A. A., & Card, J. P. (1997). Interconnected parallel circuits between rat nucleus accumbens and thalamus revealed by retrograde transynaptic transport of pseudorabies virus. Journal of Neuroscience, 17, 2143–2167. Olds, J., & Milner, P. (1954). Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain. Journal of Comparative Physiological Psychology, 47, 419–427. Olds, M. E. (1982). Reinforcing effects of morphine in the nucleus accumbens. Brain Research, 237, 429–440. Olive, M. F., Mehmert, K. K., Messing, R. O., & Hodge, C. W. (2000). Reduced operant ethanol self-administration and in vivo mesolimbic dopamine responses to ethanol in PKCepsilon-deficient mice. European Journal of Neuroscience, 12, 4131–4140.

Rada, P. V., Mark, G. P., Yeomans, J. J., & Hoebel, B. G. (2000). Acetylcholine release in ventral tegmental area by hypothalamic selfstimulation, eating, and drinking. Pharmacology Biochemistry and Behavior, 65, 375–379. Rahman, S., & McBride, W. J. (2002). Involvement of GABA and cholinergic receptors in the nucleus accumbens on feedback control of somatodendritic dopamine release in the ventral tegmental area. Journal of Neurochemistry, 80, 646–654. Ranaldi, R., Pocock, D., Zereik, R., & Wise, R. A. (1999). Dopamine fluctuations in the nucleus accumbens during maintenance, extinction, and reinstatement of intravenous D-amphetamine self-administration. Journal of Neuroscience, 19, 4102–4109. Ranaldi, R., & Woolverton, W. L. (2002). Self-administration of cocaine: Scopolamine combinations by rhesus monkeys. Psychopharmacology, 161, 442–448.

Olson, V. G., Green, T. A., Neve, R. L., & Nestler, E. J. (2007). Regulation of morphine reward and feeding by CREB in the lateral hypothalamus. Synapse, 61, 110–113.

Rice, O. V., Gordon, N., & Gifford, A. N. (2002). Conditioned place preference to morphine in cannabinoid CB1 receptor knockout mice. Brain Research, 945, 135–138.

Olson, V. G., Zabetian, C. P., Bolanos, C. A., Edwards, S., Barrot, M., Eisch, A. J., et al. (2005). Regulation of drug reward by cAMP response element-binding protein: Evidence for two functionally distinct subregions of the ventral tegmental area. Journal of Neuroscience, 25, 5553–5562.

Risinger, R. C., Salmeron, B. J., Ross, T. J., Amen, S. L., Sanfilipo, M., Hoffmann, R. G., et al. (2005). Neural correlates of high and craving during cocaine self-administration using BOLD fMRI. NeuroImage, 26, 1097–1108.

Oswald, L. M., Wong, D. F., McCaul, M., Zhou, Y., Kuwabara, H., Choi, L., et al. (2005). Relationships among ventral striatal dopamine release, cortisol secretion, and subjective responses to amphetamine. NeuroPsychopharmacology, 30, 821–832. Panagis, G., Kastellakis, A., Spyraki, C., & Nomikos, G. (2000). Effects of methyllycaconitine (MLA), an alpha 7 nicotinic receptor antagonist, on nicotine- and cocaine-induced potentiation of brain stimulation reward. Psychopharmacology, 149, 388–396. Panagis, G., Nomikos, G. G., Miliaressis, E., Chergui, K., Kastellakis, A., Svensson, T. H., et al. (1997). Ventral pallidum self-stimulation induces stimulus dependent increase in c-fos expression in reward-related brain regions. Neuroscience, 77, 175–186.

Roberts, D. C., Loh, E. A., & Vickers, G. (1989). Self-administration of cocaine on a progressive ratio schedule in rats: Dose-response relationship and effect of haloperidol pretreatment. Psychopharmacology, 97, 535–538. Robinson, T. E., & Berridge, K. C. (2003). Addiction. Annual Reviews of Psychology, 54, 25–53. Rocha, B. A. (2003). Stimulant and reinforcing effects of cocaine in monoamine transporter knockout mice. European Journal of Pharmacology, 479, 107–115. Rocha, B. A., Fumagalli, F., Gainetdinov, R. R., Jones, S. R., Ator, R., Giros, B., et al. (1998). Cocaine self-administration in dopaminetransporter knockout mice. Nature Neuroscience, 1, 132–137.

Parsons, L. H., Weiss, F., & Koob, G. F. (1998). Serotonin1B receptor stimulation enhances cocaine reinforcement. Journal of Neuroscience, 18, 10078–10089.

Rocha, B. A., Goulding, E. H., O’Dell, L. E., Mead, A. N., Coufal, N. G., Parsons, L. H., et al. (2002). Enhanced locomotor, reinforcing, and neurochemical effects of cocaine in serotonin 5-hydroxytryptamine 2C receptor mutant mice. Journal of Neuroscience, 22, 10039–10045.

Paterson, N. E., & Markou, A. (2005). The metabotropic glutamate receptor 5 antagonist MPEP decreased break points for nicotine, cocaine and food in rats. Psychopharmacology, 179, 255–261.

Rodd, Z. A., Bell, R. L., Kuc, K. A., Zhang, Y., Murphy, J. M., & McBride, W. J. (2005). Intracranial self-administration of cocaine within the posterior ventral tegmental area of Wistar rats: Evidence for involvement of

c40.indd 804

8/17/09 3:08:45 PM

References 805 serotonin-3 receptors and dopamine neurons. Journal of Pharmacology and Experimental Therapeutics, 313, 134–145. Rolls, E. T. (2006). Brain mechanisms underlying flavour and appetite. Philosophical Transactions of the Royal Society of London. Series B, 361, 1123–1136. Romach, M. K., Glue, P., Kampman, K., Kaplan, H. L., Somer, G. R., Poole, S., et al. (1999). Attenuation of the euphoric effects of cocaine by the dopamine D1/D5 antagonist ecopipam (SCH 39166). Archives of General Psychiatry, 56, 1101–1106. Sabatinelli, D., Bradley, M. M., Lang, P. J., Costa, V. D., & Versace, F. (2007). Pleasure rather than salience activates human nucleus accumbens and medial prefrontal cortex. Journal of Neurophysiology, 98, 1374–1379. Schilström, B., Svensson, H. M., Svensson, T. H., & Nomikos, G. G. (1998). Nicotine and food induced dopamine release in the nucleus accumbens of the rat: Putative role of alpha7 nicotinic receptors in the ventral tegmental area. Neuroscience, 85, 1005–1009. Schmidt, H. D., Anderson, S. M., & Pierce, R. C. (2006). Stimulation of D1-like or D2 dopamine receptors in the shell, but not the core, of the nucleus accumbens reinstates cocaine-seeking behaviour in the rat. European Journal of Neuroscience, 23, 219–228. Schmidt, H. D., & Pierce, R. C. (2006). Systemic administration of a dopamine, but not a serotonin or norepinephrine, transporter inhibitor reinstates cocaine seeking in the rat. Behavioral Brain Research, 175, 189–194. Schroeder, J. A., Hummel, M., Simpson, A. D., Sheikh, R., Soderman, A. R., & Unterwald, E. M. (2007). A role for mu opioid receptors in cocaine-induced activity, sensitization, and reward in the rat. Psychopharmacology, 195, 26–272. Schulteis, G., Markou, A., Cole, M., & Koob, G. F. (1995). Decreased brain reward produced by ethanol withdrawal. Proceedings of the National Academy of Sciences, USA, 92, 5880–5884. Schultz, W. (2007). Behavioral dopamine signals. Trends in Neuroscience, 30, 203–210. Self, D. W., Genova, L. M., Hope, B. T., Barnhart, W. J., Spencer, J. J., & Nestler, E. J. (1998). Involvement of cAMP-dependent protein kinase in the nucleus accumbens in cocaine self-administration and relapse of cocaine-seeking behavior. Journal of Neuroscience, 18, 1848–1859. Semba, K., & Fibiger, H. C. (1992). Afferent connections of the laterodorsal and the pedunculopontine tegmental nuclei in the rat: A retro- and antero-grade transport and immunohistochemical study. Journal of Comparative Neurology, 323, 387–410. Sesack, S. R., & Pickel, V. M. (1992). Prefrontal cortical efferents in the rat synapse on unlabeled neuronal targets of catecholamine terminals in the nucleus accumbens septi and on dopamine neurons in the ventral tegmental area. Journal of Comparative Neurology, 320, 5–60. Sherer, M. A., Kumor, K. M., & Jaffe, J. H. (1989). Effects of intravenous cocaine are partially attenuated by haloperidol. Psychiatry Research, 27, 117–125. Skoubis, P. D., & Maidment, N. T. (2003). Blockade of ventral pallidal opioid receptors induces a conditioned place aversion and attenuates acquisition of cocaine place preference in the rat. Neuroscience, 119, 241–249. Slattery, D. A., Markou, A., Froestl, W., & Cryan, J. F. (2005). The GABAB receptor-positive modulator GS39783 and the GABAB receptor agonist baclofen attenuate the reward-facilitating effects of cocaine: Intracranial self-stimulation studies in the rat. NeuroPsychopharmacology, 30, 2065–2072.

c40.indd 805

be established in dopamine- and in serotonin-transporter knockout mice. Proceedings of the National Academy of Sciences, USA, 95, 7699–7704. Stafford, D., LeSage, M. G., Rice, K. C., & Glowa, J. R. (2001). A comparison of cocaine, GBR 12909, and phentermine self-administration by rhesus monkeys on a progressive-ratio schedule. Drug and Alcohol Dependence, 62, 41–47. Steffensen, S. C., Stobbs, S. H., Colago, E. E., Lee, R., Koob, G. F., Gallegos, R. A., et al. (2006). Contingent and non-contingent effects of heroin on mu-opioid receptor-containing ventral tegmental area GABA neurons. Experimental Neurology, 202, 139–151. Stein, E. A., Pankiewicz, J., Harsch, H. H., Cho, J. K., Fuller, S. A., Hoffmann, R. G., et al. (1998). Nicotine-induced limbic cortical activation in the human brain: A functional MRI study. American Journal of Psychiatry, 155, 1009–1015. Steiner, M., Katz, R. J., & Carroll, B. J. (1980). Behavioral effects of dopamine agonists across the estrous cycle in rats. Psychopharmacology, 71, 147–151. Stellar, J. R., & Corbett, D. (1989). Regional neuroleptic microinjections indicate a role for nucleus accumbens in lateral hypothalamic self-stimulation reward. Brain Research, 477, 126–143. Stromberg, M. F., Mackler, S. A., Volpicelli, J. R., O’Brien, C. P., & Dewey, S.L. (2001). The effect of gamma-vinyl-GABA on the consumption of concurrently available oral cocaine and ethanol in the rat. Pharmacology Biochemistry, and Behavior, 68, 291–299. Thompson, B. E., Sachs, B. D., Kantak, K. M., & Cherry, J. A. (2004). The Type IV phosphodiesterase inhibitor rolipram interferes with druginduced conditioned place preference but not immediate early gene induction in mice. European Journal of Neuroscience, 19, 2561–2568. Thomsen, M., Woldbye, D., Wörtwein, G., Fink-Jensen, A., Wess, J., & Caine, S. (2005). Reduced cocaine self-administration in muscarinic M5 acetylcholine receptor-deficient mice. Journal of Neuroscience, 25, 8141–8149. Todtenkopf, M. S., Marcus, J. F., Portoghese, P. S., & Carlezon, W. A. (2004). Effects of kappa-opioid receptor ligands on intracranial selfstimulation in rats. Psychopharmacology, 172, 463–470. Todtenkopf, M. S., Parsegian, A., Naydenov, A., Neve, R. L., Konradi, C., & Carlezon, W. A., Jr. (2006). Brain reward regulated by AMPA receptor subunits in nucleus accumbens shell. Journal of Neuroscience, 26, 11665–11669. Tomkins, D. M., Joharchi, N., Tampakeras, M., Martin, J. R., Wichmann, J., & Higgins, G. A. (2002). An investigation of the role of 5-HT(2C) receptors in modifying ethanol self-administration behaviour. Pharmacology Biochemistry and Behavior, 71, 735–744. Tran, A. H., Tamura, R., Uwano, T., Kobayashi, T., Katsuki, M., Matsumoto, G., et al. (2002). Altered accumbens neural response to prediction of reward associated with place in dopamine D2 receptor knockout mice. Proceedings of the National Academy of Sciences, USA, 99, 8986–8991. Tran, A. H., Tamura, R., Uwano, T., Kobayashi, T., Katsuki, M., & Ono, T. (2005). Dopamine D1 receptors involved in locomotor activity and accumbens neural responses to prediction of reward associated with place. Proceedings of the National Academy of Sciences, USA, 102, 2117–2122. Unterwald, E., Sasson, S., & Kornetsky, C. (1987). Evaluation of the supraspinal analgesic activity and abuse liability of ethylketocyclazocine. European Journal of Pharmacology, 133, 275–281.

Sofuoglu, M., Singha, A., Kosten, T. R., McCance-Katz, F. E., Petrakis, I., & Oliveto, A. (2003). Effects of naltrexone and isradipine, alone or in combination, on cocaine responses in humans. Pharmacology Biochemistry and Behavior, 75, 801–808.

Uslaner, J., Kalechstein, A., Richter, T., Ling, W., & Newton, T. (1999). Association of depressive symptoms during abstinence with the subjective high produced by cocaine. American Journal of Psychiatry, 156, 1444–1446.

Sora, I., Wichems, C., Takahashi, N., Li, X., Zeng, Z., Revay, R., et al. (1998). Cocaine reward models: Conditioned place preference can

Volkow, N. D., Wang, G. J., Fowler, J. S., Logan, J., Gatley, S. J., Wong, C., et al. (1999). Reinforcing effects of psychostimulants in humans are

8/17/09 3:08:46 PM

806

Neural Basis of Pleasure and Reward

associated with increases in brain dopamine and occupancy of D(2) receptors. Journal of Pharmacology and Experimental Therapeutics, 291, 409–415.

Woolverton, W. L. (1987). Evaluation of the role of norepinephrine in the reinforcing effects of psychomotor stimulants in rhesus monkeys. Pharmacology Biochemistry and Behavior, 26, 835–839.

Volpicelli, J. R., Watson, N. T., King, A. C., Sherman, C. E., & O’Brien, C. P. (1995). Effect of naltrexone on alcohol “high” in alcoholics. American Journal of Psychiatry, 152, 613–615.

Woolverton, W. L., Goldberg, L. I., & Ginos, J. Z. (1984). Intravenous self-administration of dopamine receptor agonists by rhesus monkeys. Journal of Pharmacology and Experimental Therapeutics, 230, 678–683.

Wachtel, S. R., Ortengren, A., & de Wit, H. (2002). The effects of acute haloperidol or risperidone on subjective responses to methamphetamine in healthy volunteers. Drug and Alcohol Dependence, 68, 23–33. Wald, D., Ebstein, R. P., & Belmaker, R. H. (1978). Haloperidol and lithium blocking of the mood response to intravenous methylphenidate. Psychopharmacology, 57, 83–87. Walker, D., Mahoney, C., Ilivitsky, V., & Knott, V. J. (2001). Effects of haloperidol pretreatment on the smoking-induced EEG/mood activation response profile. Neuropsychobiology, 43, 102–112. Walsh, S. L., Preston, K. L., Sullivan, J. T., Fromme, R., & Bigelow, G. E. (1994). Fluoxetine alters the effects of intravenous cocaine in humans. Journal of Clinical Psychopharmacology, 14, 396–407. Walsh, S. L., Sullivan, J. T., Preston, K. L., Garner, J. E., & Bigelow, G. E. (1996). Effects of naltrexone on response to intravenous cocaine, hydromorphone and their combination in humans. Journal of Pharmacology and Experimental Therapeutics, 279, 524–538. Walters, C. L., & Blendy, J. A. (2001). Different requirements for cAMP response element binding protein in positive and negative reinforcing properties of drugs of abuse. Journal of Neuroscience, 21, 9438–9444. Walters, C. L., Godfrey, M., Li, X., & Blendy, J. A. (2005). Alterations in morphine-induced reward, locomotor activity, and thermoregulation in CREB-deficient mice. Brain Research, 1032, 193–199. Walters, C. L., Kuo, Y. C., & Blendy, J. A. (2003). Differential distribution of CREB in the mesolimbic dopamine reward pathway. Journal of Neurochemistry, 87, 1237–1244. Ward, A. S., Haney, M., Fischman, M. W., & Foltin, R. W. (1997). Binge cocaine self-administration in humans: Intravenous cocaine. Psychopharmacology, 132, 375–381. West, A. R., Floresco, S. B., Charara, A., Rosenkranz, J. A., & Grace, A. A. (2003). Electrophysiological interactions between striatal glutamatergic and dopaminergic systems. Annals of the New York Academy of Sciences, 1003, 53–74. Wise, R. A., Murray, A., & Bozarth, M. A. (1990). Bromocriptine selfadministration and bromocriptine-reinstatement of cocaine-trained and heroin-trained lever pressing rats. Psychopharmacology, 100, 355–360. Wojnicki, F. H., & Glowa, J. R. (1996). Effects of drug history on the acquisition of responding maintained by GBR 12909 in rhesus monkeys. Psychopharmacology, 123, 34–41.

c40.indd 806

Wu, M., Hrycyshyn, A. W., & Brudzynski, S. M. (1996). Subpallidal outputs to the nucleus accumbens and the ventral tegmental area: Anatomical and electrophysiological studies. Brain Research, 740, 151–161. Xi, Z. X., Baker, D. A., Shen, H., Carson, D. S., & Kalivas, P. W. (2002). Group II metabotropic glutamate receptors modulate extracellular glutamate in the nucleus accumbens. Journal of Pharmacology and Experimental Therapeutics, 300, 162–171. Xi, Z., & Stein, E. A. (2000). Increased mesolimbic GABA concentration blocks heroin self-administration in the rat. Journal of Pharmacology and Experimental Therapeutics, 294, 613–619. Xu, F., Gainetdinov, R. R., Wetsel, W. C., Jones, S. R., Bohn, L. M., Miller, G. W., et al. (2000). Mice lacking the norepinephrine transporter are supersensitive to psychostimulants. Nature Neuroscience, 3, 465–471. Yan, Q. S., & Yan, S. E. (2001). Activation of 5-HT(1B/1D) receptors in the mesolimbic dopamine system increases dopamine release from the nucleus accumbens: A microdialysis study. European Journal of Pharmacology, 418, 55–64. Yeomans, J. S., Takeuchi, J., Baptista, M., Flynn, D. D., Lepik, K., Nobrega, J., et al. (2000). Brain-stimulation reward thresholds raised by an antisense oligonucleotide for the M5 muscarinic receptor infused near dopamine cells. Journal of Neuroscience, 20, 8861–8867. Yeomans, M. R., & Gray, R. W. (1996). Selective effects of naltrexone on food pleasantness and intake. Physiology and Behavior, 60, 439–446. Yeomans, M. R., & Gray, R. W. (1997). Effects of naltrexone on food intake and changes in subjective appetite during eating: Evidence for opioid involvement in the appetizer effect. Physiology and Behavior, 62, 15–21. Zanetti, L., Picciotto, M. R., & Zoli, M. (2007). Differential effects of nicotinic antagonists perfused into the nucleus accumbens or the ventral tegmental area on cocaine-induced dopamine release in the nucleus accumbens of mice. Psychopharmacology, 190, 189–199. Zawertailo, L. A., Busto, U. E., Kaplan, H. L., Greenblatt, D. J., & Sellers, E. M. (2003). Comparative abuse liability and pharmacological effects of meprobamate, triazolam, and butabarbital. Journal of Clinical Psychopharmacology, 23, 269–280.

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Chapter 41

Neural Basis of Mental Representations of Motivation, Emotion, and Pleasure MORTEN L. KRINGELBACH

Motivation and emotion govern our lives for better and sometimes for worse (Kringelbach, 2005). The underlying hedonic processing is arguably at the heart of what makes us human, but at the same time it is also one of the most important factors keeping us from staying healthy (Kringelbach, 2004b; Saper, Chou, & Elmquist, 2002). A better understanding of the underlying brain mechanisms can therefore help us understand and potentially treat the serious problems of affective disorders, such as unipolar depression, bipolar disorder, chronic pain, and eating disorders. This review is centered on the functional neuroanatomy of pleasure and hedonic processing in general, where pleasure is defined as one of the positive dimensions of the more general category of hedonic processing, which also includes other negative and unpleasant dimensions such as pain (Berridge & Kringelbach, 2008; Kringelbach & Berridge, 2009; see Figure 41.1). Malignant affective disorders such as depression, chronic pain, and eating disorders are characterized by the lowered or missing ability to experience pleasure, anhedonia. The evidence reviewed comes from human neuroimaging, neuropsychology, and neurosurgery. This chapter concentrates on the evidence linking the orbitofrontal cortex to reward and hedonic processing (see Figure 41.2).

Orbitofrontal cortex Cingulate cortex Ventral tegmental area Hypothalamus PVG/PAG Nucleus accumbens Amygdala Insular cortex

Figure 41.1 ( Figure C.38 in color section) Some important brain structures in the pleasure brain. Note: The human brain seen from the side (top) and split in the middle (bottom) overlaid with the important brain structures of the pleasure brain. These include the orbitofrontal cortex (grey), the cingulate cortex (light blue), ventral tegmental area in the brain stem (light red), hypothalamus, periventricular gray/periacqueductal gray (PVG/PAG, green), nucleus accumbens (in the temporal lobes, light green), amygdala (in the temporal lobes, light red) and the insular cortices (buried between the prefrontal and temporal lobes, orange).

EMOTION AND MOTIVATION Emotion and motivation remained for many years elusive scientific topics and were generally defined in opposition to cognition as that which move us in some way, as implied by the Latin root movere, to move. Owing primarily to its perceived subjective nature, the scientific study of emotion

was stunted despite ideas put forward by pioneering individuals such as Charles Darwin (1872), who examined the evolution of emotional responses and facial expressions, and suggested that emotions allow an organism to make

This research was supported by TrygFonden Charitable Foundation, MRC, and Wellcome Trust. 807

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Figure 41.2 The orbitofrontal cortex. Note: A: The primate orbitofrontal was subdivided to reflect its heterogeneity, as first proposed by the maps by Walker (1940), with the areas in the monkey Macaca fascicularis shown in views of the medial wall and the ventral surface. Walker proposed a further parcellation of the orbitofrontal cortex into five areas (areas 10, 11, 12, 13, and 14) (leftmost). Walker’s nomenclature was then reconciled with that used in Brodmann’s primate and human brain by Petrides and Pandya (1994), with lateral parts of the orbitofrontal cortex designated area 47/12 (middle). Even further subdivisions of the orbitofrontal cortex were subsequently proposed by Carmichael and Price (Carmichael & Price, 1994; rightmost). B: The human cytoarchitectonic maps of the orbitofrontal cortex rendered on the orbital surface

in normalized space. From “The Functional Neuroanatomy of the Human Orbitofrontal Cortex: Evidence from Neuroimaging and Neuropsychology,” by M. L. Kringelbach and E. T. Rolls, 2004, Progress in Neurobiology, 72, pp. 341–372. Based on a template provided by the Montreal Neurological Institute. Adapted with permission. C: The sulcal variability of the human orbitofrontal cortex is demonstrated with ventral views of the three main types taken from the left hemisphere. From “Orbitofrontal Sulci of the Human and Macaque Monkey Brain,” by M. M. Chiavaras and M. Petrides, 2000, Journal of Comparative Neurology, 422, pp. 35–54. Adapted with permission. It is clear that this variability poses substantial challenges for normalization across brains, but some possible strategies have been offered in a recent article (Kringelbach & Rolls, 2004).

adaptive responses to salient stimuli in the environment, thus enhancing its chances of survival. A highly successful scientific strategy has been to divide the concept of emotion into two parts: the emotional state that can be measured through physiological changes

such as visceral and endocrine responses, and feelings, seen as the subjective experience of emotion (Kringelbach, 2004a). This allows emotional states to be measured in animals using, for example, conditioning, and most subsequent research has regarded emotions as states elicited by

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Emotion and Motivation 809

rewards and punishments (which, of course, is a rather circular definition; Weiskrantz, 1968). Emotional stimuli (primary and secondary reinforcers) are represented by brain structures, depending on the kind of reinforcer. Reinforcers are defined such that positive reinforcers (rewards) increase the frequency of behavior leading to their acquisition, while negative reinforcers (punishers) decrease the frequency of behavior leading to their encounter and increase the frequency of behavior leading to their avoidance. The subsequent emotional processing is a multistage process mediated by networks of brain structures. The results of this processing influence which behavior is selected, which autonomic responses are elicited, and which conscious feelings are produced (at least in humans). An early, contrasting but still influential, theory of emotion was proposed independently by William James (1890) and Carl Lange (1887) who proposed that rather than emotional experience being a response to a stimulus, it is the perception of the ensuing physiological bodily changes. The James-Lange theory of emotion suggests that contrary to popular perception we do not run from the bear because we are afraid but that we become afraid because we run. Several scientists have remained skeptical of such bodily theories of emotion. One of the initial main proponents, William Cannon (1927), offered a detailed critique of the James-Lange theory. He showed that surgical disruption of the peripheral nervous system in dogs did not eliminate emotional responses as would have been predicted by the theory. Further investigations by Schachter and Singer (1962) suggested that bodily states must be accompanied by cognitive appraisal for an emotion to occur. However, this research did not fully resolve the basic question of the extent to which bodily states influence emotion and feelings. The James-Lange theory was resurrected first by Walla Nauta (1971) with his interoceptive markers, and since—to far more popular acclaim—by Antonio Damasio (1994) in the form of his somatic marker hypothesis, in which feedback from the peripheral nervous system controls the decision about the correct behavioral response rather than the emotional feelings as postulated in the James-Lange theory. Among the objections to these and other bodily theories of emotion are that they are underspecified with regard to what constitute emotional stimuli; that signals from the body are noisy and it is not clear whether they can distinguish the different emotions; and that animals and humans with severe spinal cord damage appear to have normal emotions. Some of these objections are addressed in the somatic marker theory that includes an as-if loop for those decision-making situations with relatively low uncertainty that allows the brain to bypass the role of body (Damasio, 1996). It has also been argued that emotions are constituted

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in large measure by visceral and endocrine responses rather than through the spinal cord. The orbitofrontal cortex certainly has the connectivity to receive and integrate visceral sensory signals to influence ongoing behavior, and although it is not clear how this information is integrated, it remains possible that these signals have a significant role in decision making and emotion (Craig, 2002). It should also be noted that most primary reinforcers are signaled via an interoceptive route and that this is likely to be essential for hedonic experience. At the same time, it is clear from the evidence of, for example, successful use of various beta blockers in alleviating stage fright, anxiety, and panic attacks in stage musicians and other world-class performers that the body clearly must play a role in the regulation of emotions. Some observers have therefore suggested that the role of the body in emotion is perhaps more akin to an amplifier than to a generator. There are close links between body and brain, as was fully clear to even Descartes who is otherwise widely seen as one of the main proponents for the mind-body split. It is at best misleading to assign Descartes such a simpleminded dualistic position (Descartes, 1649; Sutton, 2001)— although he was clearly on the wrong track when he named the pineal gland as the seat of the soul. Later research has shown that this brain structure is a key structure in the control of hormones and thus an unlikely contributor to the metaphysical construction of the soul. On Hedonic Processing Past and Present From an evolutionary perspective, reward, pleasure, and hedonic processing have important roles in helping with the Darwinian imperative of survival and procreation. It has proven useful to divide hedonic processing into at least two categories: basic and higher pleasures (Kringelbach & Berridge, 2009). The basic pleasures are linked to survival and include sensory pleasures such as food as well as sex (Berridge, 1996; Kringelbach, 2004b). Similarly, the basic pleasures are linked to both survival and procreation since the social interactions with conspecifics may potentially lead to the propagation of genes. This has probably been selected for in evolution, which means that social pleasures are also likely to be part of our repertoire of basic pleasures (Kringelbach & Rolls, 2003). In the development of the social pleasures, the early attachment bonds between parents and infants are likely to be extremely important (Stein et al., 1991). In fact, in social species such as humans, it might well be that the social pleasures are at least as pleasurable as the sensory pleasures. In addition to these basic sensory and social pleasures, there are a large number of higher-order pleasures, including

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monetary, artistic, musical, altruistic, and transcendent pleasures. Such higher-order pleasures might be conceptualized as higher-dimensional combinations of the basic pleasures and as such may re-use some of the same brain mechanisms. Over the past century, a large corpus of animal experimentation has investigated reward processing in the brain. Many people have subsequently defined pleasure to be the conscious experience of reward but it is questionable whether such a narrow definition is meaningful or indeed useful. Such a definition would limit pleasure to conscious organisms, which is problematic for a number of reasons, not the least being that we do not have a good definition of consciousness. Pleasure is not a sensation because it does not fit most common definitions of sensations, as pointed out by Ryle (1954). Instead, pleasure would appear to be part of the subsequent valuation of sensory stimuli needed in decision making, including most importantly the hedonic valence, and as such may well be present in many species. While the pleasure—or hedonic impact—of a reward such as sweetness can be measured by verbal reports in conscious humans, this hedonic processing is not dependent on the presence of language. In most nonlinguistic mammals, pleasure will also elicit “acceptance wriggles” that add a hedonic gloss to the sensation which we experience as conscious pleasure. Pleasure-elicited behaviors (such as protruding tongue movements to sweet foods) are present in other animals including rodents and has been proposed as an objective measure of the pleasure elicited (Steiner, Glaser, Hawilo, & Berridge, 2001). While human infants initially exhibit similar kinds licking of their lips for sweet foods, these stereotyped behaviors disappear after a while. Humans do, however, exhibit much pleasure behavior, from the carefree smiles and laughter of pleasant social interactions to the deep groans of sensory and sexual pleasure (James, 1890). Most people instinctively feel that our pleasures would somehow not be quite the same without these pleasure-elicited behaviors. At the same time, much of our brain activity is not available for conscious introspection and the neuroscientific evidence from humans and other animals has made it clear that nonconscious brain activity is essential for controlling our behavior. Some of this nonconscious brain activity is related to hedonic processing and may lead to hedonic reactions, where we are not conscious of their origin but where we are nevertheless happy to confabulate about the causes (Kringelbach, 2004c). Similarly to how it is has proven useful to divide emotion into the nonconscious and conscious subcomponents of emotions and feelings, it might be useful and meaningful to divide pleasure into nonconscious and conscious subcomponents

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of evaluative hedonic processing (Kringelbach, 2004a). Such a definition would hold that while pleasure plays a central role for emotions and conscious feelings, it is not itself a conscious feeling. Reward and hedonic processing are closely linked with motivation and emotion. Historically, early drive theories of motivation proposed that need potentiated previously learned habits, and that need reduction strengthened new stimulus-response habit bonds (Hull, 1951). This was then taken to mean that hedonic behavior is controlled by need states. But these theories do not, for example, explain why people still continue to eat when sated. This led to theories of incentive motivation where hedonic behavior is mostly determined by the incentive value of a stimulus or its capacity to function as a reward (Bindra, 1978). Need states such as hunger are still important but only work indirectly on the stimulus’ incentive value. Alliesthesia is the principle of modulation of the hedonic value of a consummatory sensory stimulus by homeostatic factors (Cabanac, 1971). A useful distinction has been proposed between two aspects of reward: hedonic impact and incentive salience, where the former refers to the liking or pleasure related to the reward, and the latter to the wanting or desire for the reward (Berridge, 1996; Berridge & Robinson, 1998). In order to provide hedonic evaluation of stimuli, the brain regions implicated in hedonic assessment must receive salient information about stimulus identity from the primary and secondary sensory cortices. Neuroimaging offers a powerful way to investigate both the liking and wanting components in the human brain. One way to investigate liking is to take subjective hedonic ratings throughout a human neuroimaging experiment and then correlate these ratings with changes in activity in the human brain (De Araujo, Kringelbach, Rolls, & McGlone, 2003; De Araujo, Rolls, Kringelbach, McGlone, & Phillips, 2003; Kringelbach, O’Doherty, Rolls, & Andrews, 2003). This allows for a unique window on the hedonic processes evaluating the pleasantness of salient stimuli and has pointed to the central role of the orbitofrontal cortex.

THE PRIMATE ORBITOFRONTAL CORTEX The human orbitofrontal cortex has received relatively little attention in studies of the prefrontal cortex, and many of its functions remain enigmatic (Kringelbach, 2005). During primate evolution, the orbitofrontal cortex has developed considerably, and although some progress has been made through neurophysiological recordings in nonhuman primates, it is only during the past couple of years that evidence has converged from neuroimaging, neuropsychology,

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The Primate Orbitofrontal Cortex 811

and neurophysiology to allow a better understanding of the functions of the human orbitofrontal cortex. These studies indicate that the orbitofrontal cortex is a nexus for sensory integration, the modulation of autonomic reactions, and participation in learning, prediction, and decision making for emotional and reward-related behaviors. The orbitofrontal cortex functions as part of various networks that include regions of the medial prefrontal cortex, hypothalamus, amygdala, insula/operculum, dopaminergic midbrain, and areas in the basal ganglia including the ventral and dorsal striatum. These additional areas have been investigated in detail in rodents and other animals, and have been described in other reviews (Cardinal, Parkinson, Hall, & Everitt, 2002; Holland & Gallagher, 2004). Here the focus is on the functions of the human orbitofrontal cortex because the phylogenetic expansion and heterogeneous nature of this brain region mean that a full understanding of its functions must be informed by evidence from human neuroimaging and neuropsychology studies. Neuroimaging studies have found that the reward value (Kringelbach, O’Doherty, Rolls, & Andrews, 2000; O’Doherty et al., 2000), the expected reward value (Gottfried, O’Doherty, & Dolan, 2003), and even the subjective pleasantness of foods (Kringelbach et al., 2003) and other reinforcers are represented in the orbitofrontal cortex. Such findings could provide a basis for further exploration of the brain systems involved in the conscious experience of pleasure and reward and, as such, provide a unique method for studying the hedonic quality of human experience. Neuroanatomy of the Orbitofrontal Cortex The orbitofrontal cortex occupies the ventral surface of the frontal part of the brain (see Figures 40.1 and 40.2). It is defined as the part of the prefrontal cortex that receives projections from the magnocellular, medial, nucleus of the mediodorsal thalamus (Fuster, 1997). This is in contrast to areas of the prefrontal cortex that receive projections from other parts of the mediodorsal thalamus. For example, the dorsolateral prefrontal cortex (Brodmann area [BA] 46/9) receives projections from the parvocellular, lateral, part of the mediodorsal thalamic nucleus, whereas the frontal eye fields in the anterior bank of the arcuate sulcus (BA 8) receive projections from the paralamellar part of the mediodorsal thalamic nucleus. This is a broad connectional topography, in which each specific portion of the mediodorsal thalamus is connected to more than one architectonic region of the prefrontal cortex (Pandya & Yeterian, 1996), and a better definition therefore includes the cortical area’s corticocortical connectivity and morphological features.

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Brodmann (1909) carried out one of the first comprehensive cytoarchitectural analyses of both the human and the primate (Cercopithecus) brain, in which different cytoarchitectonic areas were assigned unique numbers. Unfortunately, he did not investigate the orbitofrontal cortex in detail, and his maps of the human brain include only three orbitofrontal cortical areas, 10, 11, and 47. In addition, his nomenclature was not consistent across species: Area 11 in the primate map is extended laterally, and area 12 has taken over the medial area occupied by area 11 in the human map, whereas area 47 is not included at all in the nonhuman primate map. Walker (1940) provided some clarification of the crossspecies inconsistencies present in Brodmann’s maps in his investigation of the monkey species Macaca fascicularis. He found the orbitofrontal cortex to be much less homogeneous than Brodmann specified, and he proposed to parcellate the primate orbital surface into five distinct areas (areas 10, 11, 12, 13, and 14; see Figure 41.2A). Walker ’s areas 12 and 13 occupy the lateral and medial orbital surface, respectively, whereas area 14 is on the ventromedial convexity near the gyrus rectus. More anteriorly, area 10 occupies the frontal pole, whereas area 11 occupies the remaining anterior orbital surface. However, this left the problem of area 47 from the human map, which was still not included in Walker ’s map. Petrides and Pandya (1994) subsequently tried to reconcile the remaining inconsistencies between the human and monkey cytoarchitectonic maps by labeling the lateral parts of the orbitofrontal gyri as 47/12. Even further subdivisions of the orbitofrontal cortex were subsequently proposed using nine different histochemical and immunohistochemical stains (Carmichael & Price, 1994). Two important cytoarchitectonic features of the orbitofrontal cortices are the phylogenetic differences and the considerable variability between individuals (Chiavaras & Petrides, 2000, 2001). The former poses potential problems when trying to understand functional relationships across species, and the latter poses interesting methodological challenges for those who hope to normalize individual brains to a template brain to explore the functional anatomy of the human orbitofrontal cortex. The orbitofrontal cortex receives inputs from the classic five sensory modalities: gustatory, olfactory, somatosensory, auditory, and visual (Carmichael & Price, 1995b). It also receives visceral sensory information, and all this input makes the orbitofrontal cortex perhaps the most polymodal region in the entire cortical mantle, with the possible exception of the rhinal regions of the temporal lobes (Barbas, 1988). The orbitofrontal cortex also has direct reciprocal connections with other brain structures, including the amygdala (Amaral & Price, 1984; Carmichael & Price, 1995a), cingulate

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cortex (Öngür & Price, 2000; Van Hoesen, Morecraft, & Vogt, 1993), insula/operculum (Mesulam & Mufson, 1982), hypothalamus (Rempel-Clower & Barbas, 1998), hippocampus (Cavada, Company, Tejedor, Cruz Rizzolo, & Reinoso Suarez, 2000), striatum (Eblen & Graybiel, 1995), periaqueductal grey (Rempel-Clower & Barbas, 1998), and dorsolateral prefrontal cortex (Barbas & Pandya, 1989; Carmichael & Price, 1995b).

FUNCTIONAL NEUROANATOMY OF THE HUMAN ORBITOFRONTAL CORTEX In terms of its neuroanatomical connectivity, the orbitofrontal cortex is uniquely placed to integrate sensory and visceral motor information to modulate ongoing behavior through both visceral and motor systems. This has led to the proposal that the orbitofrontal cortex is an important part of the networks involved in emotional and hedonic processing (Nauta, 1971; E. T. Rolls, 1999). The orbitofrontal cortex has direct connections to the basolateral amygdala, and these two brain areas probably have an important role in goal-directed behavior (E. T. Rolls, 1999). The orbitofrontal cortex is a comparatively large brain area in nonhuman primates and humans and is heterogeneous in terms of its connectivity and morphological features, so its constituent parts probably have different functional roles. One proposal based on neuroanatomical and neurophysiological evidence from nonhuman primates is that the orbitofrontal cortex should be viewed as part of a functional network known as the orbital and medial prefrontal cortex (OMPFC; Öngür & Price, 2000). This network includes both the orbitofrontal cortex and parts of the anterior cingulate cortex, and has distinct connections to other parts of the brain. The orbital network includes areas 11, 13, and 47/12 of the orbitofrontal cortex and receives input from all the sensory modalities, including visceral afferents and is proposed to be important for the regulation of food intake. The medial network (which includes medial areas 11, 13, 14, and lateral area 47/12s of the orbitofrontal cortex as well as areas 25, 32, and 10 on the medial wall) has extensive visceromotor outputs. The two networks might therefore serve as a crucial sensory-visceromotor link for consummatory behaviors. It should be noted that the definition of the medial network partly overlaps with the ventromedial prefrontal cortex as utilized by Bechara, Damasio, Damasio, and Anderson (1994), but the latter does not include lateral regions of the orbitofrontal cortex. Another proposal extends the OMPFC network, based on evidence from human neuroimaging and neuropsychology studies, to suggest that there are medial-lateral and posterioranterior distinctions within the human orbitofrontal cortex (Kringelbach & Rolls, 2004). A large meta-analysis of the

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existing neuroimaging data was used to show that activity in the medial orbitofrontal cortex is related to the monitoring, learning, and memory of the reward value of reinforcers, whereas lateral orbitofrontal cortex activity is related to the evaluation of punishers, which can lead to a change in ongoing behavior. There was also a posterior-anterior distinction, with more complex or abstract reinforcers (such as monetary gain and loss) represented more anteriorly in the orbitofrontal cortex than less complex reinforcers (such as taste). Other proposed functions of the orbitofrontal cortex include a role for the lateral parts in response inhibition (Elliott, Dolan, & Frith, 2000), based on the observation that humans and nonhuman primates will perseverate in choosing a previously, but no-longer rewarded, stimulus in object-reversal learning tasks (Dias, Robbins, & Roberts, 1996; E. T. Rolls, Hornak, Wade, & McGrath, 1994). There is now strong evidence that this inhibition cannot be a simple form of response inhibition. Lesion studies in monkeys have shown that errors on reversal-learning tasks may not be caused by perseverative responses, but can be caused by failure to learn to respond to the currently rewarded stimulus (Iversen & Mishkin, 1970). Similarly, simple response inhibition cannot account for the severe impairment on the reversal part of an object-reversal learning task shown by patients with discrete bilateral surgical lesions to the lateral orbitofrontal cortex (Hornak et al., 2004). It is possible that the orbitofrontal cortex has a role in more complex behavioral changes that could be interpreted as being inhibitory to behavior, and that this behavior arises in conjunction with activity in other brain structures such as the anterior cingulate cortex, as discussed later. These influential proposals share some similarities and conclusions, but the exact functions and underlying mechanisms of the various parts of the orbitofrontal cortex have yet to be discovered. Here I consider new evidence from neuroimaging and neuropsychology studies that can help to illuminate the functions of the orbitofrontal cortex in sensory integration, reward processing, decision making, reward prediction, and subjective hedonic processing. It is important to remember that studies using fMRI are prone to signal dropout, geometric distortion, and susceptibility artefacts in the orbitofrontal cortex due to its close proximity to the air-filled sinuses (Deichmann, Josephs, Hutton, Corfield, & Turner, 2002; Wilson et al., 2002), so negative findings should be treated with caution.

SENSORY PLEASURES: INTEGRATION AND REWARD VALUE Neurophysiological recordings have found that the nonhuman primate orbitofrontal cortex receives input from all of

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Social Pleasures: Face Processing

the five senses (E. T. Rolls, 1999), and neuroimaging has confirmed that the human orbitofrontal cortex is activated by auditory (Frey, Kostopoulos, & Petrides, 2000), gustatory (Small et al., 1999), olfactory (Zatorre, Jones-Gotman, Evans, & Meyer, 1992), somatosensory (E. T. Rolls, O’Doherty, Kringelbach, Francis, Bowtell, & McGlone, 2003), and visual (Aharon et al., 2001) inputs. This cortical region also receives information from the visceral sensory system (Critchley, Mathias, & Dolan, 2002), and even abstract reinforcers such as money can activate the human orbitofrontal cortex (O’Doherty, Kringelbach, Rolls, Hornak, & Andrews, 2001; Thut et al., 1997). Four computational principles have been proposed for the interaction between sensory and hedonic processing in humans: (1) motivation-independent processing of identity and intensity; (2) formation of learning-dependent multimodal sensory representations; (3) reward representations using state-dependent mechanisms including selective satiation; and (4) representations of hedonic experience, monitoring/learning or direct behavioral change (Kringelbach, 2006). Sensory inputs enter the orbitofrontal cortex mostly through its posterior parts (see next section). Here they are available for multisensory integration (DeAraujo, Rolls, et al., 2003; Kringelbach et al., 2003; Small, Jones-Gotman, Zatorre, Petrides, & Evans, 1997) and subsequent encoding of the reward value of the stimulus. One approach to demonstrate the encoding of the reward value of a stimulus is by a manipulation called selective or sensory-specific satiety (B. J. Rolls, Rolls, Rowe, & Sweeney, 1981), which is a form of reinforcer devaluation. This approach was used in neuroimaging experiments on hungry human subjects who were scanned while being presented with two food-related stimuli. Subjects were then fed to satiety on one of the corresponding stimuli, which led to a selective decrease in reward value of the food eaten, and scanned again in their satiated state using exactly the same procedure. The neuroimaging experiments using olfactory (O’Doherty et al., 2000) and whole-food (Kringelbach et al., 2000, 2003) stimuli showed that the activity in more anterior parts of the orbitofrontal cortex tracks the changes in reward value of the two stimuli such that the activity selectively decreases for the food eaten but not for the other food. This is compatible with studies in nonhuman primates where monkeys with lesions to the orbitofrontal cortex responded normally to associations between food and conditioners but failed to modify their behavior to the cues when the incentive value of the food was reduced (Butter, Mishkin, & Rosvold, 1963), and where lesions to the orbitofrontal cortex altered food preferences in monkeys (Baylis & Gaffan, 1991). Similarly, unilateral crossed lesions between the orbitofrontal cortex and the

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basolateral part of the amygdala in monkeys disrupted devaluation effects in a procedure in which the incentive value of a food was reduced by satiation on that specific food (Baxter, Parker, Lindner, Izquierdo, & Murray, 2000). A malfunction of these satiation mechanisms could explain the profound changes in eating habits (escalating desire for sweet food coupled with reduced satiety) that are often followed by enormous weight gain in patients with frontotemporal dementia, a progressive neurodegenerative disorder associated with major and pervasive behavioral changes in personality and social conduct resembling those produced by orbitofrontal lesions (Rahman, Sahakian, Hodges, Rogers, & Robbins, 1999; although it should be noted that more focal lesions to the orbitofrontal cortex have not to date been associated with obesity). Further evidence for the representation of the reward value of more abstract reinforcers comes from neuroimaging studies of, for example, social judgments (Farrow et al., 2001) and music (Blood, Zatorre, Bermudez, & Evans, 1999). A meta-analysis of neuroimaging studies found that abstract reinforcers such as money are represented more anteriorly in the orbitofrontal cortex than less complex reinforcers such as taste (Kringelbach & Rolls, 2004). These studies show that the orbitofrontal cortex represents the affective value of both primary and abstract secondary reinforcers.

SOCIAL PLEASURES: FACE PROCESSING Humans are intensely social, and experiments have shown time and again that our preferred route to health, pleasure, and perhaps even happiness is through social relationships with other people (Layard, 2005). Human social relationships are very rich and complex, and we have only started to understand some of the underlying brain processes (Adolphs, 2003). In humans and other primates, facial expressions act as important social cues to regulate behavior (Darwin, 1872; Ekman & Friesen, 1971). Much is known about the neural correlates of the decoding of face expressions from neurophysiological studies in nonhuman primates (Bruce, Desimone, & Gross, 1981; Desimone & Gross, 1979; Hasselmo, Rolls, Baylis, & Nalwa, 1989; Perrett, Rolls, & Caan, 1982) and from human lesion (Adolphs, Tranel, Damasio, & Damasio, 1994; Bodamer, 1947; Sergent & Villemure, 1989) and imaging studies (Haxby et al., 1994), but very little is known about the neural correlates of how face expressions govern human social behavior. In addition, it is clear that infant faces serve an important role in the early attachment between parents and children, which is the foundation of our hedonic brain.

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Infant and Infantile Faces as a Tool for Understanding Social Attachment The scientific interest in the infant faces started with Charles Darwin (1872) who pointed out that in order for infants to survive and to perpetuate the human species, adults need to respond and care for their young. The Nobel Prize winner Konrad Lorenz (1971) proposed that it is the specific structure of the infant face that serves to elicit these parental responses, but the biological basis for this has remained elusive. Lorenz argued that infantile features serve as “innate releasing mechanisms” for affection and nurturing in adult humans and that most of these features are evident in the face including a relatively large head, predominance of the brain capsule, large and low-lying eyes and bulging cheek region. Thus, it is argued that these “babyish” features of infants increase the infant’s chance of survival by evoking parental responses (Bowlby, 1957, 1969), and the parents’ ability to respond is important for the survival of the species (Darwin, 1872). Although a considerable body of research has focused on how the human brain processes adult faces, much less research has investigated the processing of infant faces (Frith, 2006). A number of studies have used fMRI to examine parental responses to children’s faces, which has advanced our understanding of some of the underlying neural circuitry (Swain, Lorberbaum, Kose, & Strathearn, 2007). Most studies have compared parental responses to their own children with their responses to other children. It has been found that there is stronger activity to one’s own children compared to other infants in striate and extrastriate visual areas and in reward-related areas such as the nucleus accumbens, anterior cingulate and amygdala (Ranote et al., 2004; Swain et al., 2007). While these studies have substantially increased our general knowledge of the parental neural responses to children faces, there are a number of reasons why a substantial test of the Lorenz’s theory of the specificity of infant faces requires a direct comparison between matched adult and infant faces from the first year of life; preferably where the faces are unfamiliar and using neuroimaging techniques that permit the temporal progression of brain activity to be studied. We used magnetoencephalography (MEG) to investigate the temporal and spatial distribution of the underlying neural systems for these facial responses in 12 adult human participants (Kringelbach, Lehtonen, et al., 2007; Kringelbach et al., 2008). Consistent with previous findings, we found that face processing of both adult and infant faces elicits a wave of activity starting in the striate cortices and spreading along ventral and dorsal pathways (Blair, 2003). In addition, however, we found that at around 130 ms after presentation of the infant faces, activity occurred in

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the medial orbitofrontal cortex identifying for the first time a neural basis for this vital evolutionary process (see Figure 41.3). This was not evident in response to the adult faces. Since the infant and adult faces used in the present study were carefully matched by independent panels of participants for emotional valence and arousal, and attractiveness, the findings provide evidence that it is the distinct features of the infant faces compared to adult faces that are important, rather than evaluative subjective processing such as attractiveness or emotional valence. These specific responses to unfamiliar infant faces occur so fast that they are almost certainly quicker than anything under conscious control suggesting that they are automatized. The findings are therefore potentially of interest in that they suggest a temporally earlier role than previously thought for the medial orbitofrontal cortex in guiding affective reactions, which may even be nonconscious. The medial orbitofrontal cortex may thus provide the necessary attentional—and perhaps hedonic—tagging of infant faces that predisposes humans to treat infant faces as special and elicits caring, as suggested by Lorenz. It would be of considerable interest to investigate the brain responses to infants of other species to see whether a similar effect is present. Overall, these neuroimaging studies demonstrate that faces are important stimuli to help understand how the social pleasures might govern behavior. In particular, they show that the sensory and social pleasures share a similar network of interacting brain regions.

DECISION MAKING AND PREDICTION In decision making, the brain must compare and evaluate the predicted reward value of various behaviors. This processing can be complex because the estimations will vary in quality depending on the sampling rate of the behavior and the variance of reward distributions. It is hard to provide a reliable estimate of the reward value of a food that appears to be highly desirable and is high in nutritional value but is only rarely available and varies significantly in quality. This raises the classic problem in animal learning of how to optimize behavior such that the amount of exploration is balanced with the amount of exploitation, where exploration is the time spent sampling the outcome of different behaviors and exploitation is the time spent using existing behaviors with known reward values. Food-related behaviors have to be precisely controlled because the decision to swallow toxins, microorganisms, or nonfood objects on the basis of erroneously determining the sensory properties of the food can be fatal. Humans and other animals have therefore developed elaborate food-related behaviors to balance conservative riskminimizing and life-preserving strategies (exploitation) with

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Decision Making and Prediction Adult Faces

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Figure 41.3 Infant faces elicited an early neural signature.

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occasional novelty seeking (exploration) in the hope of discovering new, valuable sources of nutrients (Rozin, 2001). The orbitofrontal and anterior cingulate cortices were implicated in decision making by the classic case of Phineas Gage, whose frontal lobes were penetrated by a metal rod (Harlow, 1848). Gage survived but his personality and emotional processing were changed completely (although the case should be viewed with caution because the available information is limited; Macmillan, 2000). In more recent cases of orbitofrontal cortex damage, patients have often shown problems with decision making, a lack of affect, and social inappropriateness and irresponsibility (S. W. Anderson, Bechara, Damasio, Tranel, & Damasio, 1999; Blair & Cipolotti, 2000; Hornak et al., 2003; E. T. Rolls et al., 1994). Such patients are impaired in identifying social signals that are important for decision making, including, for example, face and voice expressions (Hornak et al., 2003; Hornak, Rolls, & Wade, 1996). Reliable prediction underlies decision making (Schultz & Dickinson, 2000), and neuroimaging has been used to investigate the predicted reward value of rewarding and punishing stimuli. Often this is done using classical conditioning paradigms, in which an arbitrary neutral stimulus is paired with a reward or punishment. After learning, the arbitrary stimulus takes on the predictive value of the specific reward value of the unconditioned stimulus, but

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Note: (Top Row) Significant activity was present from around 130 ms in the medial orbitofrontal cortex when viewing infant faces but not when viewing adult faces. (Bottom Row) Time-frequency representations of the normalized evoked average group responses to baby and adult faces from the virtual electrodes show that the initial response to infant faces is present in the 12 to 20 Hz band from around 130 ms—and not present in adult faces. From “A Specific and Rapid Neural Signature for Parental Instinct,” by Kringelbach et al., 2008, PLoS ONE. 3(2): e1664. Reprinted with permission.

it can also code for various aspects of the sensory or general affective properties of the unconditioned stimulus. In an fMRI study using selective satiation, subjects were presented with predictive cues associated with one of two food-related odors (Gottfried et al., 2003). By comparing the brain activity in response to the cues before and after devaluation (by feeding to satiety) of the associated food, it was found that neural responses in the orbitofrontal cortex, amygdala, and ventral striatum tracked the relative changes in the specific predictive reward value of the odors. When the specific predictive reward values of different behaviors are in place, comparison and evaluation mechanisms must choose between them to optimize behavior. Bechara and colleagues developed a gambling task in which subjects were asked to select cards from four decks and maximize their winnings (Bechara et al., 1994). After each selection of a card, facsimile money is lost or won. Two of the four packs produce large payouts with larger penalties (and can thus be considered high-risk), whereas the other two packs produce small payouts but smaller penalties (low-risk). The most profitable strategy is therefore to select cards from the two low-risk decks; this strategy is adopted by normal control subjects. During the task, electrodermal activity (skin conductance responses, SCR) of the subject is measured as an index of visceral sensory arousal. Patients with damage to the ventromedial prefrontal cortex

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(including parts of the medial orbitofrontal cortex), but not the dorsolateral prefrontal cortex, persistently draw cards from the high-risk packs, and lack anticipatory SCRs while they consider risky choices (Bechara, Damasio, Tranel, & Anderson, 1998; but see also a recent critique of this experiment; Maia & McLelland, 2004). In another decision-making task, a visual discrimination reversal task, subjects had to associate an arbitrary stimulus with monetary wins or losses, and then rapidly reverse these associations when the reinforcement contingencies altered. Probabilistic reward and punishment schedules were used such that selecting either the currently rewarded stimulus or the unrewarded stimulus can lead to a monetary gain or loss, but only consistent selection of the currently rewarded stimulus results in overall monetary gain. An fMRI study of this task in normal subjects found a dissociation of activity in the medial and lateral parts of the orbitofrontal cortex: Activity in the medial orbitofrontal cortex correlated with how much money was won on single trials, and activity of the lateral orbitofrontal cortex correlated with how much money was lost on single trials (O’Doherty et al., 2001). Another PET study also found that predominantly lateral parts of the orbitofrontal cortex were significantly activated during decision making (Rogers et al., 1999). Other studies have since confirmed the role of the medial orbitofrontal cortex in monitoring and learning about the reward value of stimuli that have no immediate behavioral consequences (see Figure 41.4). Neuroimaging experiments have found activations in the medial orbitofrontal cortex that monitor the affective properties of olfaction (A. K. Anderson et al., 2003; E. T. Rolls, Kringelbach, & De Araujo, 2003), gustation (Small et al., 2003), somatosensory (E. T. Rolls, O’Doherty, et al., 2003), and multimodal (De Araujo, Rolls, et al., 2003) stimuli. This monitoring process is consistent with intriguing findings in spontaneously confabulating patients with lesions to the medial orbitofrontal cortex (Schnider, 2003; Schnider & Ptak, 1999). A PET study has found that the medial orbitofrontal cortex monitors outcomes even when no reward is at stake (Schnider, Treyer, & Buck, 2005). In contrast, the lateral orbitofrontal cortex is often co-active with the anterior cingulate cortex when subjects evaluate punishers, which, when detected, can lead to a change in behavior (see Figure 41.5). A PET study investigating analgesia and placebo found that the lateral orbitofrontal and anterior cingulate cortices were co-active in subjects who responded to the placebo, suggesting that the pain relief effect of the placebo might be related to the co-activation of these two brain areas (Petrovic & Ingvar, 2002; Petrovic, Kalso, Petersson, & Ingvar, 2002). Another neuroimaging study found evidence that the lateral orbitofrontal cortex is related to changing behavior, in this case

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when there were unexpected breaches in expectation in a visual attention task (Nobre, Coull, Frith, & Mesulam, 1999). A direct investigation of the role of the lateral orbitofrontal cortex was carried out using a face reversal–learning task that solved the problem inherent in the monetary reversallearning task (mentioned previously; O’Doherty et al., 2001), in which the probabilistic nature of the task meant that the magnitude of negative reinforcers (money loss) was slightly confounded by the reversal event per se. This new reversal-learning study showed that the lateral orbitofrontal cortex and a region of the anterior cingulate cortex are together responsible for supporting general reversal learning in the human brain (Kringelbach, 2004d; Kringelbach & Rolls, 2003; see Figure 41.6). This is consistent with the finding that passively presenting angry facial expressions—a signal that ongoing social behavior should be changed— activates the orbitofrontal and anterior cingulate cortices (Blair, Morris, Frith, Perrett, & Dolan, 1999). Further strong, causal evidence has come from a similar reversal-learning experiment in patients with discrete, surgical lesions to the orbitofrontal cortex, in which bilateral lesions to the lateral orbitofrontal cortex—but not unilateral lesions to medial parts of the orbitofrontal cortex—produce significant impairments in reversal learning (Hornak et al., 2004). A recent fMRI study investigated the interaction between decision making and performance monitoring (Walton, Devlin, & Rushworth, 2004). The orbitofrontal cortex was more involved in outcome monitoring for the externally instructed condition than in the internally generated volition condition. In contrast, the anterior cingulate cortex was more active when the selected response was internally generated than when it was externally instructed by the experimenter. In summary, decision making, performance, and outcome monitoring require complex processing that relies strongly on frontal cortical areas, and in particular on interactions between the orbitofrontal and anterior cingulate cortices. Different parts of these cortical regions have been implicated in different aspects and timings of decision making and outcome monitoring (Ullsperger & von Cramon, 2004). In particular, there is now evidence from a large meta-analysis of the neuroimaging literature for the differential roles of the medial and lateral parts of the orbitofrontal cortex (Kringelbach & Rolls, 2004). Activity in the medial orbitofrontal cortex is related to the monitoring, learning, and memory of the reward value of reinforcers, whereas activity in the lateral orbitofrontal cortex is related to the evaluation of punishers, which can lead to a change in behavior. As already mentioned, the meta-analysis also found evidence for a posterior-anterior distinction between more complex or abstract and less complex reinforcers.

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Decision Making and Prediction

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Figure 41.4 Valence coding in medial orbitofrontal cortex (OFC). Note: A: The activity in medial OFC correlates with the subjective ratings of pleasantness in an experiment with three pleasant and three unpleasant odors (Rolls, Kringelbach, et al., 2003). B: Similarly, the activity in medial OFC was also correlated with the subjective pleasantness ratings of water in a thirst experiment (De Araujo, Kringelbach, Rolls, & McGlone, 2003). A correlation in a very similar part of medial OFC was found with the pleasantness of other pure tastants used in the experiment (not shown).

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⫺1.5 Monetary Wins and Losses C: This corresponded to the findings in an experiment investigating taste and smell convergence and consonance, which found that activity in the medial OFC was correlated to subjective consonance ratings (De Araujo, Rolls, et al., 2003). D: Even higher-order rewards such as monetary reward was found to correlate with activity in the medial OFC. From “Abstract Reward and Punishment Representations in the Human Orbitofrontal Cortex,” by J. O’Doherty, M. L. Kringelbach, E. T. Rolls, J. Hornak, and C. Andrews, 2001, Nature Neuroscience, 4, p. 99. Reprinted with permission.

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Figure 41.5 ( Figure C.38 in color section) Hedonic experience. Note: A: A neuroimaging study using selective satiation found that mid-anterior parts of the orbitofrontal cortex are correlated with the subjects’ subjective pleasantness ratings of the foods throughout the experiment. On the right is shown a plot of the magnitude of the fitted haemodynamic response from a representative single subject against the subjective pleasantness ratings (on a scale from –2 to +2) and peristimulus time in seconds. From “Activation of the Human Orbitofrontal Cortex to a Liquid Food Stimulus Is Correlated with Its Subjective Pleasantness,” by M. L. Kringelbach, J. O’Doherty, E. T. Rolls, and C. Andrews, 2003, Cerebral Cortex, 13, p. 1067. Reprinted with permission. B: Additional evidence for the role of the orbitofrontal cortex in subjective experience comes from another neuroimaging experiment investigating the supra-additive effects of combining the umami tastants monosodium glutamate and inosine monophosphate. The figure shows the region of mid-anterior orbitofrontal cortex showing synergistic effects (rendered on the ventral surface of human cortical areas with the cerebellum removed). The perceived synergy is unlikely to be expressed in the taste receptors themselves and the activity in the orbitofrontal cortex may thus reflect the subjective enhancement of umami taste that must be closely linked to subjective experience. From “The Representation of Umami Taste in the Human Brain,” by I. E. De Araujo, M. L. Kringelbach, E. T. Rolls,

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and P. Hobden, 2003, Journal of Neurophysiology, 90, p. 316. Reprinted with permission. C: Adding strawberry odor to a sucrose taste solution makes the combination significantly more pleasant than the sum of each of the individual components. The supralinear effects reflecting the subjective enhancement were found to significantly correlate with the activity in a lateral region of the left anterior orbitofrontal cortex, which is remarkably similar to that found in the other experiments. From “Taste-olfactory convergence, and the representation of the pleasantness of flavour, in the human brain,” by I. E. T. De Araujo, E. T. Rolls, M. L. Kringelbach, F. McGlone, and N. Phillips, European Journal of Neuroscience, 18, p. 2064. Reprinted with permission. D: These findings were strengthened by findings using deep brain stimulation (DBS) and magnetoencephalography (MEG). Pleasurable subjective pain relief for chronic pain in a phantom limb in a patient was causally induced by effective deep brain stimulation in the PVG/PAG part of the brain stem. When using MEG to directly measure the concomitant changes in the rest of the brain, a significant change in power was found in the mid-anterior OFC. From “Deep Brain Stimulation for Chronic Pain Investigated with Magnetoencephalography,” by M. L. Kringelbach, N. Jenkinson, A. L. Green, et al., 2007, NeuroReport, 18, p. 224. Reprinted with permission.

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Subjective Pleasantness

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Figure 41.6 ( Figure C.39 in color section) Social interactions and the case of reversal learning. Note: A: The lateral orbitofrontal and parts of the anterior cingulate cortices in the rostral cingulate zone are often found to be co-active in neuroimaging studies (with the regions superimposed in red). Most often this is found in tasks where the subjects have to evaluate negative stimuli which when detected may lead to a change in current behavior. B: A recent neuroimaging study found that the lateral orbitofrontal and the anterior cingulate/paracingulate cortices are together responsible for changing behavior in an object-reversal task. This task was setup to model aspects of human social interactions (see text for full description of task). Subjects

SUBJECTIVE PLEASANTNESS Using food as stimuli in neuroimaging has proved to be an interesting avenue for studying the hedonic quality of life, which is perhaps not surprising given that the essential energy to sustain life is obtained from food intake, as is much of the pleasure of life (especially on an empty stomach; Kringelbach, 2004b). Food intake in humans is not only regulated by homeostatic processes, as illustrated by our easy overindulgence on sweet foods and by rising obesity levels, but relies on the interactions between homeostatic regulation and hedonic experience (Saper et al., 2002). This complex subcortical and cortical processing involves higher-order processes such as learning, memory, planning, and prediction, and gives rise to conscious

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were required to keep track of the faces of two people and to select the happy person, who would change mood after some time, and subjects had to learn to change, reverse, their behavior to choose the other person. The most significant activity during the reversal phase was found in the lateral orbitofrontal and cingulate cortices (red and green circles), while the main effects of faces were found to elicit activity in the fusiform gyrus and intraparietal sulcus (blue circles). From “Neural Correlates of Rapid Context-Dependent Reversal Learning in a Simple Model of Human Social Interaction,” by M. L. Kringelbach and E. T. Rolls, 2003, NeuroImage, 20, p. 1375. Reprinted with permission.

experience of not only the sensory properties of the food (such as the identity, intensity, temperature, fat content, and viscosity) but also the valence elicited by the food (including, most importantly, the hedonic experience). In humans and higher primates, the orbitofrontal cortex receives multimodal information about the sensory properties of food and is therefore a candidate region for representing the incentive salience, the hedonic impact and the subjective hedonic experience hereof. A sensoryspecific satiety neuroimaging study of activity in the midanterior region of the orbitofrontal cortex showed not only a sensory-specific decrease in the reward value of the whole food eaten to satiety (and not of the whole food not eaten), but also a correlation between brain activity and pleasantness ratings (see Figure 41.5A; Kringelbach et al., 2003).

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This indicates that the reward value of the taste, olfactory, and somatosensory components of a whole food are represented in the orbitofrontal cortex, and that the subjective pleasantness of food might be represented here. As mentioned, a related fMRI study found that activity in adjacent mid-anterior parts of the orbitofrontal cortex was selectively decreased in response to an arbitrary visual cue linked to an odor that had been devalued using a selective satiation paradigm in which subjects were fed on the associated food (Gottfried et al., 2003). Another recent PET study found that the extrinsic incentive value of foods was located in a similar part of the orbitofrontal cortex (Hinton et al., 2004). Further evidence of neural correlates of subjective experience was found in an fMRI experiment investigating true taste synergism (De Araujo, Kringelbach, Rolls, & Hobden, 2003). The results of the study showed that the strong subjective enhancement of umami taste was correlated with increased activity in a mid-anterior part of the orbitofrontal cortex (see Figure 41.5B). The perceived synergy is unlikely to be expressed in the taste receptors themselves and the activity in the orbitofrontal cortex might thus reflect the subjective enhancement of umami taste. Similarly, a neuroimaging study showed that the synergistic enhancement of a matched taste and retronasal smell (where the multimodal combination was significantly more pleasant than the sum of the unimodal stimuli) correlated with activity in a mid-anterior region of the orbitofrontal cortex (De Araujo, Kringelbach, Rolls, & Hobden, 2003; see Figure 41.5C). Other neuroimaging studies have directly correlated brain activity with the subjective ratings of the pleasantness and intensity of different positive and negative reinforcers, but crucially without devaluing or otherwise manipulating the reinforcers to change their valence during the course of the experiment. The results of these correlations are therefore perhaps better thought of in terms of the monitoring processing in the orbitofrontal cortex, described previously. Consistent with this suggestion, correlations with pleasantness have been found almost exclusively in the medial orbitofrontal cortex. Pleasantness but not intensity ratings were correlated with activity in the medial orbitofrontal and anterior cingulate cortices for taste (De Araujo, Kringelbach, Rolls, & McGlone, 2003), odor (Anderson et al., 2003; E. T. Rolls, O’ Doherty, et al., 2003), chocolate (Small, Zatorre, Dagher, Evans, & Jones-Gotman, 2001), and stimulus fat content (independent of viscosity; De Araujo & Rolls, 2004). In addition, a study of thermal stimulation showed that the perceived thermal intensity was correlated with activity in the insula and orbitofrontal cortices (Craig, Chen, Bandy, & Reiman, 2000). A correlation was also recently found between a reliable index of the rush of intravenous

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met-amphetamine in drug-naive subjects and activity in the medial orbitofrontal cortex (Völlm et al., 2004). In addition, activation of the orbitofrontal cortex correlates with the negative dissonance (pleasantness) of musical chords (Blood et al., 1999), and intensely pleasurable responses, or chills, that are elicited by music are correlated with activity in the orbitofrontal cortex, ventral striatum, cingulate, and insula cortex (Blood & Zatorre, 2001). Supporting evidence for the interpretation that the medial orbitofrontal cortex implements monitoring processing of the incentive salience comes from the study mentioned previously with patients with damage to the ventromedial prefrontal cortex who have been reported to have relatively intact SCRs when receiving the monetary rewards and punishments (Bechara et al., 1994). This monitoring process is in contrast, as shown earlier, to the mid-anterior parts of the orbitofrontal cortex which correlate directly with the subjective hedonic impact. It would thus appear that dissociable regions of the human orbitofrontal cortex represent both the wanting and the liking aspect of reward. These exciting findings from neuroimaging extend previous findings in nonhuman primates of reinforcer representations to representations of the subjective affective value of these reinforcers. One has to be careful not to overinterpret mere correlations with the elusive qualities of subjective experience, and it is unlikely that hedonic experience depends on only one cortical region. Even so, it would be interesting to obtain more evidence on this issue by investigating patients with selective lesions to these areas to investigate whether their subjective affective experiences have changed. Some evidence has already been obtained to suggest that this is the case (Hornak et al., 2003).

DEEP BRAIN PLEASURES The sensory and social pleasures can be bypassed by direct stimulation of the brain (Kringelbach, Jenkinson, Owen, & Aziz, 2007). Deep brain stimulation (DBS) is generally thought to have started with the demonstration of the localized electrical excitability of the motor cortex by Fritsch and Hitzig (1870). It was, however, only with the invention of the Horsley-Clarke frame for stereotactic neurosurgery (Horsley & Clarke, 1908) that DBS became practical for subcortical structures and the full potential awaited the adaptation of this frame for humans by Spiegel, Wycis, Mark, and Lee (1947). New targets were mostly inspired by animal experiments such as the pioneering studies by Hess and Hassler during the 1940s, and by Olds and Milner in the 1950s (Gildenberg, 2005). Early usage in humans included alleviation of movement disorders, where mostly the globus pallidus and the ventral thalamus were

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Deep Brain Pleasures

targeted (Bechtereva, Bondartchuk, & Smirnov, 1972; Hassler, 1955; Hassler, Riechert, Mundinger, Umbach, & Ganglberger, 1960). In parallel, human psychosurgery from Egas Moniz’ and Walter Freeman’s lobotomies to Heath’s electrical stimulation in schizophrenics and homosexuals drew sharp public criticism (Baumeister, 2000; Valenstein, 1973). Most of this research in both animals and humans proceeded without the use of a stereotactic frame and the exact brain targets of these early electrical stimulation studies were never clear (Peciña, Smith, & Berridge, 2006). After an initial flourish, stereotactic surgery for Parkinson’s disease was largely abandoned in the 1960s when the link was found to the degeneration of the dopamine cells of the substantia nigra pars compacta and L-Dopa became widely used for treatment. However, L-Dopa often has very serious side effects and in the 1990s lesions of the globus pallidus internus (GPi) were reintroduced for PD dyskinesia. Lesions of the subthalamic nucleus (STN) can cause hemiballismus, and instead DBS of the STN and GPi at 130–180 Hz has been shown as effective and comparably safe (Aziz, Peggs, Sambrook, & Crossman, 1991). The current DBS targets for pain are in the brain stem (periventricular gray, PVG, and periaqueductal gray, PAG) and thalamus (Nandi, Liu, Joint, Stein, & Aziz, 2002). The targets for PD are in the STN (Bittar, Burn, et al., 2005), GPi (Bittar, Burn, et al., 2005; Krack et al., 2003), and pedunculopontine nucleus in the brain stem (Jenkinson, Nandi, Aziz, & Stein, 2005; Mazzone et al., 2005; Plaha & Gill, 2005). The current target for cluster headache is in the hypothalamus (Leone, Franzini, Broggi, May, & Bussone, 2004). Some promising targets for depression have been found in the inferior thalamic peduncle (Andy & Jurko, 1987), the nucleus accumbens (Schlaepfer et al., 2007), and the subgenual cingulate cortex (Mayberg et al., 2005). Programmable stimulators are implanted subcutaneously and thousands of patients have been restored to near normal lives (Perlmutter & Mink, 2006). Mood changes linked to changes in reward and hedonic processing such as unipolar depression are found in up to 40% of PD patients often starting before the onset of PD symptoms (Cummings, 1992). This is perhaps not surprising given the important role of the basal ganglia not only in movement but also in affect. The technique of stereotactic DBS thus has wide-reaching therapeutic applications clinically and in the neurosciences generally. Patients with chronic pain who have DBS of the PVG/PAG report experiencing much less pain (Bittar, Kar-Purkayastha et al., 2005; Bittar, Otero, Carter, & Aziz, 2005). The PVG/PAG receives noxious input from ascending spinothalamic pathways and descending regulatory input from higher brain structures such as the orbitofrontal cortex. Electrical stimulation of the PAG induces

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stimulation-produced analgesia in animals and humans (Boivie & Meyerson, 1982; Reynolds, 1969). This effect is ascribed to a release of endogenous opioids because the effects are reversible with the administration of the opioid antagonist naloxone (Akil, Mayer, & Liebeskind, 1976; Hosobuchi, Adams, & Linchitz, 1977), and also to the activation of descending inhibitory systems that depress spinal noxious transmission (Fields & Basbaum, 1999). Measuring Whole Brain Activity Elicited by Deep Brain Stimulation What is particularly exciting about DBS is that it offers the potential for causally changing brain activity and thus potentially can inform us about the fundamental mechanisms of brain function (Kringelbach, Jenkinson, Owen, et al., 2007). This is particularly true when combined with a noninvasive whole-brain neuroimaging technique such as MEG (Kringelbach, Jenkinson, Green, et al., 2007). In some select patients, this chronic pain can be significantly changed over a short period of time with DBS. This subjective change can be measured with MEG when changing DBS from effective to noneffective, while acquiring repeated subjective measurements on a visual scale. This can then be used in the data analysis to reveal the brain regions that mediate the change in subjective hedonic experience. We were the first group to use MEG to make direct measurements of the whole brain elicited by DBS. When DBS was turned off, the participant reported significant increases in subjective pain. During the pain relief, we found corresponding significant changes in brain activity in a network that comprises the regions of the hedonic brain and includes the mid-anterior orbitofrontal and subgenual cingulate cortices (see Figure 41.5D; Kringelbach, Jenkinson, Green, et al., 2007). We found similar brain changes in a patient with depression and in a patient with intractable cluster headache (Ray et al., 2007). This finding is strong evidence linking the orbitofrontal cortex to pain relief. These findings raise some pertinent questions about the nature of DBS. While stimulation of the PVG/PAG clearly brings about pain relief, which is clearly pleasurable, it is not clear if this would also be the case in humans without chronic pain. It is well known that while low-frequency stimulation of the PVG/ PAG can bring about pain relief in chronic pain patients, high-frequency stimulation has the opposite effect and actually makes the pain worse (Kringelbach, Jenkinson, Green, et al., 2007). Anecdotally, some DBS patients with chronic pain relief report that the pain is still there but that DBS makes them care less about the pain (Aziz, personal communication).

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A PET study investigating analgesia and placebo found that the opioid-rich brain structures lateral orbitofrontal and anterior cingulate cortices are co-active in placeboresponders, suggesting that the pain relief effect of the placebo might be related to these two brain areas being co-active (Petrovic & Ingvar, 2002; Petrovic, Kalso, Petersson, & Ingvar, 2002). It is also potentially of interest to note that lesions to an output structure of the orbitofrontal cortex, the ventral pallidum (Öngür & Price, 2000), can lead to anhedonia (Miller et al., 2006). Similar evidence of anhedonia linked to lesions of some parts of the pallidum was also found in a large case series of 117 patients undergoing pallidotomies for movement disorders (Aziz, personal communication). This is of particular importance since lesions of the posterior ventral pallidum in rats abolishes and replaces liking reactions to sweetness with bitter-type disliking instead (e.g., gapes; Cromwell & Berridge, 1993). Similar, DBS of the nucleus accumbens, which is another output structure of the orbitofrontal cortex, can alleviate anhedonia in patients with treatment-resistant depression (Schlaepfer et al., 2007). These results are not surprising given the animal literature on lesions and brain stimulation effects, where studies of rodents have indicated pleasure (liking) reactions to be generated by a network of hedonic hotspots distributed across the brain (Peciña & Berridge, 2005). Hotspot sites include cubic-millimeter localizations in nucleus accumbens, ventral pallidum, and possibly other forebrain and limbic cortical sites, and also deep brain-stem sites including the parabrachial nucleus in the pons (Peciña et al., 2006). Each hotspot is capable of supporting opioidmediated, endocannabinoid-mediated, or other neurochemical enhancements of liking reactions to a sensory pleasure such as sweetness (Smith & Berridge, 2005). Only one hedonic hotpot so far appears to be strongly necessary to normal pleasure, in the sense that only damage to it abolishes and replaces liking reactions to sweetness with bitter-type disliking instead (e.g., gapes). This essential hotspot appears to be in the posterior ventral pallidum, and perhaps adjacent areas in substantia innominata, extended amygdala, and lateral hypothalamus (Cromwell & Berridge, 1993). These findings open up a number of interesting avenues of human research, and it would be of considerable interest to investigate the effects of DBS on the mid-anterior orbitofrontal cortex as well as on the ventral pallidum. But does DBS actually produce pleasure? One could plausibly argue that DBS may help to modulate otherwise malignant oscillatory activity in the brain, based on what is known about the neural mechanisms of DBS (Kringelbach, Jenkinson, Owen, et al., 2007). The evidence would suggest that although DBS may help to restore the brain’s normal equilibrium in pathological states, DBS might have

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little effect on long-term pleasure in the normal brain. It might be possible for DBS to perturb the brain’s equilibrium in the normal state, but such perturbations are likely to be short lived, similar to those induced by various drugs. For now, DBS remains a very useful technique for alleviating the acute symptoms of anhedonia so that people can again appreciate normal sensory and social pleasures.

SUMMARY The scientific study of emotion, motivation, pleasure, and hedonic processing in humans remains in its infancy. Some progress has been made in understanding the putative brain structures involved in emotion and pleasure, mostly based on animal models but also to some extent based on human neuroimaging studies. Animal models using primarily rodents have convincingly shown that the hypothalamus, nucleus accumbens, ventral pallidum, and various brainstem nuclei such as the periaqueductal grey are important for hedonic processing (Berridge, 1996; Peciña et al., 2006). Human neuroimaging research has implicated primarily the orbitofrontal and cingulate cortices as well as the amygdala and the insular cortices. The subcortical brain regions identified with animal models (such as the nucleus accumbens and the ventral pallidum) provide some of the necessary input and output systems for multimodal association regions such as the orbitofrontal cortex that are involved in representing and learning about the reinforcers that elicit emotions and conscious feelings (Kringelbach, 2005). The recent convergence of findings from neuroimaging, neuropsychology, neurophysiology, and neurosurgery has demonstrated that the human orbitofrontal cortex is best thought of as an important nexus for sensory integration, emotional processing, and hedonic experience (see Figure 41.7). A model for the functions of the orbitofrontal cortex could be following: The posterior parts process the sensory information for further multimodal integration. The reward value of the reinforcer is assigned in more anterior parts of the orbitofrontal cortex from where it can be modulated by hunger and other internal states, and can be used to influence subsequent behavior (in lateral parts of the anterior orbitofrontal cortex with connections to anterior cingulate cortex), stored for monitoring, learning, and memory (in medial parts of the anterior orbitofrontal cortex), and made available for subjective hedonic experience (in mid-anterior orbitofrontal cortex). At all times, there is important reciprocal information flowing between the various regions of the orbitofrontal cortex and other brain regions subserving hedonic processing including the anterior cingulate cortex, the amygdala, the nucleus accumbens, and the ventral pallidum. Lateralization does not appear to play a major

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Summary (A)

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Lateral anterior

Medial anterior 10o Lateral

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Evaluations leading to change

Correlates of hedonic experience

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Figure 41.7 Model of the functions of the orbitofrontal cortex. Note: The proposed model shows the interactions between sensory and hedonic systems in the orbitofrontal cortex using as an example one hemisphere of the orbitofrontal cortex. Information is flowing from bottom to top on the figure. Sensory information arrives from the periphery to the primary sensory cortices, where the stimulus identity is decoded into stable cortical representations. This information is then conveyed for further multimodal integration in brain structures in the posterior parts of the orbitofrontal cortex. The reward value of the reinforcer is assigned in more anterior parts of the orbitofrontal cortex from where it can then be used to influence subsequent behavior (in lateral parts of the anterior

role for the functions of the human orbitofrontal cortex as shown by the largest meta-analysis of its involvement by neuroimaging studies (Kringelbach & Rolls, 2004). This model does not posit that medial orbitofrontal cortex only codes for the valence of positive reinforcers and vice versa for the lateral parts. Instead, the evidence from neuroimaging would seem to suggest that the valence of pleasures can be represented differently in different subparts of the orbitofrontal cortex. The activity (as indexed by the blood level dependent [BOLD]signal as measured with fMRI) in the medial orbitofrontal cortex would appear to correlate with the valence of reinforcers such that positive reinforcers elicit a higher BOLD signal than negative reinforcers, which is consistent with a monitoring role for the medial orbitofrontal cortex. The inverse appears to be true for the lateral parts of the orbitofrontal cortex, but with the important caveat that only the lateral parts are mostly concerned with those negative reinforcers that can bring about a change in behavior. Finally, the mid-anterior

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Olfaction

Somato- Autonomic Visual Auditory Gustatory sensory

orbitofrontal cortex with connections to anterior cingulate cortex), stored for learning/memory (in medial parts of the anterior orbitofrontal cortex), and made available for subjective hedonic experience (in mid-anterior orbitofrontal cortex). The reward value and the subjective hedonic experience can be modulated by hunger and other internal states. In addition, there is important reciprocal information flowing between the various regions of the orbitofrontal cortex and other brain regions involved in hedonic processing. From “Food for Thought: Hedonic Experience beyond Homeostasis in the Human Brain,” by M. L. Kringelbach, 2004b, Neuroscience, 126, p. 815. Reprinted with permission.

region of the orbitofrontal cortex would appear to integrate the valence with state-dependent mechanisms such as selective satiation and is thus a candidate region for taking part in the mediation of subjective hedonic experience. The proposed link to subjective hedonic processing places the orbitofrontal cortex as an important gateway to subjective conscious experience. One possible way to conceptualize the role of the orbitofrontal and anterior cingulate cortices would be as part of a global workspace for access to consciousness with the specific role of evaluating the affective valence of stimuli (Dehaene, Kerszberg, & Changeux, 1998). In this context, the medial parts of the orbitofrontal cortex are part of a proposed network for the baseline activity of the human brain at rest (Gusnard & Raichle, 2001) placing the orbitofrontal cortex as a key node in the network subserving consciousness. This could potentially explain why all experiences have an emotional tone. There are many interesting and important issues in pleasure research that are not yet fully understood. We have

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still to understand the exact interactions and oscillations of the network of brain regions subserving hedonic processing. In particular, it is presently unclear which brain regions are necessary and sufficient for pleasure. Although conscious appraisal of pleasure is usually what we mean by referring to pleasure, many emotional stimuli can be processed on a nonconscious level as demonstrated by subliminal priming (Naccache et al., 2005; Winkielman, Berridge, & Wilbarger, 2005). The most difficult question facing pleasure research remains the nature of the subjective experience of pleasure, and while some progress has been made, it is important not to over-interpret mere correlations from neuroimaging with the elusive qualities of subjective experience. In summary, pleasure, motivations, and emotions are evolutionarily important for animals (including humans) in evaluating and preparing for appropriate actions. The evolution of conscious pleasure and emotion in humans could be adaptive because they allow us to consciously appraise our emotions and actions, and subsequently to learn to manipulate these appropriately. Pleasure and emotion may be some of evolution’s most productive breakthroughs, constantly reminding us that we are still animals at heart, but endowed with the possibility of enjoying our limited time on this planet and with the enhanced control of our subjective experience that comes with it.

Barbas, H. (1988). Anatomic organization of basoventral and mediodorsal visual recipient prefrontal regions in the rhesus monkey. Journal of Comparative Neurology, 276, 313–342. Barbas, H., & Pandya, D. N. (1989). Architecture and intrinsic connections of the prefrontal cortex in the rhesus monkey. Journal of Comparative Neurology, 286, 353–375. Baumeister, A. A. (2000). The Tulane Electrical Brain Stimulation Program a historical case study in medical ethics. Journal of the History of the Neurosciences, 9, 262–278. Baxter, M. G., Parker, A., Lindner, C. C., Izquierdo, A. D., & Murray, E. A. (2000). Control of response selection by reinforcer value requires interaction of amygdala and orbital prefrontal cortex. Journal of Neuroscience, 20, 4311–4319. Baylis, L. L., & Gaffan, D. (1991). Amygdalectomy and ventromedial prefrontal ablation produce similar deficits in food choice and in simple object discrimination learning for an unseen reward. Experimental Brain Research, 86, 617–622. Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7–15. Bechara, A., Damasio, H., Tranel, D., & Anderson, S. W. (1998). Dissociation of working memory from decision making within the human prefrontal cortex. Journal of Neuroscience, 18, 428–437. Bechtereva, N. P., Bondartchuk, A. N., & Smirnov, V. M. (1972). Therapeutic electrostimulations of the deep brain structures. Vopr Neirokhir, 1, 7–12. Berridge, K. C. (1996). Food reward: Brain substrates of wanting and liking. Neuroscience and Biobehavioral Reviews, 20, 1–25. Berridge, K. C., & Kringelbach, M. L. (2008). Affective neuroscience of pleasure: Reward in humans and animals. Psychopharmacology, 199, 457–480. Berridge, K. C., & Robinson, T. E. (1998). What is the role of dopamine in reward: Hedonic impact, reward learning, or incentive salience? Brain Research Reviews, 28, 309–369.

REFERENCES Adolphs, R. (2003). Cognitive neuroscience of human social behaviour. Nature Reviews: Neuoscience, 4, 165–178. Adolphs, R., Tranel, D., Damasio, H., & Damasio, A. (1994, December 15). Impaired recognition of emotion in facial expressions following bilateral damage to the human amygdala. Nature, 372, 669–672. Aharon, I., Etcoff, N., Ariely, D., Chabris, C. F., O’Connor, E., & Breiter, H. C. (2001). Beautiful faces have variable reward value: FMRI and behavioral evidence. Neuron, 32, 537–551. Akil, H., Mayer, D. J., & Liebeskind, J. C. (1976, March 5). Antagonism of stimulation-produced analgesia by naloxone, a narcotic antagonist. Science, 191, 961–962. Amaral, D. G., & Price, J. L. (1984). Amygdalo-cortical projections in the monkey (Macaca fascicularis). Journal of Comparative Neurology, 230, 465–496.

Bindra, D. (1978). How adaptive behavior is produced: A perceptualmotivational alternative to response-reinforcement. Behavioral and Brain Sciences, 1, 41–91. Bittar, R. G., Burn, S. C., Bain, P. G., Owen, S. L., Joint, C., Shlugman, D., et al. (2005). Deep brain stimulation for movement disorders and pain. Journal of Clinical Neuroscience, 12, 457–463. Bittar, R. G., Kar-Purkayastha, I., Owen, S. L., Bear, R. E., Green, A., Wang, S., et al. (2005). Deep brain stimulation for pain relief: A metaanalysis. Journal of Clinical Neuroscience, 12, 515–519. Bittar, R. G., Otero, S., Carter, H., & Aziz, T. Z. (2005). Deep brain stimulation for phantom limb pain. Journal of Clinical Neuroscience, 12, 399–404. Blair, R. J. (2003). Facial expressions, their communicatory functions and neuro-cognitive substrates. Philosophical Transactions of the Royal Society of London. Series B, 358, 561–572. Blair, R. J., & Cipolotti, L. (2000). Impaired social response reversal. A case of “acquired sociopathy.” Brain, 123, 1122–1141.

Anderson, A. K., Christoff, K., Stappen, I., Panitz, D., Ghahremani, D. G., Glover, G., et al. (2003). Dissociated neural representations of intensity and valence in human olfaction. Nature Neuroscience, 6, 196–202.

Blair, R. J., Morris, J. S., Frith, C. D., Perrett, D. I., & Dolan, R. J. (1999). Dissociable neural responses to facial expressions of sadness and anger. Brain, 122(Pt. 5), 883–893.

Anderson, S. W., Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1999). Impairment of social and moral behavior related to early damage in human prefrontal cortex. Nature Neuroscience, 2, 1032–1037.

Blood, A. J., & Zatorre, R. J. (2001). Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion. Proceedings of the National Academy of Sciences, USA, 98, 11818–11823.

Andy, O. J., & Jurko, F. (1987). Thalamic stimulation effects on reactive depression. Applied Neurophysiology, 50(1–6), 324–329. Aziz, T. Z., Peggs, D., Sambrook, M. A., & Crossman, A. R. (1991). Lesion of the subthalamic nucleus for the alleviation of 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP)-induced parkinsonism in the primate. Movement Disorders, 6, 288–292.

c41.indd 824

Blood, A. J., Zatorre, R. J., Bermudez, P., & Evans, A. C. (1999). Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions. Nature Neuroscience, 2, 382–387. Bodamer, J. (1947). Die prosop-agnosie. Archiv für Psychiatrie und Nervenkrankheiten, 179, 6–53.

8/17/09 5:22:19 PM

References 825 Boivie, J., & Meyerson, B. A. (1982). A correlative anatomical and clinical study of pain suppression by deep brain stimulation. Pain, 13, 113–126. Bowlby, J. (1957). An ethological approach to research in child development. British Journal of Medical Psychother, 30, 230–240. Bowlby, J. (1969). Attachment and Loss: Vol. 1. Attachment. London: Hogarth Press.

De Araujo, I. E., & Rolls, E. T. (2004). Representation in the human brain of food texture and oral fat. Journal of Neuroscience, 24, 3086–3093.

Brodmann, K. (1909). Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. Leipzig, Germany: Barth.

De Araujo, I. E., Rolls, E. T., Kringelbach, M. L., McGlone, F., & Phillips, N. (2003). Taste-olfactory convergence, and the representation of the pleasantness of flavour, in the human brain. European Journal of Neuroscience, 18, 2059–2068.

Bruce, C., Desimone, R., & Gross, C. G. (1981). Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque. Journal of Neurophysiology, 46, 369–384.

Dehaene, S., Kerszberg, M., & Changeux, J. P. (1998). A neuronal model of a global workspace in effortful cognitive tasks. Proceedings of the National Academy of Sciences, USA, 95, 14529–14534.

Butter, C. M., Mishkin, M., & Rosvold, H. E. (1963). Conditioning and extinction of a food-rewarded response after selective ablations of frontal cortex in rhesus monkeys. Clinical and Experimental Neurology, 7, 65–75.

Deichmann, R., Josephs, O., Hutton, C., Corfield, D. R., & Turner, R. (2002). Compensation of susceptibility-induced BOLD sensitivity losses in echo-planar fMRI imaging. NeuroImage, 15, 120–135.

Cabanac, M. (1971, September 17). Physiological role of pleasure. Science, 173, 1103–1107. Cannon, W. B. (1927). The James-Lange theory of emotion. American Journal of Psychology, 39, 106–124. Cardinal, R. N., Parkinson, J. A., Hall, J., & Everitt, B. J. (2002). Emotion and motivation: The role of the amygdala, ventral striatum, and prefrontal cortex. Neuroscience and Biobehavioural Reviews, 26, 321–352. Carmichael, S. T., & Price, J. L. (1994). Architectonic subdivision of the orbital and medial prefrontal cortex in the macaque monkey. Journal of Comparative Neurology, 346, 366–402. Carmichael, S. T., & Price, J. L. (1995a). Limbic connections of the orbital and medial prefrontal cortex in macaque monkeys. Journal of Comparative Neurology, 363, 615–641. Carmichael, S. T., & Price, J. L. (1995b). Sensory and premotor connections of the orbital and medial prefrontal cortex of macaque monkeys. Journal of Comparative Neurology, 363, 642–664. Cavada, C., Company, T., Tejedor, J., Cruz Rizzolo, R. J., & Reinoso Suarez, F. (2000). The anatomical connections of the macaque monkey orbitofrontal cortex. A review. Cerebral Cortex, 10, 220–242. Chiavaras, M. M., & Petrides, M. (2000). Orbitofrontal sulci of the human and macaque monkey brain. Journal of Comparative Neurology, 422, 35–54. Chiavaras, M. M., & Petrides, M. (2001). Three-dimensional probabilistic atlas of the human orbitofrontal sulci in standardized stereotaxic space. NeuroImage, 13, 479–496. Craig, A. D. (2002). Opinion: How do you feel? Interoception: The sense of the physiological condition of the body. Nature Reviews: Neuroscience, 3, 655–666. Craig, A. D., Chen, K., Bandy, D., & Reiman, E. M. (2000). Thermosensory activation of insular cortex. Nature Neuroscience, 3, 184–190.

Descartes, R. (1649). Les passions de l’âme. [The passions of the Soul] Paris: Henry LeGras. Desimone, R., & Gross, C. G. (1979). Visual areas in the temporal cortex of the macaque. Brain Research, 178(2–3), 363–380. Dias, R., Robbins, T., & Roberts, A. (1996, March 7). Dissociation in prefrontal cortex of affective and attentional shifts. Nature, 380, 69–72. Eblen, F., & Graybiel, A. M. (1995). Highly restricted origin of prefrontal cortical inputs to striosomes in the macaque monkey. Journal of Neuroscience, 15, 5999–6013. Ekman, P., & Friesen, W.-V. (1971). Constants across cultures in the face and emotion. Journal of Personality and Social Psychology, 17, 124–129. Elliott, R., Dolan, R. J., & Frith, C. D. (2000). Dissociable functions in the medial and lateral orbitofrontal cortex: Evidence from human neuroimaging studies. Cerebral Cortex, 10, 308–317. Farrow, T. F., Zheng, Y., Wilkinson, I. D., Spence, S. A., Deakin, J. F., Tarrier, N., et al. (2001). Investigating the functional anatomy of empathy and forgiveness. NeuroReport, 12, 2433–2438. Fields, H. L., & Basbaum, A. (1999). Central nervous system mechanisms of pain modulation. In P. D. Wall & R. Melzack (Eds.), Textbook of pain (pp. 309–329). Edinburgh, Scotland: Churchill Livingstone. Frey, S., Kostopoulos, P., & Petrides, M. (2000). Orbitofrontal involvement in the processing of unpleasant auditory information. European Journal of Neuroscience, 12, 3709–3712. Frith, C. D. (2006). The value of brain imaging in the study of development and its disorders. Journal of Child Psychology and Psychiatry, 47, 979–982. Fritsch, G., & Hitzig, E. (1870). Über die elektrische Erregbarkeit des Grosshirns. Archives of Anatomica Physiology, 37, 300–332. Fuster, J. M. (1997). The prefrontal cortex (3rd ed.). New York: Raven Press.

Critchley, H. D., Mathias, C. J., & Dolan, R. J. (2002). Fear conditioning in humans: The influence of awareness and autonomic arousal on functional neuroanatomy. Neuron, 33, 653–663.

Gildenberg, P. L. (2005). Evolution of neuromodulation. Stereotactic and Functional Neurosurgery, 83(2–3), 71–79.

Cromwell, H. C., & Berridge, K. C. (1993). Where does damage lead to enhanced food aversion: The ventral pallidum/substantia innominata or lateral hypothalamus? Brain Research, 624(1–2), 1–10.

Gottfried, J. A., O’Doherty, J., & Dolan, R. J. (2003, August 22). Encoding predictive reward value in human amygdala and orbitofrontal cortex. Science, 301, 1104–1107.

Cummings, J. L. (1992). Depression and Parkinson’s disease: A review. American Journal of Psychiatry, 149, 443–454.

Gusnard, D. A., & Raichle, M. E. (2001). Searching for a baseline: Functional imaging and the resting human brain. Nature Reviews: Neuroscience, 2, 685–694.

Damasio, A. R. (1994). Descartes’ error. New York: Putnam. Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society of London. Series B, 351, 1413–1420. Darwin, C. (1872). The expression of the emotions in man and animals (3rd ed.). Chicago: University of Chicago Press. De Araujo, I. E., Kringelbach, M. L., Rolls, E. T., & Hobden, P. (2003). The representation of umami taste in the human brain. Journal of Neurophysiology, 90, 313–319.

c41.indd 825

De Araujo, I. E., Kringelbach, M. L., Rolls, E. T., & McGlone, F. (2003). Human cortical responses to water in the mouth, and the effects of thirst. Journal of Neurophysiology, 90, 1865–1876.

Harlow, J. M. (1848). Passage of an iron rod through the head. Boston Medical and Surgical Journal, 39, 389–393. Hasselmo, M. E., Rolls, E. T., Baylis, G. C., & Nalwa, V. (1989). Objectcentred encoding by face-selective neurons in the cortex in the superior temporal sulcus of the the monkey. Experimental Brain Research, 75, 417–429. Hassler, R. (1955). The influence of stimulations and coagulations in the human thalamus on the tremor at rest and its physiopathologic

8/17/09 5:22:19 PM

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mechanism. Proceedings of the Second International Congress of Neuropathology, 2, 637–642. Hassler, R., Riechert, T., Mundinger, F., Umbach, W., & Ganglberger, J. A. (1960). Physiological observations in stereotaxic operations in extrapyramidal motor disturbances. Brain, 83, 337–350. Haxby, J. V., Horwitz, B., Ungerleider, L. G., Maisog, J. M., Pietrini, P., & Grady, C. L. (1994). The functional organization of human extrastriate cortex: A PET-rCBF study of selective attention to faces and locations. Journal of Neuroscience, 14, 6336–6353. Hinton, E. C., Parkinson, J. A., Holland, A. J., Arana, F. S., Roberts, A. C., & Owen, A. M. (2004, September). Neural contributions to the motivational control of appetite in humans. European Journal of Neuroscience, 20, 1411–1418. Holland, P. C., & Gallagher, M. (2004, April). Amygdala-frontal interactions and reward expectancy. Current Opinion in Neurobiology, 14, 148–155. Hornak, J., Bramham, J., Rolls, E. T., Morris, R. G., O’Doherty, J., Bullock, P. R., et al. (2003). Changes in emotion after circumscribed surgical lesions of the orbitofrontal and cingulate cortices. Brain, 126, 1671–1712. Hornak, J., O’Doherty, J., Bramham, J., Rolls, E. T., Morris, R. G., Bullock, P. R., et al. (2004). Reward-related reversal learning after surgical excisions in orbitofrontal and dorsolateral prefrontal cortex in humans. Journal of Cognitive Neuroscience, 16, 463–478. Hornak, J., Rolls, E. T., & Wade, D. (1996). Face and voice expression identification in patients with emotional and behavioural changes following ventral frontal lobe damage. Neuropsychologia, 34, 247–261. Horsley, V., & Clarke, R. H. (1908). The structure and functions of the cerebellum examined by a new method. Brain, 31, 45–124. Hosobuchi, Y., Adams, J. E., & Linchitz, R. (1977, July 8). Pain relief by electrical stimulation of the central gray matter in humans and its reversal by naloxone. Science, 197, 183–186. Hull, C. L. (1951). Essentials of behavior. New Haven, CT: Yale University Press. Iversen, S. D., & Mishkin, M. (1970). Perseverative interference in monkeys following selective lesions of the inferior prefrontal convexity. Experimental Brain Research, 11, 376–386. James, W. (1890). The principles of psychology. New York: Henry Holt.

Kringelbach, M. L., Jenkinson, N., Green, A. L., Owen, S. L. F., Hansen, P. C., Cornelissen, P. L., et al. (2007). Deep brain stimulation for chronic pain investigated with magnetoencephalography. NeuroReport, 18, 223–228. Kringelbach, M. L., Jenkinson, N., Owen, S. L. F., & Aziz, T. Z. (2007). Translational principles of deep brain stimulation. Nature Reviews: Neuroscience, 8, 623–635. Kringelbach, M. L., Lehtonen, A., Squire, S., Harvey, A. G., Craske, M. G., Holliday, I. E., et al. (2007). Infant faces evoke a highly specific and rapid neural signature in the human brain. Society for Neuroscience, 782.1. Kringelbach, M. L., Lehtonen, A., Squire, S., Harvey, A. G., Craske, M. G., Holliday, I. E., et al. (2008). A specific and rapid neural signature for parental instinct. PLoS ONE. 3(2): e1664. Kringelbach, M. L., O’Doherty, J., Rolls, E. T., & Andrews, C. (2000). Sensory-specific satiety for the flavour of food is represented in the orbitofrontal cortex. NeuroImage, 11, S767. Kringelbach, M. L., O’Doherty, J., Rolls, E. T., & Andrews, C. (2003). Activation of the human orbitofrontal cortex to a liquid food stimulus is correlated with its subjective pleasantness. Cerebral Cortex, 13, 1064–1071. Kringelbach, M. L., & Rolls, E. T. (2003). Neural correlates of rapid context-dependent reversal learning in a simple model of human social interaction. NeuroImage, 20, 1371–1383. Kringelbach, M. L., & Rolls, E. T. (2004). The functional neuroanatomy of the human orbitofrontal cortex: Evidence from neuroimaging and neuropsychology. Progress in Neurobiology, 72(5), 341–372. Lange, C. G. (1887). Über Gemüstbewegungen. (Org. Om Sindsbevægelser). Leipzig, Germany: Thomas Theodor. Layard, R. (2005). Happiness: Lessons from a new science. London: Penguin Press. Leone, M., Franzini, A., Broggi, G., May, A., & Bussone, G. (2004). Longterm follow-up of bilateral hypothalamic stimulation for intractable cluster headache. Brain, 127, 2259–2264. Lorenz, K. (1971). Studies in animal and human behavior (Vol. 2). London: Methuen. Macmillan, M. (2000). An odd kind of fame: Stories of Phineas Gage. Cambridge, MA: MIT Press.

Jenkinson, N., Nandi, D., Aziz, T. Z., & Stein, J. F. (2005). Pedunculopontine nucleus: A new target for deep brain stimulation for akinesia. NeuroReport, 16, 1875–1876.

Maia, T. V., & McLelland, J. L. (2004). A reexamination of the evidence for the somatic marker hypothesis: What participants really know in the Iowa gambling task. Proceedings of the National Academy of Sciences, USA, 101, 16075–16080.

Krack, P., Batir, A., Van Blercom, N., Chabardes, S., Fraix, V., Ardouin, C., et al. (2003). Five-year follow-up of bilateral stimulation of the subthalamic nucleus in advanced Parkinson’s disease. New England Journal of Medicine, 349, 1925–1934.

Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C., et al. (2005). Deep brain stimulation for treatment-resistant depression. Neuron, 45, 651–660.

Kringelbach, M. L. (2004a). Emotion. In R. L. Gregory (Ed.), The Oxford companion to the mind (2nd ed., pp. 287–290). Oxford: Oxford University Press. Kringelbach, M. L. (2004b). Food for thought: Hedonic experience beyond homeostasis in the human brain. Neuroscience, 126, 807–819. Kringelbach, M. L. (2004c). Hjernerum: Den følelsesfulde hjerne. Copenhagen, Denmark: People’s Press. Kringelbach, M. L. (2004d). Learning to change. PLoS Biology, 2(5), e140. Kringelbach, M. L. (2005). The orbitofrontal cortex: Linking reward to hedonic experience. Nature Reviews: Neuroscience, 6, 691–702. Kringelbach, M. L. (2006). Cortical systems involved in appetite and food consumption. In S. J. Cooper & T. C. Kirkham (Eds.), Appetite and body weight: Integrative systems and the development of anti-obesity drugs (pp. 5–26). London: Elsevier. Kringelbach, M. L., & Berridge, K. C. (2009). Pleasures of the brain. New York: Oxford University Press.

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Mazzone, P., Lozano, A. M., Stanzione, P., Galati, S., Scanati, E., Peppe, A., et al. (2005). Implantation of human pedunculopontine nucleus: A safe and clinically relevant target in Parkinson’s disease. NeuroReport, 16, 1877–1881. Mesulam, M.-M., & Mufson, E. J. (1982). Insula of the old world monkey: Pt. III. Efferent cortical output and comments on function. Journal of Comparative Neurology, 212, 38–52. Miller, J. M., Vorel, S. R., Tranguch, A. J., Kenny, E. T., Mazzoni, P., van Gorp, W. G., et al. (2006). Anhedonia after a selective bilateral lesion of the globus pallidus. American Journal of Psychiatry, 163, 786–788. Naccache, L., Gaillard, R., Adam, C., Hasboun, D., Clemenceau, S., Baulac, M., et al. (2005). A direct intracranial record of emotions evoked by subliminal words. Proceedings of the National Academy of Sciences, USA, 102, 7713–7717. Nandi, D., Liu, X., Joint, C., Stein, J., & Aziz, T. (2002). Thalamic field potentials during deep brain stimulation of periventricular gray in chronic pain. Pain, 97(1/2), 47–51.

8/17/09 5:22:20 PM

References 827 Nauta, W. J. (1971). The problem of the frontal lobe: A reinterpretation. Journal of Psychiatric Research, 8, 167–187. Nobre, A. C., Coull, J. T., Frith, C. D., & Mesulam, M. M. (1999). Orbitofrontal cortex is activated during breaches of expectation in tasks of visual attention. Nature Neuroscience, 2, 11–12. O’Doherty, J., Kringelbach, M. L., Rolls, E. T., Hornak, J., & Andrews, C. (2001). Abstract reward and punishment representations in the human orbitofrontal cortex. Nature Neuroscience, 4, 95–102.

Rolls, E. T., Kringelbach, M. L., & De Araujo, I. E. T. (2003). Different representations of pleasant and unpleasant odors in the human brain. European Journal of Neuroscience, 18, 695–703.

O’Doherty, J., Rolls, E. T., Francis, S., Bowtell, R., McGlone, F., Kobal, G., et al. (2000). Sensory-specific satiety-related olfactory activation of the human orbitofrontal cortex. NeuroReport, 11, 893–897.

Rolls, E. T., O’Doherty, J., Kringelbach, M. L., Francis, S., Bowtell, R., & McGlone, F. (2003). Representations of pleasant and painful touch in the human orbitofrontal and cingulate cortices. Cerebral Cortex, 13, 308–317.

Öngür, D., & Price, J. L. (2000, March). The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebral Cortex, 10, 206–219.

Rozin, P. (2001). Food preference. In N. J. Smelser & P. B. Baltes (Eds.), International encyclopedia of the social and behavioral sciences (pp. 5719–5722). Amsterdam: Elsevier.

Pandya, D. N., & Yeterian, E. H. (1996). Comparison of prefrontal architecture and connections. Philosophical Transactions of the Royal Society of London, Series B, 351, 1423–1432.

Ryle, G. (1954). Pleasure. Proceedings of the Aristotelian Society, 28, 135–146.

Peciña, S., & Berridge, K. C. (2005). Hedonic hot spot in nucleus accumbens shell: Where do mu-opioids cause increased hedonic impact of sweetness? Journal of Neuroscience, 25, 11777–11786. Peciña, S., Smith, K. S., & Berridge, K. C. (2006). Hedonic hot spots in the brain. Neuroscientist, 12, 500–511. Perlmutter, J. S., & Mink, J. W. (2006). Deep brain stimulation. Annual Reviews of Neuroscience, 29, 229–257. Perrett, D. I., Rolls, E. T., & Caan, W. (1982). Visual neurons responsive to faces in the monkey temporal cortex. Experimental Brain Research, 47, 329–342. Petrides, M., & Pandya, D. N. (1994). Comparative cytoarchitectonic analysis of the human and the macaque frontal cortex. In F. Boller & J. Grafman (Eds.), Handbook of neuropsychology (Vol. 9, pp. 17–58). Amsterdam: Elsevier. Petrovic, P., & Ingvar, M. (2002). Imaging cognitive modulation of pain processing. Pain, 95(1/2), 1–5. Petrovic, P., Kalso, E., Petersson, K. M., & Ingvar, M. (2002, March 1). Placebo and opioid analgesia: Imaging a shared neuronal network. Science, 295, 1737–1740. Plaha, P., & Gill, S. G. (2005). Bilateral deep brain stimulation of the pedunculopontine nucleus for idiopathic Parkinson’s disease. NeuroReport, 16, 1883–1887. Rahman, S., Sahakian, B. J., Hodges, J. R., Rogers, R. D., & Robbins, T. W. (1999). Specific cognitive deficits in mild frontal variant frontotemporal dementia. Brain, 122, 1469–1493. Ranote, S., Elliott, R., Abel, K. M., Mitchell, R., Deakin, J. F., & Appleby, L. (2004). The neural basis of maternal responsiveness to infants: An fMRI study. NeuroReport, 15, 1825–1829.

Saper, C. B., Chou, T. C., & Elmquist, J. K. (2002). The need to feed: Homeostatic and hedonic control of eating. Neuron, 36, 199–211. Schachter, S., & Singer, J. (1962). Cognitive, social and physiological determinants of emotional state. Psychological Review, 69, 387–399. Schlaepfer, T. E., Cohen, M. X., Frick, C., Kosel, M., Brodesser, D., Axmacher, N., et al. (2007). Deep brain stimulation to reward circuitry alleviates anhedonia in refractory major depression. Neuropsychopharmacology, 22, 368–377. Schnider, A. (2003, August). Spontaneous confabulation and the adaptation of thought to ongoing reality. Nature Reviews: Neuroscience, 4, 662–671. Schnider, A., & Ptak, R. (1999). Spontaneous confabulators fail to suppress currently irrelevant memory traces. Nature Reviews: Neuroscience, 2, 677–681. Schnider, A., Treyer, V., & Buck, A. (2005). The human orbitofrontal cortex monitors outcomes even when no reward is at stake. Neuropsychologia, 43, 316–323. Schultz, W., & Dickinson, A. (2000). Neuronal coding of prediction errors. Annual Review of Neuroscience, 23, 473–500. Sergent, J., & Villemure, J. G. (1989). Prosopagnosia in a right hemispherectomized patient. Brain, 112, 975–995. Small, D. M., Gregory, M. D., Mak, Y. E., Gitelman, D., Mesulam, M. M., & Parrish, T. (2003). Dissociation of neural representation of intensity and affective valuation in human gustation. Neuron, 39, 701–711. Small, D. M., Jones-Gotman, M., Zatorre, R. J., Petrides, M., & Evans, A. C. (1997). Flavor processing: More than the sum of its parts. NeuroReport, 8, 3913–3917. Small, D. M., Zald, D. H., Jones-Gotman, M., Zatorre, R. J., Pardo, J. V., Frey, S., et al. (1999). Human cortical gustatory areas: A review of functional neuroimaging data. NeuroReport, 10, 7–14.

Ray, N. J., Kringelbach, M. L., Jenkinson, N., Owen, S. L. F., Davies, P., Wang, S., et al. (2007). Using magnetoencephalography to investigate deep brain stimulation for cluster headache. Biomedical Imaging and Intervention Journal, 3, e25.

Small, D. M., Zatorre, R. J., Dagher, A., Evans, A. C., & Jones-Gotman, M. (2001). Changes in brain activity related to eating chocolate: From pleasure to aversion. Brain, 124, 1720–1733.

Rempel-Clower, N. L., & Barbas, H. (1998). Topographic organization of connections between the hypothalamus and prefrontal cortex in the rhesus monkey. Journal of Comparative Neurology, 398, 393–419.

Smith, K. S., & Berridge, K. C. (2005). The ventral pallidum and hedonic reward: Neurochemical maps of sucrose “liking” and food intake. Journal of Neuroscience, 25, 8637–8649.

Reynolds, D. V. (1969, April 25). Surgery in the rat during electrical analgesia induced by focal brain stimulation. Science, 164, 444 – 445.

Spiegel, E. A., Wycis, H. T., Marks, M., & Lee, A. J. (1947, October 10). Stereotaxic apparatus for operations on the human brain. Science, 106, 349–350.

Rogers, R. D., Owen, A. M., Middleton, H. C., Williams, E. J., Pickard, J. D., Sahakian, B. J., et al. (1999). Choosing between small, likely rewards and large, unlikely rewards activates inferior and orbital prefrontal cortex. Journal of Neuroscience, 19, 9029–9038. Rolls, B. J., Rolls, E. T., Rowe, E. A., & Sweeney, K. (1981). Sensory specific satiety in man. Physiology and Behavior, 27, 137–142. Rolls, E. T. (1999). The brain and emotion. Oxford: Oxford University Press.

c41.indd 827

Rolls, E. T., Hornak, J., Wade, D., & McGrath, J. (1994). Emotion-related learning in patients with social and emotional changes associated with frontal lobe damage. Journal of Neurology, Neurosurgery, and Psychiatry, 57, 1518–1524.

Stein, A., Gath, D. H., Bucher, J., Bond, A., Day, A., & Cooper, P. J. (1991). The relationship between post-natal depression and mother-child interaction. British Journal of Psychiatry, 158, 46–52. Steiner, J. E., Glaser, D., Hawilo, M. E., & Berridge, K. C. (2001). Comparative expression of hedonic impact: Affective reactions to taste by human infants and other primates. Neuroscience and Biobehavioral Reviews, 25, 53–74.

8/17/09 5:22:21 PM

828

Neural Basis of Mental Representations of Motivation, Emotion, and Pleasure

Sutton, J. (2001). Descartes, René. In Encyclopedia of life sciences. Retrieved November 14, 2008, from http://doi.wiley.com/10.1038/npg.els.0002472.

circuitry in drug naïve human subjects. Neuropsychopharmacology, 29, 1715–1722.

Swain, J. E., Lorberbaum, J. P., Kose, S., & Strathearn, L. (2007). Brain basis of early parent-infant interactions: Psychology, physiology, and in vivo functional neuroimaging studies. Journal of Child Psychology and Psychiatry, 48(3/4), 262–287.

Walker, A. E. (1940). A cytoarchitectural study of the prefrontal area of the macaque monkey. Journal of Comparative Neurology, 73, 59–86.

Thut, G., Schultz, W., Roelcke, U., Nienhusmeier, M., Missimer, J., Maguire, R. P., et al. (1997). Activation of the human brain by monetary reward. NeuroReport, 8, 1225–1228. Ullsperger, M., & von Cramon, D. Y. (2004). Decision making, performance and outcome monitoring in frontal cortical areas. Nature Neuroscience, 7, 1173–1174. Valenstein, E. S. (1973). Brain control: A critical examination of brain stimulation and psychosurgery. London: Wiley-Interscience. Van Hoesen, G. W., Morecraft, R. J., & Vogt, B. A. (1993). Connections of the monkey cingulate cortex. In B. A. Vogt & M. Gabriel (Eds.), The Neurobiology of the cingulate cortex and limbic thalamus: A comprehensive handbook (pp. 249–284). Boston: Birkhäuser. Völlm, B. A., de Araujo, I. E. T., Cowen, P. J., Rolls, E. T., Kringelbach, M. L., Smith, K. A., et al. (2004). Methamphetamine activates reward

c41.indd 828

Walton, M. E., Devlin, J. T., & Rushworth, M. F. (2004, November). Interactions between decision making and performance monitoring within prefrontal cortex. Nature Neuroscience, 7, 1259–1265. Weiskrantz, L. (1968). Emotion. In L. Weiskrantz (Ed.), Analysis of behavioural change (pp. 50–90). New York and London: Harper & Row. Wilson, J., Jenkinson, M., de Araujo, I. E. T., Kringelbach, M. L., Rolls, E. T., & Jezzard, P. (2002). Fast, fully automated global and local magnetic field optimization for fMRI of the human brain. NeuroImage, 17, 967–976. Winkielman, P., Berridge, K. C., & Wilbarger, J. L. (2005). Unconscious affective reactions to masked happy versus angry faces influence consumption behavior and judgments of value. Personality and Social Psychology Bulletin, 31, 121–135. Zatorre, R. J., Jones-Gotman, M., Evans, A. C., & Meyer, E. (1992, November 26). Functional localization and lateralization of human olfactory cortex. Nature, 360, 339–340.

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Chapter 42

Neural Perspectives on Emotion: Impact on Perception, Attention, and Memory DAMIAN STANLEY, EMMA FERNEYHOUGH, AND ELIZABETH A. PHELPS

the range of processes that comprise emotion (e.g., Scherer, 2005) is represented in several regions and circuits of the brain (Dagleish, 2004), we focus on insights from studies of the amygdala. The amygdala, because of its wide connectivity, is suggested to play a primary role in the modulation of cognition in the presence of emotional events (Anderson & Phelps, 2000). Furthermore, due to its prominence in studies of emotion across species, the amygdala is one of the most thoroughly investigated brain regions in studies of emotion (e.g., Aggleton, 1992; LeDoux, 1996). In particular, we focus on how studies of the human amygdala have enhanced our understanding of emotion’s impact on perception, attention, and memory.

In behavioral science, investigations of the structure and impact of emotion have traditionally occurred within the domains of social, personality, or clinical psychology. Studies of cognition, inspired by the computer metaphor and the information processing approach (Miller, 2003), have generally focused on characterizing functions such as perception, attention, and memory assuming little role for emotion. This traditional divide between emotion and cognition has been debated in psychological science (Lazarus, 1984; Zajonc, 1984). Underlying these debates is the notion that the mechanisms of emotion and cognition can be separated and investigated independently. This assumption has been challenged as behavioral science has started to incorporate methods from neuroscience. One of the striking characteristics of the brain is the extensive connectivity between regions thought to underlie independent processes. This is particularly apparent in studies of emotion. Investigations of neural connectivity have shown that the amygdala, a region thought to be more or less specialized for emotion, has extensive connections throughout the brain (Young, Scannell, Burns, & Blakemore, 1994). The amygdala is a small, almondshaped structure in the medial temporal lobe that both receives input from and projects to a wide array of brain regions implicated in cognitive processes, including those underlying perception, attention, and memory. What is the role of these connections? An examination of neural circuitry suggests that emotion and cognition are intertwined at several stages of stimulus processing. From an evolutionary perspective, this makes sense. The purpose of emotion is to highlight what is relevant (Frijda, 1986) and potentially important for survival. Given this, we might expect the mechanisms of cognition to be tuned to highlight emotional events. In this chapter, we review recent research exploring the relation between cognition and emotion that has been informed by advances in cognitive neuroscience. Although

EMOTION’S INFLUENCE ON ATTENTION AND PERCEPTION The influence of emotional stimuli on attention and perception has been documented using a variety of techniques. Emotion is thought to influence attention and perception in two distinct ways. First, emotion has been found to enhance or facilitate attentional and perceptual processes, thereby increasing the salience of emotional stimuli. Second, emotion has been shown to capture our attention, leading to impaired processing of nonemotional stimuli present in the environment. Emotion Enhances Attention and Perception A number of psychological and psychophysical paradigms have demonstrated emotion’s facilitation of attention and perception. Often these are paradigms used by vision and attention researchers to show that a particular stimulus or class of stimuli has preferential access to awareness as a result of its saliency. One example is visual search, in which observers search for a target in an array of distracters. When the target 829

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and the distracters are equally salient, people take longer to find the target because they must inspect each item in the array to determine whether it is a target or a distracter. However, when a target is salient compared to surrounding distracters, it is immediately distinguishable and is said to “pop out” (Treisman & Gelade, 1980). Ohman and colleagues (Ohman, Flykt, & Esteves, 2001; Ohman, Lundqvist, & Esteves, 2001) used the visual search paradigm to show that emotional targets (e.g., threatening faces, snakes, or spiders) pop out when embedded in an array of neutral distracters (e.g., friendly faces, flowers, or mushrooms) but the reverse is not true for neutral targets. It is proposed that an enhanced ability to detect a snake in a field of flowers may have been advantageous to survival and subject to evolutionary pressures. A similar ability to detect a flower in a pit of snakes would confer little evolutionary advantage on the possessor. Other studies demonstrating emotion’s facilitation of attention have adopted paradigms used by vision researchers to suppress awareness of visual stimuli in order to determine whether affective stimuli are less susceptible to such suppression. The attentional blink (AB; Raymond, Shapiro, & Arnell, 1992) is one such paradigm. In the AB, observers view a continuous stream of rapidly changing stimuli (e.g., 6 to 20 items per second) and indicate when one of two target stimuli appears. Typically, observers experience an attentional blink for a short period (180 to 450 ms) during which the presentation of a second target goes unnoticed. Researchers interested in the influence of emotion on attention have manipulated the affective value of the targets in the AB paradigm. Anderson and Phelps (2001) found that when the target presented during the AB period is an emotionally significant word, it is often detected, suggesting that emotional stimuli have preferential access to awareness at times when attention is limited (Anderson, 2005; Anderson & Phelps, 2001). Interestingly, Anderson and Phelps (2001) also found that patients with lesions of the amygdala do not show enhanced detection of emotional stimuli, indicating a role for the amygdala in our enhanced sensitivity to affectively salient events. Another visual suppression paradigm used to demonstrate that emotional stimuli receive preferential access to awareness (Yang, Zald, & Blake, 2007) is continuous flash suppression (CFS; Tsuchiya & Koch, 2005), a variant of binocular rivalry. In binocular rivalry, two different images are presented simultaneously, one to each eye, in overlapping regions of visual space. The brain, unable to merge the dissimilar images, compromises by switching between the two so that only one is perceived at a time. If the images are equally salient, the dominant percept will switch back and forth between the two images. If the images are not equally salient, then one will tend to dominate

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over the other. In CFS, the visual properties of the stimulus presented to one eye are manipulated so that it is constantly dominant—preventing the input to the other eye from reaching awareness. Yang et al. (2007) used this paradigm to show that fearful faces (both upright and inverted) were less susceptible to CFS than neutral or happy ones. Other studies involving binocular rivalry and emotional faces or aversively conditioned stimuli have found similar effects in that the emotional stimuli dominate perception compared to their neutral counterparts (Alpers & Gerdes, 2007; Alpers, Ruhleder, Walz, Mühlberger, & Pauli, 2005). These studies demonstrate that when attentional resources are limited, emotional stimuli have preferential access to awareness. Emotion’s facilitation of attention also extends to the perception of nonemotional stimuli in the vicinity of emotional stimuli. This was demonstrated using an attentional cuing paradigm (Posner, 1980) in which fearful or neutral faces were used to cue the location of a subsequent target (Phelps, Ling, & Carrasco, 2006). The target was a simple gabor patch that varied in its visibility from trial to trial by altering its contrast. Observers had to indicate the orientation of the target. Cuing with a fearful face enhanced the perceived contrast of the subsequent target relative to cuing with a neutral face. That is, observers were able to identify the orientation of lower contrast targets when they were preceded by a fearful cue. There was an overall effect of the fear face cues on perception as well as a positive interaction between emotion and attention, providing support for separate but interacting mechanisms through which emotion and attention enhance perception. Orientation discrimination is a basic perceptual function known to rely on early visual cortex for processing. This suggests that one mechanism whereby emotion enhances perception and interacts with attention may be through the modulation of early visual areas by attentional and emotional systems. An examination of the connectivity of the brain also suggests the amygdala may play a role in modulating the visual cortex at multiple levels. As displayed in Figure 42.1, tract-tracing studies in macaque monkeys found that the majority of the ventral visual sensory cortex (from area V1 to area TE) receives direct, topographically organized projections from the amygdala (Amaral, Behniea, & Kelly, 2003). This topographic organization indicates that the amygdala’s input to the visual cortex does not serve as a diffuse, general modulator, but rather it has the spatial specificity to selectively activate different (presumably stimulus-relevant) visual regions. Further study of the termination pattern of the amygdala projections to visual cortices has revealed that they are most likely excitatory and modulatory in nature (Freese & Amaral, 2005, 2006), resembling other feedback projections into the visual

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Emotion’s Influence on Attention and Perception

V4 V1 V2 TEO Bmc L

TE

Bi

Figure 42.1 The connectivity of subregions of the amygdala (L, Bi, Bmc), and ventral visual cortical regions (V1, V2, V4, TEO, TE) in the macaque monkey. Note: From “The Organization of Projections from the Amygdala to Visual Cortical Areas TE and V1 in the Macaque Monkey,” by J. L. Freese and D. G. Amaral, 2005, Journal of Comparative Neurology, 486, p. 314. Reprinted with permission.

cortex that are known to facilitate neuronal responses to visual stimuli (Hupé et al., 1998, 2001). This pattern of connectivity between the amygdala and the sensory cortex is not unique to vision. Other neuroanatomical studies have found a similar topographic pattern of amygdala input to the sensory auditory cortex (Yukie, 2002) and evidence of projections to the early somatosensory cortex (Amaral & Price, 1984). These anatomical studies suggest a pathway by which the amygdala may facilitate attention and perception in the presence of emotional stimuli by modulating processing in sensory cortices. Consistent with this, functional brain imaging studies have shown amygdala involvement in the enhancement of responses to emotional stimuli in the visual cortex. Morris and colleagues (Morris, Buchel, & Dolan, 2001; Morris, Friston, et al., 1998) reported a correlation between the magnitude of amygdala activation in response to fearful faces and fear-conditioned stimuli, relative to neutral stimuli, and the magnitude of the enhanced response to these emotional stimuli in the visual cortex. Vuilleumier, Armony, Driver, and Dolan (2001) found that activation in the fusiform face area (FFA), a region in the ventral visual stream involved in face processing (Kanwisher, McDermott, & Chun, 1997), was modulated both by attention and emotion. They also looked for regions in which there was an interaction between emotion and attention and found that this was the case in the primary visual cortex. These functional imaging studies show a correlation between amygdala activation and enhanced responses in the visual cortex to emotional stimuli, but do not indicate that the amygdala is mediating this enhanced

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cortical response. By combining lesion and brain imaging techniques, Vuilleumier, Richardson, Armony, Driver, and Dolan (2004) were able to demonstrate that the enhanced response in the visual cortex to emotional stimuli may be the result of feedback from the amygdala. They showed a lack of visual cortex activation to fearful versus neutral faces in patients with focal damage to the amygdala. This finding supports the proposed role of the amygdala suggested by the anatomical connectivity studies and strongly implicates the amygdala in the enhancement of sensory cortical responses to emotionally salient stimuli. One possible means by which the amygdala may modulate sensory cortical regions is through reciprocal connections. The amygdala receives input from the sensory cortex that allows the detection of an emotionally salient event and then, in turn, it modulates further perceptual processing. However, findings that emotionally salient stimuli both induce and, almost simultaneously, are the target of their own modulatory influences on perception and attention imply that the amygdala might also receive information about these stimuli rapidly so that it can act on early sensory representations. Researchers investigating auditory fear conditioning in rats have identified a critical, rapid, subcortical route to the amygdala from the auditory thalamus (see Phelps & LeDoux, 2005, for a review). The existence of a similar visual subcortical route from the retina to the amygdala via the superior colliculus and pulvinar nucleus of the thalamus has been suggested (see Ohman, Carlsson, Lundqvist, & Ingvar, 2007, for a review). In support of this subcortical visual pathway for the rapid detection of emotional events, studies in monkeys have shown that the earliest neural responses in the amygdala to visual stimuli occur 60 ms after the presentation of the stimulus, which suggests little-to-no cortical processing (Nakamura, Mikami, & Kubota, 1992). In humans, strong evidence of a subcortical route for conveying the emotional nature of visual stimuli to the amygdala has been difficult to obtain because of the correlational nature of functional imaging techniques and the difficulty in interpreting null results. However, some studies have reported responses to fear faces in the proposed subcortical route (i.e., pulvinar and superior colliculus) and not in cortical regions to subliminal, unseen stimuli, as well as increased functional connectivity between the amygdala and these subcortical nuclei for unseen versus seen stimuli (Lidell et al., 2005; Morris, Friston, & Dolan, 1997; Morris, Ohman, & Dolan, 1998, 1999). Another imaging study by Vuilleumier, Armony, Driver, and Dolan (2003) took advantage of the hypothesis that a subcortical route would be more sensitive to crude, low spatial frequency visual information, whereas visual cortical processing is necessary to detect detailed, high-spatial frequency information. They showed

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that the superior colliculus, the pulvinar nucleus, and the amygdala are all preferentially sensitive to differences in low-spatial frequency information between emotional and neutral faces. In contrast, visual areas involved in face processing were more sensitive to the high-spatial frequency content of emotional faces. These imaging studies provide some support for the existence of a visual subcortical route and indicate that it may be an important component of the amygdala’s sensitivity to emotional stimuli. However, they are correlative in nature and therefore cannot be used to establish causality. The strongest evidence for the existence of a visual subcortical route involved in the processing of emotional stimuli comes from patient studies involving blindsight. Patients with blindsight have lesions in their primary visual cortex and as a result are unaware of stimuli that are presented to the corresponding visual field. However, they maintain a residual ability to discriminate certain stimuli in the absence of conscious perception (Weiskrantz, 1990). This residual visual ability is thought to rely on the retinocolliculo-pulvinar pathway. De Gelder, Vroomen, Pourtois, and Weiskrantz (1999) showed that a patient with blindsight was able to distinguish between faces with different emotional facial expressions presented in the blind visual field. Morris, DeGelder, Weiskrantz, and Dolan (2001) used functional imaging in the same patient to demonstrate that amygdala sensitivity to fearful faces presented in the blind field remained intact while sensitivity in cortical regions involved in face processing did not. Moreover, the authors found that this amygdala sensitivity to subliminal, unseen fearful faces was correlated with activity in the superior colliculus and pulvinar, further supporting a role for these subcortical regions in the processing of emotional stimuli. Emotion Captures Attention, Impairing Perception The majority of work examining the effects of emotion on attention and perception has focused on the enhanced processing of emotional stimuli. However, this enhancement does not come without a price. Much like talking on a mobile phone while driving impairs one’s driving ability (Strayer, Drews, & Johnston, 2003), focusing attention on one stimulus often comes at the perceptual cost of others (e.g., Carrasco, 2006; Pestilli & Carrasco, 2005). Is the attentional facilitation observed for emotional stimuli accompanied by an attentional cost for other, nonemotional, stimuli in the environment? A few studies have examined this question and found that perception of neutral stimuli is indeed impaired in the presence of emotional

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stimuli, a phenomenon referred to as the emotional capture of attention. As discussed, previous research has shown that emotional stimuli are more resistant to the attentional blink than neutral stimuli (Anderson & Phelps, 2001). Using modified versions of this task, other research groups have found that emotional stimuli can also exacerbate the AB. The important difference between these two types of findings lies in the placement of the emotional stimulus. In contrast to the AB paradigm used by Andersen and Phelps (2001), Most, Chun, Widders, and Zald (2005) placed emotional stimuli in a stream of distracters and investigated what would happen when an emotional distracter preceded a neutral target. They found that observers detected the target less often when it followed an emotional distracter at a delay of 200 ms, but not at a delay of 800 ms. A second study by the same group (Smith, Most, Newsome, & Zald, 2006) found a similar impairment for targets following an aversively conditioned stimulus. These findings not only provide support for the emotional capture of attention, but they also show that this capture is transient, lasting less than 800 ms. The effects of this emotional capture may be reduced using cognitive strategies, though the rate of success depends on observers’ propensity to avoid harm (Most et al., 2005). The emotional capture of attention, as demonstrated by the impaired processing of neutral stimuli, has been observed in a range of paradigms (e.g., Mathews & MacLeod, 1985), but these studies do not isolate the component of attention that is mediating this effect. In an effort to determine if emotion results in faster shifts of attention, or perhaps a difficulty in disengaging from a detected emotional stimulus to process other stimuli, spatial attention was examined with the Posner cuing paradigm (Posner, 1980). Observers fixate on a central point while a stimulus is briefly flashed (100 ms) on either the left or right side of the screen. This stimulus automatically shifts covert attention to that side of the screen. Immediately after, a target dot appears on either the left or right side, and the observer ’s task is to indicate on which side the target appeared. The dependent measure is reaction time, which is expected to be longer for invalid trials (when the location of the stimulus and target are not the same). Fox, Russo, Bowles, and Dutton (2001) used this paradigm to present neutral, positive, or negative words and examine whether the affective value of the words altered observers’ ability to detect the subsequent target dot. They found that on valid trials, where the target appeared in the same location as the words, there was no difference in reaction time for the three types of words. This suggests that emotion does not result in faster shifts of attention to the target location. However, on invalid trials, reaction times were significantly longer for trials with negative words compared

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to those with neutral or positive words. This finding indicates that once attention has shifted to the location of a negative stimulus, it is harder to disengage attention from this location to respond to nonemotional targets elsewhere. They also found that this disengagement cost for threatrelated stimuli was greater in subjects who scored highly on scales of state-anxiety. There have been very few studies of neurobiological mechanisms mediating the capture of attention, and it is not yet known precisely what role the amygdala may play in this effect (Pessoa, 2008). However, a study by Pourtois and Vuilleumier (2006) identified one component of the neural circuitry underlying the emotional capture of attention. They used electroencephalography (EEG), which provides high-resolution temporal information about neural events, and functional magnetic resonance imaging (fMRI) in two experiments using a Posner-style cuing paradigm. They found that enhanced EEG responses to targets following valid fearful faces in early visual cortex were preceded by heightened responses in regions of contralateral parietal lobe that are known to be involved in attentional control to that region of visual space. This suggests that emotional stimuli can modulate attentional control mechanisms. In addition, in the fMRI study, they found evidence of an “invalidity” effect. When a face was flashed to one side of the screen, the region of the parietal attentional control network that would be engaged in shifting attention to the other side of the screen showed less activation when this face had a fearful expression (relative to neutral). In other words, fearful faces on invalid trials captured attention, producing disengagement costs reflected in fMRI activity in the parietal cortex and resulting in impaired processing of the target. These data provide convincing evidence that emotional stimuli can modulate the neural systems involved in attentional control, effectively capturing attention and impairing processing for other, nonemotional, stimuli in the environment.

and retrieval. Episodic memory depends on the orchestration of a number of brain regions, most critically the hippocampal complex (Eichenbaum, 2002; Squire & ZolaMorgan, 1991), which lies adjacent to the amygdala in the medial temporal lobe (see Figure 42.2). In the presence of emotional events, the amygdala may modulate the neural circuitry of memory by influencing a range of mnemonic functions. Emotion’s Impact on Encoding The first stage of memory, encoding, refers to the processes that occur when a stimulus is first encountered. At encoding, factors that influence attention and perception will also influence later memory (Craik, Govoni, NavehBenjamin, & Anderson, 1996). As outlined, emotion can influence attention in two ways. Emotion facilitates attention and perception for emotional events, or those cued by emotion, and also captures attention resulting is less processing of some other, nonemotional aspects of the environment. In memory research, it has been proposed that this resulting “narrowing-of-attention” around the emotional aspects of an event results in enhanced memory for details within this narrow focus of attention and worse memory for other details (Easterbrook, 1959). This pattern of memory performance has been observed in a number of psychological studies (Heuer & Reisberg, 1992). For instance, studies of weapon focus have demonstrated how the presence of a threatening stimulus in a scene, such as a gun, can result in enhanced memory for details of the gun and stimuli in close proximity, and impaired memory for

EMOTION’S INFLUENCE ON MEMORY In the Principles of Psychology, William James (1890) suggested, “An impression may be so exciting emotionally as almost to leave a scar upon the cerebral tissues” (p. 670). James was reflecting on the common insight that emotion creates memories that have unique qualities and are often perceived to be more vivid and accurate than memories for mundane events. Both psychological and neuroscience studies of emotion have specified a number of ways emotion can change memory. It has been proposed that emotion can alter all three stages of memory: encoding, storage,

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Figure 42.2 ( Figure C. 40 in color section ) The amygdala (left, darker grey) and hippocampus (right, lighter grey). Note: Adapted from “The Human Amygdala and Awareness: Interaction of the Amygdala and Hippocampal Complex,” by E. A. Phelps, 2004, Current Opinion in Neurobiology, 14, p. 199. Reprinted with permission.

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other scene details, such as the face of the person holding the gun (Kramer, Buckhout, & Eugenio, 1990). A study by Adolphs, Tranel, and Buchanan (2005) found that the amygdala may mediate this trade-off in memory for details. They observed that patients with amygdala damage fail to show the normal memory enhancement for details of the gist of the central aspects of an emotional scene, with no difference in memory for peripheral details. Emotion Modulates Consolidation By influencing encoding, emotion is altering the nature of the stimuli that are available for the second stage of memory: retention or storage. Although the storage stage is essentially passive in that it is simply the interval between encoding and retrieval and requires no action or thought, the existing evidence suggests that it is actually a neurobiologically active process. For a period of time after encoding, the neural representation that leads to a lasting memory trace can either be strengthened, in which case the event is available for later retrieval, or weakened, in which case the event may be forgotten. This time-dependent process that helps strengthen or solidify memories is consolidation. One of the primary means by which emotion can influence memory is by enhancing the consolidation or storage of emotional events (McGaugh, 2000). Kleinsmith and Kaplan (1963) demonstrated the impact of emotion on memory storage in a classic study in which subjects were presented with word-digit pairs. The words varied in how arousing they were. At later testing, subjects were presented each word and asked to recall the digit. Some subjects were given the memory test immediately after encoding and others were given the memory test after a variety of delays ranging from 20 minutes to 1 week. When comparing recall for the digits paired with the highversus low-arousal words, arousal impaired the immediate cued recall of the digits, but by 45 minutes after encoding, memory for digits paired with high-arousal words was significantly enhanced. This effect emerged because recall for digits paired with high-arousal words did not decline, and even improved slightly, whereas recall for the digits paired with the low-arousal words showed the standard delay dependent decay in memory (Ebbinghaus, 1885/2003). This enhanced retention with arousal is consistent with an active storage process that is modulated by emotion (see also Berlyne, 1969; Heuer & Reisberg, 1992). In an elegant series of studies in nonhuman animals, McGaugh (2000) outlined the neural mechanism mediating this enhanced retention for arousing events. Physiological arousal is linked to the release of stress hormones from the adrenal gland, including epinephrine and cortisol, a glucocorticoid. Epinephrine indirectly activates ␤-adrenergic

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receptors in the amygdala, which in turn play an important role in mediating the impact of arousal on glucocorticoid receptors in the hippocampus. This critical role for the amygdala in modulating hippocampal consolidation has been demonstrated with a range of neuroscience techniques that impact the different components of this proposed neural mechanism. The evidence that the amygdala’s modulatory role impacts the consolidation or storage of hippocampaldependent memories comes from studies showing that influencing ␤-adrenergic receptors or lesioning the amygdala immediately after memory encoding alters arousal’s impact on memory consolidation. McGaugh suggested that a possible role for a slow consolidation process is to allow the emotional impact of an event, which may unfold after the event occurs, to modulate subsequent memory. In this way, emotional events, which may provide important lessons for future survival, are more likely to be retained. Evidence from studies with humans suggesting a role for the amygdala in the modulation of hippocampal consolidation has been observed with a number of cognitive neuroscience techniques. Brain imaging studies have reported that activation of the amygdala at encoding can predict later memory for emotional stimuli (Cahill et al., 1996; Canli, Zhao, Brewer, Gabrieli, & Cahill, 2000). The amygdala has direct projections to the anterior portion of the hippocampus (Stefanacci, Suzuki, & Amaral, 1996). The activation of the amygdala and anterior hippocampus is correlated during the encoding of emotional scenes that were later remembered (Dolcos, LaBar, & Cabeza, 2004). Although these studies do not indicate if the enhanced amygdala activation observed at encoding is related to altered encoding or consolidation processes, it has been demonstrated that this activation is more strongly correlated with delayed compared to immediate recall (Hamann, Ely, Grafton, & Kilts, 1999). It has also been reported that patients with amygdala damage fail to show the normal enhancement of memory with arousal (Cahill, Babinsky, Markowitsch, & McGaugh, 1995). Amygdala damage results in similar forgetting curves for arousing and neutral stimuli, in contrast to normal control subjects who show enhanced retention for arousing stimuli. This lack of a retention difference for arousing and neutral stimuli is consistent with a role for the amygdala in modulating storage or consolidation (LaBar & Phelps, 1998). Finally, in a direct test of the neurochemical mechanisms thought to mediate the amygdala’s influence on hippocampal consolidation, Cahill, Prins, Weber, and McGaugh (1994) administered a drug that blocked ␤-adrenergic receptors. The administration of this drug also blocked the normal enhanced memory observed for arousing stimuli, consistent with animal models. One of the difficulties in isolating an effect of altered storage or consolidation for arousal on memory performance is

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Emotion’s Influence on Memory 835

separating encoding from consolidation because memory retrieval reflects their combined effects. In a clever series of studies, Cahill, Gorski, and Le (2003) were able to isolate an effect of arousal on memory consolidation in humans by manipulating arousal immediately after stimulus encoding. In these studies, subjects were presented with scenes that had an emotional meaning (valence), but did not elicit a strong arousal response. Immediately after these scenes were encoded, half of the subjects were given an arousal manipulation and the other half a control manipulation. In one study, this arousal manipulation required subjects to place their arms in freezing water (cold pressor stress), while control subjects were required to place their arms in warm water. In a second study, subjects were given a drug that elicits physiological arousal (epinephrine), while control subjects were given a placebo (Cahill & Alkire, 2003). Both arousal manipulations resulted in enhanced memory for the low-arousal scenes immediately followed by arousal relative to the control manipulation. This effect of postencoding arousal on later memory only emerged for scenes that have an emotional meaning or valence. Memory for neutral scenes did not improve as a result of postencoding arousal. These results are consistent with the proposal that arousal modulates the consolidation or storage of events independent of emotion’s impact on encoding, but suggests some limits on the types of stimuli that can elicit this effect. Emotion Enhances the Subjective Sense of Remembering at Retrieval Through its impact on attention, emotion facilitates encoding of salient aspects of the environment, sometimes at the decrement of nonemotional details. Those details that are encoded are then more likely to be retained via arousal’s influence on memory consolidation. The results of these combined processes are observed at the final stage of memory, retrieval. However, studies of the retrieval of emotional events have also reported an effect of emotion on memory that is not readily apparent from its influence on encoding and consolidation. Independent of its effect on memory accuracy, emotion also enhances the subjective sense of remembering. Remembering is not only accompanied by the retrieval of past events, but also by the subjective judgment that the memory is or is not vivid, detailed, and held with confidence in its accuracy. These qualities make up the subjective sense of remembering. Brown and Kulik (1977) coined the term “flashbulb memory” to describe the impact of emotion on recollection. By examining memories for highly emotional, public events, such as the assassination of John F. Kennedy, they determined that emotion results in highly vivid, detailed,

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and confident memories, almost as if one is looking at a picture taken with a flashbulb. Brown and Kulik assumed these vivid recollections were also highly accurate because memory confidence is generally linked to memory accuracy (Lindsay, Read, & Sharma, 1998). However, when researchers examined memories for emotional public events over time, they found that these memories were often highly inaccurate in their details and were distinguished primarily by their sense of vividness and confidence in their accuracy (Neisser & Harsch, 1992; Talarico & Rubin, 2003). In other words, the subjective sense of remembering was enhanced for emotion, even when accuracy for details of the memory was not. Laboratory studies have reported a similar effect. When subjects are asked to indicate if they “remember” previously presented emotional or neutral scenes along with details of the encoding context, or simply “know” that the scene is familiar, they are more likely to indicate they “remember” emotional scenes, even when there is no difference in overall accuracy for emotional and neutral scenes (Ochsner, 2000; Sharot, Delgado, & Phelps, 2004). The neural systems mediating the impact of emotion on the subjective sense of remembering have been explored using this remember/know paradigm. Studies examining both memory encoding (Dolcos et al., 2004) and retrieval (Sharot et al., 2004) have reported that different neural mechanisms may mediate the subjective judgment of remembering for emotional and neutral scenes. For neutral scenes judged as “remembered,” stronger activation is observed in the posterior parahippocampal cortex, relative to those judged as “known.” This region of the parahippocampal cortex is also known as the parahippocampal place area (PPA; Epstein & Kanwisher, 1998) and has been shown to be involved in the processing of scenes and the encoding of scene details (Kohler, Crane, & Milner, 2002). Because subjects in this task are asked to judge if their recollection of the scene is accompanied by encoding details, it is not surprising that activation of this region would be linked to this judgment. What is surprising is that emotional scenes judged as “remembered” versus “known” do not show differential activation in this region. Instead, activation of the amygdala differentiates this mnemonic judgment for emotional scenes. In other words, there is a double dissociation. Even though subjects are making the same subjective assessment of their memory, different medial temporal lobe structures seem to be mediating this judgment for emotional and neutral stimuli. A similar pattern was observed when examining memories for September 11, 2001 (Sharot, Martorella, Delgado, & Phelps, 2007). Subjects who were in Manhattan on 9/11 were asked to retrieve personal autobiographical events from that day or the summer of 2001. They then rated these memories for arousal, vividness, confidence, sense

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of reliving, and “remembering.” About half of the participants showed enhanced ratings for the 9/11 versus summer memories. What differentiated those subjects whose 9/11 memories yielded these “flashbulb” qualities is proximity to the World Trade Center (WTC) during the terrorist attack. Those closest to the WTC not only rated their memories as more vivid, but also were more likely to report experiencing threat and direct sensory experience of the attack (e.g., seeing, hearing, smelling). An examination of the neural systems underlying the retrieval of 9/11 versus summer memories revealed that proximity was correlated with greater amygdala activation and decreased posterior parahippocampal activation, mirroring the pattern observed in laboratory studies using the remember/know paradigm. The enhanced subjective sense of memory for emotional events suggests a possible functional role for this mnemonic judgment. Our judgments about our memories can influence how we use them to guide our future actions. If we are confident in the accuracy of a memory for an event in the past, we may act decisively when encountering a similar situation in the future. However, if a memory is held with less confidence, we might seek to gather more information before acting. It has been proposed that emotion’s impact on the subjective sense of remembering, in the absence of absolute veridicality, may serve to promote faster and less ambiguous action in the face of familiar, emotional situations (Phelps & Sharot, 2008). Even if a memory is inaccurate in its details, the strong memory for the gist of an emotional event may be sufficient to enhance the vividness and confidence of memory and allow for fast action in a similar circumstance in the future.

Emotion and cognition interact at all stages of information processing, from early perception to higher reasoning. By highlighting what is important, emotion provides a framework by which cognition can be tuned to respond adaptively to environmental demands.

REFERENCES Adolphs, R., Tranel, D., & Buchanan, T. W. (2005). Amygdala damage impairs emotional memory for gist but not details of complex stimuli. Nature Neuroscience, 8, 512–518. Aggleton, J. P. (1992). The amygdala: Neurobiological aspects of emotion, memory, and mental dysfunction. New York: Wiley-Liss. Alpers, G. W., & Gerdes, A. B. M. (2007). Here is looking at you: Emotional faces predominate in binocular rivalry. Emotion, 7, 495–506. Alpers, G. W., Ruhleder, M., Walz, N., Mühlberger, A., & Pauli, P. (2005). Binocular rivalry between emotional and neutral stimuli: A validation using fear conditioning and EEG. International Journal of Psychophysiology, 57, 25–32. Amaral, D. G., Behniea, H., & Kelly, J. L. (2003). Topographic organization of projections from the amygdala to the visual cortex in the macaque monkey. Neuroscience, 118, 1099–1120. Amaral, D. G., & Price, J. L. (1984). Amygdalo-cortical projections in the monkey. Journal of Comparative Neurology, 230, 465–496. Anderson, A. K. (2005). Affective influences on the attentional dynamics supporting awareness. Journal of Experimental Psychology: General, 134, 258–281. Anderson, A. K., & Phelps, E. A. (2000). Perceiving emotion: There’s more than meets the eye. Current Biology, 10, R551–R554. Anderson, A. K., & Phelps, E. A. (2001, May 17). Lesions of the human amygdala impair enhanced perception of emotionally salient events. Nature, 411, 305–309. Berlyne, D. E. (1969). Arousal, reward and learning. Annals of the New York Academy of Sciences, 159, 1059–1070. Brown, R., & Kulik, J. (1977). Flashbulb memories. Cognition, 5, 73–99.

SUMMARY

Cahill, L., & Alkire, M. T. (2003). Epinephrine enhancement of human memory consolidation: Interaction with arousal at encoding. Neurobiololgy of Learning and Memory, 79, 194–198.

By incorporating neuroscience methods in our efforts to understand human behavior a different perspective on behavioral science is beginning to emerge. Psychologists and other behavioral scientists have divided the study of human behavior into distinct domains, which have yielded unique subsets of academic departments, conferences, and journals. These boundaries, however, are not recognized by the brain. As the range of behavioral disciplines incorporating neuroscience methods expands, so does the necessity to adopt a broader and more integrative approach to the study of human behavior. In this chapter, we highlighted how neuroscience research has informed our understanding of the relation between emotion and cognition. In light of our knowledge of the brain and its connectivity, it no longer appears feasible to clearly differentiate emotion processes from cognitive functions.

Cahill, L., Babinsky, R., Markowitsch, H. J., & McGaugh, J. L. (1995, September 28). The amygdala and emotional memory. Nature, 377, 295–296.

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Cahill, L., Gorski, L., & Le, K. (2003). Enhanced human memory consolidation with post-learning stress: Interaction with the degree of arousal at encoding. Learning and Memory, 10, 270–274. Cahill, L., Haier, R. J., Fallon, J., Alkire, M. T., Tang, C., Keator, D., et al. (1996). Amygdala activity at encoding correlated with long-term, free recall of emotional information. Proceedings of the National Academy of Sciences, USA, 93, 8016–8021. Cahill, L., Prins, B., Weber, M., & McGaugh, J. L. (1994, October 20). Beta-adrenergic activation and memory for emotional events. Nature, 371, 702–704. Canli, T., Zhao, Z., Brewer, J., Gabrieli, J. D., & Cahill, L. (2000). Eventrelated activation in the human amygdala associates with later memory for individual emotional experience. Journal of Neuroscience, 20, RC99. Carrasco, M. (2006). Covert attention increases contrast sensitivity: Psychophysical, neurophysiological and neuroimaging studies. Progress in Brain Research, 154, 33–70.

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References 837 Craik, F. I. M., Govoni, R., Naveh-Benjamin, M., & Anderson, N. D. (1996). The effects of divided attention on encoding and retrieval processes in human memory. Journal of Experimental Psychology. General, 125, 159–180.

LeDoux, J. E. (1996). The emotional brain. New York: Simon & Schuster.

Dagleish, T. (2004). The emotional brain. Nature Reviews: Neuroscience, 5, 582–589.

Lindsay, D. S., Read, J. D., & Sharma, K. (1998). Accuracy and confidence in person identification: The relationship is strong when witnessing conditions vary widely. Psychological Science, 9, 215–218.

de Gelder, B., Vroomen, J., Pourtois, G., & Weiskrantz, L. (1999). Non-conscious recognition of affect in the absence of striate cortex. NeuroReport, 10, 3759–3763. Dolcos, F., LaBar, K. S., & Cabeza, R. (2004). Interaction between the amygdala and the medial temporal lobe memory system predicts better memory for emotional events. Neuron, 42, 855–863. Easterbrook, J. A. (1959). The effect of emotion on cue utilization and the organization of behavior. Psychological Review, 66, 183–201. Ebbinghaus, H. (1885/2003). Memory: A contribution to experimental psychology. New York: Cosmo Publications. Eichenbaum, H. (2002). The hippocampal system and declarative memory in animals. Journal of Cognitive Neuroscience, 4, 217–231. Epstein, R., & Kanwisher, N. (1998, April 9). A cortical representation of the local visual environment. Nature, 392, 598–601. Fox, E., Russo, R., Bowles, R., & Dutton, K. (2001). Do threatening stimuli draw or hold visual attention in subclinical anxiety? Journal of Experimental Psychology: General, 130, 681–700. Freese, J. L., & Amaral, D. G. (2005). The organization of projections from the amygdala to visual cortical areas TE and V1 in the macaque monkey. Journal of Comparative Neurology, 486, 295–317. Freese, J. L., & Amaral, D. G. (2006). Synaptic organization of projections from the amygdala to visual cortical areas, T E, & V1 in the macaque monkey. Journal of Comparative Neurology, 496, 655–667. Frijda, N. H. (1986). The emotions. Cambridge: Cambridge University Press. Hamann, S. B., Ely, T. D., Grafton, S. T., & Kilts, C. D. (1999). Amygdala activity related to enhanced memory for pleasant and aversive stimuli. Nature Neuroscience, 2, 289–293. Heuer, F., & Reisberg, D. (1992). Emotion, arousal, and memory for detail. In S. Christianson (Ed.), The handbook of emotion and memory (pp. 151–164). Hillsdale, NJ: Erlbaum. Hupé, J. M., James, A. C., Girard, P., Lomber, S. G., Payne, B. R., & Bullier, J. (2001). Feedback connections act on the early part of the responses in monkey visual cortex. Journal of Neurophysiology, 85, 134–145. Hupé, J. M., James, A. C., Payne, B. R., Lomber, S. G., Girard, P., & Bullier, J. (1998, August 20). Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons. Nature, 394, 784–787.

Mathews, A. M., & MacLeod, C. (1985). Selective processing of threat cues in anxiety states. Behavioral Research Therapy, 23, 563–569. McGaugh, J. L. (2000, January 14). Memory: A century of consolidation. Science, 287, 248–251. Miller, G. A. (2003). The cognitive revolution: A historical perspective. Trends in Cognitive Neuroscience, 7, 141–144. Morris, J. S., Buchel, C., & Dolan, R. J. (2001). Parallel neural responses in amygdala subregions and sensory cortex during implicit fear conditioning. Neuroimage, 13, 1044–1052. Morris, J. S., DeGelder, B., Weiskrantz, L., & Dolan, R. J. (2001). Differential extrageniculostriate and amygdala responses to presentation of emotional faces in a cortically blind field. Brain, 124, 1241–1252. Morris, J. S., Friston, K. J., Büchel, C., Frith, C. D., Young, A. W., Calder, A. J., et al. (1998). A neuromodulatory role for the human amygdala in processing emotional facial expressions. Brain, 121, 47–57. Morris, J. S., Friston, K. J., & Dolan, R. J. (1997). Neural responses to salient visual stimuli. Proceedings of the Royal Society, B, Biological Sciences, 264, 769–775. Morris, J. S., Ohman, A., & Dolan, R. J. (1998, June 4). Conscious and unconscious emotional learning in the human amygdala. Nature, 393, 467–470. Morris, J. S., Ohman, A., & Dolan, R. J. (1999). A subcortical pathway to the right amygdala mediating “unseen” fear. Proceedings of the National Academy of Sciences, USA, 96, 1680–1685. Most, S. B., Chun, M. M., Widders, D. M., & Zald, D. H. (2005). Attentional rubbernecking: Cognitive control and personality in emotion-induced blindness. Psychonomic Bulletin and Review, 12, 654–661. Nakamura, K., Mikami, A., & Kubota, K. (1992). Activity of single neurons in the monkey amygdala during performance of a visual discrimination task. Journal of Neurophysiology, 67, 1447–1463. Neisser, U., & Harsch, N. (1992). Phantom flashbulbs: False recollections of hearing news about the Challenger. In E. Winograd & U. Neisser (Eds.), Affect and accuracy in recall: Studies of “flashbulb” memories (pp. 9–31). Cambridge: Cambridge University Press.

James, W. (1890). The principles of psychology. New York: Dover.

Ochsner, K. N. (2000). Are affective events richly recollected or simply familiar? The experience and process of recognizing feelings past. Journal of Experimental Psychology: General, 129, 242–261.

Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 4302–4311.

Ohman, A., Carlsson, K., Lundqvist, D., & Ingvar, M. (2007). On the unconscious subcortical origin of human fear. Physiology and Behavior, 92, 180–185.

Kleinsmith, L. J., & Kaplan, S. (1963). Paired-associate learning as a function of arousal and interpolated interval. Journal of Experimental Psychology, 65, 190–193.

Ohman, A., Flykt, A., & Esteves, F. (2001). Emotion drives attention: Detecting the snake in the grass. Journal of Experimental Psychology: General, 130, 466–478.

Kohler, S., Crane, J., & Milner, B. (2002). Differential contributions of the parahippocampal place area and the anterior hippocampus to human memory for scenes. Hippocampus, 12, 718–723.

Ohman, A., Lundqvist, D., & Esteves, F. (2001). The face in the crowd revisited: A threat advantage with schematic stimuli. Journal of Personality and Social Psychology, 80, 381–396.

Kramer, T. H., Buckhout, R., & Eugenio, P. (1990). Weapon focus, arousal, and eyewitness memory: Attention must be paid. Law and Human Behavior, 14, 167–184.

Pessoa, L. (2008). On the relationship between emotion and cognition. Nature Reviews: Neuroscience, 9, 148–158.

LaBar, K. S., & Phelps, E. A. (1998). Arousal-mediated memory consolidation: Role of the medial temproal lobe in humans. Psychological Science, 9, 490–493. Lazarus, R. S. (1984). On the primacy of cognition. American Psychologist, 39, 124–129.

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Liddell, B. J., Brown, K. J., Kemp, A. H., Barton, M. J., Das, P., Peduto, A., et al. (2005). A direct brainstem-amygdala-cortical “alarm” system for subliminal signals of fear. NeuroImage, 24, 235–243.

Pestilli, F., & Carrasco, M. (2005). Attention enhances contrast sensitivity at cued and impairs it at uncued locations. Vision Research, 45, 1867–1875. Phelps, E. A. (2004). The human amygdala and awareness: Interaction of the amygdala and hippocampal complex. Current Opinion in Neurobiology, 14, 198–202.

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Phelps, E. A., & LeDoux, J. E. (2005). Contributions of the amygdala to emotion processing: From animal models to human behavior. Neuron, 48, 175–187. Phelps, E. A., Ling, S., & Carrasco, M. (2006). Emotion facilitates perception and potentiates the perceptual benefits of attention. Psychological Science, 17, 292–299. Phelps, E. A., & Sharot, T. S. (2008). How (and why) emotion enhances the subjective sense of recollection. Current Directions in Psychological Science, 17, 147–152. Posner, M. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 32, 3–25. Pourtois, G., & Vuilleumier, P. (2006). Dynamics of emotional effects on spatial attention in the human visual cortex. Progress in Brain Research, 156, 67–91. Raymond, J. E., Shapiro, K. L., & Arnell, K. M. (1992). Temporary suppression of visual processing in an RSVP task: An attentional blink? Journal of Experimental Psychology: Human Perception and Performance, 18, 849–860. Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Information, 44, 695–729. Sharot, T., Delgado, M. R., & Phelps, E. A. (2004). How emotion enhances the feeling of remembering. Nature Neuroscience, 7, 1376–1380.

parahippocampal cortices in macaque monkeys. Journal of Comparative Neurology, 375, 552–582. Strayer, D. L., Drews, F. A., & Johnston, W. A. (2003). Cell phoneinduced failures of visual attention during simulated driving. Journal of Experimental Psychology: Applied, 9, 23–32. Talarico, J. M., & Rubin, D. C. (2003). Confidence, not consistency, characterizes flashbulb memories. Psychological Science, 14, 455–461. Treisman, A., & Gelade, G. (1980). A feature integration theory of attention. Cognitive Psychology, 12, 97–136. Tsuchiya, N., & Koch, C. (2005). Continuous flash suppression reduces negative afterimages. Nature Neuroscience, 8, 1096–1101. Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2001). Effects of attention and emotion on face processing in the human brain: An eventrelated fMRI study. Neuron, 30, 829–841. Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2003). Distinct spatial frequency sensitivities for processing faces and emotional expressions. Nature Neuroscience, 6, 624–631. Vuilleumier, P., Richardson, M. P., Armony, J. L., Driver, J., & Dolan, R. J. (2004). Distant influences of amygdala lesion on visual cortical activation during emotional face processing. Nature Neuroscience, 7, 1271–1278. Weiskrantz, L. (1990). Blindsight: A case study and its implications. (New Ed.). Oxford: Oxford University Press.

Sharot, T., Martorella, E. A., Delgado, M. R., & Phelps, E. A. (2007). Remembering 9/11: How personal experience modulates the neural circuitry of recollection. Proceedings of the National Academy of Sciences, USA, 104, 389–394.

Yang, E., Zald, D. H., & Blake, R. (2007). Fearful expressions gain preferential access to awareness during continuous flash suppression. Emotion, 7, 882–886.

Smith, S. D., Most, S. B., Newsome, L. A., & Zald, D. H. (2006). An emotion-induced attentional blink elicited by aversively conditioned stimuli. Emotion, 6, 523–527.

Young, M. P., Scannell, J. W., Burns, G. A., & Blakemore, C. (1994). Analysis of connectivity: Neural systems in the cerebral cortex. Reviews in the Neurosciences, 5, 227–250.

Squire, L. R., & Zola-Morgan S. (1991, September 20). The medial temporal lobe memory system. Science, 253, 1380–1386.

Yukie, M. (2002). Connections between the amygdala and auditory cortical areas in the macaque monkey. Neuroscience Research, 42, 219–229.

Stefanacci, L., Suzuki, W. A., & Amaral, D. G. (1996). Organization of connections between the amygdaloid complex and the perirhinal and

Zajonc, R. B. (1984). On the primacy of affect. American Psychologist, 39, 117–123.

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Chapter 43

Face Perception NANCY KANWISHER AND GALIT YOVEL

For highly social organisms like us, faces reign supreme among visual stimuli. Faces inform us not only about a person’s identity, but also about his or her mood, sex, age, and direction of gaze. The ability to extract this information within a fraction of a second of viewing a face is crucial for normal social interactions and has likely played a critical role in the survival of our primate ancestors. Considerable evidence from behavioral, neuropsychological, and neurophysiological investigations supports the hypothesis that the perception of faces is conducted by specialized cognitive and neural machinery distinct from that engaged in the perception of objects (the face specificity hypothesis). This fact has important implications for social cognition. It shows that at least one aspect of social cognition is domain specific (Fodor, 1983): Fundamentally different mental processes apparently go on, and different neural systems become engaged, when we think about people compared to objects (see also Downing, Jiang, Shuman, & Kanwisher, 2001; Saxe & Powell, 2006). The domain specificity of face perception invites a much broader investigation of domain specificity of other aspects of social cognition (Saxe & Powell, 2006). In this chapter, we review the literature on the three main cortical regions engaged in face perception in humans. We begin with a broad survey of the evidence from multiple methods for the face specificity hypothesis.

Evidence from Neuropsychology: Prosopagnosia and Agnosia The first evidence that face perception engages specialized machinery distinct from that engaged during object perception came from the syndrome of acquired prosopagnosia, in which neurological patients lose the ability to recognize faces after brain damage. Prosopagnosia is not a general loss of the concept of the person because prosopagnosic subjects can easily identify individuals from their voice or from a verbal description of the person. Impairments in face recognition are often accompanied by deficits in other related tasks such as object recognition, as expected given the usually large size of lesions relative to functional subdivisions of the cortex. However, a few prosopagnosic patients show very selective impairments in which face recognition abilities are devastated despite the lack of discernible deficits in the recognition of nonface objects (Wada & Yamamoto, 2001). Some prosopagnosic subjects have a preserved ability to discriminate between exemplars within a category (Duchaine, Yovel, Butterworth, & Nakayama, 2006; Henke, Schweinberger, Grigo, Klos, & Sommer, 1998; McNeil & Warrington, 1993), including objects of expertise (Sergent & Signoret, 1992) arguing against the idea that mechanisms of face perception are engaged more broadly on any visual stimuli requiring fine-grained discrimination. Some cases of “developmental prosopagnosia” (Duchaine et al., 2006), a lifelong impairment in face recognition (Behrmann & Avidan, 2005) with no apparent neurological lesion, show remarkably specific deficits in face perception only (Duchaine et al., 2006). Is face recognition merely the most difficult visual recognition task we perform, and hence the most susceptible to

SPECIALIZED MECHANISMS FOR FACE PERCEPTION: EVIDENCE FROM NEUROPSYCHOLOGY, BEHAVIOR, ELECTROPHYSIOLOGY, AND NEUROIMAGING Evidence from neuropsychology, behavior, electrophysiology, and neuroimaging supports the hypothesis that special mechanisms are engaged for the perception of faces.

We thank Bettiann McKay for help with manuscript preparation. This research was supported by NIH grant EY13455 and a grant from the Ellison Foundation to Nancy Kanwisher.

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brain damage? Apparently not: The striking case of patient CK (Moscovitch, Winocur, & Behrmann, 1997; see also McMullen, Fisk, Phillips, & Maloney, 2000), who had shown deficits in object recognition but normal face recognition, indicates a double dissociation between the recognition of faces and objects. Further, patient CK, who had been a collector of toy soldiers, lost his ability to discriminate these stimuli, showing a further dissociation between face recognition (preserved) and visual expertise (impaired). Thus, taken together, these selective cases of prosopagnosia and agnosia support the face specificity hypothesis and are inconsistent with its domain-general alternatives. Behavioral Signatures of Face-Specific Processing Classic behavioral work in normal subjects has also shown dissociations between the recognition of faces and objects by demonstrating a number of differences in the ways that these stimuli are processed. Best known among these signatures of face-specific processing is the face inversion effect, in which the decrement in performance that occurs when stimuli are inverted (i.e., turned upsidedown) is greater for faces than for nonface stimuli (Yin, 1969). Other behavioral markers include the “part-whole” effect (Tanaka & Farah, 1993), in which subjects are better able to distinguish which of two face parts (e.g., two noses) appeared in a previously shown face when they are presented in the context of the whole face than when they are shown in isolation, and the “composite effect” (Young, Hellawell, & Hay, 1987), in which subjects are slower to identify one half of a chimeric face if it is aligned with an inconsistent other half-face than if the two half-faces are misaligned. Consistent with the holistic hypothesis, the probability of correctly identifying a whole face is greater than the sum of the probabilities of matching each of its component face halves (Yovel, Paller, & Levy, 2005). Taken together, these effects suggest that upright faces are processed in a distinctive “holistic” manner (McKone, Martini, & Nakayama, 2001; Tanaka & Farah, 2003), that is, that faces are processed as wholes rather than as sets of parts processed independently. All the holistic effects mentioned are either absent or reduced for inverted faces and nonface objects (Robbins & McKone, 2007; Tanaka & Farah, 1993), indicating that this holistic style of processing is specific to upright faces. According to one alternative to the face specificity hypothesis, it is our extensive experience with faces that leads us to process them in this distinctive holistic and orientation-sensitive fashion. The original impetus for this hypothesis came from Diamond and Carey’s (1986) classic report that dog experts show inversion effects for dog stimuli (see Figure 43.1). Since then, claims that nonface objects of expertise exhibit facelike processing have been

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widespread. However, an extensive effort to replicate the original Diamond and Carey result met with total failure (Robbins & McKone, 2007). Further, McKone, Kanwisher, and Duchaine (2007) reviewed all of the relevant published behavioral experiments (including those that claim to support the expertise hypothesis) and found no evidence for facelike processing of objects of expertise (see Figure 43.1). Electrophysiology in Humans Face-selective electrophysiological responses occurring 170 ms after stimulus onset have also been measured in humans using scalp electrodes (Bentin, Allison, Puce, Perez, & McCarthy, 1996; Downing, Chan, Peelen, Dodds, & Kanwisher, 2006; Jeffreys, 1996). These results have been replicated with both ERPs and MEG in numerous studies that show face-selective responses both as early as 100 ms after stimulus onset (Itier & Taylor, 2004; Liu, Harris, & Kanwisher, 2002), and around 170 ms after stimulus onset (Halgren, Raij, Marinkovic, Jousmaki, & Hari, 2000; Liu et al., 2002). Although it has been claimed that the face-selective N170 response is sensitive to visual expertise with nonface stimuli (Gauthier, Curran, Curby, & Collins, 2003; Rossion, Curran, & Gauthier, 2002; Tanaka & Curran, 2001), these studies are hard to interpret because none of them include all three critical conditions: faces, objects of expertise, and control objects (McKone & Kanwisher, 2005). The one published study that included all three conditions investigated the faceselective magnetic (M170) response (Halgren et al., 2000; Liu et al., 2002) and found no elevated response to cars in car experts, and no trial-by-trial correlation between the amplitude of the M170 response and successful identification of cars by car experts (Xu, 2005) . Thus, the N170 and M170 appear to be truly face-selective and at least the M170 response is not consistent with any alternative domain-general hypotheses. What is the nature of the face representation that is manifested by the N170? Initial studies suggested that the N170 is not sensitive to identity information, but instead primarily reflects structural encoding of facial information (Bentin & Deouell, 2000). However, other studies have shown that the N170 amplitude is smaller for subsequent presentation of similar faces (within the perceptual category boundary) than different identity faces (Jacques & Rossion, 2006), which suggest that identity is processed by 170 ms after stimulus onset. (For further discussion of the logic of adaptation studies, see the discussion that follows.) Importantly, this adaptation of the N170 to identity information (i.e., the lower response for repeated compared to unrepeated face stimuli) is shown for upright faces but not inverted faces (Jacques, d’Arripe, & Rossion, 2007). Evidence for holistic processing at 170 ms after stimulus

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Specialized Mechanisms for Face Perception Recognition memory Upright–inverted difference

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Note: A: Inversion effects for homogeneous objects increase little with expertise and do not become facelike, even in a recent direct replication (aii) of the classic Diamond & Carey experiment using dogs (ai). (Instead, in most studies, experts improve relative to novices for both upright and inverted stimuli, which suggests expertise in part-based processing.) B: The part-whole effect does not increase with expertise and does not become facelike; unlike inversion, this task is a direct measure

of measures holistic processing directly. C: The composite effect is not found for objects of expertise, despite strong effects for upright faces. The two double-panel plots in (iii) and (iv) show cases where both accuracy (% correct) and reaction time (RT) were reported. For further details and references, see McKone et al. (2007). NS  p  .05. From “Can Generic Expertise Explain Special Processing for Faces?” by E. McKone, N. Kanwisher, and B. C. Duchaine, 2007, Trends in Cognitive Sciences, 11, p. 9. Reprinted with permission. * p  .05.

onset has been recently demonstrated in another adaptation study that revealed a composite effect on the N170 (Jacques et al., 2007; for more information, see Schiltz & Rossion, 2006, for similar findings with functional magnetic resonance imaging or fMRI). Taken together, these

findings suggest that by 170 ms a face-specific holistic representation, which includes all aspects of facial information, is already generated. One important unanswered question concerns the neural source of the face-selective electrophysiological responses,

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which could be either the fusiform face area (FFA; Halgren et al., 2000) and/or the superior temporal sulcus (STS) (Bentin et al., 1996). Although this question is difficult to answer given the relatively low spatial resolution of eventrelated potentials (ERP) and magnetoencephalography (MEG ), subdural ERP measurements in epilepsy patients have shown strongly face-selective responses in discrete patches of the temporal lobe (Allison et al., 1994; Allison, Puce, Spencer, & McCarthy, 1999). Further, a powerful demonstration of the causal role of these regions in face perception comes from two studies demonstrating that electrical stimulation of these ventral temporal sites can produce a transient inability to identify faces (Mundel et al., 2003; Puce, Allison, & McCarthy, 1999). Neurophysiology and Functional Magnetic Resonance Imaging in Monkeys Face specificity has been demonstrated in monkeys at both the single-cell level and at the level of cortical regions. Numerous studies dating back decades have reported faceselective responses from single neurons (face cells) in the temporal lobes of macaques (Desimone, Albright, Gross, & Bruce, 1984). Face-selective regions have been reported in macaques using fMRI (Pinsk, DeSimone, Moore, Gross, & Kastner, 2005; Tsao, Freiwald, Knutsen, Mandeville, & Tootell, 2003) and in vervets using a novel dual-activity mapping technique based on induction of the immediate early gene zif268 (Zangenehpour & Chaudhuri, 2005). Strong claims of face selectivity entail the prediction that no nonface stimulus will ever produce a response as strong as a face; because the set of nonface stimuli is infinite, there is always some possibility that a future study will show that a putative face-selective cell or region actually responds more to some previously untested stimulus (say, armadillos) than to faces. However, studies in neurophysiology have addressed this problem about as well as can practically be hoped for. Foldiak, Xiao, Keysers, Edwards, and Perrett (2004) used rapid serial visual presentation (RSVP) to test each cell on over 1,000 natural images, and found some cells that were truly face-selective: For some cells, the 70 stimuli producing the strongest responses all contained faces, and the next best stimuli produced less than one-fifth the maximal response. Although these data demonstrate individual cells that are strikingly face-selective, they don’t address the face selectivity of whole regions of the cortex. However, a more recent study demonstrates a spectacular degree of selectivity of whole regions of the cortex. Tsao, Freiwald, Tootell, and Livingstone (2006) directed eletrodes into the face-selective patches they had previously identified

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with fMRI (Tsao et al., 2003), and found that 97% of the visually responsive cells in these regions responded selectively (indeed, for most cells, exclusively) to faces. These stunning data suggest that the weak responses of the FFA to nonface stimuli may result from “partial voluming,” that is, from the inevitable blurring of face-selective and nonface-selective regions that arise when voxel sizes are large relative to the size of the underlying functional unit. Thus, these data provide some of the strongest evidence to date on the extreme selectivity of some cortical regions for face processing (Kanwisher, 2006). Brain Imaging in Humans In the early 1990s, positron emission tomography (PET) studies showed activation of the ventral visual pathway, especially the fusiform gyrus, in a variety of face perception tasks (Haxby et al., 1991; Sergent, Ohta, & MacDonald, 1992). fMRI studies of the specificity of these cortical regions for faces per se began in the mid-1990s, with demonstrations of fusiform regions that responded more strongly to faces than to letterstrings and textures (Puce, Allison, Asgari, Gore, & McCarthy, 1996), flowers (McCarthy, Puce, Gore, & Allison, 1997), and other stimuli, including mixed everyday objects, houses, and hands (Kanwisher, McDermott, & Chun, 1997). Although facespecific fMRI activations could also be seen in many subjects in the region of the face-selective STS (fSTS) and in the occipital lobe in a region named the occipital face area (OFA), the most consistent and robust face-selective activation was located on the lateral side of the mid-fusiform gyrus in a region we named the fusiform face area (FFA; Kanwisher et al., 1997; see Figure 43.2). One of the most consistent findings about face-selective activations in the occipital-temporal cortex is its hemispheric asymmetry. All three face-selective regions are larger over the right than the left hemisphere. Furthermore, this asymmetry is stable across sessions (even when they take place more than 1 year apart) in particular for the FFA (Yovel, Tambini, & Bradman, 2008). This asymmetric response to faces is consistent with the finding that right hemisphere damage is necessary (though not always sufficient) for prosopagnosia (Barton, Press, Keenan, & O’Connor, 2002; Sergent & Signoret, 1992). We recently assessed whether individual differences in the asymmetric brain response to faces is associated with the behavioral left visual field superiority for face recognition. Numerous behavioral laterality studies have shown that normal individuals recognize better faces that are presented in the left visual field that projects directly to the right hemisphere than the right visual field that projects directly to the left

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Specialized Mechanisms for Face Perception Right Hemisphere

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Figure 43.2 Face-selective activation (faces  objects, p  .0001) on an inflated brain of one subject, shown from lateral and ventral views of the right and left hemispheres. Note: Three face-selective regions are typically found: the FFA in the fusiform gyrus along the ventral part of the brain, the OFA in the lateral occipital area, and the fSTS in the posterior region of the superior temporal sulcus.

hemisphere (Rhodes, 1985; Sergent & Bindra, 1981). The known right-hemisphere dominance for face processing has been suggested to account for this behavioral laterality effect. However, this association has never been demonstrated directly. We recently found that the asymmetry of the volume of the FFA was correlated across subjects with the magnitude of the behavioral asymmetry that was collected on a different session outside the scanner. That is, subjects who showed better performance for right- than left-side faces also had a larger FFA over the right than left hemisphere. This correlation was not found with the laterality of the occipital face area or with nearby object-selective regions (lateral occipital complex or LOC). These findings suggest that the asymmetric response of the brain to faces is a stable characteristic of each individual, which is manifested both at the neural and the behavioral level (Yovel et al., 2008). The FFA region can be reliably identified in almost every normal subject in a short “localizer” fMRI scan contrasting the response to faces versus objects. In the functional region of interest (f ROI) approach, the FFA is first functionally localized in each individual, then its response magnitude is measured in a new set of experimental conditions; this method enables the FFA to be studied directly despite its anatomical variability across subjects, in a statistically powerful yet unbiased fashion (Saxe, Brett, & Kanwisher, 2006). In contrast, group studies often cannot identify the FFA at all because of the variability in its precise location across subjects. Because the FFA is the most robust of the three face-selective regions (Kanwisher et al., 1997; Yovel &

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Kanwisher, 2004), it has been investigated most fully, more is known about it, and we focus on it in this section. We return to other face-selective regions later in the chapter. Three lines of evidence indicate that the FFA responds specifically to faces and not to lower-level stimulus features usually present in faces (such as a pair of horizontally arranged dark regions). First, the FFA responds strongly and similarly to a wide variety of face stimuli that would appear to have few low-level features in common, including front and profile photographs of faces (Tong, Nakayama, Moscovitch, Weinrib, & Kanwisher, 2000), line drawings of faces (Spiridon & Kanwisher, 2002), cat faces (Tong et al., 2000), and two-tone stylized Mooney faces. Second, the FFA response to upright Mooney faces is almost twice as strong as the response to inverted Mooney stimuli in which the face is difficult to detect (Kanwisher, Tong, & Nakayama, 1998; Rhodes, Byatt, Michie, & Puce, 2004), even though most low-level features (such as spatial frequency composition) are identical in the two stimulus types. Finally, for bistable stimuli such as the illusory face-vase (Hasson, Hendler, Ben Bashat, & Malach, 2001), or for binocularly rivalrous stimuli in which a face is presented to one eye and a nonface is presented to the other eye (Pasley, Mayes, & Schultz, 2004; Tong, Nakayama, Vaughan, & Kanwisher, 1998; Williams, Moss, & Bradshaw, 2004), the FFA responds more strongly when subjects perceive a face than when they do not see a face even though the retinal (Andrews, Schluppeck, Homfray, Matthews, & Blakemore, 2002) stimulation is unchanged. For all these reasons, it is difficult to account for the selectivity of the FFA in terms of lower-level features that covary with faceness. Instead, these findings support the face specificity hypothesis. However, before the specificity of the FFA can be accepted, several other alternatives must be considered. First, is the FFA engaged whenever subjects must discriminate between similar exemplars within a category, whether or not the stimulus is a face (Gauthier, Behrmann, & Tarr, 1999). No: When subjects perform within-category discrimination for faces and houses that have been matched for discriminability, the FFA still responds about three times as strongly during face discrimination as house discrimination (Yovel & Kanwisher, 2004). This experiment also rules out a second alternative to the face specificity hypothesis, according to which the FFA is involved in domain-general configural processing of any stimulus types: The FFA response was no higher when subjects discriminated faces or houses on the basis of the spacing between parts than when they discriminated faces or houses based on the appearance of the parts. Thus, the FFA is not involved in a domain-general way in either fine-grained discrimination, or configural processing, or any stimulus type; instead, it is specific for faces per se.

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In the most widely discussed third alternative hypothesis, it is claimed that the FFA is not face specific because it responds more strongly to objects of expertise than to control objects. However, this effect is in fact significant in only three (Gauthier, Skudlarski, Gore, & Anderson, 2000; Gauthier et al., 1999; Xu, 2005) of the nine studies that have tested the hypothesis (see also Grill-Spector, Knouf, & Kanwisher, 2004; Jiang et al., 2007; Moore, Cohen, & Ranganath, 2006; Op de Beeck, Baker, DiCarlo, & Kanwisher, 2006; Rhodes et al., 2004; Yue, Tjan, & Biederman, 2006). A plausible alternative account of this weak and unreliable effect is that it reflects a general increase in attentional engagement for objects of expertise compared to control objects, not a special role of face regions in expertise. This alternative attentional account predicts that increased responses to objects of expertise should be found not only in the FFA but in nearby object-processing regions such as the LOC. Indeed, all four studies that have tested for effects of expertise in both the FFA and the LOC find larger effects of expertise in the LOC than in the FFA (Jiang et al., 2007; Moore et al., 2006; Op de Beeck et al., 2006; Yue et al., 2006). These findings are inconsistent with the expertise hypothesis, instead supporting the face specificity hypothesis (see also Kanwisher & Yovel, 2006; McKone et al., 2007). Summary Taken together, these lines of research make a compelling case for the existence of specialized cognitive and neural machinery for face perception per se (the face specificity hypothesis), and argue against a variety of alternative hypotheses. First, neuropsychological double dissociations exist between face recognition and visual expertise for nonface stimuli, casting doubt on the claim that these two phenomena share processing mechanisms. Second, behavioral data from normal subjects show a number of “signatures” of holistic face processing that are not observed for other stimulus classes, such as inverted faces and objects of expertise. Third, electrophysiological measurements indicate face-specific processing at or before 200 ms after stimulus onset (N170). Fourth, fMRI and physiological investigations in monkeys show strikingly selective (and often exclusive) responses to faces both within individual neurons, and more recently also within cortical regions. Finally, extensive investigation of the most robust faceselective cortical region in humans, the FFA, supports the face specificity hypothesis (see also Kanwisher & Yovel, 2006) . This strong evidence for face-specific mechanisms invites a more detailed investigation of the precise nature of the computations and representations extracted in each of the face-selective regions of the cortex, which we turn to next.

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THE NATURE OF THE FACE REPRESENTATIONS IN THE FUSIFORM FACE AREA Many experiments implicate the FFA in determining face identity, that is, in extracting the perceptual information used to distinguish between individual faces. For example, we showed a higher FFA response on trials in which subjects correctly identified a famous face than on trials in which they failed to recognize the same individual (Grill-Spector et al., 2004), implicating this region in the extraction of information about face identity. (No comparable correlation between the FFA response and performance was seen for identification of specific types of cars, guitars, buildings, etc.) Further evidence that the FFA is critical for distinguishing between individual faces comes from studies that use fMRI-adaptation, discussed later. Finally, the critical lesion site for prosopagnosia is very close to the FFA (Barton et al., 2002; Bouvier & Engel, 2006). However, these results tell us nothing about the nature of the representations extracted from faces in the FFA, which we turn to next. Invariances of Face Representations To understand the representations of faces extracted by the FFA, we need to determine their equivalence classes. If the FFA is involved in discriminating between individuals, then it must extract different representations for different individuals. But are these representations invariant across images of the same face that differ in size, position, view, and so on? The best current method for approaching this problem with fMRI is fMRI-adaptation (Grill-Spector et al., 1999; Kourtzi & Kanwisher, 2001), in which the bloodoxygen-level dependent (BOLD) response to two (or more) stimuli in a given region of the brain is lower when they are the same than when they are different, indicating a sensitivity of that brain region to that stimulus difference. This sensitivity to the sameness of two stimuli enables us to ask each brain region which stimulus pairs it takes to be the same and which it takes to be different, that is, to discover equivalence classes and invariances in neural representations of faces. Several studies have found robust fMRI- adaptation for faces in the FFA, that is a lower response to identically repeated faces than to new faces (e.g., Avidan, Hasson, Malach, & Behrmann, 2005; Eger, Schweinberger, Dolan, & Henson, 2005; Gauthier & Nelson, 2001; Pourtois, Schwartz, Seghier, Lazeyras, & Vuilleumier, 2005; Rotshtein, Henson, Treves, Driver, & Dolan, 2005; Yovel & Kanwisher, 2004). Does this adaptation reflect a representation of face identity that is invariant across different

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The Nature of the Face Representations in the Fusiform Face Area 847

images of the same person? Several studies have found adaptation across repeated images of the same face even when those images differ in position (Grill-Spector et al., 1999), image size (Andrews & Schluppeck, 2004; GrillSpector et al., 1999), and spatial scale (Eger, Schyns, & Kleinschmidt, 2004). Further, Rotshtein et al. (2005) used categorical perception of morphed faces to show adaptation across physically different images that were perceived to be the same (i.e., two faces that were on the same side of a perceptual category boundary), but not across physically different images that were perceived to be different (i.e., two faces that straddled the category boundary). A similar study with unfamiliar morphed faces also revealed a close correspondence between the perceptual boundary and the magnitude of adaptation (Gilaie-Dotan & Malach, 2007). Thus, representations in the FFA are not tied to low-level image properties, but instead show at least partial invariance to simple image transformations. However, representations in the FFA do not appear to be invariant to nonaffine changes such as changes in lighting direction (Bradshaw, 1968), viewpoint (Pourtois et al., 2005; Warrington, Logue, & Pratt, 1971), and combinations thereof (Avidan et al., 2005; Pourtois et al., 2005). Fang, Murray, and He (2007) found sharp view-specific tuning in the FFA and STS. View-specific tuning in the FFA was more precise after very long presentations of the adaptor (25 sec) than after shorter presentations (0.3 sec). These studies indicate that the FFA treats two images of the same face that differ in viewpoint and lighting as two different images. In sum, studies conducted to date converge on the conclusion that neural representations of faces in the FFA discriminate between faces of different individuals and are partly tolerant to simple image transformations including size, position, and spatial scale. However, these representations are not invariant to nonaffine changes in viewpoint or lighting. Discriminating between Familiar and Unfamiliar Faces A finding that the FFA responds differently to familiar and unfamiliar faces would support the role of this region in face recognition (though it is not required by this hypothesis as discussed shortly). Several fMRI studies have investigated this question (George et al., 1999; Gorno-Tempini et al., 1998; Haxby et al., 1999; Henson, Shallice, GornoTempini, & Dolan, 2002; Leveroni et al., 2000; Sergent et al., 1992; Wiser et al., 2000) using as familiar faces either famous faces or faces studied in the lab. Two studies that investigated faces learned in the lab found opposite results, one showing an increase in the

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response to familiar compared to unfamiliar faces in the FFA (Lehmann et al., 2004) and the other (using PET) found a decrease in the response to familiar faces (Rossion, Schiltz, & Crommelinck, 2003). Although this discrepancy may be due to the use of different tasks in the two experiments (Rossion et al., 2003; see also Henson et al., 2002), studies of famous faces, which provide a stronger manipulation of familiarity, do not give a much clearer picture. One study found a small but significant increase in the response to famous compared to nonfamous faces (Avidan et al., 2005) but two other studies found no difference in the response to famous versus nonfamous faces in the FFA (Eger et al., 2005; Pourtois et al., 2005; see also Gorno-Tempini & Price, 2001; Gorno-Tempini et al., 1998). Taken together, these studies do not show a consistently different FFA response for familiar versus unfamiliar faces. Although these studies do not strengthen the case that the FFA is important for face recognition, it is important to note that they do not provide evidence against this hypothesis either. These results may simply show that the FFA merely extracts a perceptual representation from faces in a bottom-up fashion, with actual recognition (i.e., matching to stored representations) occurring at a later stage of processing. It is also possible that information about face familiarity is represented not by an overall difference in the mean response but by the pattern of response across voxels within the FFA (Haxby et al., 2001; but see Kriegeskorte, Formisano, Sorger, & Goebel, 2007). Studies of face familiarity do however enable us to address a different question about the FFA—its role in processing of nonvisual semantic information about people. Because famous faces are associated with rich semantic information about the person, but nonfamous faces are not, the lack of a consistently and robustly higher response for famous rather than nonfamous faces in the FFA casts doubt on the idea espoused by some (Martin & Chao, 2001), that this region is engaged in processing not only perceptual but also semantic information about people (Turk, Rosenblum, Gazzaniga, & Macrae, 2005). Face Inversion Effect and Holistic Processing As described previously, behavioral studies have discovered distinctive “signatures” of face-like processing, including the face inversion effect (Yin, 1969) and the composite effect. Does the FFA mirror these behavioral signatures of face-specific processing? Early studies of the face inversion effect in the FFA found little (Haxby et al., 1999; Kanwisher, Stanley, & Harris, 1999) or no (Aguirre, Singh, & D’Esposito, 1999; Leube et al., 2003) difference in the response to upright and inverted faces. However, we reported a substantially

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higher FFA response for upright compared to inverted faces (Yovel et al., 2004). Further, in a subsequent study, we (Yovel & Kanwisher, 2005) reported that the FFAface inversion effect was correlated across subjects with the behavioral face inversion effect. That is, subjects who showed a large increment in performance for upright versus inverted faces also showed a large increment in the FFA response to upright versus inverted faces (see Figure 43.3). Second, we found greater fMR-adaptation for upright than inverted faces, indicating that the FFA is more sensitive to identity information in upright than in inverted faces (Yovel & Kanwisher, 2005; see also Mazard, Schiltz, & Rossion, 2006). Thus, consistent with the behavioral face inversion effect, the FFA better discriminates faces when they are upright than when they are inverted. Importantly, this pattern of response was specific to the FFA studies (see discussion of OFA and STS that follows). In summary, in contrast to previous findings that found only a weak relationship between the FFA and the face inversion effect, our findings show a close link between these behavioral and neural markers of specialized face processing. The larger behavioral inversion effect for faces rather than objects has been taken as evidence for holistic processing of upright but not inverted faces (Farah, Tanaka, & Drain, 1995). However, more direct evidence for holistic processing comes from the composite effect (Young et al., 1987) in which subjects are not able to process the upper or lower half of a composite face independently from the other half of the face even when instructed to do so, unless the two halves are misaligned. This effect is found for upright but not inverted faces. If the FFA is engaged in holistic processing of faces, then we might expect it to show an fMRI correlate of the composite effect. One study used fMRI adaptation to show evidence for a composite face effect in the FFA (Schiltz & Rossion, 2006). In particular, the FFA only showed adaptation across two identical top halves of a face (compared to two different top halves) when the bottom half of the face was also identical, consistent with the behavioral composite face effect. As with the behavioral composite effect, the fMRI composite effect was found only for upright faces and was absent for inverted faces or misaligned faces. Thus, fMRI measurements from the FFA show neural correlates of the classic behavioral signatures of face-like processing, including the face inversion effect and the composite effect. These findings link the behavioral evidence on face-specific processing with research on the FFA, as well as helping to characterize the operations and representations that occur in the FFA.

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Representation of Configuration and Parts of Faces Three prominent features of face stimuli are the classic frontal face configuration (the arrangement of two horizontally and symmetrically placed parts above two vertically placed parts), the presence of specific face parts (eyes, nose, mouth), and the bounding contour of a roughly oval shape with hair on the top and sides. Which of these stimulus properties are important in driving the response of the FFA? Liu and colleagues (Liu et al., 2003) created stimuli in which each of these three attributes were orthogonally varied. The face configuration was either canonical or scrambled (with face parts rearranged to occur in different positions), veridical face parts were either present or absent (i.e., replaced by black ovals), and external features were either present or absent (with a rectangular frame showing only internal features, omitting chin and hairline). This study found that the FFA responds to all three kinds of face properties. A prominent theory (Maurer, LeGrand, & Mondloch, 2002) suggests that the spacing among face parts plays a privileged role in our representation of faces, dissociable from the representation of the shape of the parts. However, fMRI studies consistently lead to the conclusion that the FFA is involved in processing both the parts and the spacing among the parts of faces (Maurer et al., 2007; Rotshtein, Geng, Driver, & Dolan, 2007; Yovel & Kanwisher, 2004). First, Yovel and Kanwisher (2004) scanned subjects while they performed a successive discrimination task on pairs of faces that differed in either the individual parts, or in the configuration (i.e., spacing) of those parts. Subjects were informed in advance of each block which kind of discrimination they should perform. The FFA response was similar and strong in both conditions, again indicating a role of the FFA in the discrimination of both face parts and face configurations. Second, two fMRI studies that examined the brain response when subjects discriminated faces that differ in spacing or parts also support the hypothesis that the FFA is involved in processing both spacing and part information in faces. Although Maurer et al. (2007) reported some regions that were differentially sensitive to spacing information or to part-based information, these fusiform activations were located outside the FFA and therefore do not argue against our contention that the FFA is engaged in both processes. A close examination of the face-selective region in their study did not reveal any difference between the response to spacing and parts even when very low threshold levels were applied. Similarly, Rotshtein et al. (2007) examined repetition effects for faces that differ in spacing and parts. Several regions outside face-selective regions showed differential sensitivity to

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The Nature of the Face Representations in the Fusiform Face Area 849 0.9 0.8

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Figure 43.3 The magnitude of the face inversion effect in the FFA (but not the OFA, fSTS, or LOC) is correlated across subjects with the magnitude of the behavioral face inversion effect.

Note: From “A Whole Face Is More Than the Sum of Its Halves: Interactive Processing in Face Perception,” by Yovel, et al. (2005). The neural basis of the FIE Current Biology, pp. 15, 2256–2262, Figure 2.

spacing versus parts, whereas an area that overlapped with the face-selective fusiform area was sensitive to repetition of parts and was correlated with performance on the spacing discrimination task. The authors concluded that information about spacing and parts may converge in the FFA.

Taken together, these studies show that the FFA is not sensitive to only a few specific face features, but instead seems to respond generally to a wide range of features spanning the whole face. Whereas several brain regions do show dissociated responses to information about spacing or parts,

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the FFA seems instead to generate an integrated representation that includes all aspects of face information. These findings are consistent with a recent behavioral study that found a positive correlation across subjects between performance on discrimination of spacing and discrimination of parts only for upright faces, not for inverted faces or houses (Yovel & Kanwisher, 2008). Thus, although spacing and part-based information about objects are processed by distinct mechanisms, information about the spacing and parts of upright faces are integrated into a common, holistic representation.

Norm-Based Coding of Faces The power of caricatures to capture the likeness of a face suggests that face identity is coded in terms of deviation from the norm or average face, a hypothesis supported by behavioral studies (Leopold, O’ Toole, Vetter, & Blanz, 2001; Rhodes, Brennan, & Carey, 1987). One fMRI study found higher FFA responses to atypical compared to average faces, implicating the FFA in such norm-based coding of face identity (Loffler, Yourganov, Wilkinson, & Wilson, 2005). However, efforts in this study to unconfound such face typicality effects from the greater adaptation effects expected between highly similar faces (in the average-face condition) versus very different faces (in the atypical face condition) were not entirely satisfactory. Therefore, the interesting hypothesis that the FFA codes faces in terms of deviation from the average face remains to be fully tested and explored.

Top-Heavy Figures Although several studies have found that newborns look preferentially at facelike images (Johnson, Dziurawiec, Ellis, & Morton, 1991), this preference may reflect a more general preference for top-heavy figures (Cassia, Turati, & Simion, 2004). Top-heavy figures are similar to faces in that they contain more information in their upper half. To assess whether the FFA shows a similar preference to such figures, Caldara and colleagues (2006) presented head-shape and square-shape figures that included more information in their upper or lower halves. The right FFA showed the highest response to head-shape top-heavy stimuli. The response of the right FFA to a top-heavy square and bottom-heavy stimuli was similar and lower. This pattern is consistent with behavioral ratings of faceness on these stimuli. The left FFA and the OFA showed similar responses to top and bottom-heavy stimuli. Thus, only the right FFA shows a higher sensitivity to the type of stimuli that elicit longer looking time during the first 24 hours of life.

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Fusiform Face Area and Facial Expression Information fMRI studies of face expression have primarily focused on the amygdala (e.g., Glascher, Tuscher, Weiller, & Buchel, 2004; Williams et al., 2004). However, studies that have investigated the response of the temporal cortex have also found higher responses to emotional rather than neutral faces in the fusiform gyrus (Breiter et al., 1996; Dolan, Morris, & de Gelder, 2001; Vuilleumier, Armony, Driver, & Dolan, 2001, 2003; Williams et al., 2004). It has been suggested that this effect is modulated by connections from the amygdala (Dolan et al., 2001). Consistent with this hypothesis, effects of facial expression (in contrast to face identity) are not specific to the FFA. Given the higher arousal generated by emotional faces, the higher response to expressive than neutral faces in the FFA may reflect a general arousal effect rather than specific representation of facial expression. Indeed, one fMRI-adaptation study (Winston, Vuilleumier, & Dolan, 2003), in which expression and identity were manipulated in a factorial manner, did not find significant fMRI-adaptation to expression information in the fusiform gyrus, but did find fMRI-adaptation to face expression in regions in the STS. These findings are consistent with the idea that the FFA is involved in the extraction of identity but not expression information, whereas the STS shows the opposite pattern of response (Haxby, Hoffman, & Gobbini, 2000). However, another study found a higher FFA response during expression judgments and identity judgments that were done on separate blocks on the same face stimuli (Ganel, Valyear, Goshen-Gottstein, & Goodale, 2005), casting some doubt on the simple idea that the FFA is involved exclusively in processing face identity information. Further evidence for the possible role of the FFA in expression processing comes from a recent developmental fMRI study in which adults, teenagers (13 to 17 years), and children (8 to 11 years) were asked to classify facial expressions for upright or inverted faces (Passarotti, Smith, DeLano, & Huang, 2007). Just as for face identity findings (Yovel & Kanwisher, 2005), adults showed higher responses to upright than inverted stimuli in regions that overlap with face-selective regions in the fusiform gyrus and the STS (a face localizer was not included in Passarotti et al., 2007). In contrast, teenagers and children showed weaker or absent fMRI-face inversion effects in these regions. Finally, only in adults and only in the area that overlapped with the right FFA, a correlation across subjects was found between the fMRI and behavioral inversion effects. This finding suggests that the FFA may play some role in extracting information about emotional expressions in faces.

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Function of the Face-Selective Occipital Face Area 851

Summary The results reviewed in this section provide the beginnings of a characterization of the computations and representations that occur in the FFA. The FFA is implicated in face detection and face discrimination, but evidence on the role of the FFA in discriminating familiar from unfamiliar faces or in discriminating emotional expressions in faces is inconsistent. Representations of faces in the FFA are partly invariant to simple image transformations such as changes in size, position, and spatial scale, but largely noninvariant to changes in most viewpoints and lighting direction of the face image. The FFA shows both a face inversion effect (i.e., a higher response for upright than inverted faces) and holistic processing of faces, as expected if this region plays a major role in face-processing phenomena established in previous behavioral work. Although the FFA is by far the most robust and hence most studied of the face-selective regions of the cortex, two other face-selective regions have also been investigated, the OFA in the lateral occipital cortex and what we call the fSTS (a face-selective region in the posterior part of the superior termporal gyrus). Figure 43.2 shows these face-selective activations on an inflated brain from one subject. Ongoing work has begun to reveal a functional division of labor between these three cortical regions. FUNCTION OF THE FACE-SELECTIVE OCCIPITAL FACE AREA Situated just posterior and lateral to the FFA, the most natural hypothesis is that the OFA is an earlier stage of the face-processing network that sends its output to the FFA. Although the responses of the OFA are in many ways similar to responses of the FFA, they do differ in some telling respects that are largely consistent with this hypothesis. First, the OFA has a stronger contralateral-field bias than the FFA (Hemond, Kanwisher, & Op de Beeck, 2007). Second, Rotshtein et al. (2005) showed that a posterior face-sensitive region in the inferior occipital gyrus, presumably the OFA, is sensitive to physical changes in the face stimulus, independent of whether those changes are perceived as a change in face identity, whereas a facesensitive region in the right fusiform gyrus, presumably the FFA, is sensitive only to perceived changes in face identity. Third, Yovel and Kanwisher (2005) found that the OFA showed a similar response to upright and inverted faces, and there was no correlation across subjects between the magnitude of the behavioral face inversion effect and the difference in the response of the OFA to upright and inverted faces (OFA–face inversion effect). In contrast, the FFA

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showed higher response to upright than inverted faces and this difference was correlated across subjects with the behavioral face inversion effect. Finally, whereas the FFA responds to first-order stimulus information about both face parts and face configurations, the OFA is sensitive only to face parts (Liu et al., 2003). Consistent with these findings, a transcranial magnetic stimulation (TMS) study (Pitcher, Walsh, Yovel, & Duchaine, 2007) showed that that OFA stimulation that takes place 60 to 100 ms after stimulus onset disrupts discrimination of faces that differ in parts but not in spacing among them (Pitcher, Garrido, Walsh, & Duchaine, 2008). Taken together, these findings suggest that the OFA constitutes an earlier stage of face processing, which represents information that is more closely tied to the face stimulus, whereas the FFA represents the perceived identity of the face. However, all of the data just summarized is based on the functional properties of the OFA and FFA. What do we know about the critical question of how these regions are connected? Efforts to answer this question using functional connectivity (Fairhall & Ishai, 2007) are suggestive but not yet conclusive, and fiber-tracing methods cannot distinguish direct connections between the OFA and FFA from other nearby fibers. However, evidence from disruption methods shows that the OFA is a necessary stage in the face-processing network. First, patient PS with no right OFA but intact right FFA was severely prosopagnosic (Rossion et al., 2003). Although this result by itself makes sense, a puzzle arises from the fact that the same patient shows a face-selective activation in the fusiform gyrus (FFA) in fMRI. One possibility is that the right FFA receives input form the left OFA. However, a study of another patient (DF) with bilateral lesions in the OFA also shows apparently intact face-selective activation in the FFA (Steeves et al., 2006). These data suggest that the FFA gets face input from early visual areas outside the OFA (Dricot, Sorger, Schiltz, Goebel, & Rossion, 2008). However, this input does not generate an intact representation of identity information in the FFA. fMRI adaptation has shown that the FFA in patient PS does not discriminate between individual faces (Schiltz, Sorger, Ahmed, Mayer, & Goebel, 2006), suggesting that interaction with the OFA is necessary for normal functioning of the FFA. In a follow-up study that tested adaptation to faces in nearby nonface-selective regions, the absence of adaptation for face identity in the FFA was replicated, but intact adaptation effect to faces in nearby object processing regions was observed. These findings suggest that the OFA is associated with the FFA but not with nearby nonface-selective regions that show normal adaptation response even when the OFA is damaged (Dricot et al., 2008). Finally, as mentioned

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previously, TMS to the OFA selectively disrupts perception of face parts (Pitcher et al., 2007). In sum, the OFA appears to constitute an early stage of face processing necessary for the perception of face parts, and most likely connecting directly to the FFA, but not processing the only input to the FFA.

FUNCTION OF THE FACE-SELECTIVE REGION IN THE SUPERIOR TEMPORAL SULCUS Although the FFA can be found in essentially all normal subjects, the face-selective region in the STS (fSTS) is less reliable; it is found in only half (Kanwisher et al., 1997) to three quarters (Yovel & Kanwisher, 2005) of subjects scanned individually. For this reason, this region has been studied less extensively than the FFA, although numerous studies investigate responses in the STS to face stimuli. Nonetheless, evidence suggests important functional distinctions between the fSTS and other face-selective regions of the cortex, with the fSTS more involved in processing dynamic and social aspects of faces such as emotional expression and gaze (Haxby et al., 2000). First, the fSTS does not show the same involvement in the detection of faces and the perceptual analysis of face identity that has been found in the FFA. Two studies have found that the FFA but not the fSTS is correlated with successful face detection. Andrews and Schulppeck (2004) presented ambiguous stimuli (Mooney faces) that on some trials were perceived as faces but on others were perceived as novel blobs. Whereas the FFA response was stronger for face than blob percepts (see also Kanwisher et al., 1998), the fSTS showed no difference between the two types of trials. These findings are consistent with Grill-Spector et al. (2004) who found that the response of the FFA was correlated with successful detection of faces in brief masked stimuli, but the response of the fSTS was not. The failure to find a correlation with successful face detection in the fSTS when stimuli are held constant (or are similar) is somewhat surprising given that this region by definition responds more strongly when faces are present than when they are not. In any event, the correlation with successful face detection of the FFA but not fSTS, which was found in both studies, shows a dissociation between the two regions. Given the findings just described, it is not surprising that the fSTS shows no sensitivity to face identity information. For example, Grill-Spector et al. (2004) found no correlation of the fSTS response with successful identification of faces. Similarly, studies that used fMRI-adaptation

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found sensitivity to face identity in the FFA but not in the fSTS (Andrews & Schluppeck, 2004; Yovel & Kanwisher, 2005). Several studies have found a robust face inversion effect (higher response to upright than inverted faces) in the fSTS (Haxby et al., 1999; Leube et al., 2003; Yovel & Kanwisher, 2005). However, in contrast to the FFA, this difference between upright and inverted faces was not correlated with the behavioral face inversion effect measured in a face identity discrimination task (Yovel & Kanwisher, 2005; see Figure 43.3). These findings are consistent with the idea that the fSTS is not involved in extracting individual identity from faces. Its higher response to upright rather than inverted faces may suggest that the computations that are done in the fSTS to extract dynamic aspects of facial information are specific to upright faces. Several studies have provided compelling evidence that the fSTS is involved in the processing of eye gaze, emotional expression, and dynamic information about faces. First, Hoffman and Haxby (2000) showed that although the FFA responds more strongly when subjects performed a one-back task on face identity rather than gaze information, the fSTS showed a higher response for the gaze task than the identity task. Second, an fMRI-adaptation study (Winston et al., 2003), in which expression and identity were manipulated in a factorial manner, found significant sensitivity to information about emotional expression in faces in the fSTS but none in the fusiform gyrus (see also Andrews & Ewbank, 2004). Other studies have shown strong responses in the fSTS to dynamic face stimuli in which expression or gaze changes (Calvert & Campbell, 2003; Thompson, Hardee, Panayiotou, Crewther, & Puce, 2007). Is an intact OFA necessary for an fSTS activation? fMRI studies of patients with unilateral (Sorger, Goebel, Schiltz, & Rossion, 2007) and bilateral OFA (Steeves et al., 2006) lesions show intact fSTS activation in these patients, which suggests that the fSTS gets face input from areas outside the OFA. It is still not known, however, whether more subtle fMRI measures of the fSTS activation during expression and gaze processing may be intact in these patients. In sum, whereas the FFA and OFA appear to be more involved in the analysis of face identity, the STS is more involved in the analysis of social and dynamic information in faces such as gaze, expression, and movement (Haxby et al., 2000).

ORIGINS OF FACE PROCESSING How do face-selective cortical regions and adult-like face processing arise in development? Are they constructed by a process of experience-dependent cortical self-organization (Jacobs, 1997)? Are some aspects of face processing partly

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Summary

innately specified? For the case of faces, these questions have been hard to answer because both experiential and evolutionary arguments are plausible. Evidence from individuals with face impairments due to developmental prosopagnosia and congenital cataracts are suggestive (for a review, see Kanwisher & Yovel, 2006; McKone et al., 2007), but do not yet provide definitive answers to these questions (but see Duchaine, Germine, & Nakayama, 2007; Grueter et al., 2007). However, clues are beginning to emerge from a number of recent studies. Ongoing work is characterizing the developmental trajectory of face perception abilities, and the face-selective cortex, in increasing detail. Neuroimaging studies show that the FFA is still developing into the early teenage years (Aylward et al., 2005; Golarai et al., 2007; Passarotti et al., 2003; Scherf, Behrmann, Humphrey, & Luna, 2007). In contrast, behavioral work shows that all of the key behavioral signatures of adult-like face processing are qualitatively present by 4 years of age (Kanwisher & Yovel, 2006; McKone et al., 2007). A major puzzle for future research will be to understand why the FFA changes at least twofold in volume between age 7 and adulthood, after face perceptual abilities are largely in place. Whatever the ultimate answer to this question, it is important to note that late development of face perception (whether by 4 or 14 years of age) need not indicate a critical role for experience in the construction of the FFA; maturation could explain some of all of this developmental change. To understand how face-processing mechanisms arise, we must turn to other methods. At least some aspects of face perception appear to be innately specified because infants less than 24 hours old preferentially track schematic faces compared to visually similar scrambled or inverted faces (Cassia et al., 2004; Johnson, Dziurawiec, Ellis, & Morton, 1991). Experience also affects face perception, as evidenced by the “other race effect,” in which neural responses (Golby, Gabrieli, Chiao, & Eberhardt, 2001) and behavioral performance (Malpass & Kravitz, 1969; Meissner & John, 2001) are higher for faces of a familiar than for an unfamiliar race, even if (in the latter case) the relevant experience occurs after age 3 (Sangrigoli, Pallier, Argenti, Ventureyra, & de Schonen, 2005). However, these two observations leave open a vast space of possible scenarios in which genes and environment interact in the construction of a selective region of cortex such as the FFA. Two recent findings suggest a greater role for genes than many would have guessed. First, Polk, Park, Smith, and Park (2007) compared the spatial distribution of response to various stimulus categories across the ventral visual pathway in twins. They found that for faces and places the pattern of response was more similar for monozygotic than dizygotic twins, whereas

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for pseudowords and chairs, it was not. This result indicates that differences between individuals in the pattern of response to faces in the ventral visual pathway are due in part to genes. However, note that this result does not argue against a role for experience in the construction of the FFA. Although genes might exert some kind of direct control over neural connectivity, another possibility is that genes that affect social behavior lead some individuals to look at faces more than others do, and this differential experience itself affects neural responses to these categories. Although this study presents some of the first evidence we know of for a genetic influence on the cortical machinery of face perception (see also Duchaine et al., 2007; Grueter et al., 2007), that evidence does not necessarily argue against a role for experience in the construction of the FFA. There are many possible causal pathways from genes to neural architecture, some of which crucially implicate experience as the key intervening variable. Another recent study argues that experience with faces may not be necessary for the construction of faceprocessing machinery. Sugita (2008) raised baby monkeys without ever allowing them to see faces, from birth to the age of 6, 12, or 24 months. The monkeys lived in enriched visual environments and were cared for by human caretakers who wore hoods over their faces at all times while in the presence of the monkeys. Astonishingly, when the monkeys were first tested on face perception, even after 2 years of deprivation, they showed the standard preference to look at static photographs of faces over novel object photographs. Even more surprising, they showed adultlike sensitivity to differences between faces: Given a choice between a face they had just habituated to, and a new face, they looked more at the new face, even though the differences between faces were very subtle. These findings leave little room for a role of face experience in the construction of adult face-processing performance, at least in monkeys. Crucial future work should use even more subtle tests to ask whether these monkeys have truly normal face perception abilities. Also of great interest is the question of whether these monkeys who never saw faces in the first 2 years of life nonetheless have normal face-selective cortical regions (Tsao et al., 2003), and if so, how quickly these arise after exposure to faces. Although we still don’t know how the machinery of face processing arises during development, new evidence suggests that experience with faces may not be necessary.

SUMMARY In this chapter, we described the current state of knowledge about face-selective regions of cortex in humans

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and their role in face perception. Current evidence supports the hypothesis that the FFA is specifically involved in face perception per se. The division of labor between the three face-selective cortical regions is beginning to come into focus, with the OFA apparently more involved in the analysis of face parts, the FFA involved in the construction of perceptual descriptions of faces used in face identity discrimination, and the fSTS involved in discriminating social/dynamic information in faces such as eye gaze direction and perhaps also emotional expression. Further work is characterizing the representations of faces extracted in each of these regions, though much remains to be done. Finally, although the FFA is clearly influenced both by experience and by genes, very recent work opens up the surprising possibility that experience with faces may not be necessary for the construction of adult-like face-processing abilities. Despite the wealth of knowledge accrued from the past decade of research into the cortical regions involved in face perception, fundamental questions remain unanswered. First, we have only begun to scratch the surface in understanding what information is represented in each of these regions, and we have no idea at all about the neural circuits that give rise to these representations. Second, virtually nothing is known about the connections between each of these regions, or between these regions and the rest of the brain. Third, although current methods of human cognitive neuroscience can tell us about time (via ERPs and MEG) or about space (via fMRI), we have almost no data that can tell us about the precise time course of response in specific spatially resolved regions of the brain (for powerful but rare data on this question, see Mundel et al., 2003; Puce et al., 1999). For example, despite the many fMRI studies of the FFA using fMRI, it is unknown which of its response properties arise during the initial feedforward response to a stimulus, and which may arise hundreds of milliseconds later. Fourth, with few exceptions (Afraz, Kiani, & Esteky, 2006; Pitcher et al., 2007; Puce et al., 1999), we know very little about the causal structure of the face-processing system: which regions play a necessary role in which aspects of face perception. Finally, we know next to nothing about why face-selective regions land so systematically where they do in the cortex, or about the mechanisms that wire this system up during development. These questions remain largely unanswered because current methods of human cognitive neuroscience cannot answer them. However, the bright light on the horizon is the fact that monkeys too have face-selective regions of the cortex and methods exist to tackle most of these questions in macaques. Indeed, the combination of behavioral, neuroimaging, and physiological studies in monkeys (Tsao &

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Livingstone, 2008) is likely to prove very powerful over the next decade.

REFERENCES Afraz, S.-R., Kiani, R., & Esteky, H. (2006, August 10). Microstimulation of inferotemporal cortex influences face categorization. Nature, 442, 692–695. Aguirre, G. K., Singh, R., & D’Esposito, M. (1999). Stimulus inversion and the responses of face and object-sensitive cortical areas. NeuroReport, 10, 189–194. Allison, T., Ginter, H., McCarthy, G., Nobre, A. C., Puce, A., Luby, M., et al. (1994). Face recognition in human extrastriate cortex. Journal of Neurophysiology, 71, 821–825. Allison, T., Puce, A., Spencer, D. D., & McCarthy, G. (1999). Electrophysiological studies of human face perception: Pt. I. Potentials generated in occipitotemporal cortex by face and non-face stimuli. Cerebral Cortex, 9, 415–430. Andrews, T. J., & Ewbank, M. P. (2004). Distinct representations for facial identity and changeable aspects of faces in the human temporal lobe. NeuroImage, 23, 905–913. Andrews, T. J., & Schluppeck, D. (2004). Neural responses to Mooney images reveal a modular representation of faces in human visual cortex. NeuroImage, 21, 91–98. Andrews, T. J., Schluppeck, D., Homfray, D., Matthews, P., & Blakemore, C. (2002). Activity in the fusiform gyrus predicts conscious perception of Rubin’s vase-face illusion. NeuroImage, 17, 890–901. Avidan, G., Hasson, U., Malach, R., & Behrmann, M. (2005). Detailed exploration of face-related processing in congenital prosopagnosia: Pt. 2. Functional neuroimaging findings. Journal of Cognitive Neuroscience, 1, 1150–1167. Aylward, E. H., Park, J. E., Field, K. M., Parsons, A. C., Richards, T. L., Cramer, S. C., et al. (2005). Brain activation during face perception: Evidence of a developmental change. Journal of Cognitive Neuroscience, 17, 308–319. Barton, J. J., Press, D. Z., Keenan, J. P., & O’Connor, M. (2002). Lesions of the fusiform face area impair perception of facial configuration in prosopagnosia. Neurology, 58, 71–78. Behrmann, M., & Avidan, G. (2005). Congenital prosopagnosia: Faceblind from birth. Trends in Cognitive Sciences, 9, 180–187. Bentin, S., Allison, T., Puce, A., Perez, E., & McCarthy, G. (1996). Electrophysiological studies of face perception in humans. Journal of Cognitive Neuroscience, 8, 551–565. Bentin, S., & Deouell, L. Y. (2000). Structural encoding and identification in face processing: ERP evidence for separate mechanisms. Cognitive Neuropsychology, 17(1/3), 35–54. Bouvier, S. E., & Engel, S. A. (2006). Behavioral deficits and cortical damage loci in cerebral achromatopsia. Cerebral Cortex, 16, 183–191. Bradshaw, J. L. (1968). Load and pupillary changes in continuous processing tasks. British Journal of Psychology, 59, 265–271. Breiter, H. C., Etcoff, N. L., Whalen, P. J., Kennedy, W. A., Rauch, S. L., Buckner, R. L., et al. (1996). Response and habituation of the human amygdala during visual processing of facial expression. Neuron, 17, 875–887. Caldara, R., Seghier, M. L., Rossion, B., Lazeyras, F., Michel, C., & Hauert, C. A. (2006). The fusiform face area is tuned for curvilinear patterns with more high-contrasted elements in the upper part. NeuroImage, 31, 313–319.

8/17/09 5:23:58 PM

References 855 Calvert, G. A., & Campbell, R. (2003). Reading speech from still and moving faces: The neural substrates of visible speech. Journal of Cognitive Neuroscience, 15, 57–70.

Gauthier, I., Skudlarski, P., Gore, J. C., & Anderson, A. W. (2000). Expertise for cars and birds recruits brain areas involved in face recognition. Nature Neuroscience, 3, 191–197.

Cassia, V. M., Turati, C., & Simion, F. (2004). Can a nonspecific bias toward top-heavy patterns explain newborns’ face preference? Psychological Sciencse, 15, 379–383.

Gauthier, I., Tarr, M. J., Anderson, A. W., Skudlarski, P., & Gore, J. C. (1999). Activation of the middle fusiform ‘face area’ increases with expertise in recognizing novel objects. Nature Neuroscience, 2, 568–573.

Desimone, R., Albright, T. D., Gross, C. G., & Bruce, C. (1984). Stimulusselective properties of inferior temporal neurons in the macaque. Journal of Neuroscience, 4, 2051–2062. Diamond, R., & Carey, S. (1986). Why faces are and are not special: An effect of expertise. Journal of Experimental Psychology: General, 115, 107–117. Dolan, R. J., Morris, J. S., & de Gelder, B. (2001). Crossmodal binding of fear in voice and face. Proceedings of the National Academy of Sciences, USA, 98, 10006–10010. Downing, P. E., Chan, A. W., Peelen, M. V., Dodds, C. M., & Kanwisher, N. (2006). Domain specificity in visual cortex. Cerebral Cortex, 16, 1453–1461. Downing, P. E., Jiang, Y., Shuman, M., & Kanwisher, N. (2001, September 28). A cortical area selective for visual processing of the human body. Science, 293, 2470–2473. Dricot, L., Sorger, B., Schiltz, C., Goebel, R., & Rossion, B. (2008) The roles of “face” and “non-face” areas during individual face perception: Evidence by fMRI adaptation in a brain-damaged prosopagnosic patient. Neuorimage, 40, 318–332. Duchaine, B., Germine, L., & Nakayama, K. (2007). Family resemblance: Ten family members with prosopagnosia and within-class object agnosia. Cognitive Neuropsychology, 24, 419–430.

Gilaie-Dotan, S., & Malach, R. (2007). Sub-exemplar shape tuning in human face-related areas. Cerebral Cortex, 17, 325–338. Glascher, J., Tuscher, O., Weiller, C., & Buchel, C. (2004). Elevated responses to constant facial emotions in different faces in the human amygdala: An fMRI study of facial identity and expression. BMC Neuroscience, 5, 45. Golarai, G., Ghahemani, D. G., Whitfield-Gabrieli, S., Reiss, A., Eberhardt, J. L., Gabrieli, J. D., et al. (2007). Differential development of high-level visual cortex correlates with category-specific recognition memory. Nature Neuroscience, 10, 512–522. Golby, A. J., Gabrieli, J. D., Chiao, J. Y., & Eberhardt, J. L. (2001). Differential responses in the fusiform region to same-race and otherrace faces. Nature Neuroscience, 4, 775–776. Gorno-Tempini, M. L., & Price, C. J. (2001). Identification of famous faces and buildings: A functional neuroimaging study of semantically unique items. Brain, 124(Pt. 10), 2087–2097. Gorno-Tempini, M. L., Price, C. J., Josephs, O., Vandenberghe, R., Cappa, S. F., Kapur, N., et al. (1998). The neural systems sustaining face and proper-name processing. Brain, 121, 2103–2118.

Duchaine, B., Yovel, G., Butterworth, E., & Nakayama, K. (2006). Prosopagnosia as an impairment to face-specific mechanisms: Elimination of the alternative hypotheses in a developmental case. Cognitive Neuropsychology, 23, 714–747.

Grill-Spector, K., Knouf, N., & Kanwisher, N. (2004). The fusiform face area subserves face perception, not generic within-category identification. Nature Neuroscience, 7, 555–562.

Eger, E., Schweinberger, S. R., Dolan, R. J., & Henson, R. N. (2005). Familiarity enhances invariance of face representations in human ventral visual cortex: FMRI evidence. Neuroimage, 26, 1128–1139.

Grill-Spector, K., Kushnir, T., Edelman, S., Avidan, G., Itzchak, Y., & Malach, R. (1999). Differential processing of objects under various viewing conditions in the human lateral occipital complex. Neuron, 24, 187–203.

Eger, E., Schyns, P. G., & Kleinschmidt, A. (2004). Scale invariant adaptation in fusiform face-responsive regions. Neuroimage, 22, 232–242. Fairhall, S. L., & Ishai, A. (2007). Effective connectivity within the distributed cortical network for face perception. Cerebral Cortex, 17, 2400–2406. Fang, F., Murray, S. O., & He, S. (2007). Duration-dependent FMRI adaptation and distributed viewer-centered face representation in human visual cortex. Cerebral Cortex, 17, 1402–1411. Farah, M. J., Tanaka, J. W., & Drain, H. M. (1995). What causes the face inversion effect? Journal of Experimental Psychology: Human Perception and Performance, 21, 628–634. Fodor, J. A. (1983). The modularity of mind. Cambridge, MA: MIT Press. Foldiak, P., Xiao, D., Keysers, C., Edwards, R., & Perrett, D. I. (2004). Rapid serial visual presentation for the determination of neural selectivity in area STSa. Progress in Brain Research, 144, 107–116. Ganel, R., Valyear, K. F., Goshen-Gottstein, Y., & Goodale, M. A. (2005). The involvement of the “fusiform face area” in processing facial expression. Neuropsychologia, 43, 1645–1654. Gauthier, I., Behrmann, M., & Tarr, M. J. (1999). Can face recognition really be dissociated from object recognition? Journal of Cognitive Neuroscience, 11, 349–370. Gauthier, I., Curran, T., Curby, K. M., & Collins, D. (2003). Perceptual interference supports a non-modular account of face processing. Nature Neuroscience, 6, 428–432. Gauthier, I., & Nelson, C. A. (2001). The development of face expertise. Current Opinion in Neurobiology, 11, 219–224.

c43.indd 855

George, N., Dolan, R. J., Fink, G. R., Baylis, G. C., Russell, C., & Driver, J. (1999). Contrast polarity and face recognition in the human fusiform gyrus. Nature Neuroscience, 2, 574–580.

Grueter, M., Grueter, T., Bell, V., Horst, J., Laskowski, W., Sperling, K., et al. (2007). Hereditary prosopagnosia: The first case series. Cortex, 43, 734–749. Halgren, E., Raij, T., Marinkovic, K., Jousmaki, V., & Hari, R. (2000). Cognitive response profile of the human fusiform face area as determined by MEG. Cerebral Cortex, 10, 69–81. Hasson, U., Hendler, T., Ben Bashat, D., & Malach, R. (2001). Vase or face? A neural correlate of shape-selective grouping processes in the human brain. Journal of Cognitive Neuroscience, 13, 744–753. Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001, September 28). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, 2425–2430. Haxby, J. V., Grady, C. L., Horwitz, B., Ungerleider, L. G., Mishkin, M., Carson, R. E., et al. (1991). Dissociation of object and spatial visual processing pathways in human extrastriate cortex. Proceedings of the National Academy of Sciences, USA, 88, 1621–1625. Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. (2000). The distributed human neural system for face perception. Trends in Cognitive Sciences, 4, 223–233. Haxby, J. V., Ungerleider, L. G., Clark, V. P., Schouten, J. L., Hoffman, E. A., & Martin, A. (1999). The effect of face inversion on activity in human neural systems for face and object perception. Neuron, 22, 189–199. Hemond, C. C., Kanwisher, N., & Op de Beeck, H. P. (2007). A preference for contralateral stimuli in human object- and face-selective cortex. PLoS ONE, 2(6), e574.

8/17/09 5:23:58 PM

856

Face Perception

Henke, K., Schweinberger, S. R., Grigo, A., Klos, T., & Sommer, W. (1998). Specificity of face recognition: Recognition of exemplars of non-face objects in prosopagnosia. Cortex, 34, 289–296. Henson, R. N., Shallice, T., Gorno-Tempini, M. L., & Dolan, R. J. (2002). Face repetition effects in implicit and explicit memory tests as measured by fMRI. Cerebral Cortex, 12, 178–186. Hoffman, E. A., & Haxby, J. V. (2000). Distinct representations of eye gaze and identity in the distributed human neural system for face perception. Nature Neuroscience, 3, 80–84. Itier, R. J., & Taylor, M. J. (2004). N170 or N1? Spatiotemporal differences between object and face processing using ERPs. Cerebral Cortex, 14, 132–142. Jacobs, R. A. (1997). Nature, nurture, and the development of functional specializations: A computational approach. Psychological Bulletin and Review, 4, 2999–2309. Jacques, C., d’Arripe, O., & Rossion, B. (2007). The time course of the inversion effect during individual face discrimination. Journal of Vision, 7, 1–9. Jacques, C., & Rossion, B. (2006). The speed of individual face categorization. Psychological Science, 17, 485–492. Jeffreys, D. A. (1996). Evoked potential studies of face and object processing. Visual Cognition, 3, 1–38. Jiang, X., Bradley, E., Rini, R., Zeffiro, T., VanMeter, J., & Riesenhuber, M. (2007). Categorization training results in shape- and category-selective human neural plasticity. Neuron, 53, 891–903. Johnson, M. H., Dziurawiec, S., Ellis, H., & Morton, J. (1991). Newborns’ preferential tracking of face-like stimuli and its subsequent decline. Cognition, 40(1/2), 1–19. Kanwisher, N. (2006, February 3). Neuroscience: What’s in a face? Science, 311, 617–618. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 4302–4311. Kanwisher, N., Stanley, D., & Harris, A. (1999). The fusiform face area is selective for faces not animals. NeuroReport, 10, 183–187. Kanwisher, N., Tong, F., & Nakayama, K. (1998). The effect of face inversion on the human fusiform face area. Cognition, 68, B1–B11. Kanwisher, N., & Yovel, G. (2006). The fusiform face area: A cortical region specialized for the perception of faces. Philosophical Transactions of the Royal Society of London. Series B, 361, 2109–2128. Kourtzi, Z., & Kanwisher, N. (2001, August 24). Representation of perceived object shape by the human lateral occipital complex. Science, 293, 1506–1509. Kriegeskorte, N., Formisano, E., Sorger, B., & Goebel, R. (2007). Individual faces elicit distinct response patterns in human anterior temporal cortex. Proceedings of the National Academy of Sciences, USA, 104, 20600–20605. Lehmann, C., Mueller, T., Federspiel, A., Hubl, D., Schroth, G., Huber, O., et al. (2004). Dissociation between overt and unconscious face processing in fusiform face area. NeuroImage, 21, 75–83. Leopold, D. A., O’ Toole, A. J., Vetter, T., & Blanz, V. (2001). Prototypereferenced shape encoding revealed by high-level aftereffects. Nature Neuroscience, 4, 89–94. Leube, D. T., Yoon, H. W., Rapp, A., Erb, M., Grodd, W., Bartels, M., et al. (2003). Brain regions sensitive to the face inversion effect: A functional magnetic resonance imaging study in humans. Neuroscience Letters, 342, 143–146. Leveroni, C. L., Seidenberg, M., Mayer, A. R., Mead, L. A., Binder, J. R., & Rao, S. M. (2000). Neural systems underlying the recognition of familiar and newly learned faces. Journal of Neuroscience, 20, 878–886. Liu, J., Harris, A., & Kanwisher, N. (2002). Stages of processing in face perception: An MEG study. Nature Neuroscience, 5, 910–916.

c43.indd 856

Liu, J., Harris, A., Mangini, M., Wald, L., Kwong, K. K., & Kanwisher, J. W. (2003). Distinct representations of faces in the FFA and the OFA: An fMRI study. Paper presented at the 33rdAnnual Society for Neuroscience, New Orleans, LA. Loffler, G., Yourganov, G., Wilkinson, F., & Wilson, H. R. (2005). FMRI evidence for the neural representation of faces. Nature Neuroscience, 8, 1386–1390. Malpass, R. S., & Kravitz, J. (1969). Recognition for faces of own and other race. Personality and Social Psychology, 13, 330–334. Martin, A., & Chao, L. L. (2001). Semantic memory and the brain: Structure and processes. Current Opinion in Neurobiology, 11, 194–201. Maurer, D., LeGrand, R., & Mondloch, J. (2002). The many faces of configural processing. Trends in Cognitive Sciences, 6, 225–260. Maurer, D., O’Craven, K. M., Le Grand, R., Mondloch, C. J., Springer, M. V., Lewis, T. L., et al. (2007). Neural correlates of processing facial identity based on features versus their spacing. Neuropsychologia, 45, 1438–1451. Mazard, A., Schiltz, C., & Rossion, B. (2006). Recovery from adaptation to facial identity is larger for upright than inverted faces in the human occipito-temporal cortex. Neuropsychologia, 44, 912–922. McCarthy, Puce, A. Gore, J., & Allison, T. (1997). Infrequent events transiently activate human prefrontal and parietal cortex as measured by functional MRI. Journal of Neurophysiology, 77, 1630–1634. McKone, E., & Kanwisher, N. (2005). Does the human brain process objects of expertise like faces? A review of the evidence. In S. Dehaene, J. R. Duhamel, M. Hauser, & G. Rizzolatti (Eds.), From monkey brain to human brain (pp. 339–356). Cambridge, MA: MIT Press. McKone, E., Kanwisher, N., & Duchaine, B. C. (2007). Can generic expertise explain special processing for faces? Trends in Cognitive Sciences, 11, 8–15. McKone, E., Martini, P., & Nakayama, K. (2001). Categorical perception of face identity in noise isolates configural processing. Journal of Experimental Psychology: Human Perception and Performance, 27, 573–599. McMullen, P. A., Fisk, J. D., Phillips, S. J., & Maloney, W. J. (2000). Apperceptive agnosia and face recognition. Neurocase, 6, 403–414. McNeil, J. E., & Warrington, E. K. (1993). Prosopagnosia: A face-specific disorder. Quarterly Journal of Experimental Psychology: A Human Experimental Psychology, 46, 1–10. Meissner, C. A. B., & John, C. (2001). Thirty years of investigating the own-race bias in memory for faces: A meta-analytic review. Psychology, Public Policy, and Law, 7(1), 3–35. Moore, C. D., Cohen, M. X., & Ranganath, C. (2006). Neural mechanisms of expert skills in visual working memory. Journal of Neuroscience, 26, 11187–11196. Moscovitch, M., Winocur, G., & Behrmann, M. (1997). What is special about face recognition? Nineteen experiments on a person with visual object agnosia and dyslexia but normal face recognition. Journal of Cognitive Neuroscience, 9, 555–604. Mundel, T., Milton, J. G., Dimitrov, A., Wilson, H. W., Pelizzari, C., Uftring, S., et al. (2003). Transient inability to distinguish between faces: Electrophysiologic studies. Journal of Clinical Neurophysiology, 20, 102–110. Op de Beeck, H. P., Baker, C. I., DiCarlo, J. J., & Kanwisher, N. G. (2006). Discrimination training alters object representations in human extrastriate cortex. Journal of Neuroscience, 26, 13025–13036. Pasley, B. N., Mayes, L. C., & Schultz, R. T. (2004). Subcortical discrimination of unperceived objects during binocular rivalry. Neuron, 42, 163–172. Passarotti, A., Paul, B., Bussiere, J., Buxton, R., Wong, E., & Stiles, J. (2003). The development of face and location processing: An fMRI study. Developmental Science, 6(1), 100–117.

8/17/09 5:23:59 PM

References 857 Passarotti, A., Smith, J., DeLano, M., & Huang, J. (2007). Developmental differences in the neural bases of the face inversion effect show progressive tuning of face-selective regions to the upright orientation. NeuroImage, 34, 1708–1722. Pinski, M. A., DeSimone, K., Moore, T., Gross, C. G., & Kastner, S. (2005). Representations of faces and body parts in macaque temporal cortex: A functional MRI study. Proceedings of the National Academy Sciences, USA, 102, 6996–7001. Pitcher, D., Garrido, L., Walsh, V., & Duchaine, B. (2008) TMS disrupts the perception and embodiment of facial expressions. Journal of Neuroscience, 28, 8929–8933.

Schiltz, C., Sorger, B. R. C., Caldara, R., Ahmed, F., Mayer, E., Goebel, R., et al. (2006). Impaired face discrimination in acquired prosopagnosia is associated with abnormal response to individual faces in the right middle fusiform gyrus. Cerebral Cortex, 16, 574–586. Sergent, J., & Bindra, D. (1981). Differential hemispheric processing of faces: Methodological considerations and reinterpretation. Psychological Bulletin, 89, 541–554. Sergent, J., Ohta, S., & MacDonald, B. (1992). Functional neuroanatomy of face and object processing: A positron emission tomography study. Brain, 115, 15–36.

Pitcher, D., Walsh, V., Yovel, G., & Duchaine, B. (2007). TMS evidence for the involvement of the right occipital face area in early face processing. Current Biology, 17, 1568–1573.

Sergent, J., & Signoret, J. L. (1992). Implicit access to knowledge derived from unrecognized faces in prosopagnosia. Cerebral Cortex, 2, 389–400.

Polk, T. A., Park, J., Smith, M. R., & Park, D. C. (2007). Nature versus nurture in ventral visual cortex: A functional magnetic resonance imaging study of twins. Journal of Neuroscience, 27, 13921–13925.

Sorger, B., Goebel, R., Schiltz, C., & Rossion, B. (2007). Understanding the functional neuroanatomy of acquired prosopagnosia. NeuroImage, 35, 836–852.

Pourtois, G., Schwartz, S., Seghier, M. L., Lazeyras, F., & Vuilleumier, P. (2005). Portraits or people? Distinct representations of face identity in the human visual cortex. Journal of Cognitive Neuroscience, 17, 1043–1057.

Spiridon, M., & Kanwisher, N. (2002). How distributed is visual category information in human occipito-temporal cortex? An fMRI study. Neuron, 35, 1157–1165.

Puce, A., Allison, T., Asgari, M., Gore, J. C., & McCarthy, G. (1996). Differential sensitivity of human visual cortex to faces, letterstrings, and textures: A functional magnetic resonance imaging study. Journal of Neuroscience, 16, 5205–5215. Puce, A., Allison, T., & McCarthy, G. (1999). Electrophysiological studies of human face perception: Pt. III. Effects of top-down processing on face-specific potentials. Cerebral Cortex, 9, 445–458. Rhodes, G. (1985). Lateralized processes in face recognition. British Journal of Psychology, 76, 249–271. Rhodes, G., Brennan, S., & Carey, S. (1987). Identification and ratings of caricatures: Implications for mental representations of faces. Cognitive Psychology, 19, 473–497. Rhodes, G., Byatt, G., Michie, P. T., & Puce, A. (2004). Is the fusiform face area specialized for faces, individuation, or expert individuation? Journal of Cognitive Neuroscience, 16, 189–203. Robbins, R., & McKone, E. (2007). No face-like processing for objectsof-expertise in three behavioural tasks. Cognition, 103, 34–79. Rossion, B., Curran, T., & Gauthier, I. (2002). A defense of the subordinatelevel expertise account for the N170 component. Cognition, 85, 189–196. Rossion, B., Schiltz, C., & Crommelinck, M. (2003). The functionally defined right occipital and fusiform “face areas” discriminate novel from visually familiar faces. NeuroImage, 19, 877–883. Rotshtein, P., Geng, J. J., Driver, J., & Dolan, R. J. (2007). Role of features and second-order spatial relations in face discrimination, face recognition, and individual face skills: Behavioral and functional magnetic resonance imaging data. Journal of Cognitive Neuroscience, 19, 1435–1452. Rotshtein, P., Henson, R. N., Treves, A., Driver, J., & Dolan, R. J. (2005). Morphing Marilyn into Maggie dissociates physical and identity face representations in the brain. Nature Neuroscience, 8, 107–113. Sangrigoli, S., Pallier, C., Argenti, A. M., Ventureyra, V. A., & de Schonen, S. (2005). Reversibility of the other-race effect in face recognition during childhood. Psychological Sciences, 16, 440–444. Saxe, R., Brett, M., & Kanwisher, N. (2006). Divide and conquer: A defense of functional localizers. NeuroImage, 3, 1088–1096. Saxe, R., & Powell, L. J. (2006). It’s the thought that counts: Specific brain regions for one component of theory of mind. Psychological Science, 17, 692–699. Scherf, K. S., Behrmann, M., Humphrey, K., & Luna, B. (2007). Visual category-selectivity for faces, places and objects emerges along different developmental trajectories. Developmental Science, 10(4), F15–F30.

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Schiltz, C., & Rossion, B. (2006). Faces are represented holistically in the human occipito-temporal cortex. NeuroImage, 32, 1385–1394.

Steeves, J. K., Culham, J. C., Duchaine, B. C., Pratesi, C. C., Valyear, K. F., Schindler, I., et al. (2006). The fusiform face area is not sufficient for face recognition: Evidence from a patient with dense prosopagnosia and no occipital face area. Neuropsychologia, 44, 594–609. Sugita, Y. (2008). Face perception in monkeys reared with no exposure to faces. Proceedings of the National Academy of Sciences, USA, 105, 394–398. Tanaka, J. W., & Curran, T. (2001). A neural basis for expert object recognition. Psychological Science, 12, 43–47. Tanaka, J. W., & Farah, M. J. (1993). Parts and wholes in face recognition. Quarterly Journal of Experimental Psychology: A Human Experimental Psychology, 46, 225–245. Tanaka, J. W., & Farah, M. (2003). Holistic face recognition. In M. Peterson & G. Rhodes (Eds.), Analytic and holistic processes in the perception of faces, objects and scenes (Vol. 2, pp. 53–74). New York: Oxford University Press. Thompson, J. C., Hardee, J. E., Panayiotou, A., Crewther, D., & Puce, A. (2007). Common and distinct brain activation to viewing dynamic sequences of face and hand movements. NeuroImage, 37, 966–973. Tong, F., Nakayama, K., Moscovitch, M., Weinrib, O., & Kanwisher, N. (2000). Response properties of the human fusiform face area. Cognitive Neuropsychology, 17(1/3), 257–279. Tong, F., Nakayama, K., Vaughan, J. T., & Kanwisher, N. (1998). Binocular rivalry and visual awareness in human extrastriate cortex. Neuron, 21, 753–759. Tsao, D. Y., Freiwald, W. A., Knutsen, T. A., Mandeville, J. B., & Tootell, R. B. (2003). Faces and objects in macaque cerebral cortex. Nature Neuroscience, 6, 989–995. Tsao, D. Y., Freiwald, W. A., Tootell, R. B., & Livingstone, M. S. (2006, February 3). A cortical region consisting entirely of face-selective cells. Science, 311, 670–674. Tsao, D. Y., & Livingstone, M. S. (2008). Mechanisms of face perception. Annual Review of Neuroscience, 31, 411–437. Turk, D. J., Rosenblum, A. C., Gazzaniga, M. S., & Macrae, C. N. (2005). Seeing John Malkovich: The neural substrates of person categorization. NeuroImage, 24, 1147–1153. Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2001). Effects of attention and emotion on face processing in the human brain: An eventrelated fMRI study. Neuron, 30, 829–841. Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2003). Distinct spatial frequency sensitivities for processing faces and emotional expressions. Nature Neuroscience, 6, 624–631.

8/17/09 5:23:59 PM

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Wada, Y., & Yamamoto, T. (2001). Selective impairment of facial recognition due to a haematoma restricted to the right fusiform and lateral occipital region. Journal of Neurology, Neurosurgery, and Psychiatry, 71, 254–257.

Young, A. W., Hellawell, D., & Hay, D. C. (1987). Configurational information in face perception. Perception, 16, 747–759.

Warrington, E. K., Logue, V., & Pratt, R. T. C. (1971). The anatomical localisation of selective impairment of auditory-verbal short-term memory. Neuropsychologia, 9, 377–387.

Yovel, G., & Kanwisher, N. (2005). The neural basis of the behavioral face-inversion effect. Current Biology, 15, 2256–2262.

Yovel, G., & Kanwisher, N. (2004). Face perception domain specific, not process specific. Neuron, 44, 889–898.

Williams, M. A., Moss, S. A., & Bradshaw, J. L. (2004). A unique look at face processing: The impact of masked faces on the processing of facial features. Cognition, 91, 155–172.

Yovel, G., & Kanwisher, N. (2008) The representations of spacing and partbased information are associated for upright faces but dissociated for objects: Evidence from individual differences. Psychonomics Bulletin and Review, 15, 933–939.

Winston, J. S., Vuilleumier, P., & Dolan, R. J. (2003). Effects of low-spatial frequency components of fearful faces on fusiform cortex activity. Current Biology, 13, 1824–1829.

Yovel, G., Paller, K. A., & Levy, J. (2005). A whole face is more than the sum of its halves: Interactive processing in face perception. Visual Cognition, 12, 337–352.

Wiser, A. K., Andreasen, N., O’Leary, D. S., Crespo-Facorro, B., BolesPonto, L. L., Watkins, G. L., et al. (2000). Novel vs. well-learned memory for faces: A positron emission tomography study. Journal of Cognitive Neuroscience, 12, 255–266.

Yovel, G., Tambini, A., & Brandman, T. (2008) The asymmetry of the fusiform face area is a stable individual characteristic which underlies the left visual field superiority for faces. Neuropsychologia, 46, 3061–3068.

Xu, Y. (2005). Revisiting the role of the fusiform face area in visual expertise. Cerebral Cortex, 15, 1234–1242. Yin, R. (1969). Looking at upside down faces. Journal of Experimental Psychology, 81, 141–145.

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Yue, X., Tjan, B. S., & Biederman, I. (2006). What makes faces special? Vision Research, 46, 3802–3811. Zangenehpour, S., & Chaudhuri, A. (2005). Patchy organization and asymmetric distribution of the neural correlates of face processing in monkey inferotemporal cortex. Current Biology, 15, 993–1005.

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Chapter 44

Self versus Others/Self-Regulation ANNE C. KRENDL AND TODD F. HEATHERTON

Humans are fundamentally social beings. Collectively, we crave social interactions and actively seek them out whenever possible (Baumeister & Leary, 1995). In fact, social interactions are so vital to our physical and mental wellbeing that being without them has grave consequences. For instance, social isolation can lead to severe depression and loneliness (Rubin & Mills, 1988) and is a major risk factor for mortality (Cacioppo, Hughes, Waite, Hawkley, & Thisted, 2006; House, Landis, & Umberso, 1988). As humans, we expend a great deal of energy on maintaining and promulgating our social groups. Many of the most popular modern technological advances serve the sole purpose of increasing the quantity of our social interactions. For instance, there has been an unprecedented increase in chat rooms, online gaming, and virtual reality worlds that center around promoting social interactions on the Internet. Meanwhile, increasing obsessions with personal digital assistants, e-mail, text messaging, and cell phones allow us to maintain constant social contact at all times, no matter where we are physically located. Arguably, a primary cause for the amount of effort we exert on maintaining social interactions stems from the fact that human social interactions are remarkably complex. Determining when someone might have a crush on you, when your friends are no longer your friends, and the best time to ask your boss for a raise each involves highly developed levels of social awareness. Understanding the social rules required to successfully navigate through these myriad social interactions requires complex levels of cognitive processing. It is therefore not surprising that the brain has developed an intricate system of neural networks to support and facilitate social interactions. In this chapter,

we describe the fundamental psychological components necessary for social behavior and we review what is currently known regarding known neural correlates. Before we begin our review, it is important to emphasize that the social brain is not located in one discrete location in the brain. Rather, it is comprised of multiple systems throughout the brain. Although we describe each of these systems separately, we do so with the following two caveats: First, each of the subcomponents of the social brain serve multiple functions, many of them overlapping; second, these subcomponents work together to give rise to the social brain as a whole. As we argue in this chapter, damage to any one system leads to profound social deficits. We begin by describing the theoretical basis of our model and then discuss relevant research.

THE SOCIAL BRAIN Our overall approach follows a social brain sciences perspective that merges evolutionary theory, experimental social cognition, and neuroscience to elucidate the neural mechanisms that support social behavior (Adolphs, 2003; Heatherton, Macrae, & Kelley, 2004). Initial findings using neuroimaging have shown that unique neural substrates are associated with processing social information as compared to general semantic knowledge. For instance, Mitchell, Heatherton, and Macrae (2002) showed that when participants make semantic judgments about words that could either describe a person (e.g., assertive, fickle) or an object such as fruit (e.g., sundried, seedless), two separate networks were engaged. One system was activated when participants made judgments about objects (e.g., left inferior prefrontal cortex and left posterior parietal cortex), and a separate network was engaged when they made judgments about people (e.g., left temporal sulcus, medial prefrontal cortex, and fusiform gyrus; Figure 44.1). Similarly, Mason, Banfield, and Macrae (2004) found that when participants made judgments about whether an action (e.g., running,

The work described in this chapter was supported by an NSF Graduate Fellowship to ACK and NIMH 59282, NIDA 022582 and NSF BCS 0354400 to TH. We acknowledge our group members of the Dartmouth Center for Social Brain Sciences as collaborators on much of the research described. 859

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(A)

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Figure 44.1 Activation maps show brain areas to be more active during person trials than during object trials. Note: Regions of modulation included A: the left temporal sulcus B: the dorsal and ventral mPFC C: the right fusiform gyrus, and D: the right parietal temporal occipital junction.

sitting, or biting) could be performed by a person or a dog, they showed dissociable patterns of neural activation. When making judgments about people, participants revealed a distinct pattern of activation in the prefrontal cortex (e.g., right middle frontal gyrus and medial prefrontal cortex) as compared to when they made judgments about dogs (e.g., occipital and parahippocampal gyri). There are countless other examples of discrete neural networks being recruited to process social as compared to nonsocial information, several of which we explore in more detail throughout this chapter. From an evolutionary perspective, the brain is an organ that has evolved over millions of years to solve problems related to survival and reproduction. Those ancestors who were able to solve survival problems and adapt to their environments were most likely to reproduce and pass along their genes. Whether certain aspects of the social brain (i.e., a sense of self) truly are adaptive is open to some debate (Leary, 2005), although there is ample evidence that the ability to engage in social interactions provided considerable advantages over the course of evolution, such as facilitating communication and cooperation with group members (Sedikides & Skowronski, 1997). From the social brain sciences perspective, just as there are dedicated brain mechanisms for breathing, walking, and talking, the brain has evolved specialized mechanisms for processing information about the social world. It is important to emphasize here that we are not suggesting that there is a specific “social” module or region of the brain. Rather, psychological processes are distributed throughout the brain, with contributions from multiple subcomponents

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determining discrete mental activities that come together to support the social brain (Turk, Heatherton, Macrae, Kelley, & Gazzaniga, 2003). Various cognitive, sensory, motor, somatosensory, and affective processes are essential to the successful navigation of social interactions, and these processes reflect the contribution of several cortical and subcortical regions. Much of our approach relies on the idea that humans have evolved a fundamental need to belong, which encourages behavior that prevents people from being evicted from their social groups (Baumeister & Leary, 1995; Bowlby, 1969). According to the need-to-belong theory, the need for interpersonal attachments is a fundamental motive that has evolved for adaptive purposes. Over the course of human evolution, those who lived with others were more likely to survive and pass along their genes. Adults who were capable of developing long-term committed relationships were more likely to reproduce and also more likely to have their offspring survive to reproduce. Effective groups shared food, provided mates, and helped care for offspring. As such, human survival has long depended on living within groups; banishment from the group was effectively a death sentence. Baumeister and Leary (1995) argued that the need to belong is a basic motive that activates behavior and influences cognition and emotion, and that it leads to ill effects when not satisfied. Even today not belonging to a group increases a person’s risk for a number of adverse consequences, such as illnesses and premature death (see Cacioppo et al., 2000, 2006). In essence, the social brain allows individuals to operate as effective group members, which allows them to maintain their status within the group as well as cooperate with other group members in service of the group’s survival needs. Such a system requires four components, each of which is likely to have a discrete neural signature. First, people need self-awareness—to be aware of their behavior so as to gauge it against societal or group norms. Both the psychologist William James and the sociologist George Herbert Mead differentiated between the self as the knower (“I”) and the self as the object that is known (“me”). In the sense of the knower, the self is the subject doing the thinking, feeling, and acting, which we will consider later as part of the executive self. In the sense of the objectified self, the self consists of the knowledge that people hold about themselves, as when they contemplate their best and worst qualities. The experience of self as the object of attention is the psychological state known as self-awareness, which allows people to reflect on their actions and understand the extent to which those actions match both personal values and beliefs as well as group standards. For instance, people who violate societal rules (i.e., by using more than their

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fair share of resources by cheating their group mates) tend to feel ashamed of their behavior. In order to experience social emotions, such as shame, people need to understand how people are reacting to their behavior so as to predict how others will respond to them (Heatherton & Krendl, 2007). In other words, they need the capacity to attribute mental states to others so that they are able to accurately interpret the beliefs and emotional states of those individuals. For instance, to feel guilty about hurting a loved one, people need to understand that other people have feelings. All social emotions related to empathy require the capacity to attribute specific mental states to others (Heatherton & Krendl, 2007). Similarly, feeling shy is the belief that one is being evaluated by others (thereby giving rise to emotions such as embarrassment), which at its core means recognizing that other people make evaluative judgments. The ability to infer the mental states of others is commonly referred to as mentalizing, or having the capacity for theory of mind (ToM). ToM enables the ability to empathize and cooperate with others, interpret other people’s behavior, and even deceive others when necessary. This capacity is vital to ensure that people understand how others are viewing them in the group. One value of having ToM is that it supports a third mechanism—threat detection—especially in complex situations. If humans have a fundamental need to belong, then there ought to be mechanisms for detecting inclusionary status (Leary, Tambor, Terdal, & Downs, 1995; Macdonald & Leary, 2005). Put another way, given the importance of group inclusion, humans need to be sensitive to signs that the group might exclude them. There is evidence that people feel anxious when they face exclusion from their social groups. Thus, feeling socially anxious or worrying about potential rejection should lead to heightened social sensitivity. Research has demonstrated that people who worry most about social evaluation (i.e., the shy and lonely) show enhanced memory for social information, are more empathetically accurate, and show heightened abilities to decode social information (Gardner, Pickett, & Brewer, 2000; Gardner, Pickett, Jefferis, & Knowles, 2005; Pickett, Gardner, & Knowles, 2004). Not all threats, however, are related to social exclusion. Just as people naturally fear dangerous animals (i.e., poisonous snakes and spiders, tigers, and wolves), they also fear (and encounter) harm from other humans. Other group members can transmit disease, act carelessly to place bystanders at risk, waste or steal vital group resources, or poach one’s mate. Similarly, people from other groups also can be dangerous; over the course of human evolution, competition between small groups over scarce resources led to intergroup violence. Hence, there is also a need for

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mechanisms that detect threats from people from other groups. We argue that threatening people are accorded a special status that identifies them as potentially burdensome or dangerous—they are stigmatized. Thus, as research in social psychology has documented, humans quickly and efficiently categorize others based on information that is evolutionarily meaningful (Macrae & Bodenhausen, 2000). Finally, there needs to be a mechanism for resolving discrepancies between self-knowledge and social expectations or norms, thereby motivating behavior to resolve any conflict that exists. This executive aspect of self (the “I” as knower) is responsible for ensuring that behaviors that might lead one to be expelled from the group are discouraged and, conversely, that behaviors that promote harmonious social relations within the group are encouraged. The control of one’s own behavior is known as self-regulation, which is the process by which people change themselves, including their thoughts, their emotions and moods, their impulsive acts, and their performance at school or work. Although people can delay gratification, control appetites and impulses, and persevere in order to attain goals, many people have difficulties with self-regulation from time to time. Failures of self-regulation are implicated in most of the major problems of contemporary society, including addiction, obesity, risky sex, drunk driving, alcohol abuse, excessive gambling, spiraling consumer debt, marital infidelity, impulsive crimes, and school violence. Many of these behaviors threaten group inclusion; accordingly, understanding their neural basis is of considerable importance. We do not contend that other psychological processes are unimportant for social functioning. Capacities such as language, memory, vision, along with motivational and basic emotional states, are generally important for functioning within the social group. However, they are not necessary for a person to be a good group member; the blind and deaf can contribute substantially to their groups. By contrast, people with fundamental disturbances in the primary components of self, ToM, threat detection, or self-regulation have fundamental and often specific impairments in social function. The literature is replete with case studies of individuals with brain injuries who suffer social impairments while having other capacities intact (e.g., Phineas Gage). Likewise, individuals with a disturbed sense of self often have interpersonal problems (Stuss & Alexander, 1994): Those who have difficulties with ToM (i.e., autistics) or impoverished empathy (i.e., psychopaths) are socially impaired, and those who fail to regulate their behavior are often ostracized and even imprisoned. Although space limitations preclude a theory discussion of social impairments, our contention is that many of them are due to fundamental problems with one of the core processes we have identified.

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Thus, according to our model, the brain has evolved distinct mechanisms for knowing ourselves (self-awareness), knowing how others respond to us (which requires theory of mind), detecting threats from within our social group and beyond, and regulating our actions in order to avoid being ejected from our social groups. Together, these abilities form the foundation of the social brain. In the next few sections, we consider how neuroimaging can elucidate each of these discrete processes. Self-Reflection and Awareness The concept of self forms the foundation of the social brain. The self-concept consists of all that we know about ourselves, including things such as name, race, likes, dislikes, beliefs, values, and even whether we possess certain personality traits. According to Baumeister (1998), “the capacity of the human organism to be conscious of itself is a distinguishing feature and is vital to selfhood” (p. 683). Given that self-knowledge plays a critical role in understanding who we are, researchers have long debated whether the brain gives privileged status to information that is self-relevant or alternatively if information processed about the self is treated in the same manner as any other type of information (Bower & Gilligan, 1979; Klein & Kihlstrom, 1986; Klein & Loftus, 1988; Maki & McCaul, 1985; Markus, 1977; Rogers, Kuiper, & Kirker, 1977). This is the key issue underlying the question of whether self is “special” in any meaningful way. Gillihan and Farah (2005) argue that the majority of the patient and neuroimaging research does not provide sufficient support to conclude that self-relevant information is processed in any “special” way. In this section, we explore several studies that argue that self-relevant information is given a unique and “special” status in the brain. Perhaps one of the most striking examples of the uniqueness of self is reflected in the self-relevant memory enhancement effect. A seminal study by Timothy Rogers and colleagues (1977) found a memory advantage for information encoded with reference to self. They found that asking people to make personal judgments on trait adjectives (e.g., “Are you mean?”) produced significantly improved memory for the words than if the participants were asked to make semantic judgments (e.g., “Define the word mean”). This self-reference memory enhancement effect has been observed in many contexts, such as when people remember information processed with reference to self better than information processed with reference to other people (Symons & Johnson, 1997). The overall picture that emerges is that self-relevant information is especially memorable. Even people who can remember very little can often remember information that is self-relevant. For

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instance, patients who suffer from severe amnesia (resulting from brain injury, developmental disorders, or Alzheimer ’s disease) retain the ability to accurately describe whether specific traits are true of the self (Klein, 2004). Klein provides the example of patient K.C., who showed a preserved ability to accurately identify his “new” personality traits after becoming profoundly amnesic and undergoing a radical personality change following a motorcycle accident (Tulving, 1993). Even patients with Alzheimer ’s disease who suffer severe temporal disorientation and have difficulty recognizing their own family have shown evidence of self-knowledge. Patient K.R., for instance, suffered from profound Alzheimer ’s disease, yet she was still able to identify self-relevant personality traits accurately (Klein, Cosmides, & Costabile, 2003). But why is information about the self particularly memorable? During the 1980s, research in social cognition examined the self-reference superiority effect in memory. Although it was undeniable that memory was better for self-relevant information, it was widely debated whether self-relevant information was supported by discrete cognitive systems (Rogers, Kuiper, & Kirker, 1977), or if superior memory could occur simply because people had greater knowledge about the self, which in turn could produce more elaborate encoding and, hence, better memory (Greenwald & Banaji, 1989; Klein & Kihlstrom, 1986). Neuroimaging techniques are exceptionally positioned to resolve the debate regarding the self-reference effect in memory. The first group to use brain imaging to examine this question was Fergus Craik and his colleagues at the University of Toronto. Using positron emission tomography (PET), Craik et al. (1999) examined brain activity while participants rated personality traits for the self or for a familiar other (in this case, the Canadian Prime Minister). These researchers did not replicate the self-relevant memory enhancement effect, but they did observe distinct activations for self-referential material in frontal regions, notably medial prefrontal cortex (mPFC) and areas of the right prefrontal cortex. Using rapid event-related functional magnetic resonance imaging (fMRI), we asked participants to judge 270 trait adjectives in one of three ways: self (“Does the trait describe you?”); other (“Does the trait describe George Bush?”); and case (“Is the trait presented in uppercase letters?”; Kelley et al., 2002). The expected self-relevant enhancement effect was observed in this study, and a direct comparison of “self” trials to “other” trials revealed heightened activation in a number of different brain regions, most notably the mPFC (Figure 44.2). We conducted a subsequent study that showed that mPFC activity during the encoding of self-relevant words later predicted memory for these words (i.e., the greater the mPFC activity during the encoding of each item, the more likely that item was to be remembered;

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Figure 44.2 Statistical activation maps comparing self and other trials demonstrated greater activity during self-encoding trials in the mPFC.

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of the brain. Time courses (right panel) were computed for each condition within a 3-D region surrounding the peak voxel identified all contiguous voxels within the 10 mm of the peak that reached the significance level (p ⬍ .0001). Bars indicate standard error of the mean (SEM). Activity in the mPFC was uniquely sensitive to self-encoding trials.

Macrae, Moran, Heatherton, Banfield, & Kelley, 2004). Thus, we were able to demonstrate that mPFC contributes to the formation of self-relevant memories. This supports our contention that self-referential processing is functionally dissociable from general semantic processing. In other words, the brain has discrete neural substrates that give rise to the self. The extent to which we include others in our selfconcept has been a topic of particular interest for social psychologists. Theories of intimacy and personal relationships might suggest that the self-reference effect is affected by the closeness of a relationship with the other used as a target. Aron and colleagues define closeness as the extension of self into other and suggest that one’s cognitive processes about a close other develop in a way so as to include that person as part of the self (Aron & Aron, 1996; Aron, Aron, Tudor, & Nelson, 1991; Aron & Fraley, 1999). Consistent with this idea, the memorial advantage afforded to self-referenced material can be diminished or eliminated when the comparison target is an intimate other such as a parent, friend, or spouse (Bower & Gilligan, 1979; Keenan & Baillet, 1980). To address this question, we conducted a study nearly identical to the Kelley et al. (2002) method, but this time we had people make judgments for their best friend rather than for George Bush (Heatherton et al., 2006). Although differences in recognition memory performance for self and intimate other judgments were modest, neural response differences in the mPFC were robust, with self showing much greater activity in mPFC than for best friend or case judgments, which did not differ from one another. These

results indicate an mPFC response that is self-specific; that is, in the brain, judgments pertaining to the self were distinct from those made for friends. Our findings diverge from others that have been reported in which mPFC activity was similar for self and intimate others (Ochsner et al., 2005; Schmitz, KawaharaBaccus, & Johnson, 2004; Seger, Stone, & Keenan, 2004). Two methodological issues may account for this discrepancy. First, the three previous studies used block designs with fairly long intertrial intervals, whereas our study used an event-related design; the former may obscure singletrial events because brain activity is averaged across the entire block. It is possible that participants engaged in self-reflection between trials within the blocks, thereby mixing self-referential processing with judgments about the intimate others (e.g., such as recalling episodes in which the person acted in accordance with the trait during an interaction with the subject). We also used an unusually large number of research participants (N ⫽ 30) and therefore we had the power to detect differences between self and other; the finding of no difference between self and other in the previous studies might be due to power issues. Further research is necessary to resolve the importance of these methodological factors. Considered together, the findings from our three studies support the idea that mPFC is involved in self-referential processing, and that the actions of this region support greater memory for material encoded with reference to self. These findings are also consistent with those obtained by other researchers (Gusnard, 2005). For instance, Gusnard, Akbudak, Shulman, and Raichle (2001) used a blocked-design fMRI

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paradigm to examine judgments about affectively normed pictures and observed mPFC activity that was preferentially associated with self-referential judgments. Johnson et al. (2002) asked participants to respond to a series of questions that demanded access to either personal knowledge (e.g., “I have a quick temper”) or general semantic knowledge (e.g., “Ten seconds is more than a minute”). Their results revealed that self-reflective thought was accompanied by activity in anterior regions of mPFC. Cabeza et al. (2004) found heightened mPFC activation for episodic memory retrieval of autobiographical events. In the study, participants were presented with photographs that either they had taken around campus, or that someone else had taken. The participants showed heightened mPFC activity for photographs they themselves had taken. More recently, Mitchell, Banaji, and Macrae (2005) showed participants a series of faces and asked them to judge physical (i.e., how symmetrical the face appeared) or mental features (i.e., how pleased the people were to have their photographs taken). They found that the activity in mPFC was correlated with the extent to which participants judged the people in the photographs to be similar to them, but only for the mentalizing task. The mPFC has also been implicated in autobiographical memory, an important component of self-awareness. Knowing yourself requires remembering events unique to your own past experiences. These memories play a large role in your understanding of who you are. Steinvorth, Corkin, and Halgren (2006) asked participants to recall past autobiographical memories (e.g., “A birthday party: Who spilled wine on your pants?”) as well as semantic memories (e.g., “A cartoon figure: What is the color of the fur on Garfield?”). They found that participants engaged mPFC when recalling autobiographical memories, but not for semantic memories. Further, Addis, Wong, and Schacter (2006) found that ruminating on future biographical events (i.e., “Imagine your future child”) also elicited activation in the mPFC. Thus, the imaging literature is quite clear regarding tasks that involve self-reflection; they activate mPFC (Gusnard, 2005). The view that mPFC plays a prominent role in self-referential processing is also supported by neuropsychological evidence of patients with frontal lobe injuries (Feinberg & Keenan, 2005; Stuss & Benson, 1984; Wheeler, Stuss, & Tulving, 1997). However, this is not to suggest that the mPFC is the only neural region that selectively responds to self-relevant information. For instance, Northoff and Bermpohl (2004) suggest that the parietal cortex is vital to understanding the location of self in space (Vogeley & Fink, 2003) and the orbitomedial prefrontal cortex tags incoming information as self-relevant so it can be processed by the appropriate system (see also Schore, 2003). The mPFC, however, plays an important role in

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self-awareness and extensive literature has consistently implicated this region as being particularly important to processing self-relevant information. Emerging evidence suggests that the same areas implicated in processing self-relevant information also appear to be tonically active when the brain is “at rest”; that is, not performing a cognitive task (Raichle et al., 2001). This so-called “default state” includes the same network of regions observed when participants perform self-relevant processing tasks—mPFC, precuneus, and posterior cingulate (Gusnard et al., 2001). This finding has led to the supposition that when people are “doing nothing,” the default state of the brain is to self-reflect (Gusnard et al., 2001; Kelley et al., 2002; Mason, Norton, Van Horn, Wegner, Grafton, & Macrae, 2007). The converging evidence the studies described in this section suggests that mPFC plays a critical role in the social brain by giving rise to the execution and storage of selfrelevant information. Self-awareness provides the ability us with the necessary information to understand our own social goals in the world. This necessitates being able to take the perspective of another. In the next section, we review the findings from emerging neuroimaging research attempting to isolate the neural mechanisms engaged in theory of mind. Theory of Mind The social brain requires more than just being aware of our own mental states and feelings. A vital component of the social brain is the ability to recognize the mental states of others so we can engage in deception, empathize and cooperate with others, and accurately interpret other people’s behavior (Gallagher & Frith, 2003). Our ability to infer the mental states of others is commonly referred to as theory of mind (ToM). The extent to which ToM is a uniquely human trait is highly contentious, and evidence on this point is mixed. Primitive forms of apparent mentalizing have been recorded in the animal literature, but it is widely debated whether these studies demonstrate ToM or just learned behavior (for review, see Seyfarth & Cheney, 2003). For instance, research with baboons has shown that if a dominant female grunts to a subordinate following aggression, the subordinate’s behavior immediately changes (Cheney & Seyfarth, 1997). Based on this observation, the authors posit two possible explanations: First, the subordinate has recognized a change in the dominant’s attitude toward her (e.g., the dominant is trying to make the subordinate feel less anxious), so the subordinate baboon changes her behavior accordingly (an explanation that necessitates ToM). The second possible explanation for this behavioral

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change is that it simply reflects a learned behavior—the subordinate baboon has learned through experience that this type of grunt typically leads to reduced aggression (an explanation that does not require ToM). Of the extant animal literature, the most compelling evidence for ToM has been observed in chimpanzees (for review, see Seyfarth & Cheney, 2003). Unlike other nonhuman primates, chimpanzees are able to recognize intentional gestures such as pointing. Povinelli, Nelson, and Boysen (1990) found that when chimpanzees were given clues by two different experimenters as to where food was hidden, they would follow the hints of the experimenter whom they had observed hide the food, instead of the clues provided by the second experimenter who had waited outside the room while the food was hidden. The authors argued that the chimpanzees’ ability to correctly determine that the person they had seen hide the food would know its true location is clear evidence that the chimpanzees possess ToM. Additionally, chimpanzees follow the gaze of a human or other group member to a specific location, an action that, when observed in children, is believed to be evidence of ToM (Tomasello, Hare, & Agnetta, 1999). However, not all research with chimpanzees points to evidence that they have acquired ToM. Chimpanzees exhibit no preference between begging an experimenter who could plainly see them for food, or begging another experimenter whose face or eyes were covered, and therefore could not see them (Povinelli & Eddy, 1996). Emerging research on ToM in humans has sought to identify the neural correlates that are selectively engaged during mentalizing tasks. These studies have consistently implicated a network of brain areas, including mPFC, posterior cingulate, and tempero-parietal junction, as the central components of ToM. Of central interest in this work has been the role of the mPFC in ToM tasks. As discussed in the previous section, the mPFC plays a central role in processing self-relevant information. It is therefore not surprising that the same region that supports our ability to determine our own mental states would be involved in our ability to infer the mental states of others. Compelling evidence has emerged in the patient literature to support the assertion that the mPFC plays a central role in ToM. For instance, research with people who are either autistic or suffer from Asperger ’s syndrome (which both have impairments in the ability to mentalize) has revealed that the deficits may result, at least in part, from deficiencies in the mPFC (Gallagher & Frith, 2003). Further, Stuss, Gallup, and Alexander (2001) found that patients with frontal lobe lesions (particularly to the right mPFC) were unable to detect deception, a task requiring ToM. The advent of PET and fMRI has allowed researchers to examine the neural correlates engaged in ToM tasks in

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healthy participants. In one of the first attempts to identify brain regions involved in ToM, Fletcher and colleagues (1995) measured neural activity while participants made judgments (based on ToM reasoning) about the motivations of an actor ’s behavior. For example, participants had to work out that an actor ’s behavior (giving himself up to the police) was based on his assumption about the policeman’s beliefs (the policeman knew he had robbed a shop). Because the policeman’s beliefs were not made explicit in the story, mental state attribution (i.e., ToM reasoning) was required to perform the task. These researchers found that these mental state attributions were accompanied by a relative increase in activation in mPFC. Activation in the mPFC has also been observed in ToM paradigms that use pictures that require mental state attribution to be understood (Gallagher et al., 2000).1 mPFC activation has been observed even in contrived tasks in which participants are led believe they must infer the mental states of others. Gallagher, Jack, Roepstorff, and Frith (2002) had participants play a game of “rock, paper, scissors” while in the PET scanner. Participants were told during some blocks that they were playing against the experimenter, and in others that they were playing against the computer. In truth, they received randomly generated stimuli from the computer throughout the experiment. However, on trials during which participants believed they were playing the experimenter, they showed robust activation in the mPFC (suggesting they were using ToM to determine how the experimenter might play the next hand), whereas they did not show this activation when they believed they were playing the computer. Similarly, mPFC activation was observed on tasks in which cooperation was required among team members. McCabe, Houser, Ryan, Smith, and Trouard (2001) had participants compete in a trust game with either human or computer partners. The authors found that participants showed heightened activation in mPFC when they were cooperating with a human partner. They argue that such cooperation requires ToM because participants must be able to infer the mental states

1

In several studies described in this chapter, changes in mPFC activity often appear to be decreases in activity from an arbitrary baseline. As discussed earlier in this chapter, mPFC is tonically active when the brain is at rest. In other words, when the brain is engaged in cognitive tasks, mPFC activity appears to decrease relative to resting state (which is measured by the arbitrary baseline) because its most active state occurs at rest (for a more detailed discussion of the default state and mPFC, please see Raichle et al., 2001). Thus, for the sake of clarity, our use the term “activations” in this chapter refers to changes in neural activity that are significantly greater in the experimental than control conditions.

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of their partners in order to form mutual expectations of making cooperative choices. Thus, the mPFC can be engaged when perceivers either are interacting with another human, or simply believe that they are. Further, the extent to which the perceiver can identify with an individual with whom he is interacting may also modulate the extent to which mPFC is engaged. Mitchell, Macrae, and Banaji (2006) had participants who were self-proclaimed liberals and conservatives make judgments about themselves, other liberals, or other conservatives (e.g., “Would this individual enjoy having a roommate from a different country?” “Would this individual drive a small car entirely for environmental reasons?”). The authors found that the activation in the ventral mPFC was greater for judgments made about individuals that participants perceived to be similar to themselves (i.e., participants who were identified as liberals by a measure of implicit political bias showed heightened vmPFC when making judgments about other liberals than when evaluating conservatives), whereas activation was greater in the dorsal mPFC for individuals that participants perceived as being dissimilar to themselves (e.g., liberals evaluating conservatives). Although the extant literature on ToM has clearly emphasized the role of the mPFC in mentalizing tasks, emerging research has also implicated the tempero-parietal junction (TPJ) in inferring mental states. Saxe and Kanwisher (2003) observed heightened activation in this region when participants read stories that uniquely described the goals or beliefs of an individual (a task that requires ToM), as opposed to when participants read stories that simply described people in physical detail (a task that does not require ToM). In a later study, Saxe and Wexler (2005) had participants make judgments about the mental states of others who either came from similar (familiar) or dissimilar (foreign) backgrounds. In both cases, participants were given a short story about an individual with either a familiar (e.g., Your friend is happily married) or foreign (e.g., Your friend belongs to a cult that promotes extramarital affairs) background. Each story then had a “normal” (e.g., Your friend confided that he hoped his wife never cheated on him) or “norm-violating” (e.g., Your friend confided that he hoped his wife would have a relationship outside of marriage) desire. The authors found that the right TPJ was recruited when perceivers were trying to reconcile incongruent information (e.g., a protagonist from a foreign background who expressed a “normal” mental state), which they argued requires greater mentalizing. The mPFC, however, did not discriminate between stories that would require differing levels of mentalizing. Thus, the authors argue that the right TPJ, not the mPFC, is uniquely engaged in the attribution of mental states. To further explore the possibly diverging roles of the TPJ and mPFC, Saxe and Powell (2006) gave participants

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stories that described either the appearance of a person (e.g., “Joe was a heavy-set man, with a gut that fell over his belt. He was balding and combed his blonde hair over the top of his head. His face was pleasant, with large brown eyes”), a bodily sensation (e.g., “Sheila skipped breakfast because she was late for the train to her mother’s. By time she got off the train, she was starving. Her stomach was rumbling, and she could smell food everywhere”), or thoughts (e.g., “Nicky knew that his sister’s flight from San Francisco was delayed 10 hours. Only one flight was delayed so much that night, so when he got to the airport, he knew that flight was hers”). The authors found that only the TPJ (bilaterally) and posterior cingulate were selectively recruited when participants read stories about a protagonist’s thoughts or beliefs (the only task that would require ToM), but not in the other two conditions. They observed similar patterns of mPFC activation in all three conditions. In other words, participants recruited mPFC equally when they were reading any story describing an individual, and not just in conditions in which they were making mental state inferences. The authors therefore argue that the TPJ is uniquely engaged in mentalizing tasks, whereas the mPFC may play a broader role in social processing. Although the findings suggest that TPJ is involved in mentalizing, its precise role in theory of mind is widely debated. Gobbini, Koralek, Bryan, Montgomery, and Haxby (2007) presented participants with false belief stories and pictures of geometric shapes moving in a socially relevant manner: two tasks that both require mentalizing. They only observed TPJ activation in the story condition, not in the social animation condition. Based on these findings, the authors argue that TPJ may play a role in interpreting “covert mental states” to help predict future behavior, but that it is not involved in interpreting beliefs and actions based on perceived actions. In an attempt to dissociate the recruitment of TPJ from mPFC in theory of mind tasks, Ciaramidaro and colleagues (2007) presented participants with cartoons depicting unique actions with disparate goals: private intentions (i.e., fixing a broken light bulb to read), prospective social intentions (i.e., observing a single person prepare dinner for someone else, reflecting a social intention to engage in a social interaction), and communicative intention (i.e., observing person A obtain a glass of water for person B after being asked to do so). The authors found that the right TPJ was active for all three conditions relative to control scenarios, whereas mPFC was only active for the social intention conditions, and not the private intentions. Importantly, the authors found a functional dissociation within the TPJ in that right TPJ in engaged in processing all intentions, but left TPJ is only engaged in processing discrete social intentions (communicative intentions). Together, these findings suggest that the neural network engaged during theory of mind tasks may be more complex than previously thought.

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Although the studies discussed in this section reveal conflicting evidence for identifying the central mechanisms that give rise to ToM, one point remains clear: The brain has a specialized network engaged in mentalizing tasks. This key ingredient to the social brain allows us to engage in complex social interactions involving cooperation, deception, and empathy. ToM provides us a specialized tool by which to detect threats from within our social group. For instance, the ability to understand when you have committed a social error, and then determine how to overcome that social error, requires ToM. Successfully detecting threats from the environment—both from within the social group and beyond—is an important aspect of the social brain. We explore the unique mechanisms dedicated to threat detection in the next section. Threat Detection An important aspect of the social brain is the ability to detect threats in the environment. These threats can be threats to our physical self (i.e., quickly detecting when a predator is pursuing us or recognizing individuals who may threaten our social group or resources) or they may be threats to our psychological self (i.e., threats that affect our status within our social group via social rejection or social exclusion). It is apparent that the brain has developed an efficient system to respond to threats from the environment. For instance, the superior temporal sulcus is uniquely sensitive to detecting biological motion (i.e., movement of the eyes, mouth, hands, and body; Allison, Puce, & McCarthy, 2000; Grezes et al., 2001). However, the amygdala is central to this threat detection system (for review, see Whalen, 1998). The amygdala automatically detects potentially aversive stimuli in the environment, sometimes causing us to jump away from an object that resembles a threat (i.e., a curved branch in the forest that we mistake for a snake) even without knowing what it is. Research with nonhuman primates has shown that amygdala lesions impair appropriate fear responses to novel stimuli (Amaral, 2002). For instance, Amaral showed that primates with amygdala lesions approach and play with a toy snake, while primates with intact amygdala cower from the toy. Evidence from patient research with humans suggests that the amygdala damage impairs patients’ ability to accurately identify the arousal associated with negative stimuli. Intriguingly, however, patients with amygdala damage are able to accurately identify the valence of positive and negative stimuli (Bernston, Bechara, Damasio, Tranel, & Cacioppo, 2007). The amygdala may also play an important role in social threats. The amygdala shows heightened activation in response to fearful faces (Whalen, 1998). Research has

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shown that people are remarkably adept at recognizing fearful faces (Schubo, Gendolla, Meinecke, & Abele, 2006), and this ability is pervasive over the life span (Mather & Knight, 2006). Chiu, Ambady, and Deldin (2004) demonstrated that high-prejudiced White individuals more quickly evaluated angry Black faces as compared to happy Black faces. In other words, high-prejudice participants in this study could more quickly detect and evaluate an outgroup member when that individual conveyed threat (i.e., via an angry facial expression). Norris, Chen, Zhu, Small, and Cacioppo (2004) demonstrated that images with social and emotional content have an additive effect on the amygdala. They showed participants images that contained (a) social information only, (b) emotional information only, and (c) social and emotional information together along with neutral controls. They found the amygdala activation was significantly stronger in response to the pictures that conveyed both social and emotional information as compared to all other conditions. A similar finding emerged from a study by Ito and Cacioppo (2000) using event-related potentials. They found that negative images with social information (e.g., negative pictures that included people) received heightened processing as compared to images that were void of social information. Together these findings suggest that negative social information elicits heightened neural activation as compared to negative nonsocial information, particularly in the amygdala. Given the important role of the amygdala in detecting threats in the environment, it is not surprising that the amygdala also is largely involved in our ability to detect social threats. Emerging research on threat detection from outgroup members has focused primarily on the role of the amygdala in automatically detecting threats from the environment (for review, see Eberhardt, 2005). We next explore these findings in more detail. Threats from Outgroups There is a ubiquitous tendency among humans to stigmatize outgroup members (Kurzban & Leary, 2001). Stigma refers to an attribute that renders individuals “devalued, spoiled or flawed in the eyes of others” (Crocker, Major, & Steele, 1998). Broadly defined, common stigmas include people of different races, people who are physically disabled (e.g., paraplegics), or people with mental disabilities (i.e., schizophrenics; Goffman, 1963). Extensive research has revealed that stigmas automatically elicit powerful and often negative responses from perceivers (for review, see Crocker et al., 1998). Kurzban and Leary (2001) argued that outgroup members are stigmatized as a way of helping social groups protect themselves from outside threats. Emerging neuroscience research has focused primarily on the stigma of race and has revealed that the amygdala plays a central role

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in perceiving other races (Cunningham et al., 2004; Hart et al., 2000; Lieberman, Hariri, Jarcho, Eisenberger, & Bookheimer, 2005; Phelps et al., 2000; Richeson et al., 2003). However, it remains an open question what role the amygdala is playing in perceiving outgroup members. Specifically, is the amygdala responding because we experience negative emotions upon seeing outgroup members, or is the amygdala simply responding to the novelty of the outgroup member? Phelps and colleagues (2000) found a strong positive correlation between heightened amygdala activation of White participants to their anti-Black bias (as measured by the Implicit Association Test; Greenwald, McGhee, & Schwartz, 1998), which may suggest that the amygdala’s role in perceiving race is more emotion based. Conversely, Hart and colleagues (2000) demonstrated the amygdala activation of White participants habituated to the presentation of White, but not Black, faces over time, suggesting that the amygdala simply responds to the novelty of the social stimulus. Further suggesting a more subtle role of the amygdala in processing outgroup members, Wheeler and Fiske (2005) found that the types of judgments that participants make about faces affects amygdala activity. For instance, when White participants were asked to evaluate Black faces, amygdala activity was observed only when the target was socially categorized (e.g., “Is this individual over 21 years old?”), and not when participants were asked to individuate the target (“Would this individual like this vegetable?”). The amygdala is only one of several neural areas engaged during the evaluation of an outgroup member. Emerging research from neuroimaging has revealed that areas of the prefrontal cortex involved in cognitive control are also engaged in these tasks. For instance, Cunningham and colleagues (2004) showed that the amygdala responded to pictures of Black faces when presented very quickly (30 ms). However, when the faces were presented longer (525 ms), the amygdala response was dampened, and instead increased activation was observed in the prefrontal cortex. The authors argue that the heightened activation in the prefrontal cortex may have been inhibiting the automatic response elicited by the amygdala. Richeson and colleagues (2003) also found that White participants engage prefrontal control mechanisms (i.e., dorsolateral prefrontal cortex and anterior cingulate cortex) in response to viewing Black faces (Figure 44.3). However, they found that the activation of these areas was positively correlated with anti-Black bias. In other words, they found that White individuals with greater anti-Black bias recruit some of these cognitive control areas to a greater extent than White individuals with less anti-Black bias. They argue that this heightened activation results from the higher bias Whites’ attempts to mask their prejudice (see also Richeson & Shelton, 2003).

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Figure 44.3 Statistical activation maps of Black faces versus White faces contrast, showing regions in right and left middle frontal gyri, as well as right anterior cingulate cortex.

Neuroscience research on nonrace stigmas is scant, but two studies examine the neural mechanisms engaged in perceiving nonrace stigmas (Harris & Fiske, 2006; Krendl, Macrae, Kelley, Fugelsang, & Heatherton, 2006). Krendl and colleagues (2006) examined the neural mechanisms engaged during explicit (conscious) and implicit (unconscious) evaluations of socially stigmatized individuals (e.g., people who are unattractive or who have numerous facial piercings). We demonstrated that individuals also engaged inhibitory mechanisms in response to viewing socially stigmatized targets, even when the perceivers were unaware that they were evaluating the targets. We also showed that stigmas that were explicitly rated as being more aversive elicited heightened amygdala response (Figure 44.4). Harris and Fiske (2006) examined nonrace stigmas from the perspective of mentalizing. Specifically, they sought to identify whether activation in the medial prefrontal cortex (mPFC) is modulated by the type of stigma group being evaluated. They found that the mPFC was less active than when participants evaluated “extreme outgroup members” (homeless people, drug addicts) as compared to other stigmatized groups (e.g., older adults, disabled people). However, in a subsequent study, they found that the activity of mPFC was further modulated by the type of judgment individuals made about the stigmatized individuals (Harris & Fiske, 2007). For instance, making judgments about whether individuals would like certain vegetables elicited heightened mPFC activity in response to both extreme outgroup members and other stigmatized individuals as compared to making age judgments about the individuals. Considered as a whole, these results revealed two important points: (1) outgroup members appear to automatically activate aversive responses in perceivers, particular when the perceiver is high in prejudice; and (2) perceivers

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Figure 44.4 Parametric modulation of disgust ratings: Analysis conducted with individual disgust ratings modeled linearly as a covariate of interest.

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must successfully engage cognitive control mechanisms to inhibit aversive responses to stigmatized outgroup members. Together these studies reveal a unique social function for the amygdala and prefrontal cortex in detecting threats from outgroup members. In the next section, we explore the manner in which the social brain can detect threats from the ingroup. Threat from Ingroups Ingroup threats (i.e., social rejection or isolation) pose arguably the most potential harm to group members because they can result in ejection from one’s social group. There are several possible causes for ingroup threat. For instance, Kurzban and Leary (2001) argue that individuals will be ostracized from the social group if they endanger the group (i.e., they have a disease that poses a risk to the group) or if they do not contribute to the group (i.e., they are missing a limb and thereby unable to help gather resources). Such individuals, according to Kurzban and Leary, are stigmatized and socially isolated from the group. Further, they argue that individuals who directly violate the rules of the group are subjected to social isolation. In other words, people who steal from the group or intentionally harm other group members will be ejected from the group. Thus, to maintain group membership, one must adhere to

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Note: A: Coronal slice at the 0 point on the z-axis shows robust activity in the bilateral amygdala (L: –18, –4, –17; R: 18, –4, –17) and left insula (BA 13: –42, –3, 9). B: Plots show change in signal amplitude across conditions relative to a baseline control condition (fixating a cross-hair). Error bars indicate SEM. Left amygdala (left panel) demonstrated sensitivity to highly negative stigmas as compared to controls in the evaluative (explicit) condition, but only to the most negative stigma (unattractive) in the gender (implicit) condition. Right amygdala (right panel) demonstrated sensitivity to stigma conditions in both the evaluative and gender conditions.

the social norms of the group. It is on this final point that we focus most of our attention in this section. Violations of social norms are met with harsh punishment or ostracism, even among nonhuman primates. Rhesus macaques, for instance, will unleash significantly more aggression on group members who find food and do not share it with their cohort (Hauser, 1992). For humans, such deviations from social group norms may result in social rejection, or even ejection from one’s social group (Kurzban & Leary, 2001), a punishment most individuals want to avoid at all costs. Thus, the social brain has evolved an extensive network to detect violations of social group norms, thereby serving to protect our membership in the group. The ability to detect ingroup social threats appears to rely, at least in part, on the anterior cingulate cortex (ACC), a region that has been implicated in conflict resolution (Kerns et al., 2004). The ACC has been implicated in responding to social interactions that provide conflicting social feedback (Eisenberger, Lieberman, & Williams, 2003). Somerville, Heatherton, and Kelley (2006) provided subjects with false feedback that was either negative or positive (rejection or not), and also that was incongruent or congruent with their expectations (expectancy violation or not). Results revealed a double dissociation between dorsal and

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ventral ACC regions. The dorsal ACC (dACC) was uniquely sensitive to expectancy violations, and ventral (vACC) was uniquely sensitive to social feedback (Figure 44.5), with significantly greater response to negative feedback than positive feedback, irrespective of expectancy violations.

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Figure 44.5 (Figure C.41 in color section) Differential ACC response to expectancy violation and social feedback. Note: A: A three-dimensional rendering of the medial surface of the brain illustrates a functional dissociation between dorsal (dACC0) and ventral (vACC) anterior cingulate. A whole-brain voxel-by-voxel analysis of variance (ANOVA) was used to identify voxels that showed a significant main effect (p ⬍ .001, uncorrected) of expectancy violation (blue) and a main effect of feedback (yellow). B–C: Voxels in the dACC (BA 32: ⫺6, 28, 32; 13 voxels) demonstrated greater sensitivity to expectancy violation (incongruent ⬎ congruent) (B) whereas voxels in the vACC (BA 32/10: ⫺6, 49, ⫺13: 16 voxels) demonstrated greater sensitivity to feedback (accepted ⬎ rejected). (C) Error bars denote s.e.m.

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Similarly, Krendl, Richeson, Kelley, and Heatherton (2008) found vACC activation during a social threat task. We conducted an fMRI study in which women were reminded of gender stereotypes about math ability while they were completing difficult math problems. Women showed an increase in vACC activity while performing difficult math problems after a social threat was induced (reminding them of gender stereotypes), whereas in the absence of social threat, women instead showed heightened activation over time in regions associated with math learning, and no change in vACC activation (Figure 44.6). Not surprisingly, women who were threatened exhibited a decrease in math performance over time whereas women who were not threatened improved in performance over time. Based on these findings, we conclude that the vACC is engaged in social and emotional processing. Perhaps one of the most immediate sources of ingroup threats stems from violating social norms. In order to protect against such threats, the social brain has developed an intricate network of aptly named social emotions to warn us when we have violated social norms. Social emotions are complex emotions (e.g., admiration, jealousy, envy, irritation, flirtatiousness) that promote long-term social relationships and interactions. These emotions are critical in ensuring that we adhere to social norms in social interactions. Powerful social emotions commonly referred to as moral emotions are engaged to identify when we have acted inappropriately and violated a social norm. These emotions include guilt, pity, embarrassment, shame, pride, awe, contempt, indignation, moral disgust, and gratitude (Moll, de Oliveira-Souza, Zahn, & Grafman, 2008). Their purpose is to elicit negative reactions that will (hopefully) prevent us from committing the violation in the future. Emerging neuroimaging research has begun to identify the unique neural mechanisms that are selectively engaged to process social and moral emotions. Not surprisingly, social and moral emotions engage many of the same structures activated by basic emotions (e.g., anger, fear), but they also selectively engage neural networks involved in assessing affect (e.g., amygdala, orbitofrontal cortex), as well as those regions that are involved in building cognitive schemas about the social world (left prefrontal cortex, right parietal cortex, anterior and posterior cingulate cortex; Adolphs, 2003). The role of the amygdala in processing social emotions is a recent and novel finding that has emerged from patient and neuroimaging research. For instance, Adolphs, BaronCohen, and Tranel (2002) presented facial expressions of social emotions (arrogant, guilt, admiration, flirtatiousness) to patients with amygdala damage. Patients with unilateral or bilateral amygdala damage were impaired when recognizing those specific emotions; moreover, they were

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Figure 44.6 ( Figure C.42 in color section) Changes in neural activation over time for controls and threatened participants. Note: Statistical maps A: lateral view of the left hemisphere of an inflated brain, B: medial view of the right hemisphere of an inflated brain) depicting neural regions that are more active during the second math task than the first for both controls and threatened participants. Activation for controls is depicted in blue, whereas activation for threatened participants is depicted in orange. Images are thresholded at p ⬍ .001 uncorrected, with

more impaired at recognizing social emotions than basic emotions. Ruby and Decety (2004) conducted a PET study in which participants were asked to choose the appropriate reaction (from varying perspectives) to sentences that represented different social emotions (embarrassment, pride, shame, guilt, admiration, irritation), or nonsocial emotions and nonemotional sentences. Results revealed heightened amygdala activation during the processing of all social emotions, regardless of the perspective taken during the task. However, it is important to note that the authors do not dissociate between types of social emotions in the task. Thus, it is unclear whether the amygdala activation observed was driven by a specific emotion. Berthoz, Grezes, Armony, Passingham, and Dolan (2006) conducted an fMRI study to examine intentional violations of social norms. In the study, participants were presented with stories (e.g., “You are invited for a Japanese

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0 ⫺0.2

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a minimum t of 3.5 and maximum of 6.0 for all. C and D: signal change from a fixation control task for left inferior prefrontal cortex (C: BA 47) and ventral anterior cingulate cortex (D: BA 32/10). Controls recruited greater activity in the left inferior prefrontal cortex, whereas the threatened participants showed no change in activation in this region. Conversely, threatened participants recruited greater ventral anterior cingulate activity over time, whereas controls did not.

dinner at your friend’s house”), and one of three endings: one was descriptive of normal social behavior (e.g., “You have a bite of the first course, like it, and congratulate your friend for her good cooking”); one that described an embarrassing conclusion (e.g., “You have a bite of the first course, you choke and spit out the food while you are coughing”); or one in which the protagonist violated a social norm (e.g., “You have a bite of the first course, but do not like it and spit the food back into your plate”). Participants evaluated the statements from their own perspective, or someone else’s. When taking their own perspective, participants showed greater amygdala activation in response to intentional violations of social norms. However, the amygdala is only one part of the neural network engaged in perceiving social and moral emotions. Shin and colleagues (2000) used PET to isolate the neural correlates of guilt, a moral emotion. Prior to the PET scan, participants were asked to provide written accounts of three

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distinct events: one involving the most guilt the participant had ever experienced, and two additional events involving no clear emotion. During the scan, a tape-recording of the autobiographical events was presented aurally to participants, and they were asked to reexperience the event to which they were listening. Direct comparisons between guilt-induction conditions versus neutral elicited heightened activation in anterior cingulate gyrus and left anterior insula. A recent fMRI study on embarrassment, also a moral emotion, found that the anterior cingulate cortex was activated when it was clear that a social norm had been violated. Berthoz, Armony, Blair, and Dolan (2002) used fMRI to investigate the neural systems supporting embarrassing situations and violations of social norms using the same paradigm described previously. Violation of social norms yielded greater activation in the anterior cingulate than embarrassment. Both violation of social norms and embarrassment led to greater activation in the medial prefrontal cortex (such as observed in self-awareness and ToM tasks) and the left orbitofrontal cortex. mPFC activation was also observed in an fMRI study by Takahashi and colleagues (2004) when they compared guilt and embarrassment to neutral emotions. In the study, participants were shown sentences that had been previously rated as inducing guilt (e.g., “I shoplifted a dress from the store”), embarrassment (e.g., “I soiled my underwear”), or no emotion (neutral; e.g., “I washed my clothes”). Both guilt and embarrassment elicited mPFC activation, but direct comparisons between the two revealed heightened activation for mPFC in the guilt condition as compared to embarrassment. The involvement of mPFC in social and moral emotions suggests that these emotions may uniquely engage some form of mentalizing in order to be effective. One possible explanation for why social and moral emotions may engage an extensive network of activation is that simply experiencing social emotions does not make them effective. Instead, social emotions must inform the perceiver of what social norms were followed or violated and provide either a reward or punishment (respectively) to encourage or deter future reoccurrences. However, in order to be effective, the perceiver of the social emotion must possess selfawareness and ToM. For instance, when we commit a social error during a social interaction, we may feel embarrassed. However, to recognize that we are embarrassed because of the social error, we must have self-awareness. Conversely, we may recognize the social error first by realizing that the person to whom our comment was directed is upset by our remark. This recognition may then lead to a feeling of embarrassment. In this case, we would assess violations of social norms by making an inference about the mental state of someone else, which requires ToM.

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Possessing self-awareness and ToM only allow us to understand when a social norm has been violated and does little to prevent us from committing the errors in the future. Here is where self-regulation plays a vital role in the social brain. Self-regulation allows us to control our behavior to ensure that we do not violate social norms. We next explore how the social brain gives rise to self-regulation. Self-Regulation A unique aspect of human behavior is the ability to regulate and control thoughts and actions, an ability commonly referred to as self-regulation. Self-regulation allows us to make plans, choose from alternatives, focus attention on pursuit of goals, inhibit competing thoughts, and regulate social behavior (Baumeister, Heatherton, & Tice, 1994; Baumeister & Vohs, 2004; Metcalfe & Mischel, 1999; Wegner, 1994). Extensive evidence from neuroimaging and patient research demonstrates that the prefrontal cortex is imperative in successfully engaging self-regulatory processes, as befitting its label as “chief executive” of the brain (Goldberg, 2001). The vital role of the prefrontal cortex in self-regulation was famously observed in the case of Phineas Gage, who suffered profound frontal lobe damage when a railroad spike misfired into his head. Formerly described by friends as dependable, polite, and hardworking, Gage became capricious and volatile after the accident. Gage’s failure to regulate his social behavior after his injury was among the first lines of evidence that the prefrontal cortex supports the inhibitory mechanisms necessary to regulate behavior. Since Gage’s accident, abundant patient and neuroimaging research has identified discrete brain regions within prefrontal cortex that are critical for self-regulation (for review, see Banfield, Wyland, Macrae, Münte, & Heatherton, 2004), primarily the dorsolateral prefrontal cortex (DLPFC; involved in modulating of cognitive control), the orbitofrontal cortex (OFC; involved in integrating cognitive and affective information), and the anterior cingulate cortex (ACC; involved in conflict resolution). The DLPFC has been associated with planning, novelty processing, choice, the control of memory, and working memory and language function (see D’Esposito et al., 1995; Dronkers, Redfern, & Knight, 2000; Fuster, Brodner, & Kroger, 2000; Goldman-Rakic, 1987). Damage to this area often results in patients’ experiencing an inability to inhibit certain behaviors (Pandya & Barnes, 1987). Damage to the OFC, which controls our behavioral and emotional output and how we interact with others (Dolan, 1999), often results in striking, and sometimes aggressive, behavioral changes (e.g., Rolls, Hornak, Wade, & McGrath, 1994). Damage to the OFC usually results in personality changes

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such as indifference, impaired social judgment and responsiveness, poor self-regulation, lack of impulse control, and poor judgment and insight (Damasio, 1994; Stone, Baron-Cohen, & Knight, 1998; Stuss & Alexander, 2000). Patients with OFC damage often cannot inhibit desires for instant gratification and thus may commit thefts or sexually aggressive behavior (Blumer & Benson, 1975; Grafman et al., 1996). The ACC is essential for initiating actions, evaluating conflicts, and also inhibiting prepotent responses, processes heavily involved in self-regulation (Kerns et al., 2004). The ACC is functionally dissociated into the dorsal ACC that evaluates cognitive conflict, and the ventral ACC that evaluates emotional conflict (Bush, Luu, & Posner, 2000). The ACC is often engaged whenever any kind of “supervisory input” is required (Badgaiyan & Posner, 1998). In fact, it is widely accepted that the ACC is somehow involved in evaluating the degree and nature of conflict, whereas other parts of the brain (particularly the PFC) may be involved in resolving the conflict itself (Botvinick, Cohen, & Carter, 2004; Cohen, Botvinick, & Carter, 2000; Kerns et al., 2004). Emerging neuroimaging research has sought to more clearly identify the neural structures in self-regulation by examining the structures engaged in emotion and cognitive regulation. Ochsner, Bunge, Gross, and Gabrieli (2002) showed participants highly negative pictures and instructed them either to “attend” (study the picture and be aware of, but not try to alter, their feelings toward it) or “reappraise” (reinterpret the picture in such a way that it would no longer elicit a negative response) the photograph. The authors found that reappraising the photographs led to decreased subjective negative affect, and this was reflected in a reduction of activity in the amygdala (a region implicated in processing fear) and OFC, and increased activation in the lateral and medial prefrontal cortex, as well as in the anterior cingulate cortex. In a later study, Ochsner and colleagues (2004) instructed participants to increase negative affect toward the image (by imagining themselves or a loved one as the central figure in a highly negative photograph) or decrease their negative affect to the photograph (by psychologically distancing themselves from it). Here, the authors found that extensive networks in the prefrontal cortex (left prefrontal cortex, dorsal anterior cingulate, and dorsal mPFC) were engaged when participants were using self-regulatory processes either to increase or decrease their affective response to the photographs. They observed that enhancing negative emotions engaged primarily left-lateralized prefrontal regions, whereas suppressing negative affect engaged bilateral prefrontal regions. Importantly, they also observed that activity in the amygdala decreased when participants actively

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decreased their negative affect to the picture, and increased when they increased their negative affect. Self-regulation research has not been limited to modulating negative affect. Kim and Hamann (2007) observed increased activation in the left prefrontal cortex when participants were asked to increase either positive or negative affective responses to stimuli, and predominantly bilateral prefrontal activity in response to suppressing positive or negative affect. Importantly, the dorsal mPFC and the OFC were engaged for regulating both positive and negative emotions. Activation of the amygdala increased during the increase condition for both positive and negative pictures, and decreased in the suppress conditions in response to both positive and negative stimuli as well. Beauregard, Levesque, and Bourgouin (2001) had male participants view erotic films clips while undergoing fMRI. Participants were instructed either to allow themselves to become aroused during the clips, or to suppress any arousal they might be feeling. The authors found that suppressing arousal resulted in heightened activation in the right superior prefrontal and right anterior cingulate cortices with no accompanying activation in the limbic areas (e.g., amygdala, hypothalamus) that were active during the arousal condition. Together, these findings have had important implications in patient populations. For instance, emerging research on patients with severe depression has revealed that their prefrontal cortex is unable to suppress amygdala activation in emotion regulation tasks (Johnstone, van Reekum, Urry, Kalin, & Davidson, 2007). When depressed patients are asked to suppress negative affect to highly aversive pictures, depressed patients show a positive correlation between the amygdala and vmPFC, whereas controls demonstrate a negative correlation between the two. In other words, the more controls engage vmPFC to suppress negative affect, the greater decrease is observed in activity in the amygdala. However, the more depressed patients try to suppress negative affect, the greater the activity in their amygdala. Another important form of self-regulation that is critical for daily living is mental control. Successfully controlling the contents of consciousness is a difficult task—worries intrude when people least desire them and it is not uncommon for the mind to wander when people should be focused on a particular task or objective. Research by Wegner (1994) demonstrated that trying to suppress a particular thought can paradoxically lead to an increase in the very thought one is attempting to suppress. One open question is whether cognitive control of thoughts and actions involves similar component processes and therefore recruits common brain regions. If so, one might expect to observe ACC activity during attempts to control thoughts. To address this issue, we conducted an fMRI study of attempted

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thought suppression (Wyland, Kelley, Macrae, Gordon, & Heatherton, 2003). Participants were each asked to provide a personally relevant thought that was especially salient to them at that moment (e.g., “study for an exam” or “a phone call with a distant girlfriend”). During the scan, they were given visual prompts that instructed them to: (a) suppress the personally relevant thought they had generated before the task (“suppress”), (b) think about nothing (“clear”), or (c) think about anything (“free thought”). Both the “suppress” and “clear” conditions required a form of thought suppression. To dissociate these two processes, no overt behavioral response was collected (e.g., pushing a button to index thought intrusions) as such a requirement contaminates thought suppression with response generation. Moreover, we were not concerned with failures of cognitive control per se, but rather the ongoing process of mental regulation. The results indicated that the brain regions previously implicated in the suppression of overt behavior were also active during attempts to control the emergence of unwanted thoughts. Specifically, we found greater activation in the ACC for the “suppress” condition than for the “free thought” condition. When the “clear” and “free thought” conditions were compared, a more diverse pattern of neural activation was observed. Specifically, greater activation was observed in the anterior cingulate, left inferior frontal cortex, right insula, right parietal cortex, and right medial frontal cortex in the “clear” as compared to the “free thought” condition. The greater activity for the “clear” condition may have occurred because it is more difficult to suppress all thoughts than to suppress a specific thought. Our participants reported having a great deal of difficulty with this condition (the interested reader should go ahead and try this; it is nearly impossible). As previously demonstrated by Wegner and his colleagues (1989), suppressing a specific thought can be achieved relatively easily by thinking of something else, especially if that other thought captures attention. Because we are also interested in examining failures of mental control, we conducted a second study in which participants were asked to suppress the specific thought of white bears (Mitchell, Heatherton, Kelley, Wyland, and Macrae (2006). In this study, participants were scanned while alternatively trying to suppress the thought of a white bear or freely thinking about anything that came to mind; in either case, they pressed a button to indicate a white bear thought (we also had a third condition in between blocks that required participants to press a key when a yellow light appeared in order to control for simple motor movement). We found that right dorsolateral prefrontal cortex showed a sustained response when participants were attempting to suppress thoughts (i.e., the tonic state), whereas the ACC

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was activated by the intrusion of forbidden thoughts (i.e., transient events). This pattern of results is consistent with neural models of cognitive control that emphasize the interplay between PFC and ACC in cognitive control (Kerns et al., 2004) as well as with the Wegner ’s (1994) model of mental control. The use of imaging is well suited to contribute to our ability to examine theoretical models of selfregulation, both in emotion and mental regulation tasks.

SUMMARY In this chapter, we described how neuroimaging has informed our knowledge of the unique components of the social brain. Our research has identified a number of frontal lobe regions that appear to subserve important human talents, such as the ability to introspect, evaluate ourselves and others, detect social threats around us, and to purposefully modify our thoughts and behaviors in the pursuit of goals. Knowing where in the brain something happens doesn’t by itself tell you very much. But, knowing that there are consistent patterns of brain activation associated with specific psychological tasks provides evidence that the two are connected, and provides an opportunity to identify component processes that might be important for a full understanding of mental constructs. We believe that a social brain sciences approach will be useful for understanding the nature of the social brain. Now that we have identified various regions of the brain that comprise the social brain, one next logical step is to try to identify the specific role of these regions. Many of the regions discussed in this chapter (e.g., the amygdala, ACC, DLPFC) have been implicated in nonsocial tasks. It would therefore be misleading to suggest that these regions are solely “social brain areas.” More than likely, these regions have a broader role in the brain (e.g., threat detection, conflict resolution) that renders them useful both in certain cognitive and social tasks. However, the role of the mPFC in the social brain is particularly puzzling because it is robustly activated during self-relevant and theory of mind tasks, but otherwise it is deactive during most cognitive tasks (Raichle et al., 2001). It is thus thought that the mPFC is engaged when the brain is “inactive,” suggesting perhaps that the “default state” of the brain is introspection (Raichle et al., 2001). One possible explanation for this is that the deactivations observed in mPFC during cognitive tasks are due to the fact that available neural resources are required to perform the task at hand, and therefore fewer are available for inward reflection, thus causing a decline in activity in the mPFC (Gusnard, 2005). Another possibility is that mPFC operates by binding together various physical experiences and

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cognitive operations that have implications for self. The prefrontal cortex receives input from all sensory modalities, and is therefore the brain region where inputs from internal sources conjoin with information received from the outside world. This region may act in a metacognitive fashion to monitor all stimuli, whether internal or external, so that our conscious sense of self at any particular moment reflects a workspace determined by which brain regions are most active. Finally, evidence is accumulating that the medial prefrontal cortex is important not only for processing information about the self, but also for inferring mental states in others (Macrae et al., 2004; Mitchell et al., 2005). This raises the possibility that having a self might be adaptive because it allows us to simulate the mental lives of others, thereby allowing us to better know others and predict their behavior. Functionally, having ToM allows us to be good group members because we can predict how others will respond to our actions and ensure that we act in accordance with group norms and values. Such a theory is consistent with the argument that a symbolic self is adaptive (Sedikides & Skowronski, 1997). These and other theories will inspire further research on the social brain.

Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1994). Losing control: How and why people fail at self-regulation. San Diego, CA: Academic Press. Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117, 497–529. Baumeister, R. F., & Vohs, K. D. (2004). Handbook of self-regulation: Research, theory, and applications. New York: Guilford Press. Beauregard, M., Levesque, J., & Bourgouin, P. (2001). Neural correlates of conscious self-regulation of emotion. Journal of Neuroscience, 21, 1–5. Bernston, G. G., Bechara, A., Damasio, H., Tranel, D., & Cacioppo, J. T. (2007). Amygdala contribution to selection dimensions of emotion. Social Cognitive and Affective Neuroscience, 2, 123–129. Berthoz, S., Armony, J. L., Blair, R. J., & Dolan, R. J. (2002). An fMRI study of intentional and unintentional (embarrassing) violations of social norms. Brain, 125, 1696–1708. Berthoz, S., Grezes, J., Armony, J. L., Passingham, R. E., & Dolan, R. J. (2006). Affective response to one’s own moral violations. NeuroImage, 31, 945–950. Blumer, D., & Benson, D. (1975). Personality changes with frontal and temporal lesions. New York: Grune & Stratton. Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: An update. Trends in Cognitive Sciences, 8, 539–546. Bower, G. H., & Gilligan, S. G. (1979). Remembering information related to one’s self. Journal of Research in Personality, 13, 420–432. Bowlby, J. (1969). Attachment and loss (Vol. 1). New York: Basic Books. Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences, 4, 215–222.

REFERENCES Addis, D. R., Wong, A. T., & Schacter, D. L. (2006). Remembering the past and imagining the future: Common and distinct neural substrates during event construction and elaboration. Neuropsychologia, 45, 1363–1377. Adolphs, R. (2003). Cognitive neuroscience of human social behaviour. Nature Reviews: Neuroscience, 4, 165–178. Adolphs, R., Baron-Cohen, S., & Tranel, D. (2002). Impaired recognition of social emotions following amygdala damage. Journal of Cognitive Neuroscience, 14, 1264–1274. Allison, T., Puce, A., & McCarthy, G. (2000). Social perception from visual cues: Role of the STS region. Trends in Cognitive Sciences, 4, 267–278. Amaral, D. G. (2002). The primate amygdala and the neurobiology of social behavior: Implications for understanding social anxiety. Biological Psychiatry, 51, 11–17. Aron, A., & Aron, E. N. (1996). Self and self-expansion in relationships. Hillsdale, NJ: Erlbaum. Aron, A., Aron, E. N., Tudor, M., & Nelson, G. J. (1991). Close relationships as including other in the self. Journal of Personality and Social Psychology, 60, 241–253. Aron, A., & Fraley, B. (1999). Relationship closeness as including other in the self: Cognitive underpinnings and measures [Special issue: Social Cognition and Relationships]. Social Cognition, 17, 140–160. Badgaiyan, R., & Posner, M. (1998). Mapping the cingulate cortex in response selection and monitering. NeuroImage, 7, 255–260. Banfield, J. F., Wyland, C. L., Macrae, C. N., Münte, T. F., & Heatherton, T. F. (2004). The cognitive neuroscience of self-regulation. New York: Guilford Press. Baumeister, R. F. (1998). The self (4th ed. Vol. 2). New York: McGraw-Hill.

c44.indd 875

Cabeza, R., Prince, S. E., Daselaar, S. M., Greenberg, D. L., Budde, M., Dolcos, F., et al. (2004). Brain activity during episodic retrieval of autobiographical and laboratory events: An fMRI study using a novel photo paradigm. Journal of Cognitive Neuroscience, 16, 1583–1594. Cacioppo, J. T., Ernst, J. M., Burleson, M. H., McClintock, M. K., Malarkey, W. B., Hawkley, L. C., et al. (2000). Lonely traits and concomitant physiological processes: The MacArthur social neuroscience studies. International Journal of Pshchophysiology, 35, 143–154. Cacioppo, J. T., Hughes, M. E., Waite, L. J., Hawkley, L. C., & Thisted, R. A. (2006). Loneliness as a specific risk factor for depressive symptoms: Cross sectional and longitudinal analyses. Psychology and Aging, 21, 140–151. Cheney, D. L., & Seyfarth, R. M. (1997). Reconciliatory grunts by dominant female baboons influence victims’ behavior. Animal Behaviour, 54, 409–418. Chiu, P., Ambady, N., & Deldin, P. (2004). Contingent negative variation to emotional in- and out-group stimuli differentiates high- and low-prejudiced individuals. Journal of Cognitive Neuroscience, 16, 1830–1839. Ciaramidaro, A., Adenzato, M., Enrici, I., Erk, S., Pia, L., Bara, B. G., et al. (2007). The intentional network: How the brain reads varieties of intentions. Neuropsychologia, 45, 3105–3113. Cohen, J. D., Botvinick, M., & Carter, C. S. (2000). Anterior cingulate and prefrontal cortex: Who’s in control? Nature Neuroscience, 3, 421–423. Craik, F. I. M., Moroz, T. M., Moscovitch, M., Stuss, D. T., Winocur, G., Tulving, E., et al. (1999). In search of the self: A positron emission tomograph study. Psychological Science, 10, 26–34. Crocker, J., Major, B., & Steele, C. (1998). Social stigma (Vol. 2, 4th ed.). New York: McGraw-Hill. Cunningham, W. A., Johnson, M. K., Raye, C. L., Gatenby, C. J., Gore, J. C., & Banaji, M. R. (2004). Separable neural components in the processing of black and white faces. Psychological Sciences, 15, 806–813.

8/17/09 5:25:03 PM

876

Self versus Others/Self-Regulation

Damasio, A. R. (1994). Descartes’ error and the future of human life. Scientific American, 271, 144.

Gusnard, D. A. (2005). Being a self: Considerations from functional imaging. Consciousness and Cognition, 14, 679–697.

D’Esposito, M., Detre, J. A., Alsop, D. C., Shin, R. K., Atlas, S., & Grossman, M. (1995, November 16). The neural basis of the central executive system of working memory. Nature, 378, 279–281.

Gusnard, D. A., Akbudak, E., Shulman, G. L., & Raichle, M. E. (2001). Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function. Proceedings of the National Academy of Sciences, USA, 98, 4259–4264.

Dolan, R. J. (1999). On the neurology of morals. Nature Neuroscience, 2, 927–929. Dronkers, N. F., Redfern, B. B., & Knight, R. T. (2000). The neural architecture of language disorders. Cambridge, MA: MIT Press. Eberhardt, J. L. (2005). Imaging race. American Psychologist, 60, 181–190. Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003, October 10). Does rejection hurt? An fMRI study of social exclusion. Science, 302, 290–292. Feinberg, T. E., & Keenan, J. P. (2005). The lost self: Pathologies of the brain and identity. New York: Oxford University Press. Fletcher, P. C., Happe, F., Frith, U., Baker, S. C., Dolan, R. J., Frackowiak, R. S., et al. (1995). Other minds in the brain: A functional imaging study of “theory of mind” in story comprehension. Cognition, 57, 109–128. Fuster, J. M., Brodner, M., & Kroger, J. K. (2000, May 18). Cross-modal and cross-temporal associations in neurons of frontal cortex. Nature, 405, 347–351. Gallagher, H. L., & Frith, C. D. (2003). Functional imaging of ‘theory of mind.’ Trends in Cognitive Sciences, 7, 77–83. Gallagher, H. L., Happe, F., Brunswick, N., Fletcher, P. C., Frith, U., & Frith, C. D. (2000). Reading the mind in cartoons and stories: An fMRI study of ‘theory of mind’ in verbal and nonverbal tasks. Neuropsychologia, 38, 11–21.

Harris, L. T., & Fiske, S. T. (2006). Dehumanizing the lowest of the low: Neuroimaging responses to extreme out-groups. Psychological Science, 17, 847–853. Harris, L. T., & Fiske, S. T. (2007). Social groups that elicit disgust are differentially processed in mPFC. Social Cognitive and Affective Neuroscience, 2, 45–51. Hart, A. J., Whalen, P. J., Shin, L. M., McInerney, S. C., Fischer, H., & Rauch, S. L. (2000). Differential response in the human amygdala to racial outgroup vs ingroup face stimuli. NeuroReport, 11, 2351–2355. Hauser, M. D. (1992). Costs of deception: Cheaters are punished in rhesus monkeys (Macaca mulatta). Proceedings of the National Academy of Sciences, USA, 89, 12137–12139. Heatherton, T. F., & Krendl, A. C. (2007). Social emotions: Imaging social emotions. New Encyclopedia of Neuroscience. Oxford: Elsevier. Heatherton, T. F., Macrae, C. N., & Kelley, W. M. (2004). What the social brain sciences can tell us about the self. Current Directions in Psychological Science, 13, 190–193. Heatherton, T. F., Wyland, C., Macrae, C. N., Denny, B. T., Demos, K. D., & Kelley, W. M. (2006). Friends among us? Medial prefrontal activity is specific for self-referential processing. Unpublished manuscript. House, J. S., Landis, K. R., & Umberso, D. (1988, July 29). Social relationships and health. Science, 241, 540–545.

Gallagher, H. L., Jack, A. I., Roepstorff, A., & Frith, C. D. (2002). Imaging the intentional stance in a competitive game. NeuroImage, 3, 814–821.

Ito, T. A., & Cacioppo, J. T. (2000). Electrophysical evidence of implicit and explicit categorization processes. Journal of Experimental Social Psychology, 36, 660–676.

Gardner, W. L., Pickett, C. L., & Brewer, M. B. (2000). Social exclusion and selective memory: How the need to belong influences memory for social events. Personality and Social Psychology Bulletin, 26, 486–496.

Johnson, S. C., Baxter, L. C., Wilder, L. S., Pipe, J. G., Heiserman, J. E., & Prigatano, G. P. (2002). Neural correlates of self-reflection. Brain, 125, 1808–1814.

Gardner, W. L., Pickett, C. L., Jefferis, V., & Knowles, M. (2005). On the outside looking in: Loneliness and social monitoring. Personality and Social Psychology Bulletin, 31, 1549–1560.

Johnstone, T., van Reekum, C. M., Urry, H. L., Kalin, N. H., & Davidson, R. J. (2007). Failure to regulate: Counterproductve recruitment of topdown prefrontal-subcortical circuitry in major depression. Journal of Neuroscience, 27, 8877–8884.

Gillihan, S. J., & Farah, M. J. (2005). Is self special? A critical review of evidence from experimental psychology and cognitive neuroscience. Psychological Bulletin, 13, 76–97.

Keenan, J. M., & Baillet, S. D. (1980). Memory for personally and socially significant events. Hillsdale, NJ: Erlbaum.

Gobbini, M. I., Koralek, A. C., Bryan, R. E., Montgomery, K. J., & Haxby, J. V. (2007). Two takes on the social brain: A comparison of theory of mind tasks. Journal of Cognitive Neuroscience, 19, 1803–1814.

Kelley, W. M., Macrae, C. N., Wyland, C. L., Caglar, S., Inati, S., & Heatherton, T. F. (2002). Finding the self?: An event-related fMRI study. Journal of Cognitive Neuroscience, 14, 785–794.

Goffman, E. (1963). Stigma: Notes on the management of spoiled identity. New York: Simon & Schuste.

Kerns, J. G., Cohen, J. D., MacDonald, A. W., III, Cho, R. Y., Stenger, V. A., & Carter, C. S. (2004, February 13). Anterior cingulate conflict monitoring and adjustments in control. Science, 303, 1023–1026.

Goldberg, E. (2001). The executive brain: The frontal lobes and the civilized mind. New York: Oxford University Press.

Kim, S. H., & Hamann, S. (2007). Neural correlates of positive and negative emotion regulation. Journal of Cognitive Neuroscience, 19, 776–798.

Goldman-Rakic, P. S. (1987). Development of cortical circuitry and cognitive function. Child Development, 58, 601–622.

Klein, S. B. (2004). The cognitive neuroscience of knowing one’s self (Vol. 3). Cambridge, MA: Institute of Technology.

Grafman, J., Schwab, K., Warden, D., Pridgen, A., Brown, H. R., & Salazar, A. M. (1996). Frontal lobe injuries, violence, and aggression: A report of the Vietnam Head Injury Study. Neurology, 46, 1231–1238.

Klein, S. B., Cosmides, L., & Costabile, K. A. (2003). Preserved knowledge of self in a case of Alzheimer’s dementia. Social Cognition, 21, 157–165.

Greenwald, A. G., & Banaji, M. R. (1989). The self as a memory system: Powerful, but ordinary. Journal of Personality and Social Psychology, 57, 41–54. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74, 1464–1480. Grezes, J., Fonlupt, P., Bertenthal, B., Delon-Martin, C., Segebarth, C., & Decety, J. (2001). Does perception of biological motion rely on specific brain regions? NeuroImage, 13, 775–785.

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Klein, S. B., & Kihlstrom, J. F. (1986). Elaboration, organization, and the self-reference effect in memory. Journal of Experimental Psychology: General, 115, 26–38. Klein, S. B., & Loftus, J. (1988). The nature of self-referent encoding: The contributions of elaborative and organizational processes. Journal of Personality and Social Psychology, 55, 5–11. Krendl, A. C., Macrae, C. N., Kelley, W. M., Fugelsang, J. F., & Heatherton, T. F. (2006). The good, the bad, and the ugly: An fMRI investigation of the functional anatomic correlates of stigma. Social Neuroscience, 1, 5–15.

8/17/09 5:25:04 PM

References 877 Krendl, A. C., Richeson, J. A., Kelley, W. M., & Heatherton, T. F. (2008). The negative consequences of threat: An fMRI investigation of the neural mechanisms underlying women’s underperformance in math. Psychological Science, 19, 168–175. Kurzban, R., & Leary, M. R. (2001). Evolutionary origins of stigmatization: The functions of social exclusion. Psychological Bulletin, 127, 187–208. Leary, M. R. (2005). Interpersonal cognition and the quest for social acceptance: Inside the sociometer. New York: Guilford Press. Leary, M. R., Tambor, E. S., Terdal, S. K., & Downs, D. L. (1995). Selfesteem as an interpersonal monitor: The sociometer hypothesis. Journal of Personality and Social Psychology, 68, 518–530. Lieberman, M. D., Hariri, A., Jarcho, J. M., Eisenberger, N. I., & Bookheimer, S. Y. (2005). An fMRI investigation of race-related amygdala activity in African-American and Caucasian-American individuals. Nature Neuroscience, 8, 720–722. Macdonald, G., & Leary, M. R. (2005). Why does social exclusion hurt? The relationship between social and physical pain. Psychological Bulletin, 131, 202–223. Macrae, C. N., & Bodenhausen, G. V. (2000). Social cognition: Thinking categorically about others. Annual Review of Psychology, 51, 93–120. Macrae, C. N., Moran, J. M., Heatherton, T. F., Banfield, J. F., & Kelley, W. M. (2004). Medial prefrontal activity predicts memory for self. Cerebral Cortex, 14, 647–654. Maki, R. H., & McCaul, K. D. (1985). The effects of self-reference versus other reference on the recall of traits and nouns. Bulletin of the Psychonomic Society, 23, 169–172. Markus, H. (1977). Self-schemata and processing information about the self. Journal of Personality and Social Psychology, 35, 63–78.

Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. (2002). Rethinking feelings: An FMRI study of the cognitive regulation of emotion. Journal of Cognitive Neuroscience, 14, 1215–1229. Ochsner, K. N., Ray, R. D., Cooper, J. C., Robertson, E. R., Chopra, S., Gabrieli, J. D. E., et al. (2004). For better or for worse: Neural systems supporting the cognitive dow- and up-regulation of negative emotion. NeuroImage, 2, 483–499. Pandya, D. N., & Barnes, C. L. (1987). Architecture and connections of the frontal lobe. New York: IRBN Press. Phelps, E. A., O’Connor, K. J., Cunningham, W. A., Funayama, E. S., Gatenby, J. C., Gore, J. C., et al. (2000). Performance on indirect measures of race evaluation predicts amygdala activation. Journal of Cognitive Neuroscience, 12, 729–738. Pickett, C. L., Gardner, W. L., & Knowles, M. (2004). Getting a cue: The need to belong and enhanced sensitivity to social cues. Personality and Social Psycgology Bulletin, 30, 1095–1107. Povinelli, D. J., & Eddy, T. J. (1996). What chimpanzees know about seeing. Monographs of the Society for Reseach in Child Development, 61, 1–152. Povinelli, D. J., Nelson, K. E., & Boysen, S. T. (1990). Inferences about guessing and knowing by chimpanzees (Pan troglodytes). Journal of Comparative Psychology, 104, 203–210. Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, USA, 98, 676–682.

Mason, M. F., Banfield, J. F., & Macrae, C. N. (2004). Thinking about actions: The neural substrates of person knowledge. Cerebral Cortex, 14, 209–214.

Richeson, J. A., Baird, A. A., Gordon, H. L., Heatherton, T. F., Wyland, C. L., Trawalter, S., et al. (2003). An fMRI investigation of the impact of interracial contact on executive function. Nature Neuroscience, 6, 1323–1328.

Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C.N. (2007). Wandering minds: The default network and stimulus independent thought. Science, 315, 393–395.

Richeson, J. A., & Shelton, J. N. (2003). When prejudice does not pay: Effects of interracial contact on executive function. Psychological Science, 14, 287–290.

Mather, M., & Knight, M. R. (2006). Angry faces get noticed quickly: Threat detection is not impaired among older adults. Journals of Gerontology. Series B, Psychological Sciences, 61(1), P54–P57.

Rogers, T. B., Kuiper, N. A., & Kirker, W. S. (1977). Self-reference and the encoding of personal information. Journal of Personality and Social Psychology, 35, 677–688.

McCabe, K., Houser, D., Ryan, L., Smith, V., & Trouard, T. (2001). A functional imaging study of cooperation in two-person reciprocal change. Proceedings of the National Academy of Sciences, USA, 98, 11832–11835.

Rolls, E. T., Hornak, J., Wade, D., & McGrath, J. (1994). Emotion-related learning in patients with social and emotional changes associated with frontal lobe damage. Journal of Neurology, Neurosurgery, and Psychiatry, 57, 1518–1524.

Metcalfe, J., & Mischel, W. (1999). A hot/cool-system analysis of delay of gratification: Dynamics of willpower. Psychological Review, 106, 3–19. Mitchell, J. P., Banaji, M. R., & Macrae, C. N. (2005). General and specific contributions of the medial prefrontal cortex to knowledge about mental states. NeuroImage, 28, 757–762.

Rubin, K. H., & Mills, R. S. (1988). The many faces of social isolation in childhood. Journal of Consulting and Clinical Psychology, 56, 916–924. Ruby, P., & Decety, J. (2004). How would you feel versus how do you think she would feel? A neuroimaging study of perspective-taking with social emotions. Journal of Cognitive Neuroscience, 16, 988–999.

Mitchell, J. P., Heatherton, T. F., Kelley, W. M., Wyland, C. L., & Macrae, C. N. (2006). Controlling the contents of consciousness: The neural substrates of thought suppression. Unpublished manuscript.

Saxe, R., & Kanwisher, N. (2003). People thinking about people: The role of the termpero-parietal junction in “theory of mind.” NeuroImage, 19, 1835–1842.

Mitchell, J. P., Heatherton, T. F., & Macrae, C. N. (2002). Distinct neural systems subserve person and object knowledge. Proceedings of the National Academy of Sciences, USA, 99, 15238–15243.

Saxe, R., & Powell, L. J. (2006). It’s the thought that counts: Specific brain regions for one component of theory of mind. Psychological Science, 17, 692–699.

Mitchell, J. P., Macrae, C. N., & Banaji, M. R. (2006). Dissociable medial prefrontal contributions to judgments of similar and dissimilar others. Neuron, 50, 655–663.

Saxe, R., & Wexler, A. (2005). Making sense of another mind: The role of the right tempero-parietal junction. Neuropsychologia, 43, 1391–1399.

Moll, J., de Oliveira-Souza, R., Zahn, R., & Grafman, J. (2008). The cognitive science of morality (Vol. 2). Cambridge, MA: MIT Press. Norris, C. J., Chen, E. E., Zhu, D. C., Small, S. L., & Cacioppo, J. T. (2004). The interaction of social and emotional processes in the brain. Journal of Cognitive Neuroscience, 16, 1818–1829. Northoff, G., & Bermpohl, F. (2004). Cortical midline structures and the self. Trends in Cognitive Sciences, 8, 102–107.

c44.indd 877

Ochsner, K. N., Beer, J. S., Robertson, E. R., Cooper, J. C., Gabrieli, J. D., Kihsltrom, J. F., et al. (2005). The neural correlates of direct and reflected self-knowledge. NeuroImage, 28, 797–814.

Schmitz, T. W., Kawahara-Baccus, T. N., & Johnson, S. C. (2004). Metacognitive evaluation, self-relevance, and the right prefrontal cortex. NeuroImage, 22, 941–947. Schore, A. N. (2003). Affect regulation and repair of the self. New York: W. W. Norton. Schubo, A., Gendolla, G. H., Meinecke, C., & Abele, A. E. (2006). Detecting emotional faces and features in a visual search paradigm: Are faces special? Emotion, 6, 246–256.

8/17/09 5:25:05 PM

878

Self versus Others/Self-Regulation

Sedikides, C., & Skowronski, J. J. (1997). The symbolic self in evolutionary context. Personality and Social Psychology Review, 1, 80–102. Seger, C. A., Stone, M., & Keenan, J. P. (2004). Cortical activations during judgments about the self and an other person. Neuropsychologia, 42, 1168–1177.

Takahashi, H., Yahata, N., Koeda, M., Matsuda, T., Asai, K., & Okubo, Y. (2004). Brain activation associated with evaluative processes of guilt and embarassment: An fMRI study. NeuroImage, 23, 967–974. Tomasello, M., Hare, B., & Agnetta, B. (1999). Chimpanzees follow gaze direction geometrically. Animal Behaviour, 58, 769–777.

Seyfarth, R. M., & Cheney, D. L. (2003). Signalers and receivers in animal communcation. Annual Review of Psychology, 54, 145–173.

Tulving, E. (1993). Self-knowledge of an amnesic individual is represented abstractly. Hillsdale, NJ: Erlbaum.

Shin, L. M., Dougherty, D. D., Orr, S. P., Pitman, R. K., Lasko, M., Macklin, M. L., et al. (2000). Activation of anterior paralimbic structures during guilt-related script-driven imagery. Biological Psychiatry, 48, 43–50.

Turk, D. J., Heatherton, T. F., Macrae, C. N., Kelley, W. M., & Gazzaniga, M. S. (2003). Out of contact, out of mind: The distributed nature of the self. Annals of the New York Academy of Sciences, USA, 1001, 65–78.

Somerville, L. H., Heatherton, T. F., & Kelley, W. M. (2006). Dissociating expectancy violation from social rejection. Nature Neuroscience, 9, 1007–1008.

Vogeley, K., & Fink, G. R. (2003). Neural correlates of first-person perspective. Trends in Cognitive Sciences, 7, 38–42.

Steinvorth, S., Corkin, S., & Halgren, E. (2006). Ecphory of autobiographical memories: An fMRI study of recent and remote memory retrieval. NeuroImage, 30, 285–298. Stone, V. E., Baron-Cohen, S., & Knight, R. T. (1998). Does frontal lobe damage produce theory of mind impairment? Journal of Cognitive Neuroscience, 10, 640–656. Stuss, D. T., & Alexander, M. P. (1994). Functional and anatomical specificity of frontal lobe functions. New York: Plenum Press. Stuss, D. T., & Alexander, M. P. (2000). Executive functions and the frontal lobes: A conceptual view. Psychology Research, 63(3–4), 289–298. Stuss, D. T., & Benson, D. F. (1984). Neuropsychological studies of the frontal lobes. Psychological Bulletin, 95, 3–28. Stuss, D. T., Gallup, G. G., & Alexander, M. P. (2001). The frontal lobes are necessary for ‘theory of mind.’ Brain, 124, 279–286. Symons, C. S., & Johnson, B. T. (1997). The self-reference effect in memory: A meta-analysis. Psychological Bulletin, 121, 371–394.

c44.indd 878

Wegner, D. M. (1989). White bears and other unwanted thoughts: Suppression, obsession, and the psychology of mental control. New York: Penguin Press. Wegner, D. M. (1994). Ironic processes of mental control. Psychological Review, 101, 34–52. Whalen, P. J. (1998). Fear, vigilance, and ambiguity: Initial neuroimaging studies of the human amygdala. Current Directions in Psychological Science, 7, 177–188. Wheeler, M. A., Stuss, D. T., & Tulving, E. (1997). Toward a theory of episodic memory: The frontal lobes and autonoetic consciousness. Psychological Bulletin, 121, 331–354. Wheeler, M. E., & Fiske, S. T. (2005). Controlling racial prejudice: Social-cognitive goals affect amygdala and stereotype activation. Psychological Sciences, 16, 56–63. Wyland, C. L., Kelley, W. M., Macrae, C. N., Gordon, H. L., & Heatherton, T. F. (2003). Neural correlates of thought suppression. Neuropsychologia, 41, 1863–1867.

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Chapter 45

Language Processes HOWARD C. NUSBAUM AND DANIEL MARGOLIASH

HUMAN UNIQUENESS AND COMPUTATIONAL MECHANISMS

Although physicists have debated the possibility of action at a distance for quite some time, the biological form of action at a distance is well established, as achieved through vocal communication in an extremely broad range of behaviors and settings. Many species of fish, amphibians, reptiles, birds, and mammals commonly exchange information at a distance through their calls. The learned songs (and some calls) of songbirds are particularly rich sources of information, conveying to the receiver individual identity and a host of other characteristics of the sender (Kroodsma & Miller, 1996). Frogs are generally characterized by their low frequency hearing, yet some species have evolved ultrasonic communication to communicate in high noise environments near streams and waterfalls (Feng et al., 2006). Some mammals exchange information at great distances through calling behavior. Humpback whale (Megaptera novaeangliae) vocalizations are perceived over extremely long distances and may be used to maintain social groups at distances as great as 5 km (Frankel, Clark, Herman, & Gabriele, 1995). African elephants (Loxodonta africana) can recognize friends and relatives from their calls at a distance of 2.5 km (McComb, Reby, Baker, Moss, & Sayialel, 2003). Among the nonhuman primates, flanged male orangutans (Pongo pygmaeus) are notable for maintaining spacing with other males and advertising for receptive females over distances of at least 1 km with their long calls (Galdikas, 1983; MacKinnon, 1974). Human sheepherders keep each other company from the top of one mountain to another in the Canary Islands using a whistled language called Silbo Gomero (Busnel & Classe, 1976). Whether for purposes of mating, threat, warning, or social organization, conveying information regarding location, identity, and motivation, and directed at one individual or toward far-flung groups, vocal communication plays an important role in the social connection and behavior of a great number of vertebrate species. This affords insight into biological constraints on the evolution of vocal communication systems.

The differences often loom larger when we compare human spoken language with nonhuman vocal communication. Certainly, the issue of the “human uniqueness” of language, whether taken broadly across a variety of properties (e.g., Pinker & Jackendoff, 2005) or defined minimally (Hauser, Chomsky, & Fitch, 2002; Hauser, Barner, & O’Donnell, 2007) has become a point of substantial controversy (see Gentner, Fenn, Margoliash, & Nusbaum, 2006). An extreme view, primarily attributed to Chomsky (1988), Piattelli-Palmarini (1989), and by Pinker (2003) to Gould (1997), is that language “could not” have evolved because certain aspects of language such as recursive processing in grammar could not have emerged gradually. This argumentation unwittingly supports “creation science” (see Bates, Thal, & Marchman, 1991), and appropriately so—it is wholly ignorant of evolutionary process whereby radical specialization can emerge. Consider, for example, the existence of ultrasonic frogs, or recursive processing in other human cognitive domains. By contrast, Pinker (2003) and Pinker and Bloom (1990) argue that language is the result of evolution through adaptation. Pinker contrasts this with the idea that language is manifest as the result of more general cognitive evolution. In other words, from the Chomskian perspective, language emerged without evolutionary antecedents. But from Pinker ’s perspective, language evolved, and Pinker and Bloom (1990) assert the possibility of natural selection operating as the basis for the intelligent “design” of the syntactic complexity demonstrated in human language. According to this argument, comparative biology is not helpful in understanding this evolutionary development because it all occurred over the 200,000 to 300,000 generations that have occurred since our divergence from our common ancestor with modern apes (Pinker, 2003). Thus, the theoretical sanctity of human uniqueness is entirely

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preserved by the evolutionary isolation of humans from other species from that point on, as is true during the course of evolution as each species emerges at the point of reproductive isolation. Human vocalizations do not leave a fossil image, but neither do those vocalizations of other animals whose behavior has been usefully analyzed from an evolutionary perspective. Those who write with confidence regarding the kind of evidence that future research will bring to bear on the notion of human uniqueness do so with unjustified confidence. The issue of the human uniqueness of language is fundamentally not a deep philosophical problem, but a pragmatic problem of a sparse evolutionary history (one surviving species of Homo) during a period of rapid evolutionary change. Imagine if we could investigate the vocal repertoire of Cro Magnum and Neanderthal. So far we cannot, so it is unlikely that there will be a simple resolution to this debate because psychologists are obligated to investigate the internal “mental” states (goals etc.) to understand deeply what could be communicated among nonhuman animals. However, we do know that the set of communications (regularly used, differentiable vocalizations) is substantially restricted in nonhuman species compared to humans and the flexibility in the circumstances of the use of these vocalizations appears much more restricted (Hauser, 1996). Thus, regardless of the ultimate theoretical or empirical resolution to the human uniqueness debate, humans appear to have more things to talk about and use more ways to say the same thing when compared with nonhuman vocal communication systems. This enormous breadth (perhaps unlimited) of vocal repertoire in speech is attributed to a core property of language to generate entirely novel utterances to express new concepts. The productivity of human language is one of its most interesting properties, which contrasts with vocal communication systems in other species that are much smaller, and often fixed throughout adult life. In human language, there is a system for producing new patterns from some foundation of shared knowledge and a way for recipients to understand these entirely new communication patterns. Animals have a much smaller range of variation in this context, but it is not zero. Some species such as mockingbirds (Mimus polyglottus) acquire new vocalizations throughout life, and birds such as thrushes (e.g., American robin, Turdus migratorius) produce continually variable song patterns, albeit how much information is conveyed in that variation remains unresolved. With a fixed message set, communication systems can (but need not be) relatively simple using specific predetermined motor plans for production and matching communicative signals to previously learned templates or patterns. However, when the communicative needs of a novel situation demand expression, new

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utterances must be generated from idiosyncratic experience, but these messages must be understandable to a listener without the same prior experience. For human language, Hockett (1960) considered this one of the fundamental “design” principles. The property of productivity (producing novel utterances) can be thought of as resulting from the property called “duality of patterning” together with the property of arbitrariness (the pattern of an expression is arbitrary in relation to the meaning of an expression). Duality of patterning refers to the fact that, in language, pattern elements without intrinsic meaning (i.e., phonemes) combine to make meaningful patterns (i.e., words), and meaningful patterns can be combined to make other meaningful patterns (e.g., sentences). Given that the meaning of patterns is arbitrary with respect to the form of the pattern itself, it is possible to make new meaning patterns by different combinations of existing meaningful units (e.g., to make new sentences) or by novel combinations of the meaningless units to make new meaningful units (e.g., novel words). As a result, the range of expression for human language is not bounded by a fixed set of vocalization patterns. Moreover, based on the principle of arbitrariness, a language can be constructed on the basis of almost any set of intrinsically meaningless elements that can be combined, thus licensing sign languages and printed languages. New words can be coined and combined in novel ways and these two levels of analysis—the lexical and syntactic—have thus been the focus of much language research. Indeed, Pinker (2003) distinguishes two attributes of language—word recognition and grammar—with respect to the importance of evolution and human uniqueness. From his perspective, words are simply a memorized pairing of pattern form (e.g., sound pattern) with meaning. Therefore, this kind of associative system, which is found in many animals, is not the locus of the intelligent design of language through adaptation (Pinker & Bloom, 1990). Several studies (e.g., Cohen & Dehaene, 2004; Cohen, Jobert, LeBihan, & Dehaene, 2004; Vinckier et al., 2007) have examined the role of a part of the fusiform gyrus in recognizing visual word forms. This work suggests that visual word recognition may be understood as an exaptation (Gould, 1991; Gould & Vrba, 1982) of visual object recognition processing (Dehaene & Cohen, 2007). Similarly, spoken word recognition may be viewed as arising from the neural mechanisms involved in general auditory and conceptual processing (see Price, Thierry, & Griffiths, 2005) and thus as an exaptation of more general auditory pattern recognition and cognitive processes. According to Pinker (2003), it is grammar that provides Hockett’s (1960) productivity. Grammar consists of syntax, morphology, and phonology. Syntax is the rule system that

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Production and Comprehension

governs the ordering of words into linguistically acceptable sentences. Morphology is the rule system that governs affixing and the extensibility of lexical forms. Phonology is the rule system that governs the sound patterns of a language. Together these rule systems provide for the productive extensibility that comes from creating words and creating sentences. It is the grammar that is viewed as unique to humans and has evolved uniquely to suit the specific information and social demands of humans. These descriptions define a complexity of human vocalizations beyond those of nonhuman species, but not categorically so. For example, the songs of European starlings (Sturnus vulgaris) comprise sequences of motifs, and different motifs comprise sequences of notes. Notes apparently are arbitrary, but motifs have meaning if not semantic value, in the sense that motifs appear to be the fundamental unit that drives vocal recognition. There is an overall structure to the sequence of motifs within song (whistles, variable phrases, rattles, and high-frequency phrases) but this varies from one song rendition to the next (Adret-Hausberger & Jenkins, 1988; Eens, Pinxten, & Verheyen, 1989). Again, the full extent of information conveyed in all this variation is unknown, but there is good evidence that at least some features of variation carry information (Gentner & Hulse, 2000). In communication systems with a fixed vocal repertoire, utterance recognition could be quite simple, at least when described abstractly in a pristine research setting isolated from the real world. A set of pattern templates can be compared to auditory transforms (minimizing distortion and noise by filtering) of utterances and the closest match selected similar to the way the first small vocabulary speech recognition systems operated. This vastly oversimplifies real vocal recognition because multiple signals can overlap in time, vocalizations show variations due to the individual and the environment that can defeat linear separability of even a small set of patterns unless constructed robustly by design. These limitations hampered the success of even small vocabulary speech recognition systems (e.g., Nusbaum & Pisoni, 1987) for human speech. However, the problems become insurmountable when small fixedvocabulary recognition problems become open-ended, fluent continuous spoken language understanding problems (cf. Klatt, 1977). Small vocabulary recognition systems are successful because of the constraints provided by the task at hand (which could be limited to flight status or account numbers) and the choice of vocabulary (that can be prompted within the task) using highly discriminable items. The validity of this viewpoint is best tested with the more complex animal vocalizations such as those of starlings or mockingbirds, which have much larger repertoires and greater variability than single-song fixed repertoire species, but far less variation than do humans. In principle,

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it should be possible to design a highly accurate song recognition system for such species of birds, but so far none have yet been reported (c.f. Kogan & Margoliash, 1998; Tao, Johnson, & Osiejuk, 2008). When expanded beyond these bounds, the recognition possibilities increase dramatically and a different approach is needed (Klatt, 1977) that is more about language and less about task constraints. Moreover, if not all utterances we understand have been heard previously (even if many of them have been heard) and thus cannot be stored in some form ahead of time, it becomes necessary to compose new interpretations out of utterance parts. This suggests that the computational demands of recognition for spoken language might well be different from the computational requirements of recognizing a limited set of possible utterances, or in the case of some species, presumptively limited recognition within a large possible set of utterances. This places constraints on what can be gained from a comparative neuroethology of vocal communication.

PRODUCTION AND COMPREHENSION The functional linkage between production and perception of biologically significant signals has often suggested that there should be interactions among the underlying neural systems, whether in crickets (Hoy & Paul, 1973), songbirds (Gentner & Margoliash, 2002; Margoliash, 2002; Marler & Peters, 1980), bats (e.g., Moss & Sinha, 2003), or humans (Liberman & Mattingly, 1985). When signal producers are also signal perceivers, there is a tendency to assume that some kind of common processing system may underlie aspects of both. From an engineering perspective, if one were building a device to both produce and interpret a set of signals, there needs to be a way to relate the meaning of signals, even if the generation of a signal is physically different from the sensory encoding of a signal. However, for organisms, the common principles derive not only from the physical constraints of the environments and the signals (which engineers will know about), but also from evolutionary constraints (which engineers will not know about). This can have unexpected consequences. For example, in the electric fish, “timed” electric pulses are used as a kind of sonar system for perceiving the environment, and when two fish are close, their signals can interfere. To avoid this interference, the fish that is sending lower-frequency signals reduces frequency and the higher-frequency sender increases signal frequency. One might assume that part of this jamming avoidance response depends on the use of a common “pacer” or “clock” neuron both in production and perception, but this is not correct—the system is much more complicated (Heiligenberg, 1991; Nelson & MacIver,

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2006). This suggests the cautionary point that the assumption of processing commonality between production and perception simply on the face of apparent similarity can be wholly misleading. Theories of speech perception that depend on the motor system have a long history in modern speech research (at least over the past 40 years). For example, Liberman and his colleagues (e.g., Liberman, Cooper, Shankweiler, & Studdert-Kennedy, 1967; Liberman & Mattingly, 1985) argued that speech perception could only be accounted for by the involvement of the motor system. In the most general terms, the theory was intended to account for our ability to recognize phonemes in spite of the many-to-many relationship between acoustic patterns and linguistic categories. This lack of invariance between acoustic patterns and phonetic categories is observed, for example, for vowel categories between speakers (Peterson & Barney, 1952) and for stop consonants across vowel contexts (Liberman et al., 1967) and for consonants at different rates of speech (Francis & Nusbaum, 1996). A particular acoustic pattern for a phoneme (e.g., the second formant transition for the consonant /d/) may be different in two contexts (e.g., /i/ and /u/) and yet listeners hear the same phoneme regardless of context. Moreover, the same acoustic pattern (e.g., formant transition rate) may indicate two different phonemes (e.g., /b/ or /w/) depending on context (e.g., speaking rate). In theoretical terms, perhaps the most critical feature of the lack of invariance problem is that it makes speech recognition an inherently nondeterministic process (see Nusbaum & Magnuson, 1997). A deterministic process is one in which the prior history, the current state of the system, and the current input to the system uniquely specify a single next state for the system. In a simple case, each unique input pattern has a unique corresponding invariant interpretation. By contrast, a nondeterministic process is one in which there is more than one possible state even given the prior history, current state, and input state. As pointed out by Nusbaum and Henly (unpublished manuscript), this is a general problem for language at all levels of analysis: The phonetic interpretation of acoustic speech patterns, the lexical interpretation of phonetic sequences, and the sentential interpretation of lexical sequences all have a many-to-many mapping that makes the process of interpreting linguistic patterns nondeterministic.

NONDETERMINISM AND ACTIVE PROCESSES To understand the significance of this, we need to consider briefly the definition of a finite state automaton (Gross,

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1972; Hopcroft & Ullman, 1969). A finite state automaton is an abstract computational mechanism that can represent (in terms of computational theory) a broad class of different “real” computational processes. A finite state automaton consists of a set of states (that differ from each other), a vocabulary of symbols, a mapping process that denotes how to change to a new state given an old state and an input symbol, a starting state, and a set of ending states. Finite state automata have been used to represent and analyze grammars (e.g., Gross, 1972) and other formal computational problems (Hopcroft & Ullman, 1969). For our purposes, the states can be thought of as representing internal linguistic states such as phonetic features or categories and the symbols can be thought of as acoustic properties present in an utterance. The possible orderings of permissible states in the automaton can be thought of as the phonotactic constraints inherent in language. The transition from one state to another that determines those orderings is based on acoustic input with acoustic cues serving as the input symbols to the system. This is actually a relatively uncontroversial conceptualization of speech recognition (e.g., Klatt, 1979; Levinson, Rabiner, & Sondhi, 1983; Lowerre & Reddy, 1980) and is similar to the use of finite state automata in other areas of language processing such as syntactic analysis (e.g., Charniak, 1993; Woods, 1973). A deterministic finite state automaton changes from one state to another such that the new state is uniquely determined by the information (i.e., next symbol) that is processed. In speech, this means that if there were a oneto-one relationship between acoustic information and the phonetic classification of that information (i.e., each acoustic cue denotes one and only one phonetic category or feature), a wide variety of relatively simple deterministic computational mechanisms (e.g., some simple context-free grammars, Chomsky, 1957; feature detectors, Abbs & Sussman, 1971) could be invoked to explain the apparent ease with which we recognize speech. As we know all too well by now, this is not the case. Instead, there is a manyto-many mapping between acoustic patterns and phonetic categories, which is referred to as the lack of invariance problem. Theories of Perceptual Invariance Any particular phonetic category (or feature) may be instantiated acoustically by a variety of different acoustic patterns. Conversely, any particular acoustic pattern may be interpreted as a variety of different phonetic categories (or features). Although a many-to-one mapping can still be processed by a deterministic finite state automaton because each new category or state is still uniquely determined, albeit by different symbols or information (e.g., the set of

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Nondeterminism and Active Processes

different cues any one of which could denote a particular feature), the one-to-many mapping represents a nondeterministic problem. Given the current state of the system and the acoustic information, there are multiple possible states to which the system could change. There is nothing inherent in the input symbol or acoustic information, or in the system itself, that uniquely determines the classification of that information (i.e., the next state of the system). In other words, there is a computational ambiguity that is unresolvable given just the information that describes the system. Thus, interpreting speech sounds and linguistic structure depends on additional information. What form that information takes and to what degree it is innately specified or acquired and modified during development represent issues of central importance for language research. Motor theory originally proposed that knowledge of the process of articulation could be used in resolving this computational ambiguity (Liberman, Cooper, Harris, & MacNeilage, 1962). Subsequently, the motor theory was revised (Liberman & Mattingly, 1985) to suggest that the sound patterns of speech are not the proximal stimuli for phoneme perception; rather listeners directly perceive the motor gestures that gave rise to the sounds. Under this revised version, speech perception is treated as a module (see Fodor, 1983) that is explicitly independent of other processes involved in auditory perception or cognition. However, none of these approaches has been entirely successful or convincing in explaining phonetic constancy. All depend on the assumption that the appropriate kind of (e.g., motor) knowledge or representation will be sufficient to restructure the nondeterministic relationship between the acoustic patterns of speech and the linguistic interpretation into a deterministic relationship. Measures of speech production show as much lack of invariance in the motor system as there is in the relationship between acoustics and phonetics (e.g., MacNeilage, 1970). As noted earlier, there is as much lack of invariance between sound patterns and larger linguistic units (e.g., syllables, words) as there is with phonemes or phonetic features. And the perspective that better acoustic knowledge would provide an invariant mapping has failed as well. One interpretation of the problem of lack of invariance is that the kinds of acoustic analyses being carried out in typical acoustic-phonetic research (e.g., spectrographs for examining time-frequency-amplitude plots of speech patterns) do not reveal acoustic invariance that other types of analyses might show. For example, Stevens and Blumstein (1978, 1981) provided evidence that when the power spectrum of the initial 25.6 ms of the release of a stop consonant (e.g., /b/ or /d/) is examined, the distribution of energy is invariant with respect to place of articulation (e.g., /d/ or /t/ versus /b/ or /p/), and listeners can use this

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to classify the speech. The argument was that this energy distribution would provide an invariant acoustic signature for one important phonetic aspect of consonants previously thought to show lack of invariance (Liberman et al., 1967). However, when the putative spectral invariant cue was tested directly against the variable cue formant patterns over time, Walley and Carrell (1983) demonstrated that listeners carry out phonetic classification by using the noninvariant portions of the signal rather than the invariant portions. Listeners appear to use context-dependent acoustic information even when context-independent cues are present. Theories of speech perception have largely failed to explain phonetic constancy given the problem of lack of invariance because they have taken the wrong tack on analyzing the problem (see Nusbaum & Magnuson, 1997). Although place of articulation is taken as the paradigm case of lack of invariance, the same issues hold for vowel perception and talker variability (Nusbaum & Morin, 1992) and for speaking rate and manner (Francis & Nusbaum, 1996). Theories focus on addressing one particular limited aspect of lack of invariance and then in broad strokes assume that other aspects of the problem will be addressed similarly in spite of important differences among these problems. By focusing on a content analysis of the lack of invariance problem, these theories have tried to specify the type of information or knowledge that would permit accurate recovery of phonetic structure from acoustic patterns. In other words, there is an assumption that if we only had the right acoustic analysis (e.g., onset power spectrum), the right decoding information (e.g., the motor codebook), or some broader rule-based knowledge (e.g., syntactic structure), the lack of invariance problem would disappear. However, there is no reason to believe that these approaches can basically change what is fundamentally a nondeterministic problem into a deterministic problem that could be solved by a simple computational architecture like a finite-state automaton. To understand the process of speech perception, it may be critical to analyze the computational considerations inherent in a nondeterministic system. The point of this section has been to argue that it is important to shift the focus of theories from a consideration of the problem of lack of invariance as a matter of determining the correct representation of the information in speech to a definition of the problem in computational terms. We claim speech perception is a nondeterministic computational problem. Furthermore, we claim that deterministic mechanisms and passive systems are incapable of accounting for phonetic constancy. Human speech perception requires an active control system in order to resolve the fundamental lack of invariance between acoustic patterns and phonetic categories. By

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focusing on an analysis of the specific nature of the active system used in speech perception, it will be possible to develop theories that provide better explanations of phonetic constancy. Active systems have been proposed as explanations of speech perception in the past (see Nusbaum & Schwab, 1986), including analysis-by-synthesis (Stevens & Halle, 1967) and Trace (McClelland & Elman, 1986). These theories have acknowledged the importance of complex control systems in accounting for phonetic constancy. However, even in these theories, the focus has been on the nature of the information (e.g., articulatory in analysis-by-synthesis and acoustic-phonetic, phonological, and lexical in Trace); the active control system has subserved the role of delivering the specific information at the appropriate time or in the appropriate manner. Unfortunately, from our perspective, these earlier theories took relatively restricted views of the problem of lack of invariance. Although all theories of speech perception have generally acknowledged that lack of invariance arises from variation in phonetic context, speaking rate, and the vocal characteristics of talkers, most theories have focused on the problem of variation in phonetic context alone. By focusing on the specific knowledge or representations needed to maintain phonetic constancy over variability in context, these theories developed highly specific approaches that do not generalize to problems of talker variability or variability in speaking rate (e.g., see Klatt, 1986, for a discussion of this problem in Trace). For these theories, there is no clear set of principles for dealing with nondeterminism in speech that would indicate how to generalize these theories to other sources of variability such as talker variability. However, the theory of analysis by synthesis (Stevens & Halle, 1967) presented an active theory of speech perception in which the motor system was invoked in service of resolving the lack of invariance between acoustic patterns and phonetic categories. In this theory, the auditory transforms of acoustic speech patterns were matched against stored representations of phonetic categories. When a clear match could be made, the phoneme was recognized. However, when there was a problem matching the pattern to a single phonetic category due to lack of invariance, a motor simulation of speech production was carried out internally to synthesize the patterns that would arise in the context that had been recognized. This is a kind of internal active processing similar to active processing seen in other species for resolving complex sensory inputs (e.g., Moss, Bohn, Gilkenson, & Surlykke, 2006; Nelson & McIver, 2006). Although proponents of motor theory have argued that there is substantial behavioral evidence supporting motor theory (e.g., Liberman & Mattingly, 1985), alternative

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theories based only on passive auditory processes (e.g., Diehl, Lotto, & Holt, 2004) can account for much of the same data. In many respects, this situation parallels the debate about the representation of mental imagery in terms of perceptual processes or propositional-symbolic processes; behavioral data can be explained by very different alternative theories and neurophysiological evidence was viewed as the only basis for testing among the theories (see Anderson, 1978). The parallel deficits in perception and mental imagery for patients with visual agnosia (Farah, 1984) provided strong evidence in support of the perceptual representation theory. Furthermore, neuroimaging data showing activity in visual areas during mental imagery (Kosslyn & Sussman, 1994; Kosslyn, Thompson, & Alpert, 1997) converges with the neuropsychology data to strongly support this type of theory. Thus, neurophysiological evidence can be critical to testing specific aspects of psychological theories when behavioral evidence is equivocal. However, this takes a very narrow view of the role of neuroscience in understanding biologically important processes such as vocal communication. Metaphors and Mechanisms Perhaps a more important role for neuroscience is to offer new ways to understand behavior such as vocal communication. Psychological explanations and theories derive metaphors from a wide range of human experience, from steam engines to telephone switchboards to digital computers. However, neuroscience offers new metaphors and mechanisms to explain psychological processes (cf. Churchland, 1999). Historically, some research has tried to impose linguistic analyses on specific brain regions, whereas other research has tried to use language-processing deficits associated with brain injuries as the basis for neuroscience theories. By using research on neurophysiology as a basis for theory and by understanding the systems neuroscience research of other complex behaviors, it is possible that new theories of language processing may be developed. Researchers often make strong theoretical pronouncements about the mechanisms of speech perception with little or no empirical support but based on a functionalist analysis and introspection. For over half a century, the scientific study of language has been strongly influenced by the perspective of linguistics (e.g., Chomsky, 1957). Note that linguistic investigation represents just one way to study language, and that psychology, sociology, computer science, anthropology, neurobiology, and philosophy all play important roles in understanding language. During this time, two basic assumptions shaped a broad range of language research particularly in psychology and neurobiology. The first

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Nondeterminism and Active Processes

assumption is that we can separate out the study of language as an object of inquiry from the study of language use. The competence/performance distinction is rooted in the notion that there is a Platonic idealization of language that can be studied independent of the moment-by-moment limitations, distractions, and disturbances to which all human performance is subject. In other words, language can be studied independently of other aspects of psychology including cognitive processing, social interaction, and affective processing, and language areas of the brain can be understood apart from other complementary regions and networks, such as the motor system or the limbic system. It is this perspective—separating language from all of psychology and much of biology—that allows extreme views to arise (such as the claim that language could not have evolved). The second assumption derives from the notion that we can analytically separate putative language functions from each other, and having done so, empirically study each as if truly dissociated from other language functions. The prototypical example is syntax. Chomsky (1957) initially viewed the explanation of syntactic processing as a component of the language system that was scientifically tractable and isolatable from other aspects of language. One aspect of Chomsky’s work was an attempt to outline the way in which competing theories of syntax could be tested under the assumption that the problem of syntax was sufficiently tractable that it would give rise to a number of such theories. Unfortunately, given the lack of any sufficient theory of syntax, testing among equally competent theories has not been a serious problem for linguistics. But we infer that the entire enterprise of isolating for analysis a psychological process that appears to be tractable on its own terms may be misleading. If the fundamental assumption of an area of inquiry is flawed, progress, even considerable progress, can be made within that area, but with the underlying errors unchallenged. Consider attempts to understand airflow over the wing of an airplane (see Gleick, 1987). Under some conditions, airflow over a wing is smooth and laminar whereas under other conditions, airflow is noisy and turbulent. For a long time, these two types of physical behavior were viewed as having entirely different theoretic explanations, one being simple and linear and the other complex and nonlinear. However, this separation into systems based on apparently different modes of behavior led researchers away from a single, simpler explanation in terms of a chaotic dynamical system. These are not just philosophical considerations of metaphorical examples, but are highly relevant to understanding behavior at all levels of analysis. In the neurosciences, sensory processing had long been analyzed with far greater

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attention paid to ascending than to descending connections. Yet, we now know that in the auditory system, descending input from the cortex fundamentally shapes the receptive field properties of subcortical neurons (e.g., Yan & Suga, 1996). Thus, auditory receptive fields can be studied ignoring the role of descending inputs, but this will limit the understanding of time-domain, frequency domain, and attentional influences. In the visual system, it is established that principle ascending connections to the cortex arising from the thalamus account for approximately 5% of all input to cortical cells (Douglas & Martin, 1991). Receptive fields can be studied ignoring the role of descending inputs and intercortical connections, but this will limit the understanding of numerous psychophysical phenomena such as illusory contours (von der Heydt, Peterhans, & Baumgartner, 1984). In many areas of inquiry, neurobiology has been highly successful taking the approach to study isolated systems, but behavior is the sum total of the interactions of all those systems. This perspective helps to motivate neuroethological studies, which tend to be more holistic. These examples help to emphasize that it is deeply problematic to assume that because we can be analytically introspective about syntactic structure or any particular psychological process, it has an independent psychological and biological reality from other psychological processes. As described by Fodor (1983), the notion of an autonomous, biologically fixed, evolutionarily expert, mandatory, and automatic syntactic processor has given rise to a great deal of research and controversy (e.g., Appelbaum, 1998; Garfield, 1987; Sperber, 2001). As in the case of all language processing modules (e.g., lexical module), the controversy has generally focused on whether processing falling within the domain of expertise of the putative module interacts with other kinds of processing outside the module (beyond the operation of a discrete input and output stage). Proponents of modularity deny such interaction whereas opponents support it. Theoretical considerations aside, this kind of debate has had important practical consequences, limiting experimental design and isolating theory from verification and falsification. Language processing is often studied independent of context and separately for each putative unit of analysis— syllables, words, or sentences. Language processing is typically studied without much consideration for the relationship of the interlocutors to each other in a conversation—in those relatively rare instances when actual face-to-face conversation is studied (see Pickering & Garrod, 2004). Language processing is often studied as independent of other cognitive, affective, and social mechanisms. Language processing is studied without consideration for the speaker’s intentions or goals or the listener’s intentions or goals. In theoretic terms,

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it is generally accepted in much of language research that messages can be understood independent of situation (i.e., pragmatic context) or even independent of the contents of the message. In other words, it is assumed that the possible importance (i.e., personal relevance) of a message’s meaning to a listener has little or no bearing on the way the message is processed as language. Understanding the message is typically viewed as independent of what we understand about the person who was talking (although, e.g., see Holtgraves, 1994). On the one hand, the assumption of modular language mechanisms is often viewed as a “null hypothesis” to be rejected by specific experiments and therefore only a testable theoretic proposition. On the other hand, this is a theoretic foundation that shifts research away from the basic evolutionary forces that actually shaped the biological development of linguistic communication—the need for social interaction between people. This limits the potential impact of the large body of ethological and neuroethological literature investigating the vocal communication and social interaction for understanding language processing. Dewey (1896) pointed out that although researchers can analytically decompose a mental process into separated components, the reality of such components is not substantiated by that analysis. Furthermore, just starting with the assumption of separability or isolability leads to research questions and methods that would not otherwise be employed but that could distort, in various ways, our understanding of a psychological system such as language. Dewey recognized that in many cases goals and motives may actually determine (i.e., restructure or change) the nature of the entire processing system rather than simply affect the outcome of processing. This notion goes well beyond the standard view of interactions in which one process provides inputs to another process. Instead, this is closer to the idea emerging from some neuroscience research (e.g., Barrie, Freeman, & Lenhart, 1996; Freeman, 2003; Freeman & Skarda, 1990), that neural processes and representations are context-sensitive and can change dynamically with goals and motivations and experience. Churchland (1999) has argued that we need to move from a theoretical vocabulary rooted in functionalism to a new “eliminative materialism” in which explanations would be grounded directly in the theoretical constructs of neuroscience. It is possible by examining the functional anatomy of vocal communication, we may come to a different parsing of language processing, one grounded in neurobiological operation and structure, rather than introspection and analysis. Of course, there is a long history of theories of spoken language use that is grounded in neuroscience.

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NEURAL MECHANISMS OF LANGUAGE USE Before we had the technology to systematically measure brain responses during psychological processing, the primary method of studying neural mechanisms was through intervention by natural accident or surgery. Thus, Broca (1861) described a patient Leborgne who had severe brain damage to the left side of his brain (see Figure 45.1) and as a result could only utter a single nonsense syllable. Broca’s aphasia, widely associated with damage to the left inferior frontal gyrus (IFG, although damage is seldom confined to that region) has long been identified as “expressive aphasia.” An aphasia is defined as a selective disruption of a language function and expressive aphasia was described as selective damage to the ability to produce language. Wernicke (1874) subsequently identified damage to a posterior region of the superior temporal gyrus but extending into the parietal cortex (see Figure 45.1, although c.f. Bogen & Bogen, 1976) with a selective loss of comprehension or “receptive aphasia.” This led to a proposed neural model of language processing in which the respective functions of talking and understanding were assigned to different cortical areas, one in the IFG and one in the posterior (pSTG)/inferior parietal region. Lichtheim (1885) developed a more elaborated model of language use that related neural mechanisms for production, comprehension, and conceptual representation and their relationships in processing. This relatively simple view of the neural mechanisms of language processing was being advocated as late as the 1970s (Geschwind, 1970), suggesting little theoretical development in the neuroscience of language over 100 years. While subjectively the difference between talking and listening (expressive and receptive language processing) seems like a plausible division of language function, this is a very different view of the parts of language than has developed in either modern linguistics or psychology.

Broca

Wernicke

Figure 45.1 Left lateral surface of the brain showing (roughly) the location of Broca’s area (inferior frontal gyrus or IFG) and Wernicke’s area (posterior superior temporal gyrus, or pSTG, and inferior parietal, or iPL).

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Toward a New Motor Theory of Speech Perception

Areas of language “competence” such as phonetics, phonology, morphology, semantics, syntax, and pragmatics represent the divisions that linguists view as domains of research, whereas psychological research at these levels of analysis considers aspects of processing such as speech perception, word recognition, syntactic constraint, and so on. The behavioral approach has long guided the neuroethological branch of auditory research (Capranica, 1972; Roeder, 1964), and in visual science, animal neurophysiological research has more recently emerged as providing potent evidence to guide psychophysical and perceptual theory and interpretation (e.g., von der Heydt et al., 1984; Newsome, Shadlen, Zohary, Britten, & Movshon, 1995; Teller, 1984) and vice versa (see Chalupa & Werner, 2003). Similarly, for issues of low-level speech processing, animal models have provided valuable guidance. Evidence for categorical perception and thus an aspect of phonetic constancy in nonhuman mammals (Kuhl & Miller, 1975) and in birds (Kluender, Diehl, & Killeen, 1987) helped to evaluate claims of human uniqueness for those attributes of speech perception. Analysis of peripheral representations of speech signals (e.g., Sachs & Young, 1979) informed theories of population coding of speech. Analysis of sensitivity of neurons to missing fundamentals has identified a region of auditory cortex in monkeys and humans specialized for pitch processing (Bendor & Wang, 2005). A state-dependent neuronal replay phenomenon (Dave & Margoliash, 2000; Dave, Yu, & Margoliash, 1998) has stimulated research on the role of sleep in vocal learning (Derégnaucourt, Mitra, Feher, Pytte, & Tchernichovski, 2005; Shank & Margoliash, in press), and the role of sleep in speech perceptual learning (Fenn, Nusbaum, & Margoliash, 2003). A similar approach has not been exploited for language processing. Neuroimaging data makes it possible to compare neurophysiological measures in humans and animals further advancing research. Without a clear animal model and without direct neurophysiological measures, however, the neuroscience of language has until recently been constrained to study patients with brain damage and some interventions in clinical patients such as electrical stimulation. The advent of neuroimaging and other modern neuroscience methods (e.g., transcranial magnetic stimulation, human electrophysiology) has changed the scientific and conceptual landscape of human neuroscience research. Neuroimaging methods make it possible to relate specific ongoing psychological processing to patterns of brain activity (electrical, hemodynamic, or metabolic) in particular anatomical regions. This minimally provides a common theoretical reference for cognitive neuroscientists, in terms of brain anatomy. By identifying brain regions that,

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through damage, transcranial magnetic stimulation (TMS; see Devlin & Watkins, 2007), or electrical stimulation (e.g., Ojemann & Mateer, 1979), affect behavior and that are activated in association with behavior and that can be related to research on animals, it becomes possible to develop a more coherent neuroscientific explanation of psychological processing in language.

TOWARD A NEW MOTOR THEORY OF SPEECH PERCEPTION As noted, the neuroscience of language processing has long been shaped by studies of patients with relatively focal brain lesions. In many areas of cognitive and social neuroscience (e.g., memory and behavioral regulation), research has been influenced by notable patients such as HM and Phineas Gage (e.g., see Farah & Feinberg, 2000). However, this foundation has always been tempered by animal models of perception, memory, and even aspects of behavioral control and emotion that make possible neurophysiological measures and intervention that could not be carried out with human participants until recently. However, a different approach to thinking about the neuroscience of communication has emerged from research on the motor system. This research indicated that neurons previously thought to play a role in the production of action also play a role in understanding observed action. Rizzolatti and colleagues (see Rizzolatti & Craighero, 2004; Rizzolatti, Fogassi, & Gallese, 2002) demonstrated that some neurons that fire during execution of a learned action also fire during observation of that action. In other words, when learning to perform a behavior, some parts of the motor system that fire during performance also respond during perception of that specific action. These neurons, located in area F5 of the macaque cortex, respond in an effector specific manner (mouth actions different from hand actions) to observed action (Gallese, Fadiga, Fogassi, & Rizzolatti, 1996) and are putatively sensitive to the goal of the action rather than the specific motor behavior (Rizzolatti, Fogassi, & Gallese, 2001). Based on these findings, this observation has given rise to the mirror neuron hypothesis (Rizzolatti & Craighero, 2004) in which such neurons are responsible for comprehension of such observed actions. Moreover, this theory of the mirror system has been extended more broadly to include understanding social cognition and others’ behavior more broadly (Gallese, Keysers, & Rizzolatti, 2004). While other research has suggested substantially greater limits on the role this system may play in empathy and social cognition (e.g., Decety & Lamm, 2006), the

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recognition of the importance of motor mimicry (e.g., Lakin & Chartrand, 2003) in interpersonal interaction is growing, even in respect to language processing (Giles, Mulac, Bradac, & Johnson, 1987; Pickering & Garrod, 2004). Rizzolatti and Arbib (1998) have suggested that the mirror neuron system may provide an evolutionary foundation for human language. Rather than ground language in a vocal communication process, they take the genesis of language to be the comprehension of other people’s actions through mirror neurons. Thus, vocal sound production and perception is not the basis for language. Instead, they start with the assumption that mirror neurons serve to decode observed actions and that gestures or symbolic actions (e.g., iconic gestures, McNeill, 2007) abstracted actions into communication. From this abstraction, gestures were presumably conventionalized initially to share a common form vocabulary. Mouth gestures (e.g., lip smacks) that were produced along with manual gestures then presumably evolved into an acoustic basis for language when the sounds accompanying the mouth gestures provided additional communicative utility. According to this view, language processing is a form of action understanding. The actions involved are abstractions from the real behavioral actions one might engage in. Nonetheless, this suggests a view of language processing that is based on a projection from sensory areas of the brain in the visual cortex and auditory cortex to motor areas of the brain. Understanding then becomes a kind of motor resonance with sound patterns and visual patterns that correspond to articulatory gestures and manual gestures localizing the seat of comprehension (if such a thing were to exist) in posterior portions of the inferior frontal gyrus (homologue of macaque F5) and premotor cortex which are purported to code for the goals of actions (Rizzolatti & Craighero, 2004; Wilson & Iacoboni, 2006). This presents a very different view of the evolution of language from a more traditional view in which language evolved by the refinement of vocal communication systems from other species. The model of language processing as grounded in action production and perception is quite different from the more traditional linguistic view of language as a conventional system of symbol use and organization that evolved from the communicative signs of other species. On a linguistic analysis, the fundamental properties of language consist of the meaningless form elements (e.g., phonemes or syllables) organized into hierarchically meaningful symbolic patterns (e.g., words and sentences). This suggests that the sound patterns of speech must be recognized and combined according to abstract rules that allow for the generativity of any novel legal utterance (Hockett, 1960). This view of language processing has a large group

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of proponents (e.g., Caplan, 1996; Friederici, Fiebach, et al., 2006), who espouse a view of spoken language understanding that progresses from auditory patterns to phonological analysis in the superior temporal gyrus (Hickok & Poeppel, 2004) to lexical processing in the more posterior regions proximal to traditional Wernicke’s area, to syntactic processing in Broca’s area (Friederici, Bahlmann, et al., 2006) and sentence understanding (Hagoort, 2005). Indeed, this is essentially the model that Hickok and Poeppel (2004) present as the “ventral stream” in speech perception. They suggest that speech perception is mediated by networks that are similar to the ventral/dorsal pathway difference in vision (Goodale & Milner, 1992; Mishkin, Ungerleider, & Macko, 1983). In vision, these two systems are respectively viewed as important for object recognition (ventral stream) and for object location or object-oriented action (dorsal stream). Although others (e.g., Rauschecker, 1998) have argued for a ventral/dorsal distinction in auditory processing, Hickok and Poeppel specifically make this distinction for speech perception. The ventral stream, projecting from the primary auditory cortex ventrally and laterally to the posterior inferior temporal cortex is responsible for mapping sound onto meaning, ultimately projecting to anterior temporal cortex (aSTS) for sentence processing. This represents a kind of bottom-up soundto-phoneme-to-word-to-sentence mapping process that bears some general similarity to the traditional neural models of vision in which simple features map to complex features which map onto object form and then visual memory in a progression along the ventral visual stream. By contrast, the dorsal speech stream, projects from auditory areas to parietal cortex to the IFG and premotor cortex, is functionally more vague. Hickok and Poeppel allude to its role in language development and maintaining information in working memory though the use of the “phonological loop” to subvocalize (Baddeley & Hitch, 1974). In other words, according to this model, speech perception is fundamentally a bottom-up process of progressive interpretation within the ventral stream, while the dorsal stream projection to the premotor system plays little or no role in normal speech understanding. There has been one longstanding distinction among theories of speech perception. On the one hand, auditory theories (e.g., Diehl & Kluender, 1989; Diehl et al., 2004; Fant, 1967; Stevens & Blumstein, 1981) view speech perception as a purely auditory process in which acoustic features are coded and matched to stored representations of phonetic categories that are then mapped onto words and so on. On the other hand, motor theory (Liberman & Mattingly, 1985) and analysis-by-synthesis (Stevens & Halle, 1967) have viewed speech perception as involving the motor system as part of the recognition process (albeit in very

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Toward a New Motor Theory of Speech Perception

different ways). Neuroimaging methods have begun to present evidence that increasingly supports the involvement of the motor system in the process of speech perception. The enthusiasm to embrace this new evidence for motor theory needs to be tempered by the fact that the same neuroimaging methods implicate broader arrays of cortical activation than previously anticipated for numerous tasks. If so, then the new data help to define language mechanisms, placing them in the context of other perceptual processes (e.g., Price et al., 2005). If Rizzolatti and Arbib (1998) are correct in their view of the evolution of spoken language, then face-to-face communication is a much better match to the conditions under which language emerged than long-range communication (as might be exemplified by the telephone model investigated in most speech research). If spoken language understanding is an abstracted form of action understanding, we might expect to see evidence of ventral premotor activity (corresponding to mouth movements) when a talking face is visible. However, the ventral stream described by Hickok and Poeppel does not involve the premotor cortex. There is no reason why seeing the face of a talker should necessarily recruit the premotor cortex in service of speech perception, given that the ventral stream is responsible for understanding spoken language. Skipper, Nusbaum, and Small (2005) examined patterns of neural activity while listeners heard auditory-only spoken stories or watched and heard an audio-video recording of a person telling stories. Hearing and seeing someone tell stories significantly increases ventral premotor activity compared to either the speech alone or the talking face with no speech. In addition, hearing and seeing the talker significantly increased neural activity in the superior temporal cortex as well. Moreover, for stories with greater viseme content (visual mouth shapes that are informative about the phonetic structure of speech), premotor activity was greater than for stories with less viseme content. This suggests that the premotor activity was specifically related to phonetic information visible in the talker ’s mouth movements. Sumby and Pollack (1954) demonstrated that speech presented in noise is significantly more intelligible when the talking face can be seen by listeners. Clearly, there is phonetically useful information in the visible mouth movements made during speech production. In fact, McGurk and MacDonald (1976) demonstrated that visible mouth movements dramatically change the perception of the acoustic speech signal. For example, when an acoustic recording of /pa/ is dubbed onto a video recording of a face producing /ka/, listeners often perceive an illusory syllable /ta/. The increased activity in the ventral premotor region during audiovisual speech perception may indicate a mechanism by which this illusion is created. This mechanism

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may operate in much the same way that Stevens and Halle (1967) suggested in analysis-by-synthesis. Skipper, Nusbaum, and Small (2006) suggested that the motor system may operate as part of the active process of speech perception (see Nusbaum & Schwab, 1986). Sensory information from visual and auditory representations in the occipital and temporal cortex are decoded into motor representations in the premotor cortex. These motor representations can feedback to the sensory systems as part of a distributed recognition network. This suggests that the sensory and motor cortex may interact over time to determine the phonetic percept experienced by the observer (see Figure 45.2). To test this, Skipper, van Wassenhove, Nusbaum, and Small (2007) examined patterns of neural activity during the perception of the McGurk illusion. The time course of BOLD response to audiovisual /pa/, /ta/, and /ka/ in the right ventral premotor, left supermarginal, and right middle occipital cortices were measured as estimated population responses to these syllables. The correlation between the McGurk syllable (audio /ka/ and visual /pa/) and each of these population responses was measured to determine how similar the neural response to the McGurk syllable was to each of the three consistent audiovisual syllables. The results (see Figure 45.3) showed that in the ventral premotor region, /ta/ (consistent with the actual percept) was the best fitting representation of the McGurk syllable, whereas in the supermarginal gyrus, the initial best fitting syllable was /pa/ (consistent with the acoustic signal) which then shifted to fitting best with /ta/. In the middle occipital gyrus, the initial best fit was to /ka/ (consistent with visual information about the mouth movements) which shifted to /ta/. Thus, in the sensory cortex, the best fitting representations for the McGurk stimulus started out consistent with their respective sensory input signals but then shifted to fit best with the percept. However, in the ventral premotor region, the /ta/ representation fit best throughout the entire time course of processing. This is consistent with the hypothesis that visual and auditory information, when fed to the premotor cortex give rise to an activity pattern consistent with the McGurk illusion that then may interact with sensory cortices resulting in a final activity pattern consistent across all regions with the McGurk percept. These results are entirely consistent with an active theory of perception by which the lack of invariance between acoustic patterns and phonetic categories is resolved by an interaction between articulatory knowledge in the premotor representation of speech and the sensory representation of speech. One interpretation of this kind of interaction is the notion of covert motor simulation: The activity within the premotor region leads to associated sensory activity that would be associated with the actual act of speech

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Language Processes Motor plan (PM/M1)

Auditory and somatosensory consequences (STp, SMG, S1/SII) Efference copy

Motor goal (POp)

Multisensory description (STp)

Observed talker

Figure 45.2 A model of sensory-motor interaction. Note: Sensory information from the sounds and sight of an observed talker result in a multisensory description in the posterior STG (from primary auditory and visual cortex) that activates pars opercularis in the IFG that gives rise to associated sensory activity. From “Hearing lips and

production (Skipper et al., 2006). The premotor representation can be taken as a kind of “hypothesis” regarding the phonetic interpretation of sensory input and this hypothesis can constrain the alternative interpretations possible given the sensory representations alone. This is exactly the kind of mechanism described by Nusbaum and Magnuson (1997) in considering how an active process might resolve the nondeterministic relationship between acoustic speech patterns and the possible phonetic interpretations of those patterns, although it was not clear that articulatory knowledge would provide sufficient phonetic constraint. Cross-modal interactions are typically poorly explored but there is increasing evidence for such interactions (Bensmaïa, Killebrew, & Craig, 2006). Thus, this new perspective on language processing may be illuminating general properties of cortical organization, not those that are unique to language. One apparent problem with this active theory of speech perception, by contrast to Hickok and Poeppel’s dual pathway model, is that the supporting evidence derives largely from audiovisual speech perception. Although the support from audiovisual speech is consistent with the idea that speech has evolved as face-to-face communication (and thus audiovisual speech may be the “ethologically appropriate” stimulus), the ventral stream of the dual pathway does not really use any premotor processing. Thus, if the dorsal stream were modified to play a role in speech recognition when face information is present, the dual pathway model would be largely unchanged. Moreover, this raises the question as to whether there is any role for premotor processing when face information is not present. Can premotor activity serve as a constraint on phonetic

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seeing voices: How cortical areas support speech production mediate audiovisual speech perception,” by J. I. Skipper, V. van Wassenhove, H. C. Nusbaum, and S. L. Small, S. L.,2007, Cerebral Cortex, 17, p. 2388. Reprinted with permission.

interpretation even without visual input about mouth movements? Part of the motivation for the revision of the motor theory proposed by Liberman and Mattingly (1985) was to assert the proximal equivalence of mouth movements and acoustic patterns in speech perception given the McGurk effect. Although this also entailed the theoretically implausible claim that auditory sensory processing plays no role in speech perception, the notion was that motor knowledge should be important in speech perception whether a face is seen or not. If we take seriously the current premise that covert motor simulation (or prior associations between speech production experiences and the sensory consequences of production) provide a constraint on phonetic interpretation that can reduce acoustic-phonetic uncertainty, there should be evidence of premotor activity even without face input. Indeed, there is a substantial body of research on motor imagery that demonstrates substantial premotor activity during imagery that overlaps with motor execution (see Hanakawa et al., 2003; Jeannerod, 1994). Moreover, this evidence of premotor simulation during imagery has been functionally interpreted as relevant to understanding others’ actions (Decety & Grezes, 2006). Fadiga, Craighero, Buccino, and Rizzolatti (2002) showed some evidence for the involvement of the premotor system in speech perception, even without input from the mouth movements seen while watching a talker ’s face. Audio-only speech perception alone was not sufficient to produce peripheral electromyography (EMG) signals in tongue muscles, nor did single-shot TMS to the ventral premotor region alone produce a tongue EMG. However, the combination of speech input and premotor TMS did produce measurable tongue EMG. Fadiga et al. suggested

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Toward a New Motor Theory of Speech Perception

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Figure 45.3 Population code representations in the ventral premotor, supramarginal, and middle occipital cortex during the McGurk stimulus (audio /p/, visual /k/). Note: A: The McGurk stimulus produces patterns of activity that are most like the audiovisual /ta/ (which is the perception of this stimulus) in motor regions and the auditory and somatosensory cortex whereas the activity in occipital cortex is more like the visual channel of input or an audiovisual /ka/. B: Time course of the mean correlation coefficient for the activity associated with the McGurk stimulus with each of the three audiovisual syllables (/pa/, /ta/, and /ka/) indicating how the population response in ventral PM is most similar to the perceptual experience over the entire time course of processing. C: The time course of population response indicates that initially the McGurk stimulus activates the left supramar-

that during audio-only speech perception, premotor cortex is active but well below the threshold to produce a peripheral EMG in articulators. However the addition of TMS-induced neural activity in the premotor system was sufficient to produce measurable EMG. Wilson, Saygin, Sereno, and Iacoboni (2004) presented acoustic speech syllables to listeners (with no visual information) and found evidence for ventral premotor activity. Subsequently, Wilson and Iacoboni (2006) presented listeners with familiar phonemes and unfamiliar phonemes from other languages and measured brain activity during passive listening (no classification task involved). They

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Occipital Regions

(D) Mean Correlation Coefficient

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Right Middle Occipital Gyrus 0.15 0.1 0.05 0 1.5 3 4.5 6 7.5 9 10.5 12 13.5 15 16.5 18 ⫺0.05 ⫺0.1 ⫺0.15 ⫺0.2 ⫺0.25 Time (seconds)

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ginal gyrus most like the consistent audiovisual /pa/ but then changes to match the audiovisual /ta/. This suggests the initial interpretation is most like the audio portion of the McGurk stimulus but it changes to match the interpretation in the vPM cortex. D: The time course of processing in the middle occipital cortex suggests an initial population response to the McGurk stimulus is based on visual information (best match to audiovisual /ka/) but this changes to match the perceived category of /ta/ becoming consistent with the vPM region. From “Hearing lips and seeing voices: How cortical areas support speech production mediate audiovisual speech perception,” by J. I. Skipper, V. van Wassenhove, H. C. Nusbaum, and S. L. Small, S. L., 2007, Cerebral Cortex, 17, p. 2394. Reprinted with permission.

reported significantly more premotor activity for unfamiliar phonemes although this activity did not change with producibility of the speech. They interpret this result, along with activity in the superior temporal gyrus as evidence for the role of ventral premotor activity in speech perception. Specifically, they interpret their data as suggesting that premotor categorizations serve as phonetic hypotheses that are tested against auditory representations in sensory areas arguing for speech perception as a neither purely sensory nor purely motor but instead a sensorimotor process. Hasson, Skipper, et al. (2007) investigated the neural representation of abstract phonetic categories. In the

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McGurk percept, we have an experience that does not correspond to either the acoustic information or the visual information, but transcends them as an abstraction. By examining the effects of perceiving one syllable on the neural activity associated with perception of a subsequent syllable, it is possible to investigate the neural representation of the the phonetic information carried over in processing. When a stimulus is repeated, the neural response to the second occurrence is typically reduced (called repetition suppression) compared to the activity associated with the first occurrence (see Grill-Spector, Henson, & Martin, 2006). To the extent that this repetition suppression occurs between syllables that do not share sensory components, but do share the same perceptual experience or phonetic category, it is possible to assess how such sensoryindependent phonetic information is represented. By examining how brain activity is suppressed in the McGurk illusory /ta/ by an antecedent audiovisual /ta/ we provided evidence demonstrating that the “abstract” phonetic representations of speech that are independent of sensory information are distributed between the posterior part of the inferior frontal gyrus (pars opercularis) in the motor system and the planum polare. This indeed demonstrates that phonetic representation has both a motor and sensory aspect.

BEYOND SPEECH PERCEPTION TO SPOKEN LANGUAGE UNDERSTANDING The proposal of language understanding as symbolic action understanding has broader support than simply the evolutionary arguments made by Rizzolatti and Arbib (1998). There is a growing body of research on embodied cognition that understanding is grounded in aspects of concrete sensorimotor representation. Barsalou (1999) has argued for sensory and motor simulation as a fundamental basis for conceptual representation. Glenberg and Kaschak (2002) demonstrated that motor responses are facilitated or inhibited when they are directionally compatible versus different from the directionality inherent the main verb of a stimulus sentence (toward or away from oneself). This suggests that the semantics of verbs are closely linked to our understanding of motor plans. Tettamanti et al. (2005) demonstrated that listening to sentences describing action increases brain activity in the motor system in pars opercularis and the premotor cortex. Similarly, Hauk, Johnsrude, and Pulvermüller (2004) showed that reading action words that are specific to the hand, foot, or mouth (e.g., grasp, kick, kiss) activate a pattern of cortical activity in the premotor cortex that displays somatopic organization similar to the performance of those actions. These studies demonstrate that there is a close

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correspondence between the patterns of cortical activity seen in the motor system when understanding the meaning of action words and sentences and when engaged in motor behavior. In the anatomical model of neural language processing derived from Broca, Wernicke, and Lichtheim, the conceptual representation of word and sentence meaning was not considered to be in the motor system (e.g., see Geschwind, 1970; Pulvermuller, 2003). In the model proposed by Hickok and Poeppel (2004), within the ventral stream where sentences are understood, meaning is not represented in the motor system either. Given the kind of evidence for motor system activity during word and sentence comprehension, it is unlikely there is a single amodal conceptual representation system. An abstract conceptual representational system alone cannot account for why the motor system would be involved in comprehending language. Barsalou’s (1999) view of a sensory and motor grounded conceptual system seems much more consistent the behavioral and neural evidence regarding embodied comprehension. Moreover, this kind of semantic system appears to be relatively plastic both in the long term and in the short term. Holt and Beilock (2006) have shown that expert sports players (hockey or football) understand sentences containing sport-specific objects or actions differently from people with little or no experience in that sport. In recognizing whether an object was named in a sentence (cf. Zwaan, Stanfield, & Yaxley, 2002), Holt and Beilock showed that amount of sports expertise interacted with the response time to make that decision about compatibility of the depiction of the sport object with the rest of the sentence. This interaction is not found for nonsport-specific sentences indicating that it is the domain of expertise that is determining this comprehension effect: Long-term experience with an action domain shapes the understanding and representations we draw from utterances. Beilock, Lyons, Mattarella-Micke, Nusbaum, and Small (2008) used fMRI neuroimaging data to show that the effect of hockey expertise in understanding hockey sentences is entirely mediated by premotor activity. Thus, even among adults, increased experience in a domain of action can significantly modify the neural processing of language to recruit more strongly brain areas that are not part of the traditional language network (i.e., IFG and pSTG). This suggests that many neural models of language processing need to consider how other domains of experience and cognitive processing may operate during language understanding. Hasson, Nusbaum, and Small (2007) investigated the neural activity associated with discourse comprehension. Specifically, they examined how a sentence at the end of a story was processed if the sentence was informative in the

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Beyond Speech Perception to Spoken Language Understanding

context of the antecedent discourse. Two different neural networks were identified as relevant to discourse understanding and memory: A fronto-temporal network overlapping with regions involved in language comprehension and working memory increased in activity with improved memory for discourse and a second network, often called the default network or resting state network (e.g., Gusnard & Raichle, 2001), showed a biphasic relationship between activity and subsequent memory. Within this default network, often considered to reflect “mind wandering” or diffuse cognitive processes, memory for informative sentences was predicted by increased activity but increased activity also predicted forgetting for uninformative sentences. These data suggest that comprehension of language is closely tied to memory (seen in an overlap between regions sensitive to discourse semantics and those that predicted subsequent recall). Moreover, it is interesting to note that contrasting informative and uninformative story endings revealed activity in a set of brain regions often associated with attention including the right SFG and MFG, superior parietal, and the thalamus. This suggests a close coupling between the more traditional fronto-temporal (IFG, STG) language areas and areas more typically associated with general cognitive processes of attention and memory. Even in considering aspects of speech perception, there is evidence of the involvement of other cognitive processes not typically considered part of language processing. As discussed earlier, the problem of nondeterministic mapping of acoustic patterns onto phonetic categories may require an active system of hypothesis testing to resolve phonetic ambiguity (see Nusbaum & Magnuson, 1997). This problem has traditionally been discussed as a lack of invariance between acoustic patterns and phonetic patterns that motor theory was originally proposed to resolve (Liberman et al., 1967). The problem of lack of invariance has typically been attributed to the process of speech production. Phonemes are coarticulated with each other, meaning that the articulatory gestures used to produce speech sounds are not discrete and separable from one another. The physics and neurophysiology of motor control result in temporal overlap of the articulatory gestures in speech production—one mouth movement comes from another and must anticipate future movements. This means that the acoustic patterns at any point in time are shaped by previous and subsequent phonemes. Moreover, as the rate of speech production changes, this coarticulation changes as well so that acoustic patterns can be dilated or compressed in time. Different talkers, with different vocal tract architectures and different motor systems, will respond to and produce phonemes in different ways. As a result, the relationship between any particular acoustic pattern and phonetic categories is nondeterministic.

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Nusbaum and Morin (1992) examined the way listeners adjust to the differences between talkers. Using a speeded target-monitoring paradigm, comparing recognition for speech produced by a single talker with recognition for speech produced by a mix of talkers, they reported that talker variability slowed recognition time reliably (about 20 to 40 ms) for consonants, vowels, and words (see also Mullennix & Pisoni, 1990). Moreover, they showed that this increase in recognition time was due to an increase in cognitive load demonstrated by an interaction between a secondary working memory load and talker variability. Furthermore, they showed that when there was talker variability, listeners directed attention to specific cues (fundamental frequency and formants above F2) as a way of providing information about talker vocal characteristics; the absence of these cues significantly reduced recognition accuracy but only when there was talker variability. Francis and Nusbaum (1996) found that speaking rate variability produced similar results, slowing recognition and interacting with a working memory load. These studies suggest that resolving the nondeterministic mapping between acoustic patterns and phonetic categories is an active attention-demanding process (see Nusbaum & Schwab, 1986). Listeners shift attention to different acoustic cues in order to reduce the increased working memory demands of alternative phonetic interpretations under conditions of increased acoustic-phonetic variability. Moreover, Magnuson and Nusbaum (2007) demonstrated that talker calibration is not an automatic bottom-up process, but can be driven by listeners’ expectations about the interpretation of a talker-specific cue such as voice fundamental frequency. If relatively fast calibration to a talker ’s voice occurs as a result of shifting attention to different cues in speech, there should be evidence for this in terms of the recruitment of neural mechanisms involved in attention. Just as we saw evidence for the recruitment of cortical and subcortical brain areas typically involved in attention in processing informative versus noninformative sentences, there should be evidence of attention processing in speech perception when there is talker variability. Wong, Nusbaum, and Small (2004) presented neuroimaging evidence that talker variability significantly increases cognitive demands and recruits brain areas involved in attention that are not typically involved in language processing. Talker variability increases neural activity in the STG and in superior parietal cortex. Given the hypothesized role of the premotor cortex in generating phonetic hypotheses described earlier, we might expect to see increased premotor activity as well. Moreover, we might predict that there should be a significant covariance relationship between activity within the attention network and the premotor activity. Unfortunately,

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Language Processes

this study was run with a lower strength scanner (1.5 T compared to 3 T more typically used) and the evidence for premotor activity was not reliable. As a result, it will be necessary to replicate this study at a higher field strength to test this prediction. However, it is clear that language processing takes place in a much broader network that has been typically considered. Speech perception may draw on the motor system and attention mechanisms. Word and sentence understanding may also depend on the use of the motor system, although in different ways from phoneme perception. Finally, discourse comprehension, and the comprehension of sentences in the context of discourse also may recruit attention and memory systems. These kinds of results suggest that the basic processing of language depends broadly on neural networks subserving more general cognitive processes than would be considered under the rubric of “neural mechanisms of language comprehension.”

A BROADER VIEW OF NEURAL LANGUAGE PROCESSING Bar (2003) speculated on a direct connection between the ventral and dorsal pathway in vision. Rather than view these as playing functionally distinct roles in vision (ventral ⫽ recognition and dorsal ⫽ action), he suggested that the dorsal route may represent a coarse route to object recognition that connects through goal-directed attention systems linked to working memory to affectivemodulation systems that may bias responses. The dorsal route projects through motor, attention, affective, and working memory systems and then projects back to the inferior temporal cortex to constrain aspects of perceptual analysis carried out in parallel by the ventral pathway. Given the proximity (even overlap) in brain systems involved in emotion, executive function and behavioral control, working memory, and attention, and the relationship between these systems and other aspects of perceptual processing, it seems surprising that this kind of proposal has not been made previously. Moreover, it raises the possibility of thinking about language networks in much the same way. Roland (1993) argued that the frontal cortex can generate sensory expectations that can “tune” the sensitivity of more posterior areas. Bar ’s (2003) argument is that affective goals and evaluations are important in shifting attention for perception. This process of shifting attention involves frontal and prefrontal systems changing the sensitivity of posterior sensory processes, presumably by modifying receptive fields (e.g., Moran & Desimone, 1985). This could be the way in which visual information about mouth movements during speech production changes

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neural population responses in the superior temporal cortex during comprehension of the talker ’s speech. To postulate that goals, motives, and expectations may change the processing of auditory information goes well beyond the notion that immediately available visual information about motor movements changes phonetic perception. Goals, motivation, and expectations are not manifest in the sensory signal about the articulations underlying speech production—they are completely endogenous information. Consider that seeing the race of a talker ’s face (shown as a static image) can change the perception of the talker ’s speech (Rubin, 1992) and the gender of a talker ’s face can change interpretation of speech (Johnson, Strand, & D’Imperio, 1999). These effects are driven by expectations of the listener rather than the sensory information from a talker ’s mouth movements. Even without dynamic real-time perceptual input about a talker ’s mouth movements, expectations can change the interpretation of speech. Furthermore, language processing is shaped by expectations even when they do not involve information about a talker ’s face. As noted previously, Magnuson and Nusbaum (2007) showed the expectations about the importance of an acoustic fundamental frequency difference can drive a low-level perceptual process such as calibrating to the vocal characteristics of a talker. However, the impact of listener expectations is much broader and deeper than just calibrating talker differences. Remez, Rubin, Pisoni, and Carrell (1981) demonstrated that when presented with sinusoidal replicas of a sentence, listeners generally report that these signals sound like nonspeech bird chirps but when told that the signals are language, listeners can correctly understand the speech. Wymbs, Nusbaum, and Small (2004) measured the cortical activity of listeners presented with sinewave speech before and after language instructions. The results showed that linguistic expectations activate an attentional-motor network involving the inferior frontal gyrus and superior parietal cortex. Changing a listener ’s expectations about speech fundamentally changes the pattern of cortical processing that mediates perception of speech. How can we explain the role of expectations in recognizing and understanding speech? One approach would be to posit two different systems, a cognitive system for maintaining and applying expectations (e.g., working memory and attention) and a sensory system for speech perception. This approach would be consistent with the kind of theory proposed by Hickock and Poeppel in that recognition within the ventral stream (speech recognition) would then be modulated by processing within the dorsal stream (working memory and attention). However, as described, this model does not make clear how such interactions would take place. Indeed, the model itself does not specifically try to explain

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Plasticity and Development 895

how expectations might play a role in speech perception. In general, expectations, attention, and other processes that are not specifically linguistic in nature are treated as external to a language processing system, even given the evidence that such processes might be critical to understanding language comprehension. Social expectations (e..g., Johnson et al., 1999; Rubin, 1992) and emotional expectations (Luks, Nusbaum, & Levy, 1998) seem to interact with the basic processes of phonetic and prosodic recognition. This would suggest a more direct interaction akin to Bar ’s (2003) model of vision. Indeed, Erthal et al. (2005) demonstrated how attentional load can down regulate affective and perceptual responses to emotional faces. Increasing demands on perceptual attention can reduce amygdale and visual cortex responses to fear faces. This kind of research is needed to begin to investigate the interrelationship of cognitive and affective and social information in language. The shift to consider information beyond the propositional content of utterances, such as social and emotional information leads to a consideration of prosody. While the linguistic categories used in the “message” content of speech are considered discrete (Hockett, 1960), speech also conveys less categorical and more continuous information in prosody. Although there is a general assumption that much of the processing of prosodic information is carried out by the right hemisphere whereas the categorical information of a linguistic message is carried out in the left hemisphere, this seems to be an oversimplification. For example, Luks et al. (1998) investigated the cortical lateralization of intonation information relevant to emotion and syntactic judgments using a behavioral method similar to research investigating the lateralization of phonetic processing. (It is important to point out that, with recent neuroimaging methods, such lateralization results seem to reflect more a subtle shift in the balance of cortical processing rather than strong evidence that mechanisms in just one hemisphere are carrying out the perceptual task.) When judging whether a sentence was spoken in an angry or sad intonation, there was a reliable left ear advantage, suggesting greater right hemisphere processing. In contrast, when listeners made decisions about whether a sentence was a question or a statement (based on rising or falling intonation of the same declarative syntactic form), there was no advantage in processing sentences in either ear, suggesting both right and left hemispheres were contributing to processing equally. However, when the same sentences that differed in rising or falling pitch were judged as indicating either surprise or neutral attitude (thus the same speech signals with a different listener decision), a left ear advantage was found. This suggests that the way listeners attend to the speech determines aspects of the neural processing of the speech.

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Although there has been some research using neuroimaging to measure brain responses to emotional speech (e.g., Schirmer & Kotz, 2006; Wildgruber et al., 2004), it is not yet clear how attention modulates this kind of processing (cf. Sander et al., 2005). Because we know that variation in working memory load does affect behavioral measures of processing in talker variability (Nusbaum & Morin, 1992) it seems plausible that it might affect other aspects of speech perception. Similarly, there is evidence that perceptual load affects other aspects of auditory processing (e.g., Watkins, Dalton, Lavie, & Rees, 2007), and it seems plausible that varying perceptual load may affect our perception of emotional speech. Given the social importance of language and faces, it is surprising that there has been relatively little research on the role of face information beyond phonetic processing (although see Massaro, 1998). It is important to go beyond the long-standing limited linguistic notions of the meaning of language and start to develop an understanding of the emotional and social impact of language use and how this impact is realized in terms of neural mechanisms. The interaction between affective systems and cognitive systems also raises the question of understanding the relationship between cortical and subcortical mechanisms. Subcortical structures are clearly important in a number of psychological processes such as attention (thalamus), emotion and reward (ventral striatum), and motor control (basal ganglia) and are heavily connected to cortical systems that are typically studied in language processing. However, few studies have explicitly investigated the relationship between subcortical and cortical processing of language. Musacchia et al. (Musacchia, Sams, Nicol, & Kraus, 2006; Musacchia, Sams, Skoe, & Kraus, 2007) demonstrated that auditory brain stem responses are shaped by higher-order sensory inputs so that visual information such as seeing a talker ’s mouth produce speech sharpens the subcortical coding of auditory information such as speech. This suggests that neural theories such as Suga’s corticofugal (e.g., Suga & Ma, 2003) model of bat echolocation may provide new insights about active processing in speech perception.

PLASTICITY AND DEVELOPMENT There is great potential for the neuroscience of language processing to be informed by the research in neuroethology. However, it also possible that in some cases comparisons among species may be taken directly in a way that could be misleading. The notion of a developmentally critical period, derived from ethology (see Michel & Tyler, 2005) was applied as a theory about the development of

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language (Lenneberg, 1967). This theory states that there is a biologically determined limited time for the “natural development” of a native language (e.g., see Johnson & Newport, 1989). This can be seen as similar to the critical period in the development of bird song (Marler, 1970; Thorpe, 1961). Although we understand much about the neural mechanisms underlying the development of bird song, we understand little about the neural mechanisms underlying human language acquisition. Prather, Peters, Nowicki, and Mooney (2008) demonstrated the existence of “mirror neurons” in the swamp sparrow forebrain area HVC that respond in the same way when a bird sings or hears the same song (see also Dave & Margoliash, 2000). These HVC neurons innervate neurons in area X (which includes the song system part of the avian basal ganglia) that are important in song acquisition. The authors suggested that one important role for such mirror neurons is to guide the development of bird song. Although the suggestion of mirror neurons operating in speech perception is more specifically linked to the problem of recognizing speech rather than learning to recognize speech, it is possible that they may play an analogous role in birds and humans. Rosenblum, Miller, and Sanchez (2007), showed that the experience of lip-reading a talking face without hearing the speech can aid in recognizing subsequent novel speech (without the face) from the same talker. Given the evidence that phonetic categorization can take place within the motor system (Hasson, Skipper, et al., 2007) it seems entirely plausible that this plasticity of subsequent auditory recognition of speech is mediated in part by activity within the motor system. While this might seem to be a special case of spoken language plasticity in the short term, and thus not germane to native language learning, there is now substantial evidence for robust language learning abilities in the adult human. For example, after eight one-hour sessions of being trained on computer-generated (synthetic speech) words and sentences, never hearing the same words twice, adults show huge improvements from 20% correct word identification to almost 70% correct (Schwab, Nusbaum, & Pisoni, 1985). Even after 6 months without subsequent exposure, when tested on a new set of words not heard during training, listeners retain most of what they have learned. This is substantial, robust, generalized, and long-lasting learning of the phonetic properties of speech that results from a perceptual reorganization of the phonetic space (see Francis, Fenn, & Nusbaum, 2007). This perceptual reorganization suggests that listeners change the way they direct attention to the speech signal as a result of learning. Francis, Baldwin, and Nusbaum (2000) showed experimentally that the kind of feedback presented to listeners in this type of training can direct attention to specific acoustic cues, even

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when listeners are not subjectively aware of those cues, and thus change the phonetic classification of speech sounds. Although this work focuses on perceptual learning of new acoustic patterns from computer-generated synthetic speech rather than learning new phonetic categories, a similar process seems to be at work in learning a new phonology. Yamada and Tohkura (1992) showed that the reason that the /r/-/l/ distinction is difficult for Japanese listeners is because, compared to American English listeners, they direct attention to different acoustic cues in this speech that are not the ones used in English. In learning a new phonological category, listeners must learn to shift attention from one set of cues to another (see Nusbaum & Lee, 1992). Francis and Nusbaum (2002) showed that listeners can do this with appropriate training: American English listeners have a single phonetic dimension which they apply to Korean stop consonants that Korean listeners perceive as having two phonetic dimensions. Following appropriate training, American English listeners learn to redirect attention to the speech signal and induce the second dimension. This means that listeners can show substantial flexibility, with appropriate feedback and training, in shifting attention to the acoustic properties of speech either to adapt to a novel talker (such as a computer) or to a new phonological system. The problems in acquiring a new phonological system may be more a result of the critical mass of first language knowledge we have that, as a system, may direct attention to different acoustic properties than are needed for a new phonological system (see Nusbaum & Lee, 1992). Studies of birdsong share at least some of the same flavor. The original concept of a fixed critical period for auditory exposure has been modified. In general, the early laboratory studies failed to identify the much richer context in the field of song learning, where there are multiple influences on the sensitivity and duration to song exposure (Beecher, 1996). Memory acquisition is sensitive to social cues (e.g., Baptista & Petrinovich, 1984; Payne, 1981). The duration of the sensitive phase for song memory acquisition depends not only on timely sensory exposure (Marler, 1970), but depends strongly on environmental factors that vary throughout the year (Kroodsma & Pickert, 1980). Songs memories are not simple veridical templates but may be laid down in packages or “chunks” (Hultsch & Todt, 1989; Williams & Staples, 1992). Memory representations of the tutor songs are associative in nature (Rose et al., 2004). Many songbird species retain plasticity into adulthood, but this can be affected by the quality of developmental song learning. Most generally, there are some 3,500 species of songbirds that learn to sing, so picking a simple early model of song memorization born of laboratory observation of a few species in an attempt to model

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Summary

human language acquisition may arrive at only limited success. We need to think about song and language learning in a broader biological context than the simple notion of a genetically programmed critical period suggests. If we take language acquisition as a learning process, rather than a result of a critical period of developmental plasticity, why does this learning persist? The critical period hypothesis suggests that acquisition can only occur for a limited time and then sensitivity to new language forms shuts down after this period. However, a learning model for language acquisition does not by itself indicated why this learning is retained, even for adult learners. What makes synthetic speech learning by adults robust over long time periods such as 6 months without subsequent training? The answer may come from understanding the biological mechanisms involved in the consolidation of learning (McGaugh, 2000). Consolidation is the process whereby learning becomes robust against subsequent interference and decay. One of the primary candidate processes for consolidation of learning is sleep, although the actual mechanism by which sleep might consolidate learning is a topic of intense research and speculation (e.g., see Huber, Ghilardi, Massimini, & Tononi, 2004; Tononi & Cirelli, 2001; Walker & Stickgold, 2006; Shank & Margoliash, in press). Although much of the research showing sleep consolidation of learning has focused on rote learning of a specific stimulus or a particular single motor behavior, there is evidence that sleep consolidates perceptual learning of speech. Fenn, Nusbaum, and Margoliash (2003) examined the effects of sleep on perceptual learning of synthetic speech. Unlike the prior studies of sleep consolidation, participants never heard the same words twice. In a onehour training session, participants improved in recognizing novel words in synthetic speech by 20 percentage points. After a 12-hour waking retention interval, performance dropped by almost half suggesting that over this waking period, experience with spoken language may have interfered with learning. By comparison, a 12-hour period that included sleep did not show a similar reduction in performance on a subsequent posttest. Moreover, by comparing two different 24-hour retention groups, it was possible to see that sleep had two different benefits for consolidation of learning: First, sleep actually could restore performance following a waking interval suggesting that what appeared to be lost due to interference could actually be regained simply through sleep. Second, sleep prior to a waking retention interval inoculated the learner against loss during the waking interval. Clearly then, sleep consolidation plays an important role in making generalized learning of phonetic information robust and may contribute to maintaining the robustness and resilience of native language learned early.

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However, learning language is not simply a matter of learning the phonetic constituents of speech. Saffran (2003) demonstrated that learning the phonotactic patterns of a language can be thought of as a statistical learning process that is not specific to language. Similarly, Gentner et al. (2006) demonstrated that learning complex syntactic patterns is not unique to humans nor specific to language. If starlings can learn finite state and context-sensitive grammars and can generalize this learning to patterns and pattern lengths with which they have no prior experience, it seems that the mechanisms needed to learn complex hierarchical patterns are quite general. Moreover, there is some evidence that sleep may play a role in consolidating this kind of rule-based abstraction, at least for human infants (see Gomez, Bootzin, & Nadel, 2006). Thus, learning syntactic patterns may reflect a combination of memory for specific instances and generalization and abstraction over those instances. This suggests that there should be some enduring trace of the processing of a sentence that can form part of the basis for generalization. In some sense, we might think of sentences as objects in their own right. Hasson, Nusbaum, and Small (2007) showed that repeating the same sentence resulted in repetition suppression—a reduction in neural activity for the second occurrence (see Grill-Spector et al., 2006). This change in neural processing resulting from previous experience occurred primarily in the middle temporal gyrus for passive listening. However, with more specific processing goals the change in neural processing was seen in a broader network, including the IFG. Thus, there is indeed a trace of prior processing of a specific sentence and the nature of this effect depends on the cognitive processing carried out. This may provide a neural basis for subsequent generalization and abstraction of sentence properties such as syntax.

SUMMARY Rather than think about language simply as a signal (multimodal rather than just acoustic, of course) for transmitting information, we can think about the communicative interaction itself as a psychologically significant act that was part of the basic force shaping the evolution of the brain. By this construal, the listener ’s goal may not be to interpret the linguistic message but to interact with the interlocutor in a way that satisfies specific social goals and motives. This would suggest that communicative behavior—broadly construed—should be affected by a conversational partner ’s behavior, even beyond the simple process of interpretation.

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There is substantial evidence to support this hypothesis. Giles (1973; Giles & Smith, 1979) has demonstrated speech accommodation in conversations, in which one interlocutor (or both) converges on the speech of the other, in terms of speaking patterns. This kind of vocal accommodation or indexical mimicry is increased between members of the same social group and decreased between groups (Giles & Coupland, 1991). It is also increased, when one interlocutor is trying to persuade the other of something (Giles & Coupland, 1991). Moreover, this kind of behavioral convergence in a conversation is not restricted to speech patterns. Chartrand and Bargh (1999) demonstrated that other motor behaviors that are not speech related show similar accommodation between conversational partners and depend on social goals. One conversational partner tapping her foot can start the other partner tapping as well even though this is not a linguistically relevant behavior. And this can serve to socially link the interlocutors, increasing the sense of interpersonal affiliation (Lakin & Chartrand, 2003) as well as shifting attention from one’s self to the broader environment (Van Baaren, Horgan, Chartrand, & Dijkmans, 2004). The work by Giles (1973; Giles et al., 1987; Giles, Taylor, & Bourhis, 1973) on speech accommodation—the convergence of speech patterns between conversational partners—reflects a kind of short-term interactive plasticity in language use that suggests that spoken language use is always sensitive to the inputs that occur in conversations. Moreover, the motor mimicry research that suggests that there is a social connection that is formed on this basis (Lakin & Chartrand, 2003) reflects that this as unconscious communication and adaptive plasticity that is always present in conversations. If we take the view that language evolved in the context of face-to-face interaction seriously, then understanding this kind of behavioral plasticity simply echoes the role of the motor system in spoken language comprehension. Rizzolatti and Arbib (1998) suggested that language evolved based on the availability of the mirror neuron system as a way of understanding goaldirected action. Although the observation of mirror neurons has been extended into theories of empathy (Decety & Jackson, 2006; Decety & Lamm, 2006) and social understanding (Gallese et al., 2004), the notion that the motor system may aid in spoken language understanding has received empirical support in recent neuroimaging studies. Moreover, there is also a shift in the paradigms used to study language processing, moving from isolated sentences to conversational interaction (Pickering & Garrod, 2004) and from the study of decontextualized reading to contextualized spoken language that refers to an immediately present real world (Sedivy, Tanenhaus, Chambers, & Carlson, 1999; Tanenhaus, Spivey-Knowlton, Eberhard,

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& Sedivy, 1995). Taken together with the growing interest in understanding cognition as embodied and grounded in sensory and motor representations, neuroscience researchers are becoming more focused on understanding real faceto-face communication rather than linguistic competence. There is much more information in conversations than is represented in the propositional utterances and the prosodic expression of speaker attitude and emotion. In addition to the mouth movements that correspond to articulatory gestures, speakers routinely make manual gestures accompanying speech (see McNeill, 1992). These hand movements represent a channel of information that is not entirely independent of speech but not completely redundant either. McNeill argued that the gestures accompanying speech present more continuous and analogical representations of information than discrete and conventionalized linguistic forms. It seems entirely plausible that such gestures are processed using neural mechanisms in the dorsal premotor region, given the representation of hand movements in that area and there is some evidence suggesting that this may be the case. Gestures, more than other nongesture hand movements, differentially activate the dorsal premotor and have increased connectivity to the anterior portion of the STG which is associated with sentence processing (Skipper, Goldin-Meadow, Nusbaum, & Small, 2007a, 2007b). This suggests that gesture may be understood through the use of sensorimotor networks that, in some sense, parallel the sensorimotor networks that process spoken language. If gestures represent a first abstraction of observed action, and the underpinning of speech, then perhaps there should be some form of speech that is more similar to gesture. In other words, is there a vocal signal that is descriptive and referential, as is true of the propositional content of language, but is also continuous (as opposed to the discrete representation in language) and analogical rather than arbitrary (as are words and sentences)? Speech researchers typically describe the arbitrary-discrete and analogical-continuous aspects of language as corresponding to the propositional content in words and the attitude and affect content in prosody. However, Shintel, Nusbaum, and Okrent (2006) described a third channel of spoken language that may serve as a kind of “missing link” between manual gesture and propositional speech. They showed that when speakers are asked to describe a dot moving up or down or moving fast or slow, they modulate the fundamental frequency of their voice or speaking rate to refer to these events. Speakers do this even without instruction and even when the propositional content is about a different aspect of the event. Speakers can say “It’s going right” quickly or slowly to indicate dot speed, even when the task is to describe dot direction. Furthermore, this information is communicative for listeners who can decide

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References 899

what the speaker was referring to. Shintel and Nusbaum (2007) demonstrated that this kind of analogue acoustic expression or spoken gesture can actually create an image in the head of the listener so that describing a horse with a fast rate of speech implies to the listener a horse in motion. Finally, listeners’ interpretation of spoken gesture is not a simple motor resonance with the articulatory gestures that produced the speech. Shintel and Nusbaum (2008) showed that the interpretation of spoken gesture depends on the discourse context that preceded it. Thus, as with all other aspects of spoken language, there is no unique and invariant interpretation of the acoustic signature of spoken gesture. Spoken language understanding is not a process of determining an objectively specified meaning for a particular utterance. It is a process of construing the intended meaning of an utterance as a construction within the context of a social interaction. The listener must bring to bear a broad set of mechanisms including some that are language specific and some that are more general to cognition such as attention and memory and some that are more general still to social interaction and affective evaluation. The primary function of language is as a social signal. As social communication, it may share much in common with the vocal communication systems of other species, even if the evolutionary history of those systems is quite different. Although much of the research in the neuroscience of language started from a traditional linguistic perspective that separates out the propositional content from other aspects of communication such as manual gesture, spoken gesture, and emotional information, by understanding spoken language use as social communication, we may derive new insights into the neural basis of language.

Bar, M. (2003). A cortical mechanism for triggering top-down facilitation in visual object recognition. Journal of Cognitive Neuroscience, 15, 600–609. Barrie, J. M., Freeman, W. J., & Lenhart, M. D. (1996). Spatiotemporal analysis of prepyriform, visual, auditory, and somesthetic surface EEGs in trained rabbits. Journal of Neurophysiology, 76, 520–539. Barsalou, L. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577–660. Bates, E., Thal, D., & Marchman, V. (1991). Symbols and syntax: A Darwinian approach to language development. In N. Krasnegor, D. Rumbaugh, R. Schiefelbusch, & M. Studdert-Kennedy (Eds.), Biological and behavioral determinants of language development (pp. 29–65). Hillsdale, NJ: Erlbaum. Beecher, M. D. (1996). Birdsong learning in the laboratory and field. In D. E. Kroodsma & E. H. Miller (Eds.), Ecology and evolution of acoustic communication in birds (pp. 61–78). Ithaca, NY: Cornell University Press. Beilock, S. L., Lyons, I., Mattarella-Micke, A., Nusbaum, H., & Small, S. (2008). Sports experience changes the neural processing of language. Paper to be presented at the 15th Annual Meeting of the Cognitive Neuroscience Society, San Francisco. Bendor, D., & Wang, X. (2005). The neuronal representation of pitch in primate auditory cortex. Nature, 436, 1161–1165. Bensmaïa, S. J., Killebrew, J. H., & Craig, J. C. (2006). Influence of visual motion on tactile motion perception. Journal of Neurophysiology, 96, 1625–1637. Bogen, J. E., & Bogen, G. M. (1976). Wernicke’s region: Where is it? Annals of the New York Academy of Sciences, 280, 834–843. Broca, P. P. (1861). Nouvelle observation d’aphémie produite par une lesion de la partie postérieure des deuxième et troisième circonvolutions frontales. [New observation of aphemia produced by a lesion in the posterior half of the second and third frontal convolution.]. Bulletins de la Societe Anatomique, 6, 398–407. Busnel, R. G., & Classe, A. (1976). Whistled languages. New York: Springer-Verlag. Caplan, D. (1996). Language: Structure, processing, and disorders. Cambridge, MA: MIT Press. Capranica, R. R. (1972). Why auditory neurophysiologists should be more interested in animal sound communication. Physiologist, 15(2), 55–60. Chalupa, L. M., & Werner, J. S. (Eds.). (2003). Visual neurosciences. Cambridge, MA: MIT Press. Charniak, E. (1993). Statistical language learning. Cambridge, MA: MIT Press.

REFERENCES

Chartrand, J. L., & Bargh, J. A. (1999). The chameleon effect: The perception-behavior link and social interaction. Journal of Personality and Social Psychology, 76, 893–910.

Abbs, J. H., & Sussman, H. M. (1971). Neurophysiological feature detectors and speech perception: A discussion of theoretical implications. Journal of Speech and Hearing Research, 14, 23–36.

Chomsky, N. (1988). Language and problems of knowledge: The managua lectures. (Current studies in linguistics series 16). Cambridge, MA: MIT Press.

Adret-Hausberger, M., & Jenkins, P. F. (1988). Complex organization of the warbling song in the European Starling (Sturnus vulgaris). Behaviour, 107, 138–156.

Churchland, P. M. (1999). Eliminative materialism and the propositional attitudes. In W. G. Lycan (Ed.), Mind and cognition: An anthology (2nd ed., pp. 120–132). Malden, MA: Blackwell Publishers.

Anderson, J. R. (1978). Arguments concerning representations for mental imagery. Psychological Review, 85, 249–277.

Cohen, L., & Dehaene, S. (2004). Specialization within the ventral stream: The case for the visual word form area. NeuroImage, 22, 466–476.

Appelbaum, I. (1998). Fodor, modularity, and speech perception. Philosophical Psychology, 11, 317–330.

Cohen, L., Jobert, A., LeBihan, D., & Dehaene, S. (2004). Distinct unimodal and multimodal regions for word processing in the left temporal cortex. NeuroImage, 23, 1256–1270.

Chomsky, N. (1957). Syntactic structures. Hague: Mouton.

c45.indd 899

Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47–89). New York: Academic Press.

Dave, A. S., & Margoliash, D. (2000). Song replay during sleep and computational rules for sensorimotor vocal learning. Science, 290, 812–816.

Baptista, L. F., & Petrinovich, L. (1984). Social interaction, sensitive phrases and the song template hypothesis in the white-crowned sparrow. Animal Behaviour, 32, 172–181.

Dave, A. S., Yu, A. C., & Margoliash, D. (1998). Behavioral state modulation of auditory activity in a vocal motor system. Science, 282, 2250–2254.

8/17/09 5:25:35 PM

900

Language Processes

Decety, J., & Grezes, J. (2006). The power of simulation: Imagining one’s own and other ’s behavior. Brain Research, 1079, 4–14. Decety, J., & Jackson, P. L. (2006). A social neuroscience perspective on empathy. Current Directions in Psychological Science, 15, 54–58. Decety, J., & Lamm, C. (2006). Human empathy through the lens of social neuroscience. Scientific World Journal, 6, 1146–1163. Dehaene, S., & Cohen, L. (2007). Cultural recycling of cortical maps. Neuron, 56, 384–398. Derégnaucourt, S., Mitra, P. P., Feher, O., Pytte, C., & Tchernichovski, O. (2005). How sleep affects the developmental learning of bird song. Nature, 433, 710–716. Devlin, J. T., & Watkins, K. E. (2007). Stimulating language: Insights from TMS. Brain, 130, 610–622.

Freeman, W. J., & Skarda, C. A. (1990). Representations: Who needs them. In J. L. McGaugh, N. M. Weinberger (Eds.), Brain organization and memory: Cells, systems, and circuits (pp. 375–380). New York: Oxford University Press. Friederici, A. D., Bahlmann, J., Heim, S., Schubotz, R. I., & Anwander, A. (2006). The brain differentiates human and non-human grammars: Functional localization and structural connectivity. Proceedings of the National Academy of Sciences, USA, 103, 2458–2463. Friederici, A. D., Fiebach, C. J., Schlesewsky, M., Bornkessel, I., & von Cramon, D. Y. (2006). Processing linguistic complexity and grammaticality in the left frontal cortex. Cerebral Cortex, 16, 1709–1714. Galdikas, B. M. F. (1983). The orangutan long call and snag crashing at Tanjung Puting reserve. Primates, 24, 371–384.

Dewey, J. (1896). The reflex arc concept in psychology. Psychological Review, 3, 357–370.

Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119, 593–609.

Diehl, R. L., & Kluender, K. R. (1989). On the objects of speech perception. Ecological Psychology, 1(2), 121–144.

Gallese, V., Keysers, C., & Rizzolatti, G. (2004). A unifying view of the basis of social cognition. Trends in Cognitive Sciences, 8, 396–403.

Diehl, R. L., Lotto, A. J., & Holt, L. L. (2004). Speech perception. Annual Review of Psychology, 55, 149–179.

Garfield, J. L. (Ed.). (1987). Modularity in knowledge representation and natural-language understanding. Cambridge, MA: MIT Press.

Douglas, R. J., & Martin, K. A. (1991). Opening the grey box. Trends in Neurosciences, 14, 286–293.

Gentner, T. Q., & Hulse, S. H. (2000). Perceptual classification based on the component structure of song in European starlings. Journal of the Acoustical Society of America, 107, 3369–3381.

Eens, M., Pinxten, R., & Verheyen, R. F. (1989). Temporal and sequential organization of song bouts in the starling. Ardea, 77, 75–86. Erthal, F. S., Oliveira, L., Mocaiber, I., Pereira, M. G., Machado-Pinheiro, W., Volchan, E., et al. (2005). Load-dependent modulation of affective picture processing. Cognitive, Affective, and Behavioral Neuroscience, 5, 388–395. Fadiga L., Craighero L., Buccino G., & Rizzolatti G. (2002). Speech listening specifically modulates the excitability of tongue muscles: A TMS study. European Journal of Neuroscience, 15, 399–402. Fant, G. (1967). Sound, features and perception. STL-QPSR (Quarterly Progress and Status Report), 2, 1–14. Farah, M. J. (1984). The neurological basis of mental imagery: A componential analysis. Cognition, 18, 245–272. Farah, M. J., & Feinberg, T. E. (Eds.). (2000). Patient-based approaches to cognitive neuroscience. Cambridge, MA: MIT Press. Feng, A. S., Narins, P. M., Xu, C. H., Lin, W. Y., Yu, Z. L., Qiu, Q., et al. (2006). Ultrasonic communication in frogs. Nature, 440, 333–336. Fenn, K. M., Nusbaum, H. C., & Margoliash, D. (2003). Consolidation during sleep of perceptual learning of spoken language. Nature, 425, 614–616. Fodor, J. A. (1983). The modularity of mind. Cambridge, MA: MIT Press. Francis, A. L., Baldwin, K., & Nusbaum, H. C. (2000). Learning to listen: The effects of training on attention to acoustic cues. Perception and Psychophysics, 62, 1668–1680. Francis, A., Fenn, K., & Nusbaum, H. (2007). Effects of training on the acoustic-phonetic representation of synthetic speech. Journal of Speech, Language, and Hearing Research, 50, 1445–1465. Francis, A., & Nusbaum, H. C. (1996). Paying attention to speaking rate. Proceedings of the International Conference on Spoken Language Processing, SaA2L2.5. Francis, A., & Nusbaum, H. C. (2002). Selective attention and the acquisition of new phonetic categories. Journal of Experimental Psychology: Human Perception and Performance, 28, 349–366. Frankel, A. S., Clark, C. W., Herman, L. M., & Gabriele, C. M. (1995). Spatial distribution, habitat utilization, and social interactions of humpback whales, Megaptera novaeangliae, off Hawaii, determined using acoustic and visual techniques. Canadian Journal of Zoology, 73, 1134–1146. Freeman, W. J. (2003). Neurodynamic models of brain in psychiatry. Neuropsychopharmacology, 28, S54–S63.

c45.indd 900

Gentner, T. Q., Fenn, K. M., Margoliash, D., & Nusbaum, H. C. (2006). Recursive syntactic pattern learning by songbirds. Nature, 440, 1204–1207. Gentner, T. Q., & Margoliash, D. (2002). The neuroethology of vocal communication: Perception and cognition [Acoustic communication]. In A. Megela-Simmons, A. N. Popper, & R. R. Fay (Eds.), p.3 Springer handbook of auditory research (Vol. 16, pp. 324–386). Geschwind, N. (1970). The organization of language and the brain. Science, 170, 940–944. Giles, H. (1973). Accent mobility: A model and some data. Anthropological Linguistics, 15, 87–105. Giles, H., & Coupland, N. (1991). Language: Contexts and consequences. Pacific Grove: Brooks/Cole. Giles, H., Mulac, A., Bradac, J. J., & Johnson, P. (1987). Speech accommodation theory: The first decade and beyond. In M. McLaughlin (Ed.), Communication yearbook (Vol. 10, pp. 13–48). Newbury Park, CA: Sage. Giles, H., & Smith, P. (1979). Accommodation theory: Optimal levels of convergence. In H. Giles & R. N. St. Clair (Eds.), Language and social psychology (pp. 45–65). Baltimore: University Park Press. Giles, H., Taylor, D. M., & Bourhis, R. Y. (1973). Towards a theory of interpersonal accommodation through speech: Some Canadian data. Language in Society, 2, 177–192. Gleick, J. (1987). Chaos: Making a new science. New York: Viking. Glenberg, A. M., & Kaschak, M. P. (2002). Grounding language in action. Psychonomic Bulletin and Review, 9, 558–565. Gomez, R. L., Bootzin, R. R., & Nadel, L. (2006). Naps promote abstraction in language-learning infants. Psychological Science, 17, 670–674. Goodale, M. A., & Milner, A. D. (1992). Separate visual pathways for perception and action. Trends in Neurosciences, 15, 20–25. Gould, S. J. (1991). Exaptation: A crucial tool for evolutionary psychology. Journal of Social Issues, 47, 43–65. Gould, S. J. (1997). Darwinian fundamentalism. New York Review of Books, 34–37. Gould, S. J., & Vrba, E. S. (1982). Exaptation: A missing term in the science of form. Paleobiology, 8, 4–15. Grill-Spector, K., Henson, R., & Martin, A. (2006). Repetition and the brain: Neural models of stimulus-specific effects. Trends in Cognitive Sciences, 10, 14–23.

8/17/09 5:25:36 PM

References 901 Gross, M. (1972). Mathematical models in linguistics. Englewood Cliffs, NJ: Prentice-Hall.

Klatt, D. H. (1979). Speech perception: A model of acoustic-phonetic analysis and lexical access. Journal of Phonetics, 7, 279–312.

Gusnard, D. A., & Raichle, M. E. (2001). Searching for a baseline: Functional imaging and the resting human brain. Nature Reviews: Neuroscience, 2, 685–694.

Klatt, D. H. (1986). Comment. In J. S. Perkell & D. H. Klatt (Eds.), Invariance and variability in speech processes (pp. 381–382). Hillsdale, NJ: Erlbaum.

Hagoort, P. (2005). On broca, brain, and binding: A new framework. Trends in Cognitive Sciences, 9, 416–423.

Kluender, K. R., Diehl, R. L., & Killeen, P. R. (1987). Japanese quail can learn phonetic categories. Science, 237, 1195–1197.

Hanakawa, T., Immisch, I., Toma, K., Dimyan, M. A., Van Gelderen, P., & Hallett, M. (2003). Functional properties of brain areas associated with motor execution and imagery. Journal of Neurophysiology, 89, 989–1002.

Kogan, J., & Margoliash, D. (1998). Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: A comparative study. Journal of the Acoustical Society of America, 103, 2185–2196.

Hasson, U., Nusbaum, H. C., & Small, S. L. (2007). Brain networks subserving the extraction of sentence information and its encoding to memory. Cerebral Cortex, 17, 2899–2913.

Kosslyn, S. M., & Sussman, A. L. (1994). Roles of imagery in perception: Or, there is no such thing as immaculate perception. In M. S. Gazzaniga (Ed.), The cognitive neurosciences (pp. 1035–1042). Cambridge, MA: MIT Press.

Hasson, U., Skipper, J. I., Nusbaum, H. C., & Small, S. L. (2007). Abstract coding of audiovisual speech: Beyond sensory representation. Neuron, 56, 1116–1126. Hauk, O., Johnsrude, I., & Pulvermüller, F. (2004). Somatotopic representation of action words in human motor and premotor cortex. Neuron, 41, 301–307. Hauser, M. D. (1996). The evolution of communication. Cambridge, MA: MIT Press. Hauser, M. D., Barner, D., & O’Donnell, T. (2007). Evolutionary linguistics: A new look at an old landscape. Language Learning and Development, 2, 101–132. Hauser, M. D., Chomsky, N., & Fitch, T. (2002). The faculty of language: What is it, who has it, and how did it evolve? Science, 298, 1569–1579. Heiligenberg, W. F. (1991). Neural nets in electric fish. Cambridge, MA: MIT Press. Hickok, G., & Poeppel, D. (2004). Dorsal and ventral streams: A framework for understanding aspects of the functional anatomy of language. Cognition, 92, 67–99.

Kroodsma, D. E., & Miller, E. H. (Eds.). (1996). Ecology and evolution of acoustic communication in birds. Ithaca, NY: Cornell University Press. Kroodsma, D. E., & Pickert, R. (1980). Environmentally dependent sensitive periods for avian vocal learning. Nature, 288, 477–479. Kuhl, P. K., & Miller, J. D. (1975). Speech perception by the chinchilla: Voiced-voiceless distinction in alveolar plosive consonants. Science, 190, 69–72. Lakin, J., & Chartrand, T. L. (2003). Using nonconscious behavioral mimicry to create affiliation and rapport. Psychological Science, 14, 334–339. Lenneberg, E. H. (1967). Biological foundations of language. New York: Wiley.

Hockett, C. F. (1960). The origin of speech. Scientific American, 203, 88–96.

Levinson, S. E., Rabiner, L. R., & Sondhi, M. M. (1983). An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition. Bell System Technical Journal, 62, 1035–1074.

Holt, L. E., & Beilock, S. L. (2006). Expertise and its embodiment: Examining the impact of sensorimotor skill expertise on the representation of action-related text. Psychonomic Bulletin and Review, 13, 694–701.

Liberman, A. M., Cooper, F. S., Harris, K. S., & MacNeilage, P. F. (1962). A motor theory of speech perception. Proceedings of the Speech Communication Seminar (Vol. 2). Stockholm: Royal Institute of Technology.

Holtgraves, T. (1994). Communication in context: Effects of speaker status on the comprehension of indirect requests. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 1205–1218.

Liberman, A. M., Cooper, F. S., Shankweiler, D. P., & Studdert-Kennedy, M. (1967). Perception of the speech code. Psychological Review, 74, 431–461.

Hopcroft, J. E., & Ullman, J. D. (1969). Formal languages and their relation to automata. Reading, MA: Addison-Wesley.

Liberman, A. M., & Mattingly, I. G. (1985). The motor theory of speech perception revised. Cognition, 21, 1–36.

Hoy, R. R., & Paul, R. C. (1973). Genetic control of song specificity in crickets. Science, 180, 82–83.

Lichtheim, L. (1885). On aphasia. Brain, 7, 433–484.

Huber, R., Ghilardi, M. F., Massimini, M., & Tononi, G. (2004). Local sleep and learning. Nature, 430, 78–81.

Lowerre, B., & Reddy, R. (1980). The Harpy speech understanding system. In W. A. Lea (Ed.), Trends in speech recognition (pp. 340–360). Englewood Cliffs, NJ: Prentice-Hall.

Hultsch, H., & Todt, D. (1989). Memorization and reproduction of songs in nightingales (Luscinia megarhynchos): Evidence for package formation. Journal of Comparative Psychology, 165, 197–203.

Luks, T. L., Nusbaum, H. C., & Levy, J. (1998). Hemispheric involvement in the perception of syntactic prosody is dynamically dependent on task demands. Brain and Language, 65, 313–332.

Jeannerod, M. (1994). The representing brain: Neural correlates of motor intention and imagery. Behavioral and Brain Science, 17, 187–245.

MacKinnon, J. (1974). The behaviour and ecology of wild orang-utans (Pongo pygmaeus). Animal Behaviour, 22, 3–74.

Johnson, J. S., and Newport, E. L. (1989). Critical period effects in second language learning: The influence of maturational state on the acquisition of English as a second language. Cognitive Psychology, 21, 60–99.

MacNeilage, P. F. (1970). Motor control of serial ordering of speech. Psychological Review, 77, 182–196.

Johnson, K., Strand, E. A., & D’Imperio, M. (1999). Auditory: Visual integration of talker gender in vowel perception. Journal of Phonetics, 27, 359–384. Klatt, D. H. (1977). Review of the ARPA speech understanding project. Journal of the Acoustic Society of America, 62, 1345–1366.

c45.indd 901

Kosslyn, S. M., Thompson, W. L., & Alpert, N. M. (1997). Neural systems shared by visual imagery and visual perception: A positron emission tomography study. NeuroImage, 6, 320–334.

Magnuson, J. S., & Nusbaum, H. C. (2007). Acoustic differences, listener expectations, and the perceptual accommodation of talker variability. Journal of Experimental Psychology: Human Perception and Performance, 33, 391–409. Margoliash, D. (2002). Evaluating theories of bird song learning: Implications for future directions. Journal of Comparative Physiology, 188, 851–866.

8/17/09 5:25:36 PM

902

Language Processes

Marler, P. (1970). A comparative approach to vocal learning: Song learning in white-crowned sparrows. Journal of Comparative Physiological Psychology, 71, 1–25. Marler, P., & Peters, S. (1980). Birdsong and speech: Evidence for special processing. In P. Eimas & J. Miller (Eds.), Perspectives on the study of speech (pp. 75–112). Hillsdale, NJ: Erlbaum. Massaro, D. W. (1998). Perceiving talking faces: From speech perception to a behavioral principle. Cambridge, MA: MIT Press. McClelland, J. L., & Elman, J. L. (1986). The TRACE model of speech perception. Cognitive Psychology, 18, 1–86. McComb, K., Reby, D., Baker, L., Moss, C., & Sayialel, S. (2003). Longdistance communication of acoustic cues to social identity in African elephants. Animal Behaviour, 65, 317–329. McGaugh, J. L. (2000). Memory: A century of consolidation. Science, 287, 248–251. McGurk, H., & MacDonald, J. (1976, December 23). Hearing lips and seeing voices. Nature, 264, 746–748. McNeill, D. (1992). Hand and mind: What gestures reveal about thought. Chicago: University of Chicago Press. McNeill, D. (2007). Gesture and thought. Chicago: University of Chicago Press. Michel, G. F., & Tyler, A. N. (2005). Critical period: A history of the transition from questions of when, to what, to how. Developmental Psychobiology, 46(3), 156–162. Mishkin, M., Ungerleider, L. G., & Macko, K. A. (1983). Object vision and spatial vision: Two cortical pathways. Trends in Neurosciences, 6, 414–417. Moran, J., & Desimone, R. (1985). Selective attention gates visual processing in the extrastriate cortex. Science, 229, 782–784. Moss, C. F., & Sinha, S. R. (2003). Neurobiology of echolocation in bats. Current Opinion in Neurobiology, 13, 755–762. Moss, C. F., Bohn, K., Gilkenson, H., & Surlykke, A. (2006). Active listening for spatial orientation in a complex auditory scene. Public Library of Science Biology, 4, 615–626. Mullennix, J. W., & Pisoni, D. B. (1990). Stimulus variability and processing dependencies in speech perception. Perception and Psychophysics, 47, 379–380. Musacchia, G., Sams, M., Nicol, T. G., & Kraus, N. (2006). Seeing speech affects acoustic information processing in the human brainstem. Experimental Brain Research, 168, 1–10. Musacchia, G., Sams, M., Skoe, E., & Kraus, N. (2007). Musicians have enhanced subcortical auditory and audiovisual processing of speech and music. Proceedings of the National Academy of Sciences, USA, 104, 15894–15898. Nelson, M. E., & MacIver, M. A. (2006). Sensory acquisition in active sensing systems. Journal of Comparative Physiology, 192, 573–586. Newsome, W. T., Shadlen, M. N., Zohary, E., Britten, K. H., & Movshon, J. A. (1995). Visual motion: Linking neuronal activity to psychophysical performance. In M. S. Gazzaniga (Ed.), The cognitive neurosciences (pp. 401–414). Cambridge, MA: MIT Press. Nusbaum, H. C., & Henly, A. S. (unpublished manuscript). Understanding speech perception from the perspective of cognitive psychology. In J. Charles-Luce, P. A. Luce, & J. R. Sawusch (Eds.), Spoken language processing. Newark, NJ: Ablex. Nusbaum, H. C., & Lee, L. (1992). Learning to hear phonetic information. In Y. Tohkura, E. Vatikiotis-Bateson, & Y. Sagisaka (Eds.), Speech perception, production, and linguistic structure (pp. 265–273). Tokyo: Ohmsha. Nusbaum, H. C., & Magnuson, J. S. (1997). Talker normalization: Phonetic constancy as a cognitive process. In K. A. Johnson & J. W. Mullennix (Eds.), Talker variability in speech processing (pp. 109–132). New York: Academic Press.

c45.indd 902

Nusbaum, H. C., & Morin, T. M. (1992). Paying attention to differences among talkers. In Y. Tohkura, E. Vatikiotis-Bateson, & Y. Sagisaka (Eds.), Speech perception, production, and linguistic structure (pp. 113–134). Tokyo: OHM. Nusbaum, H. C., & Pisoni, D. B. (1987). Automatic measurement of speech pp.109-132 recognition performance: A comparison of six speakerdependent recognition devices. Computer Speech and Language, 2, 87–108. Nusbaum, H. C., & Schwab, E. C. (1986). The role of attention and active processing in speech perception. In E. C. Schwab & H. C. Nusbaum (Eds.), Pattern recognition by humans and machines: Speech perception (Vol. 1, pp. 113–157). San Diego, CA: Academic Press. Ojemann, G., & Mateer, C. (1979). Human language cortex: Localization of memory, syntax, and sequential motor-phoneme identification systems. Science, 205, 1401–1403. Payne, R. B. (1981). Song learning and social interaction in indigo buntings. Animal Behaviour, 29, 688–697. Peterson, G., & Barney, H. (1952). Control methods used in a study of the vowels. Journal of the Acoustical Society of America, 24, 175–184. Piattelli-Palmarini, M. (1989). Evolution, selection, and cognition: From “learning” to parameter setting in biology and the study of language. Cognition, 31, 1–44. Pickering, M. J., & Garrod, S. (2004). Towards a mechanistic Psychology of dialogue. Behavioral and Brain Sciences, 27, 169–226. Pinker, S. (2003). Language as an adaptation to the cognitive niche. In M. Christiansen & S. Kirby (Eds.), Language evolution: States of the Art (pp. 16–37). New York: Oxford University Press. Pinker, S., & Bloom, P. (1990). Natural language and natural selection. Behavioral and Brain Sciences, 13, 707–784. Pinker, S., & Jackendoff, R. (2005). The faculty of language: What’s special about it? Cognition, 95, 201–236. Prather, J. F., Peters, S., Nowicki, S., & Mooney, R. (2008). Precise auditory: Vocal mirroring in neurons for learned vocal communication. Nature, 451, 305–310. Price, C., Thierry, G., & Griffiths, T. (2005). Speech-specific auditory processing: Where is it? Trends in Cognitive Sciences, 9, 271–276. Pulvermuller, R. (2003). The neuroscience of language. Cambridge, MA: Cambridge University Press. Rauschecker, J. P. (1998). Cortical processing of complex sounds. Current Opinion in Neurobiology, 8, 516–521. Remez, R. E., Rubin, P. E., Pisoni, D. B., & Carrell, T. I. (1981). Speech perception without traditional speech cues. Science, 212, 947–950. Rizzolatti, G., & Arbib, M. A. (1998). Language within our grasp. Trends in Neurosciences, 21, 188–194. Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169–192. Rizzolatti, G., Fogassi, L., & Gallese, V. (2001). Neurophysiological mechanisms underlying the understanding and imitation of action. Nature Review Neuroscience, 2, 661–670. Rizzolatti, G., Fogassi, L., & Gallese, V. (2002). Motor and cognitive functions of the ventral premotor cortex. Current Opinion in Neurobiology, 12, 149–154. Roeder, K. D. (1964). Aspects of the noctuid tympanic nerve response having significance in the avoidance of bats. Journal of Insect Physiology, 10, 529–546. Roland, P. (1993). Brain activation. New York: Wiley-Liss. Rose, G. J., Goller, F., Gritton, H. J., Plamondon, S. L., Baugh, A. T., & Cooper, B. G. (2004). Species-typical songs in white-crowned sparrows tutored with only phrase pairs. Nature, 432, 753–758. Rosenblum, L. D., Miller, R. M., & Sanchez, K. (2007). Lip-read me now, hear me better later: Cross-modal transfer of talker-familiarity effects. Psychological Science, 18, 369–468.

8/17/09 5:25:37 PM

References 903 Rubin, D. L. (1992). Nonlanguage factors affecting undergraduates’ judgments of nonnative English-speaking teaching assistants. Research in Higher Education, 33, 511–531.

Suga, N., & Ma, X. (2003). Multiparametric corticofugal modulation and plasticity in the auditory system. Nature Reviews: Neuroscience, 4, 783–794.

Sachs, M. B., & Young, E. D. (1979). Encoding of steady-state vowels in the auditory nerve: Representation in terms of discharge rate. Journal of the Acoustical Society of America, 66, 470–479.

Sumby, W. H., & Pollack, I. (1954). Visual contribution of speech intelligibility in noise. Journal of the Acoustical Society of America, 26, 212–215.

Saffran, J. R. (2003). Statistical language learning: Mechanisms and constraints. Current Directions in Psychological Science, 12, 110–114.

Tanenhaus, M. K., Spivey-Knowlton, M., Eberhard, K., & Sedivy, J. C. (1995). Integration of visual and linguistic information is spokenlanguage comprehension. Science, 268, 1632–1634.

Sander, D., Grandjean, D., Pourtois, G., Schwartz, S., Seghier, M., Scherer, K. R., et al. (2005). Emotion and attention interactions in social cognition: Brain regions involved in processing anger prosody. NeuroImage, 28, 848–858. Schirmer, A., & Kotz, S. A. (2006). Beyond the right hemisphere: Brain mechanisms mediating vocal emotional processing. Trends in Cognitive Sciences, 10, 24–30. Schwab, E. C., Nusbaum, H. C., & Pisoni, D. B. (1985). Effects of training on the perception of synthetic speech. Human Factors, 27, 395–408. Sedivy, J. E., Tanenhaus, M. K., Chambers, C. G., & Carlson, G. N. (1999). Achieving incremental interpretation through contextual representation: Evidence from the processing of adjectives. Cognition, 71, 109–147. Shank, S., & Margoliash, D. (in press). Sleep, sensorimotor integration, and early vocal learning. Nature. Shintel, H., & Nusbaum, H. C. (2007). The sound of motion in spoken language: Visual information conveyed by acoustic properties of speech. Cognition, 105, 681–690. Shintel, H., & Nusbaum, H. C. (2008). Moving to the speed of sound: Context modulation of the effect of acoustic properties of speech. Cognitive Science, 32, 1063–1074. Shintel, H., Nusbaum, H. C., & Okrent, A. (2006). Analog acoustic expression in speech communication. Journal of Memory and Language, 55, 167–177. Skipper, J. I., Goldin-Meadow, S., Nusbaum, H. C., & Small, S. L. (2007a). Observed iconic and metaphoric speech-associated gestures are processed differently by ventral and dorsal premotor cortex. Paper presented at the 14th Annual Meeting of the Cognitive Neuroscience Society, New York, NY, USA. Skipper, J. I., Goldin-Meadow, S., Nusbaum, H. C., & Small, S. L. (2007b). Speech-associated gestures, Broca’s area, and the human mirror system. Brain and Language, 101, 260–277. Skipper, J. I., Nusbaum, H. C., & Small, S. L. (2005). Listening to talking faces: Motor cortical activation during speech perception. NeuroImage, 25, 76–89. Skipper, J. I., Nusbaum, H. C., & Small, S. L. (2006). Lending a helping hand to hearing: Another motor theory of speech perception. In M. A. Arbib (Ed.), Action to language via the mirror neuron system (pp. 250–285). New York: Cambridge University Press. Skipper, J. I., van Wassenhove, V., Nusbaum, H. C., & Small, S. L. (2007). Hearing lips and seeing voices: How cortical areas support speech production mediate audiovisual speech perception. Cerebral Cortex, 17, 2387–2399.

Teller, D. Y. (1984). Linking propositions. Vision Research, 24, 1233–1246. Tettamanti, M., Buccino, G., Saccuman, M. C., Gallese, V., Danna, M., Scifo, P., et al. (2005). Listening to action-related sentences activates fronto-parietal motor circuits. Journal of Cognitive Neuroscience, 17, 273–281. Thorpe, W. H. (1961). Bird-song: The biology of vocal communication and expression in birds. Cambridge, MA: Cambridge University Press. Tononi, G., & Cirelli, C. (2001). Some consideration on sleep and neural plasticity. HCN Italian Archives of Biology, 139, 221–241. Van Baaren, R. B., Horgan, T. G., Chartrand, T. L., & Dijkmans, M. (2004). The forest, the trees and the chameleon: Context-dependency and mimicry. Journal of Personality and Social Psychology, 86, 453–459. Vinckier, F., Dehaene, S., Jobert, A., Dubus, J. P., Sigman, M., & Cohen, L. (2007). Hierarchical coding of letter strings in the ventral stream: Dissecting the inner organization of the visual word-form system. Neuron, 55, 143–156. von der Heydt, R., Peterhans, E., & Baumgartner, G. (1984). Illusory contours and cortical neuron responses. Science, 224, 1260–1262. Walker, M. P., & Stickgold, R. (2006). Sleep, memory, and plasticity. Annual Review of Psychology, 57, 139–166. Walley, A. C., & Carrell, T. D. (1983). Onset spectra and formant transitions in the adult’s and child’s perception of place of articulation in stop consonants. Journal of the Acoustical Society of America, 73, 1011–1022. Watkins, S., Dalton, P., Lavie, N., & Rees, G. (2007). Brain mechanisms mediating auditory attentional capture in humans. Cerebral Cortex, 17, 1694–1700. Wernicke, C. (1874). Der aphasische symptomenkomplex. [HCN The aphasia symptom complex.]. Breslau: Cohn & Weigert. Wildgruber, D., Hertrich, I., Riecker, A., Erb, M., Anders, S., Grodd, W., et al. (2004). Distinct frontal regions subserve evaluation of linguistic and emotional espects of speech sntonation. Cerebral Cortex, 14, 1384–1389. Williams, H., & Staples, K. (1992). Syllable chunking: In zebra finch taeniopygia-guttata song. Journal of Comparative Psychology, 106, 278–286. Wilson, S. M., & Iacoboni, M. (2006). Neural responses to non-native phonemes varying in producibility: Evidence for the sensorimotor nature of speech perception. NeuroImage, 33, 316–325.

Sperber, D. (2001). In defense of massive modularity. In E. Dupoux (Ed). Language, brain, and cognitive development: Essays in honor of Jacques Mehler (pp. 47–57). Cambridge, MA: MIT Press.

Wilson, S. M., Saygin, A. P., Sereno, M. I., & Iacoboni, M. (2004). Listening to speech activates motor areas involved in speech production. Nature Neuroscience, 7, 701–702.

Stevens, K. N., & Blumstein, S. E. (1978). Invariant cues for place of articulation in stop consonants. Journal of the Acoustical Society of America, 64, 1358–1368.

Wong, P. C. M., Nusbaum, H. C., & Small, S. L. (2004). Neural bases of talker normalization. Journal of Cognitive Neuroscience, 16, 1173–1184.

Stevens, K. N., & Blumstein, S. E. (1981). The search for invariant acoustic correlates of phonetic features. In P. D. Eimas & J. Miller (Eds.), Perspectives on the study of speech (pp. 1–38). Hillsdale, NJ: Erlbaum. Stevens, K. N., & Halle, M. (1967). Remarks on analysis by synthesis and distinctive features. In W. Walthen-Dunn (Ed.), Models for the perception of speech and visual form (pp. 88–102). Cambridge, MA: MIT Press.

c45.indd 903

Tao, J., Johnson, M. T., & Osiejuk, T. S. (2008). Acoustic model adaptation for ortolan bunting (Emberiza hortulana L.) song-type classification. Journal of the Acoustical Society of America, 123, 1582–1590.

Woods, W. A. (1973). An experimental parsing system for transition network grammars. In R. Rustin (Ed.), Natural language processing (pp. 111–145). New York: Algorithmics Press. Wymbs, N. F., Nusbaum, H. C., & Small, S. L. (2004). The informed perceiver: Neural correlates of linguistic expectation and speech perception. Paper presented to the 11th Annual Meeting of the Cognitive Neuroscience Society, March, San Francisco.

8/17/09 5:25:37 PM

904

Language Processes

Yamada, R. A., & Tohkura, Y. (1992). Perception of American English and native speakers of Japanese. In Y. Tohkura, E. Vatikiotis-Bateson, & Y. Sagisaka (Eds.), Speech perception, production and linguistic structure (pp. 155–174). Tokyo: Ohmsha. Yan, J., & Suga, N. (1996). Corticofugal modulation of time-domain processing of biosonar information in bats. Science, 273, 1100–1103.

c45.indd 904

von der Heydt, R., Peterhans, E., & Baumgartner, G. (1984). Illusory contours and cortical neuron responses. Science, 224, 260–1262. Zwaan, R. A., Stanfield, R. A., & Yaxley, R. H. (2002). Language comprehenders mentally represent the shape of objects. Psychological Science, 13, 168–171.

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Chapter 46

Imitation and Theory of Mind PHILIP GERRANS

simulate others in the absence of a psychological theory about the relationship between inner mental states and actions. This argument suggests that simulation is a mechanism that enables direct apprehension of mental states embodied in behavior rather than inference-based attribution of mental states hypothesized as underlying causes of behavior (Gordon, 1986). The notion of simulation unites philosophical arguments about direct perception of mental states with a Gibsonian strand in psychology which also argues that perception is part of a sensory motor continuum that couples an agent to the world (Gallese & Goldman, 1998). Of course, the coupling involved in simulation is only partial: The simulator imagines performing the action, which takes the action offline (Nichols, Stich, Leslie, & Klein, 1996). Hence, it is also a mechanism of decoupling, but less abstract and theoretical than the TOM idea that decoupling requires amodal forms of representation not essentially linked to action. If we understand other people by simulating them, there is an intuitive link with the concept of imitation (Byrne, 2005; Carpenter, Call, & Tomasello, 2005; Meltzoff & Decety, 2003; Sommerville & Decety, 2006; Wolpert, Doya, & Kawato, 2003; Zaitchik, 1990). One way to simulate other people is to imagine imitating them; that is, performing the same action. If imagined performance automatically induces the same mental states in the simulator as the target, then imitation can be a mechanism of mental state simulation. Simulation theorists were therefore keen to recruit research in cognitive neuroscience, which showed automatic imitation of perceived actions such as smiles and tongue protrusion in human neonates (Meltzoff & Moore, 1983) and mirror phenomena in monkeys and humans (Rizzolatti & Craighero, 2004). In mirror phenomena, third-person observation of an action produces activation of the same premotor and parietal circuitry involved in first-person execution of the action (Gallese, Fadiga, Fogassi, & Rizzolatti, 1996; Gallese, Keysers, & Rizzolatti, 2004; Rizzolatti & Craighero, 2004; Rizzolatti, Ferrari,

THEORY OF MIND, SIMULATION, AND IMITATION In 1985, Simon Baron-Cohen, Uta Frith, and Alan Leslie published the influential paper, “Does the autistic child lack a theory of mind” (Baron-Cohen, Leslie, & Frith, 1985). What they called theory of mind (TOM) is the ability to conceive of mental states as internal representations that stand for objects in the world and, in doing so, may misrepresent it (Perner, 1991). Acquiring this concept of mental states allows the child to become a competent mind reader—to use the felicitous expression of S. Baron-Cohen (1995)—able to treat other people as agents animated by an inner mental life instead of as organisms directly coupled to the world by shallow perception-action loops. The expression “theory of mind” is sometimes used synonymously with “mind reading” to refer to the ability to attribute mental states, but it is in fact a theory about the cognitive processes on which mind reading depends. The essence of TOM is the idea that mind reading depends on metarepresentation, the representation of the relationship between a representation and its object. Metarepresentation allows the child to decouple the concept of a mental state from the world by thinking of mental states as possibly false or inaccurate (Leslie, 1994). The main competitor for TOM is the simulation theory of mind reading. Instead of using TOM to mind-read, we simulate the psychology of others by pretending to be them or imaginatively projecting ourselves into their situation (Goldman, 2006; Heal, 1996). And, intuitively, it seems that there are occasions when we simulate other people to read their minds. When playing chess or bidding against another person at an auction, we imagine ourselves in their situation to anticipate their next move. The attraction of the simulation alternative to TOM is that simulation is less conceptually demanding than theoretically embedded metarepresentation. Just as we can use a model plane or boat to make predictions in the absence of a detailed theory about how it works, we can use our own psychology to 905

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Rozzi, & Fogassi, 2006). Simulationists suggested that these types of involuntary motor imitation, which are best described as motor contagion, could be a developmental basis for simulation and ultimately for mind reading. Goldman and Gallese suggested, “Mirror Neurons represent a primitive version, or possibly a precursor in phylogeny of a simulation heuristic that might underlie mind-reading” (Gallese et al., 2004, p. 498). Results such as these combined with an increasing number of imaging and lesion studies of mind reading shifted the terrain of the debate between TOM and simulation theorists (Davies & Stone, 1995). The neural rather than conceptual basis of mind reading is now the focus with a priori and conceptual arguments restricted to providing hypotheses about the cognitive role played by different neural systems. No one now doubts that there is a conceptual difference between simulation and TOM-based mind reading, but a crucial question is whether the temporo-parietal junction (TPJ) activation elicited in some mind reading studies is computing the metarepresentation of mental states (Saxe, 2005, 2006; Saxe, Carey, & Kanwisher, 2004; Saxe & Wexler, 2005) or is involved in some less specialized integrative functions (Decety & Lamm, 2007; Stone & Gerrans, 2006a) that could be assimilated to the simulation paradigm (Decety & Lamm, 2007).

THEORY OF MIND: BEHAVIORAL PARADIGMS AND NEURAL CORRELATES The ability to decouple mental states arrives at different times for different states. Children can decouple emotions, perceptions, desires, and pretend states well before they can decouple beliefs. In effect, this means that a child can understand that another person can have different emotions, perceptual experiences, and desires from those of the child and that these states can be entertained even in the absence of their intentional object. Not only that, but children as young as 2 years old seem to have a naive psychological understanding in which these decoupled states are understood to fit together coherently. Children aged 2 years understand the relationship between desire and emotion. Asked to choose a picture that predicts the responses of a child who wanted a puppy and either did or did not get one, 2-year-olds chose pictures of happy and sad faces respectively (Wellman & Woolley, 1990). So it is not quite correct to say that toddlers cannot mind read. However, their mind reading undergoes a dramatic transformation between the ages of three and five. By the age of five, the neurotypical child can attribute beliefs understood as decoupled representations, completing the repertoire required for adult mind reading and expanding her capacities

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for social cognition. Thus some theorists have distinguished between early TOM and late or mature TOM in which the child can attribute decoupled beliefs. Others refer to social cognition as a general phenomenon of which TOM is a discrete component. Thus the acid test for the TOM interpretation of mind reading is the false belief test. In the classic false belief test, a child is seated at a table that contains two containers and a toy, opposite a partner (A). The partner leaves the room and the experimenter moves the toy from one container to another. The child is then asked, “Where will A look for the toy?” The correct answer is the previous location. Five-year-olds pass this test; 3-year-olds fail it. The 3-year-old does not seem to understand that the partner has a false belief based on perceptual information which has been falsified (Baron-Cohen et al., 1985). There are many versions of the test but a recent one by Ziv and Frye captures both the synchronic and diachronic aspects of TOM. In this test, children are read two short stories about characters called Duck and Cat and their belongings, a book and a ball. In change-of-location stories, Duck puts his ball in a bag, then leaves the room. While he is away, Cat removes the ball, in some cases replacing it with a book. The child is then asked, “Where does Duck think the ball is?/want the ball to be?” The results are the same as earlier. Three-year-olds pass the desire test but fail the belief test, 5-year-olds pass them both. In changeof-object stories, the child is asked, “What is in the bag? What does Duck think is in the bag? What does Duck want to be in the bag?” The conceptual advance of the 5-yearold is the ability to understand that someone may have a representation of the world that misrepresents it (BaronCohen et al., 1985; Wellman & Lagattuta, 2000; Ziv & Frye, 2003).This is consistent with findings that 3-yearolds have difficulty with the appearance/reality distinction. Shown a sponge painted as a rock, 3-year-olds will say both that it is a sponge and that it looks like a sponge (Astington, 1993). The other classic experiment recruited as evidence for TOM is the false photograph test in which the child watches while a Polaroid camera is used to take a photo of a scene containing an object. While the picture is developing, the object is moved and the child is asked, “Where will the object be in the photograph?” replicating the changeof-location problem in the false belief test. Unlike the false belief test, 3-year-olds have no trouble with the false photo task. Another crucial finding is that children with autism, a developmental disorder characterized by a range of impairments in social cognition, typically pass the false photo test but have impaired performance on the false belief test. Autistic and normal children exhibit the reverse pattern of performance on false belief and false photo tasks (Zaitchik, 1990).

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Theory of Mind: Behavioral Paradigms and Neural Correlates

These basic paradigms establish the framework against which the neural correlates of TOM and mind reading are investigated. All studies correlate neural activity revealed by imaging methodologies or focal lesions with performance on tasks that require the ability to attribute beliefs. Before we look at those studies, there are two important points to note, both relating to the possible modularity of TOM. Baron-Cohen et al. interpreted the false belief test as evidence that the 5-year-old forms a belief about a false belief and hence that metarepresentation of beliefs is the key to passing the false belief test. If this is the case, mind reading should develop in tandem with the development of metarepresentational capacities, scaffolded by other high-level capacities such as recursion (the embedding of one representation inside another) and executive function. Executive function, a developmental prerequisite for all forms of high-level domain-general cognition, involves the ability to selectively inhibit some representations while flexibly manipulating others. Executive function seems important to false belief tasks because the child has to inhibit the perceptual information about the new location while retrieving the memory of the original location that supplies the content for the false belief (Carlson & Moses, 2001; Moses, 2001; Perner & Lang, 1999). Another high-level ability that integrates seamlessly with executive and metarepresentational capacities arriving at about this time is a capacity for mental time travel, the use of episodic memory and imagination to plan, deliberate, and ruminate (Suddendorf & Busby, 2005; Suddendorf & Corballis, 2007). The name Mental Time Travel captures the idea that a combination of episodic memory and imagination under executive control allows a person to project herself forward and backward in time. Mental time travel appears to be a crucial cognitive adaptation, enhancing planning and deliberation by allowing a subject to simulate and evaluate contingencies in the Cartesian privacy of her own mind. Brain imaging and lesion studies indicate that episodic memory and prospection (imaginative rehearsal of future experience) recruit similar mechanisms and strongly overlapping neural systems. Rather than thinking of memory and imagination as different abilities, we should conceive them as part of a unified capacity for the construction of imagery that underwrites both episodic memory and prospection required for planning and problem solving. As Schacter et al. put it in a recent review: “[T]he medial temporal lobe system which has long been considered to be crucial for remembering the past might actually gain adaptive value through its ability to provide details that serve as building blocks of future event simulations” (Addis, Wong, & Schacter, 2007; Hassabis, Kumaran, & Maguire, 2007; Hassabis & Maguire, 2007). Some theorists note that in preschoolers, improved executive capacities consequent on maturation of prefrontal

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areas coincide with the installation of a capacity for voluntary episodic memory and (fragile) capacity for planning using prospection (Gerrans, 2002; Miller & Cohen, 2001). If this is correct, the precocious role of pretense in human cognitive development seems less anomalous (Gerrans, 1998; Leslie, 1994). Sophisticated pretend play, often taken to be an important precursor of TOM in virtue of its metarepresentational structure, can be seen as a socially sculpted mental time travel and perhaps assimilated by simulationists as a landmark in the development of third-person simulation. Thus one view of TOM is that it results from the ability to deploy a suite of higher level capacities, of which executive function and metarepresentation are perhaps the most important, to the task of understanding other minds. A mainstream view of TOM, following two decades of further research, however, is that it is a domain-specific cognitive adaptation for metarepresentation of mental states implemented in a specialized neural circuit: a “TOM module” as it is known. The initial impetus for the modular theory was the behavioral double dissociation elegantly demonstrated by Zaitchek (1990). Modular theorists treat it as evidence for the inference that the tasks are subserved by different cognitive subsystems or modules or, at the very least, for partitioning of metapresentational abilities. A stronger nativist modular hypothesis is that the development of this specialized neural circuitry is genetically specified. For nativists, TOM implemented in the TPJ or MPFC is akin to language: a universal cognitive adaptation that develops relatively autonomously. For modular nativists, impairments in social cognition characteristic of autism result from the developmental dissociation of a TOM module (Gerrans & Stone, 2008; Leslie & Thaiss, 1992; Scholl & Leslie, 2001). Thus when studies show selective activation of neural circuitry in mind-reading tasks, the role of that circuitry has been interpreted in different ways: as evidence for the modular hypothesis (Happé et al., 1996; Saxe et al., 2004; Saxe & Wexler, 2005); or as evidence that the child’s higher order intellectual capacities are focused on a mindreading task. In the latter case, the onus is on the theorist to show that the neural systems recruited by the experimental task are not specialized for TOM but have some more general, possibly executive or metarepresentational role. Apperly, Samson, Chiavarino, Bickerton, & Humphreys, 2007; (Apperly, Samson, Chiavarino, & Humphreys, 2004; Stone & Gerrans, 2006a, 2006b). Distinguishing these hypotheses is not an insuperable task: It seems to require that activation elicited by the hypothesized TOM task be compared with that produced by a task with the same conceptual structure and matched for executive and metarepresentational demands. For this

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reason, versions of the false photo and false belief task matched for difficulty have been used to pry apart the contribution to mind reading of domain general and domain specific cognitive systems (Apperly et al., 2004). Another important strategy is to examine cases of lesion to areas putatively identified by other studies as specialized for TOM. If they are specialized for TOM, we should expect (modulo the usual caveats about the distributed nature of higher level cognition and the difficulty of inferring cognitive specialization from neural localization; Saxe, 2006), that damage to them should lead to selective impairment in mind reading (Apperly et al., 2007). The other important point to note before we turn to the neural correlates of TOM is that mind reading builds on earlier developmental stages called precursors, characterized by specific forms of behavior underwritten by specialized cognitive systems. Gaze tracking, joint attention, and social referencing are precursors to mind reading in the sense that children who do not exhibit them subsequently have delays or impairments in mind reading. A satisfactory explanation of mind reading would show how the cognitive structure of precursor states produces the cognitive structure of mind reading. There are two main possibilities: In the first, weak, sense precursors are important early inputs to development of another cognitive process. This seems to be the sense in which domain general metarepresentational theorists of TOM conceive the role of early quasi-perceptual precursors such as intention detecting or social referencing: They provide social information that becomes evidence for a domain general inference system. The second, strong, sense of a precursor is that it is an earlier state of the same cognitive process (in this case mind reading), which is the explanandum. The syntactic structure of language has been proposed as a precursor to TOM in this strong sense because embedded complement clauses (he said that . . . , she saw that . . . , he is pretending that . . . ) have the argument structure required for metarepresentation of mental states (she believes that . . . ) (de Villiers, 2007). Alan Leslie followed a similar strategy in his suggestion that pretense is an important precursor of mind reading because pretense has the same argument structure as belief (S pretends of x that it is F; S believes of x that it is F; Leslie, 1994). Common to all accounts of mind reading is the idea that it requires prior development of a suite of specialized lowerlevel cognitive mechanisms. These precursor mechanisms, whether understood as weak or strong precursors of mind reading, represent vital information about the social world of infants and toddlers and mediate their earliest interactions with others. These mechanisms enable face processing, representations of gaze direction, gaze monitoring, detection of animacy, tracking of intentions and goals, and

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joint attention (Baron-Cohen, 1995; Carpenter et. al., 2001; Charman et al., 2000; Crichton & Lange-Kuttner, 1999; Csibra, Biro, Koos, & Gergely, 2003; Dawson, Meltzoff, et al., 1998; Hare, Call, Agnetta, & Tomasello, 2000; Saxe et al., 2004; Schultz, 2005; Stone, 2005; Woodward, 2003). These capacities seem to be specific to social stimuli, are shared with other primates, and appear to depend on neural circuitry that responds specifically to social stimuli (Blakemore & Decety, 2001; Campbell, Heywood, Cowey, Regard, & Landis, 1990; Csibra et al., 2003; Hare et al., 2000; Kumashiro et al., 2003; Povinelli & Vonk, 2003; Tomasello, Call, & Hare, 2003). Gaze monitoring seems to involve specific regions of the superior temporal sulcus that respond to the stimulus of eye gaze direction but not to other stimuli, even other stimuli that are physically similar to images of eyes (Campbell et al., 1990; Haxby, Hoffman, & Gobbini, 2000; Perrett, Hietanen, Oram, Benson, & Rolls, 1992). Assessing others’ goals and intentions seems to depend on specific representations of certain movement patterns—limb movement, combined with gaze, head, or body orientation—and involves both superior temporal sulcus and superior parietal and lateral frontal areas (Blakemore & Decety, 2001; Jellema, Baker, Wicker, & Perrett, 2000). These precursor mechanisms are not superseded by mind reading. Mature social cognition integrates these mechanisms together with mind-reading systems to allow a person to parse the social world. To understand an implied threat requires both an understanding of the literal meaning of the sentence as well as the contrary belief or desire expressed. Crucial clues may be given by vocalization, subtle gestures or expressions, and the overall context. People are very good at integrating this information, which requires a variety of mechanisms functioning in harmony. Any mind-reading task will activate many components of this suite of systems so a problem for an experimenter is to distinguish the contribution to the task of early components of social cognition from TOM or simulation. Given the centrality of the TOM hypothesis to many research programs, ranging from evolutionary psychology to developmental psychology and psychopathology, there are relatively few imaging studies using standardized tasks designed to elicit attribution of belief. This is in some ways unsurprising given the range of conditions under which belief attribution is normally elicited. Prediction, as in false belief contexts, is one instance, but communication, cooperation, and deception in verbal and nonverbal contexts involving shared or nonshared goals are all situations in which humans need to attribute beliefs. Versions of all these situations have been used as paradigms to elicit belief attribution. Table 46.1 summarizes the results of some studies (see also Figure 46.1).

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TABLE 46.1

Imaging studies of belief attribution.

Study/Method

Task

Neural Correlate of TOM Condition

Goel et al. (1995) PET[150]H2O

Infer others’ attribution of function to novel object Control: visual and semantic knowledge of objects

Left medial frontal lobe Left temporal lobe

Fletcher et al. (1995) PET[150]H2O

Story comprehension Control: stories not requiring mental state attribution

Left medial frontal gyrus Posterior cingulate cortex

Gallagher et al. (2000) fMRI

Story and cartoon comprehension

Medial prefrontal cortex (PFC; especially paracingulate gyrus)

Brunet et al. (2000) PET[150]H2O

Nonverbal comic strips Completion required attributing intentions, physical logic, or semantic knowledge

Right medial and middle PFC Inferior and superior temporal gyrus Left cerebellum, anterior cingulate middle temporal gyri

Russell et al. (2000) fMRI

Reading the Mind in the Eyes Test

Left inferior frontal gyrus medial frontal lobes Left middle and superior gyrus

McCabe et al. (2001) fMRI

Two-person trust and reciprocity games with human or computer counterpart

For cooperators, PFC more active when playing a human

Vogeley et al. (2001) fMRI

TOM stories, physical stories, self- and other ascription stories, and self-ascription stories

Anterior cingulate gyrus Self condition: temporo-parietal junction and anterior cingulate cortex (ACC)

Calder et al. (2002) PET[150]H2O

Eye gaze direction: 100% direct, 50% direct, 100% averted from subject

Medial prefrontal cortex with direct gaze Middle and superior temporal gyri

Ferstl & Von Cramon (2002) fMRI

Logical or TOM explanation of sentence pairs

Frontal medial cortex

Calarge et al. (2003) PET[150]H2O

Invent TOM story

Medial frontal cortex, superiori and inferior frontal regions, paracingulate and cingulated gyrus Anterior pole of temporal lobe Right cerebellum

Nieminen-von Wendt et al. (2003) PET[150]H2O

Heard TOM and physical (control) stories

Neurotypical subjects showed higher activation in medial prefrontal areas than Asperger ’s subjects

Saxe & Kanwisher (2003) fMRI

Visual stories of false belief, mechanical inference, action, and nonhuman objects

TPJ bilaterally only in false-belief condition Left TPJ objects and photos, right TPJ people

Walter et. al. (2004) fMRI

Comic strips requiring inference of intention in social and nonsocial actions Future intentional action

Anterior paracingulate gyrus in current and prospective intention STS and ACC in physical action condition

Grezes et al. (2004) fMRI

Videos of action (lifting and carrying) Infer expectations of weight from action

Dorsal premotor, left frontal operculum, left intraparietal sulcus, left cerebellum Earlier onset for observation of self than other

Rilling et al. (2004) fMRI

Feedback from computer or partner in game theoretic interactions Prisoner ’s dilemma Ultimatum game

Right mid STS in both tasks Ultimatum game with computers only activated dorsomedial PFC rostral ACC

German et al. (2004) fMRI

Video of actions and pretend actions

Medial PFC Inferior frontal gyrus, TPJ

Harris et al. (2005) fMRI

Inferring dispositions in high and low consensus conditions

Medial PFC in conditions of low consensus

Saxe & Powell (2005) fMRI

Parse “early” versus “late” social cognition stories

Medial PFC for all story conditions TPJ bilaterally for “late”: inferring thoughts or socially relevant information

Kobayashi et al. (2006) fMRI

Verbal and nonverbal false-belief tasks with children (8–12) and adults

TPJ bilaterally and right inferior parietal lobule for both groups Age-related differences in inferior frontal gyrus and left TPJ

Sommer et al. (2007) fMRI

Nonverbal false-belief test (cartoon) contrast with true-belief test

False belief versus true belief activated dorsal ACC Right lateral rostral PFC and right TPJ

Mitchell (2007) fMRI

Nonsocial attentional reorienting task Aim to see whether TPJ recruited

Attentional reorienting recruits the TPJ

Wakusawa et al. (2008) fMRI

Detection of irony

Medial orbitofrontal cortex (OFC)

Abraham et al. (2008) fMRI

Infer intentional versus nonintentional relations between people

Precuneus, temporal poles medial prefrontal cortex

Lissek et al. (2008) fMRI

Detecting cooperation and deception in cartoon scenarios

Both conditions activated TPJ parietal in cingulate regions Deception alone OFC and medial PFC

ACC ⫽ Anterior cingulate cortex; OFC ⫽ Orbitofrontal cortex; PFC ⫽ Prefontal cortex; STS ⫽ Superior temporal sulcus; TOM ⫽ Theory of Mind; TPJ ⫽ Temporo-parietal junction.

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4

1 2

3

Figure 46.1 ( Figure C.43 in color section) Lateral and medial view of the human brain showing main areas activated in belief attribution tasks summarized in Table 46.1. Note: 1: bilateral temporo-parietal junction (TPJ). 2: right anterior superior temporal sulcus (STS). 3: medial prefrontal cortex (MPFC). 4: posterior cingulate. Of these areas, the MPFC and TPJ have received the most recent attention as proposed neural substrates for TOM.

SUMMARY OF IMAGING STUDIES OF TOM A brief summary suggests that early studies from approximately the 1990s to early 2000s found that the area most consistently activated in conditions requiring the attribution of mental states was the medial prefrontal cortex (MPFC; Calarge, Andreasen, & O’Leary, 2003; Ferstl & von Cramon, 2002; Fletcher et al., 1995; Gallagher et al., 2000; Happé et al., 1996; McCabe, Houser, Ryan, Smith, & Trouard, 2001; Nieminen-von Wendt et al., 2003; Vogeley et al., 2001). Other areas that figure consistently in these studies are paracingulate gyrus and temporal poles. Thus many commentators converged on the idea that the MPFC plays an important role in mind reading and this hypothesis is still strongly favored, at least to the extent that activation in this area is interpreted as evidence that the task involved requires mental state inferences (Decety & Lamm, 2006; Moriguchi, Ohnishi, Mori, Matsuda, & Komaki, 2007; Sommer et al., 2007). The precise nature of those inferences is difficult to determine given that the studies used different paradigms (e.g., verbal and nonverbal story completion and comprehension or games of deception and cooperation) as well as more classic false belief–type scenarios (Vogeley et al., 2001). Another pattern emerges from studies conducted since the early 2000s that contrast the false belief and false photograph task. These studies elicit the involvement of the temporo-parietal junction (TPJ) sometimes bilaterally and sometimes, the right TPJ. Saxe et al. (2004) found that the TPJ bilaterally was activated more strongly for false belief than false photo tasks and that there was a lateralization effect for inferences involving people (right) and objects and photos (left). Other studies since then have found TPJ activation correlated with parsing scenarios that require the attribution of false beliefs or contrasts between true and false belief (Kobayashi, Glover, & Temple, 2007; Sommer et al., 2007).

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Advocates of the modular and nonmodular versions of TOM and simulationists naturally give different interpretations of these results. Rather than try to adjudicate disputes within a research program that is in its infancy, we can note issues that will no doubt figure in future studies. The first is an elegant explanation of the roles of both TPJ and MPFC proposed by Saxe and Powell (advocates of the idea that TPJ is the neural substrate of TOM inference). They suggested that the MPFC takes input from early precursor subsystems involved in social cognition and hence will be activated by any scenario that involves metarepresentation of precursors to TOM (e.g., conceptualizing behavior in terms of intentions or goals). The TPJ in contrast is specialized for aspects of social cognition requiring the metarepresentation of beliefs, or what they call “late” social cognition. This may be consistent with a finding by Moriguchi et al. that maturation of the prefrontal cortex is associated with different patterns of activation in the MPFC for TOM tasks. They noted an age-related shift from the ventral to the dorsal part of the MPFC during late childhood and adolescence. Perhaps this shift is associated with the recruitment of the TPJ for metarepresentation of belief that reduces processing demands on MPFC (Moriguchi et al., 2007). Another way to establish the role of circuits identified by fMRI is to look at lesion studies. If the MPFC and TPJ are specialized for TOM, lesions to those areas should produce selective deficits in TOM. Subjects with medial frontal damage who have TOM deficits all have accompanying executive function deficits (Happé et al., 2001; Stone, 2005). Furthermore, extensive medial frontal damage does not necessarily cause impairment in TOM (Bird, Castelli, Malik, Frith, & Husain, 2004). The patient in this case had extensive medial frontal damage with characteristic executive impairments but “no significant impairment on tasks requiring her to construct a theory of mind.” Hence while MPFC is recruited during TOM tasks, its contribution does not seem to be specific to TOM inferences. Similarly a hypothesis that TPJ is the site of the TOM module suggests that TPJ patients should have specific TOM deficits. Indeed, initial research with TPJ patients showed deficits on false belief tasks and not other EF tasks even when the general demands of executive function were controlled for (Apperly et al., 2004). However, more recent research shows that all TPJ lesion patients who performed below chance on false belief tasks also performed poorly on a false photograph task (Apperly et al., 2007). Thus, there is as yet no conclusive evidence from neurological patients that supports the claim that the TPJ is the site of a domain-specific mechanism dedicated to TOM inferences.

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Contagion, Imitation, Simulation

Just as the idea that MPFC is not specialized for TOM fits with general considerations about its executive role, general anatomical and processing considerations support a similar domain general interpretation of TPJ function. Mitchell (2008) found that the TPJ was preferentially activated by the Posner cueing task in which subjects are required to attend to predicted or unpredicted cues. This finding is not inconsistent with a role for TPJ in false belief tasks, only with the idea that the TPJ is somehow specialized for TOM. Decety and Lamm in a meta-analysis of fMRI studies of the TPJ (Decety & Lamm, 2007) concluded that it has a role in low level (bottom up) processes associated with the sense of agency and attention to salient stimuli. Once again, these results are not inconsistent with Saxe’s findings that TPJ is more activated by false belief than by nonsocial representations, just the idea that it is only activated by false belief stories. A speculative interpretation consistent with Decety and Lamm’s findings is that the TPJ is recruited by tasks that involve comparison of predicted and actual states. The comparison can be between intrapersonal perceptual modalities (giving rise to the sense of agency); interpersonally, between self and other perspectives, or even between beliefs entertained by self and other where attribution requires comparison of perceptually acquired information as in false belief tests. While mind reading constitutes a specific cognitive domain constituted by interacting subsystems, the jury is still out on whether one of those components is subserved by a neural circuitry (typically hypothesized to lie in the MPFC or TPJ) specialized for the metarepresentation of mental states.

CONTAGION, IMITATION, SIMULATION Most of the interest in imitation involves the idea that it might be an important precursor of simulation in the strong sense; that is, the cognitive architecture of imitation is transformed in development into an architecture supporting imitation-based simulation. Combined with the idea that mind reading depends on simulation rather than TOM, imitation could thus be a crucial cognitive bridge between early social cognition and mind reading. For this strategy to work, the simulation theorist needs to challenge the conception of decoupling that subtends the TOM interpretation of the false belief test. Imitation is at heart a motor phenomenon, the reproduction of a movement or an action. Thus the simulation theorist needs to build a cognitive bridge between motor cognition and social cognition. The attraction of this approach is that it fits well with the idea that the brain evolved as a control system for sensorimotor cognition, which, in species like ours, developed

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special adaptations for increasingly complex forms of social cognition. The TOM theory tends to approach mind reading by conceptualizing it as an application of abstract theorizing about unobservable causes: a unique human achievement that seems to have no essential connection to the sensorimotor cognition. Each conception has different problems. For TOM, it is closing the conceptual gap between TOM and the affectively scaffolded sensorimotor precursors. For the simulation theory, it is showing how cognition can move from being essentially tied to action interpretation and prediction to the level of abstraction implied by success on the false belief task. Theorists in this camp suggest that the issue can be approached by situating both imitation and simulation on the other side of a contrast with TOM drawn by Sommerville and Decety this way: Traditional theories of knowledge assume that knowledge consists of amodal redescriptions of sensory motor and introspective states . . . knowledge exists independently of these states and operates according to different principles. In contrast embodied theories of cognition construe knowledge as partial simulations of these sensory, motor, and introspective states. (Sommerville & Decety, 2006, p. 180)

The most promising approach for simulationists is therefore to conceive mind reading not as a theoretical achievement but as essentially a motor phenomenon of action synchronization (Byrne, 2005; Jackson & Decety, 2004; Jackson, Meltzoff, & Decety, 2006; Meltzoff & Decety, 2003; Ruby & Decety, 2004; Sommerville & Decety, 2006). This approach emphasizes that action has both a strictly motor component (controlling the trajectory of bodily movement) and an intentional one, the representation of the goal achieved by that movement (Chaminade, Meltzoff, & Decety, 2002). Imitation of action requires the representation of goals as well as motor mimicry and hence could be a way for a subject to become aware of another ’s goals, an important step on the path to mind reading (Decety, Chaminade, Grezes, & Meltzoff, 2002; Iacoboni et al., 1999; Jackson et al., 2006; Meltzoff & Decety, 2003; Oztop, Kawato, & Arbib, 2006). If it is the case that imitating action also rehearses the appropriate intention, simulation could provide access to intentions. It would be a further step from simulation-based knowledge of intention to knowledge of beliefs, but imitation of this kind could take the subject part of the way toward a conception of decoupled mental states. This promising idea faces a problem that it will be helpful to keep in mind. I see a swimmer moving his arm back and forth. Why is he doing this? Is he drowning and signaling for help or waving in exuberance? Perhaps I could solve

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this problem or get some vital information by mentally imitating him. However, if I simply reproduce the movement, the trajectory of his arm, by motor mimicry, the problem remains the same as before. I have reproduced the means to an end: calling for help or waving a greeting, but I need to know the end communicated by this movement. The same is true if observing his movement activates the same motor circuit that I would use to produce the movement via motor contagion. Wittgenstein is no longer the flavor of the month in philosophy of mind, but he did point out that we do not solve the problem of determining the meaning of a perceived object (in this case a movement) by associating it with a mental image of that object (in this case motor

representation of the mimicked movement). On the one hand, we need to represent the end as well as the means to understand action. On the other hand, if I imitate waving or signalling for help rather than the bodily movement common to both, I have already solved my problem because to do either I must represent the ends as well as the means. So it is helpful to build the case for simulation by trying to understand how mechanisms which mimic movements could be part of cognitive systems that give rise to simulation-based knowledge of intentions and ultimately beliefs. In other words, we describe a transition from conceptualizing mechanisms in terms of contagion, to imitation and, ultimately, to simulation (Fig. 46.2.)

MOTOR CONTAGION TO MIND READING ? Shared Representation: Motor Contagion Observed and executed action involve common computational code and neural systems. These shared representations support:

Sense of Agency distinguish self from other-generated motor representations

Action Anticipation Anticipating future actions or outcomes in our own and other’s behaviour.

Action Organization Creating structurally coherent action representations organized around an overarching goal

Behavioral Control Inhibitory mechanism for self-regulation

Action Imitation Reproducing observed action and outcomes

?

Mind reading Humans (perhaps uniquely) also move beyond basic action processing to interpret other’s behavior with respect to underlying mental states. A role for shared representations in the transition from motor contagion to action-based processing to simulation-based mental state understandings the subject of a research program.

Figure 46.2 This diagram captures the idea that higher order social cognition emerges from the recruitment of motor cognition into systems that perform increasingly sophisticated forms of social cognition culminating in mind reading. Note: As Sommerville and Decety note in their review, while the mechanisms of motor cognition are well specified, a role for these mechanisms

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in mind reading has not been conclusively established. From “Weaving the Fabric of Social Interaction: Articulating Developmental Psychology and Cognitive Neuroscience in the Domain of Motor Cognition,” by J. A. Sommerville and J. Decety, 2006, Psychonomic Bulletin and Review, 13, pp. 179–200. Adapted with permission.

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Contagion

CONTAGION The attempt to bridge the gap between ends and means naturally starts from cases in which the gap is small, such as the induction of emotion from perception. If the child sees a parent’s fearful or happy expression when confronted with an ambiguous stimulus and her own fearful or happy expression or feeling state is automatically evoked, the child will be in a similar state as the parent in relation to the stimulus (Decety & Lamm, 2006; Gallese et al., 2004; Goldman & Sripada, 2005; Ruby & Decety, 2004; Saxe et al., 2004; Wicker et al., 2003). Of course, the child will not thereby gain conceptual knowledge that fear represents danger or happiness pleasure. To do so, she would have to metarepresent the relationship between the mental state that produces the expression and its object. We might say of an infant who turned up her nose at a dirty diaper and simultaneously felt the sensation of disgust, as a result of automatically imitaing her parent’s expression, “She thinks her mum thinks the diaper is disgusting” without being committed to the view that she metarepresents. Once again, however, a simulationist would argue that the explanandum here is the ability to coordinate appropriate responses to disgusting objects rather than the ability to metarepresent emotions. The same problem of inferring what, if anything, is represented by a state automatically evoked by perception appears in the case of motor contagion as the difficulty of finding out the intention realized by the bodily movement (Oztop et al., 2006). Even if perceiving an action automatically rehearses the observer ’s motor circuitry, which we can call a form of automatic motor imitation, a question remains: Is action or movement being imitated? A role for imitation in development of mind reading was initially suggested by Meltzoff and Moore who observed neonatal imitation of facial gestures such as tongue protrusion within an hour of birth. This was consistent with films of infants dating from the 1970s showing preferential attention to and mimicry of facial expressions. As Meltzoff and Moore observed, this type of imitation requires the infant to map perception of another ’s movements onto her own motor system to reproduce the observed action (Meltzoff & Moore, 1983). Interestingly, this type of motor contagion has two properties not shared by all mirror systems. It is overt in that it proceeds all the way to behavior: The neonate reproduces the behavior she observes. And it is intransitive. Transitive actions have a goal or target. Reaching and grasping for example are aimed at a target. Tongue protrusion is a gesture without a target but it is mirrored nonetheless. Sommerville and Decety (2006) have suggested that overt intransitive imitation is a consequence of the lack

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of prefrontal inhibition of sensory motor processes. The ability to inhibit overt action or to imitate more selectively is something that arrives later as prefrontal cortical connectivity matures. Until then, observation rehearses some motor schemas all the way to completion. A parsimonious interpretation of the role of neonatal imitation and emotional contagion is therefore that they are weak precursors of mind reading. Infants who lack them will lose an important developmental resource. Interest in motor contagion as a possible strong precursor of mind reading was ignited by the discovery of mirror neurons in the premotor and parietal cortices of macaque monkeys in the 1990s. Some cells in the ventral premotor cortex, area F5, fire both when an action is observed and when the same action is executed by the monkey. Observation and execution of hand and mouth grasping will produce firing of the same premotor neurons involved in grasp. Some of these neurons fire even when the target of an action is occluded. Other neurons in the posterior parietal cortex (area PF) fire when the consequence of an action, such as tearing paper or breaking a peanut is perceived through another modality such as audition (Gallese et al., 1996, 2004; Grezes, Armony, Rowe, & Passingham, 2003; Iacoboni et al., 2005). These monkey mirror neurons are all transitive: They fire only when the action has a target or object and not when objectless gestures are perceived although they will fire when observing the last segment of reach to a previously seen currently occluded target (Umilta et al., 2001). Because single neuron studies are not performed on humans, an homologous mirror system in humans has been discovered through MEG and fMRI studies that have shown overlapping activation in the premotor and parietal cortices for both observation and execution of both transitive and (some) intransitive actions (Iacoboni et al., 2005; Rizzolatti & Craighero, 2004). A final piece of evidence about the human mirror systems is that premotor and parietal areas show overlapping activation for execution and for imagination of actions (Decety, et. al. 1997; Fig. 46.3). Thus there is strong evidence for shared circuits activated by third-person observation and first-person production of actions (Currie & Ravenscroft, 2002; Hurley & Chater, 2005; Jeannerod, 2006). Some theorists have suggested as a consequence that mirror neurons provide a neural basis for imitation, and hence for imitation-based simulation. However, as Oztop et al. point out, “Neurophysiological data simply show that a mirror neuron fires when a monkey [or human in the case of mirror circuitry] fires both when the monkey executes a certain action and when he observes a more or less congruent action”(Chaminade, Oztop, Cheng, & Kawato, 2008; Oztop et al., 2006).

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5

6

Figure 46.3 ( Figure C.44 in color section) The human mirror system. Note. Areas activated in both performance and observation of actions. 5: Right inferior parietal cortex. 6: Inferior frontal gyrus.

To show that motor contagion also plays an important role in imitation, we need to understand its role in imitation.

IMITATION The human ability to imitate as opposed to emulate arrives quite early. Infants 12 to 18 months old who observe an action such as hopping a mouse across a rug into a house will reproduce both ends and means. If the means are unavailable, they will reproduce the ends only, placing the mouse in the house (Carpenter et al., 2005). By contrast, chimpanzees will not reproduce the means. If a rake is used to pull a banana toward the demonstrator, a child who watches will use the rake. A chimpanzee will obtain the banana by other means (although a more recent experiment found some ability of chimpanzees to use the observed means; Buttelmann, Carpenter, Call, & Tomasello, 2007; Carpenter et al., 2005). Infants of about 9 months can also detect if they are being imitated, and toddlers engage in games of reciprocal imitation. Imaging studies with adults suggest that reciprocal imitation engages the STS (an area known to be involved in response to goal-directed movement) as well as left inferior parietal cortex when the participant imitates the other and right parietal cortex when the participant is imitated by the other (Decety et al., 2002). This is consistent with many other studies which detect lateralization for representation of self and other produced actions in the inferior parietal cortex. These studies suggest that a sense of agency, that is of being the initiator and controller of an action, is associated with activity in the right inferior parietal cortex (Blakemore, Oakley, & Frith, 2003; Uddin, Molnar-Szakacs, Zaidel, & Iacoboni, 2006; Vosgerau & Newen, 2007). Imaging studies of adult imitation following observation of means (arm and hand movements without the completion of the action) and goals (building a house out of LEGO blocks) suggest imitation of goals and means recruits different circuitry. The medial prefrontal cortex was involved in imitating means only, whereas the imitation

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of goals produced activation in left premotor cortex. The experimenters suggested that dorsolateral prefrontal cortex and cerebellum are active in imitating both ends and means (Chaminade et al., 2002). In fact, the human capacity for imitation cannot depend essentially on mirror circuits homologous to those of macaques. Macaques, who possess mirror neurons cannot imitate. When they are presented with a novel action and are rewarded for performing it, they cannot reproduce the action (Oztop et al., 2006; although see Kumashiro et al., 2003). This may be a consequence of the fact that mirror neuron firing in monkeys is limited to learned goaldirected action, and when presented with a novel action, they cannot readily infer the goal. Similarly, when presented with an intransitive action, chimpanzees cannot imitate it (Myowa-Yamakoshi & Matsuzawa, 2000). This suggests that what is driving the chimpanzee is an attempt to acquire a salient object rather than reproduce the motor component of an action. Monkeys and apes emulate rather than imitate. If they can infer a goal, they can produce an action from their motor repertoire to pursue the goal as a means. A monkey prevented from grasping by mouth will reach by hand for a target after observing a conspecific grasp the target by mouth. Thus they fail to meet the first two of the three essential conditions for imitation proposed by Tomasello et al.: 1. The behavior should be novel. 2. The behavior must reproduce the observed behavior. 3. The behavior should share the same final goal. Consequently, the consensus is that “imitation and language are not inherent in a macacque-like mirror system but instead depend on the embedding of circuitry homologous to that of the macaque in more extended systems in the human brain” (Oztop et al., 2006 p. 255, my italics). Thus to understand the contribution of mirror neurons to human imitation, it becomes important to understand the nature and contribution of the delete wider system in which the mirror systems are embedded. An interesting finding is that neural network models trained to imitate, or that evolve from a starting state to an end state in which they reproduce transitive actions, also show mirror properties. Cells in the middle layers are activated by both observation and production of target action see (Oztop et al., 2006) for a review. These results are consistent with findings that imitation of actions produces activation of the mirror systems in humans, Iacoboni et al. found that the left pars opercularis of the inferior frontal gyrus (considered to be an homologue of the monkey F5 area) was more active in an imitation than an observation condition for simple finger movements (Iacoboni et al., 1999). In another important

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Imitation 915

experiment, Decety et al. (1997) asked participants to observe an action in two conditions: one for later reproduction of the action (imitation), and another for later recognition of the action. In the imitation condition, elements of the mirror systems were active (relevant to a control condition in which static postures were shown). In the action recognition condition, the contrast with the control was in different areas: inferior frontal gyrus STS, posterior STS, and parietal cortex. Recognizing a movement as intentional, purposeful movement, seems to involve the superior temporal sulcus (STS) rather than the motor mirror system. A series of experiments have shown that STS neurons, which have no motor properties, respond to goal-directed movement such as reaching when the actor is looking at the target. That the STS seems to play a crucial role in the detection of purposeful bodily movement is also shown in experiments involving point light displays of human movement and Heider and Simmel type experiments in which subjects overattribute intentions to moving geometric shapes. Watching a montage of moving shapes, they say the triangle is “chasing” the circle and so on. When these tasks were contrasted with false belief tests, the STS was activated by the moving shape task, and as predicted by the TPJ hypothesis of mind reading, the TPJ was activated by false belief tasks (Gobbini, Koralek, Bryan, Montgomery, & Haxby, 2007). These cases are particularly interesting since no motor or mirror activity is evoked by perception of geometric shapes (the human motor system is indifferent to the trajectories of geometric shapes), and yet the subjects still attribute goals to the triangles, squares, and circles purely in virtue of their trajectories. This last finding is important because it suggests that the mirror system is not required for action recognition and classification. Although recruited by imitation of action, which requires recognizing an action qua action, it seems that the role of the mirror system is different. What then is the mirror system doing during imitation of observed actions? One suggestion is that mirror neurons are essentially motor control neurons. They encode movement kinematics, not intended goals. If that is the case, why do they exist and what is their role in the distinctively human capacity for imitation? It is necessary to encode motor trajectories for control of first-person action, but why should mechanisms evolved for that purpose also respond to observation of actions? An answer is provided by the forward model hypothesis of action control. On that hypothesis, to control action we need perceptual information to check that our movements are correct. In reaching to pick up a glass, we use perceptual information, first to calculate the difference between the current state of the system and the goal state (e.g., if the target is 30cm from the hand, we need to move the hand

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30 cm to pick it up); this is known as an inverse model. We then use the inverse model to create an instruction to produce the instrumental movements necessary to achieve the goal. In this case, move the hand 30 cm. This instruction is then executed by motor systems that decompose it into elements executable by different subsystems that control force, trajectory of limb movement, power, and precision grip. These instructions are used to create a series of forward models that include predictions of the outcome of movements. The forward model for the whole movement predicts that the target will be grasped. The forward model for precision grip predicts the consequences for the fingertips of fine motor movements. The prediction made by the forward model is then compared with the perceived actual state of the system that results from the movement. If there is a mismatch, the procedure is repeated until the error signal is zero. If motor prediction was that the hand projects 30 cm but we see it overshoot the target by 5 cm, we can produce another instruction to withdraw the hand 5 cm closer to the body (Blakemore & Decety, 2001; Miall, 2003; Wolpert et al., 2003; Wolpert & Kawato, 1998). This is a description of a process whose actual cognitive architecture is the topic of ongoing computational modelling that tries to replicate actual neurocognitive processes (Oztop et al., 2006). Despite controversy, one conclusion can be drawn. The comparison process decomposes the action into a series of finer-grained forward models. Only at the top level are goals represented. To determine whether the intention to pick up the cup is fulfilled, we compare perception of the action to the intention. At lower levels, the comparison is between perceptual information and motor information, to which the goal is irrelevant. This is made clear by consideration of cases in which the same motor movement can implement two different intentions/goals. Tracing a horizontal line on a page can be the completion of an architectural plan or an underlining of text. But the motor representations of the movement are the same in each case. The perceptual information used to control the movement is compared with the motor representation, not the overarching goal. A persuasive suggestion about mirror neurons is that they are part of the system implementing the predictive component of the forward model (Oztop et al., 2006). When a canonical representation of a movement is produced, a prediction of the consequences (motor and perceptual) is also created. The mirror neurons in F5 premotor cortex are not canonical neurons but part of this predictive system (Grezes et al., 2003). This is shown by experiments in which canonical neurons are deactivated, leading to inability to produce movement. Deactivating mirror neurons does not affect movement. In essence, the prediction is a copy of the motor representation not used for action but as the basis for subsequent comparison with sensory

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feedback. The parietal cortex is a sensory integration area that maps perceptual to motor information as part of the process of controlling movement (Rizzolatti et al., 2006). This is why the mirror neurons are activated by perception of goal-directed movement: They help implement the comparison of the predictive motor representation to perceptual feedback. Consequently, mirror systems will be active in imitation because they encode the motor component of the imitated action, which is shared between both perceiver and target. They are indifferent to whether the movement originates with the first or third person because they encode kinematic information. When subjects imitate a perceived intransitive movement, the contralateral sensory motor cortex is more active if that movement is presented from the firstperson perspective (Jackson et al., 2006). Presumably this is because the mapping from first-person perspective to a copy of a first-person motor prediction requires less transformation than the mapping from third- to first-person perspective. Thus it is entirely consistent with the finding that mirror neurons are active during imitation and are activated by goal-directed movement that their role should be essentially motor control rather than the “mentalistic” aspects of social cognition such as inferring intentions and goals. The fact that mirror neurons fire for goal-directed action does not mean that they encode the goal of the action. The evidence we have reviewed suggests that goal representation is subserved by different circuitry that operates in harmony with the mirror system. The information encoded by the mirror system is part of a hierarchy of representations involved in producing and reproducing action. A recent imaging study parsed the action understanding system into different levels of representational complexity and content and mapped those components to different areas of a distributed motor system (Lestou, Pollick, & Kourtzi, 2008). The experimenters summarized their findings this way: [T]he ventral premotor cortex encodes the physical similarity between movement trajectories and action goals that are important for exact copying of actions and the acquisition of complex motor skills. In contrast, whereas parietal regions and the superior temporal sulcus process the perceptual similarity between movements and may support the perception and imitation of abstract action goals and movement styles. Thus, our findings propose that fronto-parietal and visual areas involved in action understanding mediate a cascade of visual-motor processes at different levels of action description from exact movement copies to abstract action goals achieved with different movement styles. (p. 324, my italics)

This is consistent with the findings of Decety and collaborators, who found that imitation of action requires

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frontal and executive control as well as the integration of perceptual and motor information (Decety et al., 1997). In fact, this is what we could expect of imitation: Motor aspects need to be controlled by higher level plans (Decety et al., 1997; Jeannerod, 2006; Pacherie, 2006). The intention to imitate a movement must be decomposed into less complex intentions and ultimately to motor plans executable by the motor systems. The representations encoded by the mirror system are of movement kinematics necessary to compare observed movement with intended movements, not with intentions. This minimal interpretation of the role of mirror systems is not unanimous. Rizzolatti and Craighero and Iacoboni et al. argue that mirror systems have an important role in inferring intentions (Iacoboni et al., 2005; Rizzolatti & Craighero, 2004). The basic reason is for this interpretation is that mirror neurons are selectively activated by different types of action (reaching and grasping) rather than intransitive movements. As Octopi et al. remark, however, it is not clear why this interpretation rather than the narrower one should be accepted. One possibility is that mirror neurons could function not only as part of a forward model which transforms intentions to motor commands to movements and compares actual to intended movements but as a part of an inverse model that maps perceived movements to (stored copies of) motor commands to intentions. This comparator system thus could be used to gain knowledge of third-person intentions if (a), the movement kinematics and hence motor representations are the same in observer and actor and (b), there is correspondence between the movement kinematics and the representations of action. In such a case, recovering the intention governing a perceived action would be a matter of retracing the path from perceived action to intention via the mirror system. In effect, this strategy uses mirror neurons, not only as part of a forward model for controlling movements, but as part of an inverse model for discovering intentions (Blakemore & Decety, 2001; Iacoboni et al., 2005; Miall, 2003; Rizzolatti et al., 2006). It is worth noting that conditions (a) and (b) seem to be met in the case of reaching tasks for monkeys. The movements made by target and subject are the same and, given that there are only a small number of movements (mouth and hand grasping) and one action (grasping), it should be possible to recover the intention from the perception of an action via the motor system, which encodes the movements (Fig. 46.4). A difficulty with this idea is that it could not work for novel intentions or actions, so as a general analysis of imitation and learning by imitation, it is unsatisfactory. Consequently, if we conceive of imitation as the attainment of an already understood or novel goal by copying the means to that goal, motor contagion cannot be the basis for

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Simulation 917

Forward Prediction Delay compensation Prefrontal cortex Mental State/ intention

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Figure 46.4 This diagram shows how mirror neurons fit into the forward model for action monitoring. Note: The bottom half of the diagram shows how an observer ’s motor systems mirror those of the target: The same predictive circuitry is activated by both observation and performance. The crucial question raised

imitation. However, such imitation will inevitably involve mirroring to the extent that the encoding of the kinematics of the action copied is neutral between first and third person. We should expect to see mirror activity in the premotor cortex and parietal lobule (PF) of someone learning a musical instrument, such as a guitar, by imitation because the kinematics are similar for observer and actor (Vogt et al., 2007). Even in cases such as this, however, it seems that the first-person perspective recruits the motor system more extensively, which suggests that the motor representations are not completely neutral between perspectives. It might be advantageous to teach your child to tie her shoes, not by having her observe you but by reaching around her from behind and tying them yourself so that, from her perspective, the action looks as if she were doing it. Consequently, despite the suggestiveness of links between motor contagion, imitation, simulation, and mind reading, there is as yet no evidence that they share a common computational structure. Motor contagion is embedded within imitation and imitation-based simulation but does not provide information about the crucial aspect that transforms

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Sensory Processing

兰兰

⌬B

Parietal areas (PF) Control variable computation

by models such as these is whether the observer ’s mirror system does in fact play a role in inferring the mental state expressed by the imitated action. From “Mirror Neurons and Imitation: A Computationally Guided Review,” by E. Oztop, M. Kawato, and M. Arbib, 2006, International Neural Network Society, 19, 254–271. p. 264. Reprinted with permission.

movement to action: the mental states that provide the intentions and goals to which the movement is a means.

SIMULATION Initial discussion of simulation focused on the case of explicit simulation involving imagination or pretence. The problem arising automatically is that where the target of a simulation has different beliefs and desires to the simulator, the simulation will be inaccurate. If the simulator likes chocolate and the target is allergic to it, simulation will produce an inaccurate prediction about the response to a gift of an Easter egg. Both children and adults make egocentric errors of this kind all the time, but we also compensate for them, and the ability to compensate seems to require precisely the kind of abstract amodal theorizing that led to the TOM theory. We could refine the simulation in this case by imagining that the person did not like chocolate, or believed that the chocolate contains allergens, but that kind of refinement

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seems to involve possession of the concepts simulation was proposed to explain This is the main objection to hybrid theories in which simulation is the default process involved in mind reading and TOM is engaged to resolve errors of prediction or interpretation in the same way aeronautical engineers resort to theory to explain unpredicted results produced by their simulations. The hybrid theory needs to explain why, if we already possess the TOM concepts and can employ them as backup, we do not simply employ them in the first instance. And indeed, rather than simulate and then compensate for errors using TOM, children and adults often use naive psychological theories about others’ beliefs to understand them. In one experiment, a child and an adult observer (A) are seated in front of two dishes of beads. The round dish contains red and green beads, but the square dish contains only yellow. The child and A watch while an assistant moves a bead from the round dish into an opaque bag. The child, but not A, sees that the bead is green. When the child is asked, “What color does A think the bean in the bag is?” the typical reply is “red.” Surely if the child was simulating, she would say red or green. The TOM theorist suggests that the child is using a theoretical rule of thumb: “ignorance equals wrong.” Whether that is correct, the error made by the child is not easy to explain as the result of conflating first- and third-person perspectives (Ruffman, 1996) . Adults also make errors in situations that require them to make inferences about the mental states of other people. Saxe suggests that a large body of research in social psychology on reasoning errors is best explained in terms of reliance on intuitive theories of psychology: that is as stereotyping, categorization, biases, and heuristics. People’s ratings of their own trustworthiness, responsibility, and fidelity often differ from their predictions about others (Epley, Keysar, Van Boven, & Gilovich, 2004; Gilovich, 1993; Kahneman, Slovic, & Tversky, 1982; Malle, 2004; Pronin, 2002; Pronin, Gilovich, & Ross, 2004; Ross, Amabile, & Steinmetz, 2005). These differences seem to result from beliefs about other people’s mental states and character traits. Of itself, egocentric bias in social judgment is not evidence for either TOM or simulation. The difference between first- and third-person judgments of say trustworthiness could result from egocentric bias, a version of an availability heuristic, that makes us rely on stereotypes about others while taking a more sympathetic and nuanced, if often wrong, view about ourselves. This is consistent with the idea that in our own case we imagine or remember details about our character or dispositions to predict our behavior, using mental time travel to simulate outcomes, while invoking theory-based stereotypes to explain others.

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The concept of simulation seems to require that that we imaginatively project ourselves into others’ situations to predict their behavior: exporting our egocentric biases so to speak and assimilating others to ourselves. Noting that we have egocentric biases does not refute the “argument from error” against simulation. The simulationist might reply that perhaps what we do is inhabit the shoes of the other person by slightly changing some parameters (“If I were stressed and had just lost my job, I would . . . ”) but if those parameters are mental states, the simulationist needs an account of mental states fed into any simulation that does not beg the question against competing theories (Fig. 46.5). In an imaging study (Delgado, Frank, & Phelps, 2005) of subjects playing a game involving trust (a classic situation in which one might project oneself into the other ’s situation to predict a response to an offer), participants choices reflected knowledge acquired about the other person’s traits. Their choices about whether to make large or small offers varied according to the content of stories describing the character and previous actions of the other participant. These varied independently of the rewards obtained in the game for “good” and “bad” partners suggesting that preexisting beliefs about the other person’s mental states overrode quite powerful reinforcement mechanisms (which were operative in the case of “neutral” partners). We cannot use the fact that we make egocentric errors to support a simulation case for mind reading. However, that still leaves many issues unresolved. A stronger argument against pure simulation-based mind reading is suggested by the discussion of relationship between mirror neurons and imitation. We considered the hypothesis that we could recover intentions from observation of an action by retracing the path from effect (perceived action) to intentional cause via the mirror system. We noted that movements are the means and the goal of an action is the end, but that the inverse modeling approach would not work unless the representation of the means also carried information about the end. And, as we saw previously, ends and means are represented by different neural systems. As part of their elegant and exhaustive review of the relationship between motor and social cognition, Sommerville and Decety (2006) noted, “[A]lthough goals and means are closely intertwined in the act of imitation they are, to some extent dissociable and may therefore tap partly distinct neural processes” (p. 190). Consequently, we do not need to use the computationally baroque method of inferring ends from means via the mirror system. The same is true for imitation-based simulation. If we want to find out the mental states of someone performing an action, we cannot simply rehearse the relevant

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Summary

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SUMMARY

(A) Pure simulation: Pretend inputs

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(B) Context-dependent simulation

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Figure 46.5 Varieties of simulation. Note: In the simplest case (A), we predict others mental states by pretending to be them. We use our own psychology as a black box. (B) describes the case where we alternate between simulation and TOM as required by the context. Perhaps we use more theoretically based knowledge for situations in which we are aware that the other person is unlikely to behave like us or that our own database is inadequate. (C) is only weakly a simulation alternative to TOM. It suggests that we simulate using inputs characterized in TOM terms (If I believed that P and desired that QI would . . . ). (D) suggests that we could use simulations to provide inputs to TOM processing using a “like me” heuristic. Once again, the hard work is being done by the metarepresentation of mental states and their representational properties.

movement and read off the ends to which it is a means. Jacob and Jeannerod give an example that dramatizes the point. Dr. Jekyll and Mr. Hyde are names for two personalities who inhabit the mind of one person. One is a caring and competent surgeon who never manifests emotions for fear of compromising the quality of his work, the other is a coldblooded sadist. When we observe Jekyll/Hyde vivisecting a patient, how can we tell whether what we are watching is surgery or torture? The motor component is identical. Even if observation rehearses our own motor systems so that we simulate movements involved in incisions and excisions, how could we use that rehearsal to recover the mental states involved; that is, to tell whether Dr. Jekyll or Mr. Hyde is wielding the scalpel? Their answer is that we cannot (Jacob & Jeannerod, 2005).

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There is no undisputed signature for either TOM or imitation/ simulation. Even putatively clear candidates for the neural substrate of TOM such as the TPJ have been disputed by theorists arguing that the role played by the TPJ is a component process such as attention switching or expectationmatching, which is consistent with a role in simulation. Attempts to determine experimentally whether mind reading depends on pure TOM, pure simulation, or a hybrid in which inputs to the simulation or its outputs are characterized using TOM concepts tend to founder on the difficulty of operationalizing a TOM or simulation concept of belief for both first and third persons. Is there a clear behavioral and neural difference between belief that it is hot in Ecuador, reached by imagining being in Ecuador (simulation), and the same belief reached by inference from the fact that it is on the equator (theory-based inference)? Most of the experiments on perspective taking recruited by simulationists are, for good methodological reasons, unable to address this type of question. Similarly, can we operationalize differences between tacit, explicit, and dispositional (long-term memory) or occurrent (working memory and executive function) beliefs? Many beliefs seem to depend on a mixture of theoretical inference and imaginative simulation. To decide whether it is hot in Quito, I might infer that it is hot in Ecuador from background knowledge but then remember freezing at the high altitude of Quito. This is a real difficulty for TOM experiments that must probe possession of beliefs using nonverbal paradigms (Apperly, 2008). Recognizing this, some theorists in both camps hoped that neuroscience might distinguish theoretically based inference from simulation by finding a neural signature for each type of process. While there is no overlap between mirror circuits in the motor cortex and circuits activated in false belief tasks, we are a long way from determining whether attribution of mental states is essentially a matter of TOM or imitation-based simulation. False belief experiments, still the gold standard for mind reading, present the subjects with vignettes, stories, and psychodramas and ask them to predict or interpret the target’s actions. We report the results in folk psychological vocabulary. If Sally predicts that James will say an animal lives in the zoo with the tigers, we might report Sally’s prediction using the sentence, “Sally believes that James believes the cat is a tiger.” The deep issue here is whether the neurocomputational processes performed by neural circuits recruited in that type of prediction have the conceptual structure and logical syntax of the sentence that reports it. Oztop et al. (2006) complained with justification that theories about the role of mirror neurons in imitation were unsupported by neurally

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constrained computational models of the development and deployment of imitation. The same complaint could be made about the inference to the computational nature of the systems involved in false belief tests (Addis et al., 2007). Ian Apperly has made the same point about simulation and TOM in general, “[O]n the basis of the current literature it seems possible that these theories will become redundant as new findings about TOM motivate the development of new models based on well characterized cognitive and neural processes” (Apperly, 2008, p282). REFERENCES Abraham, A., Werning., M., Rakoczy, H., von Cramon, Y., & Schubotz, R. I., (2008). Minds, persons, and space: An fMRI investigation into the relational complexity of higher-order intentionality. Consciousness and Cognition, 17, 438–450. Addis, D. R., Wong, A. T., & Schacter, D. L. (2007). Remembering the past and imagining the future: Common and distinct neural substrates during event construction and elaboration. Neuropsychologia, 45, 1363–1377. Apperly, I. A. (2008). Beyond simulation-theory and theory-theory: Why social cognitive neuroscience should use its own concepts to study “theory of mind.” Cognition, 107, 266–283. Apperly, I. A., Samson, D., Chiavarino, C., Bickerton, W. L., & Humphreys, G. W. (2007). Testing the domain-specificity of a theory of mind deficit in brain-injured patients: Evidence for consistent performance on non-verbal, “reality-unknown” false belief and false photograph tasks. Cognition, 103, 300–321. Apperly, I. A., Samson, D., Chiavarino, C., & Humphreys, G. W. (2004). Frontal and temporo-parietal lobe contributions to theory of mind: Neuropsychological evidence from a false-belief task with reduced language and executive demands. Journal of Cognitive Neuroscience, 16, 1773–1784. Astington, J. W. (1993). The child’s discovery of the mind. Cambridge, MA: Harvard University Press. Baron-Cohen, S. (1995). Mindblindness: An essay on autism and theory of mind. Cambridge, MA: MIT Press. Baron-Cohen, S., Leslie, A., & Frith, U. (1985). Does the autistic child have a theory of mind. Cognition, 21, 37–46. Bird, C. M., Castelli, F., Malik, O., Frith, U., & Husain, M. (2004). The impact of extensive medial frontal lobe damage on “theory of mind” and cognition. Brain, 127, 914–928. Blakemore, S. J., & Decety, J. (2001). From the perception of action to the understanding of intention. Nature Reviews: Neuroscience, 2, 561–567. Blakemore, S. J., Oakley, D. A., & Frith, C. D. (2003). Delusions of alien control in the normal brain. Neuropsychologia, 41,1058–1067. Brunet, E., Sarfati, Y., Hardy-Bayle, M., & Decety, J. (2000). A PET investigation of the attribution of intentions with a nonverbal task. NeuroImage, 11, 157–166. Buttelmann, D., Carpenter, M., Call, J., & Tomasello, M. (2007). Enculturated chimpanzees imitate rationally. Developmental Science, 10(4), F31–F38. Byrne, R. W. (2005). Social cognition: Imitation, imitation, imitation. Current Biology, 15, R498–R500. Calarge, C., Andreasen, N. C., & O’Leary, D. S. (2003). Visualizing how one brain understands another: A PET study of theory of mind. American Journal of Psychiatry, 160, 1954–1964. Calder, A. J., Lawrence, A. D., Keane, J., Scott, S. K., Owen, A. M., Christoffels, I., & Young, A. W. (2002). Reading the mind from eye gaze. Neuropsychologia, 40, 1129–1138.

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Campbell, R., Heywood, C. A., Cowey, A., Regard, M., & Landis, T. (1990). Sensitivity to eye gaze in prosopagnosic patients and monkeys with superior temporal sulcus ablation. Neuropsychologia, 28, 1123–1142. Carlson, S. M., & Moses, L. J. (2001). Individual differences in inhibitory control and children’s theory of mind. Child Development, 72, 1032–1053. Carpenter, M., Call, J., & Tomasello, M. (2005). Twelve- and 18-month-olds copy actions in terms of goals. Developmental Science, 8(1), F13–F20. Chaminade, T., Meltzoff, A. N., & Decety, J. (2002). Does the end justify the means? A PET exploration of the mechanisms involved in human imitation. Neuroimage, 15, 318–328. Chaminade, T., Oztop, E., Cheng, G., & Kawato, M. (2008). From selfobservation to imitation: Visuomotor association on a robotic hand. Brain Research Bulletin, 75, 775–784. Charman, T., S. Baron-Cohen, et al. (2000). Testing joint attention, imitation, and play as infancy precursors to language and theory of mind. Cognitive Development 15(4), 481–498. Crichton, M. T. and C. Lange-Kuttner (1999). Animacy and propulsion in infancy: Tracking, waving and reaching to self-propelled and induced moving objects. Developmental Science, 2(3), 318–324. Csibra, G., Biro, S., Koos, O., & Gergely, G. (2003). One-year-old infants use teleological representations of actions productively. Cognitive Science, 27, 111–133. Currie, G., & Ravenscroft, I. (2002). Recreative minds: Imagination in philosophy and psychology. New York: Oxford University Press. Davies, M., & Stone, T. (1995). Mental simulation. Cambridge, MA: Blackwell. Decety, J., Chaminade, T., Grezes, J., & Meltzoff, A. N. (2002). A PET exploration of the neural mechanisms involved in reciprocal imitation. NeuroImage, 15, 265–272. Decety, J., Grezes, J., Costes, N., Perani, D., Jeannerod, M., Procyk, E., et al. (1997). Brain activity during observation of actions: Influence of action content and subject’s strategy. Brain, 120, 1763. Decety, J., & Lamm, C. (2006). Human empathy through the lens of social neuroscience. Scientific World Journal, 6, 1146–1163. Decety, J., & Lamm, C. (2007). The role of the right temporoparietal junction in social interaction: How low-level computational processes contribute to meta-cognition. Neuroscientist, 13, 580–593. Delgado, M. R., Frank, R. H., & Phelps, E. A. (2005). Perceptions of moral character modulate the neural systems of reward during the trust game. Nature Neuroscience, 8, 1611–1618. de Villiers, J. (2007). The interface of language and theory of mind. Lingua, 117, 1858–1878. Epley, N., Keysar, B., Van Boven, L., & Gilovich, T. (2004). Perspective taking as egocentric anchoring and adjustment. Journal of Personality and Social Psychology, 87, 327–339. Ferstl, E. C., & von Cramon, D. Y. (2002). What does the frontomedian cortex contribute to language processing: Coherence or theory of mind? NeuroImage, 17, 1599–1612. Fletcher, P. C., Happé, F., Frith, U., Baker, S. C., Dolan, R. J., Frackowiak, R. S., et al. (1995). Other minds in the brain: A functional imaging study of “theory of mind” in story comprehension. Cognition, 57, 109–128. Gallagher, H. L., Happé, F., Brunswick, N., Fletcher, P. C., Frith, U., & Frith, C. D. (2000). Reading the mind in cartoons and stories: An fMRI study of “theory of mind” in verbal and nonverbal tasks. Neuropsychologia, 38, 11–21. Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119, 593–609. Gallese, V., & Goldman, A. (1998). Mirror neurons and the simulation theory of mind-reading. Trends in Cognitive Sciences, 2, 493–501. Gallese, V., Keysers, C., & Rizzolatti, G. (2004). A unifying view of the basis of social cognition. Trends in Cognitive Sciences, 8, 396–403. German, T. P., Niehaus, J. L., Roarty, M. P., Giesbrecht, B., Miller, M. B. (2004). Neural correlates of detecting pretense: Automatic engagement

8/19/09 4:58:47 PM

References 921 of the intentional stance under covert conditions. Journal of Cognitive Neuroscience, 16, 1805–1817. Gerrans, P. (1998). The norms of cognitive development. Mind and Language, 13, 56–75. Gerrans, P. (2002). The theory of mind module in evolutionary psychology. Biology and Philosophy, 17, 305–321. Gerrans, P. & Stone V. (2008). Generous or Parsimonious Cognitive Architecture. Cognitive Neuroscience and Theory of Mind. The British Journal for the Philosophy of Science, 59(2), 121–141.

Jellema, T., Baker, C. I., Wicker, B., & Perrett, D. I. (2000). Neural representation for the perception of the intentionality of actions. Brain and Cognition, 44(2), 280–302. Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases: New York: Cambridge University Press.

Gilovich, T. (1993). How we know what isn’t so: The fallibility of human reason in everyday life. New York: Free Press.

Keisuke Wakusawa, K., Sugiura, M., Sassa, Y., Jeong, H., Horie, K., Sato, S., Yokoyama, H., Tsuchiya, S., Inuma, K., & Kawashima, R., (2007). Comprehension of implicit meanings in social situations involving irony: A functional MRI study. NeuroImage 37, 1417–1426

Gobbini, M. I., Koralek, A. C., Bryan, R. E., Montgomery, K. J., & Haxby, J. V. (2007). Two takes on the social brain: A comparison of theory of mind tasks. Journal of Cognitive Neuroscience, 19, 1803–1814.

Kobayashi, C., Glover, G. H., & Temple, E. (2007). Children’s and adults’ neural bases of verbal and nonverbal “theory of mind.” Neuropsychologia, 45, 1522–1532.

Goel, V., Grafman, J., Sadato, N., & Hallett, M. (1995). Modeling other minds. NeuroReport, 6, 1741–1746.

Kobayashi, C., Gary, H., Glover, G. H., & Temple, E. (2007). Children’s and adults’ neural bases of verbal and nonverbal ‘theory of mind.’ Neuropsychologia, 45, 1522–1532.

Goldman, A. I. (2006). Simulating minds: The philosophy, psychology, and neuroscience of mindreading. New York: Oxford University Press. Goldman, A. I., & Sripada, C. S. (2005). Simulationist models of face-based emotion recognition. Cognition, 94, 193–213.

Kumashiro, M., Ishibashi, H., Uchiyama, Y., Itakura, S., Murata, A., & Iriki, A. (2003). Natural imitation induced by joint attention in Japanese monkeys. International Journal of Psychophysiology, 50(1/2), 81–99.

Gordon, R. M. (1986). Folk psychology as simulation. Mind and Language, 1, 158–171.

Leslie, A. and Thaiss, L. (1992). Domain specificity in conceptual development: evidence from autism. Cognition, 43, 225–251.

Grezes, J., Armony, J. L., Rowe, J., & Passingham, R. E. (2003). Activations related to “mirror” and “canonical” neurones in the human brain: An fMRI study. NeuroImage, 18, 928–937.

Leslie, A. M. (1994). Pretending and believing: Issues in the theory of ToMM. Cognition 50, 211–238.

Happé, F., Ehlers, S., Fletcher, P., Frith, U., Johansson, M., Gillberg, C., et al. (1996). “Theory of mind” in the brain: Evidence from a PET scan study of Asperger syndrome. NeuroReport, 8, 197–201. Happé, F., H. Brownell, et al. (1999). Acquired ’theory of mind’ impairments following stroke. Cognition, 70, 211–240. Hare, B., Call, J., Agnetta, B., & Tomasello, M. (2000). Chimpanzees know what conspecifics do and do not see. Animal Behaviour, 59, 771–785. Harris, L. R., Todorov, T., & Fiske, S. T. (2005). Attributions on the brain: Neuro-imaging dispositional inferences, beyond theory of mind. NeuroImage, 28, 763–769 Hassabis, D., Kumaran, D., & Maguire, E. A. (2007). Using imagination to understand the neural basis of episodic memory. Journal of Neuroscience, 27, 14365–14374. Hassabis, D., & Maguire, E. A. (2007). Deconstructing episodic memory with construction. Trends in Cognitive Sciences, 11, 299–306. Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. (2000). The distributed human neural system for face perception. Trends in Cognitive Sciences, 4, 223–233. Hogrefe, G.-J., H. Wimmer, et al. (1986). Ignorance versus False Belief: A Developmental Lag in Attribution of Epistemic States. Child Development, 57, 567–82.

c46.indd 921

Jeannerod, M. (2006). Motor cognition: What actions tell the self. Oxford, New York: Oxford University Press.

Leslie, A. M. (1994). Pretending and believing: Issues in the theory of ToMM. Cognition, 50(1/3), 211–238. Lestou, V., Pollick, F. E., & Kourtzi, Z. (2008). Neural substrates for action understanding at different description levels in the human brain. Journal of Cognitive Neuroscience, 20, 324–341. Lissek, S., Peters, S., Fuchs, N., Witthaus, H., Nicolas, V., et al. (2008). Cooperation and deception recruit different subsets of the theory-ofmind network. PLoS ONE 3: e2023. doi:10.1371/journal.pone.000202 Malle, B. F. (2004). How the mind explains behavior: Folk explanations, meaning, and social interaction. Bradford Books. McCabe, K., Houser, D., Ryan, L., Smith, V., & Trouard, T. (2001). A functional imaging study of cooperation in two-person reciprocal exchange. Proceedings of the National Academy of Sciences, USA, 98, 11832–11835. Meltzoff, A. N., & Decety, J. (2003). What imitation tells us about social cognition: A rapprochement between developmental psychology and cognitive neuroscience. Philosophical Transactions of the Royal Society of London. Series B, 358, 491–500. Meltzoff, A. N., & Moore, M. K. (1983). Newborn infants imitate adult facial gestures. Child Development, 54, 702–709. Miall, R. C. (2003). Connecting mirror neurons and forward models. NeuroReport, 14, 2135–2137.

Heal, J. (1996). Simulation, theory, and content. In Peter Carruthers & Peter Smith ( Eds.), Theories of Theories of Mind, Cambridge, CUP. 75–89.

Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202.

Hurley, S., & Chater, N. (2005). Perspectives on imitation: From cognitive neuroscience to social science. New York: Cambridge University Press.

Mitchell, J. P. (2008). Activity in right temporo-parietal junction is not selective for theory-of-mind. Cereb Cortex, 18, 262–271.

Iacoboni, M., Molnar-Szakacs, I., Gallese, V., Buccino, G., Mazziotta, J. C., & Rizzolatti, G. (2005). Grasping the intentions of others with one’s own mirror neuron system. PLoS Biology, 3(3), e79.

Moriguchi, Y., Ohnishi, T., Mori, T., Matsuda, H., & Komaki, G. (2007). Changes of brain activity in the neural substrates for theory of mind during childhood and adolescence. Psychiatry and Clinical Neuroscience, 61, 355–363.

Iacoboni, M., Woods, R. P., Brass, M., Bekkering, H., Mazziotta, J. C., & Rizzolatti, G. (1999, December 24). Cortical mechanisms of human imitation. Science, 286, 2526–2528.

Moses, L. J. (2001). Executive accounts of theory-of-mind development. Child Development, 72, 688–690.

Jackson, P. L., & Decety, J. (2004). Motor cognition: A new paradigm to study self-other interactions. Current Opinion in Neurobiology, 14, 259–263.

Myowa-Yamakoshi, M., & Matsuzawa, T. (2000). Imitation of intentional manipulatory actions in chimpanzees (Pan troglodytes). Journal of Comparative Psychology, 114, 381–391.

Jackson, P. L., Meltzoff, A. N., & Decety, J. (2006). Neural circuits involved in imitation and perspective-taking. NeuroImage, 31, 429–439.

Nichols, S., Stich, S., Leslie, A., & Klein, D. (1996). Varieties of off-line simulation. Theories of Theories of Mind, 24, 39–74.

Jacob, P., & Jeannerod, M. (2005). The motor theory of social cognition: A critique. Trends in Cognitive Sciences, 9, 21–25.

Nieminen-von Wendt, T., Metsahonkala, L., Kulomaki, T., Aalto, S., Autti, T., Vanhala, R., et al. (2003). Changes in cerebral blood flow in Asperger

8/19/09 4:58:48 PM

922

Imitation and Theory of Mind

syndrome during theory of mind tasks presented by the auditory route. European Child and Adolescent Psychiatry, 12(4), 178–189. Oztop, E., Kawato, M., & Arbib, M. (2006). Mirror neurons and imitation: A computationally guided review. International Neural Network Society, 19, 254–271. Pacherie, E. (2006). Towards a dynamic theory of intentions: Does consciousness cause behavior? 145–167. Perner, J. (1991). The representational theory of mind. Cambridge, MA: MIT Press. Perner, J., & Lang, B. (1999). Development of theory of mind and executive control. Trends in Cognitive Sciences, 3, 337–344. Perrett, D. I., Hietanen, J. K., Oram, M. W., Benson, P. J., & Rolls, E. T. (1992). Organization and functions of cells responsive to faces in the temporal cortex [and discussion]. Philosophical Transactions of the Royal Society of London, Series B, 335, 23–30. Povinelli, D. J., & Vonk, J. (2003). Chimpanzee minds: Suspiciously human? Trends in Cognitive Sciences, 7, 157–160. Pronin, E. (2002). Understanding mindreading: Social psychological perspectives: Heuristics and biases. Psychology of Intuitive Judgment, 636–665. Pronin, E., Gilovich, T., & Ross, L. (2004). Objectivity in the eye of the beholder: Divergent perceptions of bias in self versus others. Psychological Review, 111, 781–799. Rilling, J. K., Sanfey, A. G., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2004). Opposing bold responses to reciprocated and unreciprocated altruism in putative reward pathways. NeuroReport, 15, 2539–2543. Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Reviews of Neuroscience, 27, 169–192. Rizzolatti, G., Ferrari, P. F., Rozzi, S., & Fogassi, L. (2006). The inferior parietal lobule: Where action becomes perception. Novartis Foundation Symposium, 270, 129–140; discussion 140–125, 164–129. Ross, L. D., Amabile, T. M., & Steinmetz, J. L. (2005). Social roles, social control, and biases in social-perception processes. In David Hamilton (Ed.), Social Cognition: Key Readings. Psycholgy Press. Hove. Ruby, P., & Decety, J. (2004). How would you feel versus how do you think she would feel? A neuroimaging study of perspective-taking with social emotions. Journal of Cognitive Neuroscience, 16, 988–999. Ruffman, T. E. D. (1996). Do children understand the mind by means of simulation or a theory? Evidence from their understanding of inference. Mind and Language, 11, 388–414. Russell, T. A., Rubia, K., Bullmore, E. T., Soni, W., Suckling, J., Brammer, M. J., Simmons, A., Williams, S. C. R., & Sharma, T. Exploring the social brain in schizophrenia: Left prefrontal underactivation during mental state attribution. American Journal of Psychiatry, 157, 2040 –2042. Saxe, R., & Kanwisher, N. (2003). People thinking about thinking people: The role of the temporo-parietal junction in theory of mind. NeuroImage, 9, 1835–1842. Saxe, R., & Powell, L. (2006). It’s the thought that counts: Specific brain regions for one component of Theory of Mind. Psychological Science, 17, 692–699. Saxe, R. (2005). Against simulation: The argument from error. Trends in Cognitive Sciences, 9, 174–179. Saxe, R. (2006). Why and how to study theory of mind with fMRI. Brain Research, Saxe, R., Carey, S., & Kanwisher, N. (2004). Understanding other minds: Linking developmental psychology and functional neuroimaging. Annual Reviews of Psychology, 55, 87–124. Saxe, R., & Wexler, A. (2005). Making sense of another mind: The role of the right temporo-parietal junction. Neuropsychologia, 43, 1391–1399. Schultz, J., Friston, K. J. et al. (2005). Activation in posterior superior temporal sulcus parallels parameter inducing the percept of animacy. Neuron, 45, 625–635. Sommer, M., Dohnel, K., Sodian, B., Meinhardt, J., Thoermer, C., & Hajak, G. (2007). Neural correlates of true and false belief reasoning. Neuroimage, 35, 1378–1384.

c46.indd 922

Sommerville, J. A., & Decety, J. (2006). Weaving the fabric of social interaction: Articulating developmental psychology and cognitive neuroscience in the domain of motor cognition. Psychonomic Bulletin and Review, 13, 179–200. Stone, V. E., & Gerrans, P. (2006a). Does the normal brain have a theory of mind? Trends in Cognitive Sciences, 10, 3–4. Stone, V. E., & Gerrans, P. (2006b). What’s domain-specific about theory of mind? Social Neuroscience, 1, 309–319. Stone, V., Cosmides, L., Tooby, J., Kroll, N., & Knight, R (2002). Selective impairment of reasoning about social exchange in a patient with bilateral limbic system damage. Proceedings of the National Academy of Sciences, 99, 11531–11536. Suddendorf, T., & Busby, J. (2005). Making decisions with the future in mind: Developmental and comparative identification of mental time travel. Learning and Motivation, 36, 110–125. Suddendorf, T., & Corballis, M. C. (2007). The evolution of foresight: What is mental time travel, and is it unique to humans? Behavioral and Brain Sciences, 30, 299–313. Tomasello, M., Call, J., & Hare, B. (2003). Chimpanzees understand psychological states: The question is which ones and to what extent. Trends in Cognitive Sciences, 7, 153–156. Uddin, L. Q., Molnar-Szakacs, I., Zaidel, E., & Iacoboni, M. (2006). RTMS to the right inferior parietal lobule disrupts self-other discrimination. Social Cognitive and Affective Neuroscience, 1, 65–71. Umilta, M. A., Kohler, E., Gallese, V., Fogassi, L., Fadiga, L., Keysers, C., et al. (2001). I know what you are doing: A neurophysiological study. Neuron, 31, 155–165. Vogeley, K., Bussfeld, P., Newen, A., Herrmann, S., Happé, F., Falkai, P., et al. (2001). Mind reading: Neural mechanisms of theory of mind and self-perspective. Neuroimage, 14, 170–181. Vogt, S., Buccino, G., Wohlschlager, A. M., Canessa, N., Shah, N. J., Zilles, K., et al. (2007). Prefrontal involvement in imitation learning of hand actions: Effects of practice and expertise. NeuroImage, 37, 1371–1383. Vosgerau, G., & Newen, A. (2007). Thoughts, motor actions, and the self. Mind and Language, 22, 22–43. Walter, H., Adenzato, M., Ciaramidaro, A., Enrici, I., Pia, L., Bara, B. G. (2004). Understanding intentions in social interaction: The role of the anterior paracingulate cortex. Journal of Cognitive Neuroscience, 16, 1854–1863. Wellman, H. M., & Woolley, J. D. (1990). From simple desires to ordinary beliefs: The early development of everyday psychology. Cognition, 35, 245–275. Wellman, H. L. (2000). Developing Understandings of Mind. Understanding Other Minds. H. T. F. D. C. S Baron-Cohen. Oxford, Oxford University Press. Wicker, B., Keysers, C., Plailly, J., Royet, J. P., Gallese, V., & Rizzolatti, G. (2003). Both of us disgusted in my insula: The common neural basis of seeing and feeling disgust. Neuron, 40, 655–664. Wolpert, D. M., Doya, K., & Kawato, M. (2003). A unifying computational framework for motor control and social interaction. Philosophical Transactions of the Royal Society of London. Series B, 358, 593–602. Wolpert, D. M., & Kawato, M. (1998). Multiple paired forward and inverse models for motor control. International Neural Network Society, 11, 1317–1329. Woodward, A. L. (2003). Infants’developing understanding of the link between looker and object. Developmental Science, 6(3), 297–311. Zaitchik, D. (1990). When representations conflict with reality: The preschooler ’s problem with false beliefs and “false” photographs. Cognition, 35, 41–68. Ziv, M., & Frye, D. (2003). The relation between desire and false belief in children’s theory of mind: No satisfaction? Developmental Psychology, 39, 859–876.

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Chapter 47

Social Cognition RALPH ADOLPHS AND MICHAEL SPEZIO

(It is noteworthy that attention and consciousness are also separable; Koch & Tsuchiya, 2006.) Examples perhaps provide the best way to characterize social cognition. When we recognize someone from their face, voice, or gait; when we think about how we feel about someone; when we empathize with them; when we ponder how we can outsmart them: These are aspects of social cognition. Some aspects are rapid, some slow, some involve conscious access, and some do not. This variety may seem bewildering, but it can be placed in an overall processing hierarchy. Roughly, early perception feeds into identification and recognition, which feed into memory and decision making. Modulating these levels of processing are arousal, attention, and emotion. This level at which we identify social cognition does not preclude specializations at other levels that also show differentiation among social and nonsocial stimuli. There are receptors in the olfactory epithelium and in the vomeronasal organ already specialized for smelling certain socially informative conspecific molecules, and indeed there are highly specific routes of social information transmission whereby a specific molecule (called a pheromone) can bind to a specific receptor and elicit a specific social behavior (e.g., aggressive behavior; Chamero et al., 2007). Similarly, the auditory system of many mammals shows some specialization for representing sound frequencies that are the most important in social communication already at the level of the cochlea (e.g., in humans, we are most sensitive to, and our auditory system overrepresents, frequencies around 4 kHz). Nonetheless, while these peripheral specializations certainly contribute to the bandwidth of stimuli to which an organism is sensitive and can emphasize the processing of socially relevant stimuli, they are not generally thought of as cognitive, because they do not involve the flexible, inferential central processing that is the hallmark of cognition. In addition to being central in this sense of being post early perception and prior to action, social cognition in particular also offers several distinctive features—these all these come into play for cognition more generally, but arguably more so for social cognition.

WHAT IS SOCIAL COGNITION? As the name says, social cognition pertains to the cognitive processing of socially relevant information. The meaning of the term social is clear enough, although there is debate about its boundaries and properties: It is that domain relating to other people. Social cognition may come into play even when the stimuli are not people as such, but are animals, computers, or nonsocial stimuli about which we anthropomorphize (Heberlein & Adolphs, 2004). Nonetheless, processing under these circumstances would be “social” in the sense that it is derivatively social. There is yet another wrinkle: There is some evidence that the brain’s default mode of processing stimuli may be to treat stimuli as social. Many of the brain structures implicated in social processing, such as the medial prefrontal cortex, are activated at rest, remain activated when social stimuli are processed, and become deactivated when nonsocial stimuli are processed (Mitchell, Heatherton, & Macrae, 2002; Raichle et al., 2001). Thus, at least one contribution to the observed differences in regional brain activation between social and nonsocial stimuli appears to arise from an evoked deactivation to the nonsocial stimuli. The meaning of the term cognition is more problematic. A standard view of cognition is that it is the level of processing that is inferential, going beyond the simple transformation of stimulus-response processing that would be seen in reflexes, and involving the kind of creative modeling of the world that is typical of human thought. Under this interpretation, emotional appraisal is cognitive. It is important not to equate cognition with consciousness—although there are interesting relationships between the two, a large research effort is in fact directed at elucidating the extent to which social cognition can occur without conscious awareness of the stimuli, the processing, or even the behavioral consequences of such processing (Tsuchiya & Adolphs, 2007). It may be more relevant to associate cognition with attention, and certainly with “top-down” attentional effects that are not driven solely by the saliency of the stimuli per se. 923

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A feature that has received considerable attention because of its relevance to psychopathology is self-regulation: our ability (in healthy adults, and with notable individual differences) to reinterpret and control social information processing, at least to some degree (Ochsner, Bunge, Gross, & Gabrieli, 2002; Ochsner & Gross, 2005). Many of the cultural norms for appropriate social behavior that are internalized over a lifetime depend on successful self-regulation for their implementation (cf. Chapter 51, this volume), and this aspect shows pronounced changes during development and into old age (cf. the sections on Development and Aging, Part VIII) as does the associated brain region thought to be most important for these processes: the prefrontal cortex. Social cognition is thus in part distinguished from other aspects of cognition by its high level of cultural internalization and the rich interplay between automatic and effortful processing, exemplified by emotion regulation in social contexts. Another important feature of social cognition is its reliance on long-term future planning, counterfactual, and strategic thinking. To anticipate other people’s behavior, to prepare our own, and to navigate a complex and interactive social environment, we need to imagine what others are up to, and how they will react to what we do. This aspect has also received a lot of interest, with some speculation that

Coarse Perceptual Processing Superior Colliculus

Motivational Evaluation Amygdala Orbitofrontal Cortex Ventral Striatum

episodic memory, thinking about the future, and the ability to imagine what goes on in other people’s minds might all have neural substrates in common (Buckner & Carroll, 2006; D. T. Gilbert & Wilson, 2007). Intriguingly, some of these same neural substrates are the ones mentioned at the outset of this chapter, such as the medial prefrontal cortex, that appear to correspond to a default mode of processing. Perhaps the best quick definition of social cognition, then, is “thinking about people” (D. T. Gilbert, 1998), with the understanding that this aspect of thinking features an especially prominent component of control and regulation, and that it requires especially rich inferences to go from the behavior that we observe in others to the internal states that we attribute to them. Some of the component processes, and a preview of the neural structures that we discuss in more detail in the next section, are given in Figures 47.1 and 47.2. The preceding short introduction to the domain of social cognition also points toward aspects that make its investigation difficult. It is complex; it shows large individual differences; it shows large context effects; and for all these reasons, it is difficult to elicit validly and to study quantitatively in the laboratory. None of these hurdles, however, has stopped neurobiologists from investigating the topic, or social psychologists from using neuroscience tools. The

Detailed Perceptual Processing Fusiform Gyrus Superior Temporal Gyrus

Representation of Perceived Action Left Frontal Operculum Superior Temporal Gyrus

Reappraisal

Self-regulation Emotional Response In Body Visceral, Autonomic, Endocrine Changes

Representation of Emotional Response Somatosensory-related Cortices

Figure 47.1 Processes that participate in social cognition. Note: Some of the processes involved are indicated in the boxes, together with some of the structures thought to participate in their implementation. The schematic is divided into those processes more aligned with emotional reactions, on the left, and those more aligned with aspects other than emotion, on the right. Self-regulation and reappraisal operate to

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Modulation of Cognition (Memory, Attention) Cingulate Cortex Hippocampus Basal Forebrain

Social Reasoning Prefrontal Cortex

control processing in each of these streams, respectively. The set of processes inside the larger L-shaped box are perhaps the best candidates for contributing directly to conscious experience. From “Is the Human Amygdala Specialized for Social Cognition” (pp. 326–340), by R. Adolphs, in The Amygdala in Brain Function, volume 985, S.-G. et al. (Eds.), 2003c, New York: Annals of the New York Academy of Sciences. Adapted with permission.

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Left Hemisphere (Dorsal Aspect)

Right Hemisphere (Dorsal Aspect)

(Dissected to Reveal Insula)

Ventral Aspect

Right Hemisphere (Medial Aspect)

Coronal Cut

Figure 47.2 Neuroanatomy of social cognition. Note: Shown on various images of brains are subsets of the structures involved in social cognition that we discuss in this chapter. (A) This right lateral view of a brain shows somatosensory cortices and superior temporal gyrus regions; roughly between them and posterior would be the TPJ, which is not shaded to preserve clarify of the figure. (B) Left prefrontal regions are also involved in making personality attributions to others, and indicated again here is the superior temporal gyrus, involved in processes

use of neuroscience methods to investigate social cognition has rapidly become a hot topic, in good part due to the enthusiasm with which many social psychologists have made use of fMRI, ERPs and other tools. The field of social neuroscience (Cacioppo et al., 2001) and, within it, social cognitive neuroscience (Ochsner & Lieberman, 2001), has fostered numerous books and meetings, and is now the target of some focused graduate programs. The tools and approaches needed to investigate social cognition remain a topic of lively debate (Adolphs, 2003b), but as this volume demonstrates, the wisest strategy is to know something about behavioral social science, and to know something about cognitive neuroscience: Put the two together (often collaboratively), and you’re doing social neuroscience. The hope of the field is that the complexity of the processes under investigation will yield best to a multipronged attack from multiple disciplines, and that the neuroscience data will help us constrain, and ultimately understand, the process models that compose social cognition. Needless to say, we cannot review all social cognition in a chapter, and we also wish to avoid redundancy with

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such as biological motion. (C) This picture of the insula is revealed when the frontal operculum is removed. (D) A ventral view of the brain shows the medial prefrontal cortex (in this ventral view, medial OFC) and, more posteriorly, the fusiform gyrus, involved in face processing. (E) A medial view of the right hemisphere shows the anterior cingulate and again the medial PFC. (F) A coronal section along the line indicated reveals the amygdala in the medial temporal lobe.

some of the contributions in this volume that overlap considerably with social cognition. We highlight some examples where the cognitive neuroscience of a higher social process is relatively well understood, while giving the reader a sense for the diversity of approaches and topics out there. This includes our own work on the amygdala, and some suggestions for future directions that have been underemphasized.

NEUROANATOMY OF SOCIAL COGNITION Given the multiple aspects of social cognition at the process level, it should come as no surprise that it draws on a large array of brain structures, some of which are shown in Figure 47.2. In this section, we provide a brief overview of the neuroanatomy of social cognition, and we give examples of recent studies that have explored specific structures or social processes. Brain regions demonstrating a differential sensitivity for social cognition, as identified by the lesion method

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and by neuroimaging, include the amygdala, the cingulate gyrus (CingG), the fusiform gyrus, the insula (Ins), the orbitofrontal cortex (OFC), the somatosensory cortex (SSC), the superior temporal sulcus (STS), the supramarginal gyrus (SMG), the temporo-parietal junction (TPJ), and the ventromedial prefrontal cortex (vmPFC), among many others (Adolphs, 2003a; also see Figure 47.2). The FG is reviewed in Chapter 43 (Kanwisher and Yovel), this volume, and we provide a detailed discussion of the amygdala in the following section; here we review the other structures. The CingG is a large structure that is typically subdivided into sectors from anterior to posterior. Posterior cingulate gyrus and adjacent retrosplenial cortex constitute a complex array of several cytoarchitectonic regions that are implicated in self-referential processing and autobiographical memory, as well as spatial cognition (Maddock, 1999); the region is known to participate in a widely distributed anatomical network for processing emotional and selfrelated information (Parvizi, Van Hoesen, Buckwalter, & Damasio, 2006). Ochsner et al. (2004) found that the posterior cingulate was differentially activated when making judgments about oneself compared with making judgments of others. Saxe and Powell (2006) sought to test the hypothesis that thinking about another agent’s thoughts drives this activation. They found that PCC was differentially activated when participants actively considered the thoughts of the protagonist in a story (what another person was believing and thinking about), compared with when they processed other information relating to the subjective states or other social information about a person (such as their appearance or whether they felt hungry or sick). While the posterior cingulate cortex has become a topic of interest recently, it is fair to say that it remains fairly poorly understood. Considerably more is known about the functions of the anterior subdivisions of the CingG, which has been associated with cognitive conflict monitoring and anticipation of cognitive conflict (Botvinick, Cohen, & Carter, 2004; Sohn, Albert, Jung, Carter, & Anderson, 2007). The fMRI actions within it have been associated also with ERP responses that can be measured at the scalp. In addition, there are lesion studies that more definitively point toward its possible functions. Macaque monkeys with bilateral lesions to the anterior cingulate cortex (ACC) exhibit deficits in social behaviors, such as fewer social interactions, reduced time near conspecifics, and fewer vocalizations (Hadland, Rushworth, Gaffan, & Passingham, 2003). At the same time, the lesioned monkeys showed increases in manipulating inanimate objects, so the social deficits were not the result of an overall decrease in activity. ACC, particularly ventral ACC, has been associated with affective/ emotional processing (Devinsky, Morrell, & Vogt, 1995)

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and shows differential activation for empathy, social intuition (Vollm et al., 2006), and cooperation (Rilling et al., 2002). From monkey lesion studies, there is evidence that the ACC contributes more to impairments in social behavior than does the orbitofrontal cortex (Rudebeck, Buckley, Walton, & Rushworth, 2006), a nearby region that is often damaged together with the ACC in humans who have lesions of the prefrontal cortex. The ACC has long been known to be involved in pain perception (its white matter connections are a target of neurosurgery for treating intractable pain; neurosurgical recordings in humans have documented single neurons that response to pain; Hutchison, Davis, Lozano, Tasker, & Dostrovsky, 1999) and motivated behavior more generally (Vogt, 2005). A clinical outcome of acute bilateral lesions to the anterior cingulate cortex is a phenomenon called akinetic mutism—a complete lack of willed, volitional behavior. Such patients can perceive stimuli in their environment, and they are not paralyzed, but they lack any motivation to behave. All these findings taken together argue that the involvement of the ACC in social cognition is related to the strong motivation to behave and the need to monitor conflict, both of which may feature more prominently, on average, in social behavior than in other aspects of behavior. The SSC and SMG have both been demonstrated to be associated with decisions about the emotion in a face. Adolphs, Damasio, Tranel, Cooper, and Damasio (2000) demonstrated that lesions to the right SSC and the right SMG severely impaired performance on a task in which participants judged emotion from facial expression in static images. Keysers et al. (2004) showed that “tactile empathy,” in which someone reports feeling what they see happen to another person, associates with differential activation in secondary, but not primary, SSC. These findings support the notion that social cognition about others draws upon emotional information in the form of body-state representations, and is related to the by now very large literature on simulation and mirror neurons. While this literature has emphasized the motor aspects of mirroring other people, the involvement of the SSC argues also for sensory components of this mechanism. The TPJ is a structure most frequently associated with thought about another ’s state of mind (Saxe & Kanwisher, 2003; Saxe & Wexler, 2005; but see Mitchell, 2007). Saxe and Wexler (2005) found that the right TPJ was more differentially selective for the attribution of mental states than even other areas known to be associated with representing another ’s mind, such as the PCC and the mPFC. There is an active debate about how to interpret the activations in the TPJ that have been observed—whether they are specific to social processing or reflect a more general, but correlated process. On the one side, Rebecca Saxe (Saxe & Powell,

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2006) has argued that the TPJ is activated relatively specifically when we attribute beliefs to others; on the other side, Jason Mitchell (Mitchell, 2007) has argued that its activation reflects more general (not specifically social) attentional orienting—a process that certainly also comes into play when we attribute beliefs to others, but that is by no means unique to it. The debate is a good example of an attempt to find a socially specific function for a brain region (Saxe, 2006), similar in spirit to the much longer standing debates about whether ventral regions of the temporal lobe are specialized for processing faces (Kanwisher, 2000; Tarr & Gauthier, 2000). Throughout the literature, there is a close association between social cognition and emotion. Those brain regions that most consistently show an association between emotional experience and social processing are the ventromedial prefrontal cortex, the right insula, and right somatosensory cortices, and the amygdala. Bar-On, Tranel, Denburg, and Bechara (2003) tested 6 subjects with bilateral focal lesions of anterior and posterior vmPFC, 3 subjects with unilateral lesions of the right Ins and SSC, and 3 subjects with unilateral lesions of the Amy on emotional intelligence (Bar-On, 1997) and social functioning (Tranel, Bechara, & Denburg, 2002). They compared performance of these groups with a group of control subjects who had lesions that did not involve the vmPFC, the right Ins and SSC, or the Amy. The study found no differences between any of the experimental groups and control group on full IQ, executive function, perception, or memory, nor were there any indications of psychopathology. But each experimental group was significantly impaired on emotional intelligence compared with the control group. Combining all three experimental groups yielded significant deficits in social functioning compared with controls. The vmPFC was one of the first brain regions to catch the attention of neuroscientists with respect to regulating social behavior, in large part because of several highly influential lesion studies. The landmark case is that of Phineas Gage, a nineteenth-century railroad worker who had an iron rod blasted longitudinally through the front of his head in an explosives accident (A. R. Damasio, 1994; H. Damasio, Grabowski, Frank, Galaburda, & Damasio, 1994). Not only did Gage survive, but the only notable enduring effect of Gage’s head injury was a change in personality. Gage changed from shrewd, persistent, and respectable to profane, capricious, and unreliable (although the historical details of this account have been the topic of some debate; MacMillan, 2000). The association of impairments in social behavior with ventromedial prefrontal cortex (VMPC) damage has since been investigated in much greater detail. Perhaps the most illustrative modern example of this phenomenon is patient EVR (A. R. Damasio,

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Tranel, & Damasio, 1990). At age 35, EVR underwent resection of a bilateral orbitofrontal meningioma. Mesial orbital and lower mesial frontal cortices (collectively referred to as ventromedial prefrontal cortex; VMPC) were excised with the tumor. Following the surgery, EVR exhibited a remarkable decline in his personal and professional life, including two divorces, the loss of his job, and bankruptcy. Despite the gross alteration of his social conduct and decision making, neuropsychological testing indicates EVR’s intellectual abilities remained superior (Saver & Damasio, 1991). While striking, EVR is not an isolated case. Subsequent group studies of VMPC patients have identified typical personality changes associated with VMPC damage: blunted affect, poor frustration tolerance, impaired goal-directed behavior, inappropriate social conduct, and marked lack of insight into these changes (Barrash, Tranel, & Anderson, 2000). Further experimental work has demonstrated that VMPC damage impairs subjective and autonomic responses to emotionally charged pictures (e.g., mutilated bodies, nudes) and to emotional memories. Studies involving gambling games indicate that VMPC patients experience diminished emotional arousal before making risky choices (Bechara, Damasio, Damasio, & Anderson, 1994), as well as diminished regret when considering alternate outcomes after making risky choices (Camille et al., 2004). In such games, VMPC patients persistently make disadvantageous choices. These results suggest that emotional signals mediated by VMPC may be a critical influence on social conduct and decision making (Bechara, Damasio, & Damasio, 2000). Experimental tests that directly assess social knowledge provide further support for the role of VMPC in social cognition. VMPC patients have deficits in interpreting nonverbal social information such as facial expression, gestures, or body posture. VMPC patients typically have preserved declarative knowledge of basic social and moral norms, but detection of complex verbal social information, such as faux pas, sarcasm, and aspects of moral judgment, may be impaired (Koenigs et al., 2007; Saver & Damasio, 1991). Studies of moral cognition underscore the importance of VMPC in social decision making. Functional imaging experiments reveal that areas within VMPC are active during viewing of unpleasant moral pictures and judgment of moral statements (Moll, Zahn, de Oliveira-Souza, Krueger, & Grafman, 2005). When evaluating hypothetical moral dilemmas, patients with VMPC damage exhibit an abnormally utilitarian pattern of moral judgments, basing their judgments more on the consideration of outcomes than on the moral permissibility of the means to obtain the outcomes (Koenigs et al., 2007). Shamay-Tsoory, Tomer, Berger, and Aharon-Peretz (2003) tested 12 subjects with focal lesions to the vmPFC

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on empathy and the recognition of social faux pas. They found that these subjects, as a group, provided significantly lower empathy scores and were significantly more impaired at recognizing social faux pas than age-matched (but not IQ-matched) controls and people with unilateral lesions to the posterior cortex of the brain. To test if this was due primarily to impairments in emotional processing, ShamayTsoory and coworkers (Shamay-Tsoory, Tomer, Berger, Goldsher, & Aharon-Peretz, 2005) conducted another study in which they used a second-order false belief task, an ironic utterance task, and a social faux pas task, and compared the performance of participants with lesions localized to the vmPFC with that of participants with lesions localized to the dorsolateral PFC or to the posterior cortex. In line with their hypothesis that vmPFC contributes to social cognition primarily via affective/emotional processing, vmPFC lesions did not impair second-order belief formation, but severely impaired performance in detection of irony and social faux pas, compared with the controls. While the majority of studies have focused on, and the largest effects have been found, for patients who have bilateral damage to the vmPFC, unilateral damage also causes the pattern of impairments previously described, only more mildly. There appears to be an interesting asymmetry in that unilateral right-sided lesions seem to cause a more severe impairment than do unilateral left-sided lesions. A further wrinkle on this story is that unilateral right lesions are more severe than left in males, whereas unilateral left lesions may be more severe than right in females (Tranel, Damasio, Denburg, & Bechara, 2005). Patients with early onset damage involving VMPC are a unique resource for investigating the development of social cognition. Like patients with adult-onset damage, individuals acquiring VMPC damage in infancy or early childhood manifest defects in social conduct and decision making despite intact language, memory, and IQ. However, the social defects following early onset VMPC damage appear more severe than in the adult-onset cases. Common features include apathy and unconcern; lack of guilt, empathy, or remorse; violent outbursts; lewd and irresponsible behavior; petty criminal behavior; and lack of awareness of behavioral problems (S. W. Anderson, Damasio, Tranel, & Damasio, 2000). Unlike adult-onset cases, early onset VMPC patients may have impaired knowledge of social/ moral conventions (S. W. Anderson, Bechara, Damasio, Tranel, & Damasio, 1999). These results indicate that VMPC is critically involved in the acquisition of social and moral knowledge during development. Adult-onset VMPC patients, who presumably undergo normal social development, retain declarative access to social/moral facts, but they appear to lose access to emotional signals that are necessary to guide appropriate social and decision-making

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behavior in real-life situations. Early-onset VMPC patients seem to have never acquired appropriate levels of factual social knowledge, nor do they have access to normal “on-line” emotional processing, resulting in an even greater level of social impairment. As the review and these brief examples illustrate, some regions of the brain are certainly more important for social cognition than others, and there is good evidence to suggest that there is a network, or networks, for social cognition. Yet this does not mean that the structure or the network in question is limited in its function to social cognition, nor even that it is disproportionately involved in social cognition. Attempts to demonstrate such a disproportionate involvement have either all met with deconstructions that reduce higher-order and domain-specific social processes to simpler, domain-general processing, or else they are still under active debate. The difficulty is that it is impossible, merely from investigating a structure in one species at one point in time, to gain much insight into its function in a teleological sense. That requires information from development and from comparative studies; the former is well represented in this Handbook, and we provide next a brief review of the latter.

EVOLUTION OF SOCIAL COGNITION: THE SOCIAL BRAIN HYPOTHESIS The social brain hypothesis attempts to explain the extraordinary size and complexity of the human brain (Barrett & Henzi, 2005; Barrett, Henzi, & Dunbar, 2003; Bradbury, 2005; Byrne & Whiten, 1988; Dunbar & Shultz, 2007a; Roth & Dicke, 2005; Whiten & Byrne, 1997). The evolution of human brain size to its present 1.3 kg has been relatively stable within the past 100,000 to 150,000 years, with some evidence of a slight decline between 70,000 and 50,000 years ago (Ruff, Trinkaus, & Holliday, 1997). By contrast, the brain size of the great ape species closest in evolution to humans, such as chimpanzees (Pan troglodytes) and bonobos (Pan paniscus), is only 25% to 35% of this size (about the size of the brain our hominid ancestors would likely have had about 4 million years ago), while body size is comparable (~100 pounds; Bradbury, 2005; see Figure 47.3). Given the increased maternal investment required to produce offspring with large brains (Martin, 2007), and the increased metabolic costs of maintaining a large brain (Isler & van Schaik, 2006), the central puzzles of human brain evolution are—why so large and how could this possibly have taken place as recently as 100,000 years ago? Responses to these puzzles have included a focus on ethology, with attention to problem solving in a complex

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Evolution of Social Cognition: The Social Brain Hypothesis

Figure 47.3 Comparison of brains. Note: Top: The brain and skull of a modern chimpanzee. Bottom: A human brain. The chimp brain is only a quarter or so the size of the human brain.

environment, a focus on developmental costs, and a focus on social cognition, with attention to problem solving in a complex social environment (Dunbar & Shultz, 2007b). Byrne and Whiten (1988) were among the first to propose the so-called Machiavellian Intelligence hypothesis to argue in favor of complex social environments being the primary selective pressure for human brain size. They contended that individuals who could navigate the complexity required to maintain group cohesion in challenging environments would enjoy a strong evolutionary advantage over individuals who could not. Despite the title of their first edited volume, Byrne and Whiten included all aspects of social problem solving, both prosocial and deceitful, in their proposal. Subsequently, they and others renamed this approach to explaining human brain evolution the “social brain hypothesis” (Whiten & Byrne, 1997). One class of empirical tests for this hypothesis seeks to determine whether those regions where the human brain differs most in size from brains of apes correspond to regions important for social cognition in humans. Zilles and coworkers operationalized regional size similarity by the level of deformation a region requires when spatially transforming an ape brain into human brain space (Bradbury, 2005). They found that the regions requiring the largest spatial transformations were in ventromedial and orbitofrontal cortex, frontopolar cortex (e.g., BA 10), and regions for hypothalamic and neuroendocrine function (Bradbury, 2005). Though the frontal cortex as a whole is not differentially enlarged in humans compared with that of apes (Semendeferi & Damasio, 2000; Semendeferi,

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Damasio, Frank, & Van Hoesen, 1997), humans were shown to have greater complexity in orbitofrontal cortex rostral to BA 13, compared with other apes (Semendeferi, Armstrong, Schleicher, Zilles, & Van Hoesen, 1998). Additionally, BA 10 in humans was found to be enlarged and to show increased specialization for communication with other higher order association areas (Allman, Hakeem, & Watson, 2002; Semendeferi, Armstrong, Schleicher, Zilles, & Van Hoesen, 2001). Finally, evidence suggests that in humans, the insula is relatively larger than in apes, after overall brain size is controlled for (Semendeferi & Damasio, 2000). Besides being a brain region involved in social cognition, the anterior insula is also known to contain von Economo neurons, spindle-like neurons observed thus far among primates only in humans and some ape species, whose proportional representation in the brain increases with brain size (Allman et al., 2002; Nimchinsky et al., 1999). This class of neurons has been proposed to underlie complex social functioning, such as social intuition (Allman, Watson, Tetreault, & Hakeem, 2005), and it is intriguing to note that they may also be present in some other mammals known to have complex social behavior, such as whales and elephants. Additional empirical tests of the social brain hypothesis focus on operationalizing social complexity in ways that include size of the overall group, size of an average grooming clique, size and frequency of temporally limited subgroups (e.g., coalitions), number and complexity of mating strategies, frequency and complexity of social play, frequency and complexity of deception, and the extent of any social learning (Dunbar & Shultz, 2007b). Some of these operationalizations have parallels in the study of social capital in human communities (Putnam, 2000; Veenstra, 2002). In general, results from these empirical investigations are consistent with the social brain hypothesis, not just in primates but across species (Dunbar & Shultz, 2007a, 2007b). That is, the more social the species (e.g., high pair-bonding), the greater the brain volume. Evidence suggests that prevalence of prosocial behaviors, specifically pair-bonding behaviors, explains more variance in brain size than do other types of social complexity (R. Dunbar & Shultz, 2007). A third class of empirical test of the social brain hypothesis proceeds by agent-based modeling (ABM), in which computer models of reproducing individual agents, and their interactions, are built to study whether pressures from social complexity do in fact result in increased cognitive complexity, increased cooperative behavior, and so on (Bryson, Ando, & Lehmann, 2007; Burtsev & Turchin, 2006; Conte, 2002; N. Gilbert & Bankes, 2002). Gavrilets and Vose (2006) investigated the evolution of cognitive capacity due to social pressure by constructing a model

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that focused on male mating strategies in a sexually diploid population. They modeled the genetic control of each individual’s ability to learn new strategies (learning ability) and capacity to store learned strategies (cerebral capacity) using separate, independent loci that were equal in effect on survival. They chose these two traits specifically because of their association with increased brain size and complexity. The model also used an overall carrying capacity to control the size of the modeled population of agents, which was kept to between 50 and 150 individuals. The authors used small, constant rates to model an individual’s forgetting of learned strategies and invention of new strategies, and they classified each strategy according to its “Machiavellian fitness” and complexity. Machiavellian fitness was just their measure of how well an individual did in competition with other individuals for mates. Complexity regulated how easily the strategy could be learned. The rate of learning a strategy was proportional to learning ability, and inversely proportional to the strategy’s complexity and the number of strategies already learned. The fitness of an individual was just the sum of the fitnesses of all the strategies the individual learned, and the probability of winning a contest with another individual depended on the difference in fitness values. Offspring of matings were produced probabilistically using naturalistic models of recombination, segregation, and random mutation. With this model, in which social intelligence and social pressure were the operative forces, the authors demonstrated that the number of total strategies, the average learning ability, the average cerebral capacity, and the average fitness all increased substantially in only 10–20 thousand generations (~150,000 to 300,000 years). Notably, however, the model did not explicitly model the kind of pair-bonding that may be more relevant to recent associations between social complexity and brain size in apes (Dunbar & Shultz, 2007b). This suggests directions for future work using agent-based modeling more tightly constrained by research findings in the social cognition of nonhuman primates. A final point of interest that brings together evolutionary and developmental aspects of human brain size is that humans are highly altricial: At birth, we are helpless, and our development, notably including social development, occurs over a protracted period lasting many years. One way of appreciating this statement is to note that human brains are only about 25% their adult volume at birth— constraints imposed in part by our bipedal nature and the evolution of the female pelvis, with the consequence that infant human brains are highly immature. By comparison, chimpanzee brains are nearly 50% their adult size at birth, and macaque monkey brains are about 70% of their adult size at birth. These differences in the size of the neonatal brain relative to the adult brain mirror the species

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differences in the length of their development and their dependency on social support during this development. A recently found skull from a 1.8 million-year-old hominid child provided evidence that our ancestors 1.8 MY ago had a cranial capacity at birth that is essentially like that of apes, rather than like that of modern humans, arguing that this developmental shift occurred relatively recently and may be one of the features that contributed to the evolution of homo sapiens (Coqueugnlot, Hublin, Vellon, Houet, & Jacob, 2004).

THE AMYGDALA: FROM EMOTION TO SOCIAL ATTENTION Much of our own work has been on the amygdala, and this together with other recent findings in both animals and humans has yielded a fairly detailed compendium of what it is that the amygdala might contribute to social cognition. If we look at the history of work on the amygdala, it appears to follow two largely independent paths. One begins with the classical studies of Kluver and Bucy (although there were even earlier studies; Brown & Shafer, 1888) who made large bilateral lesions of the temporal lobes in monkeys. The syndrome that was named after them features a mixture of impairments due to amnesia, agnosia, and also emotional changes (Kluver & Bucy, 1939). A small number of investigators have modernized these early studies and made more selective lesions of the amygdala in monkeys by injecting a neurotoxin, ibotenic acid. This achieves a focal lesion of the amygdala that spares surrounding structures (unlike Kluver and Bucy’s very large lesions that did not permit any specific conclusions to be drawn about the amygdala per se), and importantly, it also spares fibers of passage—white matter coursing near or within a structure whose destruction (e.g., by electrolytic or aspiration lesions) would additionally introduce impairments due to disconnection of distal structures that normally communicate through these fibers. These much more selective ibotenic-acid lesions of the amygdala have revealed that amygdala damage in primates is subtle and also sensitive to the context and species (Machado & Bachevalier, 2006). In general, the animals still appear to have a normal repertoire of social behaviors (Amaral et al., 2003), but they don’t apply them normally. They fail to take into account the circumstances and behave in social inappropriate ways, often appearing very passive and tame (Emery et al., 2001). Moreover, the effects of amygdala damage depend critically on the age at which the lesions are introduced— lesioning the amygdala in infant monkeys actually induces a social phobia of sorts (Prather et al., 2001), which has made this of some interest as a possible model for autism

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(see Chapter 52, Section VII, this volume). We take up the human analogue of such lesion studies in detail later in this chapter. The second and parallel effort of studies on the amygdala, one which has involved a much larger number of laboratories and which has focused mostly on rodents, is the amygdala’s role in associative emotional memory— specifically, its role in fear conditioning (although its role in the modulation of declarative memory by emotion has also received some attention—see Chapter 31, Volume 1). This line of work has investigated how an animal learns that a stimulus or context is potentially aversive, and how it learns to avoid the stimulus as a consequence (Davis, 2000). There remains debate over the precise role of the amygdala in learned fear, but it is clear that it plays an important role. This line of work has used selective lesions of nuclei within the amygdala in rodents (although monkeys have also been investigated), and it has been borne out (at a much coarser level of anatomical and process resolution) also in humans. The human amygdala appears necessary for normal fear conditioning (Bechara et al., 1995) and is activated during the acquisition of such conditioning (Buechel, Morris, Dolan, & Friston, 1998). These studies in humans have now been taken also into neuroeconomics, which provides a mathematical framework for analyzing the decisions people make on the basis of the prior outcomes they have experienced. Some of this work has also examined the connections between multiple structures known to be important to decision making, such as those between the amygdala and prefrontal cortex. Fear conditioning, social behavior, and decision making seem to be a disparate batch of processes, and it has been hard to come up with any unified view of amygdala function. In part, this problem is real, and it is not surprising. After all, the primate amygdala consists of over a dozen nuclei, and the more detailed studies of fear conditioning in rodents have demonstrated that these nuclei all subserve different functions. They receive inputs from different parts of the brain, they send output to different parts, and they have different internal connectivity (Amaral, Price, Pitkanen, & Carmichael, 1992; Figure 47.4). The basolateral nucleus is the primary source of cortical sensory input to the amygdaloid complex, whereas the central nucleus is a main source of output to autonomic control centers. The individual roles of particular nuclei have often not been studied in primate studies of social behavior, and also not in studies in humans, certainly accounting for some of the discrepancies in the literature. Nonetheless, there is a common theme in functions assigned to the amygdala: its role related to prioritizing information processing and allocating attention (Adolphs & Spezio, 2006). This idea goes back to earlier proposals

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Sensory Cortex Sensory Thalamus

Hypothalamus, Midbrain, Pons, and Medulla

Ventral Striatum

Lateral Nucleus

Central Nucleus

Dorsomedial Nucleus of Thalamus

Basolateral Nucleus

Medial Nucleus

Basal Nucleus

Hippocampal Formation

Periaqueductal Gray Matter Main and Medial Basal Accessory Forebrain and Olfactory Bulb Hypothalamus

Figure 47.4 Amygdala nuclei. Note: A schematic showing some of the inputs and outputs, as well as intrinsic connections, of the primate amygdala. Note that none of the arrows are intended to denote monosynaptic connections. A major input nucleus is the basolateral region, whereas a major output for eliciting emotional reactions is the central nucleus.

by Paul Whalen, that the amygdala is involved in detecting ambiguous stimuli in the environment about which an organism must find out more—stimuli that are potentially threatening and dangerous, and toward which processing resources and attention must be directed (Davis & Whalen, 2001; Whalen, 1999). The amygdala’s modulation of attention had also been studied in rats (Holland & Gallagher, 1999), and was consistent with large number of studies in primates including humans. A recent neuroeconomics study found amygdala activation under decision-making circumstances where the outcomes of a choice were especially uncertain: The odds of getting a particular outcome weren’t even known (Hsu, Bhatt, Adolphs, Tranel, & Camerer, 2005). Assigning a function to the amygdala for detecting highly uncertain, unpredictable, and potentially important events ties together a lot of findings in the literature. Our own studies of the consequences of amygdala lesions in humans, complemented by those of others, followed a similar development from a role in basic emotion processing toward a more cognitive role in allocating processing resources and attention, and we review this next. Several lesion studies (Adolphs, Tranel, Damasio, & Damasio, 1994; A. K. Anderson & Phelps, 2000; A. K. Anderson, Spencer, Fulbright, & Phelps, 2000; Calder et al.,

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1996; Young, Hellawell, Van de Wal, & Johnson, 1996), complemented by functional imaging studies (Breiter et al., 1996; Morris et al., 1996; Whalen et al., 2001), had originally demonstrated that the human amygdala was critical for normal judgments about the internal states of others from viewing pictures of their facial expressions. Some studies have found a disproportionately severe impairment in recognizing fear (Adolphs, Tranel, Damasio, & Damasio, 1995; A. K. Anderson & Phelps, 2000; Broks et al., 1998; Calder et al., 1996; Sprengelmeyer et al., 1999), whereas others found evidence for a broader or more variable impairment in recognizing multiple emotions of negative valence in the face, including fear, anger, disgust, and sadness (Adolphs, 1999; Adolphs et al., 1999; Schmolck & Squire, 2001; Siebert, Markowitsch, & Bartel, 2003). Across the majority of studies, impairments in recognition of emotion were found despite an often normal ability to discriminate perceptually among the same stimuli. Many patients with bilateral amygdala damage perform in the normal range on the Benton Face Matching Task (Benton, Hamsher, Varney, & Spreen, 1983), in which subjects are asked to match different views of the same, unfamiliar person’s face, and they also perform normally in discriminating subtle changes in facial expression, even for facial expressions that they are nonetheless unable to recognize (Adolphs & Tranel, 2000; Adolphs, Tranel, & Damasio, 1998). Back in the early 1990s, one of us (R.A.) studied in detail a rare subject, SM, who has been especially informative because of the specificity of both her lesion and her impairment (Adolphs & Tranel, 2000; Tranel & Hyman, 1990). SM is a 42-year-old woman who has complete bilateral amygdala damage resulting from a rare disease (Urbach-Wiethe disease; Hofer, 1973). On a series of tasks, she shows a relatively disproportionate impairment in recognizing the intensity of fear from faces alone, and a lesser impairment also in recognizing the intensity of related emotions such as surprise and anger (Adolphs et al., 1994). A further role for the amygdala in processing aspects of faces comes from studies of the interaction between facial emotion and eye gaze. The direction of eye gaze in other individuals’ faces is an important source of information about their emotional state, intention, and likely future behavior. Eye gaze is a key social signal in many species (Emery, 2000), especially apes and humans, whose white sclera makes the pupil more easily visible and permits better discrimination of gaze. Human viewers make preferential fixations onto the eye region of others’ faces (Janik, Wellens, Goldberg, & Dell’Osso, 1978), a behavior that appears early in development and may contribute to the socioemotional impairments seen in developmental

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disorders like autism (Baron-Cohen, 1995). Eyes signal important information about emotional states, and there is evidence from functional imaging studies that at least some of this processing recruits the amygdala (Baron-Cohen et al., 1999; Kawashima et al., 1999; Wicker, Perrett, Baron-Cohen, & Decety, 2003). The interaction between facial emotion and direction of eye gaze has been explored. It was found that direct gaze facilitated processing of approach-oriented emotions such as anger, whereas averted gaze facilitated the processing of avoidance-oriented emotions such as fear (Adams & Kleck, 2003) and that this processing facilitation correlated with increased activation of the amygdala in a functional imaging study (Adams, Gordon, Baird, Ambady, & Kleck, 2003). The amygdala’s role is not limited to making judgments about basic emotions, but includes a role in making social judgments. This fact was already suggested by the earlier studies in nonhuman primates alluded to earlier (Kling & Brothers, 1992; Kluver & Bucy, 1937; Rosvold, Mirsky, & Pribram, 1954), which demonstrated impaired social behavior following amygdala damage, confirmed to some extent by the more recent studies that produced a more restricted set of impairments with more anatomically restricted damage to the amygdala (Emery & Amaral, 1999; Emery et al., 2001). What is the analogue in humans? Some studies of the human amygdala suggest a general role for the amygdala in the collection of abilities whereby we attribute internal mental states, intentions, desires, and emotions to other people (“theory of mind”; Baron-Cohen et al., 2000; Fine, Lumsden, & Blair, 2001). Relatedly, the amygdala shows differential habituation of activation to faces of people of another race (Hart et al., 2000), and amygdala activation has been found to correlate with race stereotypes of which the viewer may be unaware (Phelps et al., 2000). However, the amygdala’s role in processing information about race is still unclear: Other brain regions, in extrastriate visual cortex, are also activated differentially as a function of race (Golby, Gabrieli, Chiao, & Eberhardt, 2001), and lesions of the amygdala do not appear to impair race judgments (Phelps, Cannistraci, & Cunningham, 2003). The preceding findings support the simulation view of how emotional expressions might be recognized, an issue we briefly mentioned when discussing somatosensory cortices. The story roughly runs like this. Visual cortices in the temporal lobe would be involved in perceptual processing of facial features, would then convey a perceptual representation of the face to the basolateral amygdala, which in turn would associate it with its emotional response, likely effected by amygdala nuclei and corresponding to changes in a number of measures. One such change would be the somatic response triggered by the central nucleus

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The Amygdala: From Emotion to Social Attention 933

of the amygdala (e.g., changes in autonomic tone). These emotional responses, in turn, would be perceived and represented in somatosensory cortices including insula, and would for the direct substrate for sharing the observed person’s feeling of the emotion. The preceding account of how we might infer another ’s emotional state via simulation (Goldman & Sripada, 2005) has turned out to be an incomplete picture. A more recent study from our lab gave the surprising finding that the amygdala comes into play in a more abstract, and earlier, processing component (Adolphs et al., 2005; see Figure 47.5). Amygdala damage was found to impair the ability to use information from a diagnostic facial feature— the eye region of the face. Following amygdala damage, the eye region of faces was no longer used effectively by the viewer to discriminate fear. These findings were consistent with other results showing amygdala activation to fearful eyes (Morris, deBonis, & Dolan, 2002), or only to the briefly presented whites of eyes (Whalen et al., 2004). The experiment that demonstrated this finding used a new technique, called “bubbles,” in which small portions of an image of a face were revealed to viewers. On a particular

(A)

Controls

Subject SM

trial, a viewer might see only the ear of an underlying face, or perhaps part of the cheek and part of the forehead. Some quick reflection immediately suggests that not all regions of the face would be equally informative about the emotion: Seeing part of an ear does not distinguish emotions, whereas seeing part of the eyes or the mouth is much more discriminative. We can take advantage of this fact using a procedure similar to reverse correlation. When shown these randomly revealed pieces of faces, subjects are asked to judge the emotion. Those trials they get correct are all summed, and we now subtract all those trials (those pieces of faces) that they get incorrect. This procedure (or its continuous analogue, regressing performance accuracy on the regions of the face that are revealed in each trial) generates a so-called classification image that denotes the regions of the face on the basis of which subjects can discriminate the emotion. Perhaps not too surprisingly, the classification image for discriminating fear from happiness (the particular discrimination we used in our experiment) prominently shows the eyes and the mouth. However, when the same experiment was conducted in subject SM, mentioned earlier, who has

(C)

(B) (D) 1

0.6

SM eyes

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SM free

0.4 Controls

Accuracy

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Figure 47.5 Bilateral amygdala lesions impair the use of the eyes and gaze to the eyes during emotion judgment. Note: Applying the bubbles method (see Adolphs et al., 2005) to identify face areas used during emotion judgment, patient SM (brain shown in C) differed from controls such that controls exhibited much greater use of the eyes than SM, whereas SM did not rely more on any area of the face than did controls (A). While looking at whole faces, SM exhibited abnormal

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face gaze (B), making far fewer fixations to the eyes than did controls. This was observed across emotions (free viewing, emotion judgment, gender discrimination). When SM was instructed to look at the eyes (D, “SM eyes”) in a whole face, she could do this, resulting in a remarkable recovery in ability to recognize the facial expression of fear. From “A Mechanism for Impaired Fear Recognition after Amygdala Damage,” by R. Adolphs et al., January 6, 2005, Nature, 433, pp. 68–72. Reprinted with permission.

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bilateral amygdala damage, the classification image did not contain as much of the eyes. In fact, the impaired use of visual information from the face in subject SM was specific: She failed to make use of high spatial frequency information from the eye region of faces. In fact, the deficit was even more basic than that: Information about the eye region was not used effectively in subject SM because the eye region was not fixated in the first place. A second experiment measured viewers’ eye movements as they judged the emotion shown in facial expressions. While healthy individuals spend a lot of time fixating the eye region of faces, subject SM failed to do so. Thus, her impaired use of visual information about the eye region of the face was likely derivative to an impaired ability to allocate visual attention and fixate the eye region in the first place. Her brain did not possess the mechanism to decide which regions of a face to explore preferentially to glean relevant information about the emotion. The preceding findings could provide the basis for impaired fear recognition following amygdala damage. Since the eye region of faces is most important to distinguish fear from other emotions, and since SM fails to fixate and make use of information about the eye region of faces, her impaired fear recognition apparently results from her impaired fixation of the eyes in faces. A final experiment tested this interpretation directly: We instructed SM to direct her gaze onto the eyes of other people’s faces, and found that this manipulation temporarily allowed her to generate a normal performance on a fear recognition task in which she was otherwise severely impaired. It is worth noting two key further results from this study. First, SM failed to fixate the eyes in any face, not just facial expressions of fear. In fact, she simply failed to explore faces in general, which included a failure to direct her gaze toward the eye region. Similarly, the abnormal use of information from the eye region held for happy faces as well as for fearful faces. So the impairment in use of information from, and fixation onto, the eyes in faces was general for faces. This general impairment resulted in a relatively specific impairment in fear recognition because the eye region of the face was in fact the most diagnostic for signaling fear, rather than other emotions. Given the recognition tasks we used, this resulted in a severe impairment in recognizing fear, but not in recognizing other emotions. (Interestingly, unpublished data indicate that the same subject does fixate the eye region when the faces are shown inverted. So, while the brain does not need to know that the face is showing fear for the impaired eye fixations to occur, it apparently does need to know that the stimulus is a face; the impairment in fixation does not seem to generalize to objects other than faces, including inverted faces.)

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Another point worth noting is that the explicit instruction to fixate the eyes in faces, while rescuing SM’s impaired recognition of fear, did so only transiently (as long as that block of the experiment lasted). When later asked to view faces, SM spontaneously reverted to her lack of exploration of the face, and once again showed impaired fear recognition. It may well be that the improvement was not more permanent because SM was not given additional information about her impairment. She was unaware that she failed to fixate the eyes, as she was unaware that her performance in fear recognition was impaired. This raises further questions: Why did she not ask about her performance, why did she not notice that she failed to fixate the eyes? These questions may point toward a broader interpretation of the impairment. SM, as a result of damage to the amygdala, lacked a normal mechanism to explore the environment. An aspect of such an impairment was a failure to fixate the eyes in faces, to explore them normally with her gaze. Another aspect of the impairment was a failure to question what was going on in the experiment in any way, or to monitor her own performance during it. In both cases, there remains a passive ability to process sensory information, but the instrumental component of seeking out such information in the first place has been severely compromised. A study carried out in rodents and humans provides yet further detail to this picture. The study suggests that the amygdala can respond to stimuli on the basis of their unpredictability alone, apparently without any emotional significance (Herry et al., 2007). In this study, sequences of tones that were unpredictable in time ( jittered randomly around a mean intertone interval) activated the amygdala more in both mice and humans and resulted in anxiety-like consequences on cognition and behavior in both species. There was nothing emotional about the tones (they had not been paired with shock, they were not dissonant or particularly loud, and there was nothing else about the experiment that would be expected to make the tones emotional, let alone to make the jittered tones differentially emotional with respect to the regular tones). Yet the mere unpredictability in time of the jittered tones appeared to be sufficient to activate the amygdala (by a cellular mechanism likely relying on differential habituation) and to induce anxiety-like consequences. This example brings us back to our question about the domain-specificity of social cognition—are there structures for social cognition? Or can all aspects of social cognition ultimately be reduced to more basic and more abstract computations that have nothing to do with the social or with emotions, but merely happen to be important also for those domains (Adolphs, 2003c)? As noted, to fully understand this issue, we would need comparative and developmental studies that can shed light on the evolution of

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Summary

the amygdala’s role in social behavior, and the emergence of its function in childhood and adolescence—studies that are now beginning to appear in the literature. There also remain important open questions about the story thus far: While the amygdala indeed appears to respond to unpredictable and ambiguous stimuli, it is unclear whether this needs to be temporal (rather than unpredictable sequences of tones in time, could we perhaps test stimuli that are unpredictable in space?) or is really independent of emotion. (So far, all the unpredictable and ambiguous stimuli used have also been aversive to some extent—is it possible to devise unpredictable yet completely neutral, or even pleasant, stimuli? Would those still activate the amygdala?) Perhaps the most important open question is the relationship between unpredictability and informativeness. It is unclear to what extent unpredictable tone sequences are stimuli that an animal is desperately trying to figure out and to predict, trying to glean information from a stimulus that is in fact uninformative. Perhaps the conjunction of trying hard to predict a stimulus and continuously failing at that effort is driving the saliency of the unpredictable stimuli in the preceding experiment. What would happen if we presented the same kind of unpredictable stimuli but in a context (or, for humans, perhaps with explicit instructions) that makes them uninteresting, so that the animal or the human does not try to process them? Some stimuli in the environment are important if they are unpredictable; they are then surprising. But other stimuli may be boring if they unpredictable because they are deemed to be uninformative noise. Without any basis for making the distinction between these two possibilities, it may be that the brain defaults to assuming the stimuli could be informative, and that is the reason for the observed effects.

SUMMARY We began this chapter by suggesting ways in which social cognition was distinguished from cognition in general (self-regulation and counterfactual thinking among them), and we have had space only to review the main underpinnings of social cognition. In closing, we want to point toward another distinguishing feature of social cognition that bears considerably on psychopathology: individual differences. It is now commonplace to see neuroimaging studies show at least some figures in which regional brain activation is correlated with a personality dimension, or shown to vary with a genetic polymorphism. Most of these investigations—and much of the history of neuroscientific exploration of social cognition generally—has focused on the implications for pathology. Essentially every psychiatric disorder involves impairments in social behavior and

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functioning of some kind—autism, schizophrenia, depression, and anxiety disorders are obvious examples (Cacioppo et al., 2007). Yet, work on individual differences has pointed to the importance of looking for deviations from the norm in the opposite direction as well: What about people who are especially socially skilled, especially empathic, or especially sensitive to social information? This last question has been taken up by only a handful of investigators (it is telling that the last two chapters of this volume are about pathology, and about avoiding pathology, but not about being “extrasocial”). Two case studies in this literature are worth highlighting—one genetic, the other largely acquired (although this is unclear). The genetic example comes from Williams syndrome, a multisystemic disorder due to a hemideletion of a set of contiguous genes on chromosome 7 (Ewart et al., 1993), all of which have been mapped and are being explored for the individual contributions to the phenotype (Korenberg et al., 2000). In addition to deleterious consequences on organs like the heart (supravalvular aortic stenosis is one aspect of the phenotype that often kills patients relatively early in life), people with Williams syndrome show an unusual cognitive profile: Although language function and face processing are relatively spared, they are devastated in visuospatial cognition (Meyer-Lindenberg et al., 2004). But perhaps most intriguing is their hypersocial behavior: They are extremely interested in other people, take a great interest in how others feel, and use unusual attention-getting devices in their speech. The interest that people with Williams syndrome show for others is certainly not normal, and it is unlikely that it represents very prosocial behavior on a continuum with normal individual differences. Rather, it seems to arise from dysfunction in specific neural structures such as the amygdala and the prefrontal cortex, that results in an absence of fear of strangers and a disinhibition of approach behaviors. People with Williams syndrome fail to show normal amygdala activation to social stimuli such as fearful facial expressions, yet show exaggerated amygdala activation to nonsocial fearful stimuli, consistent with the high incidence of anxiety and specific phobias in the disorder (Meyer-Lindenberg et al., 2005). Neuropsychological studies have suggested that a failure to inhibit normal social approach behaviors (e.g., caution when approaching strangers) may arise also from frontal lobe dysfunction (Porter, Coltheart, & Langdon, 2007). All these findings argue that hypersociability here is not really an aspect of unusually positive, healthy social behavior; rather, it is pathological social behavior that superficially shows some of the attributes that we would thing of as prosocial. There are, of course, examples of truly exemplary prosocial behavior—accounts of people who sacrificed their

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lives to help others are known well enough, and they remain perhaps the strongest examples of truly altruistic behavior in humans. Yet, next to nothing is known about either the processes or the neural substrates behind those individual differences. Are they genetic? Are they learned? Can the behavior be explained by the current frameworks that explain nonreciprocal altruism, and is it mediated by reward and motivation-related structures in the brain that are the same as those revealed in all other studies of motivated instrumental behavior? Or are there examples of prosocial behavior in humans that are really of a different kind? These are provocative questions, and given the increasingly global effects of human social interaction, there is a sense of urgency about gaining insight into them. The next generation of young investigators in social cognitive neuroscience should rise to the challenge.

REFERENCES Adams, R. B., Gordon, H. L., Baird, A. A., Ambady, N., & Kleck, R. E. (2003, June 6). Effects of gaze on amygdala sensitivity to anger and fear faces. Science, 300, 1536. Adams, R. B., & Kleck, R. E. (2003). Perceived gaze direction and the processing of facial displays of emotion. Psychological Science, 14, 644–647. Adolphs, R. (1999). The human amygdala and emotion. Neuroscientist, 5, 125–137. Adolphs, R. (2003a). Cognitive neuroscience of human social behavior. Nature Reviews: Neuroscience, 4, 165–178. Adolphs, R. (2003b). Investigating the cognitive neuroscience of social behavior. Neuropsychologia, 41, 119–126. Adolphs, R. (2003c). Is the human amygdala specialized for social cognition. In Shinnick-Gallagher et al., (Eds.), The amygdala in brain function (Vol. 985, pp. 326–340). New York: Annals of the New York Academy of Sciences. Adolphs, R., Damasio, H., Tranel, D., Cooper, G., & Damasio, A. R. (2000). A role for somatosensory cortices in the visual recognition of emotion as revealed by 3-D lesion mapping. Journal of Neuroscience, 20, 2683–2690. Adolphs, R., Gosselin, F., Buchanan, T. W., Tranel, D., Schyns, P. G., & Damasio, A. (2005, January 6). A mechanism for impaired fear recognition after amygdala damage. Nature, 433, 68–72. Adolphs, R., & Spezio, M. L. (2006). Role of the amygdala in processing visual social stimuli. Progressive Brain Research, 156, 363–378. Adolphs, R., & Tranel, D. (2000). Emotion recognition and the human amygdala. In J. P. Aggleton (Ed.), The amygdala: A functional analysis (pp. 587–630). New York: Oxford University Press. Adolphs, R., Tranel, D., & Damasio, A. R. (1998, June 4). The human amygdala in social judgment. Nature, 393, 470–474. Adolphs, R., Tranel, D., Damasio, H., & Damasio, A. (1994, December 15). Impaired recognition of emotion in facial expressions following bilateral damage to the human amygdala. Nature, 372, 669–672. Adolphs, R., Tranel, D., Damasio, H., & Damasio, A. R. (1995). Fear and the human amygdala. Journal of Neuroscience, 15, 5879–5892. Adolphs, R., Tranel, D., Hamann, S., Young, A., Calder, A., Anderson, A., et al. (1999). Recognition of facial emotion in nine subjects with bilateral amygdala damage. Neuropsychologia, 37, 1111–1117.

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Allman, J. M., Hakeem, A., & Watson, K. (2002). Two phylogenetic specializations in the human brain. Neuroscientist, 8, 335–346. Allman, J. M., Watson, K. K., Tetreault, N. A., & Hakeem, A. (2005). Intuition and autism: A possible role for von economo neurons. Trends in Cognitive Sciences, 9, 367–373. Amaral, D. G., Capitanio, J. P., Jourdain, M., Mason, W. A., Mendoza, S. P., & Prather, M. (2003). The amygdala: Is it an essential component of the neural network for social cognition? Neuropsychologia, 41, 235–240. Amaral, D. G., Price, J. L., Pitkanen, A., & Carmichael, S. T. (1992). Anatomical organization of the primate amygdaloid complex. In J. P. Aggleton (Ed.), The amygdala: Neurobiological aspects of emotion, memory, and mental dysfunction (pp. 1–66). New York: Wiley-Liss. Anderson, A. K., & Phelps, E. A. (2000). Expression without recognition: Contributions of the human amygdala to emotional communication. Psychological Science, 11, 106–111. Anderson, A. K., Spencer, D. D., Fulbright, R. K., & Phelps, E. A. (2000). Contribution of the anteromedial temporal lobes to the evaluation of facial emotion. Neuropsychology, 14, 526–536. Anderson, S. W., Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1999). Impairment of social and moral behavior related to early damage in human prefrontal cortex. Nature Neuroscience, 2, 1032–1037. Anderson, S. W., Damasio, H., Tranel, D., & Damasio, A. R. (2000). Longterm sequelae of prefrontal cortex damage acquired in early childhood. Developmental Neuropsychology, 18, 281–296. Bar-On, R. (1997). The Bar-On Emotional Quotient Inventory (EQ-i): A test of emotional intelligence. Toronto, Ontario, Canada: Multi-Health Systems. Bar-On, R., Tranel, D., Denburg, N., & Bechara, A. (2003). Exploring the neurological substrate of emotional and social intelligence. Brain, 126, 1790–1800. Baron-Cohen, S. (1995). Mindblindness: An essay on autism and theory of mind. Cambridge, MA: MIT Press. Baron-Cohen, S., Ring, H. A., Bullmore, E. T., Wheelwright, S., Ashwin, C., & Williams, S. C. R. (2000). The amygdala theory of autism. Neuroscience and Biobehavioral Reviews, 24, 355–364. Baron-Cohen, S., Ring, H. A., Wheelwright, S., Bullmore, E. T., Brammer, M. J., Simmons, A., et al. (1999). Social intelligence in the normal and autistic brain: An fMRI study. European Journal of Neuroscience, 11, 1891–1898. Barrash, J., Tranel, D., & Anderson, S. W. (2000). Acquired personality disturbances associated with bilateral damage to the ventromedial prefrontal region. Developmental Neuropsychology, 18, 355–381. Barrett, L., & Henzi, P. (2005). The social nature of primate cognition. Proceedings: Biological Sciences, 272, 1865–1875. Barrett, L., Henzi, P., & Dunbar, R. (2003). Primate cognition: From “what now?” to “what if?” Trends in Cognitive Sciences, 7, 494–497. Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7–15. Bechara, A., Damasio, H., & Damasio, A. R. (2000). Emotion, decisionmaking, and the orbitofrontal cortex. Cerebral Cortex, 10, 295–307. Bechara, A., Tranel, D., Damasio, H., Adolphs, R., Rockland, C., & Damasio, A. R. (1995, August 25). Double dissociation of conditioning and declarative knowledge relative to the amygdala and hippocampus in humans. Science, 269, 1115–1118. Benton, A. L., Hamsher, K., Varney, N. R., & Spreen, O. (1983). Contributions to neuropsychological assessment. New York: Oxford University Press. Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: An update. Trends in Cognitive Sciences, 8, 539–546. Bradbury, J. (2005). Molecular insights into human brain evolution. PLoS Biology, 3(3), e50.

8/17/09 5:26:27 PM

References Breiter, H. C., Etcoff, N. L., Whalen, P. J., Kennedy, W. A., Rauch, S. L., Buckner, R. L., et al. (1996). Response and habituation of the human amygdala during visual processing of facial expression. Neuron, 17, 875–887.

Dunbar, R., & Shultz, S. (2007b). Understanding primate brain evolution. Philosophical Transactions of the Royal Society of London. Series B, 362, 649–658.

Broks, P., Young, A. W., Maratos, E. J., Coffey, P. J., Calder, A. J., Isaac, C., et al. (1998). Face processing impairments after encephalitis: Amygdala damage and recognition of fear. Neuropsychologia, 36, 59–70.

Emery, N. J. (2000). The eyes have it: The neuroethology, function and evolution of social gaze. Neuroscience and Biobehavioral Reviews, 24, 581–604.

Brown, S., & Shafer, E. A. (1888). An investigation into the functions of the occipital and temporal lobes of the monkey’s brain. Philosophical Transactions of the Royal Society of London. Series B, 179, 303–327.

Emery, N. J., & Amaral, D. G. (1999). The role of the amygdala in primate social cognition. In R. D. Lane & L. Nadel (Eds.), Cognitive neuroscience of emotion (pp. 156–191). Oxford: Oxford University Press.

Bryson, J. J., Ando, Y., & Lehmann, H. (2007). Agent-based modelling as scientific method: A case study analysing primate social behaviour. Philosophical Transactions of the Royal Society of London. Series B, 362, 1685–1698.

Emery, N. J., Capitanio, J. P., Mason, W. A., Machado, C. J., Mendoza, S. P., & Amaral, D. G. (2001). The effects of bilateral lesions of the amygdala on dyadic social interactions in rhesus monkeys. Behavioral Neuroscience, 115, 515–544.

Buckner, R. L., & Carroll, D. C. (2006). Self-projection and the brain. Trends in Cognitive Sciences, 11, 49–57.

Ewart, A. K., Morris, C. A., Atkinson, D., Jin, W., Sternes, K., Spallone, P., et al. (1993). Hemizygosity at the elastin locus in a developmental disorder, Williams syndrome. Nature Genetics, 5, 11–16.

Buechel, C., Morris, J., Dolan, R. J., & Friston, K. J. (1998). Brain systems mediating aversive conditioning: An event-related fMRI study. Neuron, 20, 947–957. Burtsev, M., & Turchin, P. (2006, April 20). Evolution of cooperative strategies from first principles. Nature, 440, 1041–1044. Byrne, R., & Whiten, A. (Eds.). (1988). Machiavellian intelligence: Social expertise and the evolution of intellect in monkeys, apes, and humans. Oxford: Clarendon Press. Cacioppo, J. T., Amaral, D. G., Blanchard, J. J., et al. (2007). Social neuroscience: Progress and implications for mental health. Social Neuroscience, 2, 99–123. Cacioppo, J. T., Berntson, G. G., Adolphs, R., Carter, C. S., Davidson, R. J., McClintock, M. K., et al. (Eds.). (2001). Foundations in social neuroscience. Cambridge, MA: MIT Press. Calder, A. J., Young, A. W., Rowland, D., Perrett, D. I., Hodges, J. R., & Etcoff, N. L. (1996). Facial emotion recognition after bilateral amygdala damage: Differentially severe impairment of fear. Cognitive Neuropsychology, 13, 699–745. Camille, N., Coricelli, G., Sallet, J., Pradat-Diehl, P., Duhamel, J.-R., & Sirigu, A. (2004, May 21). The involvement of the orbitofrontal cortex in the experience of regret. Science, 304, 1167–1170. Chamero, P., Marton, T. F., Logan, D. W., Flanagan, K., Cruz, J. R., Saghatelian, A., et al. (2007, December 6). Identification of protein pheromones that promote aggressive behavior. Nature, 450, 899–902. Conte, R. (2002). Agent-based modeling for understanding social intelligence. Proceedings of the National Academy of Sciences, USA, 99, 7189–7190. Coqueugnlot, H., Hublin, J.-J., Vellon, F., Houet, F., & Jacob, T. (2004, September 16). Early brain growth in homo erectus and implications for cognitive ability. Nature, 431, 299–332. Damasio, A. R. (1994). Descartes’ error: Emotion, reason, and the human brain. New York: Grosset/Putnam. Damasio, A. R., Tranel, D., & Damasio, H. (1990). Individuals with sociopathic behavior caused by frontal damage fail to respond autonomically to social stimuli. Behavioral Brain Research, 41, 81–94. Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., & Damasio, A. R. (1994, May 20). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. Science, 264, 1102–1104. Davis, M. (2000). The role of the amygdala in conditioned and unconditioned fear and anxiety. In J. Aggleton (Ed.), The amygdala. A functional analysis (pp. 213–288). New York: Oxford University Press. Davis, M., & Whalen, P. J. (2001). The amygdala: Vigilance and emotion. Molecular Psychiatry, 6, 13–34. Devinsky, O., Morrell, M. J., & Vogt, B. A. (1995). Contributions of anterior cingulate cortex to behavior. Brain, 118, 279–306. Dunbar, R. & Shultz, S. (2007a, September 7). Evolution in the social brain. Science, 317, 1344–1347.

c47.indd Sec5:937

937

Fine, C., Lumsden, J., & Blair, R. J. R. (2001). Dissociation between “theory of mind” and executive functions in a patient with early left amygdala damage. Brain, 124, 287–298. Gavrilets, S., & Vose, A. (2006). The dynamics of machiavellian intelligence. Proceedings of the National Academy of Sciences, USA, 103, 16823–16828. Gilbert, D. T. (1998). Ordinary personology. In S. T. Fiske, D. T. Gilbert, & G. Lindzey (Eds.), The handbook of social psychology (pp. 89–150). New York: McGraw-Hill. Gilbert, D. T., & Wilson, T. D. (2007, September 7). Prospection: Experiencing the future. Science, 317, 1351–1354. Gilbert, N., & Bankes, S. (2002). Platforms and methods for agent-based modeling. Proceedings of the National Academy of Sciences, USA, 99, 7197–7198. Golby, A. J., Gabrieli, J. D. E., Chiao, J. Y., & Eberhardt, J. L. (2001). Differential responses in the fusiform region to same-race and otherrace faces. Nature Neuroscience, 4, 845–850. Goldman, A. I., & Sripada, C. S. (2005). Simulationist models of facebased emotion recognition. Cognition, 94, 193–213. Hadland, K. A., Rushworth, M. F., Gaffan, D., & Passingham, R. E. (2003). The effect of cingulate lesions on social behaviour and emotion. Neuropsychologia, 41, 919–931. Hart, A. J., Whalen, P. J., Shin, L. M., McInerney, S. C., Fischer, H., & Rauch, S. L. (2000). Differential response in the human amygdala to racial outgroup vs ingroup face stimuli. NeuroReport, 11, 2351–2355. Heberlein, A. S., & Adolphs, R. (2004). Impaired spontaneous anthropomorphizing despite intact social knowledge and perception. Proceedings of the National Academy of Sciences, 101, 7487–7491. Herry, C., Bach, D. R., Esposito, F., DiSalle, F., Perrig, W. J., Scheffler, K., et al. (2007). Processing of temporal unpredictability in human and animal amygdala. Journal of Neuroscience, 27, 5958–5966. Hofer, P.-A. (1973). Urbach-Wiethe disease: A review. Acta Derm Venerol, 53, 5–52. Holland, P. C., & Gallagher, M. (1999). Amygdala circuitry in attentional and representational processes. Trends in Cognitive Sciences, 3, 65–73. Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., & Camerer, C. F. (2005, December 9). Neural systems responding to degrees of uncertainty in human decision-making. Science, 310, 1680–1683. Hutchison, W. D., Davis, K. D., Lozano, A. M., Tasker, R. R., & Dostrovsky, J. O. (1999). Pain-related neurons in the human cingulate cortex. Nature Neuroscience, 2, 403–405. Isler, K., & van Schaik, C. P. (2006). Metabolic costs of brain size evolution. Biology Letters, 2, 557–560. Janik, S. W., Wellens, A. R., Goldberg, M. L., & Dell’Osso, L. F. (1978). Eyes as the center of focus in the visual examination of human faces. Perceptual and Motor Skills, 47, 857–858.

8/17/09 5:26:28 PM

938

Social Cognition

Kanwisher, N. (2000). Domain specificity in face perception. Nature Neuroscience, 3, 759–763.

and great apes. Proceedings of the National Academy of Sciences, 96, 5268–5273.

Kawashima, R., Sugiura, M., Kato, T., Nakamura, A., Natano, K., Ito, K., et al. (1999). The human amygdala plays an important role in gaze monitoring. Brain, 122, 779–783.

Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. E. (2002). Rethinking feelings: An fMRI study of the cognitive regulation of emotion. Journal of Cognitive Neuroscience, 14, 1215–1229.

Keysers, C., Wicker, B., Gazzola, V., Anton, J.-L., Fogassi, L., & Gallese, V. (2004). A touching sight: SII/PV activation during the observation and experience of touch. Neuron, 42, 335–346.

Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotions. Trends in Cognitive Sciences, 9, 242–249.

Kling, A. S., & Brothers, L. A. (1992). The amygdala and social behavior. In J. P. Aggleton (Ed.), The amygdala: Neurobiological aspects of emotion, memory, and mental dysfunction (pp. 353–378). New York: Wiley-Liss. Kluver, H., & Bucy, P. C. (1937). Psychic blindness and other symptoms following bilateral temporal lobectomy in rhesus monkeys. American Journal of Physiology, 119, 352–353. Kluver, H., & Bucy, P. C. (1939). Preliminary analysis of functions of the temporal lobes in monkeys. Archives of Neurology and Psychiatry, 42, 979–997. Koch, C., & Tsuchiya, N. (2006). Attention and consciousness: Two distinct brain processes. Trends in Cognitive Sciences, 11, 16–22.

Ochsner, K. N., Knierim, K., Ludlow, D. H., Hanelin, J., Ramachandran, T., Glover, G., et al. (2004). Reflecting upon feelings: An fMRI study of neural systems supporting the attribution of emotion to self and other. Journal of Cognitive Neuroscience, 16, 1746–1772. Ochsner, K. N., & Lieberman, M. D. (2001). The emergence of social cognitive neuroscience. American Psychologist, 56, 717–734. Parvizi, J., Van Hoesen, G. W., Buckwalter, J., & Damasio, A. R. (2006). Neural connections of the posteromedial cortex in the macaque. Proceedings of the National Academy of Sciences, USA, 103, 1563–1568. Phelps, E. A., Cannistraci, C. J., & Cunningham, W. A. (2003). Intact performance on an indirect measure of race bias following amygdala damage. Neuropsychologia, 41, 203–209.

Koenigs, M., Young, L., Adolphs, R., Tranel, D., Cushman, F., Hauser, M., et al. (2007, April 19). Damage to the prefrontal cortex increases utilitarian moral judgments. Nature, 446, 908–911.

Phelps, E. A., O’Connor, K. J., Cunningham, W. A., Funayama, E. S., Gatenby, J. C., Gore, J. C., et al. (2000). Performance on indirect measures of race evaluation predicts amygdala activation. Journal of Cognitive Neuroscience, 12, 729–738.

Korenberg, J. R., Chen, X.-N., Hirota, H., Lai, Z., Bellugi, U., Burian, D., et al. (2000). Genome structure and cognitive map of Williams syndrome. Journal of Cognitive Neuroscience, 12, 89–107.

Porter, M. A., Coltheart, M., & Langdon, R. (2007). The neuropsychological basis of hypersociability in Williams and Down syndrome. Neuropsychologia, 45, 2839–2849.

Machado, C. J., & Bachevalier, J. (2006). The impact of selective amygdala, orbital frontal cortex, or hippocampal formation lesions on established social relationships in rhesus monkeys. Behavioral Neuroscience, 120, 761–786.

Prather, M. D., Lavenex, P., Mauldin-Jourdain, M. L., Mason, W. A., Capitanio, J. P., Mendoza, S. P., et al. (2001). Increased social fear and decreased fear of objects in monkeys with neonatal amygdala lesions. Neuroscience, 106, 653–658.

MacMillan, M. (2000). An odd kind of fame: Stories of Phineas Gage. Cambridge, MA: MIT Press.

Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. New York: Simon & Schuster.

Maddock, R. J. (1999). The retrosplenial cortex and emotion: New insights from functional neuroimaging of the human brain. Trends in Neurosciences, 22, 310–316.

Raichle, M. A., Macleod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, USA, 98, 676–682.

Martin, R. D. (2007). The evolution of human reproduction: A primatological perspective. American Journal of Physical Anthropology, 134(Suppl. 45), 59–84.

Rilling, J., Gutman, D., Zeh, T., Pagnoni, G., Berns, G., & Kilts, C. (2002). A neural basis for social cooperation. Neuron, 35, 395–405.

Meyer-Lindenberg, A., Hariri, A., Munoz, K. E., Mervis, C. B., Mattay, V. S., Morris, C. A., et al. (2005). Neural correlates of genetically abnormal social cognition in Williams syndrome. Nature Neuroscience, 8, 991–993.

Rosvold, H. E., Mirsky, A. F., & Pribram, K. (1954). Influence of amygdalectomy on social behavior in monkeys. Journal of Comparative and Physiological Psychology, 47, 173–178. Roth, G., & Dicke, U. (2005). Evolution of the brain and intelligence. Trends in Cognitive Sciences, 9, 250–257.

Meyer-Lindenberg, A., Kohn, P., Mervis, C. B., Kippenhan, J. S., Olsen, R. K., Morris, C. A., et al. (2004). Neural basis of genetically determined visuospatial construction deficit in Williams syndrome. Neuron, 43, 623–631.

Rudebeck, P. H., Buckley, M. J., Walton, M. E., & Rushworth, M. F. (2006). A role for the macaque anterior cingulate gyrus in social valuation. Science, 313, 310–313.

Mitchell, J. P. (2007). Activity in right temporo-parietal junction is not selective for theory-of-mind. Cereb Cortex, 18, 262–271.

Ruff, C. B., Trinkaus, E., & Holliday, T. W. (1997, May 8). Body mass and encephalization in pleistocene homo. Nature, 387, 173–176.

Mitchell, J. P., Heatherton, T. F., & Macrae, C. N. (2002). Distinct neural systems subserve person and object knowledge. Proceedings of the National Academy of Sciences, 99, 15238–15243.

Saver, J. L., & Damasio, A. R. (1991). Preserved access and processing of social knowledge in a patient with acquired sociopathy due to ventromedial frontal damage. Neuropsychologia, 29, 1241–1249.

Moll, J., Zahn, R., de Oliveira-Souza, R., Krueger, F., & Grafman, J. (2005). The neural basis of human moral cognition. Nature Reviews: Neuroscience, 6, 799–809.

Saxe, R. (2006). Why and how to study theory of mind with fMRI. Brain Research, 1079, 57–65.

Morris, J. S., deBonis, M., & Dolan, R. J. (2002). Human amygdala responses to fearful eyes. NeuroImage, 17, 214–222. Morris, J. S., Frith, C. D., Perrett, D. I., Rowland, D., Young, A. W., Calder, A. J., et al. (1996, October 31). A differential neural response in the human amygdala to fearful and happy facial expressions. Nature, 383, 812–815. Nimchinsky, E. A., Gilissen, E., Allman, J. M., Perl, D. P., Erwin, J. M., & Hof, P. R. (1999). A neuronal morphologic type unique to humans

c47.indd Sec5:938

Saxe, R., & Kanwisher, N. (2003). People thinking about thinking people: The role of the temporo-parietal junction in theory of mind. NeuroImage, 19, 1835–1842. Saxe, R., & Powell, L. J. (2006). It’s the thought that counts: Specific brain regions for one component of theory of mind. Psychological Sciences, 17, 692–699. Saxe, R., & Wexler, A. (2005). Making sense of another mind: The role of the right temporo-parietal junction. Neuropsychologia, 43, 1391–1399.

8/17/09 5:26:28 PM

References Schmolck, H., & Squire, L. R. (2001). Impaired perception of facial emotions following bilateral damage to the anterior temporal lobe. Neuropsychology, 15, 30–38.

Tranel, D., Bechara, A., & Denburg, N. L. (2002). Asymmetric functional roles of right and left ventromedial prefrontal cortices in social conduct, decision-making, and emotional processing. Cortex, 38, 589–612.

Semendeferi, K., Armstrong, E., Schleicher, A., Zilles, K., & Van Hoesen, G. W. (1998). Limbic frontal cortex in hominoids: A comparative study of area 13. American Journal of Physical Anthropology, 106, 129–155.

Tranel, D., Damasio, H., Denburg, N., & Bechara, A. (2005). Does gender play a role in functional asymmetry of ventromedial prefrontal cortex? Brain, 128, 2872–2881.

Semendeferi, K., Armstrong, E., Schleicher, A., Zilles, K., & Van Hoesen, G. W. (2001). Prefrontal cortex in humans and apes: A comparative study of area 10. American Journal of Physical Anthropology, 114, 224–241.

Tranel, D., & Hyman, B. T. (1990). Neuropsychological correlates of bilateral amygdala damage. Archives of Neurology, 47, 349–355.

Semendeferi, K., & Damasio, H. (2000). The brain and its main anatomical subdivisions in living hominoids using magnetic resonance imaging. Journal of Human Evolution, 38, 317–332. Semendeferi, K., Damasio, H., Frank, R., & Van Hoesen, G. W. (1997). The evolution of the frontal lobes: A volumetric analysis based on threedimensional reconstructions of magnetic resonance scans of human and ape brains. Journal of Human Evolution, 32, 375–388. Shamay-Tsoory, S. G., Tomer, R., Berger, B. D., & Aharon-Peretz, J. (2003). Characterization of empathy deficits following prefrontal brain damage: The role of the right ventromedial prefrontal cortex. Journal of Cognitive Neuroscience, 15, 324–337. Shamay-Tsoory, S. G., Tomer, R., Berger, B. D., Goldsher, D., & AharonPeretz, J. (2005). Impaired “affective theory of mind” is associated with right ventromedial prefrontal damage. Cognitive and Behavioral Neurology, 18(1), 55–67. Siebert, M., Markowitsch, H. J., & Bartel, P. (2003). Amygdala, affect and cognition: Evidence from 10 patients with Urbach-Wiethe disease. Brain, 126, 2627–2637. Sohn, M. H., Albert, M. V., Jung, K., Carter, C. S., & Anderson, J. R. (2007). Anticipation of conflict monitoring in the anterior cingulate cortex and the prefrontal cortex. Proceedings of the National Academy of Sciences, USA, 104, 10330–10334. Sprengelmeyer, R., Young, A. W., Schroeder, U., Grossenbacher, P. G., Federlein, J., Buttner, T., et al. (1999). Knowing no fear. Philosophical Transactions of the Royal Society, Series B, Biological Sciences, 266, 2451–2456. Tarr, M. J., & Gauthier, I. (2000). FFA: A flexible fusiform area for subordinate-level visual processing automatized by expertise. Nature Neuroscience, 3, 764–769.

c47.indd Sec5:939

939

Tsuchiya, N., & Adolphs, R. (2007). Emotion and consciousness. Trends in Cognitive Sciences, 11, 158–167. Veenstra, G. (2002). Explicating social capital: Trust and participation in the civil space. Canadian Journal of Sociology, 27, 547–572. Vogt, B. A. (2005). Pain and emotion interactions in subregions of the cingulate gyrus. Nature Reviews: Neuroscience, 6, 533–544. Vollm, B. A., Taylor, A. N., Richardson, P., Corcoran, R., Stirling, J., McKie, S., et al. (2006). Neuronal correlates of theory of mind and empathy: A functional magnetic resonance imaging study in a nonverbal task. NeuroImage, 29, 90–98. Whalen, P. J. (1999). Fear, vigilance, and ambiguity: Initial neuroimaging studies of the human amygdala. Current Directions in Psychological Science, 7, 177–187. Whalen, P. J., Kagan, J., Cook, R. G., Davis, F. C., Kim, H., Polis, S., et al. (2004, December 17). Human amygdala responsivity to masked fearful eye whites. Science, 306, 2061. Whalen, P. J., Shin, L. M., McInerney, S. C., Fischer, H., Wright, C. I., & Rauch, S. L. (2001). A functional MRI study of human amygdala responses to facial expressions of fear versus anger. Emotion, 1, 70–83. Whiten, A., & Byrne, R. (Eds.). (1997). Machiavellian intelligence: Pt. II. Extensions and evaluations. Cambridge, England: Cambridge University. Wicker, B., Perrett, D. I., Baron-Cohen, S., & Decety, J. (2003). Being the target of another ’s emotion: A PET study. Neuropsychologia, 41, 139–146. Young, A. W., Hellawell, D. J., Van de Wal, C., & Johnson, M. (1996). Facial expression processing after amygdalotomy. Neuropsychologia, 34, 31–39.

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Chapter 48

Empathy and Intersubjectivity JEAN DECETY AND CLAUS LAMM

empathy deficits, and a wide array of psychotherapeutic approaches stress clinical empathy as a fundamental component of treatment (Farrow & Woodruff, 2007). These are all good reasons to investigate the computational and biological mechanisms that underpin the processes involved in interpersonal sensitivity and intersubjectivity. There has been a great upsurge in neuroimaging investigations of empathy. Most of these investigations reflect the approach of social neuroscience, which combines research designs and behavioral measures used in social psychology with neuroscience markers (Cacioppo, 2002; Cacioppo, Berntson, Sheridan, & McClintock, 2000). Such an approach plays an important role in disambiguating competing theories in social psychology in general and in empathy-related research in particular (Decety & Hodges, 2006). For example, a critical question debated among social psychologists is whether perspective-taking instructions induce empathic concern or personal distress, and to what extent prosocial motivation springs from self-other overlap. In this chapter, we focus on social neuroscience research exploring how people respond behaviorally and neurally to the pain of others. The perception of pain in others constitutes an ecologically valid way to investigate the mechanisms underpinning the experience of empathy for two reasons: First everybody knows what pain is—it is a common and universal experience—and knows its physical and psychological manifestations; second, we have good knowledge about the neurophysiological pathways involved in processing nociceptive information. Findings from recent functional neuroimaging studies of pain empathy demonstrate that an observer ’s mere perception of another individual in pain results in the activation of the neural network involved in processing the firsthand experience of pain. This intimate (yet not complete) overlap between the neural circuits responsible for our ability to perceive the pain of others and those underpinning our own self-experience of pain supports the shared-representation theory of social cognition (Decety & Sommerville, 2003). This theory posits that perceiving someone else’s emotion

Human beings are intrinsically social. Our survival critically depends on social interactions with others, the formation of alliances, and accurate social judgments (Cacioppo, 2002). We are motivated to form and maintain positive and significant relationships (Baumeister & Leary, 1995), and most of our actions are directed toward or are responses to others (Batson, 1990). No single factor can account for human social cognitive evolution (e.g., diet and climate), but the single most important factor is the increasing complexity of hominid social groups (Bjorklund & Bering, 2003). It is therefore logical that dedicated mechanisms have evolved to perceive, understand, predict, and respond to the internal states (subjective in nature) of other individuals. The construct of empathy accounts for a fundamental aspect of social interaction (see Table 48.1 for definitions). Philosophers and psychologists have long debated the nature of empathy (e.g., Ickes, 2003; Smith, 1790; Thompson, 2001), and whether the capacity to share and understand other people’s emotions sets humans apart from other species (e.g., de Waal, 2005). Here, we consider empathy as a construct accounting for a sense of similarity in feelings experienced by the self and the other, without confusion between the two individuals (Decety & Jackson, 2004; Decety & Lamm, 2006; Eisenberg, Spinrad, & Sadovsky, 2006). The experience of empathy can lead to sympathy (concern for another based on the apprehension or comprehension of the other ’s emotional state or condition), or even personal distress (an aversive, self-focused emotional reaction to the apprehension or comprehension of another ’s emotional state or condition) when there is confusion between self and other. Knowledge of empathic behavior is essential for an understanding of human social and moral development (Eisenberg et al., 1994). It is generally assumed that people who experience others’ emotion and feel concerned for them are motivated to help (Hoffman, 2000). Furthermore, psychopathologies are marked by The writing was supported by grant #BCS-0718480 to Dr. Jean Decety from the National Science Foundation. 940

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TABLE 48.1 Despite the abundance of definitions of empathy, it is possible and recommended to differentiate emotional contagion, empathy, sympathy, and personal distress. Term

Definition

Emotional contagion

The tendency to automatically mimic and synchronize facial expressions, vocalizations, postures, and movements with those of another individual.

Empathy

An emotional response that stems from another ’s state and is congruent with the other ’s emotional state. It involves at least a minimal distinction between self and other. Empathy is not a separate emotion, but a kind of induction process by which emotions, both positive and negative, are shared.

Personal distress

An aversive state (e.g., anxiety, worry) that is not congruent with the other ’s state and that leads to a self-oriented, egoistic reaction.

Sympathy (or empathic concern)

A reference to feelings of sorrow or of being sorry for another. It is often the consequence of empathy, although it is possible that sympathy results from cognitive perspective taking. Sympathy is believed to involve an other-oriented, altruistic motivation.

Emotion

A process that facilitates appropriate physiological responses to aid the survival of the organism.

and having an emotional response or subjective feeling state fundamentally draw on the same computational processes and rely on somatosensory and motor representations (Sommerville & Decety, 2006). However, a complete self-other overlap in neural circuits can lead to personal distress and possibly be detrimental to empathic concern and prosocial behavior. Personal distress may even result in a more egoistic motivation to reduce it by withdrawing from the stressor, thereby decreasing the likelihood of prosocial behavior (Tice, Bratslavsky, & Baumeister, 2001). The chapter starts with a discussion of the evolutionary origins of empathy focusing on the role of the autonomic nervous system, followed by a section on the role of hormones. Then, we review the empirical evidence that supports the notion of shared neural circuits for the generation of behavior in oneself and its perception from others. We emphasize recent functional neuroimaging studies showing the involvement of shared neural circuits during the observation of pain in others and during the experience of pain in the self. Next, we discuss how perspective taking and the ability to differentiate the self from the other affect this sharing mechanism. Finally, we examine how some interpersonal variables modulate empathic concern and personal distress. The chapter concludes with cautionary considerations about the social neuroscience approach to intersubjective processes.

THE EVOLUTIONARY ORIGINS OF EMPATHY Natural selection has fine-tuned the mechanisms that serve the specific demands of each species’ ecology. MacLean (1985) has proposed that empathy emerged in relation with the evolution of mammals (180 million years ago). In the evolutionary transition from reptiles to mammals, three

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key developments were (1) nursing, in conjunction with maternal care; (2) audiovocal communication for maintaining maternal-offspring contact; and (3) play. The development of this behavioral triad may have depended on the evolution of the thalamocingulate division of the limbic system, a derivative from early mammals. The thalamocingulate division (which has no distinctive counterpart in the reptilian brain) is, in turn, geared in with the prefrontal neocortex that, in human beings, may be inferred to play a key role in familial acculturation. When mammals developed parenting behavior, the stage was set for increased exposure and responsiveness to emotional signals of others including signals of pain, separation, and distress. Indeed, parenting involves the protection and transfer of energy, information, and social relations to offspring. African hominoids, including chimpanzees (Pan troglodytes), gorillas (Gorilla gorilla), and humans (Homo sapiens), share a number of parenting mechanisms with other placental mammals, including internal gestation, lactation, and attachment mechanisms involving neuropeptides such as oxytocin (Geary & Flinn, 2001). The phylogenic origin of behaviors associated with social engagement has been linked to the evolution of the autonomic nervous system and how it relates to emotion. According to Porges (2001), social approach or withdrawal stems from the implicit computation of feelings of safety, discomfort, or potential danger. He proposed that the evolution of the autonomic nervous system (sympathetic and parasympathetic systems) provides a means to understand the adaptive significance of mammalian affective processes including empathy and the establishment of lasting social bonds. These basic evaluative systems are associated with motor responses that aid the adaptive responding of the organism. At this primitive level, appetitive and aversive behavioral responses are modulated by specific neural circuits in the brain that share common neuroarchitectures

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among mammals (Parr & Waller, 2007). These brain systems are genetically hardwired to enable animals to respond unconditionally to threatening, or appetitive, stimuli using specific response patterns that are most adaptive to the particular species and environmental condition. The limbic system, which includes the hypothalamus, the parahippocampal cortex, the amygdala, and several interconnected areas (septum, basal ganglia, nucleus accumbens, insula, retrospenial cingulate cortex, and prefrontal cortex) is primarily responsible for emotion processing. What unites these regions are their roles in motivation and emotion, mediated by connections with the autonomic system. The limbic system also projects to the cingulate and orbitofrontal cortices, which are involved with the regulation of emotion. There is evidence for a lateralization of emotion processing in humans and primates that has been marshaled under two distinct theories. One theory states that the right hemisphere is primarily responsible for emotional processing (Cacioppo & Gardner, 1999), whereas another one suggests that the right hemisphere regulates negative emotion and the left hemisphere regulates positive emotion (Davidson, 1992). This asymmetry is anatomically based on an asymmetrical representation of homeostatic activity that originates from asymmetries in the peripheral autonomic nervous system, and fits well with the homeostatic model of emotional awareness, which posits that emotions are organized according to the fundamental principle of autonomic opponency for the management of physical and mental energy (Craig, 2005). Supporting evidence for the lateralization of emotion comes from neuroimaging studies and neuropsychological observations with brain-damaged patients, as well as studies in nonhuman primates. In one study, tympanic membrane temperature (Tty) was used to assess asymmetries in the perception of emotional stimuli in chimpanzees (Parr & Hopkins, 2000). The tympanic membrane is an indirect, but reliable, site from which to measure brain temperature and is strongly influenced by autonomic and behavioral activity. In that study, chimpanzees were shown positive, neutral, and negative emotional videos depicting scenes of play, scenery, and severe aggression, respectively. During the negative emotional condition, right Tty was significantly higher than the baseline temperature. This effect was relatively stable, long lasting, and consistent across individuals. Temperatures did not change significantly from baseline in the neutral or positive emotion condition, although a significant number of measurements showed increased left Tty during the neutral emotion condition. These data suggest that viewing emotional stimuli results in asymmetrical changes in brain temperature (in particular increased right Tty during the negative emotion condition), providing evidence of emotional

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arousal in chimpanzees and support for right hemispheric asymmetry in our closest living ancestor. At the behavioral level, it is evident from the descriptions of comparative psychologists and ethologists that behaviors homologous to empathy can be observed in other mammalian species. Notably, reports on ape empathic reactions suggest that, apart from emotional connectedness, apes have an explicit appreciation of the other ’s situation (de Waal, 1996). A good example is consolation, defined as reassurance behavior by an uninvolved bystander toward one of the combatants in a previous aggressive incident (de Waal & van Roosmalen, 1979). De Waal (1996) has convincingly argued that empathy is not an all-or-nothing phenomenon, and many forms of empathy exist between the extremes of mere agitation at the distress of another and full understanding of their predicament. Many other comparative psychologists view empathy as a kind of induction process by which emotions, both positive and negative, are shared, and by which the probabilities of similar behavior are increased in the participants. In the view developed in this chapter, this is a necessary but not a sufficient mechanism to account for the full-blown ability of human empathy. It does provide the basic primitive, yet crucial mechanism on which empathy develops. Some aspects of empathy are present in other species, such as motor mimicry and emotion contagion (see de Waal & Thompson, 2005). Parr (2001) conducted an experiment in which peripheral skin temperature (indicating greater negative arousal) was measured in chimpanzees while they were exposed to emotionally negative video scenes. Results demonstrate that skin temperature decreased when subjects viewed videos of conspecifics injected with needles or videos of the needles themselves, but not videos of a conspecific chasing the veterinarian. Thus, when chimpanzee are exposed to meaningful emotional stimuli, they are subject to physiological changes similar to those observed during fear in humans, which is similar to the dispositional effects of emotional contagion (Hatfield, 2009). In humans, the construct of empathy accounts for a more complex psychological state than the one associated with the automatic sharing of emotions. As in other species, emotions and feelings may be shared between individuals, but humans also can intentionally “feel for” and act on behalf of other people whose experiences may differ greatly from their own (Batson et al., 1991; Decety & Hodges, 2006). This phenomenon, called empathic concern or sympathy, is often associated with prosocial behaviors such as helping kin, and has been considered as a chief enabling process for altruism. According to Wilson (1988), empathic helping behavior has evolved because of its contribution to genetic fitness (kin selection). In humans and other mammals, an impulse to care for offspring is almost

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Neuroendocrinological Aspects

certainly genetically hardwired. It is far less clear that an impulse to care for siblings, more remote kin, and similar nonkin is genetically hardwired (Batson, 2006). The emergence of altruism, of empathizing with and caring for those who are not kin is thus not easily explained within the framework of neo-Darwinian theories of natural selection. Social learning explanations of kinship patterns in human helping behavior are thus highly plausible. One of the most striking aspects of human empathy is that it can be felt for virtually any target—even targets of a different species. In addition, as emphasized by Harris (2000), humans, unlike other primates, can put their emotions into words, allowing them not only to express emotion but also to report on current as well as past emotions. These reports provide an opportunity to share, explain, and regulate emotional experience that is not found in other species. Conversation helps to develop empathy, for it is often here that people learn of shared experiences and feelings. Moreover, this self-reflexive capability (which includes emotion regulation) may be an important difference between humans and other animals (Povinelli, 2001). Two key regions, the anterior insula and anterior cingulate cortex (ACC), involved in affective processing in general and empathy for pain in particular, have singularly evolved in apes and humans. Cytoarchitectonic work by Allman, Watson, Tetreault, and Hakeem (2005) indicates that a population of large spindle neurons is uniquely found in the anterior insula and anterior cingulate of humanoid primates. Most notably, they reported a trenchant phylogenetic correlation, in that spindle cells are most numerous in aged humans, but progressively less numerous in children, gorillas, bonobos and chimpanzees, and nonexistent in macaque monkeys. Craig (2007) recently suggested that these spindle neurons interconnect the most advanced portions of limbic sensory (anterior insula) and limbic motor (ACC) cortices, both ipsilaterally and contralaterally. This is in sharp contrast to the tightly interconnected and contiguous sensorimotor cortices, which are situated physically far apart as a consequence of the pattern of evolutionary development of limbic cortices. Thus, the spindle neurons could enable fast, complex, and highly integrated emotional behaviors. In support of this view, convergent functional imaging findings reveal that the anterior insula and the anterior cingulate cortices are conjointly activated during all human emotions. According to Craig (2002), this indicates that the limbic sensory representation of subjective feelings (in the anterior insula) and the limbic motor representation of volitional agency (in the anterior cingulate) together form the fundamental neuroanatomical basis for all human emotions, consistent with the definition of an emotion in humans as both a feeling and a motivation with concomitant autonomic sequelae (Rolls, 1999).

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Overall, this evolutionary conceptual view is compatible with the hypothesis that advanced levels of social cognition may have arisen as an emergent property of powerful executive functioning assisted by the representational properties of language (Barrett, Henzi, & Dunbar, 2003). These higher levels operate on previous levels of organization and should not be seen as independent of, or conflicting with one another. Evolution has constructed layers of increasing complexity, from nonrepresentational (e.g., emotion contagion) to representational and meta-representational mechanisms (e.g., sympathy), which need to be taken into account for a full understanding of human empathy.

NEUROENDOCRINOLOGICAL ASPECTS Although we prefer to believe that our behavior is largely dependent on the processes in the central nervous system, other factors such as hormonal status or autonomic nervous system activity have to be taken into account if we want to achieve an accurate understanding of human social interaction. Hormones affect the body and the nervous system in various ways and shape the way in which our bodies and minds are affected by social interactions. As will be exemplified later in this chapter, individual differences in stress coping mechanisms may determine how we respond to another’s distress. A potential mechanism for these individual differences is the release of stress hormones such as cortisol, which has been shown to affect approach and withdrawal responses to threatening social stimuli (Roelofs, Elzinga, & Rotteveel, 2005). In a similar vein, the neuropeptides oxytocin (OT) and vasopressin (VP) have been repeatedly associated with individual differences in social cognition and behaviors. In nonhuman mammals, oxytocin is a key mediator of complex emotional and social behaviors, including attachment, social recognition, and aggression. It has been shown in prairie voles (Microtus ochrogaster) that OT facilitates positive social behaviors and promotes social attachment, such as parental and pair bonding behavior (Carter, Williams, Witt, & Insel, 1992). Among other mechanisms, OT seems to exert these effects by enabling a more adaptive response of the organism to stressful events. OT achieves this by modulating autonomic arousal, in particular by reducing activity of the hypothalamic-pituitary-adrenal (HPA) axis. The HPA axis strongly affects how we react to stressors by increasing sympathetic arousal via the release of stress hormones such as cortisol in humans or corticosterone in rodents. Importantly, the effects of the HPA axis are rather slow and tonic and sometimes persist over extended periods—acting via changes in gene expression both in the

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body and in the brain. This is in contrast to the second system involved in stress responses, the sympatheticadrenomedullary system, which triggers fast mobilization of vital resources by the release of adrenalin and noradrenalin (Gunnar & Quevedo, 2007, for review). Interestingly, OT and VP receptors are found in areas of the nervous system associated with regulation of HPA axis and autonomic nervous system activity. In addition, OT and VP receptors in brain structures associated with social behaviors and emotional processing might be the central nervous system substrates for the facilitating effects these hormones have on social interactions. For example, OT receptors are found in the amygdala, the medial prefrontal cortex, and the septum. Selective effects of OT on these regions have been demonstrated both in nonhuman mammals and in humans. The amygdala plays a central role in autonomic function and has been linked to fear and associated automatic responses to environmental threats (e.g., LeDoux, 2000). The anxiolytic and calming role of OT might be achieved by acting on receptors in this subcortical structure. Microinjections of OT in the central nucleus of the amygdala inhibit aggressive maternal behavior in female rats (Consiglio, Borsoi, Pereira, & Lucion, 2005), in line with the finding that OT excites inhibitory neurons in the central amygdala (Huber, Veinante, & Stoop, 2005). In humans, neuroimaging demonstrates reduced amygdala responses to social and nonsocial stimuli after intranasal administration of OT (Kirsch et al., 2005). Individual OT levels also seem to be related to human trust and trustworthiness—as shown by a higher level of trust in an economic exchange game requiring participants to accept social risks (Kosfeld, Heinrichs, Zak, Fischbacher, & Fehr, 2005) or as expressed by higher OT levels with higher intentional trust in a similar experimental context (Zak, Kurzban, & Matzner, 2005). The effects of OT in promoting social attachment led to speculations that it also plays a role for empathic concern, sympathy, and prosocial behavior. OT and VP are also potential candidates to explain the etiology of autism spectrum disorders (Carter, 2007, for review). ASD are characterized by deficits in social behavior and communication, with one distinctive feature being deficits in theory of mind (the ability to reason about intentions and beliefs of others) and empathy. In line with this idea, preliminary evidence from a small sample of ASD participants showed better comprehension of affective speech and the assignment of emotional significance to speech intonation with OT administration (Hollander et al., 2007). In the normal behavioral spectrum, OT enhanced the ability to infer others’ mental states by interpreting subtle social cues expressed in the eye region. Notably, this effect was only observed for more difficult emotional-social expressions

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(Domes, Heinrichs, Glascher, et al., 2007). In a companion study, the same group demonstrated that activation in the right (but not the left) amygdala is reduced when presenting facial displays of emotions—irrespective of their valence (Domes, Heinrichs, Michel, Berger, & Herpertz, 2007). A potential mechanism of OT in enabling empathy and reading the intentions of others might be a general reduction of arousal and anxiety usually triggered by social and nonsocial stressors. This should allow for a more controlled processing of social cues and a more adaptive response to the emotional state of others. This hypothesis would be in line with evidence from social psychology showing that the need to belong to others—which can be interpreted as a measure of social attachment or the motivation for it—correlates with higher empathic accuracy for both positive and negative affective information (Pickett, Gardner, & Knowles, 2004). Alternatively, and as speculated by Domes and colleagues, OT-induced increases in empathy and mind reading might reduce social ambiguity and by this means encourage social approach and trusting behavior. While research on OT and human social behavior is still in its infancy, future investigations will have to show which neural and neuronal-hormonal mechanisms are at play when it exerts its effects. The available evidence indicates a promising role of OT for promoting intersubjective understanding and prosocial behavior.

SHARED NEURAL CIRCUITS BETWEEN SELF AND OTHER It has long been suggested that empathy involves resonating with another person’s unconscious affect. Ax in 1964 proposed that empathy can be thought of as an autonomic nervous system state that tends to simulate the state of another person. In the same vein, Basch (1983) speculated that, because their respective autonomic nervous systems are genetically programmed to respond in a similar fashion, a given affective expression by a member of a particular species can trigger similar responses in other members of that species. This idea fits neatly with the notion of embodiment, which refers both to actual bodily states and to simulations of experience in the brain’s modalityspecific systems for perception, action, and the introspective systems that underlie conscious experiences of emotion, motivation, and cognitive operations (Niedenthal, Barsalou, Winkielman, Krauth-Gruber, & Ric, 2005). Furthermore, the view that unconscious automatic mimicry of a target generates in the observer the autonomic response associated with that bodily state and facial expression subsequently received empirical support from

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Perceiving Others in Pain 945

behavioral and physiological studies marshaled under the perception-action coupling account of empathy (Preston & de Waal, 2002). The core assumption of the perceptionaction model of empathy is that perceiving a target’s state automatically activates the corresponding representations of that state in the observer, which in turn activates somatic and autonomic responses. The discovery of sensory-motor neurons (called mirror neurons) in the premotor and posterior parietal cortex discharging both during the production of a given action and the perception of the same action performed by another individual provides the physiological mechanism for this direct link between perception and action (Rizzolatti & Craighero, 2004). In line with the perception-action matching mechanism, a number of behavioral and electromygraphic studies demonstrated that viewing facial expressions triggers similar expressions on viewer ’s own face, even in the absence of conscious recognition of the stimulus (Hatfield, Cacioppo, & Rapson, 1994). While watching someone smile, the observer activates the same facial muscles involved in producing a smile at a subthreshold level, and this would create the corresponding feeling of happiness in the observer. There is evidence for such a mechanism in the recognition of emotion from facial expression. Viewing facial expressions triggers expressions on one’s own face, even in the absence of conscious recognition of the stimulus (Dimberg, Thunberg, & Elmehed, 2000). Making a facial expression generates changes in the autonomic nervous system and is associated with feeling the corresponding emotion. In a series of experiments, Levenson, Ekman, and Friesen (1990) instructed participants to produce facial configurations for anger, disgust, fear, happiness, sadness, and surprise while heart rate, skin conductance, finger temperature, and somatic activity were monitored. They found that such a voluntary facial activity produced significant levels of subjective experience of the associated emotions as well as specific and reliable autonomic measures. A functional magnetic resonance imaging (fMRI) experiment extended these results by showing that when participants are required to observe or to imitate facial expressions of various emotions, increased neurodynamic activity is detected in the brain regions implicated in the facial expressions of these emotions, including the superior temporal sulcus, the anterior insula, and the amygdala, as well as specific areas of the premotor cortex (Carr, Iacoboni, Dubeau, Mazziotta, & Lenzi, 2003). Accumulating evidence suggests that a mirroring or resonance mechanism is also at play both when one experiences sensory and affective feelings in the self and perceives them in others. Even at the level of the somatosensory cortex, seeing another ’s neck or face being touched elicits appropriately organized somatotopic activations in the mind of the observer (Blakemore, Bristow, Bird, Frith, &

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Ward, 2005). Robust support for the involvement of shared neural circuits in the perception of affective states comes from recent neuroimaging and transcranial magnetic stimulation (TMS). The firsthand experience of disgust and the sight of disgusted facial expressions in others both activate the anterior insula (Wicker et al., 2003). Similarly, the observation of hand and face actions performed with an emotion engages regions that are also involved in the perception and experience of emotion and communication (Grosbras & Paus, 2006).

PERCEIVING OTHERS IN PAIN Pain is conceived as a subjective experience triggered by the activation of a mental/neural representation of actual or potential tissue damage. This representation involves somatic sensory features, as well as affective-motivational reactions associated with the promotion of protective or recuperative visceromotor and behavioral responses. It is the affective experience of pain that signals an aversive state and motivates behavior to terminate, reduce, or escape exposure to the source of noxious stimulation (Price, 2000). The expression of pain also provides a crucial signal that can motivate soothing and caring behaviors in others. It is therefore a valuable and ecologically valid means to investigate the mechanisms underlying the experience of empathy. A growing body of research demonstrates shared physiological mechanisms for the firsthand experience of pain and the perception of pain in others (Figure 48.1). A specific indication for such a shared neural mechanism comes from a single-cell recordings study in neurological patients by Hutchison, Davis, Lozano, Tasker, and Dostrovsky (1999). These authors recorded with microelectrodes from the dorsal ACC as several types of painful stimuli were delivered to the patients’ hands, and found stimulusspecific pain responses in Brodmann area 24. Some of these cells displayed mirror-like properties, as they responded to the pinprick whether it was administered to the patient’s own hand or to that of the experimenter. The first functional MRI experiment that investigated neural responses to both the firsthand experience of pain and perception of pain in others was conducted by Morrison, Lloyd, di Pellegrino, and Roberts (2004). Study participants were scanned during a condition of feeling a moderately painful pinprick stimulus to the fingertips and another condition in which they watched another person’s hand undergo similar stimulation. Both conditions resulted in common hemodynamic activity in a pain-related area in the right dorsal ACC. In contrast, the primary somatosensory cortex showed significant activations in response to noxious tactile, but not visual, stimuli.

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Empathy and Intersubjectivity M1

Prefrontal Cortex

Cingulate Cortex

S1

SMA

Somatosensory Cortices

S2 ACC

Basal Ganglia Insula

PF

PCC

PPC

INSULA MDVC VMPO VPL HT

Amygdala Hippocampus

PAG

AMYG PB

Thalamus

Brainstem PAG

A-Delta Afferents C-Fibre Afferents

Spinothalamic Tract

Spinal Cord

The primary (SI) and secondary (SII) sensory cortices are involved in the sensory-discriminative aspect of pain, e.g., the bodily location and intensity of the stimulus. ACC and insula subserve the affective-motivational component, i.e., the evaluation of subjective discomfort and response preparation in the context of painful or aversive stimuli

Schematic diagram of the main anatomical component of the ‘pain matrix’

Figure 48.1 Neurophysiological research on pain points out a distinction between the sensory-discriminative aspect of pain processing and the affective-subjective one.

demonstrated that the observation of pain in others recruits brain areas chiefly involved in the affective and motivational processing of direct pain perception (areas colored in gray).

Note: These two aspects are underpinned by discrete yet interacting neural networks. A growing number of neuroimaging studies recently

Another fMRI study demonstrated that the dorsal ACC, the anterior insula, cerebellum, and brain stem were activated when healthy participants experienced a painful stimulus, as well as when they observed a signal indicating that another person was receiving a similar stimulus. However, only the actual experience of pain resulted in activation in the somatosensory cortex and a more ventral region of the ACC (Singer et al., 2004). The different response patterns in the two areas are consistent with the ACC’s role in coding the motivational-affective dimension of pain, which is associated with the preparation of behavioral responses to aversive events. These findings are supported by an fMRI study in which participants were shown still photographs depicting right hands and feet in painful or neutral everyday-life situations, and asked to imagine the level of pain that these situations would produce (Jackson, Meltzoff, & Decety, 2005). Significant activation in regions involved in the affective aspects of pain processing, notably the dorsal ACC, the thalamus, and the anterior insula was detected, but no activity in the somatosensory cortex. Moreover, the level of activity within the dorsal ACC was strongly correlated with participants’ mean ratings of pain attributed to the different situations.

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Crying is a universal vocalization in human infants as well as in the infants of other mammals (Newman, 2007). In all studied mammals, young infants emit a speciesspecific cry when in distress, and mothers generally respond with caretaking behavior (e.g., Bell & Ainsworth, 1972). A functional MRI study measured brain activity in healthy, breastfeeding first-time mothers with young infants while they listened to infant cries, white noise control sounds, and a rest condition (Lorberbaum et al., 2002). Signal increase was detected in ACC, anterior insula, the medial thalamus, and medial prefrontal and right orbitofrontal cortices. Several other structures thought important in rodent maternal behavior also displayed increased activity, including the midbrain, hypothalamus, dorsal and ventral striatum, and the vicinity of the lateral septal region. Facial expressions of pain constitute an important category of facial expression that is readily understood by observers. One study investigated the neural response to pain expressions by performing functional magnetic resonance imaging (fMRI) as subjects viewed short video sequences showing faces expressing either moderate pain or, for comparison, no pain (Botvinick et al., 2005). In alternate blocks, the same subjects received both painful

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Perceiving Others in Pain 947

and nonpainful thermal stimulation. Facial expressions of pain were found to engage cortical areas also engaged by the firsthand experience of pain, including the anterior cingulate cortex and anterior insula. Using fMRI, Saarela and colleagues (2006) showed that not only the presence of pain but also the intensity of the observed pain is encoded in the observer ’s brain—as occurs during the observer ’s own pain experience. When subjects observed pain from the faces of chronic pain patients, activations in bilateral anterior insula, left anterior cingulate cortex, and left inferior parietal lobe in the observers’ brains correlated with their estimates of the intensity of observed pain. Furthermore, the strengths of activation in the left anterior insula and left inferior frontal gyrus during observation of intensified pain correlated with subjects’ self-rated empathy. Overall, these fMRI studies consistently detected activation of the anterior insula and dorsal ACC (two key regions that belong to the processing of the affective-motivational dimension of pain) during the perception of pain in others, and thus lend support to the idea that common neural substrates are involved in representing one’s own and others’ affective states. Most of these neuroimaging studies (except Moriguchi et al., 2007) did not report significant signal change in the somatosensory cortex/posterior insula (the region involved in the sensory discriminative dimension of pain). This result seems at odds with the perception-action coupling mechanism (mirror-neuron system) that underlies the automatic resonance between self and others. The somatosensory cortex/posterior dorsal insula contributes to the sensory discriminative dimension of pain as demonstrated by neuroimaging investigations and lesion studies (e.g., Symonds, Gordon, Bixby, & Mande, 2006). Two recent studies indicate involvement of motor cortex during the perception of pain in others. These studies used transcranial magnetic stimulation (TMS) and found changes in the corticospinal motor representations of hand muscles in individuals observing needles penetrating hands or feet of a human model (Avenanti, Bueti, Galati, & Aglioti, 2005). Using electroencephalography (EEG), another study found modulation of somatosensory cortex activity contingent on observation of others’ pain (Bufalari, Aprile, Avenanti, Di Russo, & Aglioti, 2007). Two possibilities can explain the discrepancy of the EEG and TMS with the fMRI studies. One is that the TMS and EEG methods can sense subtle changes in the sensorimotor cortex that are below the significance threshold in fMRI techniques. The other possibility is that attending to a specific body part elicits somatosensory activity in the corresponding brain region. This has been demonstrated in a positron emission tomography study in which participants were instructed

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to focus their attention either on the unpleasantness or on the location of the noxious stimuli delivered on the participants’ hands (Kulkarni et al., 2005), with the latter condition resulting in increased regional cerebral blood flow in the contralateral primary somatosensory cortex. To test if the perception of pain in others involves the somatosensory cortex, Cheng, Yang, Lin, Lee, and Decety (2008) measured neuromagnetic oscillatory activity from the primary somatosensory cortex in participants while they observed static pictures depicting body parts in painful and nonpainful situations. The left median nerve was stimulated at the wrist, and the poststimulus rebounds of the ~10-Hz somatosensory cortical oscillations were quantified. Compared with the baseline condition, the level of the ~10-Hz oscillations was suppressed during both observational situations, indicating activation of the somatosensory cortex. Importantly, watching painful compared with nonpainful situations suppressed somatosensory oscillations to a significantly stronger degree. In addition, these suppressions negatively correlated with the perspective taking subscale of the interpersonal reactivity index. These results, consistent with the mirror-neuron system, demonstrate that the perception of pain in others modulates neural activity in somatosensory cortex and supports the idea that the perception of pain in others elicits subtle somatosensory activity that may be difficult to detect by fMRI techniques. Most neuroimaging studies that have explored the overlap in brain response between the observation of behavior performed by others and the generation of the same behavior in self have relied on simple subtraction methods and generally highlight the commonalities between self and other processing, and ignore the differences. This is particularly true for the recent series of fMRI studies that have reported shared neural circuits for the firsthand experience of pain and the perception of pain in others (see Jackson, Rainville, & Decety, 2006). It is possible, as argued by Zaki, Ochsner, Hanelin, Wager, and Mackey (2007), that common activity in ACC and AI may reflect the operation of distinct but overlapping networks of regions that support perception of self or other pain. To address this issue, they scanned participants while they received noxious thermal stimulation (self pain condition) or watched short videos of other people sustaining painful injuries (other pain condition). Analyses identified areas whose activity covaried with ACC and AI activity during self or other pain either across time (intraindividual connectivity) or across participants (interindividual connectivity). Both connectivity analyses identified clusters in the midbrain and periaqueductal gray with greater connectivity to the AI during self-pain as opposed to other pain. The opposite pattern was found in the dorsal medial prefrontal cortex, which

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showed greater connectivity to the ACC and AI during other pain than during self-pain using both types of analysis. Intraindividual connectivity analyses also revealed regions in the superior temporal sulcus, posterior cingulate, and precuneus that became more connected to ACC during other pain compared with self-pain. The results of this experiment document distinct neural networks associated with ACC and anterior insula in response to firsthand experience of pain and response to seeing other people in pain. These networks could not have been detected in prior work that examined overlap between self and other pain in terms of average activity, but not connectivity. Morrison and Downing’s (2007) analyses of single-subject data in generic space similarly suggest that distinct neural networks in anterior and medial cingulate cortex (MCC) are activated during the firsthand versus thirdhand experience of pain. This is in line with a quantitative meta-analysis of published studies on empathy for pain versus pain in the self using Activation Likelihood Estimation. This analysis

Left Insula

reveals distinct subclusters in both ACC/MCC and the insular cortices (Figures 48.2 and 48.3). While activation in MCC seems to be more left-lateralized, caudal, and dorsal during empathy for pain, a rostro-caudal activation gradient is evident in the insular cortex. These distinct activation patterns suggest the involvement of only partially overlapping neural subpopulations and indicate the involvement of distinct cognitive and affective processes. It should also be kept in mind that the effective spatial resolution of fMRI, the different experimental paradigms as well as the inherently complex mapping from cognitive to neural/hemodynamic processes make it difficult to achieve a definite conclusion about how much of the activation during empathy for pain can be attributed to shared neural and mental representations. Summing up, current neuroscientific evidence suggests that merely perceiving another individual in a painful situation yields responses in the neural network associated with the coding of the motivational-affective and the sensory dimensions of pain in oneself. It is worth noting that vicariously instigated activations in the pain matrix are not necessarily specific to the emotional experience of pain, but to other processes such as somatic monitoring, negative stimulus evaluation, and the selection of appropriate skeletomuscular movements of aversion. Thus, the shared neural representations in the affective-motivational part of the pain matrix might not be specific to the sensory qualities of pain, but instead might be associated with more general survival mechanisms such as aversion and withdrawal.

Empathy ⬎ Pain Pain ⬎ Empathy

Right Insula

Figure 48.2 ( Figure C.45 in color section) Results of a metaanalysis comparing empathy for pain with the first experience of pain. Note: Activation differences are projected onto a flattened representation of the left and right hemispheres. Observe that empathy predominantly activates the anterior parts of the insula while pain sensations lead to more rostral activation—especially in the contralateral (left) hemisphere.

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11

Empathy  Pain

Pain  Empathy

8

Figure 48.3 ( Figure C.46 in color section) Results of the same meta-analysis for the MCC/ACC. Note: Observe the more left-lateralized and dorsal activations for empathy for pain.

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Perspective-Taking 949

PERSPECTIVE-TAKING There is consensus among theorists that the ability to adopt and entertain the psychological perspective of others has important consequences in social interaction. Well-developed perspective-taking abilities allow us to overcome our usual egocentrism and tailor our behaviors to others’ expectations (Davis, Conklin, Smith, & Luce, 1996). Further, successful role-taking has been linked to moral reasoning and altruism (Batson et al., 1991). Adopting another person’s perspective involves more than simply focusing our attention on the other. It involves imagining how that person is affected by his or her situation without confusion between the feelings experienced by the self versus feelings experienced by the other person (Decety, 2005). We see others as similar to ourselves on a variety of dimensions and consequently assume that they act as we act, know what we know, and feel what we feel. This default mode is based on a shared representations mechanism between self and other (Decety & Sommerville, 2003) driven by the automatic link between perception and action (Jackson & Decety, 2004). Thus, for successful social interaction, and empathic understanding in particular, an adjustment must operate on the shared representations that are automatically activated through the perception-action coupling mechanism. Whereas the projection of self-traits onto the other does not necessitate any significant storage of knowledge about the other, empathic understanding requires the inclusion of other characteristics within the self. An essential aspect of empathy is to recognize the other person as being like ourselves, while maintaining a clear separation between ourselves and other. Hence, mental flexibility and self-regulation are important components of empathy. We need to calibrate our own perspective that has been activated by the interaction with the other, or even by its mere imagination. Such calibration requires the prefrontal cortex executive resources in conjunction with the temporo-parietal junction, as demonstrated by neuroimaging experiments in healthy participants as well as lesion studies in neurological patients. Several neuroimaging studies have consistently reported that the medial prefrontal cortex is specifically involved in tasks requiring the processing of information relevant to the self, such as traits and attitudes (e.g., Johnson et al., 2002). An fMRI study investigated the neural regions mediating self-referential processing of emotional stimuli and explored how these regions are influenced by the emotional valence of the stimulus (Fossati et al., 2003). Results showed that the self-referential condition induced bilateral activation in the dorsomedial prefrontal cortex, whereas the other referential condition induced activation in lateral prefrontal areas. Activation in the right dorsomedial prefrontal cortex was specific to the self-referential condition

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regardless of the valence of the words. The authors of that study proposed that a specific role of the right dorsomedial prefrontal cortex is to represent states of an emotional episodic self and then to process emotional stimuli with a personally relevant perspective. This proposition is in line with studies showing activations within both the left and right dorsomedial prefrontal cortex during “theory of mind” tasks (Brunet-Gouet & Decety, 2006). Because emotions generally signal issues related to the self, subjects may use emotional cues during some theory of mind tasks to differentiate self from other; this self-related emotional processing is indicated by an increase of activity in the right dorsomedial prefrontal cortex. The medial prefrontal cortex is involved not only when we reflect on ourselves, but also when individuals imagine the subjective perspective of others. Using mental imagery to take the perspective of another is a powerful way to place ourselves in the situation or emotional state of that person. Mental imagery not only enables us to see the world of our conspecifics through their eyes or in their shoes, but may also result in similar sensations as the other person’s (Decety & Grèzes, 2006). A series of neuroimaging studies with healthy volunteers investigated the neural underpinning of perspective taking in three different modes (motor, conceptual, and emotional) of self-other representations. In a first study, participants were scanned while they were asked to either imagine themselves performing everyday actions (e.g., winding up a watch), or to imagine another individual performing similar actions (Ruby & Decety, 2001). Both conditions were associated with common activation in the supplementary motor area (SMA), premotor cortex, and the TPJ region. This neural network corresponds to the shared motor representations between the self and the other. Taking the perspective of the other to simulate his or her behavior resulted in selective activation of the frontopolar cortex and right inferior parietal lobule. In a second study, medical students were shown a series of affirmative health-related sentences (e.g., taking antibiotic drugs causes general fatigue) and were asked to judge their truthfulness either according to their own perspective (as experts in medical knowledge) or according to the perspective of a layperson (Ruby & Decety, 2003). The set of activated regions recruited when the participants put themselves in the shoes of a layperson included the medial prefrontal cortex, the frontopolar cortex, and the right TPJ. In a third study, the participants were presented with short written sentences that depicted real-life situations (e.g., someone opens the toilet door that you have forgotten to lock), which are likely to induce social emotions (e.g., shame, guilt, pride), or other situations that were emotionally neutral (Ruby & Decety, 2004). In one condition, they were asked to imagine how they would feel if they were

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experiencing these situations. And in another condition, they were asked to imagine how their mothers would feel in those situations. Reaction times were prolonged when participants imagined emotional-laden situations compared with neutral ones, both from their own perspective and from the perspective of their mothers. Neurodynamic changes were detected in the frontopolar cortex, the ventromedial prefrontal cortex, the medial prefrontal cortex, and the right inferior parietal lobule when the participants adopted the perspective of their mothers, regardless of the affective content of the situations depicted. Cortical regions that are involved in emotional processing, including the amygdala and the temporal poles, were found activated in the conditions that integrated emotional-laden situations. A recent functional MRI study used a factorial design to examine the neural correlates of self-reflection and perspective taking (D’ Argembeau et al., 2007). Participants were asked to judge the extent to which trait adjectives described their own personality (e.g., ‘‘Are you sociable?’’) or the personality of a close friend (e.g., ‘‘Is Caroline sociable?’’) and were also asked to put themselves in the place of their friend (to take a third-person perspective) and estimate how this person would judge the adjectives, with the target of the judgments again being either the self (e.g., ‘‘According to Caroline, are you sociable?’’) or the other person (e.g., ‘‘According to Caroline, is she sociable?’’). The results showed that self-referential processing ( judgments targeting the self versus the other person) was associated with activation in the ventral and dorsal anterior MPFC, whereas perspective taking (adopting the other person’s perspective, rather than their own, when making judgments) resulted in activation in the posterior dorsal MPFC; the interaction between the two dimensions yielded activation in the left dorsal MPFC. Findings from this study indicate that self-referential processing and perspective taking recruit distinct regions of the MPFC and suggest that the left dorsal MPFC may be involved in decoupling a person’s own perspective from other people’s perspectives on the self. Social psychology has for a long time been interested in the distinction between imagining the other and imagining oneself, and in particular in the emotional and motivational consequences of these two perspectives. A number of these studies show that focusing on another ’s feelings may evoke stronger empathic concern, whereas explicitly putting oneself into the shoes of the target (imagine self) induces both empathic concern and personal distress. Batson, Early, and Salvarini (1997) investigated the affective consequences of different perspective-taking instructions when participants listened to a story about Katie Banks, a young college student struggling with her life after the death of her parents. This study demonstrated that different

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instructions had distinct effects on how participants perceived the target’s situation. Participants imagining themselves in Katie’s place showed stronger signs of discomfort and personal distress as participants focusing on the target’s responses and feelings (imagine other), or as participants instructed to take on an objective, detached point of view. Also, both perspective-taking instructions differed from the detached perspective by resulting in higher empathic concern. This observation may help explain why observing a need situation does not always yield to prosocial behavior: If perceiving another person in an emotionally or physically painful circumstance elicits personal distress, then the observer may tend not to fully attend to the other ’s experience and as a result lack sympathetic behaviors. Two functional MRI studies recently investigated the neural mechanisms subserving the effects of perspectivetaking during the perception of pain in others. One study used pictures of hands and feet in painful scenarios and instructed the participants to imagine and rate the level of pain perceived from two different perspectives (self versus other; Jackson, Brunet, Meltzoff, & Decety, 2006). Results indicated that both the self and the other perspectives are associated with activation in the neural network involved in the processing of the affective aspect of pain, including the dorsal ACC and the anterior insula. However, the self-perspective yielded higher pain ratings and involved the pain matrix more extensively, including the secondary somatosensory cortex, the midinsula, and the caudal part of the anterior cingulate cortex. Adopting the perspective of the other was associated with increased activation in the right temporo-parietal junction and precuneus. In addition, distinct subregions were activated within the insular cortex for the two perspectives (anterior aspect for others and more posterior for self ). These neuroimaging data highlight both the similarities and the distinctiveness of self and other as important aspects of human empathy. The experience of pain in oneself is associated with more caudal activations (within area 24), consistent with spino-thalamic nociceptive projections, whereas the perception of pain in others is represented in more rostral (and dorsal) regions (within area 32). A similar rostro-caudal organization is observed in the insula, which is consistent with its anatomical connectivity and electrophysiological properties (Jackson, Rainville, & Decety, 2006). Painful sensations are evoked in the posterior part of the insula (and not in the anterior part) by direct electrical stimulation of the insular cortex in neurological patients (Ostrowsky et al., 2002). Altogether, these findings are in agreement with the fact that indirect pain representations (as elicited by the observation of pain in others) are qualitatively different from the actual experiences of pain.

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In a second neuroimaging study, the distinction between empathic concern and personal distress was investigated more specifically by using a number of behavioral measures and a set of ecological and extensively validated dynamic stimuli (Lamm, Batson, & Decety, 2007). Participants watched a series of video-clips featuring patients undergoing painful medical treatment. They were asked to either put themselves explicitly in the shoes of the patient (imagine self), or to focus on their feelings and affective expressions (imagine other). The behavioral data confirmed that explicitly projecting oneself into an aversive situation leads to higher personal distress, whereas focusing on the emotional and behavioral reactions of another ’s plight is accompanied by higher empathic concern and lower personal distress. The neuroimaging data are consistent with this finding and provide some insights into the neural correlates of these distinct behavioral responses. The self-perspective evoked stronger hemodynamic responses in brain regions involved in coding the motivationalaffective dimensions of pain, including bilateral insular cortices, anterior MCC, the amygdala, and various structures involved in action control (Figure 48.4). The amygdala plays a critical role in fear-related behaviors, such as the evaluation of actual or potential threats. Imagining oneself to be in a painful and potentially dangerous situation might therefore have triggered a stronger fearful or aversive response than imagining someone else to be in the same situation. This pattern of results fits well with the pioneering research of Stotland (1969) on the effects of perspective taking on empathy and distress. Participants observed an

individual experiencing a painful diathermy using either an imagine self or an imagine other instruction. Stotland found higher vasoconstriction for the other-perspective, and more palmar sweat and higher tension and nervousness in the self-perspective. This finding was interpreted as being more in resonance with the feelings of the target when focusing on his affective expressions and motor responses (imagine other), whereas the first-person perspective led to more self-oriented responding that was less closely matched to the actual feelings of the target. Corresponding with Jackson and colleagues (2006), the insular activation found by Lamm & coworkers was also located in a more posterior, mid-dorsal subsection of this area. The mid-dorsal part of the insula plays a role in coding the sensory-motor aspects of painful stimulation, and it has strong connections with the basal ganglia where activity was also higher during the selfperspective (see also Figure 48.2). Taken together, it appears that the insular activity during the self-perspective reflects simulation of sensory aspects of the painful experience. Such a simulation might serve to mobilize motor areas for the preparation of defensive or withdrawal behaviors, as well as instigate the interoceptive monitoring associated with autonomic changes evoked by this simulation process (Critchley, Wiens, Rotshtein, Öhman, & Dolan, 2005). Finally, the higher activation in premotor structures might connect with a stronger mobilization of motor representations by the more stressful and discomforting firstperson perspective. Further support for this interpretation is provided by a positron emission tomography study investigating the relationship between situational empathic

4.60

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Figure 48.4 Neural and behavioral consequences of two different perspective-taking instructions. Note: (adapted from Lamm et al., 2007). The flat-map representation of the left hemisphere shows higher activations during the self-perspective in limbic/paralimbic (medial and anterior cingulate cortex MCC and ACC,

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insula INS) and cortical brain structures (temporo-parietal junction TPJ, inferior frontal gyrus IFG, postcentral gyrus PCG). From “The Neural Basis of Human Empathy: Effects of Perspective-Taking and Cognitive Appraisal,” by C. Lamm, C. D. Batson, and J. Decety, 2007, Journal of Cognitive Neuroscience, 19, p. 49. Adapted with permission.

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accuracy and brain activity, which also found higher activation in medial premotor structures, partially extending into MCC, when participants witnessed the distress of others (Shamay-Tsoory et al., 2005). This study also pointed to the importance of prefrontal areas in the understanding of distress. Altogether, the available empirical findings reveal important differences between the neural systems involved in first- and third-person perspective-taking and contradict the hypothesis/notion that the self and other completely merge in empathy. The specific activation differences in both the affective and sensorimotor aspects of the pain matrix, along with the higher pain and distress ratings, reflect the selfperspective’s need for more direct and personal involvement. A key region that might facilitate self versus other distinctions is the right temporo-parietal junction (TPJ). The TPJ is activated in most neuroimaging studies on empathy (Decety & Lamm, 2007) and seems to play a decisive role in self-awareness and the sense of agency (the awareness of oneself as an agent who is the initiator of actions, desires, thoughts, and feelings). Agency is essential for a successful navigation of shared representations between self and other. Decety and Lamm (2007) conducted a quantitative meta-analysis of 70 functional neuroimaging studies on agency, empathy, theory of mind, as well as on reorienting of attention. The results demonstrate a substantial overlap in brain activation between low-level processing such as reorienting of attention or the sense of agency and higherlevel social-cognitive abilities such as empathy or theory of mind (see Figure 48.5). These results provide strong empirical support for a domain-general mechanism implemented in the right TPJ, and show that this area is also Activation Overlap: Empathy and Reorienting Attention

Attention Empathy Empathy and Attention

Figure 48.5 ( Figure C.47 in color section) Activation overlap in right TPJ for empathy for pain and reorienting of attention/ change detection. Note: From “The Role of the Right Temporoparietal Junction in Social Interaction: How Low-Level Computational Processes Contribute to Meta-Cognition,” by J. Decety and C. Lamm, 2007, Neuroscientist, 13, 580–593. Adapted with permission.

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engaged in lower-level (bottom-up) computational processes associated with the sense of agency and in reorienting attention to salient stimuli. Thus, self-awareness and a sense of agency both play pivotal roles in empathy and significantly contribute to social interaction. These important aspects are likely to be involved in distinguishing emotional contagion, which relies heavily on the automatic link between perceiving the emotions of another and our own experience of the same emotion, from empathic responses that call for a more detached and aware relation. The neural responses identified in these studies as nonoverlapping between self and other may take advantage of available processing capacities to plan appropriate future actions concerning the other. Furthermore, awareness of our own feelings and the ability to (consciously and automatically) regulate our own emotions may allow us to disconnect empathic responses to others from our own personal distress, with only the former leading to prosocial behavior.

MODULATION OF EMPATHIC RESPONDING Although the mere perception of the behavior of others activates similar circuits in the self, and in the case of empathy for pain neural circuits involved in the firsthand experience of pain, there is also evidence that this unconscious level can be modulated by situational and dispositional variables. Research in social psychology has identified these factors, such as the relationship between target and empathizer, the empathizer ’s dispositions, and the context in which the social interaction takes place. Therefore, whether observing the distress of a close friend results in empathic concern and helping behavior or withdrawal from the situation is influenced by the complex interaction between these factors. Emotion regulation seems to have a particularly important role in social interaction, and it has a clear adaptive function for both the individual and the species (Ochsner & Gross, 2005). It has been demonstrated that individuals who can regulate their emotions are more likely to experience empathy, and also to interact in morally more desirable ways with others (Eisenberg et al., 1994). In contrast, people who experience their emotions intensely, especially negative emotions, are more prone to personal distress, an aversive emotional reaction, such as anxiety or discomfort based on the recognition of another ’s emotional state or condition. In the case of perception of others in pain, the ability to downregulate our emotions is particularly valuable when the distress of the target becomes overwhelming. A mother alarmed by her baby’s cries at night has to cope with her

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Modulation of Empathic Responding 953

own discomfort to provide appropriate care for her distressed offspring. A strategy to regulate emotions is based on cognitive reappraisal. This involves reinterpreting the valence of a stimulus to change the way that we respond to it. It can either be intentionally achieved or result from additional information provided about the emotion-eliciting stimulus. By providing different context information about the consequences of the observed pain, we investigated the effects of cognitive appraisal on the experience of empathy in the previously mentioned fMRI study by Lamm and colleagues (2007). The observed target patients belonged to two different groups. In one group, health and quality of life improved after the painful therapy, while members of the other group did not benefit from the treatment. Thus, stimuli of identically arousing and negatively valenced emotional content were watched with different possibilities to appraise the patients’ pain. The results confirmed our hypotheses and demonstrated that appraisal of an aversive event can considerably alter our responses to it. Patients undergoing noneffective treatment were judged to experience higher levels of pain, and personal distress in the observers was more pronounced when watching videos of these patients. Brain activation was modulated in two subregions of the orbitofrontal cortex (OFC) and the rostral part of aMCC. The OFC is known to play an important role in the evaluation of positive and negative reinforcements and is also involved in emotion reappraisal. Activity in the OFC may thus reflect valence evaluations of the presented stimuli. Interestingly, watching effectively versus noneffectively treated patients did not modulate hemodynamic changes in either the visual-sensory areas or the insula. This suggests that both patient groups triggered an emotional reaction, and that top-down mechanisms did not alter stimulus processing at an early perceptual stage. Since the mere perception of others in pain may lead to personal distress and discomfort, regulatory mechanisms must operate in people who inflict painful procedures in their practice with patient populations to prevent their distress from impairing their ability to be of assistance. In one functional MRI study, physicians who practice acupuncture were compared with naive participants (matched for age, gender, and level of academic education) while observing animated visual stimuli depicting needles being inserted into different body parts including the mouth region, hands, and feet (Cheng, Lin, et al., 2008). Results indicate that the anterior insula, periaqueductal gray, and anterior cingulate cortex were significantly activated in the control participants, but not in the expert participants, who instead showed activation of the medial prefrontal cortices and right inferior parietal lobule. This study establishes that the perception of pain can be modulated by the expertise of the observer.

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Another intrapersonal factor affecting the empathic response is the emotional background state of the observer (Niedenthal, Halberstadt, Margolin, & Innes-Ker, 2000). A depressive mood can affect the way we perceive the expression of emotions by others. In a developmental neuroscience study, limbic structures such as the amygdala and the nucleus accumbens became hyperactive when participants with pediatric bipolar disorder attended to the facial expression of emotion (Pavuluri, O’Connor, Harral, & Sweeney, 2007). Similarly, patients with generalized social phobia show increased amygdala activation when exposed to angry or contemptuous faces (Stein, Goldin, Sareen, Zorrilla, & Brown, 2002). Whether individual differences in dispositional empathy and personal distress modulate the occurrence and intensity of self- versus other-centered responding is currently a matter of debate. Several recent neuroimaging studies demonstrate specific relationships between questionnaire measures of empathy and brain activity. Both Singer and colleagues (2004) and Lamm and colleagues (2007) detected significantly increased activation in insular and cingulate cortices in participants with higher self-reported empathy during perception. This shows modulation of neural activity in the very brain regions that are involved in coding the affective response to the other ’s distress. No such correlations were found in a similar study (Jackson et al., 2005). Also, no correlations with self-report data on personal distress were observed by Lamm et al. (2007) or Jackson, Rainville, and Brunet (2006). However, Lawrence and collaborators (2006) did report such correlations in cingulate and prefrontal regions of participants labeling a target’s mental and affective state. Part of this discrepancy between neuroscience research and dispositional measures may be related to the low validity of self-report measures in predicting actual empathic behavior (Davis & Kraus, 1997). Brain-behavior correlations should be treated with caution, and care must be taken to formulate specific hypotheses about the neural correlates of the dispositional measures as well as what the questionnaire actually measures. The personal distress subscale of the Interpersonal Reactivity Index (Davis et al., 1996) showed correlations close to zero with the experimentally derived distress measures and no significant correlations with brain activation. This indicates that the subscale is probably not an appropriate measure of situative discomfort evoked by the observation of another ’s distress. The effects of interpersonal factors—such as the similarity or closeness of empathizer and target—have been investigated at the behavioral, psychophysiological, and neural levels. Cialdini, Brown, Lewis, Luce, and Neuberg (1997) have documented that perceived oneness—that is, the perceived overlap between self and other—is an

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important predictor of helping behavior and correlates strongly with empathic concern. The work of Lanzetta and Englis (1989) made interesting observations concerning the effects of attitudes on social interaction. Their studies show that, in a competitive relationship, observation of joy can result in distress, while pain in the competitor leads to positive emotions. These findings reflect an important and often ignored aspect of empathy, that this ability can also be used in a malevolent way—as when knowledge about the emotional or cognitive state of competitors is used to harm them. The study by Singer and colleagues (2006) revealed the neural correlates of such counterempathic responding. In that study, participants were first engaged in a sequential prisoner ’s dilemma game with confederate targets who were playing the game either fairly or unfairly. Following this behavioral manipulation, fMRI measures were taken during the observation of fair and unfair players receiving painful stimulation. Compared with the observation of fair players, activation in brain areas coding the affective components of pain was significantly reduced when participants observed unfair players in pain. This effect, however, was detected in male participants only, who also exhibited a concurrent increase of activation in rewardrelated areas. In sum, there is strong behavioral evidence demonstrating that the experience of empathy and personal distress can be modulated by social-cognitive factors. In addition, a few recent neuroscience studies indicate that such a modulation leads to activity changes in the neural systems that process social information. Further studies are required to increase our knowledge about the various factors, processes, and neural and behavioral effects involved in and resulting from the modulation of empathic responses. This knowledge will inform us how empathy can be promoted to ultimately increase humankind’s ability to act in more prosocial and altruistic ways.

SUMMARY In the recent decades, there has been an increased interest in the biological mechanisms that underpin the experience of empathy (shared feelings without confusion between self and others). Much of this new work relies on functional neuroimaging studies with an emphasis on the perception of pain. The combined results of these studies demonstrate that when individuals perceive others in pain or distressful situations, they use the same neural mechanisms as when they are in painful situations themselves. Such a shared neural mechanism offers an interesting foundation for intersubjectivity because it provides a functional bridge between first-person information and third-person information, is

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grounded in self-other equivalence (Decety & Sommerville, 2003; Sommerville & Decety, 2006), allows analogical reasoning, and offers a possible route to understanding others. Yet a minimal distinction between self and other is essential for social interaction in general and for empathy in particular, and new work in social neuroscience has demonstrated that the self and other are distinguished at both the behavioral and neural levels. A handful of cognitive neuroscience studies also indicate that the neural response to others in pain can be modulated by situational and dispositional variables. Taken together, these data support the view that empathy (at least for pain) operates by way of conscious as well as automatic processes, which far from functioning independently, represent different aspects of a common subjective experience. These accounts of empathy are in harmony with theories of embodied cognition, which contend that cognitive representations and operations are fundamentally grounded in bodily states and in the brain’s modality-specific systems (Niedenthal et al., 2005). Future research should continue to investigate which pain-related brain areas are active in response to the perception of pain in others, and which are not, to help determine what aspects of pain are involved in the experience of empathy. Another interesting issue is whether there are gender differences in empathy. If so, are they learned or related to hormonal and innate differences in the way our brain is shaped? The work in social psychology, though not entirely conclusive, has seriously questioned the alleged female-superiority in empathic understanding, suggesting motivational differences between the genders instead (Ickes, 2003). More specific differences might be biologically based, as suggested by the results of a recent fMRI study that investigated neural response in men and women to infant crying and laughing showing significant differences between the two groups (Seifritz et al., 2003). Women but not men, independent of their parental status, showed neural deactivation in the anterior cingulate cortex in response to infant crying and laughing. In addition, the response pattern in the amygdala and interconnected limbic structures changed fundamentally with parental experience in both men and women. Nonparents showed stronger activation from laughing, whereas parents showed stronger activation for crying. These results seem to demonstrate that the emotion-sharing component may be subjected to personal experience, and that emotion regulation is differently prepared biologically in men and women. Finally, it is often assumed that empathy is a necessary prerequisite for altruism and compassion, and that we are, by nature, moral creatures. Despite this supposed link between empathy and prosocial behavior, no neuroscience evidence

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References 955

as yet demonstrates the existence of such a link. Empathy may well be viewed as a neutral capacity whose outcome, depending on a complex interplay of situational and personality factors, will result in positive or negative consequences for the target. One of the challenges for a social neuroscience approach to empathy and prosocial behavior is the difficulty of taking into account situational variables. To provide interpretable data, neuroscience experiments—especially functional neuroimaging studies—require intraindividual comparisons and repeatedmeasures designs. To be financially feasible, they require small samples. These conditions limit opportunities to study the effects of potentially important situational variables. This is but one example of the perennial challenge objective science faces in the attempt to understand human subjectivity in all its richness and complexity.

Allman, J. M., Watson, K. K., Tetreault, N. A., & Hakeem, A. Y. (2005). Intuition and autism: A possible role for von economo neurons. Trends in Cognitive Sciences, 9, 367–373. Avenanti, A., Bueti, D., Galati, G., & Aglioti, S. M. (2005). Transcranial magnetic stimulation highlights the sensorimotor side of empathy for pain. Nature Neuroscience, 8, 955–960. methods

of

psychophysiology.

Barrett, L., Henzi, P., & Dunbar, R. I. M. (2003). Primate cognition: From what now to what if. Trends Cognitive Sciences, 7, 494–497. Basch, M. F. (1983). Empathic understanding: A review of the concept and some theoretical considerations. Journal of the American Psychoanalytic Association, 31, 101–126. Batson, C. D. (1990). How social an animal? American Psychologist, 45, 336–346. Batson, C. D. (2006). Folly bridges. In P. A. M. van Lange (Ed.), Bridging social psychology (pp. 59–64). Mahwah, NJ: Erlbaum.

c48.indd Sec4:955

Brunet-Gouet, E., & Decety, J. (2006). Social brain dysfunctions in schizophrenia: A review of neuroimaging studies. Psychiatry Research, 148, 75–92. Bufalari, I., Aprile, T., Avenanti, A., Di Russo, F., & Aglioti, S. M. (2007). Empathy for pain and touch in the human somatosensory cortex. Cerebral Cortex, 17, 2553-2561. Cacioppo, J. T. (2002). Social neuroscience: Understanding the pieces fosters understanding the whole and vice versa. American Psychologist, 57, 819–831. Cacioppo, J. T., Berntson, G. G., Sheridan, J. F., & McClintock, M. K. (2000). Multilevel integrative analyses of human behavior: Social neuroscience and the complementing nature of social and biological approaches. Psychological Bulletin, 126, 829–843. Cacioppo, J. T., & Gardner, W. L. (1999). Emotion. Annual Review of Psychology, 50, 191–214. Carr, L., Iacoboni, M., Dubeau, M. C., Mazziotta, J. C., & Lenzi, G. L. (2003). Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas. Proceedings of the National Academy of Sciences, USA, 100, 5497–5502. Carter, C. S. (2007). Sex differences in oxytocin and vasopressin: Implications for autism spectrum disorders? Behavioral Brain Research, 176, 170–186.

REFERENCES

Ax, A. F. (1964). Goals and Psychophysiology, 62, 8–25.

cortical areas involved in the direct experience of pain. NeuroImage, 25, 312–319.

Carter, C. S., Williams, J. R., Witt, D. M., & Insel, T. R. (1992). Oxytocin and social bonding. Annals of the New York Academy of Sciences, 652, 204–211. Cheng, Y., Lin, C. P., Liu, H. L., Hsu, Y., Hung, D., & Decety, J. (2008). Expertise modulates the perception of pain in others. Submitted. Cheng, Y., Yang, C. Y., Lin, C. P., Lee, P. R., & Decety, J. (2008). The perception of pain in others suppresses somatosensory oscillations: A MEG study. Manuscript submitted for publication. Cialdini, R. B., Brown, S. L., Lewis, B. P., Luce, C., & Neuberg, S. L. (1997). Reinterpreting the empathy-altruism relationship: When one into one equals oneness. Journal of Personality and Social Psychology, 73, 481–494. Consiglio, A., Borsoi, A., Pereira, G., & Lucion, A. (2005). Effects of oxytocin microinjected into the central amygdaloid nucleus and bed nucleus of stria terminalis on maternal aggressive behavior in rats. Physiology and Behavior, 85, 354–362. Craig, A. D. (2002). How do you feel? Interoception: The sense of the physiological condition of the body. Nature Reviews: Neuroscience, 3, 655–666.

Batson, C. D., Batson, J. G., Singlsby, J. K., Harrell, K. L., Peekna, H. M., & Todd, R. M. (1991). Empathic joy and the empathy-altruism hypothesis. Journal of Personality and Social Psychology, 61, 413–426.

Craig, A. D. (2005). Forebrain emotional asymmetry: A neuroanatomical basis? Trends in Cognitive Sciences, 9, 566–571.

Batson, C. D., Early, S., & Salvarini, G. (1997). Perspective taking: Imagining how another feels versus imagining how you would feel. Personality and Social Personality Bulletin, 23, 751–758.

Craig, A. D. (2007). Interoception and emotion: A neuroanatomical perspective. In M. Lewis, J. M. Haviland-Jones, & L. F. Barrett (Eds.), Handbook of emotion (3rd ed., pp. 272-288). New York: Guilford Press.

Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117, 497–529.

Critchley, H. D., Wiens, S., Rotshtein, P., Öhman, A., & Dolan, R. D. (2005). Neural systems supporting interoceptive awareness. Nature Neuroscience, 7, 189–195.

Bell, S., & Ainsworth, M. (1972). Infant crying and maternal responsiveness. Child Development, 43, 1171–1190.

D’Argembeau, A., Ruby, P., Collette, F., Degueldre, C., Balteau, E., Luxen, A., et al. (2007). Distinct regions of the medial prefrontal cortex are associated with self-referential processing and perspective taking. Journal of Cognitive Neuroscience, 19, 935–944.

Bjorklund, D. F., & Bering, J. M. (2003). Big brains, slow development and social complexity: The development and evolutionary origins of social cognition. In M. Brune, H. Ribbert, & W. Schiedfenhovel (Eds.), The social brain: Evolution and pathology (pp. 113–151). Hoboken, NJ: Wiley.

Davidson, R. J. (1992). Anterior cerebral asymmetry and the nature of emotion. Brain and Cognition, 20, 125–151.

Blakemore, S.-J., Bristow, D., Bird, G., Frith, C., & Ward, J. (2005). Somatosensory activations during the observation of touch and a case of vision-touch synaesthesia. Brain, 128, 1571–1583.

Davis, M. H., Conklin, L., Smith, A., & Luce, C. (1996). Effect of perspective taking on the cognitive representation of persons: A merging of self and other. Journal of Personality and Social Psychology, 70, 713–726.

Botvinick, M., Jha, A. P., Bylsma, L. M., Fabian, S. A., Solomon, P. E., & Prkachin, K. M. (2005). Viewing facial expressions of pain engages

Davis, M. H., & Kraus, L. A. (1997). Personality and empathic accuracy. In W. Ickes (Ed.), Empathic accuracy (pp. 144–168). New York: Guilford Press.

8/19/09 5:29:19 PM

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Decety, J. (2005). Perspective taking as the royal avenue to empathy. In B. F. Malle & S. D. Hodges (Eds.), Other minds: How humans bridge the divide between self and other (pp. 135–149). New York: Guilford Press. Decety, J., & Grèzes, J. (2006). The power of simulation: Imagining one’s own and other ’s behavior. Brain Research, 1079, 4–14. Decety, J., & Hodges, S. D. (2006). A social cognitive neuroscience model of human empathy. In P. A. M. van Lange (Ed.), Bridging social psychology: Benefits of transdisciplinary approaches (pp. 103–109). Mahwah, NJ: Erlbaum.

Hatfield, E. (2009). Emotional contagion and empathy. In J. Decety & W. Ickes (Eds.), the social neuroscience of empathy (pp. 19–30). Cambridge, MA: MIT Press. Hatfield, E., Cacioppo, J., &, Rapson, R. (1994). Emotional contagion. New York: Cambridge University Press. Hoffman, M. L. (2000). Empathy and moral development: Implication for caring and justice. Cambridge, MA: Cambridge University Press. Hollander, E., Bartz, J., Chaplin, W., Phillips, A., Sumner, J., Soorya, L., et al. (2007). Oxytocin increases retention of social cognition in autism. Biological Psychiatry, 61, 498–503.

Decety, J., & Jackson, P. L. (2004). The functional architecture of human empathy. Behavioral and Cognitive Neuroscience Reviews, 3, 71–100.

Huber, D., Veinante, P., & Stoop, R. (2005, April 8). Vasopressin and oxytocin excite distinct neuronal populations in the central amygdala. Science, 308, 245–249.

Decety, J., & Lamm, C. (2006). Human empathy through the lens of social neuroscience. Scientific World Journal, 6, 1146–1163.

Hutchison, W. D., Davis, K. D., Lozano, A. M., Tasker, R. R., & Dostrovsky, J. O. (1999). Pain-related neurons in the human cingulate cortex. Nature Neuroscience, 2, 403–405.

Decety, J., & Lamm, C. (2007). The role of the right temporoparietal junction in social interaction: How low-level computational processes contribute to meta-cognition. Neuroscientist, 13, 580–593.

Ickes, W. (2003). Everyday mind reading: Understanding what other people think and feel. Amherst, NY: Prometheus Books.

Decety, J., & Sommerville, J. A. (2003). Shared representations between self and others: A social cognitive neuroscience view. Trends in Cognitive Sciences, 7, 527–533.

Jackson, P. L., Brunet, E., Meltzoff, A. N., & Decety, J. (2006). Empathy examined through the neural mechanisms involved in imagining how I feel versus how you feel pain. Neuropsychologia, 44, 752–761.

de Waal, F. B. M. (1996). Good natured: The origins of right and wrong in humans and other animals. Harvard: Harvard University Press.

Jackson, P. L., & Decety, J. (2004). Motor cognition: A new paradigm to study self other interactions. Current Opinion in Neurobiology, 14, 259–263.

de Waal, F. B. M. (2005). Primates, monks and the mind. Journal of Consciousness Studies, 12, 1–17. de Waal, F. B. M., &, Thompson, E. (2005). Primates, monks and the mind: The case of empathy. Journal of Consciousness Studies, 12, 38–54.

Jackson, P. L., Meltzoff, A. N., & Decety, J. (2005). How do we perceive the pain of others: A window into the neural processes involved in empathy. NeuroImage, 24, 771–779.

de Waal, F. B. M., & van Roosmalen, A. (1979). Reconciliation and consolation among chimpanzees. Behavioral Ecology and Sociobiology, 5, 55–66.

Jackson, P. L., Rainville, P., & Decety, J. (2006). From nociception to empathy: The neural mechanism for the representation of pain in self and in others. Pain, 125, 5–9.

Dimberg, U., Thunberg, M., & Elmehed, K. (2000). Unconscious facial reactions to emotional facial expressions. Psychological Science, 11, 86–89.

Johnson, S. C., Baxter, L. C., Wilder, L. S., Pipe, J. G., Heiserman, J. E., & Prigatano, G. P. (2002). Neural correlates of self-reflection. Brain, 125, 1808–1814.

Domes, G., Heinrichs, M., Glascher, J., Buchel, C., Braus, D. F., & Herpertz, S. C. (2007). Oxytocin attenuates amygdala responses to emotional faces regardless of valence. Biological Psychiatry, 62, 1197–1190.

Kirsch, P., Esslinger, C., Chen, Q., Mier, D., Lis, S., & Siddhanti, S. (2005). Oxytocin modulates neural circuitry for social cognition and fear in humans. Journal of Neuroscience, 25, 11489–11493.

Domes, G., Heinrichs, M., Michel, A., Berger, C., & Herpertz, S. C. (2007). Oxytocin improves mind-reading in humans. Biological Psychiatry, 61, 731–733. Eisenberg, N., Fabes, R. A., Murphy, B., Karbon, M., Maszk, P., Smith, M., et al. (1994). The relations of emotionality and regulation to dispositional and situational empathy-related responding. Journal of Personality and Social Psychology, 66, 776–797. Eisenberg, N., Spinrad, T. L., & Sadovsky, A. (2006). Empathy-related responding in children. In M. Killen & J. Smetana (Eds.), Handbook of moral development (pp. 517–549). Mahwah, NJ: Erlbaum. Farrow, T., & Woodruff, P. W. (2007). Empathy in mental illness and health. Cambridge, MA: Cambridge University Press. Fossati, P., Hevenor, S. J., Graham, S. J., Grady, C., Keightley, M. L., Craik, F., et al. (2003). In search of the emotional self: An fMRI study using positive and negative emotional words. American Journal of Psychiatry, 160, 1938–1945. Geary, D. C., & Flinn, M. (2001). Evolution of human parental behavior and the human family. Parenting: Science and Practice, 1, 5–61. Grosbras, M. H., & Paus, T. (2006). Brain networks involved in viewing angry hands or faces. Cerebral Cortex, 16, 1087–1096. Gunnar, M., & Quevedo, K. (2007). The neurobiology of stress and development. Annual Review of Psychology, 58, 145–173. Harris, P. L. (2000). Understanding emotion. In M. Lewis & J. M. Haviland-Jones (Eds.), Handbook of emotions (pp. 281–292). New York: Guilford Press.

c48.indd Sec7:956

Kosfeld, M., Heinrichs, M., Zak, P. J., Fischbacher, U., & Fehr, E. (2005, June 2). Oxytocin increases trust in humans. Nature, 435, 673–676. Kulkarni, B., Bentley, D. E., Elliott, R., Youell, P., Watson, A., Derbyshire, S. W., et al. (2005). Attention to pain localization and unpleasantness discriminates the functions of the medial and lateral pain systems. European Journal of Neuroscience, 21, 3133–3142. Lamm, C., Batson, C. D., & Decety, J. (2007). The neural basis of human empathy: Effects of perspective-taking and cognitive appraisal. Journal of Cognitive Neuroscience, 19, 42–58. Lanzetta, J. T., & Englis, B. G. (1989). Expectations of cooperation and competition and their effects on observers’ vicarious emotional responses. Journal of Personality and Social Psychology, 56, 543–554. Lawrence, E. J., Shaw, P., Giampietro, V. P., Surguladze, S., Brammer, M. J., & David, A. S. (2006). The role of “shared representations” in social perception and empathy: An fMRI study. NeuroImage, 29, 1173–1184. LeDoux, J. E. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 23, 155–184. Levenson, R. W., Ekman, P., & Friesen, W. V. (1990). Voluntary facial action generates emotion-specific autonomic nervous system activity. Psychophysiology, 27, 363–384. Lorberbaum, J. P., Newman, J. D., Horwitz, A. R., Dubno, J. R., Lydiard, R. B., Hammer, M. B., et al. (2002). A potential role for thalamocingulate circuitry in human maternal behavior. Biological Psychiatry, 51, 431–445. MacLean, P. D. (1985). Brain evolution relating to family, play, and the separation call. Archives of General Psychiatry, 42, 405–417.

8/19/09 5:29:19 PM

References Moriguchi, Y., Decety, J., Ohnishi, T., Maeda, M., Matsuda, H., & Komaki, G. (2007). Empathy and judging other ’s pain: An fMRI study of alexithymia. Cerebral Cortex, 9, 2223–2234.

Ruby, P., & Decety, J. (2003). What you believe versus what you think they believe? A neuroimaging study of conceptual perspective taking. European Journal of Neuroscience, 17, 2475–2480.

Morrison, I., & Downing, P. E. (2007). Organization of felt and seen pain responses in anterior cingulate cortex. NeuroImage, 37, 642–651.

Ruby, P., & Decety, J. (2004). How would you feel versus how do you think she would feel? A neuroimaging study of perspective taking with social emotions. Journal of Cognitive Neuroscience, 16, 988–999.

Morrison, I., Lloyd, D., di Pellegrino, G., & Roberts, N. (2004). Vicarious responses to pain in anterior cingulate cortex: Is empathy a multisensory issue? Cognitive and Affective Behavioral Neuroscience, 4, 270–278. Newman, J. D. (2007). Neural circuits underlying crying and cry responding in mammals. Behavioural Brain Research, 182, 155–165. Niedenthal, P. M., Halberstadt, J. B., Margolin, J., & Innes-Ker, A. H. (2000). Emotional state and the detection of change in the facial expression of emotion. European Journal of Social Psychology, 30, 211–222. Niedenthal, P.M., Barsalou, L.W., Winkielman, P., Krauth-Gruber, S., & Ric, F. (2005). Embodiment in attitudes, social perception, and emotion. Personality and Social Psychology Review, 9, 184–211. Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotion. Trends in Cognitive Sciences, 9, 242–249. Ostrowsky, K., Magnin, M., Ryvlin, P., Isnard, J., Guenot, M., & Mauguiere, F. (2002). Representation of pain and somatic sensation in the human insula: A study of responses to direct electrical cortical stimulation. Cerebral Cortex, 12, 376–385. Parr, L. A. (2001). Cognitive and physiological markers of emotional awareness in chimpanzees (Pan troglodytes). Animal Cognition, 4, 223–229. Parr, L. A., & Hopkins, W. D. (2000). Brain temperature asymmetries and emotional perception in chimpanzees (Pan troglodytes). Physiology and Behavior, 71, 363–371. Parr, L. A., &, Waller, B. (2007). The evolution of human emotion. In J. Kaas (Ed.), Evolution of the nervous system (Vol. 5, pp. 447–472). New York: Elsevier. Pavuluri, M. N., O’Connor, M. M., Harral, E., & Sweeney, J. A. (2007). Affective neural circuitry during facial emotion processing in pediatric bipolar disorder. Biological Psychiatry, 62, 158–167. Pickett, C. L., Gardner, W. L., & Knowles, M. (2004). Getting a cue: The need to belong and enhanced sensitivity to social cues. Personality and Social Psychology Bulletin, 30, 1095–1107.

Saarela, M. V., Hluschuk, Y., Williams, A. C., Schurmann, M., Lalso, E., & Hari, R. (2006). The compassionate brain: Humans detect intensity of pain from another ’s face. Cerebral Cortex, 17, 230–237. Seifritz, E., Esposito, F., Neuhoff, J. G., Lüthi, A., Mustovic, H., Dammann, G., et al. (2003). Differential sex-independent amygdala response to infant crying and laughing in parents versus nonparents. Biological Psychiatry, 54, 1367–1375. Shamay-Tsoory, S. G., Lester, H., Chisin, R., Israel, O., Bar-Shalom, R., Peretz, A., et al. (2005). The neural correlates of understanding the other ’s distress: A positron emission tomography investigation of accurate empathy. NeuroImage, 15, 468–472. Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R. J., & Frith, C. D. (2004, February 20). Empathy for pain involves the affective but not the sensory components of pain. Science, 303, 1157–1161. Singer, T., Seymour, B., O’Doherty, J. P., Stephan, K. E., Dolan, R. J., & Frith, C. D. (2006, January 26). Empathic neural responses are modulated by the perceived fairness of others. Nature, 439, 466–469. Smith, A. (1790). The theory of moral sentiments. London: Millar. Sommerville, J. A., & Decety, J. (2006). Weaving the fabric of social interaction: Articulating developmental psychology and cognitive neuroscience in the domain of motor cognition. Psychonomic Bulletin and Review, 13, 179–200. Stein, M. B., Goldin, P. R., Sareen, J., Zorrilla, L. T., & Brown, G. G. (2002). Increased amygdala activation to angry and contemptuous faces in generalized social phobia. Archives of General Psychiatry, 59, 1027–1034. Stotland, E. (1969). Exploratory investigations of empathy. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 4, pp. 271–313). New York: Academic Press. Symonds, L. L., Gordon, N. S., Bixby, J. C., & Mande, M. M. (2006). Right-lateralized pain processing in the human cortex: An FMRI study. Journal of Neurophysiology, 95, 3823–3830. Thompson, E. (2001). Empathy and consciousness. Journal of Consciousness Studies, 8, 1–32.

Porges, S. W. (2001). The polyvagal theory: Phylogenetic substrates of a social nervous system. International Journal of Psychophysiology, 42, 123–146.

Tice, D. M., Bratslavsky, E., & Baumeister, R. F. (2001). Emotional distress regulation takes precedence over impulse control: If you feel bad, do it! Journal of Personality and Social Psychology, 80, 53–67.

Povinelli, D. J. (2001). Folk physics for apes. New York: Oxford University Press.

Van Essen, D. C., Dickson, J., Harwell, J., Hanlon, D., Anderson, C. H., & Drury, H. A. (2001). An integrated software system for surface-based analyses of cerebral cortex. Journal of American Medical Informatics Association, 41, 1359–1378.

Preston, S. D., & de Waal, F. B. M. (2002). Empathy: Its ultimate and proximate bases. Behavioral and Brain Sciences, 25, 1–72. Price, D. D. (2000, June 9). Psychological and neural mechanisms of the affective dimension of pain. Science, 288, 1769–1772. Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review in Neuroscience, 27, 169–192. Roelofs, K., Elzinga, B.M., & Rotteveel, M. (2005). The effects of stress-induced cortisol responses on approach–avoidance behavior. Psychoneuroendocrinology, 30, 665–677. Rolls, E. T. (1999). The brain and emotion. Oxford: Oxford University Press. Ruby, P., & Decety, J. (2001). Effect of subjective perspective taking during simulation of action: A PET investigation of agency. Nature Neuroscience, 4, 546–550.

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Wicker, B., Keysers, C., Plailly, J., Royet, J. P., Gallese, V., & Rizzolatti, G. (2003). Both of us disgusted in my insula: The common neural basis of seeing and feeling disgust. Neuron, 40, 655–664. Wilson, E. O. (1988). On human nature. Cambridge, MA: Harvard University Press. Zak, P. J., Kurzban, R., & Matzner, W. T. (2005). Oxytocin is associated with human trustworthiness. Hormones and Behavior, 48, 522–552. Zaki, J., Ochsner, K. N., Hanelin, J., Wager, T. D., & Mackey, S. C. (2007). Different circuits for different pain: Patterns of functional connectivity reveal distinct networks for processing pain in self and others. Social Neuroscience, 2, 276–291.

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Chapter 49

Defense and Aggression D. CAROLINE BLANCHARD, YOAV LITVIN, NATHAN S. PENTKOWSKI, AND ROBERT J. BLANCHARD

and the composition of the hunting group, the defensive behaviors of the prey in these hunts are quite successful. Looking at this from a slightly different angle, the world is a dangerous place, and has been so throughout evolution. The remarkable fact is that each of us is the product of an incredibly long line of ancestors, each of whom showed successful defensive behavior to all the threats they encountered, at least until the individuals were old enough to reproduce the next in the line of our ancestors. As a result of this heritage, mechanisms related to defense are widely represented in the nervous systems of higher animals, providing a substrate for normal defensive behavior as well as a potential site for abnormal functioning.

DEFENSE The Functions of Defense Defensive behaviors constitute the immediate and direct behavioral response to threats to life and bodily safety (D. C. Blanchard & Blanchard, 2008). For most species, including many mammals, defensive behaviors are less dependent on individual learning than on evolved responses to the stimuli and situations that were frequent dangers in the evolutionary histories of that species (see Chapter 36, this volume). They have evolved because of the differential survival/reproductive success that such behaviors afford to individuals displaying them appropriately. In this context, “appropriately” means not only that the defenses be well executed, but that each individual defensive behavior has been particularly successful in response to that particular type of threat and in that particular type of situation. The notion that defensive behaviors reflect evolved neurobehavioral systems, rather than a set of purely learned responses based on pain (see Chapter 39, this volume) is crucial for understanding their cross-species commonalities, and their propensity to emerge in the absence of experience that would permit such learning. How successful is defensive behavior? One index of this is the success of predation. Overall and across species, most predators kill prey on less than 50% of their encounters with them (Vermeij, 1982), attesting to the value of prey behavior in thwarting such predatory attacks. In mammals, much of what we know about the efficacy of defensive behavior is based on estimates of the success of hunts by lions, cheetahs, and the like. This information, which may or may not be generalized to other predatorprey relationships, is available largely because such predators, and their prey, are large and live in open areas where visibility is good and the outcome of a hunt relatively easy to determine. Estimates that only about 1 in 3 hunts ends in a kill (Schaller, 1972, pp. 251–255) suggest that while lion hunting success may vary considerably with type of habitat

Types of Defensive Behavior Flight, avoidance, freezing, defensive threat, defensive attack, and risk assessment to threatening stimuli have been characterized in a variety of species (e.g., D. C. Blanchard, 1997), as have some other behaviors (e.g., burying of novel, aversive, or potentially dangerous objects; Treit, Pinel, & Fibiger, 1981) that may be functional in particular threat situations, or functionally related to one or more of the preceding defense patterns. Alarm cries warning potentially related conspecifics of the presence of danger are also adaptive, insofar as the increased safety to genetic relatives exceeds the risk of damage to the alarm caller. Thus alarm cries would be expected to occur more frequently in species in which related conspecifics typically live in close proximity (Litvin, Blanchard, & Blanchard, 2007). Reducing attention to oneself (e.g., rat pups cease separation/distress vocalizations in the presence of an adult male; Takahashi, 1992), is also a common element of defense across many species and situations. Although the category of defense remains somewhat open-ended, typical forms of defense are common to most if not all mammalian species (Edmunds, 1974; Hedinger, 1969) and to many nonmammals as well. Virtually all vertebrates and many invertebrates show some form of 958

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flight, avoidance, or hiding, while most also show freezing, defensive threat, and defensive attack. Although no single defensive behavior is yet recognized as genuinely “speciesspecific,” or restricted to a single species, some defenserelated behaviors such as stotting, shooting poison liquids at the threat source, or rolling up into a ball are restricted to a few species. In addition, the functions of some of these rare defenses are not always clear, as the conditions of observation in free-living animals typically do not facilitate analysis of the relationship between these behaviors and their outcomes in terms of success or failure. However, most defensive behaviors are common to many or most vertebrate species: The threat stimuli that higher animals face have a good deal in common, such that appropriate and effective responses to them show many parallels from one species to another. Threat Stimuli and Their Impact on Defensive Behaviors Endler (1986) divided the types of threat stimulus that animals are likely to encounter into three categories: predators, attack by conspecifics, and dangerous features of the environment. It is probably safe to say that all these classes of threat are relevant to the overwhelming majority of animal species living now or in the past. Predators Of the three types of threat, predators may show the most variation across prey species. Small animals all tend to be preyed on by something, regardless of whether they may themselves be predators of something else. The prototypical evolved response to this situation for terrestrial invertebrates, all of which are quite small, has been structural, involving the development of armor for protection; cryptic coloration or patterning for concealment; or bad tastes or poison to reduce palatability. Immobility and habitat choices provide some behavioral additions to this armamentarium, but the range of behavioral defenses of invertebrates is impoverished compared with that of vertebrates, and especially in comparison to mammals. Small mammals have relatively few structural defenses. Only pangolins and armadillos have sufficient armor to be effective, and it is questionable whether any mammals taste bad enough or are poisonous enough on consumption to make them immune to predator attack. Crypsis remains an important mode of defense, although utilized as much by stealth predators as by prey. Nonetheless, mammals overwhelmingly defend themselves from predators by their actions, not their body parts. A core feature of the analysis of antipredator defense is that different types of attack call for different defenses:

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As the abilities and hunting styles of predator species differ widely, it is important that heavily predated animals show appropriately different responses to different types of predators. Seyfarth and Cheney (2003) point out that some birds and mammals, including vervet and diana monkeys, have distinct alarm cries or calls to terrestrial, aerial, and even subterranean predators, and that these cries in turn produce different and appropriate defensive responses in the recipients. Suricates (meercats) also have different cries for different types of predator and can acoustically signal the urgency of danger, as indexed by the distance between the threat stimulus and the caller (Manser, Seyfarth, & Cheney, 2002). This urgency factor for an alarm vocalization may contribute to an important motivational or emotional response parameter that results in lesser or greater strength of responding by the recipient. Ydenberg and Dill (1986) have shown that prey flight speed increases in response to decreases in the distance to a chasing predator. In the present context, such phenomena provide evidence that many animals can differentiate predators in terms of characteristics that impact the most appropriate form of defense, and respond suitably to this differentiation. Conspecific Threat Responses to conspecific threat vary in several ways from those to predators. First, conspecific defenses are responsive to an adaptive peculiarity of conspecific attack that, particularly in social species, such attack tends to be aimed at nonlethal targets on the body of the defender (see Offensive Aggression, later in this chapter, for details). This provides an additional mode of defense; concealment of these specific nonlethal targets, a strategy that can sharply reduce the effectiveness of conspecific attack in reaching its targets. This specific mode of defense is prominent in conspecific defense but useless, indeed counterproductive, in defense against predators, as it involves the interposition of more vulnerable sites such as the ventrum, to conspecific attack. While these coordinated target-concealing movements may occur in naive animals of some species, they also improve with practice, such that experienced fighter rats can better avoid being bitten by a conspecific attacker, even though the basic patterns of defense that they use are similar to those of inexperienced males (R. J. Blanchard, Fukunaga, Blanchard, & Kelley, 1975). A second feature of the conspecific defense situation is that conspecific relationships may be neutral, or amiable, rather than engendering defense. From an experimental or analytic perspective, pure defensive behaviors to a conspecific may be more difficult to elicit than defensive responses to a predator, and conspecific interactions may reflect more complex motivations than simple attack and defense. For example, male rats and mice do not attack

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females with the same intensity as they attack sexually mature males. However, they may attack females if the female fails to cooperate with a sexual advance by the male (Arakawa, Blanchard, & Blanchard, 2007). A third complexity, particularly in the analysis of conspecific and also antipredator defense, is that domestication has resulted in a profound reduction in defensiveness of rats and mice due to selection against animals showing strong defensiveness to handling; an effect paralleled in programs of deliberate selection of “tame” animals (Naumenko, Popova, & Nikulina, 1989; see Figure 49.1). The selection associated with domestication was informal and variable but mainly involved rejection of animals showing defensive actions that humans interpreted as aggressive (Stone, 1932). This was measured in response to human handling, a situation that elicits defensive rather than offensive attack, with the consequence that defensive attack is sharply reduced in laboratory rats whereas offensive attack appears to be little changed (Takahashi & Blanchard, 1982). Thus, laboratory rats seldom show defense prior to being attacked by a conspecific, and defensive attack to any threat stimulus is virtually absent unless

the rat is actually subjected to a sharp pain, such as shock or a bite (R. J. Blanchard, Blanchard, & Takahashi, 1978). This, plus the fact that the majority of defense-related tests are run in small chambers where flight is impossible, avoidance difficult, and risk assessment simply doesn’t get measured, leaves freezing as the major measure of defensiveness for most studies of both conditioned and unconditioned defense. This is particularly true for research using rats, as domesticated (laboratory) rats show no diminution in freezing, although other defenses may be profoundly reduced. The situation is different in mice, as lab mice tend to freeze less than their wild congeners, in line with a more general though quantitatively less profound reduction in many aspects of defensiveness with domestication for this species (Figure 49.1). Environmental Threats The more common environmental features that pose a threat to animals in natural situations, such as spreading fire, floods, and earth movements have seldom been investigated in a laboratory context. The most prominent exception was the “visual cliff” (Walk, Gibson, & Tighe,

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Figure 49.1 A comparison of defensive behaviors in laboratory (L) and wild (W) rats and mice demonstrates the effects of domestication.

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1957) in which young animals were placed on the edge of a high table and their avoidance responses to this drop evaluated. Although innate avoidance of stepping onto an apparent drop is common in young mammals, dangerous environmental stimuli are unlikely to have had as specific and robust an effect on the shaping of defensive behaviors as have animate stimuli; that is, predators or conspecifics. This is because many environmental threats are either virtually inescapable (e.g., erupting volcanoes; earthslides; rapidly spreading fires), and leave no survivors to enjoy differential reproductive success; or they are relatively easy to avoid (bodies of water; individual stationary fires; high places), putting little emphasis on rapid and appropriate responding to threat. Potentially dangerous situations, however, including those that are novel, too dark, too light, or too open, or have other unsettling associations (e.g., problematic odors or sounds), all require some degree of vigilance to facilitate early detection of danger and preparation for appropriate reactions to it. The traditional and still most commonly used threat stimulus for laboratory research, pain, may be associated with any of these stimuli. Predators bite or slash, as do attacking conspecifics. Fire burns, thorns cut and sting, and falls break bones. By far the most effective way to deal with all such potentially painful events is to avoid them. On an evolutionary basis, the more predictable is pain from a particular source, the more adaptive it is to simply avoid that source. This fits well with findings that predators and stimuli associated with the presence of predators (e.g., predator odors) elicit strong avoidance, even in naive laboratory animals (R. J. Blanchard, Blanchard, Rodgers, & Weiss, 1990; Dielenberg & McGregor, 1999; Zangrossi & File, 1992), whereas stimuli associated with conspecifics are more varied (Arakawa et al., 2007), befitting the wider range of behaviors that conspecifics elicit. Threat Stimulus Characteristics In addition to the type of threat stimulus, several characteristics of threat stimuli may have a strong impact on the success or failure of specific defensive responses. Two of these are threat ambiguity and threat distance, that is, the distance between the threatening stimulus and the threatened subject. Threat Ambiguity Many threats are not obvious, at least until it is too late for any specific antipredator defenses to have a good chance of success. Stealth predators are successful insofar as they can remain undetected until they reach striking distance to the prey. Also, both conspecifics and predators typically form part of the landscape for many animals so that the

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detection of danger from such a source is different and more specific than simply detecting its presence. A sleeping lion poses little threat, but one that is systematically moving is to be watched very carefully! This careful watching is labeled “vigilance” in field studies. It is often measured by the animal’s activity and orientation: Nondefensive animals have clear patterns of behaviors such as foraging/eating, conspecific interactions including sexual behavior and care of young, and selfcare, including grooming and sleeping. In the presence of potential danger, such activities abruptly cease, to be replaced by a “heads up” posture and scanning or sniffing the environment. If a possible danger has been spotted, the subject will orient fixedly toward it, and may even approach. In laboratory studies, the combination of orientation, sensory attention (scanning; ears forward; sniffing) and sometimes approach are labeled “risk assessment” (RA) behaviors. In rodents, RA is typically measured by the subject’s assumption of a stretched posture oriented to threat, or by stretched approach to the threat source; both may include scanning and sniffing (R. J. Blanchard, Blanchard, & Hori, 1989; Pinel, Mana, & Ward, 1989). Extensions of these analyses have suggested that the division between approaching (as in RA) or avoiding aversive stimuli may provide an important factor in conceptualization of defense (McNaughton & Corr, 2004). RA and its sequelae constitute a dynamic event. Novelty, including new places, objects, sounds, and smells, often induces initial RA, with subsequent habituation of these behaviors if no actual danger is found. This is in contrast to actual predator stimuli, to which much less habituation is seen (R. J. Blanchard et al., 1998; Dielenberg & McGregor, 1999). Although information-gathering is certainly involved in other situations as well, the stealthy postures involved in rodent RA are not seen during foraging for food. These actions appear to be functional in reducing the animal’s visibility to others, while enabling it to focus on and approach, the potential threat. RA is strongly associated with learning about stimuli associated with danger (Pinel et|nb|al., 1989). Threat Distance The distance between an animal and a threat stimulus also strongly impacts the magnitude (Ydenberg & Dill, 1986) or type of defense offered. In fact, Fanselow and Lester (1988) have conceptualized a “predatory imminence” organization for defense, based largely on this relationship, with “preencounter,” “postencounter,” and “circa-strike” components; freezing, flight, and defensive attack, respectively. This is in general agreement with findings that, in wild rats, decreasing separation between subject and threat is an extremely robust determinant of defensive threat

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962

Defense and Aggression Flight Available

No Flight Available Defensive Attack

35 30

Defensive Threat

Intensity

25

Defensive Threat

20 Flight

15

Freezing

10 5 0

6

5

4

3 2 1 0 1 2 3 Predator-Prey Distance (meters)

4

5

6

Figure 49.2 Defensive behaviors of wild rats to an approaching human are modulated by availability of escape and predator-prey distance.

(at approximately 1 m separation from predator) and defensive attack (approximately 0.5 m separation from predator) (D. C. Blanchard, Williams, & Blanchard, 1981). However, the determination of flight versus freezing appears to involve additional factors; in particular, the presence or absence of an escape route from the area where the encounter is taking place (see Figure 49.2). Situational Stimuli That Facilitate or Hamper the Success of Specific Defenses Features of the environment, as well as of the threat stimulus strongly influence the success or failure of particular defensive behaviors. For an optimal outcome, each such behavior should represent the most effective defense possible, given the specific features of the situation in which the threat is encountered. Flight, defined as rapid locomotion away from a threat, is possible in even relatively small

TABLE 49.1

enclosures. It is only effective, however, when there is a way for the subject to actually escape the enclosure; when no way out is possible, flight simply presents the attacker with a clear shot at a particularly poorly armed portion of the defender ’s body, its back and rump. Other defenses, such as freezing, followed by defensive threat and attack, may be more effective when escape is impossible, and the switch from flight to the freezing/threat/attack sequence when an escape route is blocked can be demonstrated in rats (D. C. Blanchard, 1997). Other enabling stimuli include the presence of a concealing or protecting area, crucial for hiding or sheltering to be effective, and the presence of conspecifics, necessary for alarm cries to fulfill the function of alerting relatives to the presence of danger. Conspecific presence has indeed been shown to facilitate rat ultrasonic alarm calls after encountering a cat (R. J. Blanchard, Blanchard, Agullana, & Weiss, 1991). As with flight from an inescapable situation, it is certainly possible for animals to make alarm cries when no conspecifics are present: The actions involved in defensive attack (e.g., lunging and biting) are possible even when the threat is at such a distance that these behaviors are totally ineffective. But they don’t occur at such ineffective (large) distances. Instead defensive behaviors of even naive rats show extremely consistent relationships to such features as defensive distance, ambiguity of threat, and the presence of an escape route; attesting to the strong evolutionary relationship between threat and situational features, on the one hand, and the effectiveness of particular defenses, on the other. Some of these relationships are outlined in Table 49.1. Defensive Threat and Attack Defensive aggression, a term that tends to include both defensive threat and defensive attack, is marked out for special attention here because it is frequently and incorrectly

Defensive behaviors as a function of threat certainty, its proximity, and presence of particular enabling stimuli

Source of Threat (and distance)

Enabling Stimuli

Behavior

Typical Outcomes

Discrete

“Way out” available

Flight

Escapes

Discrete

No means of escape

Freezing

Reduces attack

Discrete

Conspecifics nearby

Alarm cry

Warn conspecifics

Discrete

Hiding place available

Hides

No detection/access

Discrete (close in)

Defensive threat

Threatens attacker

Discrete (contact)

Defensive attack

Hurts attacker

Discrete (contact)

Startle

Startles attacker

Ambiguous (potential)

Risk assessment

Localize, identify threat

Defensive burying

Elicit animate movement

Ambiguous (potential)

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Substrate

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Aggression 963

conflated with offensive aggression and needs to be differentiated from the latter. Defensive attack has different antecedents than offense, responding to threat of bodily harm or death, rather than to challenge over resources. Whereas offensive attack is reduced by fear (R. J. Blanchard, Kleinschmidt, Flannelly, & Blanchard, 1988; Harcourt & de Waal, 1992; Leyhausen, 1979), defensive attack may be enhanced by it. The circumstances under which defensive attack is most likely to occur, including close proximity or contact with a predator or conspecific attacker, or pain from this attack, are such as to maximize fear (D. C. Blanchard et al., 1981). Defensive threat signals include loud vocalizations in a range likely to be audible to predators; display of weapons such as teeth or claws (see Figure 49.3); and body orientations and distensions that enhance the animal’s apparent size; all useful in indicating the defensive capabilities of the defending animal. These are highly salient signals and generally impossible to ignore. However, mammalian threat activities appear to be more than displays (Szamado, 2003). In addition to indicating (and possibly exaggerating) the capabilities and determination of the defensive animal, these actions also present weapon systems in a state of readiness, constituting preparation for the species-typical fighting techniques that will be used if the display is not effective. In all these particulars, except for body size enhancements, offense is different. Offensive threat tends to be quieter, including, in cats, a low growl rather than the scream of the defensive cat, and to involve simple approach rather than the dramatic and sometimes contorted movements of the defensive animal (Leyhausen, 1979). In addition, as noted, the target sites for attack are different from those for offensive attack in animals of the same species: Defensive attack is aimed at particularly vulnerable sites, whereas offensive attack involves targets where damage is less lethal, or that are

protected by defensive structures such as the lion’s mane or the rat’s vibrissae. The role of defensive threat and attack has been somewhat neglected in recent years, for several reasons. First, its role in antipredator behavior can be seen only when the predator is allowed to carry out an attack on a prey animal, an event that is seldom allowed in most contemporary laboratories: Second, because of selective breeding, in lab rats defensive attack is typically elicited only by painful contact with an attacker, usually occurring only as the immediate and direct response to a bite; making defensive attack a much less prominent component in lab rat defense. This very limited role for defensive attack is misleading, as wild-trapped and first generation lab-bred wild R. norvegicus show high levels of defensive threat and considerable defensive attack to human handling (Takahashi & Blanchard, 1982). However, because of its rarity in lab rats, it is often ignored as a separate type of aggression. When it does appear in consequence of manipulations such as brain stimulation or lesions, it is often interpreted as offensive aggression or, more commonly, simply as aggression, with no distinction between the two forms of attack. The so-called septal syndrome is frequently associated with aggression, but if septal-lesioned rats are allowed to escape the stranger with which they are paired, they will do so, rather than attacking it. In this case, attack occurs if the two rats are forced into close proximity by a small enclosure, whereas there is no attack by the septal-lesioned animal when the same opponent is encountered in a large cage (D. C. Blanchard, Blanchard, Takahashi, & Takahashi, 1977). While defensive behaviors are obviously fascinating as examples of the strength and specificity of adaptive mechanisms in evolution, they have recently come to serve in a different, although related, capacity, as potential models for defense-related psychopathologies such as anxiety disorders (R. J. Blanchard et al., 2008). Their value in this capacity is that instead of measuring a global or amorphous concept of stress response or anxiety, they may provide behavioral measures that are, in some cases, similar to the behaviors that serve as core symptoms of specific anxiety disorders. One case in which this appears to be working well, in terms of both behavioral and drug response criteria, relates to flight and panic disorder (D. C. Blanchard et al., 2001; Pinheiro, Zangrossi, Del-Ben, & Graeff, 2007). AGGRESSION Anger and Aggression

Figure 49.3 A territorial aggressive display in an African Hippopotamus (Hippopotamus amphibious).

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Anger can be defined as motivation to harm another individual, and aggression as the pattern of behavior directly expressing that motive. This conceptualization works very

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Defense and Aggression

well in the context of human behavior, in which anger has a strong subjective component, and angry individuals are capable of providing a verbal description and confirmation of it in the absence of any overt aggressive action (Averill, 1982). Neuroscience research, however, is largely done on nonhuman animals, for which there is no robust and specific measure of anger except that of aggressive behavior, so the anger component is generally omitted, and emphasis placed on aggressive behaviors. An additional consideration in the analysis of aggression is that, this definition of aggression notwithstanding, the concept of aggression has gone through many phases, definitions, and valuejudgments in the history of psychology, and its basic status as an evolved and generally adaptive biobehavioral system has taken a long time to be recognized. Aggression—like defense, sexual behavior, eating, and other generally adaptive action patterns—is elicited and modulated by a range of biological, experiential, and environmental factors. There are many ways in which these eliciting and modulatory factors can prove dysfunctional, resulting in disordered aggression or, disordered defensive behavior, sexual behavior, consummatory behavior, and the like. This chapter presents the basic patterns of aggression (and defense) and merely notes that their dysfunctional aspects may be related to a range of human psychopathologies. Offensive aggressive behaviors are associated with anger, directly or indirectly motivated by resource control, and particularly elicited by challenge to such control. In contrast, defensive aggression is associated with high levels of fear, directly motivated by danger of harm or death to the individual, and is directed at the source of the danger. Other behaviors share some similarities with either the motivations or the behaviors of these two patterns, but typically are not included under the rubric of aggression. Predation is excluded on the basis that it is aimed at consumption of the opponent, not on hurting or killing it, per se. While the act of killing appears to have positive valence for at least some predators (Kruuk, 1972), most appear to be uninterested unless they or their offspring are hungry. Also, predation does not appear to be influenced by a history of either successful or unsuccessful fighting with members of the same species (e.g., Kemble, Flannelly, Salley, & Blanchard, 1985), making it different from conspecific offensive aggression. Finally, the neural systems underlying predation appear to be different from those in aggression directed toward either conspecifics, or predators (Canteras, Ribeiro-Barbosa, & Comoli, 2001). Similarly, “play fighting” during early ontogeny does not usually produce any harm to the opponent, and both members of the interacting dyad appear to regard the activity as rewarding (see Pellis & Pellis, 1998, for review).

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Although the categories of offensive and defensive aggression are typically applied to animal research and analysis, a rather different schema, of reactive versus instrumental aggression is more often utilized in human studies. This difference has had unfortunate effects on attempts to link the two fields of endeavor, as the category of reactive aggression appears to make no distinction between aggression as a reaction to the sorts of provocation that elicit offensive attack in animals, and those that elicit defensive attack. Instrumental aggression means aggression based on external rewards. Although such aggression is undoubtedly common in human life, it is not clear how this category is importantly different from other actions aimed at the acquisition of extraneous rewards, such as what might be termed “instrumental sex” (prostitution), “instrumental mothering” (nanny or nursemaid), or “instrumental allogrooming” (hairdresser or masseuse); all of which, along with “instrumental aggression,” constitute human occupational categories. Indeed, the category of “actor” makes it clear that humans can emit, convincingly and effectively, virtually any behavior for extraneous rewards. Aggression may be an easy pattern to acquire in this context, reflecting its normal role in resource competition—that the victor receives or controls the disputed resource—but the fact that aggression responds to reward says nothing exceptional about aggression. Animal aggression has also been categorized on specific antecedents or on the types (e.g., gender, gestational status, age) of animals involved, such as male-male fighting; isolation-induced aggression, or maternal aggression (see Crabtree & Moyer, 1981, for specific categories). Some of these categories exemplify offensive aggression, elicited or modulated by specific stimuli, but some may reflect defense, or a combination of offensive and defensive aggression. Many specific agonistic situations involve mixtures of offensive and defensive aggression. In both natural and laboratory situations, aggression is a dynamic event. In intense dyadic interactions, brief perturbations can dramatically alter the motives of the interacting individuals. In particular, pain can elicit defense in an animal that had initially shown high levels of offensive attack motivation, producing a switch from offensive to defensive behavior. When two animals dispute over an important resource, both with initially high levels of offensive aggressive motivation, the typical outcome of victory for one and defeat for the other involves a switch from predominantly offensive to predominantly defensive behavior on the part of the latter; this switch is often triggered by the level of pain or damage experienced. Figure 49.4 shows a play fighting encounter between immature chacma baboons. The smaller animal is additionally showing signs of fear or defensiveness. This interpretation was confirmed

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Offensive Aggression

Figure 49.4 Conspecific play fighting in young chacma baboons (Papio ursinus). Kruger National Park, South Africa.

shortly after the photograph was taken by its flight and vocalization behaviors. Mixed motivations to offense and defense also occur in laboratory tests, depending on conditions: Attacks by postparturient female rats on intruder females are typically offensive (and respond to antiaggressive drugs), whereas attacks by the same females to adult male intruders are more defensive in terms of behavior and are less responsive to drugs that are specifically effective against offensive attack (Parmigiani, Ferrari, & Palanza, 1998; Parmigiani, Rodgers, Palanza, Mainardi, & Brain, 1989). This situation of a mixture of core motivations or elements is hardly unique to aggression and defense. Being wet and being cold can easily be separated by appropriate manipulations, but under normal circumstances such as being caught in the rain or swimming in the ocean, being wet often involves being cold as well. The trick—easier for wet and cold than for offense and defense—is to find ways of separating the two for purposes of analysis.

OFFENSIVE AGGRESSION Antecedents to Offensive Aggression The core factor in offensive aggression is resource control. Resources may be functionally defined as objects or relationships that facilitate the enhanced representation of an individual’s genes in the next generation, that is, reproductive success. Offensive aggression represents a direct approach to resource control through defeating or intimidating the other combatant into abandoning its claims to that resource. In offensive aggression, the resource control benefit comes at the expense of the other individual. This

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nonzero sum game approach reflects that many resources can only be used once, at least in terms of a particular episode. Food is eaten and disappears; a nest or denning site will accommodate only a single individual or a mated pair and their offspring; an impregnated female is not available to productively breed again for quite a long time. Most offensive aggression is aimed at conspecifics. One reason for this is proximity; that conspecifics of social species are more or less constantly present and ready to use the same resources as the focal animal. Another aspect is that the adaptive resources of a focal individual are the same as those of its conspecifics of similar age and sex, bringing them into conflict over such resources when the latter are limited and sequesterable. A particularly important reason that offensive aggression is largely confined to conspecifics is that the most direct determinant of a focal animal’s reproductive, adaptive success is sexual access to an opposite-sex conspecific in breeding condition. Such a resource is of limited value to a nonconspecific, as interspecies hybrids are almost always sterile, and many species that are capable of interbreeding have evolved elaborate patterns of courtship activities and displays that serve to limit breeding to conspecific individuals (Krohmer, 2004). This core resource for animals of the same sex and species is therefore of little value to animals of other species, sharply limiting the motivation for interspecies offensive attack. It is interesting, however, to note reports of fighting between Atlantic spotted dolphins and bottlenose dolphins. These species have been reported to produce viable, though not necessarily fertile, hybrids in the wild, suggesting that reproductive access may be a contested resource for them (Herzing, Moewe, & Brunnick, 2003). The degree to which a resource can be sequestered, or set apart and protected by an individual animal, is also especially high in the case of reproduction. A female in breeding condition is a particularly valuable resource that can, in principle, be totally sequestered from competitors if sufficient time, muscle, energy, and skill are put into the effort. At the other extreme, while air to breathe is the most immediate and necessary of all resources, it is very difficult indeed to sequester. Food has a variable position along this continuum of sequesterability, with discrete items like larger-sized prey being high, and small and scattered items like grass or small insects being low on the continuum. The propensity of individuals of a given species to fight over food is highly correlated with its sequesterability. While predators or scavengers on larger prey frequently fight over food, ungulates, and other grazers show little tendency to do so, even when these small food items are also scarce: It is less productive to fight over these resources than to spend the time finding and consuming them.

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The Super Resources—Dominance and Territoriality Dominance hierarchies and territoriality are believed to have evolved because they are adaptive in facilitating access to a wide range of resources for the dominant or the territory holder, while reducing fights over specific resources. They serve as highly desirable and defensible “super resources” that initially must be gained in the same manner as other resources, through competition and, usually, fighting. Once a dominance hierarchy or a territory has been established, it often confers priority or sole access to important resources without additional fighting unless the resource holder is specifically challenged. Territoriality involves a space that may be marked (e.g., scent marks in many rodents, visual or auditory displays in many other mammalian species) and patrolled by the territory-holder to deny entry to same sex conspecifics (see Stamps & Buechner, 1985, for review). Variants to this pattern are frequent, including tolerance of same-sex individuals until they are mature; or even tolerance of sexually mature same-sex conspecifics unless they make sexual overtures to the females living in the territory. The importance of fighting over females, that is, the degree to which such fighting results in reproductive advantage in a particular species, is mirrored in the degree of sexual dimorphism of that species. Harem-holding species, in which a few dominant males strongly monopolize the reproductive services of females while nondominant males are much less likely to breed at all, show strong sexual dimorphism: Males are larger than females, and the larger the male, the more likely it is to be dominant. In contrast, monogamous species, in which male-male fighting plays a much smaller role in reproduction, tend to show little sexual size dimorphism. For animals that do not have a specific home area but roam over a number of sites, individual territoriality is more difficult: Among social species, a dominance hierarchy is likely to be the result (Abbott et al., 2003). Dominance relationships may also be more common than territoriality in situations in which the presence of a cohesive group is advantageous and the driving away of subordinates might be detrimental; such subordinates might serve as alternative targets for predators, or lend numbers to intergroup raids (Wrangham, 1999). Thus, unlike strict territoriality, dominance hierarchies usually include tolerance of the presence of sexually mature same-sex individuals (male except for a few species, such as spotted hyenas, where females are dominant; Glickman et al., 1992), so long as these animals do not present challenges to the dominant. For species living in large groups, and with a definite breeding season, dominance hierarchies may be manifest only during such a rutting period, sometimes in a territory that is defended only during this season.

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While dominance confers enhanced access to some or many resources, strategies to circumvent the dominant’s priority of access have also evolved. Until recently, access to breeding females was considered a major sinecure for dominants of many species, but DNA analyses have made it clear that “sneak mating strategies” result in a substantial portion of foreign or subordinate males siring offspring in some territorial or dominance hierarchy species (Bishop, Jarvis, Spinks, Bennett, & O’Ryan, 2004; Marvan et al., 2006). Offensive Aggression: Behaviors and Targets When laboratory animals are maintained in small groups and the status of each animal within the group is well established, dominant male rats show a highly organized form of offensive attack, with bites aimed at the back of the attacked (typically male) animal, a site in which bites are unlikely to cause debility or death (R. J. Blanchard et al., 1975). This provides a means of delivering pain to nondominant males and encouraging them to leave the group, but without much risk of killing or damaging the reproductive potential of an animal that is likely in nature to be one of the dominant’s own maturing male offspring. The same system, a bite or blow that will hurt but not kill, is also useful in species such as chimpanzees, where the continued presence of subordinate adult males in a group is important to the continuation of the group (Wrangham, 1999), and it is notable that primates as well as rodents appear to show a targeting of attack bites and blows (Adams, 1979). While target sites for bites and blows appear to be characteristic also of less social species such as mice, mice are less inhibited about making bites to potentially dangerous sites such as the ventrum or genitals than are rats (R. J. Blanchard, O’Donnell, & Blanchard, 1979; Litvin, Blanchard, Pentkowski, & Blanchard, 2007), suggesting that the dangerousness of the attack seen in conspecific fighting may be a particularly important determinant of the degree to which that species maintains stable social groups. At one end of this continuum of dangerousness may be fights involving targets for which there are specific protective structures, that is, where protective structures have evolved to protect the recipient of blows. Many ungulates aim conspecific blows at the antlers or horns of the opponent, a site where blows are seldom (although not always) without damage to the recipient. These structures may be especially necessary in species that are equipped with very dangerous weaponry. Thus lions, the only large cats to live in social groups, have a mane over the back of the head and the neck that is not found in other large (but solitary-living) cats (see Figure 49.5).

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Offensive Aggression

Figure 49.5 African lions (Panthera leo). Note: These males were two of a group of three brothers that hunted together and had recently killed the dominant male of a pride (Chobe National Park, Botswana).

As bites or blows (depending on the weapon system used) are aimed at specific target sites on the body of the opponent, and inhibited toward other sites, a highly effective defense by the recipient is to conceal or remove these targets by body manipulations or contortions. In rats, this involves “back-defense” behaviors including upright facing of the attacker, interposing the defender ’s face (which is protected by long vibrissae, another structural protection for an area that would otherwise be a target of attack; R. J. Blanchard, Blanchard, Takahashi, & Kelley, 1977) and ventrum (an area that is highly vulnerable and not attacked), between the attacker ’s jaws and the defender ’s own vulnerable dorsal (back) area. The attacker counters this defense by attempting to lunge around the defender to reach its target: The defender pivots in the same direction, to continue to face the attacker, and so on. When the attack becomes very pressing, the defender may slip backward to lie on its back, additionally rolling toward the attacker if the latter attempts to dig under for a bite. That these attack and defensive behaviors function to facilitate or deny, respectively, access to specific target sites may be seen in findings that they occur together for the two members of an attack/defense dyad, with the form of defense

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typically serving to drive the specific actions involved in attack (Takahashi & Blanchard, 1982). While targets of aggressive attack and strategies to defend them have been specifically investigated in only a few species, targeting appears to be a widespread phenomenon among mammals, and targets of play fighting have been described in a wide range of both mammals and primates (Pellis & Pellis, 1987). The tenet that damaging behaviors such as bites and blows tend to be aimed at less vulnerable sites on the opponent during intraspecific offensive attack, with this tendency higher in social species, is important to understanding a number of factors in aggression. First, it represents an important enabling factor for the adaptive value of aggression to be realized while avoiding damage to conspecific opponents. Second, it provides an explanation for the specific form of a number of attack (and defensive) behaviors seen during intraspecific fighting. Third, it is highly compatible with findings of structural adaptations for intraspecific fighting, including manes and ruffs, antlers, horns, and the like. Fourth, it provides a means of evaluating whether a specific harm-delivering action represents offensive or defensive aggression. As Pellis and Pellis (1987) note, the target site strategy applies to play fighting as well, but the targets of play fights tend to be different from those in either offensive or defensive attack. The second of these considerations, that target sites for attack provide an explanation for the specific form of attack (and also defense) in some species, is also of interest in terms of the more traditional ethological interpretation that many of these same actions represent signals to the opposing combatant (see Figure 49.6). Instead, a target site view suggests that many such movements represent offensive strategies adaptive in gaining access to the targets of attack, or defensive strategies useful in denying the opponent such access (R. J. Blanchard et al., 1977). If a particular intraspecific aggressive or defensive action demonstrably does not aid in the attainment or denial of such access, it might be examined for its potential status as a signal. Thus piloerection, erection of hairs making the combatant appear larger, might well be regarded as sending a (false or exaggerated) signal of the animal’s size. As discussed later, there is a clear and important use of target sites for offensive versus defensive attack to determine, within a paradigm or in an individual fighting sequence, what pattern of aggression is being demonstrated. This should be a particularly important consideration in work on the neural systems or neurotransmitters involved in offensive versus defensive aggression. In practice, the requirement of separating offensive from defensive attack has largely involved use of paradigms such as the resident-intruder model, in which these patterns should

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968

Defense and Aggression Attack Lateral Attack

Box

Mean Duration (seconds)

50

70 60

40

50 30

40 30

20 10

10 L

Mean Duration (seconds)

70

W

L

W

0

L

70

On-Top

60

60

50

50

40

40

30

30

20

20

10

10

0

W

L

W

On-Back

0 L

30 Mean Duration (seconds)

Offensive Interspecies Fighting

20

0

W

L

L

W 30

Chase

25

25

20

20

15

15

10

10

5

5

0

W

L

W

Flight

0 L

W

L

W

Bite

8

Mean Frequency

be polarized, but without necessarily checking individual behaviors to determine that they do represent either offensive or defensive behavior, a situation that has resulted in some potential confusion with reference to the biology of offensive and defensive aggression.

Defense

6

4

2

0 L W Alpha

L W Intruder

L 20 18 16 14 12 10 8 6 4 2 0

W

L

W

Freeze

L W Alpha

L W Intruder

Figure 49.6 Attack and defensive behaviors of laboratory (L) or wild (W) dominant rats (two left bars of each graph) and intruder (L or W right bars of each graph) during conspecific fighting of Rattus norvegicus. Behaviors in the left column (attack, on top, chase and bite) are associated with offensive attack, whereas those in the right column (box, on back, flight, and freeze) are associated with defense. These durations or frequencies were measured in within-strain (L or W) encounters in which lateral attack (dominant) and boxing (subordinate) occur as dyads, as do on top and on back, or chase and flight. Bite and freeze, however, are unrelated behaviors. Note: From “Attack and Defense in Laboratory and Wild Norway and Black Rats,” by L. K. Takahashi and R. J. Blanchard, 1982, Behavioral Processes, 7, pp. 49–62. Adapted with permission.

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Descriptions of interspecies aggression distinct from predator-prey relationships are increasing in frequency, and these are increasingly recognized as having important effects on species ecology and distributions. Among mammalian carnivores, competition over food or territories appears to be particularly common (and deadly), suggesting that these represent interspecies offensive aggression. Thus wolves and bears have been shown to contest kills in areas of low prey availability, while lions, leopards, cheetahs, and wild dogs all have been noted in altercations over food (Ballard, 1982). These interactions may be symmetrical (both species attack the other), as with hyenas and lions, in which the initial direction of attack depends on the numbers of each species involved; or asymmetrical, when one species usually dominates over the other in such interactions (e.g., lions attack cheetahs and the latter flee). That this is not a normal predation situation is reflected in the rarity of consumption of the loser by the victor. In fact, the usual outcome is that the loser flees and the victor, failing to chase, consumes the spoils of victory, the carcass of the prey. Strategies for minimizing interspecific aggression over resources include spatial and temporal differences in habitat usage. Interspecies aggressive interactions have a pronounced effect on the spatial distribution of animals, sometimes forcing the weaker participant species to completely withdraw from a given habitat. Cheetahs and wild dogs show avoidance of areas characterized by high prey densities which in turn attract the top carnivores (hyenas and lions; Caro, 1994; Creel & Creel, 1996). They thus survive and reproduce most successfully in areas of low prey and competitor density. Adults of one species may target only the young of the competing species (Palomares & Caro, 1999). In areas where arctic and red foxes are sympatric, red foxes have been shown to kill juvenile arctic foxes (Tannerfeldt & Elmhagen, 2002). As a result, arctic foxes avoid red foxes by forming dens in areas poor in resources; namely those in higher altitudes. Temporal differences in habitat usage may include seasonal as well as daily activity pattern variations. In Chile, the sympatric grey and culpeo foxes display differential daily activity patterns, with the former preferring the summer and fall seasons and the latter winter and spring (Johnson & Franklin, 1994). In Kruger National Park in South Africa, lions, wild dogs, and cheetahs show a preference for different hunting times. Schaller (1972)

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proposed that cheetahs in the Serengeti prefer hunting at midday due to reduced interference from lions and hyenas at this time. Such interspecific aggression between predators may be an important determinant of densities of other species than the combatants. Red foxes, which aggressively interact with coyotes, feed on duck nests. A decrease in coyotes produces an increase in red foxes, which ultimately leads to dramatic decreases in duck populations (Sovada, Sargeant, & Grier, 1995). Coyote-wolf interactions have an impact on populations of San Joaquin kit foxes, with predation by coyotes responsible for over 75% of kit fox mortality (Cypher & Scrivner, 1992; Linnell & Strand, 2000; Ralls & White, 1995). Thus, wolf extermination directly affects coyote numbers, which in turn negatively affect the kit fox population. Interspecific offensive aggression would be expected to be different from intraspecies offense in respect to the role of mechanisms reducing damage to the opponent. While this has not been examined systematically, the not uncommon findings of lethal attack during intraspecific fighting of predators over prey (e.g., Lawick-Goodall, 1971, p. 185) is consonant with a view that such fighting is neither inhibited nor is its lethality reduced by targeting of nonvulnerable structures, such as may occur in intraspecies fighting. Neural Systems: Defense The neural systems involved in conditioned aspects of defense appear to encompass a much wider array of structures than do those underlying unconditioned defensive behaviors (for reviews, see Chapter 39, this volume, or Myers & Davis, 2008). The present material will be confined to neural systems involved in unconditioned defense. A productive approach to this issue involves evaluation of intermediate early gene (c-fos) activation patterns in rodents following exposure to predators (Canteras & Goto, 1999) or predator odors (Dielenberg et al., 2001). These show a high degree of agreement, indicating the involvement of several forebrain, midbrain, and hindbrain structures, including the hypothalamus (anterior hypothalamic nucleus [AHN], dorsomedial part of the ventromedial nucleus [VMHdm], and the dorsal premamillary nucleus [PMd]); the posteroventral portion of the medial nucleus of the amygdala (MeApl); the bed nucleus of the stria terminalis (BNST); and the periaqueductal gray (PAG). The Medial Hypothalamic Zone Canteras (2008) proposed that the anterior hypothalamic nucleus (AHN), the dorsomedial part of the ventromedial nucleus (VMHdm), and the dorsal premamillary nucleus (PMd) of the hypothalamus serve as an integrating

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medial hypothalamic zone (MHZ) for the regulation of defense, with input from the amygdala, reflecting olfactory, and other predator-related sensory information, and from the septohippocampal system. The latter may supply information related to contextual or spatial orientation, a crucial factor in determining the form of defensive behavior. The MHZ is reciprocally connected, via the thalamus, to the cortex (Risold, Thompson, & Swanson, 1997), providing a potential avenue for cortical modulation of this level of integration. Finally, the MHD may initiate defensive responses through descending projections to brain stem regions, specifically the PAG, for which there has long been independent evidence of involvement in defensive behavior (reviewed by Bandler & Keay, 1996). Lesion studies support the involvement of many of these structures in defense (Canteras & Goto, 1999; Dielenberg, Hunt, & McGregor, 2001; Markham, Blanchard, Canteras, Cuyno, & Blanchard, 2004), with particularly strong reductions in unconditioned defensive behaviors seen following lesions of the PMd (for summary see Figure 49.7). The Hippocampus The hippocampus (HPC) is the topic of an immense literature on aversive situational conditioning (for review see Fanselow & Ponnusamy, 2008) and lesions in the ventral HPC reduce unconditioned defensive behaviors of rats to a cat (Pentkowski, Blanchard, Lever, Litvin, & Blanchard, 2006). The hippocampus sends projections to the MHD system by way of two main pathways. First, to the dorsal region of the ventrolateral zone of the rostral part of the lateral septal nucleus (LSrvld; Risold & Swanson, 1997), which then projects to the AHN and PMd (Comoli, RibeiroBarbosa, & Canteras, 2000; Risold, Canteras, & Swanson, 1994). The LSrvld contains gaba-aminobutyric acid (GABA)ergic neurons that may provide inhibitory inputs to circuits mediating defense (Canteras, 2002). Second, projections to the lateral, medial, and posterior basomedial and basolateral amygdala nuclei (Canteras & Swanson, 1992; Petrovic, Risold, & Swanson, 1996, 2001; Pikkarainen, Ronkko, Savander, Insausti, & Pitkanen, 1999), which then project through the BNST to the VMHdm and AHN, may be involved in the integration of sensory information impacting defense (Canteras, 2002). Amygdala The amygdala has long been known as a key structure in the expression of innate defensive responses of a rat to a predator, a cat (D. C. Blanchard & Blanchard, 1972). The medial amygdala, with strong projections to both the VMH and the AHN is involved in unconditioned fear responses of rats to cat odor, likely through its mediation of odor information from the accessory olfactory system

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Defense and Aggression Prefrontal cortex PLc, ILc, ACd, and ACv Pheromone-like processing of predator’s odor Temporal, prefrontal, agranular insular, and olfactory cortical processing

Amygdala MEApv

LA

BMAp

Intermediate regions of Field CA1 and Subiculum Hippocampus Retinal inputs (environmental luminescence)

Hypothalamus BSTif

LSr.vl.d LSr.dl Septum

AHN

PMd

Hypothalamic “attack area”

Figure 49.7 Summary diagram showing the organization of major parallel prosencephalic pathways involved in the control of innate fear responses to psychological threats. Note: Acd ⫽ Anterior cingulate area, dorsal part; Acv ⫽ Anterior cingulate area, ventral part; AHN ⫽ Anterior hypothalamic nucleus; BMAp ⫽ Basomedial amygdalar nucleus, posterior part; BSTif ⫽ Bed nuclei of the stria terminalis, interfascicular nucleus; Ilc ⫽ Imfralimbic area, caudal part; LA ⫽ Lateral amygdalar nucleus; LHArc ⫽ Lateral hypothalamic

(Staples, McGregor, Apfelbach, & Hunt, 2007). An n-methyld-aspartate (NMDA)-dependent pathway from the central amygdala to the lateral PAG may mediate lasting anxiogenic behaviors in the elevated plus maze (EPM) following predator stress (R. Adamec, 2001; R. E. Adamec, Blundell, & Burton, 2005). The periaqueductal grey (PAG) has a rostrocaudal columnar organization, with four columns: the dorsolateral (dlPAG), dorsomedial (dmPAG), lateral (lPAG), and ventrolateral (vlPAG) (Bandler & Keay, 1996). The MHZ has extensive projections to the PAG: From VMH to all parts of the PAG; from PMd to the dlPAG; and from AHN to dmPAG and vlPAG (Cameron, Khan, Westlund, & Willis, 1995; Canteras, 2002; Vianna & Brandao, 2003). Chemical or electrical stimulation of the PAG and some surrounding areas (e.g., superior and inferior colliculi; cuneiform nucleus) produces defensive behaviors such as freezing, arousal and escape, defecation, analgesia, and changes in autonomic measures (see Bandler & Keay, 1996, and Vienna & Bandao, 2003, for review). In particular, flight responses from activation of the dorsal PAG have been suggested to serve as an important mechanism in panic attacks (Graeff, Guimarães, De Andrade, & Deakin, 1996). Microinjection of excitatory amino acids (EAA) into the vlPAG evoked a passive coping reaction characterized by quiescence, decreased vigilance, hypotension, and

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VMHdm

PRC PAGdl

LHArc Emotional responses to psychological threats area, retinoceptive region; LSr.dl ⫽ Lateral septal nucleus, rostral part, dorsolateral zone; LSr.vl.d ⫽ Lateral septal nucleus, rostral part, ventrolateral zone, dorsal region; MEApv ⫽ Medial amygdalar nucleus, posteroventral part; PAGdl ⫽ Periaqueductal gray, dorsolateral part; PLc ⫽ Prelimbic area, caudal part; PMd ⫽ Dorsal premammillary nucleus; PRC ⫽ Precommissural nucleus; VMHdm ⫽ Ventromedial hypothalamic nucleus, dorsomedial part.

bradycardia (Bandler & Carrive, 1988; Bandler, Depaulis, & Vergnes, 1985; Bandler & Keay, 1996; Bandler & Shipley, 1994; Depaulis, Bandler, & Vergnes, 1989; Depaulis & Vergnes, 1986; Zhang, Bandler, & Carrive, 1990; Zhang, Davis, Bandler, & Carrive, 1994; but see Mongeau, De Oca, Fanselow, & Marsden, 1998, for a different view). Neural Systems for Offensive and Defensive Aggression At first glance, the neural systems associated with aggression, for example, hypothalamus, septum, periaqueductal grey (PAG), amygdala, bed nucleus of the stria terminalis (BNST), and frontal cortex (Delville, De Vries, & Ferris, 2000; Nelson & Trainor, 2007) appear to be virtually identical to those involved with defense. Immunostaining for c-Fos has shown increased labeling in the MeA, septum, BNST, hypothalamus, and amygdala during displays of intermale aggression (e.g., Veening et al., 2005), with relatively similar Fos activation patterns for rats (Halasz, Liposits, Meelis, Kruk, & Haller, 2002; Veening et al., 2005) hamsters (Delville et al., 2000; Kollack-Walker & Newman, 1995) and mice (Haller, Toth, Halasz, & De Boer, 2006). In particular, aggression appears to be associated with enhanced vasopressin representation in an anterior hypothalamus–medial preoptic area (AHN)

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in hamsters (Ferris, Axelson, Martin, & Roberge, 1989) while the “hypothalamic attack area” (HAA) in rats consists of the intermediate hypothalamic area (IHA) and the ventrolateral pole of the ventromedial nucleus of the hypothalamus (VMHvl). The view that the HAA (rats) and AHN (hamsters) are central sites in mediating rodent aggression (Delville et al., 2000; Risold et al., 1994) is supported by findings that both share reciprocal neural connections with the LS, MeA, PAG, and BNST (Coolen & Wood, 1998; Risold & Swanson, 1997; Roeling et al., 1994) sites that are commonly associated with aggressive behaviors. However, the apparent overlap between neural systems for aggression and for defense may be misleading, as different sites or systems within these brain areas may differentially mediate defensive and offensive behaviors. The posteroventral portion of the medial amygdala (MeApv) and the dorsomedial division of the VMH (VMHdm) have been implicated in mediating defensive behaviors (Canteras, 2002), whereas it is the posterodorsal region of the MeA (MeApd) and the ventrolateral section of the VMH (VMHvl) that may be components of the neural circuit subserving the expression of aggression (KollackWalker & Newman, 1995; Swanson, 2000; Veening et al., 2005). In addition, the overlap is hardly complete: The PMd, an area in which particularly robust effects on defense to stimuli have been obtained (see Markham et al., 2004, for review) appears not to be involved in aggression. An additional complication of these analyses is that the behavior changes associated with lesions and stimulations of these structures are sometimes ambiguous in terms of a distinction between offensive and defensive attack. Thus stimulation of the HAA produces a violent attack on conspecifics in rats, but Kruk (1991) makes the important observation that such hypothalamic attack is insensitive to many drugs that do affect naturally elicited attack in rats, and that it does not involve the same target-attack behaviors, such as lateral attack to obtain access to the back, that is typical of normal aggression. Similarly, electrical stimulation of the VMH in the cat produces a pattern of ear retraction, growling, hissing, and retreat intermixed with paw attack (Sweidan, Edinger, & Siegel, 1991), that the authors appropriately interpret as reflecting affective defense: In terms of behaviors of nonmanipulated cats in agonistic encounters, each of these components (except for growling, which may be seen in either situation) reflects defensive attack, rather than offensive attack (Leyhausen, 1979). This may represent cross-species variation in the organization of such behaviors or may suggest that there is an important defensive attack component to the aggression seen after stimulation of the VMH.

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The differentiation between defensive and offensive aggression, often not clearly made in the context of brain stimulation studies, may also be of interest in the context of aggression that is sharply enhanced by genetic, endocrine, drug, or experiential manipulations. For at least some of these aggression enhancements (e.g., aggression escalated by alcohol. See Miczek et al., 2007, for review), changes in serotonergic and GABAergic systems leading to reductions of cortical inhibitory influences appear to be important. Decreases in GABA activity resulting in lowered PFC inhibition may also be involved in the enhanced attack seen in adrenalectomized male rats maintained on low (and unchanging) levels of glucocorticoids. This aggression was characterized by attacks aimed at vulnerable body targets including the head, throat, and belly, and thus was more suggestive of a defensive attack pattern than of offense (Halasz, Totha, Kallob, Liposits, & Haller, 2006). Attack accompanying unilateral electrical stimulation of the hypothalamic attack area (HAA) and also aimed at vulnerable body sites was associated with increased activation of the central amygdala (CeA), the mediodorsal thalamus (MD), and several cortical areas (Halasz et al., 2002). These and other methods of enhancing aggression have seen a strong upsurge of attention. It will be of considerable interest to determine if the behaviors involved in these enhancements and the neural systems underlying them can illuminate not only patterns of offensive and defensive attack, but also how changes in (inhibitory?) factors can alter some of the constraints that normally differentiate the two types of aggression. While it is striking that there is so much overlap in brain systems associated with offense and defense, in rodents, this should be viewed in the context of other behaviors such as reproduction, parenting, and sociality, which often involve many of the same brain areas (e.g., De Vries & Panzica, 2006). A major organizing factor for behaviors involving interactions with conspecifics and other animals is olfaction. For macrosmatic rodents, the subjects for the vast majority of these research programs, olfaction is perhaps the core sensory system in recognizing and responding to biologically important, animate stimuli. These are mediated by the main and the accessory olfactory systems (for reviews, see Bakker, 2003; Keller, Douhard, Baum, & Bakker, 2006), which, along with their connections, comprise the rhinencephalic brain; traditionally the seat of the emotions. While, as noted for the MeA and the VMH, different types of input (in this case, odors of predator versus adult male conspecific) may produce within-area differentiations in activity, the gross areas affected may be similar, representing a particular sequence in an only partly differentiated olfactory input. This analysis, while crude, has an important corollary, that understanding of the systems

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underlying defense and aggression will require a great deal more specific attention to functional hodology, as well as to the crucial minutiae of behavior.

(Eds.), Hand book of anxiety and fear (pp. 63–80). Amsterdam: Elsevier Academic Press. Blanchard, D. C., Blanchard, R. J., Takahashi, L. K., & Takahashi, T. (1977). Septal lesions and aggressive behavior. Behavioral Biology, 21, 157–161. Blanchard, D. C., Williams, G., & Blanchard, R. J. (1981). Taming of wild rattus norvegicus by lesions of the mesencephalic central gray. Physiological Psychology, 9, 157–163.

REFERENCES Abbott, D. H., Kerverne, E. B., Bercovitch, F. B., Shively, C. A., Mendoza, S. P., Saltzman, W., et al. (2003). Are subordinates always stressed? A comparative analysis of rank differences in cortisol levels among primates. Hormones and Behavior, 43, 67–82. Adamec, R. E. (2001). Does long term potentiation in periacqueductal gray (PAG) mediate lasting changes in rodent anxiety-like behavior (ALB) produced by predator stress?: Effects of low frequency stimulation (LFS) of PAG on place preference and changes in ALB produced by predator stress. Behavioural Brain Research, 120, 111–135. Adamec, R. E., Blundell, J., & Burton, P. (2005). Neural circuit changes mediating lasting brain and behavioral response to predator stress. Neuroscience and Biobehavioral Reviews, 29, 1225–1241. Adams, D. B. (1979). Brain mechanisms for offense, defense and submission. Behavioral and Brain Sciences, 2, 201–241. Arakawa, H., Blanchard, D. C., & Blanchard, R. J. (2007). Colony formation of C57BL/6J mice in visible burrow system: Identification of eusocial behaviors in a background strain for genetic animal models of autism. Behavioural Brain Research, 176, 27–39. Averill, J. R. (1982). Anger and aggression: An essay on emotion. New York: Springer-Verlag. Bakker, J. (2003). Sexual differentiation of the neuroendocrine mechanisms regulating mate recognition in mammals. Journal of Neuroendocrinology, 15, 615–621. Ballard, W. B. (1982). Gray wolf-brown bear relationships in the Nelchina Basin of South-Central Alaska. In F. H. Harrington & P. C. Paquet (Eds.), Wolves of the world: Perspectives of behavior, ecology and conservation (pp. 71–80). Park Ridge, NJ: Noyes Publications. Bandler, R., & Carrive, P. (1988). Integrated defence reaction elicited by excitatory amino acid microinjection in the midbrain periaqueductal grey region of the unrestrained cat. Brain Research, 439(1–2), 95–106. Bandler, R., Depaulis, A., & Vergnes, M. (1985). Identification of midbrain neurones mediating defensive behaviour in the rat by microinjections of excitatory amino acids. Behavioural Brain Research, 15, 107–119. Bandler, R., & Keay, K. A. (1996). Columnar organization in the midbrain periaqueductal gray and the integration of emotional expression. Progress in Brain Research, 107, 285–300. Bandler, R., & Shipley, M. T. (1994). Columnar organization in the midbrain periaqueductal gray: Modules for emotional expression? Trends in Neurosciences, 17, 379–389. Bishop, J. M., Jarvis, J. U. M., Spinks, A. C., Bennett, N. C., & O’Ryan, C. (2004). Molecular insights into patterns of colony composition and paternity in the common mole-rat Cryptomys hottentotus hottentotus. Molecular Ecology, 15, 1217–1229. Blanchard, D. C. (1997). Stimulus, environmental and pharmacological control of defensive behaviors. In M. Bouton & M. S. Fanselow (Eds.), Learning, motivation and cognition: The functional behaviorism of Robert C. Bolles (pp. 283–305). Washington, DC: American Psychological Association. Blanchard, D. C., & Blanchard, R. J. (1972). Innate and conditioned reactions to threat in rats with amygdaloid lesions. Journal of Comparative and Physiological Psychology, 81, 281–290. Blanchard, D. C., & Blanchard, R. J. (2008). Defensive behaviors, fear and anxiety. In R. J. Blanchard, D. C. Blanchard, G. Griebel, & D. J. Nutt

c49.indd Sec2:972

Blanchard, R. J., Blanchard, D. C., Agullana, R., & Weiss, S. M. (1991). Twenty-two kHz alarm cries to presentation of a predator, by laboratory rats living in visible burrow systems. Physiology and Behavior, 50, 967–972. Blanchard, R., Blanchard, D.C., Griebel, G., and Nutt, D. (2007). Handbook of Anxiety and Fear. London: Elsevier. Blanchard, R. J., Blanchard, D. C., & Hori, K. (1989). An ethoexperimental approach to the study of defense. In R. J. Blanchard, P. F. Brain, D. C. Blanchard, & S. Parmigiani (Eds.), Ethoexpermental approaches to the study of behavior (Vol. 48, pp. 114–136). Boston: Kluver Academic. Blanchard, R. J., Blanchard, D. C., Rodgers, J., & Weiss, S. M. (1990). The characterization and modelling of antipredator defensive behavior. Neuroscience and Biobehavioral Reviews, 14, 463–472. Blanchard, R. J., Blanchard, D. C., & Takahashi, L. K. (1978). Pain and aggression in the rat. Behavioral Biology, 23, 291–305. Blanchard, R. J., Blanchard, D. C., Takahashi, T., & Kelley, M. J. (1977). Attack and defensive behaviour in the albino rat. Animal Behaviour, 25, 622–634. Blanchard, R. J., Fukunaga, K., Blanchard, D. C., & Kelley, M. J. (1975). Conspecific aggression in the laboratory rat. Journal of Comparative and Physiological Psychology, 89, 1204–1209. Blanchard, R. J., Kleinschmidt, C. K., Flannelly, K. J., & Blanchard, D. C. (1988). Fear and aggression in the rat. Aggressive Behavior, 10, 309–315. Blanchard, R. J., Nikulina, J. N., Sakai, R. R., McKittrick, C., McEwen, B., & Blanchard, D. C. (1998). Behavioral and endocrine change following chronic predatory stress. Physiology and Behavior, 63, 561–569. Blanchard, R. J., O’Donnell, V., & Blanchard, D. C. (1979). Attack and defensive behaviors in the albino mouse (Mus musculus). Aggressive Behavior, 5, 341–352. Cain, C. K., & LeDoux, J. E. (2008). Brain mechanisms of Pavlovian and instrumental aversive conditioning. In R. J. Blanchard, D. C. Blanchard, G. Griebel, & D. J. Nutt (Eds.), Handbook of anxiety and fear (pp. 103–124). Amsterdam: Elsevier Academic Press. Cameron, A. A., Khan, I. A., Westlund, K. N., & Willis, W. D. (1995). The efferent projections of the periaqueductal gray in the rat: A phaseolus vulgaris-leucoagglutinin study: Pt. II. Descending projections. Journal of Comparative Neurology, 351, 585–601. Canteras, N. S. (2002). The medial hypothalamic defensive system: Hodological organization and functional implications. Pharmacology, Biochemistry, and Behavior, 71, 481–491. Canteras, N. S. (2008). Neural systems activated in response to predators and partial predator stimuli. In R. J. Blanchard, D. C. Blanchard, G. Griebel, & D. J. Nutt (Eds.), Handbook of anxiety and fear (pp. 125–140). Amsterdam: Elsevier Academic Press. Canteras, N. S., & Goto, M. (1999). Fos-like immunoreactivity in the periaqueductal gray of rats exposed to a natural predator. NeuroReport, 10, 413–418. Canteras, N. S., Ribeiro-Barbosa, E. R., & Comoli, E. (2001). Tracing from the dorsal premammillary nucleus prosencephalic systems involved in the organization of innate fear responses. Neuroscience and Biobehavioral Reviews, 25(7–8), 661–668. Canteras, N. S., & Swanson, L. W. (1992). Projections of the ventral subiculum to the amygdala, septum, and hypothalamus: A PHAL anterograde tracttracing study of the rat. Journal of Comparative Neurology, 324, 180–194.

8/19/09 5:03:43 PM

References 973 Caro, T. M. (1994). Cheetahs of the Serengeti Plains. Chicago: Chicago University Press. Comoli, E., Ribeiro-Barbosa, E. R., & Canteras, N. S. (2000). Afferent connections of the dorsal premammillary nucleus. Journal of Comparative Neurology, 423, 83–98. Coolen, L. M., & Wood, R. I. (1998). Bidirectional connections of the medial amygdaloid nucleus in the Syrian hamster brain: Simultaneous anterograde and retrograde tract tracing. Journal of Comparative Neurology, 399, 189–209. Crabtree, J. M., & Moyer, K. E. (1981). Bibliography of aggressive behavior: A reader ’s guide to the research literature. New York: Alan R. Liss. Creel, S., & Creel, N. M. (1996). Limitation of African wild dogs by competition with larger carnivores. Conservation Biology, 10, 526–538. Cypher, B. L., & Scrivner, J. H. (1992). Coyote control to protect endangered San Joaquin kit foxes at the naval petroleum reserves, California. Proceedings of the Vertebrate Pest Conference, 15, 42–47. Delville, Y., De Vries, G. J., & Ferris, C. F. (2000). Neural connections of the anterior hypothalamus and agonistic behavior in golden hamsters. Brain, Behavior and Evolution, 55, 53–76. Depaulis, A., Bandler, R., & Vergnes, M. (1989). Characterization of pretentorial periaqueductal gray matter neurons mediating intraspecific defensive behaviors in the rat by microinjections of kainic acid. Brain Research, 486, 121–132. Depaulis, A., & Vergnes, M. (1986). Elicitation of intraspecific defensive behaviors in the rat by microinjection of picrotoxin, a gamma-aminobutyric acid antagonist, into the midbrain periaqueductal gray matter. Brain Research, 367(1–2), 87–95. De Vries, G. J., & Panzica G. C. (2006). Sexual differentiation of central vasopressin and vasotocin systems in vertebrates: Different mechanisms, similar endpoints. Neuroscience, 138(3), 947–55. Dielenberg, R. A., Hunt, G. E., & McGregor, I. S. (2001). “When a rat smells a cat”: The distribution of fos immunoreactivity in rat brain following exposure to a predatory odor. Neuroscience, 104, 1085–1097. Dielenberg, R. A., & McGregor, I. S. (1999). Low-dose midazolam attenuates predatory odor avoidance in rats. Pharmacology, Biochemistry, and Behavior, 62, 197–201. Edmunds, M. (1974). Defence in animals: A survey of anti-predator defences. New York: Longman Group. Endler, J. A. (1986). Defense against predators. Chicago: University of Chicago Press. Fanselow, M. S., & Lester, L. S. (1988). A functional behaviouristic approach to aversively motivated behavior: Predatory imminence as a determinant of the topography of defensive behavior. In R. C. Bolles & M. D. Beecher (Eds.), Evolution and learning (pp. 185–199). Mahwah, NJ: Erlbaum. Fanselow, M. S., & Ponnusamy, R. (2008). The use of conditioning tasks to model fear and anxiety. In R. J. Blanchard, D. C. Blanchard, G. Griebel, & D. J. Nutt (Eds.), Handbook of anxiety and fear (pp. 29–48). Amsterdam: Elsevier Academic Press.

c49.indd Sec3:973

Halasz, J., Totha, M., Kallob, I., Liposits, Z., & Haller, J. (2006). The activation of prefrontal cortical neurons in aggression: A double labeling study. Behavioural Brain Research, 175, 166–175. Haller, J., Toth, M., Halasz, J., & De Boer, S. F. (2006). Patterns of violent aggression-induced brain c-fos expression in male mice selected for aggressiveness. Physiology and Behavior, 88(1–2), 173–182. Harcourt, A. H., & de Waal, F. B. M. (1992). Coalitions and alliances in humans and other animals. New York: Oxford University Press. Hedinger, H. (1969). The psychology of animals in zoos and circuses. New York: Dover Publications. Herzing, D. L., Moewe, K., & Brunnick, B. J. (2003). Interspecies interactions between Atlantic spotted dolphins, Stenella frontalis and bottlenose dolphins, Tursiops truncatus, on Great Bahama Bank, Bahamas. Aquatic Mammals, 29, 335–341. Johnson, W. E., & Franklin, W. L. (1994). Spatial resource partitioning by sympatric grey fox (Dusicyon griseus) and culpeo fox (Dusicyon culpaeus) in southern Chile. Canadian Journal of Zoology, 72, 1788–1793. Keller, M., Douhard, Q., Baum, M. J., & Bakker, J. (2006). Destruction of the main olfactory epithelium reduces female sexual behavior and olfactory investigation in female mice. Chemical Senses, 31, 315–323. Kemble, E. D., Flannelly, K. J., Salley, H., & Blanchard, R. J. (1985). Mouse killing, insect predation, and conspecific attack by rats with differing prior aggressive experience. Physiology and Behavior, 34, 645–648. Kollack-Walker, S., & Newman, S. W. (1995). Mating and agonistic behavior produce different patterns of fos immunolabeling in the male syrian hamster brain. Neuroscience, 66, 721–736. Krohmer, R. W. (2004). The male red-sided garter snake (Thamnophis sirtalis parietalis): Reproductive pattern and behavior. ILAR Journal, 45, 54–74. Kruk, M. R. (1991). Ethology and pharmacology of hypothalamic aggression in the rat. Neuroscience and Biobehavioral Reviews, 15, 527–538. Kruuk, H. (1972). The spotted hyena: A study of predation and social behavior. Chicago: University of Chicago press. Lawick-Goodall, J. (1971). In the shadow of man. Boston: Houghton Mifflin. Leyhausen, P. (1979). Cat behavior: The predatory and social behavior of domestic and wild cats. New York & London: Garland STPM Press. Linnell, J. D. C., & Strand, O. (2000). Interference interactions, co-existence and conservation of mammalian carnivores. Diversity and Distributions, 6, 169–176. Litvin, Y., Blanchard, D. C., & Blanchard, R. J. (2007). Rat 22kHz ultrasonic vocalizations as alarm cries. Behavioural Brain Research, 182, 166–172. Litvin, Y., Blanchard, D. C., Pentkowski, N. S., & Blanchard, R. J. (2007). A pinch or a lesion: A reconceptualization of biting consequences in mice. Aggressive Behavior, 33, 1–7. Manser, M. B., Seyfarth, R. M., & Cheney, D. L. (2002). Suricate alarm calls signal predator class and urgency. Trends in Cognitive Sciences, 6, 55–57.

Ferris, C. F., Axelson, J. F., Martin, A. M., & Roberge, L. F. (1989). Vasopressin immunoreactivity in the anterior hypothalamus is altered during the establishment of dominant/subordinate relationships between hamsters. Neuroscience, 29, 675–683.

Markham, C. M., Blanchard, D. C., Canteras, N. S., Cuyno, C. D., & Blanchard, R. J. (2004). Modulation of predatory odor processing following lesions to the dorsal premammillary nucleus. Neuroscience Letters, 372(1–2), 22–26.

Glickman, S. E., Frank, L. G., Licht, P., Yalcinkaya, T., Siiteri, P. K., & Davidson, J. (1992). Sexual differentiation of the female spotted hyena: One of nature’s experiments. Annals of the New York Academy of Sciences, 662, 135–159.

Marvan, R., Stevens, J. M., Roeder, A. D., Mazura, I., Bruford, M. W., & de Ruiter, J. R. (2006). Male dominance rank, mating and reproductive success in captive bonobos (Pan paniscus). Folia Primatologica: International Journal of Primatology, 77, 364–376.

Graeff, F. G., Guimarães, F. S., De Andrade, T. G., & Deakin, J. F. (1996). Role of 5-HT in stress, anxiety, and depression. Pharmacology, Biochemistry, and Behavior, 54(1), 129–141.

McNaughton, N., & Corr, P. J. (2004). A two-dimensional neuropsychology of defense: Fear/anxiety and defensive distance. Neuroscience and Biobehavioral Reviews, 28, 285–305.

Halasz, J., Liposits, Z., Meelis, W., Kruk, M. R., & Haller, J. (2002). Hypothalamic attack area-mediated activation of the forebrain in aggression. NeuroReport, 13, 1267–1270.

Miczek, K. A., de Almeida, R. M., Kravitz, E. A., Rissman, E. F., de Boer, S. F., & Raine, A. (2007). Neurobiology of escalated aggression and violence. Journal of Neuroscience, 27(44), 11803-6.

8/19/09 5:03:43 PM

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Defense and Aggression

Mongeau, R., De Oca, B. M., Fanselow, M. S., & Marsden, C. A. (1998). Differential effects of neurokinin-1 receptor activation in subregions of the periaqueductal gray matter on conditional and unconditional fear behaviors in rats. Behavorial Neurosci 112(5), 1125–35. Myers, K. M., & Davis, M. (2008). Extinction of fear: From animal studies to clinical interventions. In R. J. Blanchard, D. C. Blanchard, G. Griebel, & D. J. Nutt (Eds.), Handbook of anxiety and fear (pp. 000–000). Amsterdam: Elsevier Academic Press. Naumenko, E. V., Popova, N. K., & Nikulina, W. M. (1989). Behavior, adrenocortical activity, and brain monoamines in Norway rats selected for reduced aggressiveness towards man. Pharmacology, Biochemistry, and Behavior, 33, 85–91. Nelson, R. J., & Trainor, B. C. (2007). Neural mechanisms of aggression. Nature Reviews: Neuroscience, 8, 536–546. Palomares, F., & Caro, T. M. (1999). Interspecific killing among mammalian carnivores. American Naturalist, 153, 492–508. Parmigiani, S., Ferrari, P. F., & Palanza, P. (1998). An evolutionary approach to behavioral pharmacology: Using drugs to understand proximate and ultimate mechanisms of different forms of aggression in mice. Neuroscience and Biobehavioral Reviews, 23, 143–153. Parmigiani, S., Rodgers, R. J., Palanza, P., Mainardi, M., & Brain, P. F. (1989). The inhibitory effects of fluprazine on parental aggression in female mice are dependent upon intruder sex. Physiology and Behavior, 46, 455–459. Pellis, S. M., & Pellis, V. C. (1987). Play fighting differs from serious fighting in both target of attack and tactics of fighting in the laboratory rat (Rattus norvegicus). Aggressive Behavior, 13, 227–242. Pellis, S. M., & Pellis, V. C. (1998). Play fighting of rats in comparative perspective: A schema for neurobehavioral analyses. Neuroscience and Biobehavioral Reviews, 23, 87–101. Pentkowski, N. S., Blanchard, D. C., Lever, C., Litvin, Y., & Blanchard, R. J. (2006). Effects of lesions to the dorsal and ventral hippocampus on defensive behaviors in rats. European Journal of Medicine, 23, 2185–2196. Petrovic, G. D., Risold, P. Y., & Swanson, L. W. (1996). Organization of the projections of the basomedial nucleus of the amygdala: A PHAL study in the rat. Journal of Comparative Neurology, 374, 387–420. Petrovic, G. D., Risold, P. Y., & Swanson, L. W. (2001). Combinatorial amygdalar inputs to hippocampal domains and hypothalamic behavior systems. Brain Research: Brain Research Reviews, 38, 247–289. Pikkarainen, M., Ronkko, S., Savander, V., Insausti, R., & Pitkanen, A. (1999). Projections from the lateral, basal, and accessory basal nuclei of the amygdala to the hippocampal formation in rat. Journal of Comparative Neurology, 403, 229–260. Pinel, J. P. J., Mana, M. J., & Ward, J. A. (1989). Stretched-approach sequences directed at a localized shock source by Rattus-norvegicus. Journal of Comparative Psychology, 103, 140–148. Pinheiro, S. H., Zangrossi, H. H., Jr., Del-Ben, C. M., & Graeff, F. G. (2007). Elevated mazes as animal models of anxiety: Effects of serotonergic agents. Anais da Academia Brasileira de Ciencias, 79(1), 71–85.

Schaller, G. B. (1972). The serengeti lion: A study of predator-prey relations. Chicago: University of Chicago Press. Seyfarth, R. M., & Cheney, D. L. (2003). Meaning and emotion in animal vocalizations. Annals of the New York Academy of Sciences, 1000, 32–55. Sovada, M. A., Sargeant, A. B., & Grier, J. W. (1995). Differential effects of coyotes and red foxes on duck nest success. Journal of Wildlife Management, 59, 1–9. Stamps, J. A., & Buechner, M. (1985). The territorial defense hypothesis and the ecology of insular vertebrates. Quarterly Review of Biology, 60, 155–181. Staples, L. G., McGregor, I. S., Apfelbach, R., & Hunt, G. E. (2007). Cat odor, but not trimethylthiazoline (fox odor), activates accessory olfactory and defense-related brain regions in rats. Neuroscience, 151, 937–947. Stone, C. P. (1932). Wildness and savageness in rats of different strains. Chicago: University of Chicago Press. Swanson, L. W. (2000). Cerebral hemisphere regulation of motivated behavior. Brain Research, 886(1–2), 113–164. Sweidan, S., Edinger, H., & Siegel, A. (1991). D2 dopamine receptormediated mechanisms in the medial preoptic-anterior hypothalamus regulate effective defense behavior in the cat. Brain Research, 549, 127–137. Szamado, S. (2003). Threat displays are not handicaps. Journal of Theoretical Biology, 221, 327–348. Takahashi, L. K. (1992). Ontogeny of behavioral inhibition induced by unfamiliar adult male conspecifics in preweanling rats. Physiology and Behavior, 52, 493–498. Takahashi, L. K., & Blanchard, R. J. (1982). Attack and defense in laboratory and wild Norway and black rats. Behavioural Processes, 7, 49–62. Tannerfeldt, M., & Elmhagen, B. (2002). Exclusion by interference competition? The relationship between red and arctic foxes. Oecologia, 132, 213–220. Treit, D., Pinel, J. P., & Fibiger, H. C. (1981). Conditioned defensive burying: A new paradigm for the study of anxiolytic agents. Pharmacology, Biochemistry, and Behavior, 15, 619–626. Veening, J. G., Coolen, L. M., de Jong, T. R., Joosten, H. W., De Boer, S. F., Koolhaas, J. M., et al. (2005). Do similar neural systems subserve aggressive and sexual behaviour in male rats? Insights from c-Fos and pharmacological studies. European Journal of Pharmacology, 526(1–3), 226–239. Vermeij, G. J. (1982). Unsuccessful predation and evolution. American Naturalist, 120, 701–720. Vianna, D. M., & Brandao, M. L. (2003). Anatomical connections of the periaqueductal gray: Specific neural substrates for different kinds of fear. Brazilian Journal of Medical and Biological Research, 36, 557–566. Walk, R. D., Gibson, E. J., & Tighe, T. J. (1957, July 12). Behavior of light- and dark-reared rats on a visual cliff. Science, 126, 80–81.

Ralls, K., & White, P. J. (1995). Predation on San Joaquin kit foxes by larger canids. Journal of Mammology, 76, 723–729.

Wrangham, R. W. (1999). Evolution of coalitionary killing. American Journal of Physical Anthropology, 29, 1–30.

Risold, P. Y., Canteras, N. S., & Swanson, L. W. (1994). Organization of projections from the anterior hypothalamic nucleus: A phaseolus vulgaris leucoagglutinin study in the rat. Journal of Comparative Neurology, 348, 1–40.

Ydenberg, R. C., & Dill, L. M. (1986). The economics of fleeing from predators. Advances in the Study of Behavior, 16, 229–249.

Risold, P. Y., & Swanson, L. W. (1997). Connections of the rat lateral septal complex. Brain Research: Brain Research Reviews, 24(2–3), 115–195.

Zangrossi, H., Jr., & File, S. E. (1992). Behavioral consequences in animal tests of anxiety and exploration of exposure to cat odor. Brain Research Bulletin, 29(3–4), 381–388.

Risold, P. Y., Thompson, R. H., & Swanson, L. W. (1997). The structural organization of connections between hypothalamus and cerebral cortex. Brain Research: Brain Research Reviews, 24(2–3), 197–254.

Zhang, S. P., Bandler, R., & Carrive, P. (1990). Flight and immobility evoked by excitatory amino acid microinjection within distinct parts of the subtentorial midbrain periaqueductal gray of the cat. Brain Research, 520(1–2), 73–82.

Roeling, T. A. P., Veening, J. G., Kruk, M. R., Peters, J. P. W., Vermelis, M. E. J., & Nieuwenhuys, R. (1994). Efferent connections of the hypothalamic “aggression area” in the rat. Neuroscience, 59, 1001–1024.

Zhang, S. P., Davis, P. J., Bandler, R., & Carrive, P. (1994). Brain stem integration of vocalization: Role of the midbrain periaqueductal gray. Journal of Neurophysiology, 72, 1337–1356.

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Chapter 50

A Neural Analysis of Intergroup Perception and Evaluation WILLIAM A. CUNNINGHAM AND JAY J. VAN BAVEL

(typically investigated in a racial context) appears to influence nearly all aspects of brain function from early visual processing to higher order aspects of executive function and deliberate thought. The widely distributed patterns of brain activity found to covary with the processing of social groups suggest that the simple categorization of people into groups influences not a single group perception system, but rather a constellation of processes that collectively give rise to a multitude of social biases. Although the processes underlying social perception require further specification, initial neuroscience research on social prejudice provides important hints for our understanding of the mechanisms of prejudice and intergroup discrimination.

In an era of increasing globalization, social and economic harmony depends on the ability of people to cooperate with others from a variety of ethnic, geographic, and religious backgrounds. A trend toward explicitly egalitarian attitudes among North Americans has been accompanied (and motivated) by legislation that makes discrimination a crime and public scrutiny that makes a single racist statement a major political liability. Yet, although a majority of Americans now report nonprejudiced attitudes and strong motivations to respond without prejudice, investigations over the past few decades have shown that the majority still have lingering automatic and perhaps unconsciously activated negative responses toward many minorities and socially disadvantaged groups (Nosek, Banaji, & Greenwald, 2002). These subtle prejudices have been shown to activate even among individuals with egalitarian motivations, and appear to take considerable cognitive effort to control once released. These prejudices have also been shown to directly predict discrimination, including negative nonverbal behavior and biased hiring decisions toward racial and other social groups (Dovidio, Kawakami, & Gaertner, 2002; Dovidio, Kawakami, Johnson, Johnson, & Howard, 1997). In a recent meta-analysis, these automatic forms of prejudice were stronger predictors of discrimination than self-report when there was a strong desire to hide or conceal one’s attitude (Greenwald, Poehlman, Uhlmann, & Banaji, in press). To deal with these challenges, basic research is needed to understand the structures and mechanisms that promote social prejudice: from large societal structures and historical events, to the genetic and neural mechanisms that provide the basic machinery humans use to understand and navigate their complex social worlds. Although research examining the neural bases of prejudice using neuroscience methods has a relatively short history, this research has already made dramatic progress. Looking across several key studies, one conclusion can be easily drawn—the processing of social group membership

SOCIAL COGNITIVE PERSPECTIVE The study of prejudice has been at the forefront of social psychology for over a half century. Ever since Allport (1954) wrote his classic book, The Nature of Prejudice, and placed research on prejudice firmly within mainstream social psychology, psychologists have sought to understand prejudice and find effective means for its elimination. For obvious reasons, much of this work studied overt acts of discrimination and verbally reported statements of prejudiced attitudes. However, evidence shows that people may also spontaneously evaluate social objects along a good-bad dimension, without necessarily being aware that they are doing so (Bargh, Chaiken, Govender, & Pratto, 1992; Fazio, Sanbonmatsu, Powell, & Kardes, 1986). Given such findings, models of social attitudes suggest at least two modes of evaluation: one that involves conscious and controlled modes of thinking and another that involves relatively automatic processes that operate without deliberate thought and sometimes without conscious awareness (Greenwald & Banaji, 1995; Nisbett & Wilson, 1977). Importantly, an evaluation following 975

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more controlled processing may differ from an evaluation based only on more automatic processing. On indirect or implicit measures that tap automatic associations, many White participants show negativity toward Blacks, the elderly, or foreigners compared with Whites, the young, or Americans, respectively; yet they report unbiased attitudes on questionnaires that allow more controlled or conscious evaluations of the same groups (Cunningham, Nezlek, & Banaji, 2004; Devine, 1989; Nosek et al., 2002). When the social context discourages expressions of prejudice, automatic biases can be a stronger predictor of discrimination than self-report measures, particularly of subtle, nonverbal acts (Greenwald et al., in press). Evidence that prejudice can operate automatically has led researchers to the troubling conclusion that verbal reports are not always bona fide indicators of prejudice. If people cannot fully report on the ways that prejudice influences their thoughts, feelings, and behaviors, then the extent to which group biases permeate social cognition may be underestimated. With this in mind, it is necessary to examine the ways that prejudice, both in its conscious and unconscious forms, influences each step of social perception, all the way from early visual processing where social categories are initially encoded and applied, to the reflective processing used to generate more or less biased perceptions as a function of high-order goals and values. Using the classic computer metaphor in cognitive science in which the mind processes information in a serial sequence of processing stages, this chapter examines how social categories shape each stage of social perception. Information processing at each stage is dependant on information outputted from preceding stages, which implies that small biases occurring during initial stages may have dramatic downstream effects. Thus, although later consciously accessible evaluative processes feel as if they are under our deliberate control, they can be heavily biased by automatic forms of prejudice that influence processing and behavior prior to conscious reflection.

“THEY ALL LOOK ALIKE TO ME”: PERCEPTION AND CATEGORIZATION The unfortunate, yet too often overheard, phrase in the title of this section underscores that prejudices can influence the very way that individuals see the world; and specifically, that people appear to be better at processing and remembering people from their own race than from other races—an effect that has been termed the same-race bias (Malpass & Kravitz, 1969). Although the same-race bias may seem relatively harmless at first glance, it can have serious implications for crucial decisions in the real world.

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With eyewitness testimony being among the most compelling pieces of evidence in criminal trials, the misidentification of a suspect from another race can literally lead to a death sentence for an innocent person (Brigham & Ready, 2005), especially when paired with certain societal stereotypes and prejudices (e.g., Seeleman, 1940). If people are less likely to identity outgroup relative to ingroup members, it is possible that this bias occurs in the early stages of visual processing—outgroup faces may not be processed at the same level of detail as ingroup faces. One brain region that has been proposed as particularly important for perceptual biases in social processing is an area of visual cortex known as the fusiform gyrus (see Figure 50.1). In particular, the fusiform face area (FFA), has been shown to respond preferentially to faces (as contrasted with almost any other type of visual stimulus) (Kanwisher, McDermott, & Chun, 1997) and involves in the extraction of low-level perceptual features that can allow for individuation. To examine the role of the FFA in the same-race effect Golby, Gabrieli, Chiao, and Eberhardt (2001) used fMRI to examine the brain regions associated with facial processing while participants viewed same-race and other-race faces, as well as objects (radios). Black and White participants viewed pictures of Black and White faces to compare the processing of ingroup and outgroup members. As expected, the FFA was more sensitive to ingroup than outgroup faces for both Black and White participants (see also Lieberman, Hariri, Jarcho, Eisenberger, & Bookheimer, 2005). Moreover, on a subsequent memory test, the degree of same-race bias (superior memory for same-race over other-race faces) was predicted by fusiform gyrus activation to racial ingroup members at encoding. Although this research provides an important link between early perceptual processing and racial biases in memory, these data are silent with regard to the psychological mechanism(s) that give rise to the difference (Levin, 2000; Sporer, 2001). On the one hand, it is possible that the same-race bias is the result of familiarity with samerace faces. According to this view, people have a lifetime of experience interacting with family, friends, and acquaintances of the same race, and consequently become experts at automatically processing and distinguishing members of their race. As such, the bias is not necessarily motivational, but rather the consequence of long accrued perceptual experience. On the other hand, it is also possible that this bias is the result of motivated social perception (Balcetis & Dunning, 2006); categorizing others as ingroup or outgroup members may alter the depth of processing that they receive. People might view ingroup members as more important and be more likely to process them as individuals, in contrast to less relevant outgroup members who are lumped together (even perceptually) simply as “them.” Thus, while

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Affective Evaluations and Prejudice

Anterior Cingulate

Medial PFC Dorsolateral PFC

Fusiform

Amygdala

Insula

Figure 50.1 Note: Circle: Fusiform gyrus: Involved in visual perception and recognition. A subregion of the fusiform, known as the fusiform face area, has been shown to be particularly active to presentations of faces, and face-like stimuli. Beyond just faces, this region appears to play a role in making distinctions within categories of stimuli (e.g., cars), especially among experts. Triangle: Amygdala: A small structure in the medial temporal lobe that plays a role in the encoding and processing of affective representations. Activation in the amygdala is commonly found following the presentation of affectively intense stimuli (e.g., fear), although a more general role for the processing of any motivationally significant stimulus has been proposed. Rectangle: Insula: A region located within the somatosensory cortex. The anterior insula, in particular, receives direct input about homeostatic and visceral information from the body and sends output to other limbic (including amygdala) and cortical regions. The insula has been linked to the experience of disgust and other emotional states. Dark grey. Medial prefrontal cortex: The medial region of the anterior frontal lobes. The medial PFC has been implicated in social and affective processes, including self-referential processing and simulating the mental states of other (termed mentalizing). Gray: Anterior cingulate: A functionally heterogeneous region of the cingulate cortex. The anterior ACC—especially the dorsal region—appears to play a key role in monitoring for cognitive conflict. Diamond: Dorsolateral prefrontal cortex: The lateral regions of the anterior frontal lobes. The lateral PFC appears to play an important role in cognitive control and executive function, including the processes involved in working memory.

a lifetime of greater experience with one’s own groups may help give rise to or enhance the same-race bias, the simple categorization of others into an ingroup or outgroup may be sufficient to generate biases in intergroup perception and memory (Bernstein, Young, & Hugenberg, 2007). A recent study tested these competing hypotheses by randomly assigning participants to one of two novel groups: the Leopards or Tigers (Van Bavel, Packer, & Cunningham, 2008). After participants learned the members of each group, they were presented with the same faces during fMRI. Importantly, participants had equal prescanning exposure to the ingroup and outgroup faces. To the extent

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that differences in group processing in the fusiform gyrus occur simply because of perceptual familiarity, no differences to ingroup and outgroup faces should be found. In contrast, if fusiform activity is sensitive to motivated aspects of social perception, including current self-categorization, then we should expect greater fusiform activity to ingroup as opposed to outgroup faces. Results supported the second hypothesis: The fusiform gyrus was more sensitive to novel ingroup than outgroup faces. This study suggests that the individuation of ingroup members as opposed to outgroup members may begin at the earliest stages of information processing and that this differentiation can be driven by the simple classification of others into groups. Together, these data are consistent with the idea that ingroup members may be processed at a more individuated level than outgroup members even at the earliest stages of information processing (Rhodes, Byatt, Michie, & Puce, 2004). Whereas ingroup members are processed as individuals, extracting information about what makes each person unique, outgroup members are processed as interchangeable members of a general social category (see also Outgroup Homogeneity Effect; Ostrom & Sedikides, 1992). As such, outgroup members are more likely to be stereotyped, and these stereotypes are less likely to be disconfirmed by individuating information. Consistent with this idea and complementing the individuation of ingroup members, people are faster to categorize other-race faces according to their race than own-race faces—an effect that has been labeled the other-race categorization advantage (Levin, 1996; Valentine & Endo, 1992). Using eventrelated potentials (ERPs), Caldara, Rossion, Bovet, and Hauert (2004) showed that the brain response to categorizing other-race faces (Asian faces) was about 20 ms faster than for own-race faces. Remarkably, these effects were seen a mere 240 ms after stimulus presentation, providing strong evidence for rapid and automatic differences in the processing and categorization of social groups. These studies indicate the extent to which race and group membership influence early aspects of social perception. From an information processing perspective, initial perceptual processes can influence subsequent processes and ultimately lead to discrimination and injustice. If group membership influences the way that we see the social world and how we unconsciously divide others into meaningful categories, it should not be surprising that these early processes are going to affect downstream evaluations and behavior.

AFFECTIVE EVALUATIONS AND PREJUDICE A central focus of the neuroscience research on intergroup relations has been prejudice—the (typically negative)

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affective response people have to social groups as a whole, as well as individual members of those groups. Although the neural networks involved in an affective evaluative response are likely diffuse (Cunningham & Zelazo, 2007; Cunningham, Zelazo, Packer, & Van Bavel, 2007), initial research began with a focus on the amygdala. The amygdala is a small structure in the temporal lobe linked to an array of social and affective processes, including learning emotional information (Phelps, 2006), perceiving emotional faces (Whalen et al., 1998), and directing attention to important stimuli (Vuilleumier, 2005). More directly, with its tight connection to fear conditioning, threat processing, and negative affect more generally (Phelps, 2006), the amygdala was a logical starting place to investigate social prejudice. Importantly for the study of automatic affective biases, amygdala activation to negative emotional expressions (e.g., greater to fearful than to neutral facial expressions) has been found to be similar whether stimuli are presented at durations long enough for the stimuli to be consciously seen (Morris et al., 1996) or more briefly (33 ms and masked) (Whalen et al., 1998). This suggests that the amygdala may play a critical role in rapid and unconscious evaluation of the environment. In the first fMRI study of prejudice, Hart and colleagues (2000) showed Black and White participants blocks of Black and White faces. While this initial study revealed greater amygdala activation to outgroup than to ingroup faces (White participants viewing Black faces and Black participants viewing White faces), these results were qualified by a small sample size (N ⫽ 8) and relatively weak effects (the reported pattern was only observed in the second half of the study). Armed with a larger sample size, Lieberman, Hariri, Jarcho, Eisenberger, and Bookheimer (2005) replicated these results for White participants, but found the opposite pattern for Black participants, who also showed greater amygdala activation to Black than White faces. Despite the discrepancies between these initial studies, they provided evidence of a link between the processing of social group membership and a subcortical (perhaps automatic and unconscious) affective response. To directly investigate the relationship between prejudiced attitudes and amygdala activity, subsequent research examined the relationship between amygdala activation and behavioral measures of prejudice. Phelps and colleagues (Phelps et al., 2000) presented White participants with Black and White faces while their amygdala was scanned during fMRI. Following the scanning procedure, participants completed both indirect (reaction time and physiological) and direct (self-report) measures of prejudice. If the amygdala was involved in prejudice, it was

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hypothesized that more prejudiced participants would show greater amygdala response to Black than White faces. Further, because the amygdala has been shown to be involved in unconscious evaluation and there tends to be greater variability on automatic/indirect than selfreport measures of prejudice, it was hypothesized that a stronger relationship would be observed for the indirect than the direct measures. Although the study did not find overall greater amygdala activation to Black than White faces, both the reaction time measure (the Implicit Association Test; Greenwald, McGhee, & Schwartz, 1998) and the physiological measure (startle eyeblink) were significantly correlated with more amygdala activity to Black than White faces. Further, the explicit measure of prejudice was uncorrelated with amygdala activity. Although Phelps et al. provided evidence for an important link between individual differences in automatic prejudice and amygdala activation, several important questions remained. Most importantly, if automatic evaluative biases are so pervasive, why were there no main effects of amygdala activation? In a follow-up study, Cunningham, Johnson, and colleagues (2004) reasoned that paradigms used in previous fMRI studies of race bias may confound multiple processes and may obscure the full power of unconscious bias. Specifically, because long blocks of Black and White faces were presented supraliminally, participants may automatically evaluate the Black faces more negatively than White faces, but may also then try to control or suppress their responses (discussed later in this chapter). To more closely examine unconscious race bias, the participants were presented with Black and White faces subliminally (for 30 ms), so that only automatic, unconscious processes could be used to evaluate the stimuli. Further confirming the role of the amygdala in the automatic evaluative processing of social groups, significantly greater amygdala activity was found for subliminal Black than White faces in all participants but one. In addition, this differential amygdala activation was correlated (r ⫽ .79) with the Implicit Association Test, the same indirect measure of prejudice used by Phelps and colleagues (2000). Although these studies link the amygdala to automatic racial bias, the exact role the amygdala plays in prejudice remains unclear. For example, a patient with bilateral amygdala damage has shown racial bias on the IAT, demonstrating that the amygdala is not necessary for the expression of automatic prejudice (Phelps, Cannistraci, & Cunningham, 2003). Moreover, a valence-specific conceptualization of amygdala activation has been called into question by studies showing that positive as well as negative stimuli both evoke amygdala activity (Hamann, Ely,

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Undoing the Automatic: The Deliberate Control of Prejudice

Hoffman, & Kilts, 2002). Further research will be critical in determining the role of amygdala when processing social group membership. These studies not only will lead to a better understanding of prejudice, but will also aid in our understanding of amygdala function per se. Power of the Ingroup Perhaps because negative visceral aspects of prejudice are the most frightening and salient to observers, dislike of outgroups has received much more attention than the reciprocal form of prejudice—positive associations toward ingroup members. However, the history of intergroup conflict provides strong evidence that ingroup love is a more common root of discrimination than “outgroup hate” (Brewer, 1999). Moreover, in contexts where discrimination arises as a result of differential evaluations of two groups, ingroup bias can lead to the same patterns of discrimination as outgroup derogation (e.g., in the context of a hiring decision, ingroup bias and outgroup derogation would both lead a White candidate to be hired over a Black candidate). Although these decisions are the result of quite different affective processes, the result is identical—a Black candidate is treated unfairly and the cycle of discrimination continues. Recent research has begun to dissociate the neural processes involved in ingroup and outgroup biases. In one study, participants were asked to think about the opinions and preferences of a person who had a similar or dissimilar political affiliation (Mitchell, Macrae, & Banaji, 2006). In the current partisan political landscape in the United States, it was assumed that more politically identified participants would process the similar person as an ingroup member, and therefore activate brain areas that have been linked to self-referential processing. Liberals were expected to be more motivated to understand the mental states of another liberal than a conservative, and the converse was expected for conservatives. This is exactly what was found. Considering the mental state of a similar other lead to activity in ventral areas of medial prefrontal cortex (PFC), whereas considering the mental state of a dissimilar other lead to activity in more dorsal areas of medial PFC. Interestingly, individuals who strongly self-categorized with a political group, as measured by an implicit measure, had greater ventral medial activity to politically similar others and less dorsal medial PFC activity to dissimilar others. Because regions of medial PFC have previously been implicated in building mental models of other minds and simulating the thoughts and feelings of other people (called mentalizing, Mitchell, 2006), with more ventral areas being more involved in

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the processing of self-relevant information (Kelley et al., 2002), the authors concluded that although similar and dissimilar others both recruit regions involved in understanding others, similar others were more likely to be processed like the self. If people are more willing or able to mentalize about people with whom they share a group membership, especially a group identity they highly value, certain outgroup members may not receive this processing. Consistent with this hypothesis, Harris and Fiske (2006) found that when participants viewed members of social outgroups that typically arouse feelings of contempt, such as drug users, less ventral medial prefrontal cortex activation was observed. Instead, for these stigmatized group members, the insula—a brain area associated with the emotion of disgust (Phillips et al., 1997)—showed more activation. This pattern of data is consistent with the idea that not only can negative emotions be activated in response to outgroup members, but there may be certain aspects of prejudice marked by less processing for outgroups compared with ingroups. People may use less mentalization for certain groups of people over others (Cortes, Demoulin, Rodriguez, Rodriguez, & Leyens, 2005; Vaes, Paladino, Castelli, Leyens, & Giovanazzi, 2003).

UNDOING THE AUTOMATIC: THE DELIBERATE CONTROL OF PREJUDICE With a large body of research demonstrating that people are evaluated as members of social groups automatically, unconsciously, and sometimes unfairly, one might be inclined to take a pessimistic view of human nature. More optimistically, however, research has also documented that, at least under some circumstances, people can control automatic responses, and sometimes even replace evaluations of one valence (a negative affective response) with another (a positive affective response). Among the more cherished aspects of human cognition is its ability to use controlled processing and abstraction to escape immediate stimulusresponse contingencies and generate more nuanced evaluations and judgments in the service of long-term goals and values (Cunningham & Zelazo, 2007; Greenwald & Banaji, 1995). Behavioral research provides evidence for this suggestion, showing that when people have the motivation and opportunity to use more deliberate forms of cognitive processing, the influence of automatically activated stereotypes and prejudice is dramatically reduced (Devine, 1989; Dovidio et al., 1997; Fazio, 1990; Fazio, Jackson, Dunton, & Williams, 1995). Thus, although initial

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intergroup categorization and evaluation have important implications for intergroup relations, human behavior is often driven by values, goals, and motivations. Social cognitive studies of prejudice regulation have tended to focus on the inhibition or suppression of initial evaluations deemed inappropriate or suboptimal (Devine, 1989; Petty & Wegener, 1993). In this view, the automatic activation of prejudiced representations and biased processing leads to discriminatory behavior unless controlled intervention eliminates these biases. For this to be successful, two sets of processes are thought to be necessary—a conflict-detection system and a regulatory control system—each with different temporal dynamics and neural generators (Botvinick, Braver, Barch, Carter, & Cohen, 2001; Cohen, Botvinick, & Carter, 2000; MacDonald, Cohen, Stenger, & Carter, 2000). The conflict-detection system automatically monitors current representations and provides a signal that additional processing resources are required when incompatible representations are active. In the case of an egalitarian person, prejudiced representations that contrast with egalitarian goals would trigger this conflict detection system, which may then recruit the regulatory control system to update and modify prejudiced representations. The conflictdetection system is thought to be mediated by the anterior cingulate gyrus, and the slower, more reflective regulation system is thought to be mediated by regions of anterior and lateral PFC. A study by Cunningham, Johnson, and colleagues (2004) provided evidence that these regulatory systems play an important role in modulating automatic affective responses to race. As noted in the section on evaluation, Cunningham, Johnson, and colleagues presented White participants with Black and White faces for 30 or 525 ms. Although these participants reported having strong egalitarian values, they also showed more automatically activated negative responses to the social category Black than White on an Implicit Association Test (IAT) (Greenwald et al., 1998). Showing that automatic prejudice operates unconsciously as well as that people can control this response White participants had greater amygdala activation to Black than to White faces (which were randomly intermixed), but only when the faces were presented subliminally (30 ms), such that participants did not report seeing the faces. In contrast, Black faces in the supraliminal (525 ms) condition were associated with activity in brain regions involved in controlled processing and executive function, such as the anterior cingulate cortex and lateral PFC. Moreover, the reduction in amygdala response during the supraliminal condition was inversely correlated with activity in these

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areas of anterior cingulate cortex and dorsolateral PFC. This pattern was consistent with the idea that people with the motivation and opportunity can control their automatic responses to social groups. Providing further evidence that the PFC is involved in the regulation of prejudice and building on the idea that explicit linguistic processing generally inhibits affective processing (Lieberman, 2003), Lieberman and colleagues (2005) asked both Black and White participants to categorize Black and White faces according to linguistic labels (“African American” versus “Caucasian”) or perceptual information (e.g., matching one Black face to another Black face). When participants categorized faces according to perceptual information, they had greater amygdala activity to Black than White faces. In contrast, there were no differences in amygdala activity when individuals categorized according to linguistic labels. Similar to the study by Cunningham, Johnson, and colleagues (2004), this lack of amygdala difference was accompanied by heightened lateral PFC to the Black than White faces, and this increase in lateral PFC activity was associated with the decrease in amygdala activation to Black faces. Again, these data show that the PFC is involved in modulating presumably more automatic responses under certain conditions. Although these fMRI studies implicate the PFC in the regulation of prejudiced responses and show that these areas can decrease differential responses to Black and White faces in the amygdala, they are silent to how quickly these processes unfold. To study the temporal aspects of the conflict-detection system in prejudice control, Amodio and colleagues (2004) measured ERPs while participants categorized rapidly presented images as tools or guns. Immediately preceding each object, a Black or White face appeared (see Payne, 2001, for more details). The task was designed so that if a Black face automatically activated concepts of negativity (or the specific stereotype of violence), then participants would be more likely to make errors misidentifying a tool as a gun following the presentation of a Black face. For egalitarian participants, these errors should activate the conflict-detection system because their behavior (prejudiced response) would be incongruent with their values and ideals. As predicted, prejudicial errors among egalitarian participants were followed by an ERP signal that has been previously associated with the anterior cingulate in general, and the conflict detection system specifically—the ERN (error-related negativity). Importantly, the ERN in this study occurred within 200 ms of response errors, providing evidence that people do automatically monitor for unconscious prejudice

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Problems with Suppression and Alternate Approaches to Regulation 981

and may be able to trigger corrective processes relatively automatically.1 Although deliberate control is typically associated with inhibition, reflective aspects of emotion regulation can also be involved in the maintenance or enhancement of affective states to construct a more intense explicit evaluation. Many social groups evoke negative affective responses without an increase in compunction or guilt (Crandall, Eshleman, & O’Brien, 2002). Few people attempt to mitigate their feelings or expressions of disgust toward child molesters, their anger at terrorists, or their distrust of particular politicians. Quite opposite to the controlled processes of inhibition, people are likely to desire to “upregulate” their emotional responses to feel more negative (or less positive) if they have the opportunity. Although these groups have received less attention, there is evidence that these normatively stigmatized groups (e.g., obese people) also lead to activity in brain regions associated with affective (amygdala and insula) and controlled processing (ACC and lateral PFC; Krendl, Macrae, Kelley, & Heatherton, 2006). Whereas Krendl and colleagues found greater amygdala and insula—a region linked to the intense feelings, including disgust (Phillips et al., 1997)—activation to stigmatized groups compared with controls, they reported that there was also greater activity to these groups in the lateral PFC. Although the positive relationship between affective and controlled brain regions was interpreted as a failed attempt to control negativity, work by Ochsner and colleagues (2004) on the upregulation of emotion raises the possibility that lateral PFC activity in this study reflected an effort to increase or maintain a negative response that is personally or culturally acceptable. PROBLEMS WITH SUPPRESSION AND ALTERNATE APPROACHES TO REGULATION For the most part, investigations of prejudice regulation have focused on the ways that people can suppress automatically 1

Not all people are motivated to control their prejudice to the same degree. Some people may strive to be egalitarian in all their thoughts and feelings, remaining vigilant at all times, whereas others may not care about their prejudiced reactions until they need to conceal it from others. Recent research explored the effect of internal versus external motivations to respond without prejudice on different aspects of controlled processing during the shooter task (Amodio & Devine, 2006). Replicating the previous research by Amodio and colleagues (Amodio et al., 2004), the more automatic dorsal ACC was linked to control of racial bias on the shooter task across conditions among White participants.

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activated affective and stereotypical responses. Yet, in his analysis of emotion regulation strategies, Gross (Gross, 1998; Gross & Thompson, 2007) provided a useful taxonomy in which response-focused emotion regulation strategies are contrasted with antecedent emotion regulation strategies. Whereas response-focused strategies typically involve the simple suppression of an affective response following its activation, antecedent focused strategies attempt to shape an affective response prior to activation, or quickly following activation through processes of reappraisal. Interestingly, in comparing the pros and cons of each class of regulation, Gross notes that whereas antecedent forms tend to provide strong and adaptive changes in affective experience, response-focused strategies (and suppression in particular) tend to work only for short periods of time, are associated with unhealthy physiological side effects (such as high blood pressure) and, most important, can backfire and result in rebound effects. Thus, an unfortunate consequence of a desire to suppress all prejudiced thoughts and feelings is that, to the extent that automatic bias is pervasive, people will need to engage in the most effort to control these biases and may therefore be the ones who suffer the largest cognitive costs (Baumeister, Bratslavsky, Muraven, & Tice, 1998). Providing evidence for this hypothesis, Richeson and Shelton (2003) found that after White participants with high levels of automatic racial bias interacted with a Black individual, they subsequently performed worse on the Stroop task, which requires cognitive control. Presumably, White participants with racial bias on implicit measures had the most bias to control, and therefore were cognitively depleted following an interracial interaction. These studies suggest that people with automatic racial biases need to engage in greater levels of controlled processing to successfully navigate interracial interactions and these extra efforts lead to subsequent impairments in controlled processing, raising doubts about their ability to suppress bias for any extended period. Ironically, However, the later rostral ACC component was only associated with control in the shooter task among participants with a high external motivation when they were in a public situation that precluded racially biased responding. That is, people motivated for social reasons only engaged in controlled processing mediated by the rostral ACC when they were in a situation where social constraints were a factor, and this aspect of control took slightly longer. These data suggest that more automatic aspects of control (dACC) were rapidly engaged and insensitive to contextual pressures, whereas external motivations and contextual pressures triggered more delayed aspects of controlled processing (rACC).

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982 A Neural Analysis of Intergroup Perception and Evaluation

people who try the hardest to suppress their biases may be the most likely to express these biases in later intergroup encounters. In a follow-up fMRI study, Richeson and colleagues (2003) scanned White participants while they viewed Black and White faces during fMRI. Afterward, participants interacted with a Black confederate and then performed the Stroop task. As would be expected if participants were attempting to control prejudice while in the scanner, heightened activation to Black than White faces was observed in areas of right lateral PFC and ACC. More importantly, these levels of activation correlated with poorer performance on an executive function task (the Stroop task) following scanning. These patterns of results provide support both for the idea that nonprejudiced participants attempt to regulate their emotional responses to Black faces and that this regulation depletes executive functioning resources. As such, this provides strong evidence that the attempt to suppress by those most wanting to think and feel without prejudice may ironically be the ones who may be most likely to fall victim to automatic bias after their cognitive resources have been depleted. Because suppression can lead to negative consequences, both for the social perceiver and the targets of prejudice, research is needed to examine alternative strategies for regulation that have the potential to avoid the unintended negative side effects of suppression. One unstudied but potentially promising approach is conscious reappraisal— the process of consciously changing the meaning and the appraisal of social groups and their members. Work by Kevin Ochsner and colleagues have consistently found that changing cognitions about an event or person changes affective responses (see Ochsner & Gross, 2005 for review). If people change their cognitions to feel more negative, greater amygdala activation is found, and if people change their cognitions to feel less negative, less amygdala activation is found (Ochsner, Bunge, Gross, & Gabrieli, 2002; Ochsner et al., 2004). This suggests that people can shape the contents of their mental space by foregrounding some pieces of information and backgrounding others to generate an emotional response that is consistent with their goals and values. Another strategy for changing the way that evaluations unfold is for people to consciously change their processing goals (see Cunningham, Van Bavel, & Johnsen, 2008). A processing goal that may be particularly useful is an explicit motivation to individuate people and place less emphasis on group membership in person perception. In this context, one hypothesis is that having the goal to look for individuating characteristics may change the level of processing and may indirectly reduce the power of automatic stereotypes and prejudices. To test this prediction,

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Wheeler and Fiske (2005) presented White participants with Black and White faces and had them engage in judgments that were designed to induce participants to either process the faces as individuals or as members of social groups. Consistent with previous research, when participants engaged in social categorization (e.g., classifying the faces by age), they showed greater amygdala activity to the Black than White faces. However, when participants were simply asked to decide whether each person preferred particular vegetables (a task thought to increase attention to individuating features), the amygdala no longer activated more to Black than White faces. To the extent that amygdala activation can be taken as affective bias, the simple act of individuation eliminated the standard race bias effect.

SUMMARY The study of intergroup relations using neuroscience methods remains relatively young. Nevertheless, the past decade of research has revealed several important insights into the complexity of intergroup perception and evaluation. This research has provided exciting evidence of the automaticity of intergroup perception and evaluation, and the complex interactions between the component processes that guide behavior. These studies highlight the speed with which individuals distinguish different groups, their ability to do so without conscious awareness, and their ability to alter these initial processes according to motivations or goals. Improvements in technology and convergence across methods will add precision and contribute novel insights about an evaluative system that influences intergroup relations. In addition, the insights gleaned from a multilevel approach will eventually lead to novel predictions for traditional behavioral investigations and ultimately interventions that improve intergroup relations.

REFERENCES Allport, G. W. (1954). The nature of prejudice. Reading, MA: Addison-Wesley. Amodio, D. M., & Devine, P. G. (2006). Stereotyping and evaluation in implicit race bias: Evidence for independent constructs and unique effects on behavior. Journal of Personality and Social Psychology, 91, 652–661. Amodio, D. M., Harmon-Jones, E., Devine, P. G., Curtin, J. J., Hartley, S. L., & Covert, A. E. (2004). Neural signals for the detection of unintentional race bias. Psychological Science, 15, 88–93. Balcetis, E., & Dunning, D. (2006). See what you want to see: Motivational influences on visual perception. Journal of Personality and Social Psychology, 91, 612–625.

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References 983 Bargh, J. A., Chaiken, S., Govender, R., & Pratto, F. (1992). The generality of the automatic attitude activation effect. Journal of Personality and Social Psychology, 62, 893–912.

Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238.

Baumeister, R. F., Bratslavsky, E., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is the active self a limited resource? Journal of Personality and Social Psychology, 74, 1252–1265.

Golby, A. J., Gabrieli, J. D. E., Chiao, J. Y., & Eberhardt, J. L. (2001). Differential fusiform responses to same- and other-race faces. Nature Neuroscience, 4, 845–850.

Bernstein, M., Young, S., & Hugenberg, K. (2007). The cross-category effect: Mere social categorization is sufficient to elicit an own-group bias in face recognition. Psychological Science, 18, 709–712.

Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102, 4–27.

Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108, 624–652. Brewer, M. B. (1999). The psychology of prejudice: Ingroup love or outgroup hate? Journal of Social Issues, 55, 429–444. Brigham, J. C., & Ready, D. J. (2005). Own-race bias in lineup construction. Law and Human Behavior, 9, 415–424. Caldara, R., Rossion, B., Bovet, P., & Hauert, C. A. (2004). Event-related potentials and time course of the “other-race” face classification advantage. NeuroReport, 15, 905–910. Cohen, J. D., Botvinick, M., & Carter, C. S. (2000). Anterior cingulate and prefrontal cortex: Who’s in control? Nature Neuroscience, 3, 421–423. Cortes, B. P., Demoulin, S., Rodriguez, R. T., Rodriguez, A. P., & Leyens, J.-P. (2005). Infrahumanization or familiarity?: Attribution of uniquely human emotions to the self, the ingroup, and the outgroup. Personality and Social Psychology Bulletin, 31, 243–253. Crandall, C. S., Eshleman, A., & O’Brien, L. (2002). Social norms and the expression and suppression of prejudice: The struggle for internalization. Journal of Personality and Social Psychology, 82, 359–378. Cunningham, W. A., Johnson, M. K., Raye, C. L., Gatenby, J. C., Gore, J. C., & Banaji, M. R. (2004). Separable neural components in the processing of Black and White Faces. Psychological Science, 15, 806–813. Cunningham, W. A., Nezlek, J. B., & Banaji, M. R. (2004). Implicit and explicit ethnocentrism: Revisiting the ideologies of prejudice. Personality and Social Psychology Bulletin, 30, 1332–1346. Cunningham, W. A., Van Bavel, J. J., & Johnsen, I. R. (2008). Affective flexibility: Evaluative processing goals shape amygdala activity. Psychological Science, 19, 152–160. Cunningham, W. A., & Zelazo, P. D. (2007). Attitudes and evaluations: A social cognitive neuroscience perspective. Trends in Cognitive Sciences, 11, 97–104. Cunningham, W. A., Zelazo, P. D., Packer, D. J., & Van Bavel, J. J. (2007). The iterative reprocessing model: A multi-level framework for attitudes and evaluation. Social Cognition, 25, 736–760. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18. Dovidio, J. F., Kawakami, K., & Gaertner, S. L. (2002). Implicit and explicit prejudice and interracial interaction. Journal of Personality and Social Psychology, 82, 62–68. Dovidio, J. F., Kawakami, K., Johnson, C., Johnson, B., & Howard, A. (1997). On the nature of prejudice: Automatic and controlled processes. Journal of Experimental Social Psychology, 33, 510–540. Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The MODE model as an integrative framework. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 23, pp. 75–109). New York: Academic Press. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027.

c50.indd 983

Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Greenwald, A. G., Poehlman, T. A., Uhlmann, E., & Banaji, M. R. (in press). Understanding and using the Implicit Association Test: Pt. III. Meta-analysis of predictive validity. Journal of Personality and Social Psychology. Gross, J. J. (1998). Antecedent- and response-focused emotion regulation: Divergent consequences for experience, expression, and physiology. Journal of Personality and Social Psychology, 74, 224–237. Gross, J. J., & Thompson, R. A. (2007). Emotion regulation: Conceptual foundations. In J. J. Gross (Ed.), Handbook of emotion regulation (pp. 3–24). New York: Guilford Press. Hamann, S. B., Ely, T. D., Hoffman, J. M., & Kilts, C. D. (2002). Ecstasy and agony: Activation of the human amygdala in positive and negative emotion. Psychological Science, 13, 135–141. Harris, L. T., & Fiske, S. T. (2006). Dehumanizing the lowest of the low: Neuroimaging responses to extreme out-groups. Psychological Science, 17, 847–853. Hart, A. J., Whalen, P. J., Shin, L. M., McInerney, S. C., Fischer, H., & Rauch, S. L. (2000). Differential response in the human amygdala to racial outgroup versus ingroup face stimuli. NeuroReport, 11, 2351–2355. Kanwisher, N., McDermott, J., & Chun, M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for the perception of faces. Journal of Neuroscience, 17, 4302–4311. Kelley, W. M., Macrae, C. N., Wyland, C. L., Caglar, S., Inati, S., & Heatherton, T. F. (2002). Finding the self? An event-related fMRI study. Journal of Cognitive Neuroscience, 14, 785–794. Krendl, A. C., Macrae, C. N., Kelley, W. M., & Heatherton, T. F. (2006). The good, the bad, and the ugly: An fMRI investigation of the functional anatomic correlates of stigma. Social Neuroscience, 1, 5–15. Levin, D. T. (1996). Classifying faces by race: The structure of face categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 1364–1382. Levin, D. T. (2000). Race as a visual feature: Using visual search and perceptual discrimination tasks to understand face categories and the cross-race recognition deficit. Journal of Experimental Psychology: General, 129, 559–574. Lieberman, M. D. (2003). Reflective and reflexive judgment processes: A social cognitive neuroscience approach. In J. P. Forgas, K. D. Williams, & W. von Hippel (Eds.), Social judgments: Explicit and implicit processes (pp. 44–67). New York: Cambridge University Press. Lieberman, M. D., Hariri, A., Jarcho, J. M., Eisenberger, N. I., & Bookheimer, S. Y. (2005). An fMRI investigation of race-related amygdala activity in African-American and Caucasian-American individuals. Nature Neuroscience, 8, 720–722. MacDonald, A. W., Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000, June 9). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science, 288, 1835–1838. Malpass, R. S., & Kravitz, J. (1969). Recognition for faces of own and other “race.” Journal of Personality and Social Psychology, 13, 330–334. Mitchell, J. P. (2006). Mentalizing and marr: An information processing approach to the study of social cognition. Brain Research, 1079, 66–75.

8/17/09 5:27:58 PM

984 A Neural Analysis of Intergroup Perception and Evaluation Mitchell, J. P., Macrae, C. N., & Banaji, M. R. (2006). Dissociable medial prefrontal contributions to judgments of similar and dissimilar others. Neuron, 50, 655–663.

Phillips, M. L., Young, A. W., Senior, C., Brammer, M., Andrew, C., Calder, A. J., et al. (1997, October 2). A specific neural substrate for perceiving facial expressions of disgust. Nature, 389, 495–498.

Morris, J. S., Frith, C. D., Perrett, D. I., Rowland, D., Young, A. W., Calder, A. J., et al. (1996, October 31). A differential neural response in the human amygdala to fearful and happy facial expressions. Nature, 383, 812–815.

Rhodes, G., Byatt, G., Michie, P. T., & Puce, A. (2004). Is the fusiform face area specialized for faces, individuation, or expert individuation? Journal of Cognitive Neuroscience, 16, 189–203.

Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84, 231–259.

Richeson, J. A., Baird, A. A., Gordon, H. L., Heatherton, T. F., Wyland, C. L., Trawalter, S., et al. (2003). An fMRI examination of the impact of interracial contact on executive function. Nature Neuroscience, 6, 1323–1328.

Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Harvesting intergroup attitudes and stereotypes from a demonstration website. Group Dynamics, 6(1), 101–115. Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. E. (2002). Rethinking feelings: An fMRI study of the cognitive regulation of emotion. Journal of Cognitive Neuroscience, 14, 1215–1299. Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotion. Trends in Cognitive Sciences, 9, 242–249. Ochsner, K. N., Ray, R. D., Robertson, E. R., Cooper, J. C., Chopra, S., Gabrieli, J. D. E., et al. (2004). For better or for worse: Neural systems supporting the cognitive down- and up-regulation of negative emotion. NeuroImage, 23, 483–499. Ostrom, T. M., & Sedikides, C. (1992). Out-group homogeneity effects in natural and minimal groups. Psychological Bulletin, 112, 536–552.

Richeson, J. A., & Shelton, J. N. (2003). When prejudice does not pay: Effects of interracial contact on executive function. Psychological Science, 14, 287–290. Seeleman, V. (1940). The influence of attitude upon the remembering of pictorial material. Archives of Psychology, 36, 6–69. Sporer, S. L. (2001). Recognizing faces of other ethnic groups: An integration of theories. Psychology, Public Policy, and Law, 7, 36–97. Vaes, J., Paladino, M. P., Castelli, L., Leyens, J.-P., & Giovanazzi, A. (2003). On the behavioral consequences of infrahumanization: The implicit role of uniquely human emotions in intergroup relations. Journal of Personality and Social Psychology, 85, 1016–1034.

Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192.

Valentine, T., & Endo, M. (1992). Towards an exemplar model of face processing: The effects of race and distinctiveness. Quarterly Journal of Experimental Psychology: Human Experimental Psychology, 44A, 671–703.

Petty, R. E., & Wegener, D. T. (1993). Flexible correction processes in social judgment: Correcting for context-induced contrast. Journal of Experimental Social Psychology, 29, 137–165.

Van Bavel, J. J., Packer, D. J, & Cunningham, W. A. (2008). The Neural substrates of in-group bias: A functional magnetic resonance imaging investigation. Psychological Science, 19, 1131–1139.

Phelps, E. A. (2006). Emotion and cognition: Insights from studies of the human amygdala. Annual Review of Psychology, 24, 27–53.

Vuilleumier, P. (2005). How brains beware: Neural mechanisms of emotional attention. Trends in Cognitive Sciences, 9, 585–594.

Phelps, E. A., Cannistraci, C. J., & Cunningham, W. A. (2003). Intact performance on an indirect measure of race bias following amygdala damage. Neuropsychologia, 41, 203–208.

Whalen, P. J., Rauch, S. L., Etcoff, N. L., McInerney, S. C., Lee, M., & Jenike, M. A. (1998). Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge. Journal of Neuroscience, 18, 411–418.

Phelps, E. A., O’Connor, K. J., Cunningham, W. A., Funayama, E. S., Gatenby, J. C., Gore, J. C., et al. (2000). Performance on indirect measures of race evaluation predicts amygdala activation. Journal of Cognitive Neuroscience, 12, 729–738.

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Wheeler, M. E., & Fiske, S. T. (2005). Controlling racial prejudice: Social-cognitive goals affect amygdala and stereotype activation. Psychological Science, 16, 56–63.

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Chapter 51

Cultural Processes SUSAN T. FISKE

complexities of social structure and culture that grow with ever-larger societies. Ample evidence indicates that humans do orient to other humans. Social psychologists have lately described this as a need to belong (Baumeister & Leary, 1995), describing it as the most basic, core motive (S. T. Fiske, 2004). Essentially, other people constitute our adaptational niche. People survive and thrive best when immersed in social relationships; isolation risks both physical and mental illness, even mortality (Cacioppo et al., 2006; Hawkley, Burleson, Berntson, & Cacioppo, 2003). Some have argued that the neural signature for social isolation mimics that for physical pain (Eisenberger, Lieberman, & Williams, 2003); at a minimum, social exclusion activates areas implicated in conflict and problem-solving (anterior cingulate cortex) as well as emotion regulation (right ventral prefrontal cortex) (see Figure 51.1). As another illustration, people’s neural resting state, the so-called default network, may indicate a background preoccupation with people (Iacoboni et al., 2004). People’s neural responses tune acutely to social interaction, perhaps because it is adaptive to get along with one’s group.

“Cultural neuroscience” is almost an oxymoron. The Venn diagrams for cultural psychology and neuroscience at present show scant overlap, as revealed by online searches and other reviews (Chiao & Ambady, 2007). At first glance, the difficulty of joining the two fields boggles the mind. How do we undertake complex neural measurements in separate cultures, given all the paraphernalia involved? Thoughts of researchers in jeeps toting fMRI magnets to remote villages somehow defy plausibility. What’s more, cultural psychology and hard-core neuroscience often caricature each other as respectively imprecise and reductionist, among more polite terms. Are the obstacles insurmountable? No. Culture constitutes a natural and even feasible topic for behavioral neuroscience. Despite the past misguided wars between those who study culture and those who study more biological factors, this marriage can work. People are biologically adapted to acquire a culture because they must coordinate with other people (A. P. Fiske, 2002); people are predisposed to acquire any of a variety of culturally specific languages, relationships, rituals, and other elements of human sociality. The a priori proclivity of people to absorb culture makes no adaptive sense without the human cultures to acquire, and the human cultures cannot be efficiently transmitted without the biological attunements to acquire them. People are biologically prepared for a cultural niche (Li, 2003). Cultures are stored in people’s brains. And people’s cultural niche reciprocally affects their brain development. The field of neuroecology dates back decades, to the interspecies comparative approaches of ethology (for a recent review, see Sherry, 2006). Here we see the mutual influence of neocortex size and social group size in primates (e.g., Dunbar, 2001; Whiten & Byrne, 1997). In humans and our ancestors, neocortex capacity and group size track each other over evolutionary time (Donald, 1991; Dunbar, 1993; Massey, 2005). People simply need more brain power to deal with the exponential increase in numbers of dyadic relationships as group size increases, let alone the

(A)

(B)

Anterior Cingulate x  8

Right Ventral Prefrontal y  28

Figure 51.1 A: Increased activity in anterior cingulate cortex (ACC) during exclusion relative to inclusion. B: Increased activity in right ventral prefrontal cortex (RVPFC) during exclusion relative to inclusion. Note: From “Does Rejection Hurt? An fMRI Study of Social Exclusion,” N. I. Eisenberger, M. D. Lieberman, and K. D. Williams, 2003, Science, 302, p. 291. Reprinted with permission.

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People’s brains thus are based in their physical and social environment—we know this from the enlarged navigationoriented hippocampi of taxi drivers (Maguire et al., 2000), from blind individuals’ increased sensori-motor representation of their braille-reading finger (Hamilton & PascualLeone, 1998), from early training in stringed-instruments predicting enlarged cortical representations of the left playing hand (Elbert, Pantev, Weinbruch, Rockstroh, & Taub, 1995), and from neural regional intensity changes in adolescents transitioning to college (Bennett & Baird, 2006), all compared with controls not experiencing those environments. To the extent that cultures are influenced by physical environments and certainly create social environments, then culture will be reflected in brain structure, both within individuals and as an adaptive response across generations abiding in that physical and social niche. The review returns to these themes in the context of specific cultural neuroscience research. Because of the nascent state of cultural neuroscience, this review essentially makes a promissory note to the future. This IOU will focus in turn on (a) some initially identified cultural differences in social responses that implicate neural systems, (b) some candidates for future research in these areas, and (c) some universal dimensions in culture that are likely candidates for neuroscience exploration. The review closes by (d) addressing theoretical and methodological challenges to cultural neuroscience. The review focuses on a few key areas most likely to interest behavioral scientists; it excludes cultural differences in patient populations responding to brain injury, cultural differences in neurological testing, and clinical population variance in genetic testing. Instead, specific topics include social cognitive processes such as perceiving and considering self, others, faces, races, and social structure, all critical indicators of culture.

CULTURAL VARIATION IN COGNITIVE AND AFFECTIVE NEURAL PROCESSES, WITH IMPLICATIONS FOR SOCIAL COGNITION In considering cultural variations, this section moves from the bottom up, from attentional focus, through visual processing, to attributions of causality, to language and other cultural artifacts, to social interaction. Some intermittent themes contrast independent-interdependent cultural emphases, as well as behavioral approach and behavioral inhibition systems. Attention From the earliest moments of perception onward, culture impacts how the brain responds to stimuli. Behavioral

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inhibition systems (BIS) and behavioral approach systems (Carver & White, 1994) provide a contrast in people’s earliest orientation to stimuli. The development of attentional networks reflects culture from an early age (Posner & Rothbart, 2007). A cultural route to attentional socialization may operate by sensitively activating the amygdala’s vigilance response to other people’s emotional expressions; this would be a social caution-related route that would emphasize noticing other people’s subtle emotions and avoiding harm that would reactivate the amygdala response system. This route relies on amygdala-related distress and empathy; these children socialize relatively easily and readily internalize moral principles. Another cultural route to socialization, in this view, would move slightly downstream to emphasize anterior cingulate cortex, which typically responds to discrepancies and triggers problem solving. This route would emphasize self-regulation and conscience in regard to others, to avoid harm (Posner & Rothbart, 2007). This route relies on effortful control and internalized conscience. It might seem that the caution-related route would more often characterize Eastern cultures, whereas the consciencerelated route would more often characterize Western cultures. If so, this would fit respectively a more reactive, hesitant, and shame-oriented socialization, compared with a more effortful, conscious, and guilt-oriented socialization. All these processes would operate on early attentional processes. Visual Processing Once attending, encoding processes also appear to be shaped by culture, here highlighting the independenceinterdependence theme. Westerners tend to operate with more analytic, piecemeal, and context-independent perceptual processes, focusing on single salient objects. Easterners tend to operate more holistically, in a more integrative, context-sensitive mode, attending to relationships between objects and their settings (Nisbett, 2003; Nisbett & Masuda, 2003; Nisbett & Miyamoto, 2005). Thus Westerners more readily recognize an object out of context, but they do not as readily recall its relationships to the other objects or the original context, compared with Easterners. Consistent with this idea, East Asians show more eye fixations on backgrounds than Americans do (Chua, Boland, & Nisbett, 2005). Illustrating these ideas with activation patterns in specific neural regions, Americans engage more object-processing areas in the ventral visual cortex, compared with Chinese participants (Gutchess, Welsh, Boduroglu, & Park, 2006). If culture affects neural activation patterns and even the size of various neural structures, then older adults should show more cultural differences in the brain than would younger

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Cultural Variation in Cognitive and Affective Neural Processes

adults (D. C. Park & Gutchess, 2002). Indeed, young Americans and young Singaporeans of Chinese descent show few object-processing differences in the lateral occipital cortex, but older adults show the predicted cultural differences (Chee et al., 2006). Attributions of Causality These visual processing differences may underlie cultural patterns of causal attributions for other people’s behavior. Americans tend to show a dispositional bias in explaining others’ behavior, whereas East and South Asians tend to emphasize situational causality to a greater extent (Nisbett, 2003). This corresponds to the independent American relative emphasis on isolated targets (whether human or not) and interdependent Asian relative emphasis on context. Other research documents neural markers of dispositional attributions (Harris, Todorov, & Fiske, 2005), specifically MPFC and STS activity to specific combinations of information that produce dispositional inferences (but not to other, nondispositional combinations of the same information; see Figure 51.2). Thus, cultural differences in context-sensitive, situational versus object-oriented, dispositional attributions may well have similar neural signatures emphasizing MPFC and STS. An individual’s cumulative life experience in a particular culture permanently marks the brain. Language Language, the basic cultural experience, creates culturespecific perceptual attunements as early as infancy (Aslin, 1981; Cheour et al., 1998; Kuhl, Williams, Lacerda, Stevens, & Lindblom, 1992; Näätänen et al., 1997). Language then leaves neural traces as well (Neville et al., 1998). The left hemisphere activations of bilinguals for their native language overlaps their second language only for early bilingual learners, and less so for late learners (Kim, Relkin, Lee, & Hirsch, 1997; Neville & Bavelier, 1998; WeberFox & Neville, 1996). As another example, color perception varies with language, and neuropsychology backs up the neural traces of culturally influenced processing (Davidoff, 2001). Reading likewise reflects culture, both in language and orthography (Schlaggar & McCandliss, 2007). A metaanalysis of largely Western studies implicates the left visual word-form area, supporting perceptual expertise for words, and especially the left phonological system, linking word forms and sounds (Bolger, Perfetti, & Schneider, 2005). Activations differ depending on the language’s complexity of letter-sound mappings (e.g., Italian is consistent; English is inconsistent; Paulesu et al., 2001), but the same areas activate consistently within culture. In contrast, Chinese logography activates phonology less, but orthography and word meaning

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more (Tan, Spinks, Eden, Perfetti, & Siok, 2005). Where Western reading links words and sounds, Chinese reading implicates words and space, with corresponding shifts in brain activity (Siok, Perfetti, Zhen, & Tan, 2004). Cultural differences in writing-reading direction (left to right or vice versa) apparently have ramifications for social perception. In Western, left-to-right settings, stereotypically more agentic group members (e.g., men) are more often represented visually to the left of less agentic group members (e.g., women) (Maass, Suitner, Favaretto, & Cignacchi, 2009). In art, photographs, and cartoons, this spatial agency bias occurs unless the man in the couple is perceived to be less agentic, and it correlates with an artist’s own beliefs in the agency of men. Europeans show this bias, whereas Arabic speakers tend to show the opposite bias, reflecting their written language’s right-toleft direction. This implicates culturally dictated forms of reading and writing in social perception. Such basic social perceptual processes likely implicate neural activations as well, but that research remains to be done. Cultural Artifacts Beyond language, specific cultural artifacts can mediate the impact of culture on neural functioning in surprisingly specific ways. Expertise on the East Asian abacus facilitates related visuospatial tasks (Hatano, Miyake, & Binks, 1977), although it also creates vulnerabilities to such distracters in the same modality (Hatano, Amaiwa, & Shimizu, 1987). Numerical memory span appears to be influenced by the logic of Chinese number words, giving an advantage to Chinese over Americans at an early age (Geary, Bow-Thomas, Fan, & Siegler, 1993; Miller, Smith, Zhu, & Zhang, 1995; Stigler, Lee, & Stevenson, 1986). Neuroimaging has differentiated numerical representations of Chinese and English native speakers (Tang et al., 2006), with the English-speakers more linguistic and cortical, and the Chinese speakers more visual-premotor. Reading experiences, language acquisition, and mathematics instruction all are implicated in these cultural differences. Another specifically neural impact of a numerical cultural artifact appears in Canadian postal workers, who sort zip codes that combine letters and numbers, showing less representational distinction in the left inferior occipitotemporal cortex between digit and letters, in comparison with coworkers who do not sort mail (Polk & Farah, 1998). Language may or may not play a role in numeracy (Gelman & Butterworth, 2005). Social Interaction Finally, moving from cognition, perception, and language directly to social interaction, recent research implicates

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cultural differences in independent-interdependent responses to social support under stress. Some individuals are homozygous for the short allele (s/s) of the serotonin transporter gene-linked polymorphic region, and this s/s combination is particularly over-represented in Asian populations. The s/s combination, in a gene-by-environment interaction, puts people particularly at risk for depression when they have had a stressful early family environment or recent adversity (Taylor et al., 2006). The authors speculate that the robust cultural difference in interdependence between Asians and Westerners (e.g., Markus & Kitayama, 1991) might have developed partly in response to Asians’ greater genetic risk

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Note: (A) Dispositional attribution predicted for cell e (high consistency, low consensus, low distinctiveness). From “Attributions on the Brain: Neuro-Imaging Dispositional Inferences, beyond Theory of Mind,” by L. T. Harris, A. Todorov, S. T. Fiske, 2005, NeuroImage, 28, pp. 766. (B) STS reflects intent, trajectory, goal-directed motion. From “Attributions on the Brain: Neuro-Imaging Dispositional Inferences, beyond Theory of Mind,” by L. T. Harris, A. Todorov, S. T. Fiske, 2005, NeuroImage, 28, p. 767 (C) MPFC reflects mentalizing, considering others’ minds; cell c represents conditions similar to cell e, except consensus, which is often ignored in attributions, considering what everyone including the target does under similar circumstances. From “Attributions on the Brain: Neuro-Imaging Dispositional Inferences, beyond Theory of Mind,” by L. T. Harris, A. Todorov, S. T. Fiske, 2005, NeuroImage, 28, p. 767(D) Locations of signal change. From “Attributions on the Brain: Neuro-Imaging Dispositional Inferences, beyond Theory of Mind,” by L. T. Harris, A. Todorov, S. T. Fiske, 2005, NeuroImage, 28, p. 768. Reprinted with permission.

factors for stress, by encouraging a supportive family and social network. Other gene-by-environment interactions appear in risk factors for aggression. Prior research indicates a relationship between the monoamine oxidase-A gene and aggression (Caspi et al., 2002; Nelson & Trainor, 2007; Shih, Chen, & Ridd, 1999). Individuals with the low-expression version of this MAO-A gene report a potentially volatile combination (Eisenberger, Way, Taylor, Welch, & Lieberman, 2007): (a) higher chronic levels of aggression, (b) hypersensitivity to interpersonal events, and (c) increased response to social rejection in the anterior cingulate cortex, which has been

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Candidates for Future Research on Neural Reflections of Cultural Variation

implicated both in processing of discrepancies (Botvinick, Cohen, & Carter, 2004) and in social and physical pain (Eisenberger et al., 2003). To the extent that aggression results from failed interpersonal control—that is, a perceived lack of correlation between an individual’s interpersonal behavior and outcomes (S. T. Fiske, 2004, chap. 10; Malamuth & Addison, 2001)—this possibly creates an aggression-prone gene-by-environment interaction. Individuals differ widely in MAO-A gene activity, but reviews do not yet indicate known population differences. Nevertheless, with time, a complex interplay may document such genetic population differences, environmental factors modulating expression, and cultural factors for coping with its expression. Focusing just on environments, some cultures mandate elaborate interpersonal signals, often heavy politeness norms, possibly to mute aggressive reactions to rejection (Cohen, Vandello, Puente, & Rantilla, 1999). Example environments would include cultures of honor (Nisbett & Cohen, 1996), in historically frontier, herding societies such as the southern United States, Euro-Mediterranean countries, and the northern British Isles. These cultural variations in environment may differentially affect people with individual variations in the MAO-A gene, even absent any differences in population distributions. Summary Cultural variation in perceptual-cognitive-affective processes, with implications for social perception and cognition, appear from the earliest moments of perception through to the most complex social-emotional responses, all with neural markers. Different routes to socialization speculatively might especially implicate either the amygdala, causing a more inhibition-oriented and interdependent, cautious, preoccupied approach to others, or alternatively, the anterior cingulate cortex, reflecting a more approach-oriented and independent, agentic but conscience-driven process. Relatively more holistic Eastern perception focuses more on environments and relationships among objects and people, whereas a relatively more target-oriented Western perceptual process focuses more on isolated objects and people; this would help explain cultural patterns of causal attribution that rely respectively more on situations versus the dispositions of individual actors. Cultural variations in acquiring numeracy, literacy, and spoken languages also may create long-term neural patterns that reflect persistent cultural patterns of social communication. And cultural differences in social interdependence may even reflect population distributions of certain genetic predispositions. Notably, all these studies involve neural moderators or mediators of cultural patterns.

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Notably, also, these studies almost all contrast Asians and Europeans or Americans. Future cultural neuroscience research must eventually expand to other regions and cultures.

CANDIDATES FOR FUTURE RESEARCH ON NEURAL REFLECTIONS OF CULTURAL VARIATION Given the promissory nature of most avenues for cultural neuroscience, this section explores some candidate cultural variations that may lend themselves to neural explorations. The most prominent classic variable in cultural psychology for many years was individualism-collectivism (Triandis, 1990), with Westerners scoring higher on individualism, defined as a focus on the autonomous person, and Asians, South Americans, and Africans scoring higher on collectivism, emphasizing groups such as the family, community, or organization. Individualism-collectivism varies regionally even in the United States (Vandello & Cohen, 1999). The Rocky Mountains and Great Plains tend to show the most individualism, whereas collectivism more characterizes immigrant-destinations such as Hawaii, California, New York, and New Jersey, or religion-centered areas such as the Deep South and Utah. More individualist cultures put each person’s own needs over the group’s needs, whereas more collectivist cultures put group needs over individual needs. A related description, interdependent versus independent selves (Markus & Kitayama, 1991) also contrasts East-West cultural orientations toward autonomous versus socially embedded selves. In individualist (independent self) settings, both theories imply interpersonal perception more focused on the isolated, agentic self and autonomous others, consistent with the earlier-cited Western attributional focus on individual dispositions. This fits Western neural patterns described earlier, and complementary neural states that fit the contrasting Eastern focus on situations and relationships. We next consider other candidates for future research on neural reflections of cultural variation. Default, Resting-State Activity The individual-collective (independent-interdependent) dimension implies untapped directions that extend far beyond attribution, such as when people’s minds wander and their brains rest in a default network (Mason et al., 2007). Among many intriguing leads, the cultural tendencies toward individualist versus collectivist focus should appear in default, resting-state preoccupation with self and others. Self-referential activation appears in resting,

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Cultural Processes

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Self Representations Consistent with some of these ideas, MPFC (and anterior cingulate cortex, ACC) activated for both Chinese and Western participants judging traits of self, compared with others. However, Chinese participants also activated MPFC for judging their mother ’s traits, suggesting a more interdependent self-representation in an overlap not shown

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Figure 51.3 Two MPFC regions reflect more broadly A: dorsal (dissimilar others) and B: ventral (similar others) social cognition. Note: Specifically, the top panels (A) contrast mentalizing (closed circles in the time course) versus not (open circles); overall, not-mentalizing reduces dorsal MPFC activation compared with mentalizing, so mentalizing relatively activates dorsal MPFC. The relative activation under mentalizing tends to be exaggerated for less similar others (striped bars, mentalizing

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2005; Mitchell, Macrae, & Banaji, 2006; see Figure 51.3), these patterns might distinguish relatively individualist and collectivist participants. In an individualist culture, resting (default) activity might focus more on self (ventral). In a more collectivist culture, people in a resting state might spontaneously think more about other people (dorsal). At least among American participants, self and intimate others activate proximate but distinct areas of MPFC (Heatherton et al., 2006). Thus, in an individualist culture, people might differentiate self from both similar and dissimilar others more than people in a collectivist culture, so the ventral-dorsal MPFC axis for self and dissimilar others might be exaggerated for individualists. Still another possibility would be differences in perspective-taking for self-other judgments, with collectivist cultures showing more activation in the posterior dorsal MPFC and individualist cultures showing more anterior MPFC activations. This would extend experimental manipulations showing self-other and perspective-taking differential activations in MPFC (D’Argembeau et al., 2007).

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default state activations for Americans (Gusnard, Akbudak, Shulman, & Raichle, 2001). If people spontaneously think about themselves as agents in more individualist cultures, then resting-state thoughts should focus especially on self in Westerners. For example, thought-listing studies suggest that among Westerners, much thought is operant: instrumental, problem solving, and volitional (Klinger, 1978; see S. T. Fiske & Taylor, 2008, pp. 36–41, for a review). All this centers on the self-as-agent, an essentially Western perspective. A more recent imaging study suggests a specific way to test this differential focus; it manipulates people’s reflective tasks (thinking about self, another person, social issues), compared with resting state in Dutch participants (D’Argembeau et al., 2005). More self-reported self-referential thought occurs in the self task, but also more in resting states than in thinking about others. According to PET data, the self task also activates ventral medial prefrontal cortex (VMPFC) more than all the other tasks, and the resting state more than the society task or other task. Crucially, the VMPFC areas overlapping self and resting activation then turn out to correlate with participant reports of self-referential thought during the tasks. In contrast, more dorsal MPFC characterizes all three reflective tasks compared with rest; the DMPFC often activates when people are thinking about (their own or others’) mental states, as discussed later (e.g., Amodio & Frith, 2006). To the extent that self-reflection and similar-other activation is more ventral and dissimilar other-activation is more dorsal in the MPFC (Mitchell, Banaji, & Macrae,

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condition). In a more ventral area of MPFC, panels (B) indicate activation above resting baseline in the mentalizing condition only; this activation correlates with others’ similarity ranging from most (dark bars on the bar graph) to least similar (striped bars). From “The Link between Social Cognition and Self-Referential Thought in the Medial Frontal Cortex,” by J. P. Mitchell, M. R. Banaji, and C. N. Macrae, 2005, Journal of Cognitive Neuroscience, 17, p. 1308. Reprinted with permission.

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Candidates for Future Research on Neural Reflections of Cultural Variation

by Western participants (Zhu, Zhang, Fan, & Han, 2007). Some of the first cognitive social neuroimaging studies focused on self-representations (Heatherton, Macrae, & Kelley, 2004). Showing that MPFC activity predicts both self-referential judgments and memory early established the link that later research has exploited so well (Macrae, Moran, Heatherton, Banfield, & Kelley, 2004). Self-referential processing in the MPFC differs according to task but not the modality, according to narrative and meta-analytic literature reviews (Northoff & Bermpohl, 2004; Northoff et al., 2006): As Figure 51.4 indicates: (a) Representation of self-relatedness implicates orbitoMPFC and paraACC (ventral MPFC); this fits the Mitchell et al. distinction between more ventral processing for self and similar others; (b) evaluation and emotional reappraisal implicate DMPFC. (Two other processes are not discussed here but are common in the literature: [c] monitoring implicates anterior cingulate cortex and [d] integration with autobiographical memory implicates the posterior CC/precuneus.) These cortical midline structures, ranging from ventral to dorsal to posterior, might show cultural variations in relative emphases ranging from immediate representation, to emotional loadings, to long-term memory for self. Interdependent selves might show less integration of new inputs with individual autobiographical long-term memory. Self-evaluations reliably activate MPFC regions (e.g., Ochsner et al., 2004, 2005). These researchers distinguish

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two kinds of self-appraisals: direct (one’s own self-evaluation) and reflected (one’s representation of others’ appraisals of self). These direct (self) and indirect (social) appraisals show different activation patterns. They further might respectively receive priority for independent versus interdependent selves. Perhaps cultural variations in autonomous self-focus would appear also in these activation patterns. Similarly, independent selves tend to self-enhance more than interdependent selves (Heine, Lehman, Markus, & Kitayama, 1999; Taylor & Brown, 1988), and these differences might be reflected in variations between orbital and less ventral PFC (Beer, 2007). Many avenues of cultural variation in self-representation would be open for the next level of cultural neuroscience. Mimicry: Bridging Self and Other We have just explored cortical midline structures that might reflect cultural differences, focusing on the MPFC, as well as anterior and posterior cingulate cortex; broadly speaking, these are implicated in abstract representations of self and others. Other brain systems bridge self and others in a more somatosensory/motoric mode, by observation and imitation (see Chapter 16, Volume 1, plus Rizzolatti & Craighero, 2004; Uddin, Iacoboni, Lange, & Keenan, 2007; see also Figure 51.5). Two right lateral cortical areas—inferior frontal and rostral inferior parietal lobule—are implicated in the human mirror neuron system (MNS). This right frontoparietal system activates for its own actions as well as the comparable actions of others. The MNS apparently supports the motor simulation of imitation in gesture, instrumental action, and language, all fundamental components of culture. One lesson from the human MNS is our neural readiness to acquire culture by observation and imitation of others. Another is the possibility of cultural differences in attunement to others, again contrasting more interdependent and independent stances. Finally, cultural differences in power distance and the importance of status might predict differences in subordinate attunement to powerful others. Regulation and Suppression

TRENDS in Cognitive Sciences

Figure 51.4 Processing of self-referential stimuli in the cortical midline structures (CMS). Note: Orbitomedial prefrontal cortex (OMPFC; sometimes called ventromedial PFC), dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex, particularly the supragenual region (AC), posterior cingulate cortex (PC) including the adjacent retrosplenial cortex. The four hypothesized subprocesses of self-referential processing associated with each region are shown. From “Cortical Midline Structures and the Self,” by G. Northoff and F. Bermpohl, 2004, Trends in Cognitive Sciences, 8, p. 104. Reprinted with permission.

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Cultures differ dramatically in their degree of selfmonitoring and emotional expressiveness (e.g., Mesquita & Frijda, 1992; Pennebaker, Rimé, & Blankenship, 1996). We might therefore expect neural reflections of these patterns. At a minimum, behavioral inhibition systems (BIS) may dominate behavioral activation systems (BAS) (Carver & White, 1994), and vice versa, correlated with cultural variations in self-regulation and mental suppression (BIS) versus agency and approach (BAS).

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Figure 51.5 Overlap between areas involved in self-recognition and mirror-neuron areas. Note: Self-recognition seems to engage mirror-neuron areas in the right hemisphere. Tasks of self-recognition produce activations that significantly overlap with those from tasks that involve imitation and action

Orbitofrontal cortex plays a role in emotion regulation, according to patient brain-damage studies (Beer, Heerey, Keltner, Scabini, & Knight, 2003; Beer, John, Scabini, & Knight, 2006; Beer, Knight, & D’Esposito, 2006; Roberts et al., 2004). As noted, amygdala responses to emotionally significant stimuli might well also vary across cultures, in line with socialization to emotional expression and response. Hence, we might expect cultural variations in activation and development of the relevant networks, with more inhibition-shame-regulated cultures developing more sensitive neural systems of emotion regulation. In contrast, more approach-guilt-unregulated cultures might develop agentic, approach systems to a greater extent, in the management of emotion. Not only emotions, but also sheer thought can be targeted for suppression. Two of the cognitive phenomena involved in thought suppression are working memory load and interference; these activate overlapping prefrontal areas (Bunge, Ochsner, Desmond, Glover, & Gabrieli, 2001). Dorsolaterial PFC activates to sustained efforts at thought and memory control, whereas ACC signals transient thought control (Anderson et al., 2004; Mitchell et al., 2007; Wyland, Kelley, Macrae, Gordon, & Heatherton, 2003), in line with its more general involvement in discrepancy detection (Botvinick et al., 2004). To the extent that people regulate unwanted thoughts, they tend to be more optimistic. And indeed, the right lateral PFC activation correlates with individual differences in agreeableness, which can be viewed as skillfully regulating negative affect (Haas, Omura, Constable, & Canli, 2007). To the extent that some cultures emphasize control, whereas others emphasize expression, neural activations might reflect those cultural proclivities. Racial Cues In U.S. culture, a major target of thought control and expression is racism. Race is a cultural phenomenon in

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observation. Frontal and parietal areas of overlapping activity for the two tasks are shown. From “The Self and Social Cognition: The Role of Cortical Midline Structures and Mirror Neurons,” by L. O. Uddin, M. Iacoboni, C. Lange, and J. P. Keenan, 2007, Trends in Cognitive Sciences, 11, p. 155. Reprinted with permission.

several respects. First, the definition of race is culturedependent, and reactions to race are certainly determined by culture (e.g., Fredrickson, 2002; Jones, 1997; Sears, 1998). What’s more, norms around expressions of those reactions are culture-specific. Because Americans usually try to suppress racist expression, American social psychologists quickly turned to implicit, unexamined, and automatic forms of racism to explain continuing discrimination and disparities (S. T. Fiske, 1998; Mays, Cochran, & Barnes, 2007). Prominent among these indicators, lately, have been neural correlates of racial perception (Eberhardt, 2005). A variety of approaches have focused, first, on event-related potentials indicating extremely early responses to race and, second, on imaging studies variously implicating the amygdala, the fusiform face area, and dlPFC. People categorize other people by race, age, and gender early and often (S. T. Fiske, 1998). Categorical information is extracted from faces more readily than individuating information (Cloutier, Mason, & Macrae, 2005) and implicates the left hemisphere (Mason & Maccrae, 2004). Early processes differentiate ingroup (“us”) from outgroup (“them”) and good from bad (Ito, Thompson, & Cacioppo, 2004). Black targets preferentially draw attention as early as 100 msec into stimulus exposure (Ito & Urland, 2003), although racially ambiguous faces may be differentiated as late as 500 msec after stimulus exposure (WilladsenJensen & Ito, 2006). The early reactions are consequential, translating to racial biases in simulated shoot/no-shoot responses (Correll, Urland, & Ito, 2006). Both affective and cognitive information influence racial categorization from its earliest moments. Phenotypically irrelevant affect-laden information (such as individual liking and disliking) influences racial categorization (Richeson & Trawalter, 2005a). Stereotypic racial assumptions affect early visual processing of race in faces, especially for visually typical racial appearances (Eberhardt, Goff, Purdie, & Davies, 2004). For some less flexible perceivers (so-called

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Candidates for Future Research on Neural Reflections of Cultural Variation

entity theorists), memory for racially ambiguous faces reverts to more racially prototypical faces, especially given a racial label for the face (Eberhardt, Dasgupta, & Banaszynski, 2003). Racial prototypicality correlates with harsher sentences in capital crimes, even controlling for relevant features of the crime (Eberhardt, Davies, PurdieVaughns, & Johnson, 2006), so racial appearance matters from early perception through consequential life-and-death decisions. Locating some of these early features of racial perception has proved fruitful, ever since the earliest forays into social neuroscience. Same-race faces are more memorable than other-race faces, and this correlates with degree of activation in the fusiform face area (FFA; Golby, Gabrieli, Chiao, & Eberhardt, 2001). Such attunement should differ with culture, intergroup experience, and cross-cultural experience, a cultural neuroscience avenue worth pursuit. Cultural variations in the definition of race should determine people’s tendency to include a face as “own race.” And given that the FFA responds to other types of perceptual expertise besides faces (Gauthier, Skudlarski, Gore, & Anderson, 2000), culturally based variations in intergroup experience will determine perceptual expertise and FFA response. Another index of early attunement to race is the vigilance system involving amygdala activation, which supports the heavy emotional loading of interracial interactions in the United States, and which could index cultural variability in such responses. Some of the most provocative early work on the neuroscience of interracial reactions documented amygdala responses correlated with negative implicit associations (IAT) but also with startle-eyeblink indicators of a defensive motivational orientation, especially in Whites responding to Blacks (Cunningham, Johnson, et al., 2004; Hart et al., 2000; Lieberman, Hariri, Jarcho, Eisenberger, & Bookheimer, 2005; Phelps et al., 2000; Wheeler & Fiske, 2005). Among Whites intrinsically concerned about prejudice (high in internal but low in external motivation to control their prejudice), their startle-eyeblink response to Black faces revealed lower automatic affective bias, compared with other participants (Amodio, Harmon-Jones, & Devine, 2003). Other neuro-related evidence converges on the immediate affective loading of interracial encounters, especially for Whites. Facial muscle activity (subtle expressive movements not yet visible but tracked electromyographically) indicates subtle racial bias (Vanman, Paul, Ito, & Miller, 1997). Whites inexperienced at interacting with Blacks show cardiovascular reactivity consistent with threat in crossracial interactions (Blascovich, Mendes, Hunter, Lickel, & Kowai-Bell, 2001; Mendes, Blascovich, Lickel, & Hunter, 2002). They then perform poorly on subsequent cognitive

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tasks, consistent with other evidence that executive control over prejudiced responses has mental costs (Richeson & Shelton, 2003; Richeson & Trawalter, 2005b). Conversely, Blacks who especially favor their ingroup also incur costs after interracial interaction (Richeson, Trawalter, & Shelton, 2005). Nevertheless, Whites’ successful control over racially biased responses still leaves neural traces, implicating dlPFC, among other indicators (Amodio et al., 2004; Cunningham, Johnson, et al., 2004; Lieberman et al., 2005; Richeson et al., 2003). American Whites apparently respond immediately to Blacks as emotionally significant and often negative, but they also quickly invoke controlled processes. Dehumanization Excluded groups vary by culture, though many cultures seem to disdain poor people, and social class can trump even race (Cuddy et al., 2009). In the United States, drug addicts and homeless people are among the lowest of the low, allegedly possessing no redeeming features. These extreme outgroups, compared with more ambivalently perceived, less extreme outgroups, and compared with all ingroups, elicit a unique neural signature: They alone fail to activate MPFC significantly above baseline (Harris & Fiske, 2006). Earlier, we noted that MPFC regions reliably activate to representations of one’s own and other people’s minds; specifically, MPFC activates when people consider other people’s dispositions (Harris et al., 2005) or form impressions of other people (Mitchell, Macrae, & Banaji, 2004). Apparently “other people” does not include some extreme outgroups such as the homeless. Reduced MPFC activity to extreme outgroups correlates with increased insula and amygdala activity to them, consistent with ratings of these people as disgusting and to be avoided (Harris & Fiske, 2008). The dimensions that typically control MPFC activity in the social neuroscience literature—similarity, familiarity, intelligence, empathy— are rated low for these targets, and people do not spontaneously mentalize them (think about their minds). All this fits the idea that the decreased MPFC activity reflects a form of dehumanization. Furthermore, a manipulation that asks perceivers to think about these targets’ preferences brings the MPFC back online (Harris & Fiske, 2007). Other allegedly disgusting stigmas (e.g., obesity, facial piercings, transsexuality, ugliness) show similar patterns of decreased MPFC activation along with increased insula and amygdala activation (Krendl, Macrae, Kelley, Fugelsang, & Heatherton, 2006). Extreme forms of prejudice, under the wrong conditions, can lead to extreme behavior (S. T. Fiske, Harris, & Cuddy, 2004). The reliability of MPFC, insula, and amygdala as neural correlates

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of extreme prejudice suggests that they might serve as a comparative measure of each culture’s untouchables. More broadly, ventral MPFC operates in reward processing, though it is particularly tuned to rewarding people (Harris, McClure, van den Bos, Cohen, & Fiske, 2007; van den Bos, McClure, Harris, Fiske, & Cohen, 2007). It plays a role in more affective theory of mind reasoning (Shamay-Tsoory, Tibi-Elhanay, & Aharon-Peretz, 2006). Just as this area can differentiate depressed and healthy individuals (Keedwell, Andrew, Williams, Brammer, & Phillips, 2005), so it might differentiate more and less optimistic cultural outlooks.

CULTURAL UNIVERSALS AS CANDIDATES FOR CULTURAL NEUROSCIENCE The previous section examined some reliable cultural variations as candidates for cultural neuroscience research. This section examines two kinds of social cultural universals as candidates for future social-cultural-neural research: the importance of perceived interpersonal intent and status. Inferring Intent: The Warmth Dimension The earliest adaptive judgment about another being is its intent toward oneself, for good or ill. The sentries’ cry “Friend or foe?” captures this basic question. Much evidence now supports the rapid early judgment of friendliness, trustworthiness, warmth, and good intention (versus hostility, untrustworthiness, coldness, and bad intention) as the first dimension of interpersonal and intergroup judgment, across cultures (S. T. Fiske, Cuddy, & Glick, 2007).1 A focal stimulus for inferring hostile or benign intent is the face. Affective reactions to others’ behaviors bind to the other ’s face (Todorov, Gobbini, Evans, & Haxby,

1

This is not equivalent to a simple valence judgment for objects or words, which are not intentional agents, and some evidence indicates these judgments operate somewhat differently. For example, pure evaluative space probably operates more by independent negative and positive dimensions (Cacioppo & Berntson, 1999). Social cognition differs from object cognition in a number of respects (S. T. Fiske & Taylor, 2008). The amygdala may reflect intensity in a valence x intensity space, in judgments of socially relevant words; in that setting, the insula and orbital frontal cortex correlate with valence (e.g., Cunningham, Raye, & Johnson, 2004; Lewis, Critchley, Rotshtein, & Dolan, 2007). The exception seems to be famous names of people, wherein amygdala apparently codes for valence, as in its reactivity to trustworthiness in actual faces (Cunningham, Johnson, Gatenby, Gore, & Banaji, 2003).

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2007), and extracting person knowledge from faces occurs unintentionally (Macrae, Quinn, Mason, & Quadfleig, 2005). Judging another person’s trustworthiness—a key element of the warmth dimension—occurs within 50 msec of exposure to the face and correlates with unconstrained longer exposures (Willis & Todorov, 2006), but is not equivalent to global face evaluation (Todorov & Pakrashi, 2008). Features of the face that create consensus about trustworthiness best correlate with amygdala activation (Engell, Haxby, & Todorov, 2007), and these facial features resemble extreme versions of emotions: happiness (trustworthiness) and anger (untrustworthiness; Todorov, Baron, & Oosterhof, 2008). Faces depicting anger are detected faster than other expressions, and again implicate the amygdala (Green & Phillips, 2004). Amygdala lesions damage the ability to detect social threat from faces (Adolphs, Tranel, & Damasio, 1998; Amaral, 2002). Systems involving the amygdala selectively respond to threatening faces, in effect assigning a certain aspect of emotional value (Frith, 2007). All this fits the role of the amygdala in alerting to emotionally important stimuli. Judging another person’s predispositions also implicates the MPFC (as noted: e.g., Harris, Todorov, & Fiske, 2005; Mitchell, Cloutier, Banaji, & Macrae, 2006; see Figure 51.6). A variety of work on intentionality implicates the precuneus, temporoparietal junction (TPJ), and the paraACC (e.g., Ciaramidaro et al., 2007; Saxe & Kanwisher, 2003; Saxe & Powell, 2006; Saxe & Wexler, 2005). Imputing social intentions appears to differ from imputing private, nonsocial intentions (Walter et al., 2004). And the false-belief paradigms so prevalent in theory-ofmind studies activate different systems (e.g., pACC, precuneus, TPJ) than do interacting animated shapes (pSTS, frontal orperculum, inferior parietal lobule; Gobbini, Koralek, Bryan, Montgomery, & Haxby, 2007). The STS seems particularly involved in biological motion and gaze shifts (e.g., Pelphrey, Morris, & McCarthy, 2004), which might be subsumed under the notion of human trajectory. So at least three systems need to be sorted out, centered respectively on MPFC, TPJ, and STS (cf. Frith, 2007). As research accumulates, clarity and nuance will doubtless emerge. Meanwhile, promising avenues open up for exploring the universal human need to understand other people’s intentions. Cultural similarities in some systems (vMPFC and precuneus) can contrast with differences in other systems (TPJ and inferior frontal gyrus) (Kobayashi, Glover, & Temple, 2007). People infer intent from others’ structural position as cooperator or competitor, another apparent cultural universal (Caprariello, Cuddy, & Fiske, 2008; Cuddy et al.,

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Figures 51.6 ( Figure C.48 in color section) Some functional divisions of the medial prefrontal cortex (mPFC). Note: According to meta-analyses, more cognitive tasks such as actionmonitoring often implicate the posterior rostral MFC (also called dorsal MPFC). More potentially emotional tasks such as emotion-ratings, self-knowledge, person perception, and mentalizing (imaging another’s mind) often implicate the more anterior rostral MFC (also sometimes called dorsal MPFC, but a relatively more ventral location). Finally, the most orbital MFC (oMPFC) is implicated in outcome-monitoring. Figure 51.6A labels these areas and 51.6B /C.32 (See insert for color figure) locates the specific studies. For purposes of these diagrams, per the authors, the MPFC includes the ACC (anterior cingulate cortex). From “Meeting of Minds: The Medial Frontal Cortex and Social Cognition,” by D. M. Amodio and C. D. Frith, 2006, Nature Reviews Neuroscience, 7, pp. 268-277. Reprinted with permission.

2009; S. T. Fiske, Cuddy, Glick, & Xu, 2002; Russell & Fiske, 2008). Arguably, people’s neurocognitive systems evolved to detect other people’s intents toward social exchange (Cosmides & Tooby, 1989; Cosmides, Tooby, Fiddick, & Bryant, 2005). People find another ’s cooperative intentions rewarding, as indicated by both self-reports and neural systems activated, and especially for human cooperators (Rilling et al., 2002; Rilling, Sanfey, Aronson, Nystrom, & Cohen, 2004; Walter, Abler, Ciaramidaro, & Erk, 2005). Again, cultural universals in preferences for cooperative versus competitive interaction partners might show similar neural signatures, with cultural variations.

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Having inferred another’s intent for good or ill, survival dictates inferring the other’s ability to enact that intent, and accordingly, competence is the second apparently universal dimension of social perception (S. T. Fiske et al., 2007; Judd, James-Hawkins, Yzerbyt, & Kashima, 2005). Of these two universal dimensions, competence is a slightly slower, lower-priority judgment than warmth, but still incredibly fast (e.g., Hack, Goodwin, & Fiske, 2008; Willis & Todorov, 2006). Inferred competence correlates highly with the structural variable of perceived status, across the world (Cuddy et al., 2009). Evolutionary psychology often singles out status/dominance hierarchies as basic to interpersonal interaction (e.g., Buss & Kenrick, 1998), and perceived competence predicts consequential status-related outcomes, such as elections (Todorov, Mandisodza, Goren, & Hall, 2005). Hence, one might expect neurocognitive systems for detecting competence/status. They differ in one being a trait (competence) and the other being a social structural relationship (status), so it is not surprising that they also differ in the relevant perceptual cues. Perceived competence, inferred from faces, correlates with facial maturity (Zebrowitz & Montepare, 2005). Perceived status correlates with size, height, numerosity, and precedence (Chiao, Bordeaux, & Ambady, 2004; A. P. Fiske, 2004). A related dimension, perceived dominance, correlates with direct eye gaze, among other nonverbal cues (Hall, Coats, & LeBeau, 2005). Direct eye gaze facilitates person perception, that is, both categorization and category-based associations (Macrae, Hood, Milne, Rowe, & Mason, 2002; Mason, Hood, & Macrae, 2004). And direct versus averted gaze interacts with neural activation patterns to angry versus fearful faces (Adams, Gordon, Baird, Ambady, & Kleck, 2003). This matters to dominance because anger is an emotion of the powerful (Tiedens, Ellsworh, & Mesquita, 2000), whereas fear is an emotion of the subordinate. Consistent with this direct-gaze-anger-dominance configuration is work showing that anger is an approach emotion (Harmon-Jones & Allen, 1998; Harmon-Jones & Sigelman, 2001) because power broadly activates the behavioral approach system (BAS; Guinote, 2007; Keltner, Gruenfeld, & Anderson, 2003). Altogether, then, a variety of results suggest a dominance configuration that implicates anger, BAS, and the neural concomitants associated with left frontal activity (Harmon-Jones & Sigelman, 2001; Putman, Hermans, & van Honk, 2004). In contrast, fear and anxiety, subordinate emotions, implicate the behavioral inhibition system (BIS; Keltner

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et al., 2003) with fallout for executive control (Putman et al., 2004). Low-status also interferes with executive control (Inzlicht, McKay, & Aronson, 2006) and correlates with reduced pACC volume (Gianaros et al., 2007). Fear and vigilance also reliably involve the amygdala (Phelps, 2006). One might also predict activation in brain areas implicated in vigilance (amygdala) because people attend to highpower others (S. T. Fiske, 1993), as well as medial prefrontal cortex because people want to know the intentions of high-power others (S. T. Fiske, 1993), and possibly anterior cingulate cortex because successful outgroup members present a cognitive conflict (Botvinick et al., 2004). In general, the cultural universal here is likely to be BIS/ BAS and some of their affective and neural correlates. Fear is socially learned (Olsson, Nearing, & Phelps, 2007), and as noted, some cultural settings might exaggerate the BIS, orienting toward deference, whereas others exaggerate the BAS, orienting toward dominance. Neurotransmitters such as serotonin (lower for low-status) and dopamine are likely to be implicated in status systems as well (Moskowitz, Pinard, Zuroff, Annable, & Young, 2001; S. B. Park et al., 1994; Raleigh, Mcguire, Brammer, & Yuwiler, 1984). Even cultural universals, such as amygdala-fear linkages, are likely to have some cultural specificity (Chiao et al., 2006). Europeans respond to fearful faces with more activation in posterior cingulate, supplementary motor cortex, and amygdala, which could be interpreted as more direct, emotional way, whereas Japanese responded with more activation in right inferior frontal, premotor cortex, and left insula, which could be interpreted as more indirect, template matching way (Moriguchi et al., 2005). Another likely cultural universal would be thinking more abstractly when in power (Smith & Trope, 2006; Smith, Wigboldus, & Dijksterhuis, 2008).

CHALLENGES TO CULTURAL NEUROSCIENCE Cultural neuroscience presents both huge opportunities and huge challenges. There is a reason cultural neuroscience is almost an oxymoron. A number of opportunities appear in prior sections, and we must collectively pursue them. They provide a unique opportunity to observe the interplay between brains and environments. Cultural similarities and differences in neural system responses offer an exciting chance to explore putative universals and cultural adaptations. How to do cultural neuroscience is another question. Short of toting magnets (or less bulky luggage such as EEG caps) about the world, we can coordinate across cultures, as we do in many cultural studies, working with existing

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research groups who use similar equipment and methodologies across cultures. In social cultural neuroscience, the same problems arise as in any cultural psychology study: translation and cultural fit of stimulus materials and self-report measures, controlling for culture/method confounds, experimenter differences, and so on (for a review, see Kitayama & Cohen, 2007). Nevertheless, neuroscience equipment can add a layer of bias, due to the idiosyncrasies of particular machines and centers, which go beyond the issues for paper-and-pencil or even web studies. Cultural researchers can hold constant particular, inevitably idiosyncratic equipment and staff if they conduct all their research in one spot but compare participants who are native-born residents versus those newly arrived from elsewhere. Of course, self-selection contaminates the subset of any given culture who choose to travel or emigrate. A promising avenue for cultural research is priming dimensions in either monocultural individuals or more often, bicultural individuals (Oyserman & Lee, 2008). Effect sizes for priming individualism and collectivism are moderate for relationality and cognition, suggesting that this is a viable route that complements the other methods. This exciting area of research is wide open for enterprising exploration. Cultural comparisons can inform our growing knowledge about brain functions, and neuroscience can inform our quest to better understand cultural universals and variations. Two words to the wise: “cultural neuroscience.”

REFERENCES Adams, R. B., Jr., Gordon, H. L., Baird, A. A., Ambady, N., & Kleck, R. E. (2003, June 6). Effects of gaze on amygdala sensitivity to anger and fear faces. Science, 300, 1536. Adolphs, R., Tranel, D., & Damasio, A. R. (1998, June 4). The human amygdala in social judgment. Nature, 393, 470–474. Amaral, D. G. (2002). The primate amygdala and the neurobiology of social behavior: Implications for understanding social anxiety. Biological Psychiatry, 51, 11–17. Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: The medial frontal cortex and social cognition. Nature Reviews Neuroscience, 7, 268–277. Amodio, D. M., Harmon-Jones, E., & Devine, P. G. (2003). Individual differences in the activation and control of affective race bias as assessed by startle eyeblink response and self-report. Journal of Personality and Social Psychology, 84, 738–753. Amodio, D. M., Harmon-Jones, E., Devine, P. G., Curtin, J. J., Hartley, S. L., & Covert, A. E. (2004). Neural signals for the detection of unintentional race bias. Psychological Science, 15, 88–93. Anderson, M. C., Ochsner, K. N., Kuhl, B., Cooper, J., Robertson, E., Gabrieli, S. W., et al. (2004, January 9). Neural systems underlying the suppression of unwanted memories. Science, 303, 232–235. Aslin, R. N. (1981). Experiential influences and sensitive periods in perceptual development: A unified model. In R. N. Aslin, J. R.

8/17/09 5:28:22 PM

References 997 Alberts, & M. R. Petersen (Series Eds.), Development of perception: Psychobiological perspectives: The visual system (Vol. 2). New York: Academic Press. Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117, 497–529. Beer, J. S. (2007). The default self: Feeling good or being right? Trends in Cognitive Sciences, 11, 187–189. Beer, J. S., Heerey, E. A., Keltner, D., Scabini, D., & Knight, R. T. (2003). The regulatory function of self-conscious emotion: Insights from patients with orbito-frontal damage. Journal of Personality and Social Psychology, 85, 594–604. Beer, J. S., John, O. P., Scabini, D., & Knight, R. T. (2006). Orbito-frontal cortex and social behavior: Integrating self-monitoring and emotioncognition interactions. Journal of Cognitive Neuroscience, 18, 871–879. Beer, J. S., Knight, R. T., & D’Esposito, M. (2006). Controlling the integration of emotion and cognition: The role of frontal cortex in distinguishing helpful from hurtful emotional information. Psychological Science, 17, 448–453. Bennett, C. M., & Baird, A. A. (2006). Anatomical changes in the merging adult brain: A voxel-based morphometry study. Human Brain Mapping, 27, 766–777. Blascovich, J., Mendes, W. B., Hunter, S. B., Lickel, B., & Kowai-Bell, N. (2001). Perceiver threat in social interactions with stigmatized others. Journal of Personality and Social Psychology, 80, 253–267. Bolger, D. J., Perfetti, C. A., & Schneider, W. (2005). Cross-cultural effect on the brain revisited: Universal structures plus writing system variation. Human Brain Mapping, 25, 92–104. Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: An update. Trends in Cognitive Sciences, 8, 539–546. Bunge, S. A., Ochsner, K. N., Desmond, J. E., Glover, G. H., & Gabrieli, J. D. E. (2001). Prefrontal regions involved in keeping information in and out of mind. Brain: A Journal of Neurology, 124, 2074–2186. Buss, D. M., & Kenrick, D. T. (1998). Evolutionary social psychology. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), Handbook of social psychology (4th ed., Vol. 2, pp. 982–1026). New York: McGraw-Hill. Cacioppo, J. T., & Berntson, G. G. (1999). The affect system: Architecture and operating characteristics. Current Directions in Psychological Science, 8, 133–137. Cacioppo, J. T., Hawkley, L. C., Ernst, J. M., Burleson, M., Berntson, G. G., Nouriani, B., et al. (2006). Loneliness within a nomological net: An evolutionary perspective. Journal of Research in Personality, 40, 1054–1085. Caprariello, P. A., Cuddy, J. C., & Fiske, S. T. (2008). Social structure shapes cultural stereotypes and emotions: A causal test of the stereotype content model. Manuscript under review. Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS Scales. Journal of Personality and Social Psychology, 67, 319–333. Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., & Craig, I. W., et al. (2002, August 2). Role of genotype in the cycle of violence in maltreated children. Science, 297, 851–854. Chee, M. W. L., Goh, J. O. S., Venkatraman, V., Tan, J. C., Gutchess, A., Sutton, B., et al. (2006). Age-related changes in object processing and contextual binding revealed using fMR adaptation. Journal of Cognitive Neuroscience, 18, 495–507. Cheour, M., Ceponiene, R., Lehtokoski, A., Luuk, A., Allik, J., Ahlo, K., et al. (1998). Development of language-specific phoneme representations in the infant brain. Nature Neuroscience, 1, 351–353. Chiao, J. Y., & Ambady, N. (2007). Cultural neuroscience: Parsing universality and diversity across levels of analysis. In S. Kitayama &

c51.indd 997

D. Cohen (Eds.), Handbook of cultural psychology (pp. 237–254). New York: Guilford Press. Chiao, J. Y., Bordeaux, A. R., & Ambady, N. (2004). Mental representations of social status. Cognition, 93, B49–B57. Chiao, J. Y., Iidaka, T., Gordon, H. L., Nogawa, J., Bar, M., Aminoff, E., et al. (2006). Cultural specificity in amygdala response to fear faces. Poster presented at the Cognitive Neuroscience Society meeting, San Francisco. Chua, H. F., Boland, J. E., & Nisbett, R. E. (2005). Cultural variation in eye movements during scene perception. Proceedings of the National Academy of Sciences, USA, 102, 12629–12633. Ciaramidaro, A., Adenzato, M., Enrici, I., Erk, S., Pia, L., Bara, B. G., et al. (2007). The intentional network: How the brain reads a variety of intentions. Neuropsychologia, 45, 3105–3113. Cloutier, J., Mason, M. F., & Macrae, C. N. (2005). The perceptual determinants of person construal: Reopening the social-cognitive toolbox. Journal of Personality and Social Psychology, 88, 885–894. Cohen, D., Vandello, J., Puente, S., & Rantilla, A. (1999). “When you call me that, smile!” How norms for politeness, interaction styles, and aggression work together in Southern culture. Social Psychology Quarterly, 62, 257–275. Correll, J., Urland, G. R., & Ito, T. A. (2006). Event-related potentials and the decision to shoot: The role of threat perception and cognitive control. Journal of Experimental Social Psychology, 42, 120–128. Cosmides, L., & Tooby, J. (1989). Evolutionary psychology and the generation of culture: Pt. II. Case study: A computational theory of social exchange. Ethology and Sociobiology, 10, 51–97. Cosmides, L., Tooby, J., Fiddick, L., & Bryant, G. A. (2005). Detecting cheaters: Comment. Trends in Cognitive Sciences, 9, 505–506. Cuddy, A. J. C., Fiske, S. T., Kwan, V. S. Y., Glick, P., Demoulin, S., Leyens, J.-Ph., et al. (2009). Is the stereotype content model culturebound? A cross-cultural comparison reveals systematic similarities and differences. British Journal of Social Psychology. Cunningham, W. A., Johnson, M. K., Gatenby, J. C., Gore, J. C., & Banaji, M. R. (2003). Neural components of social evaluation. Journal of Personality and Social Psychology, 85, 639–649. Cunningham, W. A., Johnson, M. K., Raye, C. L., Gatenby, J. C., Gore, J. C., & Banaji, M. R. (2004). Separable neural components in the processing of black and white faces. Psychological Science, 15, 806–813. Cunningham, W. A., Raye, C. L., & Johnson, M. K. (2004). Implicit and explicit evaluation: FMRI correlates of valence, emotional intensity, and control in the processing of attitudes. Journal of Cognitive Neuroscience, 16, 1717–1729. D’Argembeau, A., Collette, F., Van der Linden, M., Laureys, S., Del Fiore, G., Degueldre, C., et al. (2005). Self-referential reflective activity and its relationship with rest: A PET study. NeuroImage, 25, 616–624. D’Argembeau, A., Ruby, P., Collette, F., Degueldre, C., Balteau, E., Luxen, A., et al. (2007). Distinct regions of the medial prefrontal cortex are associated with self-referential processing and perspective taking. Journal of Cognitive Neuroscience, 19, 935–944. Davidoff, J. (2001). Language and perceptual categorisation. Trends in Cognitive Sciences, 5, 382–387. Donald, M. (1991). Origins of the modern mind: Three states in the evolution of culture and cognition. Cambridge, MA: Harvard University Press. Dunbar, R. (1993). Co-evolution of neocortical size, group size, and language in humans. Behavioral and Brain Sciences, 16, 681–735. Dunbar, R. (2001). Brains on two legs: Group size and the evolution of intelligence. In F. B. M. de Waal (Ed.), Tree of origin: What primate behavior can tell us about human social evolution (pp. 175–191). Cambridge, MA: Harvard University Press. Eberhardt, J. L. (2005). Imaging race. American Psychologist, 60, 181–190.

8/17/09 5:28:23 PM

998

Cultural Processes

Eberhardt, J. L., Dasgupta, N., & Banaszynski, T. L. (2003). Believing is seeing: The effects of racial labels and implicit beliefs on face perception. Personality and Social Psychology Bulletin, 29, 360–370. Eberhardt, J. L., Davies, P. G., Purdie-Vaughns, V. J., & Johnson, S. L. (2006). Looking deathworthy: Perceived stereotypicality of black defendants predicts capital-sentencing outcomes. Psychological Science, 17, 383–386. Eberhardt, J. L., Goff, P. A., Purdie, V. J., & Davies, P. G. (2004). Seeing black: Race, crime, and visual processing. Journal of Personality and Social Psychology, 87, 876–893. Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003, October 10). Does rejection hurt? An fMRI study of social exclusion. Science, 302, 290–292.

covaries with perceived social standing. Social Cognitive and Affective Neuroscience, 2, 161–173. Gobbini, M. I., Koralek, A. C., Bryan, R. E., Montgomery, K. J., & Haxby, J. V. (2007). Two takes on the social brain: A comparison of theory of mind tasks. Journal of Cognitive Neuroscience, 19, 1803–1814. Golby, A. J., Gabrieli, J. D. E., Chiao, J. Y., & Eberhardt, J. L. (2001). Differential responses in the fusiform region to same-race and otherrace faces. Nature Neuroscience, 4, 845–850. Green, M. J., & Phillips, M. L. (2004). Social threat perception and the evolution of paranoia. Neuroscience and Biobehavioral Reviews, 28, 333–342. Guinote, A. (2007). Power and goal pursuit. Personality and Social Psychology Bulletin, 33, 1076–1087.

Eisenberger, N. I., Way, B. M., Taylor, S. E., Welch, W. T., & Lieberman, M. D. (2007). Understanding genetic risk for aggression: Clues from the brain’s response to social exclusion. Biological Psychiatry, 61, 1100–1108.

Gusnard, D. A., Akbudak, E., Shulman, G. L., & Raichle, M. E. (2001). Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function. Proceedings of the National Academy of Sciences, USA, 98, 4259–4264.

Elbert, T., Pantev, C., Weinbruch, C., Rockstroh, B., & Taub, E. (1995, October 13). Increased cortical representation of the fingers of the left hand in string players. Science, 270, 305–307.

Gutchess, A. H., Welsh, R. C., Boduroglu, A., & Park, D. C. (2006). Cultural differences in neural function associated with object processing. Cognitive, Affective and Behavioral Neuroscience, 6, 102–109.

Engell, A. D., Haxby, J. V., & Todorov, A. (2007). Implicit trustworthiness decisions: Automatic coding of face properties in the human amygdala. Journal of Cognitive Neuroscience, 19, 1508–1519.

Haas, B. W., Omura, K., Constable, R. T., & Canli, T. (2007). Is automatic emotion regulation associated with agreeableness?: A perspective using a social neuroscience approach. Psychological Science, 18, 130–132.

Fiske, A. P. (2002). Complementarity theory: Why human social capacities evolved to require cultural complements. Personality and Social Psychology Review, 4, 76–94.

Hack, T. E., Goodwin, S. A., & Fiske, S. T. (2008). Warmth trumps competence in social evaluations. Manuscript under review.

Fiske, A. P. (2004). Four modes of constituting relationships: Consubstantial assimilation; space, magnitude, time and force; concrete procedures; abstract symbolism. In N. Haslam (Ed.), Relational models theory: A contemporary overview (pp. 61–146). Mahwah, NJ: Erlbaum. Fiske, S. T. (1993). Controlling other people: The impact of power on stereotyping. American Psychologist, 48, 621–628. Fiske, S. T. (1998). Stereotyping, prejudice, and discrimination. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), Handbook of social psychology (4th ed., Vol. 2, pp. 357–411). New York: McGraw-Hill. Fiske, S. T. (2004). Social beings. Hoboken, NJ: Wiley. Fiske, S. T., Cuddy, A. J. C., & Glick, P. (2007). Universal dimensions of social perception: Warmth and competence. Trends in Cognitive Sciences, 11, 77–83. Fiske, S. T., Cuddy, A. J., Glick, P., & Xu, J. (2002). A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition. Journal of Personality and Social Psychology, 82, 878–902. Fiske, S. T., Harris, L. T., & Cuddy, A. J. C. (2004, November 26). Why ordinary people torture enemy prisoners. Science, 306, 1482–1483. Fiske, S. T., & Taylor, S. E. (2008). Social cognition: From brains to culture. New York: McGraw-Hill. Fredrickson, G. M. (2002). Racism: A short history. Princeton, NJ: Princeton University Press. Frith, C. D. (2007). The social brain? Philosophical Transactions of the Royal Society, B: Biological Sciences, 362, 671–678. Gauthier, I., Skudlarski, P., Gore, J. C., & Anderson, A. W. (2000). Expertise for cars and birds recruits brain areas involved in face recognition. Nature Neuroscience, 3, 191–197. Geary, D. C., Bow-Thomas, C. C., Fan, L., & Siegler, R. S. (1993). Even before formal instruction, Chinese children outperform American children in mental addition. Cognitive Development, 8, 517–529.

Hall, J. A., Coats, E. J., & LeBeau, L. S. (2005). Nonverbal behavior and the vertical dimension of social relations: A meta-analysis. Psychological Bulletin, 13, 898–924. Hamilton, R. H., & Pascual-Leone, A. (1998). Cortical plasticity associated with Braille learning. Trends in Cognitive Sciences, 2, 168–174. Harmon-Jones, E., & Allen, J. J. B. (1998). Anger and frontal brain activity: EEG asymmetry consistent with approach motivation despite negative affective valence. Journal of Personality and Social Psychology, 74, 1310–1316. Harmon-Jones, E., & Sigelman, J. (2001). State anger and prefrontal brain activity: Evidence that insult-related relative left-prefrontal activation is associated with experienced anger and aggression. Journal of Personality and Social Psychology, 80, 797–803. Harris, L. T., & Fiske, S. T. (2006). Dehumanizing the lowest of the low: Neuro-imaging responses to extreme outgroups. Psychological Science, 17, 847–853. Harris, L. T., & Fiske, S. T. (2007). Social groups that elicit disgust are differentially processed in mPFC. Social Cognitive and Affective Neuroscience, 2, 45–51. Harris, L. T., & Fiske, S. T. (2008). Dehumanized perception: Less mentalizing and more avoidance of disgusting people. Manuscript under review. Harris, L. T., McClure, S. M., van den Bos, W., Cohen, J. D., & Fiske, S. T. (2007). Regions of mPFC differentially tuned to affective and social stimuli. Cognitive and Behavioral Neuroscience. Harris, L. T., Todorov, A., & Fiske, S. T. (2005). Attributions on the brain: Neuro-imaging dispositional inferences, beyond theory of mind. NeuroImage, 28, 763–769. Hart, A. J., Whalen, P. J., Shin, L. M., McInerney, S. C., Fischer, H., & Rauch, S. L. (2000). Differential response in the human amygdala to racial outgroup vs ingroup face stimuli. NeuroReport, 11, 2351–2355.

Gelman, R., & Butterworth, B. (2005). Number and language: How are they related? Trends in Cognitive Sciences, 9, 6–10.

Hatano, G., Amaiwa, S., & Shimizu, K. (1987). Formation of a mental abacus for computation and its use as a memory device for digits: A developmental study. Developmental Psychology, 23, 832–839.

Gianaros, P. J., Horenstein, J. A., Cohen, S., Matthews, K. A., Brown, S. M., Flory, J. D., et al. (2007). Perigenual anterior cingulate morphology

Hatano, G., Miyake, Y., & Binks, M. G. (1977). Performance of expert abacus operators. Cognition, 15, 95–110.

c51.indd 998

8/17/09 5:28:23 PM

References 999 Hawkley, L. C., Burleson, M. H., Berntson, G. G., & Cacioppo, J. T. (2003). Loneliness in everyday life: Cardiovascular activity, psychosocial context, and health behaviors. Journal of Personality and Social Psychology, 85, 105–120. Heatherton, T. F., Macrae, C. N., & Kelley, W. M. (2004). What the social brain sciences can tell us about the self. Current Directions in Psychological Science, 13, 190–193. Heatherton, T. F., Wyland, C. L., Macrae, C. N., Demos, K. E., Denny, B. T., & Kelley, W. M. (2006). Medial prefrontal activity differentiates self from close others. Social Cognitive and Affective Neuroscience, 1, 18–25. Heine, S. J., Lehman, D. R., Markus, H. R., & Kitayama, S. (1999). Is there a universal need for positive self-regard? Psychological Review, 106, 766–794. Iacoboni, M., Lieberman, M. D., Knowlton, B. J., Molnar-Szakacs, I., Moritz, M., Throop, C. J., et al. (2004). Watching social interactions produces dorsomedial prefrontal and medial parietal BOLD fMRI signal increases compared to a resting baseline. NeuroImage, 21, 1167–1173. Inzlicht, M., McKay, L., & Aronson, J. (2006). Stigma as ego depletion: How being the target of prejudice affects self-control. Psychological Science, 17, 262–269. Ito, T. A., Thompson, E., & Cacioppo, J. T. (2004). Tracking the timecourse of social perception: The effects of racial cues on event-related brain potentials. Personality and Social Psychology Bulletin, 30, 1267–1280. Ito, T. A., & Urland, G. R. (2003). Race and gender on the brain: Electrocortical measures of attention to the race and gender of multiply categorizable individuals. Journal of Personality and Social Psychology, 85, 616–626. Jones, J. M. (1997). Prejudice and racism (2nd ed.). New York: McGraw-Hill. Judd, C. M., James-Hawkins, L., Yzerbyt, V., & Kashima, Y. (2005). Fundamental dimensions of social judgment: Understanding the relations between judgments of competence and warmth. Journal of Personality and Social Psychology, 89, 899–913. Keedwell, P. A., Andrew, C., Williams, S. C. R., Brammer, M. J., & Phillips, M. L. (2005). A double dissociation of ventromedial prefrontal cortical responses to sad and happy stimuli in depressed and healthy individuals. Biological Psychiatry, 58, 495–503. Keltner, D., Gruenfeld, D. H., & Anderson, C. (2003). Power, approach, and inhibition. Psychological Review, 110, 265–284. Kim, K. H. S., Relkin, N. R., Lee, K. M., & Hirsch, J. (1997, July 10). Distinct cortical areas associated with native and second languages. Nature, 388, 171–174. Kitayama, S., & Cohen, D. (Eds.). (2007). Handbook of cultural psychology. New York: Guilford Press. Klinger, E. (1978). Modes of normal conscious flow. In K. S. Pope & J. L. Singer (Eds.), The stream of consciousness: Scientific investigations into the flow of human experience (pp. 225–258). New York: Plenum Press. Kobayashi, C., Glover, G. H., & Temple, E. (2007). Cultural and linguistic effects on neural bases of “theory of mind” in American and Japanese children. Brain Research, 1164, 95–107. Krendl, A. C., Macrae, C. N., Kelley, W. M., Fugelsang, J. A., & Heatherton, T. F. (2006). The good, the bad and the ugly: An fMRI investigation of the functional correlates of stigma. Social Neuroscience, 1, 5–15. Kuhl, P. K., Williams, K. A., Lacerda, F., Stevens, K. N., & Lindblom, B. (1992, January 31). Linguistic experience alters phonetic perception in infants by 6 months of age. Science, 255, 606–608. Lewis, P. A., Critchley, H. D., Rotshtein, P., & Dolan, R. J. (2007). Neural correlates of processing valence and arousal in affective words. Cerebral Cortex, 17, 742–748.

c51.indd 999

Li, S. (2003). Biocultural orchestration of developmental plasticity across levels: The interplay of biology and culture in shaping the mind and behavior across the life span. Psychological Bulletin, 129, 171–194. Lieberman, M. D., Hariri, A., Jarcho, J. M., Eisenberger, N. I., & Bookheimer, S. Y. (2005). An fMRI investigation of race-related amygdala activity in African-American and Caucasian-American individuals. Nature Neuroscience, 8, 720–722. Maass, A., Suitner, C., Favaretto, X., & Cignacchi, M. (2009). Groups in space: Stereotypes and the spatial agency bias. Journal of Experimental Psychology. Macrae, C. N., Hood, B. M., Milne, A. B., Rowe, A. C., & Mason, M. F. (2002). Are you looking at me? Eye gaze and person perception. Psychological Science, 13, 460–464. Macrae, C. N., Moran, J. M., Heatherton, T. F., Banfield, J. F., & Kelley, W. M. (2004). Medial prefrontal activity predicts memory of self. Cerebral Cortex, 14, 647–654. Macrae, C. N., Quinn, K. A., Mason, M. F., & Quadfleig, S. (2005). Understanding others: The face and person construal. Journal of Personality and Social Psychology, 89, 686–695. Malamuth, N. M., & Addison, T. (2001). Integrating social psychological research on aggression within an evolutionary-based framework. In G. J. O. Fletcher & M. S. Clark (Eds.), Blackwell handbook of social psychology: Interpersonal processes (pp. 129–161). Malden, MA: Blackwell. Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S. J., et al. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences, USA, 97, 4398–4403. Markus, H. R., & Kitayama, S. (1991). Culture and the self: Implications for cognition, emotion, and motivation. Psychological Review, 98, 224–253. Mason, M. F., Hood, B. M., & Macrae, C. N. (2004). Look into my eyes: Gaze direction and person memory. Memory, 12, 637–643. Mason, M. F., & Macrae, C. N. (2004). Categorizing and individuating others: The neural substrates of person perception. Journal of Cognitive Neuroscience, 16, 1785–1795. Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C. N. (2007, January 19). Wandering minds: The default network and stimulus-independent thought. Science, 315, 393–395. Massey, D. S. (2005). Strangers in a strange land: Humans in an urbanizing world. New York: Norton. Mays, V. M., Cochran, S. D., & Barnes, N. W. (2007). Race, race-based discrimination, and health outcomes among African Americans. Annual Review of Psychology, 58, 201–225. Mendes, W. B., Blascovich, J., Lickel, B., & Hunter, S. (2002). Challenge and threat during social interaction with White and Black men. Personality and Social Psychology Bulletin, 28, 939–952. Mesquita, B., & Frijda, N. H. (1992). Cultural variations in emotions: A review. Psychological Bulletin, 112, 179–204. Miller, K. F., Smith, C. M., Zhu, J., & Zhang, H. (1995). Preschool origins of cross-national differences in mathematical competence: The role of number-naming systems. Psychological Science, 6, 56–60. Mitchell, J. P., Banaji, M. R., & Macrae, C. N. (2005). The link between social cognition and self-referential thought in the medial frontal cortex. Journal of Cognitive Neuroscience, 17, 1306–1315. Mitchell, J. P., Cloutier, J., Banaji, M. R., & Macrae, C. N. (2006). Medial prefrontal dissociations during processing of trait diagnostic and nondiagnostic person information. Social Cognitive and Affective Neuroscience, 1, 49–55. Mitchell, J. P., Heatherton, T. F., Kelley, W. M., Wyland, C. L., Wegner, D. M., & Macrae, C. N. (2007). Separating sustained from transient aspects of cognitive control during thought suppression. Psychological Science, 18, 292–297.

8/17/09 5:28:24 PM

1000

Cultural Processes

Mitchell, J. P., Macrae, C. N., & Banaji, M. R. (2004). Encoding-specific effects of social cognition on the neural correlates of subsequent memory. Journal of Neuroscience, 24, 4912–4917. Mitchell, J. P., Macrae, C. N., & Banaji, M. R. (2006). Dissociable medial prefrontal contributions to judgments of similar and dissimilar others. Neuron, 50, 655–663. Moriguchi, Y., Ohnishi, T., Kawachi, T., Mori, T., Hirakata, M., Yamada, M., et al. (2005). Specific brain activation in Japanese and Caucasian people to fearful faces. NeuroReport, 16, 133–136.

activity in the superior temporal sulcus during social perception. Journal of Cognitive Neuroscience, 16, 1706–1716. Pennebaker, J. W., Rimé, B., & Blankenship, V. E. (1996). Stereotypes of emotional expressiveness of Northerners and Southerners: A crosscultural test of Montesquieu’s hypotheses. Journal of Personality and Social Psychology, 70, 372–380. Phelps, E. A. (2006). Emotion and cognition: Insights from studies of the human amygdala. Annual Review of Psychology, 57, 27–53.

Moskowitz, D. S., Pinard, G., Zuroff, D. C., Annable, L., & Young, S. N. (2001). The effect of tryptophan on social interaction in everyday life: A placebo-controlled study. Neuropsychopharmacology, 25, 277–289.

Phelps, E. A., O’Connor, K. J., Cunningham, W. A., Funayama, E. S., Gatenby, J. C., Gore, J. C., et al. (2000). Performance on indirect measures of race evaluation predicts amygdala activation. Journal of Cognitive Neuroscience, 12, 729–738.

Näätänen, R., Lehtokoski, A., Lennes, M., Cheour, M., Huotilainen, M., Iivonen, A., et al. (1997, January 30). Language-specific phoneme representations revealed by electric and magnetic brain responses. Nature, 385, 432–434.

Polk, T. A., & Farah, M. J. (1998). The neural development and organization of letter recognition: Evidence from functional neuroimaging, computational modeling and behavioral studies. Proceedings of National Academy of Sciences, USA, 95, 847–852.

Nelson, R. J., & Trainor, B. C. (2007). Neural mechanisms of aggression. Nature Reviews Neuroscience, 8, 536–546.

Posner, M. I., & Rothbart, M. K. (2007). Research on attention networks as a model for the integration of psychological science. Annual Review of Psychology, 58, 1–23.

Neville, H. J., & Bavelier, D. (1998). Neural organization and plasticity of language. Current Opinion in Neurobiology, 8, 254–258. Neville, H. J., Bavelier, D., Corina, D., Rauschecker, J., Karni, A., Lalwani, A., et al. (1998). Cerebral organization for language in deaf and hearing subjects: Biological constraints and effects of experience. Proceedings of the National Academy of Sciences, USA, 95, 922–929. Nisbett, R. E. (2003). The geography of thought: How Asians and Westerners think differently . . . and why. London: Nicholas Brealey. Nisbett, R. E., & Cohen, D. (1996). Culture of honor: The psychology of violence in the south. Boulder, CO: Westview Press. Nisbett, R. E., & Masuda, T. (2003). Culture and point of view. Proceedings of the National Academy of Sciences, USA, 100, 11163–11170. Nisbett, R. E., & Miyamoto, Y. (2005). The influence of culture: Holistic versus analytic perception. Trends in Cognitive Sciences, 9, 467–473. Northoff, G., & Bermpohl, F. (2004). Cortical midline structures and the self. Trends in Cognitive Sciences, 8, 102–107. Northoff, G., Heinzel, A., de Greck, M., Bermpohl, F., Dobrowolny, H., & Panksepp, J. (2006). Self-referential processing in our brain: A metaanalysis of imaging studies on the self. NeuroImage, 31, 440–457. Ochsner, K. N., Beer, J. S., Robertson, E. R., Cooper, J. C., Gabrieli, J. D. E., Kihlstrom, J. F., et al. (2005). The neural correlates of direct and reflected self-knowledge. NeuroImage, 28, 797–814. Ochsner, K. N., Knierim, K., Ludlow, D. H., Hanelin, J., Ramachandran, T., Glover, G., et al. (2004). Reflecting upon feelings: An fMRI study of neural systems supporting the attribution of emotion to self and other. Journal of Cognitive Neuroscience, 16, 1746–1772. Olsson, A., Nearing, K. I., & Phelps, E. A. (2007). Learning fears by observing others: The neural systems of social fear transmission. Social Cognitive and Affective Neuroscience, 2, 3–11. Oyserman, D., & Lee, S. W. S. (2008). Does culture influence what and how we think? Effects of priming individualism and collectivism. Psychological Bulletin. Park, D. C., & Gutchess, A. H. (2002). Aging, cognition, and culture: A neuroscientific perspective. Neuroscience and Biobehavioral Reviews, 26, 859–867. Park, S. B., Coull, J. T., McShane, R. H., Young, A. H., Sahakian, B. J., Robbins, T. W., et al. (1994). Tryptophan depletion in normal volunteers produces selective impairments in learning and memory. Neuropharmacology, 33, 575–588.

Putman, P., Hermans, E., & von Honk, J. (2004). Emotion Stroop performance for masked angry faces: It’s BAS, Not BIS. Emotion, 4, 305–311. Raleigh, M. J., McGuire, M. T., Brammer, G. L., & Yuwiler, A. (1984). Social and environmental influences on blood serotonin concentrations in monkeys. Archives of General Psychiatry, 41, 405–410. Richeson, J. A., Baird, A. A., Gordon, H. L., Heatherton, T. F., Wyland, C. L., Trawalter, S., et al. (2003). An fMRI investigation of the impact of interracial contact on executive function. Nature Neuroscience, 6, 1323–1328. Richeson, J. A., & Shelton, J. N. (2003). When prejudice does not pay: Effects of interracial contact on executive function. Psychological Science, 14, 287–290. Richeson, J. A., & Trawalter, S. (2005a). On the categorization of admired and disliked exemplars of admired and disliked racial groups. Journal of Personality and Social Psychology, 89, 517–530. Richeson, J. A., & Trawalter, S. (2005b). Why do interracial interactions impair executive function? A resource-depletion account. Journal of Personality and Social Psychology, 88, 934–947. Richeson, J. A., Trawalter, S., & Shelton, J. N. (2005). African Americans’ implicit racial attitudes and the depletion of executive function after interracial internactions. Social Cognition, 23, 336–352. Rilling, J., Gutman, D., Zeh, T., Pagnoni, G., Berns, G., & Kilts, C. (2002). A neural basis for social cooperation. Neuron, 18, 395–405. Rilling, J., Sanfey, A. G., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2004). The neural correlates of theory of mind within interpersonal interactions. NeuroImage, 22, 1694–1703. Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169–192. Roberts, N. A., Beer, J. S., Werner, K. H., Scabini, D., Levens, S. M., Knight, R. T., et al. (2004). The impact of orbital prefrontal cortex damage on emotional activation to unanticipated and anticipated acoustic startle stimuli. Cognitive, Affective, and Behavioral Neuroscience, 4, 307–316. Russell, A. M., & Fiske, S. T. (2008). It’s all relative: Social position and interpersonal perception. Manuscript under review. Saxe, R., & Kanwisher, N. (2003). People thinking about thinking people. The role of the temporo-parietal junction in “theory of mind.” Neuroimage, 19, 1835–1842.

Paulesu, E., Demonet, J. F., Fazio, F., McCrory, E., & Chanoine, V., et al. (2001, March 16). Dyslexia: Cultural diversity and biological unity. Science, 291, 2064–2065.

Saxe, R., & Powell, L. J. (2006). It’s the thought that counts: Specific brain regions for one component of theory of mind. Psychological Science, 17, 692–699.

Pelphrey, K. A., Morris, J. P., & McCarthy, G. (2004). Grasping the intentions of others: The perceived intentionality of an action influences

Saxe, R., & Wexler, A. (2005). Making sense of another mind: The role of the right temporo-parietal junction. Neuropsychologia, 43, 1391–1399.

c51.indd 1000

8/17/09 5:28:24 PM

References 1001 Schlaggar, B. L., & McCandliss, B. D. (2007). Development of neural systems for reading. Annual Review of Neuroscience, 30, 475–503. Sears, D. O. (1998). Racism and politics in the United States. In J. L. Eberhardt & S. T. Fiske (Eds.), Confronting racism: The problem and the response (pp. 76–100). Thousand Oaks, CA: Sage. Shamay-Tsoory, S. G., Tibi-Elhanay, Y., & Aharon-Peretz, J. (2006). The ventromedial prefrontal cortex is involved in understanding affective but not cognitive theory of mind stories. Social Neuroscience, 1, 149–166. Sherry, D. F. (2006). Neuroecology. Annual Review of Psychology, 57, 167–197. Shih, J. C., Chen, K., & Ridd, M. J. (1999). Monoamine oxidase: From genes to behavior. Annual Review of Neuroscience, 22, 197–217. Siok, W. T., Perfetti, C. A., Zhen, J., & Tan, L. H. (2004, September 3). Biological abnormality of impaired reading is constrained by culture. Nature, 431, 71–76. Smith, P. K., & Trope, Y. (2006). You focus on the forest when you’re in charge of the trees: Power priming and abstract information processing. Journal of Personality and Social Psychology, 90, 578–596. Smith, P. K., Wigboldus, D. H. J., & Dijksterhuis, A. (2008). Abstract thinking increases one’s sense of power. Journal of Experimental Social Psychology, 44, 378–385. Stigler, J. W., Lee, S.-Y., & Stevenson, H. W. (1986). Digit memory in Chinese and English: Evidence for a temporally limited store. Cognition, 23, 1–20. Tan, L. H., Spinks, J. A., Eden, G. F., Perfetti, C. A., & Siok, W. T. (2005). Reading depends on writing, in Chinese. Proceedings of National Academy of Sciences, USA, 102, 8781–8785. Tang, Y., Zhang, W., Chen, K., Feng, S., Ji, Y., Shen, J., et al. (2006). Arithmetic processing in the brain shaped by cultures. Proceedings of National Academy of Sciences, USA, 103, 10775–10780. Taylor, S. E., & Brown, J. D. (1988). Illusion and well-being: A social psychological perspective on mental health. Psychological Bulletin, 103, 193–210. Taylor, S. E., Way, B. M., Welch, W. T., Hilmert, C. J., Lehman, B. J., & Eisenberger, N. I. (2006). Early family environment, current adversity, the serotonin transporter promoter polymorphism, and depressive symptomatology. Biological Psychiatry, 60, 671–676. Tiedens, L. Z., Ellsworth, P. C., & Mesquita, B. (2000). Stereotypes about sentiments and status: Emotional expectations for high- and low-status group members. Personality and Social Psychology Bulletin, 26, 560–574. Todorov, A., Baron, S. G., & Oosterhof, N. N. (2008). Evaluating face trustworthiness: A model based approach. Social Cognitive and Affective Neuroscience, 3, 119–127. Todorov, A., Gobbini, M. I., Evans, K. K., & Haxby, J. V. (2007). Spontaneous retrieval of affective person knowledge in face perception. Neuropsychologia, 45, 163–173.

c51.indd 1001

Todorov, A., Mandisodza, A. N., Goren, A., & Hall, C. C. (2005, June 10). Inferences of competence from faces predict election outcomes. Science, 308, 1623–1626. Todorov, A., & Pakrashi, M. (2008). Trait judgments from faces: Rapid, unreflective, and robust. Manuscript under review. Triandis, H. C. (1990). Cross-cultural studies of individualism and collectivism. In J. Berman (Ed.), Nebraska symposium on motivation (pp. 41–133). Lincoln: University of Nebraska Press. Uddin, L. O., Iacoboni, M., Lange, C., & Keenan, J. P. (2007). The self and social cognition: The role of cortical midline structures and mirror neurons. Trends in Cognitive Sciences, 11, 153–157. van den Bos, W., McClure, S. M., Harris, L. T., Fiske, S. T., & Cohen, J. D. (2007). Dissociating affective evaluation and social cognitive processes in ventral medial prefrontal cortex. Cognitive and Behavioral Neuroscience. Vandello, J. A., & Cohen, D. (1999). Patterns of individualism and collectivism across the United States. Journal of Personality and Social Psychology, 77, 279–292. Vanman, E. J., Paul, B. Y., Ito, T. A., & Miller, N. (1997). The modern face of prejudice and structural features that moderate the effect of cooperation on effect. Journal of Personality and Social Psychology, 73, 941–959. Walter, H., Abler, B., Ciaramidaro, A., & Erk, S. (2005). Motivating forces of human actions: Neuroimaging reward and social interaction. Brain Research Bulletin, 15, 368–381. Walter, H., Adenzato, M., Ciaramidaro, A., Enrici, I., Pia, L., & Bara, B. G. (2004). Understanding intention in social perception: The role of the anterior paracingulate cortex. Journal of Cognitive Neuroscience, 16, 1854–1863. Weber-Fox, C., & Neville, H. J. (1996). Maturational constraints on functional specializations for language processing: ERP and behavioral evidence in bilingual speakers. Journal of Cognitive Neuroscience, 8, 231–256. Wheeler, M. E., & Fiske, S. T. (2005). Controlling racial prejudice: Social cognitive goals affect amygdala and stereotype activation. Psychological Science, 16, 56–63. Whiten, A., & Byrne, R. W. (Eds.). (1997). Machiavellian intelligence: Pt. II. Extensions and evaluations. Cambridge, England: Cambridge University Press. Willadsen-Jensen, E. C., & Ito, T. A. (2006). Ambiguity and the timecourse of racial perception. Social Cognition, 24, 580–606. Willis, J., & Todorov, A. (2006). First impressions: Making up your mind after a 100-ms exposure to a face. Psychological Science, 17, 592–598. Wyland, C. L., Kelley, W. M., Macrae, C. N., Gordon, H. L., & Heatherton, T. F. (2003). Neural correlates of thought suppression. Neuropsychologia, 41, 1863–1867. Zebrowitz, L. A., & Montepare, J. M. (2005). Appearance does matter. Science, 308, 1565–1566. Zhu, Y., Zhang, L., Fan, J., & Han, S. (2007). Neural basis of cultural influence on self-representation. NeuroImage, 34, 1310–1316.

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Chapter 52

Autism DAVID G. AMARAL, JOHN L. R. RUBENSTEIN, AND SALLY J. ROGERS

Autism or autistic spectrum disorders (ASD) are a group of neurodevelopmental disorders with varying degrees of behavioral impairment. The cause(s) of autism are unknown though it is now generally believed that there are multiple forms of autism with multiple etiologies. Many in the field refer to autisms rather than autism. Diagnosis, which generally occurs prior to 3 years of age, is based on observing behavioral impairments in three categories: (1) social behavior, (2) verbal and nonverbal communication, and (3) stereotyped, repetitive behaviors and interests (American Psychiatric Association, 1994). The disorder ranges from the lower functioning end of the spectrum with comorbid mental retardation, to the higher functioning end of the spectrum with normal IQ, to Asperger syndrome with normal to high IQ and relatively normal language development. Because of this heterogeneity, it is perhaps preferable to refer to autism spectrum disorders rather than autism. There are comorbid conditions at all levels of the spectrum including epilepsy, anxiety, gastrointestinal problems, sleep disorders and the inability to modulate sensory input. Current estimates of prevalence are on the order of 1:150 children. Males are four times as likely to have an autistic spectrum disorder as females. Autism was first described in 1943 by Leo Kanner, a psychiatrist at Johns Hopkins University (Kanner, 1943). He identified common features of 11 children whom he treated over a 5-year period “whose condition differ[ed] so markedly and uniquely from anything reported so far” (p. 217). Kanner described a set of core features, the most important of which was “the children’s inability to relate themselves in an ordinary way to people and situations from the beginning of life” (p. 242). The parental phrases that he quoted: “happiest when left alone, acting as if people weren’t there, oblivious to everything around him” are common even now when clinicians interview parents during the diagnostic process. Kanner (1971) followed 9 of the 11 children into adulthood. While there was some improvement in all symptoms, the individuals all continued to have problems engaging in normal social relations. Only

two adults were eventually employed but both remained single and lived with their parents. Hans Asperger, an Austrian pediatrician, wrote a paper roughly at the same time as that of Kanner (Asperger, 1944), and the parallels of his independent observations were striking. Asperger also used the term “autistic” to refer to his patients and also emphasized their social impairments. Asperger emphasized that the syndrome was present from early in life but not progressive. He underscored the pervasive nature of the condition: “[I]t totally colours affect, intellect, will, and action” (p. 39). Unlike Kanner, Asperger noted that several of his patients demonstrated high intellectual ability in math and reading. Asperger also described circumscribed interests in particular objects or topic areas leading often to lengthy monologues about these special interests. This description has spawned the term little professors in many descriptions of Asperger syndrome. Currently, the term Asperger syndrome is typically reserved for those individuals who have normal or even superior intellectual abilities and little or no evidence of language impairment while retaining social impairments and limited interests and behaviors.

EPIDEMIOLOGY OF AUTISM The current rate of autism, particularly the perceived increase in the rate portrayed by advocates, has generated substantial attention both in the popular press and in scientific and educational agencies. Prevalence estimates have increased in the past 20 years by more than tenfold (Chakrabarti & Fombonne, 2005) from a widely accepted figure of 5 cases per 10,000 in the 1980s to current estimates of 1 in 150 (Kuehn, 2007) for autism spectrum disorders. Some have claimed that we are in the midst of an autism epidemic and have blamed environmental factors and current medical practices as the culprits. There is currently inadequate scientific evidence to determine the real magnitude of the increase in autism spectrum disorders. 1005

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1006 Autism

Some of the increased prevalence is due to both increased sensitivities in diagnosis (Fombonne, 2005; Newschaffer et al., 2007) and the widening of diagnostic criteria. While definitions of autism 20 years ago reflected the classic, severe form of the disorder, the current DSM-IV-TR classification system (American Psychiatric Association, 1994) now includes children who meet full criteria for Autistic Disorder (AD), as well as those who do not meet stringent criteria and are given the diagnosis of Asperger ’s Disorder or even the less specified diagnosis of Pervasive Developmental Disorder—Not Otherwise Specified (PDDNOS). Epidemiological studies generally find that the prevalence of children diagnosed with PDD-NOS is higher than those with classic autism (Baker, 2002; Chakrabarti & Fombonne, 2005; Yeargin-Allsopp et al., 2003). Thus, the group of persons now being counted in prevalence studies includes many who would not have been counted previously based on earlier diagnostic definitions of autism. A variety of environmental and biological causes have also been suggested to account for some of the increasing prevalence rates of autism spectrum disorders (Altevogt, Hanson, & Leshner, 2008). Environmental exposures, particularly mercury exposure through vaccinations, dental procedures, and environmental contaminants, increased use of fertility treatments, interactions between immune abnormalities in mother or child and exposure to immune challenges in the uterine or postuterine environments, air and environmental pollutants such as heavy metals and PCBs (Newschaffer et al., 2007) have increased significantly in the past decade. The science of understanding autism is reliant on a true appreciation of the real increased prevalence of autism. The prospective epidemiological studies needed to get a scientifically valid appreciation of increased incidence have not yet been completed. While autism has a substantial genetic component, there is also evidence for environmental contributions to the etiology of autism. If autism is truly undergoing a substantial increase in prevalence, this would tend to point to environmental factors as critically important (Bello, 2007).

DIAGNOSIS AND BEHAVIORAL FEATURES Core Behavioral Features of Autism Kanner described that his patients were oblivious to the social world; not unaware, but uninterested. They ignored speech, such as calling their names, to such an extent that some were considered to be deaf, though none had a hearing impairment. The children ignored their parents, the presence of strangers, and the presence of other children (Kanner, 1943). Kanner described what is now considered

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to be the most severe form of a continuum of impairment in reciprocal social relatedness. Subsequently, Wing (1981) has fractionated the social impairments into three main types: aloof (as Kanner described), passive (responsive to others’ interactions but not initiating interactions themselves), and active but odd (clearly interested in social interaction but unusual or inappropriate in the way they go about it). Children demonstrate social reciprocity in a variety of ways, including making eye contact, sharing emotional expressions, and executing typical social body postures and gestures. This capacity is present in human development in the first few months of life in gestures like raising arms up to be lifted and using facial expressions directed to others to communicate feelings (D. Stern, 1985; Trevarthen & Aitken, 2001). All these forms of social expression are affected in autism across age and spectrum of ability (Hobson & Lee, 1999; Wimpory, Hobson, Williams, & Nash, 2000). Abnormal development or use of language is another key feature of autism. A significant number of persons with autism do not acquire speech. This group typically does not develop an alternative communication system without extensive instruction. Thus, this subgroup often lacks verbal and nonverbal communication. For those who develop speech in the preschool period, speech appears less a system for sharing thoughts, feelings, desires, and experiences with others and more a system of naming objects. Children with autism rely much more heavily on repetition, or echolalia, for language learning than others. Development of sentences is typically delayed and marked by echolalia (repetition of others’ words or sentences); pronoun confusion is also common. Verbal children eventually master syntactic rules but their language is literal and they often have difficulty with metaphor, irony, and humor (Tager-Flusberg, Paul, & Lord, 2005). A third area of impairment relates to the repetition of behaviors and the limited scope of interests. Children with autism have a narrow range of activities and interests and devote large amounts of time to repetitive and ritualized behaviors. Motor stereotypies such as hand flapping, toe walking, finger movements, odd visual behaviors, and repetitive words or other vocalizations are also common. Rituals and routines may involve consistent patterns of grouping objects. It is often upsetting if objects on a child’s desk or within a child’s room are moved from their typical location or configuration. This feature was quite evident in Kanner ’s first patients (Kanner, 1943), and these behaviors continue to be an important part of the diagnostic picture (DSM-IV-TR, APA, 1994). Many children and adults with autism have unusual reactions to the sensory world. Many children with autism

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Comorbid Features of Autism 1007

respond strongly to loud noises and moving objects, though they themselves could make just as loud noises without being upset. Fascination with moving objects, water, watching the wheels of cars and trains spin, elevator doors, and feeling textures may go hand in hand with severe aversions to clothing textures, food textures, certain sounds, and negative reactions to haircuts or hair washing. Sensory over- or under-responsiveness may be seen in any sensory domain, and in the same child. The adult autobiographical literature gives vivid descriptions of the degree of difficulty this symptom can cause in everyday life (Grandin, 1992; Ratey, Grandin, & Miller, 1992; D. Williams, 1992). Current Diagnostic Definitions In the United States, the diagnosis of autistic spectrum disorder (ASD) is made according to the DSM-IV-TR criteria. Pervasive Developmental Disorders (the DSMIV-TR generic term for ASD), involves five diagnostic groups: Autistic Disorder (AD), Asperger ’s Disorder (AS), Pervasive Developmental Disorder, not otherwise specified (PDD-NOS), Childhood Disintegrative Disorder (CDD), and Rett’s Disorder. Childhood Disintegrative Disorder and Rett’s Disorder are generally not currently considered part of the pervasive developmental disorder group. Rett syndrome is a single gene mutation involving the MECP2 gene. It affects mostly girls, is progressive in its course, and results in severe intellectual impairment and profound disability in all areas of functioning over time. Childhood Disintegrative Disorder is a very rare condition in which a fairly rapid regression occurs, generally between the third and fifth years, in children previously developing typically; and after that point, children appear indistinguishable from other children with fairly severe autistic disorder and intellectual deficits (Volkmar & Klin, 2005). The diagnostic criteria for autistic disorder involve demonstrating a total of six or more of the symptoms listed in the DSM-IV, including two from the social communication category, one from language category, and one from the restricted and repetitive behaviors category. The diagnosis of autism is made from three types of diagnostic procedures: a detailed history from parental interviews, parental description of current functioning in typical situations, and clinical observation and assessment of the child’s behavior. Recent developments in assessment tools have made the diagnosis of autism much more reliable. In fact, the diagnosis of autistic disorder among experienced clinicians has the highest rate of inter-rater agreement and the most stability of any of the psychiatric diagnoses (Lord, 2005). The most common tools for ascertaining autistic participants in research studies include the autism diagnostic

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inventory (ADI-R), an experimenter administered interview, the social communication questionnaire (SCQ), a parent questionnaire with key questions from the ADI-R, the autism diagnostic observational scale (ADOS), an interactive semistructured interview with the child or adult being diagnosed, and the childhood autism rating scales (CARS), an examiner behavior rating system completed after a developmental evaluation (Lord, 2005). There has been substantial controversy concerning the heterogeneity of onset of autism. While initially disputed, it is now clear that there are multiple patterns of onset (Goldberg, Thorsen, Osann, & Spence, 2007; Ozonoff, Williams, & Landa, 2005; Richler et al., 2006). Based on parental report and analysis of first birthday videotapes, some children are showing signs of pathology at 12 months of age. However, it is equally clear that a second group of children are closely hitting normal developmental milestones until approximately 18 to 24 months when they regress into a form of autism that is largely indistinguishable from early onset autism. This is typically referred to as regressive autism. It is likely to be the case that a relatively small fraction of children with autism show early onset versus regressive forms of autism. Many children appear to undergo some delays with subsequent regressions.

COMORBID FEATURES OF AUTISM The past decade of research and public scrutiny of autism has revealed that symptoms and difficulties are not confined to the nervous system. A number of common, comorbid features have become evident; some of these were already described by Kanner and Asperger. Seizures While epilepsy has long been associated with autism spectrum disorders, the proportion of patients reported to demonstrate comorbid seizure disorder varies from 5% to 44% (Tuchman & Rapin, 2002). Hara (2007) carried out a follow-up study of 135 patients with idiopathic autism. Of these, 25% exhibited epileptic seizures that had an onset between 8 and 26 years of age. While 18% of the nonepileptic group exhibited epileptic discharges on EEG, 68% of the epileptic group revealed epileptiform EEG findings before the onset of epilepsy. Some studies have found an association between low IQ and the occurrence of epilepsy (Pavone et al., 2004) or low IQ and motor deficit and epilepsy (Tuchman, Rapin, & Shinnar, 1991). Abnormal or epileptiform EEG is also observed in substantial numbers of individuals with autism who do not have seizures (Tuchman & Rapin, 1997; Tuchman et al., 1991). While

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1008 Autism

the presence of seizure disorder and its association with other aspects of autism may provide interesting clues to the underlying pathophysiology, it remains unclear to what extent epileptiform activity contributes to the generation of the core features of autism. Anxiety In Kanner ’s (Kanner, 1943) original description of autism, he noted unusual fear or anxiety in several of his young patients. One child, Herbert, was “tremendously frightened by running water, gas burners, and many other things.” He became upset by any change of an accustomed pattern. “If he notices change, he is very fussy and cries.” Another child did a “good deal of worrying.” He was upset because the moon did not always appear in the sky at night. He preferred to play alone and would get down from a play apparatus as soon as another child approached. Insistence on sameness leads children with autism to become greatly distressed when anything is broken or incomplete, and they demand consistency in the sequence of daily events. Muris, Steerneman, Merckelbach, Holdrinet, and Meesters (1998) examined the presence of co-occurring anxiety symptoms in 44 children diagnosed with autism or pervasive developmental disorder. Using parental report, they found that 84.1% of the children met criteria for at least one anxiety disorder. Gillott, Furniss, and Walter (2001) compared high-functioning children with autism to two control groups including children with specific language impairment and normally developing children on measures of anxiety and social worry. Children with autism were found to be significantly more anxious on both indexes. More recently, this same group of investigators found that adults with autism were almost three times more anxious than a comparison group and gained significantly higher scores on anxiety subscales of panic and agoraphobia, separation anxiety, obsessive-compulsive disorder, and generalized anxiety disorder (Gillott & Standen, 2007). Gastrointestinal Disorders While research on gastrointestinal (GI) problems in children with autism is somewhat limited and conflicted (Erickson et al., 2005), it appears that autistic children have a higher incidence of GI problems than typically developing children or children with developmental delays (ValicentiMcDermott et al., 2006). GI problems are a common complaint of parents of children with autism, and this factor has prompted the use of complementary and alternative medicines (Harrington, Rosen, Garnecho, & Patrick, 2006). A number of clinicians have emphasized the need to investigate GI problems particularly in low-functioning children

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who cannot communicate their distress and for whom alleviation of the GI condition may appreciably improve the quality of life. Autoimmune Disorders Immune dysfunction may play an important role in a subset of autistic spectrum disorder cases (van Gent, Heijnen, & Treffers, 1997). Some individuals with autism demonstrate abnormalities and deficits of immune system function leading to inappropriate or ineffective immune response to pathogens (Ashwood & Van de Water, 2004a). Children with autism often have recurrent infections (L. Stern et al., 2005), peripheral immune abnormalities (Ashwood et al., 2003; Croonenberghs, Bosmans, Deboutte, Kenis, & Maes, 2002; Singh, 1996), or neuroinflammatory responses in the central nervous system (Vargas, Nascimbene, Krishnan, Zimmerman, & Pardo, 2005). In addition to general immune system dysfunction, evidence suggests that certain forms of autism are associated with an autoimmune condition (Ashwood & Van de Water, 2004a, 2004b). Autoimmunity occurs when the immune system inappropriately identifies and reacts to “self” components. Antibodies produced during an autoimmune response play a critical role in the pathogenesis of several peripheral neurological diseases, including myasthenia gravis (Lang, Dale, & Vincent, 2003; Lang, Pinto, Giovannini, Newsom-Davis, & Vincent, 2003; Lang & Vincent, 2003; Newsom-Davis et al., 2003; Scoppetta et al., 2003). Autoimmunity may also play a role in central nervous system diseases, notably psychological and neural disorders associated with streptococcal infections (PANDAS) which accounts for a subgroup of childhoodonset obsessive-compulsive disorders (OCD) and tic disorders (Snider & Swedo, 2003). Autoimmune disorders also appear to be more common in family members of ASD patients compared with typically developing controls. Mothers and first-degree relatives of children with autism are more likely to have an autoimmune disorder (16% and 21%) than controls (2% and 4%) (Comi, Zimmerman, Frye, Law, & Peeden, 1999). Similar results have been obtained in a study of autoimmune disorder frequency in families with children who have pervasive developmental disorders, including autism (Sweeten, Bowyer, Posey, Halberstadt, & McDougle, 2003). Regression in autism has been significantly associated with a family history of autoimmune disorders (Richler et al., 2006). Antibodies directed against CNS proteins have been found in the sera of autistic children. Targets of autoantibodies in pervasive developmental disorder patients include neuron-axon filament protein (Singh, Warren, Averett, & Ghaziuddin, 1997), myelin basic protein (Singh, Warren,

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Comorbid Features of Autism 1009

Odell, Warren, Cole, 1993), serotonin receptor (Todd & Ciaranello, 1985), cerebellar neurofilaments (Plioplys, Greaves, Kazemi, & Silverman, 1994), nerve growth factor (Kozlovskaia et al., 2000), alpha-2-andrenergic binding sites (Cook, Perry, Dawson, Wainwright, & Leventhal, 1993b), and antibodies against the caudate nucleus (Singh & Rivas, 2004). Maternal antibodies to fetal brain tissue may also play a role in a subset of pervasive developmental disorder cases (Vincent, Dalton, Clover, Palace, & Lang, 2003). Antibodies from serum of mothers who have children with pervasive developmental disorder have been shown to react to antigens on lymphocytes from their affected children (Warren et al., 1990). Because antigens expressed on lymphocytes are also found on cells of the central nervous system, aberrant maternal immunity may be associated with the development of some cases of autism. In support of this, the presence of antibodies against brain tissue was identified in the serum of a mother whose child has autism (P. Dalton et al., 2003). Van de Water and colleagues have identified a common pattern of autoantibody production to fetal brain tissue in the serum of mothers who have two or more children with pervasive developmental disorder (Braunschweig et al., 2008). Collectively, these studies suggest that an atypical maternal antibody response directed against the fetal brain during pregnancy may be present in a subset of individuals with autistic spectrum disorders. Autism affects four times as many boys as girls, a consistent observation for which the mechanism remains elusive. Based on the concordance rates in monozygotic twins (~60% to 90%), which at least historically have been reported to be roughly 10-fold higher than in dizygotic twins and siblings, autism is considered to be the most heritable of neuropsychiatric disorders (Bailey et al., 1995; Smalley, Asarnow, & Spence, 1988). However, it is generally acknowledged that autism is genetically heterogeneous. The state of autism genetics has been cogently reviewed by Abrahams and Geschwind (2008). As they note, defined mutations, genetic syndromes, and de novo copy number variation probably account for about 10% to 20% of cases, with none of these known causes accounting for more than 1% to 2%. None of the molecules or syndromes currently linked to the autism spectrum disorders has been proven to selectively cause autism. It is generally believed that many cases of autism are due to more complex genetic mechanisms, including coinheritance of multiple alleles and/or epigenetic modifications (Freitag, 2007; Gupta & State, 2007). Approximately 10% of sporadic cases of autism are associated with de novo copy number variations in either single genes or sets of genes (Sebat et al., 2007). This finding raises the prospect that

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de novo germ line mutations (mutations not seen in the parents) are a more significant risk for autism than previously appreciated. The mechanisms that cause these copy number variations are unknown, but, interestingly, paternal age appears to contribute to autism risk (Reichenberg et al., 2006). Perhaps increasing age leads to the accumulation of these de novo germ line mutations. Autism is probably caused by alterations in the structural organization of neural systems that process social information, language, and sensorimotor integration. We have recently reviewed the components of these systems as a context for understanding the neuropathology of autism (Amaral, Schumann, & Nordahl, 2008). Neural system lesions can be localized or distributed (Rubenstein, 2006). A localized lesion that weakens or disables one component of a circuit can impede the function of the entire circuit, generating a behavioral phenotype. This phenotype can likewise be generated by defects in another component of the same circuit. Thus, related behavioral syndromes can be generated by different anatomical defects. Distributed lesions can be caused by defects that are common to many regions of a given neural system, or to multiple neural systems. Mutation of a gene that is broadly expressed, such as those that cause fragile X mental retardation (FRAXA; FMR1), Rett syndrome (MeCP2), or tuberous sclerosis (TSC1 & 2), will disrupt neural function throughout the nervous system. Localized lesions are exemplified by mutation of genes that are expressed in neurons that share common features (such as neurotransmitter type or participation in a common circuit). Members of the Dlx homeobox gene family (which encode transcription factors) are expressed during development of most forebrain GABAergic neurons, and some Dlx genes are expressed in mature forebrain GABAergic neurons (Cobos et al., 2005). Mutations that simultaneously block the function of pairs of mouse Dlx genes disrupt development of most forebrain GABAergic neurons (Anderson et al., 1997). This has the potential to massively disrupt communication between the neocortex, basal ganglia, and thalamus and disrupt cognitive functions that are dependent on these brain regions. Furthermore, individual Dlx genes are required for survival of maturing cortical interneurons and loss of Dlx1 function can result in epilepsy (Cobos et al., 2005). While mutations in the Dlx genes have been detected in some autistic individuals, it is unknown whether these contribute to the development of the disorder (Hamilton et al., 2005) or comorbid symptoms such as epilepsy. Developmental defects can alter the connectivity between brain regions or the function within a given region and thereby impair neural functions. Interregional connectivity defects can be caused by alterations in axon path finding and synapse choice. It is not known whether these

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1010 Autism

types of abnormalities are found in autism, although there is evidence for connectivity defects from functional imaging studies (Kana, Keller, Cherkassky, Minshew, & Just, 2006; Minshew & Williams, 2007). The array of genes that either cause or predispose to autism speaks to the diversity of genetic and epigenetic mechanisms that can cause this heterogeneous disorder (Abrahams & Geschwind, 2008). While it is beyond the scope of this chapter to review all the genes that have been associated with autism spectrum disorders, some mechanistic themes are beginning to emerge. Alterations of many of the genes associated with autism (FRMR1, MET, NGLN3/4, PTEN, Shank3, TSC1/2) lead to defects in molecular pathways that link synaptic and nonsynaptic signals with changes in protein synthesis that can modulate neural response properties. TSC1/TSC2 are integral regulators of signal-transduction cascades downstream of signaling pathways that activate receptor tyrosine kinases (Inoki, Corradetti, & Guan, 2005). These signals activate a family of phosphatidylinositol lipid kinases that in turn activate the serine-threonine kinase AKT, which then represses TSC1/TSC2 (Inoki et al., 2005). TSC1/TSC2 are also regulated by intracellular amino acid concentration and by the ATP/AMP ratio—the end product of this regulation is to promote appropriate levels of protein synthesis and cell size (Inoki et al., 2005). Neuroligins (NLGN3 and NLGN4) encode plasma membrane proteins that are implicated in regulating synapse development through binding neurexin proteins (Varoqueaux et al., 2006). In rare cases of autism, mutations in two X-linked neuroligins (NLGN3 and NLGN4; Xq13 and Xp22.33, respectively) have been found (Jamain et al., 2003). Furthermore, neurexin1 (NRX1) disruptions have recently been identified in a pair of autistic individuals (Kim et al., 2008). Other genetic alterations in genes such as DLX2/5, EN2, and MeCP2 lead to defects in transcriptional regulation of neural genes. MeCP2, the gene and protein linked to Rett syndrome, is a nuclear protein that binds to methylated CpG dinucleotides. It recruits a co-repressor complex that is implicated in transcriptional repression. This gene, therefore, has as its major function the regulation of other gene products. Defects in genes related to various ion channels also have been occasionally linked to autism. Mis-sense mutations in the L-type (CACNA1C, Cav1.2; 12p13.3) and the T-type (CACNA1H, Cav3.2) calcium channels have been identified in rare cases of autism (Splawski et al., 2004, 2006). Similarly rare mis-sense mutations have been identified in two sodium channel genes (SCN1A; 2q24; SCN2A; 2q23-q24.3) (Weiss et al., 2003). Finally, genes associated with peptide and transmitter systems implicated in one or more of the relevant autistic behaviors have also demonstrated alterations in some cases of autism. Oxytocin and vasopressin peptides are

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neuromodulatory hormones that are produced by neurons in the hypothalamus and the amygdala. These neuropeptides have been implicated in the mediation of certain social behaviors particularly in the vole (Young, Murphy Young, & Hammock, 2005), and the receptors for oxytocin (OXTR; 3p25-p26) and arginine vasopressin 1a (AVPR1a; 12q14–15) are associated with autism (Wu et al., 2005; Yirmiya et al., 2006). Serotonin has potent effects on many behavioral and developmental processes. One of the earliest biochemical indications that serotonin metabolism may be altered in autism was the finding of an increase in platelet serotonin levels in approximately 30% of individuals with autism (Cook et al., 1993). Although this is not a specific diagnostic finding, it increases the potential importance that some alleles of the serotonin transporter gene (SLC6A4, SERT; 17q11.2) might be associated with autism (Brune et al., 2006; Sutcliffe et al., 2005). It will be critical to understand how these molecular lesions disrupt neural systems that process cognition and social behaviors. Mutations that alter the balance of excitatory and inhibitory synapses in key brain regions may impede the ability to detect salient sensory signals above ambient noise (Levitt, Eagleson, & Powell, 2004; Rubenstein & Merzenich, 2003). Mutations in many of the genes described earlier cause epilepsy that is a gross manifestation of dysregulated excitatory/inhibitory balance. One of the several questions raised by these genetic studies is whether certain brain regions or certain cell types are selectively affected in autism. We turn next to an overview of the neuropathology of autism.

NEUROPATHOLOGY Magnetic Resonance Imaging Studies While early computed axial tomography studies carried out on individuals with autism described abnormalities such as ventricular enlargement (Damasio, Maurer, Damasio, & Chui, 1980), later studies determined that there were no consistent tomographic findings in children with classic autism (Prior, Tress, Hoffman, & Boldt, 1984). The first magnetic resonance imaging (MRI) studies were carried out in the mid-1980s and publications began appearing around 1987 (Courchesne, Hesselink, Jernigan, & Yeung-Courchesn, 1987; Gaffney, Kuperman, Tsai, Minchin, & Hassanein, 1987). Early studies were based on the areal analysis of single, relatively thick sections through the brain and focused on differences of the ventricles, cerebellum, and brain stem. Courchesne, Yeung-Courchesne, Press, Hesselink, & Jernigan (1988) proposed that hypoplasia of

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Neuropathology 1011

cerebellar vermal lobules VI and VII was characteristic of autism. These findings, however, have generally not been replicated (Piven, Saliba, Bailey, & Arndt, 1997). In fact, an increase in total cerebellar volume is one of the more consistent findings from MRI studies (Brambilla et al., 2003; Sparks et al., 2002). Interestingly, even Courchesne’s laboratory has reported substantial heterogeneity in cerebellar findings. In a study of 50 autistic patients ranging in age from 2 to 40 years (Courchesne et al., 1994), 86% of patients were reported to demonstrate vermal hypoplasia while 12% demonstrated vermal hyperplasia. Other brain regions that have been found to be abnormal in autism include the cerebral cortex (although the critical portion of the cerebral cortex varies from study to study), medial temporal lobe structures such as the amygdala and hippocampus, and the corpus callosum. In a comprehensive review of the structural MRI studies published up until May 2003, Brambilla et al. (2003) concluded: [D]espite a growing number of quantitative MRI studies, few robust findings have been observed. Structural abnormalities involving total brain volume, the cerebellum and, recently, corpus callosum have been consistently replicated. . . . In order to overcome design limitations of the previous morphometric neuroimaging reports, future quantitative MRI studies should focus on identifying possible morphological brain markers including homogeneous groups of well characterized individuals with autism and healthy controls, matched for age, gender, SES and IQ and should longitudinally investigate these groups. (p. 567).

We have recently reviewed the state of autism MRI studies through the end of 2007 (Amaral et al., 2008). We agree that previous studies were plagued by small numbers of participants, wide ranges of ages in each group and limited sampling of the autism spectrum. Since 1987, there have been 86 structural MRI publications. The largest sample in any of these studies was in Hashimoto et al. (Hashimoto et al., 1995) that evaluated 76 males with autism and 65 controls over a very large age range. The average sample size in the 86 publications was 24 individuals with autism and 20 typically developing controls for males and 3 girls with autism compared with 5 typically developing girls. Many studies included only high-functioning participants or studied participants from heterogeneous diagnostic categories (“autism” would often include individuals ranging from low functioning autism to Asperger syndrome). There is also very little longitudinal information on the development of the brain with autism. These findings have contributed to the current situation where relatively little can be said with confidence about neuropathology in the autistic brain. However, there is now reason to believe

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that the trajectory of brain changes over time provides a clearer picture of brain alterations in autism. The notion that cortical development may be altered in autism arose initially from clinical observations indicating that the head circumference of children with autism is larger than general population controls. Bailey et al. (1993) found that 37% of their subjects had a head circumference above the 97th percentile while Lainhart et al. (1997) found that 14% of autistic subjects had macrocephaly. Fombonne, Roge, Claverie, Courty, and Fremolle (1999) conducted a meta-analysis of published literature and concluded that an average estimate of macrocephaly in autism was 20.6%. These data would suggest that large head and thus brain size might be a common, though by no means universal, feature of individuals with autism. Courchesne and colleagues have published a series of provocative studies that demonstrate abnormal brain growth in autism (Carper & Courchesne, 2005; Courchesne, Carper, & Akshoomoff, 2003; Courchesne et al., 2001; Redcay & Courchesne, 2005). They propose that the brains of children with autism are either of normal size or perhaps slightly smaller than typically developing children at birth. However, the cerebral cortex, and preferentially the frontal lobe, undergoes a rapid and precocious growth during the first 2 years of life. Subsequently, brain growth plateaus and the volume of the brains of typically developing children catch up. Thus, in older children with autism, the brain is either the same size or even slightly smaller than typically developing subjects. Importantly, this finding has recently been replicated by the Piven laboratory (Hazlett et al., 2005). However, in this replication study, the brains of children with autism were only larger relative to a developmentally delayed comparison; there was no significant enlargement relative to age-matched controls. However, there is at least some evidence in support of the idea that precocious brain growth begins around the emergence of symptoms (near the end of the first year of life and is evident by the second year of life). Beyond the cerebral cortex, other brain regions have also been found to have an abnormal brain development. Perhaps most striking is the amygdala, a region of the temporal lobe that is involved in the detection of dangers in the environment and in modulating some forms of social interaction (Amaral, 2003). Interestingly, the amygdala undergoes a protracted development in boys (Giedd et al., 1996). It increases in size by nearly 40% between the ages of 8 and 18 years (Schumann et al., 2004). This is striking since the rest of the brain actually decreases in size during this same time period by about 10%. For boys who have been diagnosed with autism, the amygdala demonstrates precocious growth and has reached adult size by 8 years of age.

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1012 Autism

Many studies have gone beyond evaluating the volume of brain regions and have broken the tissue down into compartments representing gray matter and white matter. There have been some indications that alterations in white matter volumes may actually be a more sensitive indicator of pathology in autism than gray matter differences (Carper & Courchesne, 2005; Courchesne et al., 2001, 2003; Herbert, 2005; Herbert et al., 2004). In fact, some have proposed that the enlarged brain volume that has been reported can be accounted for, in large part, by disproportionate increases in the volume of white matter. There are reports of greater white matter volumes in boys with autism aged 2 to 3 years when compared with controls. Other analyses of white matter have suggested that those compartments of white matter that develop latest (the radiate regions that mature late in the first year and into the second postnatal year and beyond) are of greater volume than the earlier maturing sagittal and bridging fibers (Herbert et al., 2003). Recent studies using diffusion tensor weighted imaging of white matter indicate that in autism regionally specific disruptions of white matter integrity may persist into adulthood (Keller, Kana, & Just, 2007). Despite the heterogeneity of findings, a few clear directions are emerging: First, autism is not a disorder that affects a single brain region; second, the kind of brain pathology in a particular individual may depend on the phenotypic characteristics of autism (e.g., presence vs. lack of developmental delays) as well as comorbid features of the disorder (e.g., seizures vs. no seizures); and third, the pathology of autism may not be apparent in the mature size and shape of the brain but in the time course of development of both the structure and connections of the brain. Microscopic Neuropathology There are no obvious lesions or clear pathology in the brains of individuals with autism. In fact, at first blush the brain looks remarkably normal. One consistent finding in autism has been a reduced number of Purkinje cells in the cerebellum (Ritvo & Garber, 1988). When using neural stains that mark cell bodies, there are noticeable gaps in the orderly arrays of Purkinje cells. Whether Purkinje cell loss is due to autism, epilepsy, or the co-occurrence of both disorders is not currently clear. It is also not clear whether loss of Purkinje cells is characteristic of autism or a more general finding in many neurodevelopmental disorders. Thus, cerebellar alterations have been reported in idiopathic mental retardation, Williams syndrome, and many other childhood disorders. The cerebral cortex has also been reported to be abnormal at a microscopic level in autism. There have been some published examples of migration defects such as

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ectopias (Bailey et al., 1998). More recently, it has been proposed that the columnar organization of the cortex in autistic patients is abnormal (Casanova, 2006; Casanova, Buxhoeveden, Switala, & Roy, 2002). These provocative findings are awaiting confirmation in larger studies using sophisticated quantitative strategies. A quantitative neuroanatomical study has demonstrated that cells in the fusiform gyrus of the autistic brain are both smaller and fewer in number than age-matched control brains (van Kooten et al., 2008). The decrease in neurons was not seen in the primary visual cortex or when the entire cerebral cortex was analyzed. The fusiform gyrus is interesting because at least part of it is made up of the fusiform face area that appears to be involved in face processing. Finally, the amygdala has been found to have fewer neurons in the mature brain (Schumann & Amaral, 2006). Since this study was carried out with cases that did not have comorbid epilepsy, this appears to be a real component of autistic neuropathology.

FUNCTIONAL NEUROIMAGING Another approach to establishing which brain regions are most impacted by autism is the use of functional magnetic resonance imaging (fMRI). While this literature is growing rapidly and has provided important insights into the neural impairments of autism, it also applies only to the high-functioning segment of the population who can be compliant with the demands of the behavioral and imaging conditions. Many of the functional imaging studies have focused on brain regions thought to be involved in social function, such as the frontal lobe and amygdala, and on behaviors thought to be selectively impaired in autism, such as perception of social stimuli and theory of mind. More than 400 papers have appeared in recent years dealing with functional imaging of individuals with autism; therefore we only briefly highlight some findings related to the amygdala that, as described earlier, is pathological in autism. For more extensive reviews of fMRI in autism, see Just, Cherkassky, Keller, Kana, and Minshew (2007) and Kana et al. (2006). The amygdala has been the focus of many functional imaging studies in autism prompted, in part, by the “Amygdala Theory of Autism” proposed by Baron-Cohen and colleagues (2000). Functional neuroimaging studies have indicated that individuals with an autism spectrum disorder show abnormal patterns of amygdala activation (hypoactivation) in response to social stimuli. Highfunctioning adults with autism or Asperger syndrome demonstrate deficits in the ability to infer the mental state of another person by viewing images of their eyes (BaronCohen, Jolliffe, Mortimore, & Robertson, 1997). This

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Behavioral Treatment

task activates the amygdala and superior temporal gyrus in control subjects. In contrast, individuals with autism or Asperger syndrome activate the frontotemporal regions but not the amygdala when performing this task (BaronCohen et al., 1999). Pierce, Muller, Ambrose, Allen, and Courchesne (2001) found that the amygdala was activated when typically developing individuals viewed unfamiliar faces, but the amygdala was not activated in individuals with autism during this task. Children and adolescents with autism spectrum disorders show abnormal amygdala activation while matching faces by emotion and assigning a label to facial expressions (Wang, Dapretto, Hariri, Sigman, & Bookheimer, 2004). While children in the control group showed more amygdala activation when matching faces by emotion than when assigning a verbal label, the children with autism spectrum disorders did not demonstrate this pattern of task-dependent amygdala modulation. A caveat to interpreting findings from face processing studies is that subjects with autism are reluctant to make eye contact and there is some controversy as to whether they are actually examining the face in a similar manner as controls (Davidson & Slagter, 2000). In fact, when viewing faces, individuals with autism show abnormal visual scan paths during eye-tracking studies, typically spending little time on the eyes (Klin, Jones, Schultz, & Volkmar, 2003; Pelphrey et al., 2002). Whether these findings represent active avoidance of the eye region, potentially involving the amygdala, or a more global lack of social interest or motivation is unclear. An emerging hypothesis is that the amygdala may play a role in mediating or directing visual attention to the eyes (Adolphs et al., 2005; Grelotti et al., 2005; Schultz, 2005). Research from typically developing children indicates that children who are physiologically aroused by a distressing film are more likely to avert their gaze from the stimulus. It is plausible that children with autism use a similar strategy of gaze aversion in response to arousing social stimuli. Given the amygdala’s role in fear and anxiety, we would predict heightened amygdala activation during eye contact in persons with autism if they found the eye contact aversive. Dalton and colleagues (K. M. Dalton et al., 2005) found that the amount of time persons with autism spent looking at the eye region of the face was strongly positively correlated with amygdala activation, but this was not the case in control subjects. The autism subjects also showed greater left amygdala activation relative to controls in response to unfamiliar faces and greater right amygdala activation in response to both familiar and unfamiliar faces. This suggests a heightened emotional, or even fearful, response when autistic individuals look at another person’s eyes, regardless of whether they are familiar or a stranger. Nacewicz et al. (2006) recently found that

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1013

individuals with autism (8 to 25 years of age) who had a smaller amygdala were also slower to distinguish emotional from neutral expressions and showed least fixation on the eye regions of the face. These same individuals were also the most socially impaired in early childhood. Ashwin, Baron-Cohen, Wheelwright, O’Riordan, and Bullmore (2007) found that during the perception of fearful faces, Asperger syndrome patients showed less activation in the left amygdala relative to controls. However, these results may again be due to the abnormal way in which individuals with autism view faces. Spezio, Adolphs, Hurley, and Piven (2007a, 2007b) confirmed that participants with autism show less fixation on the eyes and mouth, but also a greater tendency to saccade away from the eyes when information was present in those regions. This study provides insight into the aberrant manner in which people with autism view faces, which likely influences face processing and subsequent fMRI results. Additional studies would benefit from measuring the physiological responses associated with arousal and anxiety (increased heart rate, skin response) during face processing in individuals with autism.

BEHAVIORAL TREATMENT Based on the experiences of the past 50 years, it is clear that autism is treatable. Early on in autism treatment, two main treatment approaches dominated the literature: treatment based on a psychodynamic conceptualization of autism (e.g., Bettelheim, 1967) and treatment based on the application of Skinnerian models of learning. The first empirically supported paper came from the latter tradition (Wolf, Risley, & Mees, 1964). The treatment was carried out virtually all day, every day, for several years in an institutional setting. The child eventually returned to his home, with greatly improved behavior, language, adaptive, and cognitive abilities. The teaching procedures involve massed trial teaching and many of the core approaches to teaching are still in use today (Leaf & McEachin, 2001; Lovaas, 1981). The view that autism is a neurobiological disorder (Rimland, 1964) had fundamental effects on treatments. Gradually, autism became viewed as a developmental disorder, like mental retardation, for which rehabilitation was the appropriate approach. Three main philosophies guided the development of interventions. One strategy involved the continued application of learning theory to reduce behavioral deficits and to decrease behavioral excesses (Wolf et al., 1964). These strategies, under the umbrella of Applied Behavior Analysis, were applied in two basic forms. The first involved massed trials with high levels of adult control and direction (Lovaas, 1987; McEachin, Smith, & Lovaas, 1993).

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1014 Autism

A more naturalistic application of learning principles capitalized on children’s own interests, preferences, and initiatives to assure high levels of motivation for learning. These approaches are best described in two well-known models, Incidental Teaching, first applied to autism by McGee, Krantz, Mason, & McClannahan (1991); McGee, Krantz, Mason, & McClannahan (1983); and Pivotal Response Training, as developed by Schriebman and Koegel (Koegel, O’Dell, & Dunlap, 1988; Schreibman & Pierce, 1993; J. A. Williams, Koegel, & Egel, 1981). A second main approach was the Treatment and Education of Autistic and Related Communication Handicapped Children (TEACCH) model of intervention (Schopler, Mesibov, & Hearsey, 1995; Schopler, Mesibov, Shigley, & Bashford, 1984). This capitalized on teaching by directing tasks to children’s visual-spatial skills. This approach focused on developing skills for independent work and independent functioning, minimized the need for ongoing social and verbal instruction, used visual communication systems to supplement verbal instruction, built a great deal of repetition and routine into the organization of the teaching, and reduced the sensory complexity of the environment to maximize attention. This approach also used parents as primary deliverers of the intervention. The third main approach focuses on autism as a developmental deficit. It is based on the premise that early compromises in social and communicative development lead to large downstream effects that impair the development of social relations (Meyer & Hobson, 2004; Rogers & Pennington, 1991; Sigman & Capps, 1997). The developmental approaches have flourished and current models include the Floortime approach (Greenspan et al., 1997), Relationship Development Intervention (Gutstien, Burgess, & Montfort, 2007), the Denver Model (Rogers & Lewis, 1989), and the Social Communication, Emotional Regulation and Transactional Support (SCERTS) model (Prizant, Wetherby, Rubin, Laurent, & Rydell, 2006). These approaches strongly emphasize the quality of the relationship between child and the teacher as well as the child and parent. They use a child-centered approach based on following children’s interests and initiatives, and strongly emphasize progress in social communication skills. A fourth treatment orientation focuses on the sensory and motor differences characteristic of autism. Some practitioners think that the sensory differences in autism are the primary impairments, with the social, communicative, and behavioral abnormalities resulting from the intense distress or confusion that the sensory impairments cause (reviewed in Baranek, 2002). Behavioral treatments may be delivered to change targeted symptoms. Virtually all the main symptoms of

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autism have been demonstrated to be modifiable with targeted treatments (Schreibman, 2005). Positive treatment outcomes for targeted skills have been documented across the entire age range and functioning range for persons with autism spectrum disorders. Moreover, the use of aversive consequences has largely disappeared as the field has become more sophisticated in the application of reinforcement strategies (Carr et al., 2002; Horner, Carr, Strain, Todd, & Reed, 2002; Howlin, 1998; Lalli, Casey, & Kates, 1995).

OTHER TREATMENTS Psychopharmacological Treatments Given the lack of specific brain or neurotransmitter systems as a target for pharmacological treatment, currently no psychopharmacological treatment is directed at the core symptoms of autism (Palermo & Curatolo, 2004). The atypical antipsychotics (olanzapine, ziprasidone, quetiapine, aripiprazole) have shown some efficacy in improving certain behavioral symptoms of autistic disorder such as aggressiveness, hyperactivity, and self-injurious behavior (Stachnik & Nunn-Thompson, 2007). Weight gain and sedation are frequently reported adverse consequences of these treatments. Risperidone has become the first approved drug for treatment of autism. Previously, risperidone was studied as an off-label medication to treat autism because of its increased safety and efficacy over conventional neuroleptics. Risperidone can be used as a potentially safe and effective treatment for disruptive behavioral symptoms in children with autism (West & Waldrop, 2006). The long-term use of these drugs in conjunction with the other alternative medications being used requires additional analysis pertaining to safety (McCracken, 2005). Complementary Alternative Medicine Treatments It would be fair to say that in no area of developmental pediatric practice is there more controversy than in the selection of treatments for children with autistic spectrum disorders. An increasing number of complementary and alternative medical therapies are often tried because they are perceived as treating the cause of the children’s symptoms (Levy & Hyman, 2005). Current treatments range from various forms of restricted diet (such as gluten- and casein-free diets; Millward, Ferriter, Calver, & ConnellJones, 2004) to hyperbaric oxygen treatment (Rossignol, 2007) to vitamin and mineral supplements (Hanson et al., 2007). Secretin provides a good example of how an incidental perception of behavioral improvement following

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References

treatment leads to widespread clinical use despite little or no scientific rationale for the therapy. And despite nearly unanimous negative results in placebo-controlled clinical trials (Esch & Carr, 2004), there still remains substantial parental interest in attempts at using secretin as a potential therapy. In many respects, this speaks to the desperate need of parents and practitioners alike to obtain more scientifically based approaches to the therapy of both the core and comorbid symptoms of autism.

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REFERENCES Abrahams, B. S., & Geschwind, D. H. (2008). Advances in autism genetics: On the threshold of a new neurobiology. Nature Reviews: Genetics, 9, 341–355. Adolphs, R., Gosselin, F., Buchanan, T. W., Tranel, D., Schyns, P., & Damasio, A. R. (2005, January 6). A mechanism for impaired fear recognition after amygdala damage. Nature, 433, 68–72. Altevogt, B. M., Hanson, S. L., & Leshner, A. I. (2008). Autism and the environment: Challenges and opportunities for research. Pediatrics, 121, 1225–1229. Amaral, D. G. (2003). The amygdala, social behavior, and danger detection. Annals of the New York Academy of Sciences, 1000, 337–347.

SUMMARY Autism is a spectrum disorder that is defined behaviorally as consisting of social and communication impairments, stereotyped behaviors, and circumscribed interests. There is consensus that autism has a variety of etiologies, each of which has different genetic and environmental contributions. While some 10% of autism cases are associated with a defined medical condition such as fragile X syndrome, the cause(s) of the remainder of idiopathic autism are currently unknown. Autism is often considered to be a polygenic disorder with multiple genes showing weak association. This may reflect the fact that better phenotyping of autism subtypes is essential before fruitful genotyping can be accomplished. Autism affects the development of several brain systems. The most common biological finding is precocious brain development of the cerebral cortex and amygdala. White matter may be more affected than gray matter. However, the neuropathology of autism is still at a very early stage of understanding, and additional structural MRI and postmortem studies are needed to better define the neural systems involved. Beyond the nervous system, there appears to be a variety of dysregulated functions in the immune system of some individuals with autism and some mothers of individuals with autism. Whether the immune dysregulation is a cause or effect of autism remains to be determined. Various behavioral therapies based on the operant conditioning literature are valuable for eliminating unwanted behaviors and bolstering language, social interaction, and pragmatic life skills. Finally, autism currently affects 1:150 children in the United States and other industrialized countries. While the increase in prevalence is due, in part, to increased public and professional awareness as well as the broadening of diagnostic criteria, there is also concern that the incidence of autism is truly increasing. This raises the prospect that environmental or other nongenetic factors may be increasingly impacting modern children. This highlights the important need for a better understanding of the biological underpinnings of autism and what factors may produce this complex disorder.

c52.indd Sec4:1015

Amaral, D. G., Schumann, C. M., & Nordahl, C. W. (2008). Neuroanatomy of autism. Trends in Neurosciences, 31, 137–145. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. Anderson, S. A., Qiu, M., Bulfone, A., Eisenstat, D. D., Meneses, J., Pedersen, R., et al. (1997). Mutations of the homeobox genes dlx-1 and dlx-2 disrupt the striatal subventricular zone and differentiation of late born striatal neurons. Neuron, 19, 27–37. Ashwin, C., Baron-Cohen, S., Wheelwright, S., O’Riordan, M., & Bullmore, E. T. (2007). Differential activation of the amygdala and the “social brain” during fearful face-processing in Asperger syndrome. Neuropsychologia, 45, 2–14. Ashwood, P., Anthony, A., Pellicer, A. A., Torrente, F., Walker-Smith, J. A., & Wakefield, A. J. (2003). Intestinal lymphocyte populations in children with regressive autism: Evidence for extensive mucosal immunopathology. Journal of Clinical Immunology, 23, 504–517. Ashwood, P., & Van de Water, J. (2004a). Is autism an autoimmune disease? Autoimmunity Reviews, 3(7–8), 557–562. Ashwood, P., & Van de Water, J. (2004b). A review of autism and the immune response. Clinical and Developmental Immunology, 11, 165–174. Asperger, H. (1944). Die autistischen psychopathen im kindesalter. Archiv fur Psychiatrie und Nervenkrankheiten, 117, 76–136. Bailey, A., Le Couteur, A., Gottesman, I., Bolton, P., Simonoff, E., Yuzda, E., et al. (1995). Autism as a strongly genetic disorder: Evidence from a British twin study. Psychological Medicine, 25, 63–77. Bailey, A., Luthert, P., Bolton, P., Le Couteur, A., Rutter, M., & Harding, B. (1993). Autism and megalencephaly. Lancet, 341, 1225–1226. Bailey, A., Luthert, P., Dean, A., Harding, B., Janota, I., Montgomery, M., et al. (1998). A clinicopathological study of autism. Brain, 121(Pt. 5), 889–905. Baker, H. C. (2002). A comparison study of autism spectrum disorder referrals 1997 and 1989. Journal of Autism and Developmental Disorders, 32, 121–125. Baranek, G. T. (2002). Efficacy of sensory and motor interventions for children with autism. Journal of Autism and Developmental Disorders, 32, 397–422. Baron-Cohen, S., Jolliffe, T., Mortimore, C., & Robertson, M. (1997). Another advanced test of theory of mind: Evidence from very high functioning adults with autism or Asperger syndrome. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 38, 813–822. Baron-Cohen, S., Ring, H. A., Bullmore, E. T., Wheelwright, S., Ashwin, C., & Williams, S. C. (2000). The amygdala theory of autism. Neuroscience and Biobehavioral Reviews, 24, 355–364. Baron-Cohen, S., Ring, H. A., Wheelwright, S., Bullmore, E. T., Brammer, M. J., Simmons, A., et al. (1999). Social intelligence in the normal and autistic brain: An fMRI study. Supplement to the European Journal of Neuroscience, 11, 1891–1898. Bello, S. C. (2007). Autism and environmental influences: Review and commentary. Reviews on Environmental Health, 22, 139–156.

8/17/09 5:28:48 PM

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Brambilla, P., Hardan, A., di Nemi, S. U., Perez, J., Soares, J. C., & Barale, F. (2003). Brain anatomy and development in autism: Review of structural MRI studies. Brain Research Bulletin, 61, 557–569.

Dalton, K. M., Nacewicz, B. M., Johnstone, T., Schaefer, H. S., Gernsbacher, M. A., Goldsmith, H. H., et al. (2005). Gaze fixation and the neural circuitry of face processing in autism. Nature Neuroscience, 8, 519–526.

Braunschweig, D., Ashwood, P., Krakowiak, P., Hertz-Picciotto, I., Hansen, R., Croen, L. A., et al. (2008). Autism: Maternally derived antibodies specific for fetal brain proteins. Neurotoxicology, 29, 226–231.

Dalton, P., Deacon, R., Blamire, A., Pike, M., McKinlay, I., Stein, J., et al. (2003). Maternal neuronal antibodies associated with autism and a language disorder. Annals of Neurology, 53, 533–537.

Brune, C. W., Kim, S. J., Salt, J., Leventhal, B. L., Lord, C., & Cook, E. H., Jr. (2006). 5-httlpr genotype-specific phenotype in children and adolescents with autism. American Journal of Psychiatry, 163, 2148–2156.

Damasio, H., Maurer, R. G., Damasio, A. R., & Chui, H. C. (1980). Computerized tomographic scan findings in patients with autistic behavior. Archives of Neurology, 37, 504–510.

Bettelheim, B. (1967). The empty fortress. New York: Free Press.

Carper, R. A., & Courchesne, E. (2005). Localized enlargement of the frontal cortex in early autism. Biological Psychiatry, 57, 126–133. Carr, E., Dunlap, G., Horner, R., Koegel, L., Turnbill, A., Sailor, W., et al. (2002). Positive behavior support: Evolution of an applied science. Journal of Positive Behavior Interventions, 4, 4–16. Casanova, M. F. (2006). Neuropathological and genetic findings in autism: The significance of a putative minicolumnopathy. Neuroscientist, 12, 435–441. Casanova, M. F., Buxhoeveden, D. P., Switala, A. E., & Roy, E. (2002). Minicolumnar pathology in autism. Neurology, 58, 428–432. Chakrabarti, S., & Fombonne, E. (2005). Pervasive developmental disorders in preschool children: Confirmation of high prevalence. American Journal of Psychiatry, 162, 1133–1141. Cobos, I., Calcagnotto, M. E., Vilaythong, A. J., Thwin, M. T., Noebels, J. L., Baraban, S. C., et al. (2005). Mice lacking dlx1 show subtype-specific loss of interneurons, reduced inhibition, and epilepsy. Nature Neuroscience, 8, 1059–1068. Comi, A. M., Zimmerman, A. W., Frye, V. H., Law, P. A., & Peeden, J. N. (1999). Familial clustering of autoimmune disorders and evaluation of medical risk factors in autism. Journal of Child Neurology, 14, 388–394. Cook, E. H., Jr., Arora, R. C., Anderson, G. M., Berry-Kravis, E. M., Yan, S. Y., Yeoh, H. C., et al. (1993). Platelet serotonin studies in hyperserotonemic relatives of children with autistic disorder. Life Sciences, 52, 2005–2015. Cook, E. H., Jr., Perry, B. D., Dawson, G., Wainwright, M. S., & Leventhal, B. L. (1993). Receptor inhibition by immunoglobulins: Specific inhibition by autistic children, their relatives, and control subjects. Journal of Autism and Developmental Disorders, 23, 67–78. Courchesne, E., Carper, R., & Akshoomoff, N. (2003). Evidence of brain overgrowth in the first year of life in autism. Journal of the American Medical Association, 290, 337–344. Courchesne, E., Hesselink, J. R., Jernigan, T. L., & Yeung-Courchesne, R. (1987). Abnormal neuroanatomy in a nonretarded person with autism: Unusual findings with magnetic resonance imaging. Archives of Neurology, 44, 335–341. Courchesne, E., Karns, C. M., Davis, H. R., Ziccardi, R., Carper, R. A., Tigue, Z. D., et al. (2001). Unusual brain growth patterns in early life in patients with autistic disorder: An MRI study. Neurology, 57, 245–254. Courchesne, E., Saitoh, O., Townsend, J. P., Yeung-Courchesne, R., Press, G. A., Lincoln, A. J., et al. (1994). Cerebellar hypoplasia and hyperplasia in infantile autism. Lancet, 343, 63–64. Courchesne, E., Yeung-Courchesne, R., Press, G. A., Hesselink, J. R., & Jernigan, T. L. (1988). Hypoplasia of cerebellar vermal lobules vi and vii in autism. New England Journal of Medicine, 318, 1349–1354. Croonenberghs, J., Bosmans, E., Deboutte, D., Kenis, G., & Maes, M. (2002). Activation of the inflammatory response system in autism. Neuropsychobiology, 45, 1–6. Cunningham, W., A., Johnson, M. K., Raye, C. L., Gatenbyn, J. C., & Bunaji, M. R. (2004), Separable neural components in the processing of Black and White faces. Psychological Science, 15, 806–813.

c52.indd Sec4:1016

Davidson, R. J., & Slagter, H. A. (2000). Probing emotion in the developing brain: Functional neuroimaging in the assessment of the neural substrates of emotion in normal and disordered children and adolescents. Mental Retardation and Developmental Disabilities Research Reviews, 6, 166–170. Erickson, C. A., Stigler, K. A., Corkins, M. R., Posey, D. J., Fitzgerald, J. F., & McDougle, C. J. (2005). Gastrointestinal factors in autistic disorder: A critical review. Journal of Autism and Developmental Disorders, 35, 713–727. Esch, B. E., & Carr, J. E. (2004). Secretin as a treatment for autism: A review of the evidence. Journal of Autism and Developmental Disorders, 34, 543–556. Fombonne, E. (2005). Epidemiology of autistic disorder and other pervasive developmental disorders. Journal of Clinical Psychiatry, 66(Suppl. 10), 3–8. Fombonne, E., Roge, B., Claverie, J., Courty, S., & Fremolle, J. (1999). Microcephaly and macrocephaly in autism. Journal of Autism and Developmental Disorders, 29, 113–119. Freitag, C. M. (2007). The genetics of autistic disorders and its clinical relevance: A review of the literature. Molecular Psychiatry, 12, 2–22. Gaffney, G. R., Kuperman, S., Tsai, L. Y., Minchin, S., & Hassanein, K. M. (1987). Midsagittal magnetic resonance imaging of autism. British Journal of Psychiatry, 151, 831–833. Giedd, J. N., Vaituzis, A. C., Hamburger, S. D., Lange, N., Rajapakse, J. C., Kaysen, D., et al. (1996). Quantitative MRI of the temporal lobe, amygdala, and hippocampus in normal human development: Ages 4–18 years. Journal of Comparative Neurology, 366, 223–230. Gillott, A., Furniss, F., & Walter, A. (2001). Anxiety in high-functioning children with autism. Autism, 5, 277–286. Gillott, A., & Standen, P. J. (2007). Levels of anxiety and sources of stress in adults with autism. Journal of Intellectual Disabilities, 11, 359–370. Goldberg, W. A., Thorsen, K. L., Osann, K., & Spence, M. A. (2008). Use of home videotapes to confirm parental reports of regression in autism. Journal of Autism and Developmental Disorders, 6, 1136–1146. Grandin, T. (1992). An inside view of autism. In E. Schopler & G. Mesibov (Eds.), High-functioning individuals with autism (pp. 105–126). New York: Plenum Press. Greenspan, S., Kalmanson, B., Shahmoon-Shanok, R., Wieder, S., GordonWilliamson, G., & Anzalone, M. (1997). Assessing and treating infants and young children with severe difficulties in relating and communicating. Washington, DC: Zero to Three. Grelotti, D. J., Klin, A. J., Gauthier, I., Skudlarski, P., Cohen, D. J., Gore, J. C., et al. (2005). FMRI activation of the fusiform gyrus and amygdala to cartoon characters but not to faces in a boy with autism. Neuropsychologia, 43, 373–385. Gupta, A. R., & State, M. W. (2007). Recent advances in the genetics of autism. Biological Psychiatry, 61, 429–437. Gutstein S. E., Burgess A. F., & Montfort K. (2007) Evaluation of the relationship development intervention program. Autism, 11, 397–411. Hamilton, S. P., Woo, J. M., Carlson, E. J., Ghanem, N., Ekker, M., & Rubenstein, J. L. (2005). Analysis of four dlx homeobox genes in autistic probands. BMC Genetics, 6, 52.

8/17/09 5:28:49 PM

References Hanson, E., Kalish, L. A., Bunce, E., Curtis, C., McDaniel, S., Ware, J., et al. (2007). Use of complementary and alternative medicine among children diagnosed with autism spectrum disorder. Journal of Autism and Developmental Disorders, 37, 628–636. Hara, H. (2007). Autism and epilepsy: A retrospective follow-up study. Brain and Development, 29, 486–490. Harrington, J. W., Rosen, L., Garnecho, A., & Patrick, P. A. (2006). Parental perceptions and use of complementary and alternative medicine practices for children with autistic spectrum disorders in private practice. Journal of Developmental and Behavioral Pediatrics, 27(Suppl. 2), S156–S161. Hashimoto, T., Tayama, M., Murakawa, K., Yoshimoto, T., Miyazaki, M., Harada, M., et al. (1995). Development of the brainstem and cerebellum in autistic patients. Journal of Autism and Developmental Disorders, 25, 1–18. Hazlett, H. C., Poe, M., Gerig, G., Smith, R. G., Provenzale, J., Ross, A., et al. (2005). Magnetic resonance imaging and head circumference study of brain size in autism: Birth through age 2 years. Archives of General Psychiatry, 62, 1366–1376. Herbert, M. R. (2005). Large brains in autism: The challenge of pervasive abnormality. Neuroscientist, 11, 417–440. Herbert, M. R., Ziegler, D. A., Deutsch, C. K., O’Brien, L. M., Lange, N., Bakardjiev, A., et al. (2003). Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys. Brain, 126(Pt. 5), 1182–1192. Herbert, M. R., Ziegler, D. A., Makris, N., Filipek, P. A., Kemper, T. L., Normandin, J. J., et al. (2004). Localization of white matter volume increase in autism and developmental language disorder. Annals of Neurology, 55, 530–540. Hobson, R. P., & Lee, A. (1999). Imitation and identification in autism. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 40, 649–659. Horner, R. H., Carr, E. G., Strain, P. S., Todd, A. W., & Reed, H. K. (2002). Problem behavior interventions for young children with autism: A research synthesis. Journal of Autism and Developmental Disorders, 32, 423–446. Howlin, P. (1998). Practitioner review: Psychological and educational treatments for autism. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 39, 307–322. Inoki, K., Corradetti, M. N., & Guan, K. L. (2005). Dysregulation of the tsc-mtor pathway in human disease. Nature Genetics, 37, 19–24. Jamain, S., Quach, H., Betancur, C., Rastam, M., Colineaux, C., Gillberg, I. C., et al. (2003). Mutations of the x-linked genes encoding neuroligins nlgn3 and nlgn4 are associated with autism. Nature Genetics, 34, 27–29. Just, M. A., Cherkassky, V. L., Keller, T. A., Kana, R. K., & Minshew, N. J. (2007). Functional and anatomical cortical underconnectivity in autism: Evidence from an fMRI study of an executive function task and corpus callosum morphometry. Cerebral Cortex, 17, 951–961. Kana, R. K., Keller, T. A., Cherkassky, V. L., Minshew, N. J., & Just, M. A. (2006). Sentence comprehension in autism: Thinking in pictures with decreased functional connectivity. Brain, 129(Pt. 9), 2484–2493. Kanner, L. (1943). Autistic disturbances of affective contact. Nervous Child, 2, 217–250. Kanner, L. (1971). Follow-up study of eleven autistic children originally reported in 1943. Journal of Autism and Childhood Schizophrenia, 1, 119–145. Keller, T. A., Kana, R. K., & Just, M. A. (2007). A developmental study of the structural integrity of white matter in autism. NeuroReport, 18, 23–27. Kim, H. G., Kishikawa, S., Higgins, A. W., Seong, I. S., Donovan, D. J., Shen, Y., et al. (2008). Disruption of neurexin 1 associated with autism spectrum disorder. American Journal of Human Genetics, 82, 199–207.

c52.indd Sec4:1017

1017

Klin, A., Jones, W., Schultz, R., & Volkmar, F. (2003). The enactive mind, or from actions to cognition: Lessons from autism. Philosophical Transactions of the Royal Society of London, B: Biological Sciences, 358, 345–360. Koegel, R. L., O’Dell, M., & Dunlap, G. (1988). Producing speech use in nonverbal autistic children by reinforcing attempts. Journal of Autism and Developmental Disorders, 18, 525–538. Kozlovskaia, G. V., Kliushnik, T. P., Goriunova, A. V., Turkova, I. L., Kalinina, M. A., & Sergienko, N. S. (2000). [nerve growth factor autoantibodies in children with various forms of mental dysontogenesis and in schizophrenia high risk group]. Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova, 100(3), 50–52. Kuehn, B. M. (2007). Cdc: Autism spectrum disorders common. Journal of the American Medical Association, 297, 940. Lainhart, J. E., Piven, J., Wzorek, M., Landa, R., Santangelo, S. L., Coon, H., et al. (1997). Macrocephaly in children and adults with autism. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 282–290. Lalli, J. S., Casey, S., & Kates, K. (1995). Reducing escape behavior and increasing task completion with functional communication training, extinction, and response chaining. Journal of Applied Behavior Analysis, 28, 261–268. Lang, B., Dale, R. C., & Vincent, A. (2003). New autoantibody mediated disorders of the central nervous system. Current Opinion in Neurology, 16, 351–357. Lang, B., Pinto, A., Giovannini, F., Newsom-Davis, J., & Vincent, A. (2003). Pathogenic autoantibodies in the Lambert-Eaton myasthenic syndrome. Annals of the New York Academy of Sciences, 998, 187–195. Lang, B., & Vincent, A. (2003). Autoantibodies to ion channels at the neuromuscular junction. Autoimmunity Reviews, 2(2), 94–100. Leaf, R., & McEachin, J. (2001). A work in progress. New York: DRL Books. Levitt, P., Eagleson, K. L., & Powell, E. M. (2004). Regulation of neocortical interneuron development and the implications for neurodevelopmental disorders. Trends in Neurosciences, 27, 400–406. Levy, S. E., & Hyman, S. L. (2005). Novel treatments for autistic spectrum disorders. Mental Retardation and Developmental Disabilities Research Reviews, 11, 131–142. Lord, C. (2005). Diagnostic instruments in autistic spectrum disorders. In F. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive development disorders (pp. 730–771). Hoboken, NJ: Wiley. Lovaas, O. I. (1981). Teaching developmentally disabled children: The me book. Baltimore: University Park Press. Lovaas, O. I. (1987). Behavioral treatment and normal educational and intellectual functioning in young autistic children. Journal of Consulting and Clinical Psychology, 55, 3–9. McCracken, J. T. (2005). Safety issues with drug therapies for autism spectrum disorders. Journal of Clinical Psychiatry, 66(Suppl. 10), 32–37. McEachin, J. J., Smith, T., & Lovaas, O. I. (1993). Long-term outcome for children with autism who received early intensive behavioral treatment. American Journal of Mental Retardation, 97, 359–372; 373–391. McGee, G. G., Krantz, P. J., Mason, D., & McClannahan, L. E. (1983). A modified incidental-teaching procedure for autistic youth: Acquisition and generalization of receptive object labels. Journal of Applied Behavior Analysis, 16, 329–338. McGee, G. G., Krantz, P. J., Mason, D., & McClannahan, L. E. (1991). A comparison of emotional facial display by children with autism and typical preschoolers. Journal of Early Intervention, 15, 237–245. Meyer, J., & Hobson, R. P. (2004). Orientation in relation to self and other: The case of autism. Interaction Studies, 5, 221–244. Millward, C., Ferriter, M., Calver, S., & Connell-Jones, G. (2004). Glutenand casein-free diets for autistic spectrum disorder [CD003498]. Cochrane Database of Systematic Reviews,(2).

8/17/09 5:28:49 PM

1018 Autism Minshew, N. J., & Williams, D. L. (2007). The new neurobiology of autism: Cortex, connectivity, and neuronal organization. Archives of Neurology, 64, 945–950.

Rossignol, D. A. (2007). Hyperbaric oxygen therapy might improve certain pathophysiological findings in autism. Medical Hypotheses, 68, 1208–1227.

Muris, P., Steerneman, P., Merckelbach, H., Holdrinet, I., & Meesters, C. (1998). Comorbid anxiety symptoms in children with pervasive developmental disorders. Journal of Anxiety Disorders, 12, 387–393.

Rubenstein, J. L. (2006). Comments on the genetic control of forebrain development. Clinical Neuroscience Research, 169–177.

Nacewicz, B. M., Dalton, K. M., Johnstone, T., Long, M. T., McAuliff, E. M., Oakes, T. R., et al. (2006). Amygdala volume and nonverbal social impairment in adolescent and adult males with autism. Archives of General Psychiatry, 63, 1417–1428. Newschaffer, C. J., Croen, L. A., Daniels, J., Giarelli, E., Grether, J. K., Levy, S. E., et al. (2007). The epidemiology of autism spectrum disorders. Annual Review of Public Health, 28, 238–258. Newsom-Davis, J., Buckley, C., Clover, L., Hart, I., Maddison, P., Tuzum, E., et al. (2003). Autoimmune disorders of neuronal potassium channels. Annals of the New York Academy of Sciences, 998, 202–210. Ozonoff, S., Williams, B. J., & Landa, R. (2005). Parental report of the early development of children with regressive autism: The delays-plusregression phenotype. Autism, 9, 461–486. Palermo, M. T., & Curatolo, P. (2004). Pharmacologic treatment of autism. Journal of Child Neurology, 19, 155–164.

Rubenstein, J. L., & Merzenich, M. M. (2003). Model of autism: Increased ratio of excitation/inhibition in key neural systems. Genes Brain Behavior, 2, 255–267. Schopler, E., Mesibov, G., & Hearsey, K. (1995). Structured teaching in the TEACCH system. In E. Schopler & G. Mesibov (Eds.), Learning and cognition in autism (pp. 243–268). New York: Plenum Press. Schopler, E., Mesibov, G., Shigley, R., & Bashford, A. (1984). Helping autistic children through their parents: The TEACCH model. In E. Schopler & G. Mesibov (Eds.), The effects of autism on the family (pp. 65–81). New York: Plenum Press. Schreibman, L. (2005). The science and fiction of autism. Boston: Harvard University Press. Schreibman, L., & Pierce, K. (1993). Achieving greater generalization of treatment effects in children with autism: Pivotal response training and self management. Clinical Psychologist, 46, 184–191.

Pavone, P., Incorpora, G., Fiumara, A., Parano, E., Trifiletti, R. R., & Ruggieri, M. (2004). Epilepsy is not a prominent feature of primary autism. Neuropediatrics, 35, 207–210.

Schultz, R. T. (2005). Developmental deficits in social perception in autism: The role of the amygdala and fusiform face area. International Journal of Developmental Neuroscience, 23(2–3), 125–141.

Pelphrey, K. A., Sasson, N. J., Reznick, J. S., Paul, G., Goldman, B. D., & Piven, J. (2002). Visual scanning of faces in autism. Journal of Autism and Developmental Disorders, 32, 249–261.

Schumann, C. M., & Amaral, D. G. (2006). Stereological analysis of amygdala neuron number in autism. Journal of Neuroscience, 26, 7674–7679.

Pierce, K., Muller, R. A., Ambrose, J., Allen, G., & Courchesne, E. (2001). Face processing occurs outside the fusiform “face area” in autism: Evidence from functional MRI. Brain, 124, 2059–2073.

Schumann, C. M., Hamstra, J., Goodlin-Jones, B. L., Lotspeich, L. J., Kwon, H., Buonocore, M. H., et al. (2004). The amygdala is enlarged in children but not adolescents with autism: The hippocampus is enlarged at all ages. Journal of Neuroscience, 24, 6392–6401.

Piven, J., Saliba, K., Bailey, J., & Arndt, S. (1997). An MRI study of autism: The cerebellum revisited. Neurology, 49, 546–551. Plioplys, A. V., Greaves, A., Kazemi, K., & Silverman, E. (1994). Lymphocyte function in autism and Rett syndrome. Neuropsychobiology, 29, 12–16.

Scoppetta, C., Onorati, P., Eusebi, F., Fini, M., Evoli, A., & Vincent, A. (2003). Autoimmune myasthenia gravis after cardiac surgery. Journal of Neurology Neurosurgery, and Psychiatry, 74, 392–393.

Prior, M. R., Tress, B., Hoffman, W. L., & Boldt, D. (1984). Computed tomographic study of children with classic autism. Archives of Neurology, 41, 482–484.

Sebat, J., Lakshmi, B., Malhotra, D., Troge, J., Lese-Martin, C., Walsh, T., et al. (2007). Strong association of de novo copy number mutations with autism. Science, 316, 445–449.

Prizant, B., Wetherby, A., Rubin, E., Laurent, A., & Rydell, P. (2006). The scerts model: A comprehensive educational approach for children with autism spectrum disorders. Baltimore: Paul H. Brookes.

Sigman, M., & Capps, L. (1997). Children with autism: A developmental perspective. Cambridge, MA: Harvard University Press.

Ratey, J. J., Grandin, T., & Miller, A. (1992). Defense behavior and coping in an autistic savant: The story of Temple Grandin, PhD. Psychiatry, 55, 382–391. Redcay, E., & Courchesne, E. (2005). When is the brain enlarged in autism? A meta-analysis of all brain size reports. Biological Psychiatry, 58, 1–9. Reichenberg, A., Gross, R., Weiser, M., Bresnahan, M., Silverman, J., Harlap, S., et al. (2006). Advancing paternal age and autism. Archives of General Psychiatry, 63, 1026–1032. Richler, J., Luyster, R., Risi, S., Hsu, W. L., Dawson, G., Bernier, R., et al. (2006). Is there a “regressive phenotype” of autism spectrum disorder associated with the measles-mumps-rubella vaccine? A CPEA study. Journal of Autism and Developmental Disorders, 36, 299–316. Rimland, B. (1964). Infantile autism. New York: Appleton-Century-Crofts. Ritvo, E. R., & Garber, H. J. (1988). Cerebellar hypoplasia and autism. New England Journal of Medicine, 319, 1152–1154. Rogers, S. J., & Lewis, H. (1989). An effective day treatment model for young children with pervasive developmental disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 28, 207–214. Rogers, S. J., & Pennington, B. (1991). A theoretical approach to the deficits in infantile autism. Development and Psychopathology, 3, 137–162.

c52.indd Sec4:1018

Singh, V. K. (1996). Plasma increase of interleukin-12 and interferon-gamma: Pathological significance in autism. Journal of Neuroimmunology, 66(1–2), 143–145. Singh, V. K., & Rivas, W. H. (2004). Prevalence of serum antibodies to caudate nucleus in autistic children. Neuroscience Letters, 355(1–2), 53–56. Singh, V. K., Warren, R., Averett, R., & Ghaziuddin, M. (1997). Circulating autoantibodies to neuronal and glial filament proteins in autism. Pediatric Neurology, 17, 88–90. Singh, V. K., Warren, R. P., Odell, J. D., Warren, W. L., & Cole, P. (1993). Antibodies to myelin basic protein in children with autistic behavior. Brain, Behavior, and Immunity, 7, 97–103. Smalley, S. L., Asarnow, R. F., & Spence, M. A. (1988). Autism and genetics: A decade of research. Archives of General Psychiatry, 45, 953–961. Snider, L. A., & Swedo, S. E. (2003). Post-streptococcal autoimmune disorders of the central nervous system. Current Opinion in Neurology, 16, 359–365. Sparks, B. F., Friedman, S. D., Shaw, D. W., Aylward, E. H., Echelard, D., Artru, A. A., et al. (2002). Brain structural abnormalities in young children with autism spectrum disorder. Neurology, 59, 184–192.

8/17/09 5:28:49 PM

References Spezio, M. L., Adolphs, R., Hurley, R. S., & Piven, J. (2007a). Abnormal use of facial information in high-functioning autism. Journal of Autism and Developmental Disorders, 37, 929–939.

Vargas, D. L., Nascimbene, C., Krishnan, C., Zimmerman, A. W., & Pardo, C. A. (2005). Neuroglial activation and neuroinflammation in the brain of patients with autism. Annals of Neurology, 57, 67–81.

Spezio, M. L., Adolphs, R., Hurley, R. S., & Piven, J. (2007b). Analysis of face gaze in autism using “bubbles.” Neuropsychologia, 45, 144–151.

Varoqueaux, F., Aramuni, G., Rawson, R. L., Mohrmann, R., Missler, M., Gottmann, K., et al. (2006). Neuroligins determine synapse maturation and function. Neuron, 51, 741–754.

Splawski, I., Timothy, K. W., Sharpe, L. M., Decher, N., Kumar, P., Bloise, R., et al. (2004). Ca(v)1.2 calcium channel dysfunction causes a multisystem disorder including arrhythmia and autism. Cell, 119, 19–31. Splawski, I., Yoo, D. S., Stotz, S. C., Cherry, A., Clapham, D. E., & Keating, M. T. (2006). Cacna1h mutations in autism spectrum disorders. Journal of Biological Chemistry, 281, 22085–22091. Stachnik, J. M., & Nunn-Thompson, C. (2007). Use of atypical antipsychotics in the treatment of autistic disorder. Annals of Pharmacotherapy, 41, 626–634. Stern, D. (1985). The interpersonal world of the human infant. New York: Basic Books. Stern, L., Francoeur, M. J., Primeau, M. N., Sommerville, W., Fombonne, E., & Mazer, B. D. (2005). Immune function in autistic children. Annals of Allergy, Asthma and Immunology, 95, 558–565. Sutcliffe, J. S., Delahanty, R. J., Prasad, H. C., McCauley, J. L., Han, Q., Jiang, L., et al. (2005). Allelic heterogeneity at the serotonin transporter locus (slc6a4) confers susceptibility to autism and rigid-compulsive behaviors. American Journal of Human Genetics, 77, 265–279. Sweeten, T. L., Bowyer, S. L., Posey, D. J., Halberstadt, G. M., & McDougle, C. J. (2003). Increased prevalence of familial autoimmunity in probands with pervasive developmental disorders. Pediatrics, 112, e420. Tager-Flusberg, H., Paul, R., & Lord, C. (2005). Language and communication in autism. In F. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (pp. 335– 364). Hoboken, NJ: Wiley. Todd, R. D., & Ciaranello, R. D. (1985). Demonstration of inter- and intraspecies differences in serotonin binding sites by antibodies from an autistic child. Proceedings of the National Academy of Sciences, USA, 82, 612–616. Trevarthen, C., & Aitken, K. J. (2001). Infant intersubjectivity: Research, theory, and clinical applications. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 42, 3–48. Tuchman, R. F., & Rapin, I. (1997). Regression in pervasive developmental disorders: Seizures and epileptiform electroencephalogram correlates. Pediatrics, 99, 560–566. Tuchman, R. F., & Rapin, I. (2002). Epilepsy in autism. Lancet Neurology, 1, 352–358. Tuchman, R. F., Rapin, I., & Shinnar, S. (1991). Autistic and dysphasic children: Pt. II. Epilepsy. Pediatrics, 88, 1219–1225. Valicenti-McDermott, M., McVicar, K., Rapin, I., Wershil, B. K., Cohen, H., & Shinnar, S. (2006). Frequency of gastrointestinal symptoms in children with autistic spectrum disorders and association with family history of autoimmune disease. Journal of Developmental and Behavioral Pediatrics, 27(Suppl 2), S128–S136.

c52.indd Sec4:1019

1019

Vincent, A., Dalton, P., Clover, L., Palace, J., & Lang, B. (2003). Antibodies to neuronal targets in neurological and psychiatric diseases. Annals of the New York Academy of Sciences, 992, 48–55. Volkmar, F., & Klin, A. (2005). Issues in the classification of autism and related conditions. In F. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (pp. 5–41). Hoboken, NJ: Wiley. Wang, A. T., Dapretto, M., Hariri, A. R., Sigman, M., & Bookheimer, S. Y. (2004). Neural correlates of facial affect processing in children and adolescents with autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 43, 481–490. Warren, R. P., Cole, P., Odell, J. D., Pingree, C. B., Warren, W. L., White, E., et al. (1990). Detection of maternal antibodies in infantile autism. Journal of the American Academy of Child and Adolescent Psychiatry, 29, 873–877. Weiss, L. A., Escayg, A., Kearney, J. A., Trudeau, M., MacDonald, B. T., Mori, M., et al. (2003). Sodium channels scn1a, scn2a and scn3a in familial autism. Molecular Psychiatry, 8, 186–194. West, L., & Waldrop, J. (2006). Risperidone use in the treatment of behavioral symptoms in children with autism. Pediatric Nursing, 32, 545–549. Williams, D. (1992). Nobody nowhere. New York: Times Books. Williams, J. A., Koegel, R. L., & Egel, A. L. (1981). Response-reinforcer relationships and improved learning in autistic children. Journal of Applied Behavior Analysis, 14, 53–60. Wimpory, D. C., Hobson, R. P., Williams, J. M., & Nash, S. (2000). Are infants with autism socially engaged? A study of recent retrospective parental reports. Journal of Autism and Developmental Disorders, 30, 525–536. Wing, L. (1981). Language, social, and cognitive impairments in autism and severe mental retardation. Journal of Autism and Developmental Disorders, 11, 31–44. Wolf, M., Risley, T., & Mees, H. (1964). Application of operant conditioning procedures to the behaviour problems of an autistic child. Behavior Research and Therapy, 1, 305–312. Wu, S., Jia, M., Ruan, Y., Liu, J., Guo, Y., Shuang, M., et al. (2005). Positive association of the oxytocin receptor gene (oxtr) with autism in the Chinese Han population. Biological Psychiatry, 58, 74–77. Yeargin-Allsopp, M., Rice, C., Karapurkar, T., Doernberg, T., Boyle, C., & Murphy, C. (2003). Prevalence of autism in a US metropolitan area. Journal of the American Medical Association, 289, 49–55.

van Gent, T., Heijnen, C. J., & Treffers, P. D. (1997). Autism and the immune system. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 38, 337–349.

Yirmiya, N., Rosenberg, C., Levi, S., Salomon, S., Shulman, C., Nemanov, L., et al. (2006). Association between the arginine vasopressin 1a receptor (avpr1a) gene and autism in a family-based study: Mediation by socialization skills. Molecular Psychiatry, 11, 488–494.

van Kooten, I. A., Palmen, S. J., von Cappeln, P., Steinbusch, H. W., Korr, H., Heinsen, H., et al. (2008). Neurons in the fusiform gyrus are fewer and smaller in autism. Brain, 131(Pt. 4), 987–999.

Young, L. J., Murphy Young, A. Z., & Hammock, E. A. (2005). Anatomy and neurochemistry of the pair bond. Journal of Comparative Neurology, 493, 51–57.

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Chapter 53

Attention-Deficit/Hyperactivity Disorder MARIA T. ACOSTA, MAURICIO ARCOS-BURGOS, AND MAXIMILIAN MUENKE

lines of scientific evidence that shape our understanding of neurobiology and clinical presentation of ADHD.

Attention-Deficit/Hyperactivity Disorder (ADHD), the most common behavioral disorder of childhood, affects 8% to 12% of children worldwide (Biederman, 2005). ADHD is defined as a persistent syndrome characterized by inattention, excessive motor activity, and impulsivity of a given developmental stage. ADHD is not only a behavioral trait carried by American children, as suggested in the past, but a human behavior variant that can be operatively defined in many populations worldwide (Faraone, Sergeant, Gillberg, & Biederman, 2003). Affected individuals are at increased risk for poor educational achievement, low income, underemployment, legal difficulties, and impaired social relationships (Faraone, Biederman, Mennin, Gershon, & Tsuang, 1996). A conservative estimate for an ADHD prevalence of 5% established that the costs attributable to ADHD in the United States are $42.5 billion per year (range $36 to $52.4 billion; Pelham, Foster, & Robb, 2007). As result of the high prevalence, life-span affection, and transcendent socioeconomic impact, ADHD is the best researched behavioral disorder of childhood (Pelham et al., 2007). Despite the high social impact, it is unclear whether ADHD should be viewed as a nosological entity or as a common variant of human behavior. ADHD occurs as a single disorder in the minority of diagnosed individuals. In many patients, comorbidities such as Oppositional Defiant Disorder (ODD), Conduct Disorder (CD), alcohol or drug abuse, and anxiety or depression are strongly associated. ADHD is more complex than was thought previously and the underlying neurobiology is not well understood. Several concepts have arisen in recent years; ADHD is a biological entity with a strong genetic component that is more heterogeneous than was previously considered. The better we understand the underlying etiology related to the clinical manifestations, the better we can design effective intervention programs. If we define human behaviors on a spectrum of hyperactive-hypoactive or hyperattentive to inattentive, there are several clusters of behavioral subtypes, categorically independent among them (Arcos-Burgos & Acosta, 2007). In this chapter, we focus on research data obtained from several

HISTORY AND EPIDEMIOLOGY Contrary to public belief, ADHD is not a new condition. Hippocrates in 493 BC described a condition that seems to be compatible with what we know as ADHD. He described patients who had “quickened responses to sensory experience, but also were less tenaciousness because the soul moves on quickly to the next impression.” Hippocrates attributed this condition to an “overbalance of fire over water.” In 1845, the German physician, Heinrich Hoffmann, wrote a series of children’s stories with poems and illustrations about children with undesirable behaviors. Two of his characters in those books were boys he called “Johnny Head-in-the Air” and “Fidgety Philip.” Their behavior could be interpreted as attributed to the inattentive or hyperactive/impulsive subtype of ADHD, respectively (Wortis, 1988). In 1902, George Sill, a British physician, delivered a series of lectures to the Royal College of Physicians in England and described a condition characterized by “lack of moral control” among children without noted physical impairments. He believed this behavior was caused by an innate hereditary dysfunction and not by poor rearing or other environment (Rowland, Lesesne, & Abramowitz, 2002). The condition we refer to as ADHD dates to the mid-twentieth century, when physicians developed a list of clinical observations that when they appeared in combination would receive a series of different names, including “minimal brain damage syndrome,” “minimal brain dysfunction,” and “hyperkinetic reaction of childhood.” These names described the disorder that we know today as attention-deficit/hyperactivity disorder. Early attempts to link attention deficits and behavioral disturbances to brain dysfunction were shaped by the experience of the encephalitis epidemic of 1917–1918. Children who survived the infection experienced subsequent problems 1020

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History and Epidemiology 1021

including hyperactivity, personality changes, and learning difficulties. However, despite many years of research attempting to identify specific etiologic correlates of the disorder, no single cause has been identified and ADHD is currently best understood as a group of behavioral symptoms that reflect excessive impulsivity, hyperactivity, or inattention. The first empirically based official set of diagnostic criteria for ADHD was delineated in the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM-III; 1980). Early focus on hyperactivity symptoms and later shifts toward inattention and impulsivity symptoms are reflected in the changes to the Diagnostic and Statistical Manual of Mental Disorders, revised third edition (DSM-III-R; American Psychiatric Association, 1987). The current classification criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV-TR; American Psychiatric Association, 2000) for ADHD allow diagnosis of subtypes as predominantly inattentive, predominantly hyperactive, or combined (see Table 53.1). TABLE 53.1

Although current diagnostic criteria include hyperactivity and impulsivity as does the nomenclature, attention deficit remains the core of the disorder. These successive changes in diagnostic criteria reflect a combination of empirical research findings and expert committee consensus. Taken as a whole, these criteria require an illness pattern that starts early, is enduring, and has led to impairment. To make this diagnosis correctly, the clinician must be familiar with normal development and behavior, gather information from several sources to evaluate the child’s symptoms in different settings, and construct an appropriate differential diagnosis for the present complaints. It is important to distinguish children with ADHD from those children whose parents or teachers are mislabeling normal behavior as pathological—children who are truly unaffected. When used by appropriated trained examiners, the diagnostic criteria demonstrate high reliability on individual items for the overall diagnosis (Shaffer et al., 1996).

Symptoms in the DSM IV-TR for ADHD: Diagnostic criteria for Attention-Deficit/Hyperactivity Disorder

Six or more of the following symptoms of Inattention and/or six or more symptoms of Hyperactivity-Impulsivity. symptoms have persisted for more than 6 months to a degree that is maladaptative and inconsistent with developmental level: 1. Inattention symptoms: Often fails to pay close attention to details or makes careless mistakes in schoolwork, work, or other activities Often has difficulty sustaining attention in tasks or play activities Often seems to not be listening when spoken to directly Often does not follow through on instructions and fails to finish schoolwork, chores, or duties in the workplace (not due to oppositional behavior or failure to understand instructions) Often has difficulty organizing tasks and activities Often avoids, dislikes, or is reluctant to engage in tasks that require sustained mental effort (such as schoolwork or homework) Often loses things necessary for tasks or activities (e.g., toys, school assignments, pencils, books, or tools) Is often easily distracted by extraneous stimuli Is often forgetful in daily activities 2. Hyperactivity-Impulsivity symptoms Often fidgets with hands or feet or squirms in seat Often leaves seat in classroom or in other situations in which remaining seated is expected Often runs about or climbs excessively in situations in which it is inappropriate (in adolescents or adults may be limited to subjective feeling of restlessness) Often has difficulty playing or engaging in leisure activities quietly Is often “on the go” or often acts as if “driven by a motor” Often talks excessively 3. Impulsivity Often blurts out answers before questions have been completed Often has difficulty awaiting a turn Often interrupts or intrudes on others (e.g., butts into conversations or games) Symptoms that cause impairment were present before age 7 years Some impairment from symptoms is present in two or more settings

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1022 Attention-Deficit/Hyperactivity Disorder

Epidemiological research in ADHD has been hampered by difficulties involving the diagnosis of ADHD and the numerous changes in its definition over the past 20 years. It has become clear that individuals with ADHD comprise a heterogeneous population sharing a cluster of symptoms. The lack of available biological markers and variable definitions make an adequate comparison of epidemiological studies difficult. Despite these difficulties, rigorous estimates indicate that ADHD has been described almost everywhere around the world. Community studies have estimated prevalence ranging between 1.7% and 21%, depending on the population and the diagnostic methods used (Faraone et al., 2003). These results suggest that across populations under diverse geographic, racial, ethnic, and socioeconomic conditions there is a sizable percentage of school-age children with ADHD. Furthermore, because the evolution of criteria from DSM-III to DSM-IV have broadened the limits of case definition, more children appear to be affected (Lahey, Schaughency, Hynd, Carlson, & Nieves, 1987). This is largely a function of the increased emphasis on attentional problems as opposed to a more narrow focus on hyperactivity in earlier diagnostic sets. As a result of these changes in definition, girls have been diagnosed as having ADHD more frequently than they were in the past (Biederman, 1998; Gaub & Carlson, 1997b). Caution must be used when comparing epidemiological data from different studies, because diverse instruments and questionnaires have been used in different trials and the DSM-IV definition of impairment is operationally vague. These issues are a source of subjective knowledge to the clinical evaluator when deciding the affection status. Furthermore, random selection of the sample versus “volunteer” participation could introduce a relevant bias in the estimation of epidemiological parameters (Acosta, Arcos-Burgos, & Muenke, 2004). On the one hand, stigmatization of ADHD patients and their families may lead to an underestimation of its prevalence. On the other hand, patients already under medication will exhibit less severe symptoms at the time of the screening. Differences in perception between parents and teachers should also be considered in ADHD studies. Teacher reports may be influenced by factors such as class size, teacher training, or disciplinary aptitudes and practices. Although the DSM-IV age criterion to establish the diagnosis is 7 years, new classifications should include a broad range of age for the diagnosis because preschoolers and adults clearly are part of the continuous spectrum of clinical manifestations in ADHD (Rowland et al., 2001).

DIAGNOSIS The diagnosis of ADHD is made by obtaining a careful clinical history. A child with ADHD is characterized by

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a considerable degree of inattentiveness, distractibility, impulsivity, and often hyperactivity that is inappropriate for the developmental stage of the child. Current clinical definitions and diagnosis are done according to the guidelines and criteria for diagnosis established by the DSMIVTR or ICD-10 (Swanson & Castellanos, 1998). As noted, the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) for ADHD allow for diagnosis of subtypes as predominantly inattentive, predominantly hyperactive, or combined. Criteria for each DSM-IV subtype require positive identification of 6 or more of the 9 symptoms in each respective category. There are 4 additional criteria: age of onset by 7, ADHD-specific adaptive impairments, pervasiveness, and separation from other existing conditions. The combined subtype is the most commonly represented subgroup accounting for 40% to 75% of all ADHD individuals, followed by the inattentive subtype (30% to 40%), and the hyperactive-impulsive subtype (less than 15%; Acosta et al., 2004, 2008; Gaub & Carlson, 1997a; Palacio et al., 2004). Alongside clinical interviews and direct observations, DSM-IV diagnosis of ADHD incorporates reports from parents and teachers. Under these diagnostic systems, individuals are classified as affected if they meet a specific number of criteria, determined with reliable and validated psychiatric instruments. Variations in interpretation of symptoms and total prevalence are influenced by cultural differences. ADHD, however, is a condition described worldwide (Faraone et al., 2003). An important genetic component in ADHD is currently accepted. Multiple lines of evidence suggest that the relationship between risk genes and ADHD symptoms is likely to be pleiotropic, that is, the same gene or genes may be associated with different aspects of the phenotypes (Jain et al., 2007; Willcutt et al., 2002). Similarly, several studies have suggested that ADHD represents one extreme of the quantitative manifestation of normal behavior (Curran et al., 2003; Levy, Hay, McStephen, Wood, & Waldman, 1997; Arcos-Burgos & Acosta, 2007; Stevenson, 1992). These observations would imply that diagnosis of ADHD requires a better understanding and definition, not only in terms of categorical diagnostic criteria, but also of the continuous trait in the population. A specific number of positive symptoms are needed to reach the diagnosis of ADHD, according to the categorical diagnostic criteria used by the DSM-IV. In contrast, ADHD behavior can be considered as a continuous trait in the population with variations in clinical manifestation along the spectrum of clinical symptoms. This spectrum would vary from normal behavior to severe forms of symptoms associated with the diagnosis of ADHD. Based on these considerations, several researchers have concluded, [B]oth categorical (diagnostic) and continuous

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Diagnosis

(quantitative trait) approaches to phenotypic dimension are valid and may be complimentary in molecular genetic studies on ADHD (Acosta et al., 2004; Curran et al., 2003). Among statistical approaches, latent class analysis (LCA)—a categorical approach to ADHD applied to parent report rating scales—is often used. The goal of LCA is to identify naturally occurring clusters of symptoms without imposition of a cutoff for the number of positive symptoms required for diagnosis (as in DSM-IV). Applied to parent reports of ADHD symptoms, LCA (Magidson & Vermunt, 2003) has repeatedly yielded six to eight clusters that appear to consistently account for the distribution of ADHD-related symptomatology across cultures, types of samples, population type, and diagnostic methods (Rasmussen et al., 2002; Rohde et al., 2001; Todd et al., 2001). Indirect evidence for the neurobiological validity of the observed latent classes derives from the observation that these clusters show higher heritability estimates than DSM-IV subtypes, that is, monozygotic cotwins are significantly more likely to resemble one another in latent class membership than on DSM-IV subtype classification (Hudziak et al., 1998; Neuman et al., 2001; Rasmussen et al., 2002; Rohde et al., 2001; Todd et al., 2001; Volk, Neuman, & Todd, 2005). The six to eight clusters typically established in LCA include three that are particularly clinically relevant: severe inattentive, severe combined, and severe hyperactive. These three clusters correspond roughly to the typically defined DSM-IV subtypes (Lahey et al., 2004) However, subjects not meeting DSM-IV criteria are also often included in clusters. Subjects included in the DSM-IV predominantly inattentive ADHD subtype are found to be divided across several latent classes and the severe inattentive latent class contains some DSM-IV predominantly inattentive subtype cases. The inattentive and combined LCA-derived subtypes demonstrate clinical stability over time. In contrast, persons assigned to the predominantly hyperactive/impulsive subtype typically evolve to a different subtype over time. These findings suggest the presence of more subtle independent groups within the ADHD phenotype than those advocated by the classical categorical classification (Acosta et al., 2008; Rasmussen et al., 2002). Figures 53.1 and 53.2 from a recent publication, Acosta and colleagues (2008) show the group of different subtypes observed in a sample of families with one affected member with ADHD. A total of 6 to 8 clusters of symptoms are present. Similar observations have been published in other cross cultural studies. This figures shown that regardless of age, cluster classification follows similar patterns. In a sample of 1,010 individuals from a nationwide recruitment of unilineal families with at least one child with ADHD and another either affected or clearly unaffected with six to eight clusters similar to other cross-cultural studies. These figures shown that regardless of age, cluster classification follows similar patterns.

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1023

To add complexity to the diagnostic process in ADHD, it is important to understand that many other conditions may mimic ADHD, as the behavioral manifestations seen in ADHD are part of the spectrum of clinical presentation for those conditions. Differences in attention and activity level are part of numerous genetic conditions, some of which are listed in Table 53.2 These conditions share symptoms also present in ADHD. Thus the additional diagnosis and clinical components of the concomitant diagnosis rule out the diagnosis of primary ADHD. In contrast if ADHD is considered as a continuous trait, these associations are highly expected. In addition, recognition of the neurobiological deficits underlying the behavioral deficits in these entities may help to better understand the neurobiology in ADHD. Alcohol fetal syndrome (Nash et al., 2006; O’Malley & Nanson, 2002) and genetic disorders such as Turner syndrome (Russell et al., 2006), neurofibromatosis type 1(Acosta, Gioia, & Silva, 2006), and Klinefelter syndrome (Giedd et al., 2007; Simpson et al., 2003) are highly associated with ADHD symptoms. Understanding the impact of these and other genetic deficits on early brain development and brain function may also help our understanding of the mechanism underlying ADHD symptoms (Acosta et al., 2006). Use of Scales for Diagnosis of ADHD Current diagnosis of ADHD is based on use of different behavioral scales that adequately assess these dimensions of ADHD. Many scales are available for the evaluation of ADHD symptoms and classifications according with the subtypes mandated by the DSM-IV. Unlike many other neuropsychiatric conditions that use self-report scales for the diagnosis, ADHD scales are usually rating scales that are completed by an adult informant, such as teachers, parents, or other caregivers. This is particularly helpful as it reflects differences in the manifestation of externalizing symptoms such as disruptive behavior compared with internalizing symptoms. By definition, the assessment of internalizing and related symptoms requires information regarding a youth’s internal experience. While internalizing symptoms may go unnoticed by caregivers, youths with externalizing behaviors are publicly observable and are typically referred because of the problems they pose to parents and teachers. In addition, although children and adolescents are the best reporters of their subjective experience, they tend to underestimate their externalizing behaviors. Thus, parents and teachers are the optimal informants (Loeber, Green, Lahey, & Stouthamer-Loeber, 1991). When a comprehensive clinical evaluation is the goal, then both teachers’ and parents’ reports should be included. Regardless of the basis for this discrepancy among informants, it highlights the need for multiinformant assessment, particularly as DSM-IV criteria require impairment across settings.

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1024 Attention-Deficit/Hyperactivity Disorder

5 Cluster Model Vb ADHD Children 4–11 Years Old

(A) 1.2

6 Cluster Model Vb ADHD Children 12–17 Years Old

(B) 1.0 6

1.0

0.8 4 7

6 0.8

0.6

4 0.6 2

0.4 0.4

3 2

5 0.2 1

1 0.2

vb_inat_q1 vb_inat_q2 vb_inat_q3 vb_inat_q4 vb_inat_q5 vb_inat_q6 vb_inat_q7 vb_inat_q8 vb_inat_q9 vb_h_i_q10 vb_h_i_q11 vb_h_i_q12 vb_h_i_q13 vb_h_i_q14 vb_h_i_q15 vb_h_i_q16 vb_h_i_q17 vb_h_i_q18

⫺0.2

(C)

⫺0.2

vb_inat_q1 vb_inat_q2 vb_inat_q3 vb_inat_q4 vb_inat_q5 vb_inat_q6 vb_inat_q7 vb_inat_q8 vb_inat_q9 vb_h_i_q10 vb_h_i_q11 vb_h_i_q12 vb_h_i_q13 vb_h_i_q14 vb_h_i_q15 vb_h_i_q16 vb_h_i_q17 vb_h_i_q18

0.0

0.0

7 Cluster Model Vb ADHD Adults 1.0

6 0.8

Probability Profile

7

1 Few 2 Mild Inattentive 3 Mild Combined 4 Moderated Combined 5 Talkactive-hyperactive 6 Severe Combined 7 Severe Inattentive

Graph A(%) Graph B(%) Graph C(%) 17.2 15.6 29 19.6 13.4 11.2 20.7 17.1 0 10.1 23.4 27.1 15.5 0 12.2 7.1 18 20.6 9.9 14.5 0

0.6 4 0.4 3 2 0.2 5 1

vb_inat_q1 vb_inat_q2 vb_inat_q3 vb_inat_q4 vb_inat_q5 vb_inat_q6 vb_inat_q7 vb_inat_q8 vb_inat_q9 vb_h_i_q10 vb_h_i_q11 vb_h_i_q12 vb_h_i_q13 vb_h_i_q14 vb_h_i_q15 vb_h_i_q16 vb_h_i_q17 vb_h_i_q18

0.0

Figure 53.1 ( Figure C.49 in color section) LCA analysis for 18 VAS-P items.

item (symptoms). From: Acosta, M.T., Castellanos, F. X., Kelly, M. D.,

Note: A: for children B: adolescents, and C: adults. Each figure shows the latent classes endorsement probabilities (y-axes) for every VAS-P

deficit/hyperactivity disorder and comorbid conditions. Journal of the

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Balog, J. Z., Eagen, P., et al. (2008). Latent class subtyping of attentionAmerican Academy of child and Adolescent Psychiatry, 47:7.

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Diagnosis ADHD Subtype Status by Cluster, 4–11 Years

100

75

75

50

50

25

25

0

0 1 1 2 4 5 6

2

4

5

6

1

2 1 2 3 4 6 7

Few Mild Inattentive Moderated Combined Talkactive-Impulsive Severe Combined

3 4 6 Few Mild Inattentive Mild Combined Moderate Combined Severe Combined Severe Inattentive

7

ADHD Subtype Status by Cluster, Adults

(C) 100

ADHD Subtype Unaffected Inattentive Hyperactive-impulsive Combined

75

Percent

ADHD Subtype by Cluster, 12–17 Years

(B)

100

Percent

Percent

(A)

1025

1 2 3 4 5 6 7

50

25

Few Mild Inattentive Mild Combined Moderate Combined Talkactive-impulsive Severe Combined Severe Inattentive

0 1

2

3

4

5

6

7

Figure 53.2 (Figure C.50 in color section) Comparison of ADHD status as defined by the DSM-IV best estimate, and posterior cluster membership (each cluster adds to 100%).

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New classifications and definitions of ADHD (e.g., upcoming DSMV) should consider the wide diversity of symptoms and manifestations throughout the life span. Advances in understanding the neurobiology

and the clinical manifestations will help to develop a more comprehensive set of diagnostic tools for primary ADHD patients. From: Acosta, M. T., Castellanos, F. X., Kelly, M. D., Balog, J. Z., Eagen, P., et al. (2008). Latent class subtyping of attention-deficit/hyperactivity disorder and comorbid conditions. Journal of the American Academy of child and Adolescent Psychiatry, 47:7.

Scales used for the diagnosis of ADHD may be classified as narrowband scales and broadband scales (Collett, Ohan, & Myers, 2003). Broadband scales cover several behaviors, symptoms, and diagnosis; thus, they are especially useful in the diagnosis of ADHD and the identification of comorbidities. Examples of these scales are the Child Behavior Checklist (Achenbach & Edelbrock, 1983; Achenbach & Ruffle, 2000) or the Behavioral Assessment System for Children (Reynolds & Kamphaus, 1992), DICA (Ezpeleta et al., 1997), DISC (Schwab-Stone et al., 1996; Shaffer et al., 1996) among others. The lengths of these instruments make them less useful for repeated measurements (e.g., when using scales for a monitored treatment

response. In contrast, narrowband scales are quick to administer and useful for focused evaluation and repeated measurements (see Table 53.3). Most of these scales have been designed and are highly suitable for school-age children. It is less clear whether they are equally appropriate for older and younger patients and for girls (several studies have demonstrated differences in scores in boys and girls; Arnold, 1996; Gaub & Carlson, 1997b). As mentioned, new classifications and tools for diagnosis are needed to better consider greater age ranges, including adults, and differences in gender—groups that are currently underdiagnosed by available tools.

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1026 Attention-Deficit/Hyperactivity Disorder TABLE 53.2

Selected genetic disorders associated with ADHD symptoms

Diagnosis

Genetic or Neurobiology Alteration

Cognitive Profile

References

Turner Syndrome

Deletion of part or all of one X chromosome

Normal verbal abilities, visuospatial deficits

Russell et al. (2006)

Neurofibromatosis type 1

Germ line mutations in the NF1 gene mapping to 17q11.2

ADHD symptoms 40%–60% Learning disabilities up to 80% Mental retardation 8%–10%

Williams syndrome

Microdeletion in at least 25 genes on chromosome 7q11.23

ADHD symptoms in 64.7%

ADHD diagnosis 24%

Relative strengths in verbal short-term memory and language and extreme weakness in visuospatial construction, including writing, drawing, and pattern construction

Acosta et al. (2006)

Leyfer, Woodruff-Borden, Klein-Tasman, Fricke, & Mervis (2006)

Social interaction characteristic Smith-Magenis syndrome

Interstitial deletion of chromosome l7p11.2.

Moderate to severe mental retardation IQ scores between 20 and 78

Smith, Dykens, & Greenberg (1998)

Relative weaknesses in sequential processing and short-term memory and relative strengths in longterm memory and perceptual closure Behavioral problems including ADHD symptoms in up to 80% Self-injurious behaviors Fragile X syndrome

Phenylketonuria

Mutation of a specific gene on the long arm of the X chromosome in which the number of cytosine-guanine-guanine (CGG) triplet repeats expands beyond normal

ADHD symptoms 55%–59%

Aberrant or absent phenylalanine hydroxylase (PAH) gene

Mental retardation and autism spectrum without treatment

IQ from normal to mental retardation

Farzin et al. (2006); Sullivan et al. (2006)

Executive function deficits Association with autism

Antshel & Waisbren (2003)

Prefrontal system deficits main deficits Manifestations dependent on exposure timing: prenatal exposure is associated with a higher likelihood of expressing hyperactive/impulsive symptoms, and postnatal exposure is associated with a higher likelihood of expressing inattentive symptoms

Fetal Alcohol syndrome

In utero exposure to alcohol

ADHD (41%), followed by learning disorder (17%), and oppositionaldefiant/conduct disorder (16%)

Nash et al. (2006)

Prevalence rates of ADHD across the groups generally were high risk IQ variable from normal to mild to moderated deficits Klinnefelter syndrome

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47,XXY, the occurrence of an additional X chromosome

Executive function impairments, high frequency ADHD symptoms

In males, aneuploidy and is found in between 1 of 6001,2 and 1 of 10003

IQ within normal limits to mild mental retardation

Giedd et al. (2007)

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Clinical Presentation TABLE 53.3

1027

Selected Narrowband scales for ADHD diagnosis

Scale

Administration Time

Ages

Scoring Items

Reference

Conners Rating Scales-Revised (CRS-R)

20–30 minutes

3–17 years

Parents: 80 items Teachers: 59 items Adolescents: 87 items

Conners et al. (1997)

IOWA Conners Teacher Rating Scales

5 minutes

6–12 years

10 items

Loney & Milich (1982)

The Swanson, Nolan and Phelam-IV Questionnaire (SNAP IV)

20–30 minutes full version

5–11 years

Full version: 90 items ADHD ⫹ ODD: 31 items

Gau et al. (2008); Swanson et al. (2001)

The Swanson, Kotkin, Atkins, M Flynn and Phelam Scale (SKAMP)

5 minutes

7–12 years

13 items

Wigal, Gupta, Guinta, & Swanson (1998)

Strengths and Weaknesses of ADHD symptoms and normal behavior (SWAN)

5 minutes

5–11 years

26 items

Hay, Bennett, Levy, Sergeant, & Swanson (2007)

ADHD Rating Scale IV (ADHD RS-IV)

5–10 minutes (Spanish translation)

5–18 years

18 items

DuPaul, Ervin, Hook, & McGoey (1998)

Vanderbilt ADHD Teacher and Parent Rating Scale (VADTRS & VADPRS)

10–15 minutes Spanish and German translations

6–12 years

43 items

Wolraich et al. (2003)

Brown Attention Deficit Disorder Scales for Children and Adolescents (BADDS)

10–15 minutes

3–12 years 8–-18 years

3- to 7-year-olds with parent form & teacher form: 44 items

Brown et al. (2001)

8- to 12-year-olds with parent and teacher forms and self-report: 50 items 12- to 18-year-olds self-report: 40 items

The decision which scales to use depends of the length of the scale, the needs for additional information such as comorbidity, and the age of the patient. Narrowband scales are based on DSM-IV and have good face validity as their items are derived from a clear diagnosis construct for ADHD. It is difficult to establish any preference as comparisons between scales are very limited. In clinical settings, a combination of broadband scales and narrow scales are used for the initial evaluation. Narrowband scales are preferred for repetitive measurements, monitoring of treatment response, and longitudinal evaluation of ADHD symptoms. In looking forward, there are proposals to expand the set of diagnostic symptoms to include executive functions (such as time management and multitasking), especially in older individuals (Barkley, Edwards, Laneri, Fletcher, & Metevia, 2001). In fact, the symptom “often has difficulties organizing” is the most complex DSM item and appeared for the first time in DSM-IV. New definitions also need to include age-related changes and manifestation according to gender, as well as a broader spectrum of severity that will include milder cases. Executive functions are currently considered a core symptom in the manifestation of ADHD. Observations and evaluations of these complex behaviors in the context of everyday life have become important for evaluation of the impact that ADHD behaviors have in daily performance. Evaluation scales like the Behavior Rating Inventory of Executive Function (BRIEF;

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Gioia, Isquith, Kenworthy, & Barton, 2002; Gioia, Isquith, Retzlaff, & Espy, 2002), are used more frequently as a complementary evaluation to provide a more comprehensive overview of the impact of ADHD components in daily life and for follow-up assessment of intervention plans.

CLINICAL PRESENTATION The diagnosis of ADHD is made by obtaining a careful clinical history. As mentioned, the child with ADHD is characterized by inattentiveness, distractibility, impulsivity, and often hyperactivity that is inappropriate for the developmental stage of the child. Although ADHD is often first observed in early childhood, many overactive toddlers will not develop ADHD. Other common symptoms include low tolerance to frustration, shifting activities frequently, difficulties in organization, and daydreaming. These symptoms are usually pervasive; however, they may not all occur in all settings. According to subtype, behavioral manifestations may be present or not. Children with predominantly inattentive subtype may have more school difficulties and fewer difficulties with peers or family. Conversely, children with excessive hyperactivity or impulsive symptoms may do relatively well in school, but have difficulties at home or in situations with less guidance and structure.

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1028 Attention-Deficit/Hyperactivity Disorder

Factors such as environment, level of tolerance, parenting styles, IQ, and socioeconomic status may play a role in the age of diagnosis of the ADHD symptomatology. Internal factors related to the diagnosis (e.g., severity, subtype, comorbidities) are also fundamental at the age of presentation.

AGE OF ONSET Even though DSM-IV requires the presence of the symptomatology compatible with the diagnosis of ADHD before age of 7 years, there is a consensus among clinicians about the enormous variability in the presentation and detection of symptoms. The symptoms of some patients are described by their parents as having been present “forever,” even before birth, meaning that those patients have been extremely hyperactive, had early motor development, and were always “on-the-go.” In other cases, the diagnosis is given several years after the cutoff of age 7 or even in adulthood. Those patients possibly can refer retrospectively to some vague symptomatology that was not severe enough to produce impairment or to diagnose this condition earlier. There are significant differences between the hyperactive/impulsive type and the inattentive type in terms of the age at which symptoms emerge. Retrospective parent reports have demonstrated that children with ADHD become noticeably impaired around the age of 3 ½, with a median age of onset of the first ADHD symptoms appearing around 1 year later. The age of onset of the hyperactive type (mean 4.21 years) is earlier than that of the combined type, and both are earlier than the inattentive type. Virtually all children with the hyperactive/ impulsive type and 83% of combined type are considered impaired before they reach the 7 years of age. By 9 years, all would meet formal ADHD criteria for diagnosis (Lahey et al., 2004). In contrast, the developmental trajectory characterizing the inattentive type is quite different: ADHD of the inattentive type is often not apparent until around the first grade (mean age 6.1 years) and in some cases not until around 14 years will patients finally meet the criteria for diagnosis according to DSM-IV symptoms. During the preschool years, symptoms like inattention without hyperactivity are less obvious and only around 50% of children will demonstrate some type of impairment by first grade. Even though up to 85% of children with inattentive type may have some symptoms before age 7, only less than 40% will have a real impairment by 7 years of age. Many children may continue having symptoms on and off, making adaptations on their own or taking advantage of environmental accommodations that allow

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them to go through the first years of education without a formal diagnosis. In some cases, only when they embark on demanding academic programs or in a profession, does the real impairment became evident. Chronic symptoms of ADHD in adults can have a significant impairment in academic, social, work, and family functioning. Frequency of ADHD in adults has been estimated to be between 2.9% to 6% (Barkley, Fischer, Smallish, & Fletcher, 2002; Biederman, 2004; Faraone, Biederman, Spencer, et al., 2000). Adults must have childhood onset, persistent, and current symptoms of ADHD to be diagnosed with the disorder. Parent reports of persistence of symptoms in adulthood in previous diagnosed patients estimate that around 50% of children with diagnosis of ADHD will continue fulfilling the criteria for the diagnosis (Barkley et al., 2002). Adults with ADHD often present with marked inattention, distractibility, organization difficulties and poor efficiency, which culminate in life histories of academic and occupational failure (Barkley et al., 2002; Biederman, 2004; Faraone, Biederman, Spencer, et al., 2000). These findings are of considerable importance to the clinician. The clinician should be able to make the diagnosis of ADHD combined type or predominantly hyperactive type during the preschool years. In addition, the age of onset criteria specified in DSM-IV for ADHD of the inattentive type, which indicates that “symptoms need to be present before age 7” needs to be considered carefully as evidence of impairments may not be present until several years later. Adults with ADHD also need careful consideration, as there is not an absolute consensus about the best diagnostic pathway to follow in those cases that present with first-time evidence of impairment in adulthood. In retrospect, most of the symptoms have been present for a long time, but individual adaptations, different styles, or variability in demands contained the evidence of impairment until later in life.

GENDER Only recently has it been recognized that girls and boys have similar risks for ADHD. It was believed that clinical manifestations were gender-specific, but it is now clear that boys and girls have a similar profile of social, behavioral, and academic performance impairment. Gender-specific differences have been observed among clinical samples and population-based samples. Externalized symptoms and behavioral problems are an important reason for early evaluation. Boys are more frequently referred for evaluation early in life than girls. In clinic-referred samples, girls more often present with inattention and internalizing symptoms and less aggression than boys with ADHD.

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Comorbidities 1029

A meta-analysis (Gaub & Carlson, 1997b) compared ADHD symptoms, intellectual and academic functioning, comorbid behavioral problems, social behavior, and family problems in boys and girls. No differences were found in impulsivity, academic performance, social functioning, fine motor skills, parental education, or parental depression. Girls, however, were less hyperactive than boys, had fewer externalizing behaviors, and were more intellectually impaired. Additional differences were observed in associated comorbidities. Girls have a greater frequency of anxiety depression, low self-esteem, a sense of limited control, and significant difficulties in social environments. Boys present more often with signs of ODD and CD than girls (Hartung et al., 2002; Lahey et al., 2007). A methodological review concluded that differences between boys and girls are not related to the diagnosis of ADHD, but are part of gender differences per se (Biederman et al., 2005). Boys and girls with ADHD do not have gender-specific differences in prefrontal executive function. When girls with ADHD are compared with controls, they have significant impairments in academic performance, behavioral problems, and disruptive behavioral disorders, and are more vulnerable to drug abuse and academic failure. Furthermore, girls with combined type of ADHD are more vulnerable to be abused and suffer more social rejection. Lastly, girls with the inattentive type have the tendency to be more isolated socially (Greene et al., 2001; Hinshaw, 2002; Hinshaw, Carte, Sami, Treuting, & Zupan, 2002; Rucklidge & Tannock, 2001). In summary, findings from different studies have demonstrated that ADHD is more frequent in girls than was previously recognized. They have a similar profile of behavioral and academic problems when compared with boys who have ADHD. Girls may be less likely identified as they have fewer externalized symptoms than boys. Thus, differences in clinical manifestations are related to gender, not to the clinical components of ADHD symptoms.

COMORBIDITIES Childhood ADHD Through the life cycle, key clinical features observed in patients with ADHD are associated comorbidities. Externalizing and internalizing disorders vary in their frequencies in the ADHD population among different studies and populations (Pliszka, 2000; Sanders, Arduca, Karamitsios, Boots, & Vance, 2005). Externalizing disorders, such as CD and oppositional defiant disorder (ODD) occur with frequencies up to 50% (Palacio et al., 2004). An estimated 20% of children diagnosed with

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ADHD have CD, and 30% to 45% have ODD (Acosta et al., 2004; August, Realmuto, Joyce, & Hektner, 1999; Burke, Loeber, Lahey, & Rathouz, 2005; Palacio et al., 2004). Among the internalizing disorders, the prevalence of co-occurrence is somewhat lower with 10% to 20% of children with ADHD exhibiting mood disorder (Acosta et al., 2004; Eiraldi, Power, & Nezu, 1997; Sanders et al., 2005; Vance, Sanders, & Arduca, 2005). In addition, the association of ADHD with both depressive disorders and anxiety disorders has been replicated by new epidemiological studies (Angold, Costello, & Erkanli, 1999; Costello, Egger, & Angold, 2005). It is now clear that assessment of the underlying structure of these disorders, to discriminate natural symptom aggregation across ADHD domains and provide insight into the cause of comorbidity, is necessary to better understand the psychopathology of these entities. Conduct Disorder and antisocial behaviors, as comorbidities in ADHD, have been better defined in terms of genetic association (Arcos-Burgos et al., 2004; Faraone, Biederman, & Monuteaux, 2001; Palacio et al., 2004). A recent study (Jain et al., 2007) supports the hypothesis that major genes underlie a broad behavioral phenotype in some families that may manifest as a range of symptoms including ADHD, disruptive behaviors, and alcohol abuse or dependence. These data are consistent with the notion that different behavioral phenotypes comprise a nosological entity and that the concept of comorbidity is inadequate (Jain et al., 2007; Palacio et al., 2004). The picture is less clear for internalizing disorders. Anxiety and depression may have different phenotypic expressions modified by comorbidity with ADHD, or by genetic and environmental factors that alter the final phenotype (Kendler, 1996). Special consideration is necessary for ODD, which is not only highly comorbid with ADHD but is also a predictor of two different developmental trajectories ending in either CD or anxiety. The path that the ODD phenotype selects is dictated by complex interactions between genetics and environment (Burke et al., 2005; Lavigne et al., 2001). As mentioned, variations of these comorbidities and their association with ADHD are also related with gender and age. In a systematic evaluation of the impact of gender on the clinical features of ADHD, Faraone et al. (Faraone, Biederman, Mick, et al., 2000) reported that girls with ADHD were at less risk for comorbid disruptive behavior disorder than boys with ADHD. Because disruptive behavioral disorder drives referral, this might explain the substantial discrepancy in the male/female ratio between clinic-referred (10:1) and community samples (3:1) of children with ADHD (Biederman et al., 2002). Furthermore, this gender discrepancy suggests that girls with ADHD might be underidentified and undertreated (Biederman et al., 2005)

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1030 Attention-Deficit/Hyperactivity Disorder

Comorbidities in Adult ADHD Follow-up studies have found that 5% to 66% of children with ADHD persist with this disorder in adulthood (Biederman, 1998; Weiss, Murray, & Weiss, 2002; Wilens et al., 2005). Current epidemiological studies estimate the prevalence of adult ADHD to be between 3% and 5% (Faraone & Biederman, 2005; Kessler et al., 2005). Furthermore, studies of referred and nonreferred adults with a clinical diagnosis of childhood onset and persistent ADHD revealed that clinical correlates—demographic, psychosocial, psychiatric, and cognitive features—mirrored well-documented findings among children with ADHD (Biederman, 2004). A study (Acosta et al., 2008) using the 18 Vanderbilt Assessment Scale for Parents (VAS-P) items for inattention, impulsivity, and hyperactivity, produced similar clustering patterns—six to eight clusters and similar cluster definitions, as shown in other Latent Class Analysis (LCA) studies of ADHD symptoms (Hudziak et al., 1998; Neuman et al., 2001; Rasmussen et al., 2002; Rohde et al., 2001; Todd et al., 2001; Volk, Henderson, Neuman, & Todd, 2006). Since the VAS-P has not been used for this purpose in adults and symptom severities are known to differ among age groups, LCA was performed separately for children, adolescents, and adults. While the age groups differed on certain hyperactivity symptoms, overall the symptom-clustering patterns between age groups were strikingly similar. Adding comorbidities had little effect on the cluster distributions. This comparability of symptom profiles among age groups with a broad range of internalizing and externalizing symptoms supports the use of LCA in genetic cohorts that include both adults and children. Lifetime prevalence rates of comorbid anxiety disorders in adults with ADHD approach 50%, whereas mood disorders, antisocial disorders, and alcohol or drugs dependency also show substantial prevalence rates (Biederman, 2004; Shekim, Asarnow, Hess, Zaucha, & Wheeler, 1990). It is clear that ADHD persists and is present in adulthood. Studies have demonstrated that ADHD in adults is similar in both genders and the validity of the diagnosis is supported (Biederman, 2004).

contribution to phenotypic variation, reaching 0.91 (Levy et al., 1997). Adoption studies have also confirmed that genetics rather than shared environment cause familial clustering of ADHD (Faraone et al., 2005; Sprich, Biederman, Crawford, Mundy, & Faraone, 2000). Family studies have confirmed the increased recurrence risk by comparing the ratio of the prevalence of ADHD in various kinds of relatives with the population prevalence using the ␭ statistic (Faraone, Biederman, & Monuteaux, 2000; Faraone et al., 2005; Sprich et al., 2000). Independent complex segregation analyses consistently demonstrated that the model best fitting the data was that of a major autosomal dominant/ codominant gene (Lopera et al., 1999; Maher, Marazita, Moss, & Vanyukov, 1999) Additionally, candidate genes selected because of their theoretical and empirical involvement in the physiopathogenesis of ADHD have shown significant association/linkage to ADHD even though they disclose very small effect sizes (reviewed in Acosta et al., 2004). Linkage Studies In linkage analysis, markers throughout the genome are screened systematically to identify chromosomal regions that are shared by affected relatives more often than expected by chance alone. In an attempt to find regions of chromosomes that might harbor genes for ADHD, four groups have conducted genome-wide linkage scans for ADHD in distinct populations. They have reported significant linkage to 4q, 5q, 5p, 11q, 16p, and 17p (Arcos-Burgos et al., 2004; Bakker et al., 2003; Fisher et al., 2002; Jain et al., 2007; Ogdie et al., 2003, 2006). Linkage to regions located in 5p, 11q, 16p, and 17p have been replicated by at least 2 studies (Arcos-Burgos et al., 2004; Bakker et al., 2003; Fisher et al., 2002; Jain et al., 2007; Ogdie et al., 2003, 2006; see Figure 53.3). Interpretations of these findings

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GENETIC STUDIES Twin and Family Studies Over the past decade, twin, adoption, family, and association studies have shown that genetic factors substantially contribute to the etiology of ADHD. Genetic studies in twins indicate a substantially high genetic (additive)

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Figure 53.3 ( Figure C.51 in color section) Sample of linkage.

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Genetic Studies

may be variable. Some authors consider that there is a lack of overlapping of the findings between studies, with the exception of regions like 17p11 and 5p13 and with regions in which candidate genes for ADHD are known to reside (Doyle et al., 2005; Faraone et al., 2005). Other interpretations of these linkage studies may consider that the overlaps represent a high degree of replication for this trait and therefore suggest some common susceptibility genes. That the studies have found potentially identical variants within those genes can be seen as evidence of the susceptibility for ADHD across various diverse populations (Arcos-Burgos & Acosta, 2007).

Evolutionary Forces in ADHD Increase of Fitness A different approach considers ADHD as a common phenotype with a common representation in the general population, instead of a rare one. This is also supported by the fact that genetic variants that have been associated with the physiopathology of ADHD (some described as candidate genes earlier) are common variants in the general population and possibly totally fixed in some of those populations (Arcos-Burgos & Acosta, 2007). A working hypothesis considers that ADHD as the most common neuropsychiatry disorder is in reality an evolutionary trait that conferred selective advantage and is the result of natural selection patterns (e.g., faster responses to predators, best hunting performance, more effective territorial defense, and improvement in the capacity of mobility and settling). All these factors may lead to an increase in fertility and survival. Studies in other psychiatric conditions such as schizophrenia and mood disorders have demonstrated lower fertility, not present in ADHD populations (Keller & Miller, 2006). As Arcos-Burgos and Acosta point out, these behavioral traits are now under scrutiny because of new emerging social necessities (Arcos-Burgos & Acosta, 2007). Previously, this trait was rewarded by natural selection over millions of years of human evolution; the fast revolution of human society during the past two centuries, however, brought new challenges rewarding behaviors such as planning, design, and attention while limiting rewards for the behaviors associated with ADHD (Arcos-Burgos & Acosta, 2007). Comparing the frequency of some of the susceptibility variants throughout populations distributed worldwide, using ALFRED (allele frequency database) a resource of gene frequency data on human populations, it is possible to observe that allelic variants conferring susceptibility to ADHD are frequent in the population. This is the case for

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variants like the seven repeat (7R) allele of the human dopamine receptor D4 (DRD4) gene, which was previously mentioned as a candidate gene for ADHD. It is especially prevalent in South America with a frequency as high as 80% of the population. Ding et al. (2002) showed that it is a young variant and it has been exposed to selective advantageous pressure because of genetic parameters such as a linkage disequilibrium (LD) extension and variability. Another example, the 10-repeat allele of a tandem repeat polymorphism located in the 3’ untranslated region of SLC6A3 (DAT), which is also associated with ADHD susceptibility, is the most frequent throughout the world, reaching fixation (gene frequency of 1.0) in some American populations. Even though clinical evidence has been used to justify the search for specific endophenotypes and related genes as potential explanations for the symptomatology in ADHD, the heterogeneity of the condition and the phenotypic differences have impacted our ability to find appropriate correlations. In pursuing the neurophysiological basis of the ADHD phenotype, definition of endophenotypes outlines a promising strategy to dissect biological causes of complex phenotypes. Following the operational definition of Castellanos and Tannock (Castellanos et al., 2002), endophenotypes are defined as heritable quantitative traits that index an individual’s liability to develop or manifest a given disease, and they are thought to be more directly related than dichotomous diagnostic categories to etiological factors. In addition endophenotypes (a) are present with higher frequency or intensity in affected people; (b) segregate throughout generations; (c) are objective, feasible for quantification, reliable, and reproducible characteristics; and finally, (d) they can be present in unaffected family members, but significantly, in a lesser frequency or lower intensity. Several studies have proposed several traits accomplishing operational criteria that outline endophenotypes, that is, deregulation of executive and inhibitory brain mechanisms, stress aversion, novelty seeking, unexpected reward responses, working memory dysfunction as well as personal time perception with poor fitness to real chronometry and wait aversion. Additionally, cognitive effort and continuous vigilance have been considered as vulnerability traits underlying ADHD symptoms. New research in genetics in ADHD needs to focus on genetic association of specific phenotypic profiles and markers. Phenomics (Bilder, 2008) is a new discipline that promises to advance our understanding of the genetics and phenotypic association in ADHD and other clinically complex behavioral and psychiatric conditions. It is perhaps the time to redefine ADHD from the clinical and neurobiological perspectives.

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1032 Attention-Deficit/Hyperactivity Disorder

NONGENETIC ASPECTS IMPLICATED IN ADHD Despite the evidence that ADHD carries a strong genetic component, it appears that the clinical manifestations in ADHD are the result of complex interactions between genetic and environmental factors. Several environmental factors have been considered as a potential explanation for ADHD symtomatology. A large body of literature suggests that maternal tobacco smoking and alcohol used during pregnancy have adverse effects and contribute to developing ADHD (Hill, Lowers, Locke-Wellman, & Shen, 2000; Knopik et al., 2005; Linnet et al., 2003; Mick, Biederman, Faraone, Sayer, & Kleinman, 2002). Tobacco smoking during pregnancy has also been associated in the offspring with many other behavioral outcomes, such as conduct disorder and antisocial behavior (Ernst, Heishman, Spurgeon, & London, 2001; Linnet et al., 2003; Thapar, van den Bree, Fowler, Langley, & Whittinger, 2006). Furthermore, studies of children whose mothers smoked during pregnancy have demonstrated neurocognitive deficits such as poor school performance and lower scores on intelligence and achievement tests. Few studies have investigated the effect of both genetic polymorphisms and prenatal smoking or alcohol use on ADHD (Brookes et al., 2006; Kahn, Khoury, Nichols, & Lanphear, 2003). Children with the DAT 480/480 homozygous genotype who were exposed to prenatal smoking had significantly elevated hyperactive— impulsive and oppositional scores on the Conners’ Parent Rating Scale Revised-Long Version (Kahn et al., 2003). The most striking association was with oppositionaldefiant behavior. Increased risk for ADHD was shown in two independent samples of children exposed to prenatal alcohol use and containing haplotypes with the DAT1 480 allele. Also ADHD, per se, is associated with increased risk for smoking (Becker, El-Faddagh, Schmidt, Esser, & Laucht, 2008). Thus it is not clear whether maternal smoking during pregnancy contributes to ADHD or whether it is really a proxy for ADHD in the mother that is being transmitted to the offspring. Jain et al. (2007) studied a highly prevalent ADHD isolated population in South America. They found that ADHD cosegregates with disruptive behaviors, as well as alcohol and nicotine dependence. Furthermore, they found highly significant LOD scores and the presence of linkage homogeneity throughout all the sets of families suggesting pleiotropic expression of specific vulnerability genes. These results suggest that major genes may underlie a broad behavioral phenotype in these families that can manifest as a range of symptoms that includes ADHD, disruptive behaviors (ODD and CD), and alcohol abuse or dependence. Thus, ADHD, disruptive behav-

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ior, and substance abuse due to specific genetic causes might be classified as a new disorder in the future. These findings would support the hypothesis that maternal smoking in a subset of ADHD patient may be related to the genetic component for ADHD rather than be the underlying etiology. However, in utero nicotine exposure by itself may also impact developmental pathways adding complexity to the definition of etiologic components in ADHD.

NEUROBIOLOGY OF ADHD Attention is a complex mechanism that implies the perfectly orchestrated function of several brain areas and biological mechanisms. For a detailed review about more detailed aspects of attention mechanism, refer to Chapter 18 (Odluda & Posner) in this Handbook. Several components of attention task have been implicated in the pathophysiology of ADHD. Symptoms of ADHD have been attributed to deficits in frontal-striatal pathways, regions of the brain that underlie executive functions. Executive functions are capacities that allow a person to generate voluntary behaviors that are controlled and actively guided (Gioia et al., 2002). Current findings from studies on the neuropharmacology, genetics, neuropsychology, and neuroimaging of ADHD attribute a central role to frontostriatal pathway disruption. These data suggest that the disorder may result from a disruption in a more distributed circuitry including the frontal brain regions as well as the basal ganglia, the cerebellar hemispheres, and the cerebellar vermis (Kieling, Goncalves, Tannock, & Castellanos, 2008). Frontostriatal and frontoparietal networks supporting an array of top-down or executive processes, such as dorsolateral prefrontal cortices, anterior cingulated cortices, and associated striatal regions, have been extensively associated with ADHD dysfunction (Barkley, 1997; Castellanos & Tannock, 2002). Recent advances of our neurobiological understanding of ADHD come from noninvasive neuroanatomic imaging, functional magnetic resonance imaging (fMRI) and neuropsychological studies (Dickstein, Bannon, Castellanos, & Milham, 2006; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). In the following sections, we explore these advances in the comprehension of ADHD considering the neuropsychological components and anatomical neuroimaging aspects. In addition, we focus on neurotransmitters and special dopaminergic pathways and developmental trajectories. As genetic components are known to play a fundamental role in the pathophysiology of ADHD, they are discussed in an independent section.

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Neurobiology of ADHD 1033

While morphometric neuroimaging studies cannot be used to diagnose ADHD, they are ideal for testing hypotheses about the locus or loci of brain dysfunction in individuals with ADHD as they provide direct assessments of both brain structure and brain dysfunction in ADHD. Structural imaging studies using magnetic resonance imaging found evidence of structural brain abnormalities among ADHD patients. The most common findings in children with ADHD are smaller volumes in frontal cortex, cerebellum, and subcortical structures. Castellanos et al., in one of the largest and most important neuroimaging studies of ADHD, found smaller total cerebral brain volumes from childhood through adolescence in children with ADHD. This study suggests that genetic or early environmental influences on brain development in ADHD are fixed, nonprogressive, and unrelated to stimulant treatment (Castellanos et al., 2002). Limitations included the combining of longitudinal and cross-sectional assessments. Brain-imaging studies fit well with the concept that dysfunctions in frontal subcortical structures (caudate, putamen, and globus pallidus) implicated by the imaging studies are part of the neural circuitry underlying motor control, executive functions, inhibition of behavior, and the modulation or reward pathways. These frontal-strialpallidal-thalamic circuits provide feedback to the cortex for the regulation of behavior (Alexander, DeLong, & Strick, 1986). Cerebellum and corpus callosum have also been implicated in the pathophysiology of ADHD. The cerebellum contributes significantly to cognitive functioning, presumably through cerebellar-cortical pathways involving the pons and the thalamus. The corpus callosum connects homotypic regions of the two cerebral hemispheres. Size variations in the callosum and volume differences in number of cortical neurons may degrade communications between these two hemispheres, which may account for some of the cognitive and behavioral symptoms of ADHD (Castellanos et al., 2002). Overall, research in the past 2 decades has confirmed these original findings, and it is now accepted that the brains of children who have ADHD are significantly smaller, on average, than the brains of healthy controls throughout childhood and adolescence.

command the clinical manifestations in ADHD. Barkley (1997) proposed a model of ADHD in which disinhibition is the core deficit, whereas others have proposed more generalized deficits in self-regulation (Houghton et al., 1999). In addition, working memory and temporal processing have also been considered as a potential core deficit (Castellanos & Tannock, 2002). Given both the strong association between executive function and the frontal lobes and the presence of executive dysfunction in ADHD, it is not surprising that nearly all neuroimaging studies have focused on cognitive paradigms assessing executive processes (Dickstein et al., 2006; Willcutt et al., 2005). Neuropsychological studies alone or in combination with neuroimaging techniques have found, in general, dysfunction in EF tasks in ADHD patients. Moreover, functional imaging studies have shown that an expected increase in prefrontal metabolism during response inhibition tasks is reduced markedly in ADHD subjects (Dickstein et al., 2006; Kieling et al., 2008; Rubia et al., 1999). In fact, a recent meta-analysis of 16 published functional neuroimaging studies of ADHD revealed that significant patterns of frontal hypoactivity are detected across studies in patients who have ADHD, affecting anterior cingulate, dorsolateral prefrontal, and inferior prefrontal cortices (Dickstein et al., 2006). A meta-analysis (Willcutt et al., 2005) of 83 studies that administered executive function measures to groups, for a total of 3,734 ADHD patients and 2,969 non-ADHD individuals found that, in general, ADHD individuals demonstrated weakness in several EF domains. The strongest and most consistent effects were obtained on measures of response inhibition, vigilance, spatial working memory, and some measures of planning. Executive function weaknesses are present in both clinic-referred and community samples and are not fully explained by group differences in intelligence, academic achievement, or symptoms of other disorders. Executive dysfunction in domains such as response inhibition, planning, vigilance, and working memory plays an important role in the complex neuropsychology of ADHD. Because the spectrum is heterogeneous and complex, further evaluation is necessary to assess the impact of diagnostic and neuropsychological heterogeneity and to clarify the relations between various EF dimensions, as well as the relations between EF and other neurocognitive and emotion-motivation domains.

Neuropsychological Studies: Executive Function

New Neuroimaging Techniques

Clinical manifestations of behavior in ADHD suggest deficits in the voluntary control of behavior. It is unclear, however, what core components in executive function (EF)

Noninvasive neuroimaging techniques have allowed researchers to examine the neural correlates of ADHD, resulting in a rapidly growing literature. A recent publication (Bush,

Neuroimaging Studies: Morphometric Magnetic Resonance Imaging

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1034 Attention-Deficit/Hyperactivity Disorder

Valera, & Seidman, 2005) surveyed over a dozen neuroimaging studies of ADHD encompassing functional imaging techniques (PET, SPECT, fMRI,1H-MRS, and EEG) and cognitive paradigms (e.g., inhibitory control, selective attention, working memory, and vigilance). The authors found a consistent pattern of frontal dysfunction with altered patterns of activity in anterior cingulate, dorsolateral prefrontal, and ventrolateral prefrontal cortices, as well as associated parietal, striatal, and cerebellar regions. Despite broad consistencies, multiple challenges are present when comparing results across studies; and interpretation is difficult due to small sample sizes and methodological issues related to statistical analysis. To date, the most consistent findings in the neuroimaging literature of ADHD are deficits in neural activity within frontostriatal and frontoparietal circuits. The distributed natures of these results fail to support models emphasizing dysfunction in any one frontal subregion. A complementary approach (Castellanos et al., 2008) is to examine the neural substrates of ADHD-relevant behaviors, such as attentional lapses, and then assess whether the underlying circuits are implicated in ADHD through analysis of the temporal correlations among distributed brain regions. This method of functional connectivity provides remarkably detailed spatial maps of putatively functionally related regions. This resting-state functional MRI technique demonstrated that adults who have ADHD exhibit decreased functional connectivity in long-range connections linking the anterior cingulate region and two posterior components of the so-called default-mode network (precuneus and posterior cingulated). These new findings in brain activation in addition to previous results in terms of variations in anatomic structures in individua with ADHD, may imply that the neural mechanism implicated in ADHD symptoms are more complex that was suggested before. Perhaps long-range connections, such as those linking dorsal anterior cingulate to posterior cingulate and precuneus should be considered as a candidate locus of dysfunction in ADHD (Castellanos et al., 2008). These are all new and exciting findings that deserve further investigation. Proton magnetic resonance spectroscopy (1H-MRS) is another noninvasive technique for evaluating brain chemistry in vivo (Sun et al., 2005). 1H-MRS can obtain the spectra of metabolites linked directly as well as indirectly to neurotransmission pathways including N-acetylaspartate (NAA), Inositol (Ins), Choline (Cho), and GlutamateGlutamine complex (Glx) to creatine (Cr) ratios. Such brain metabolic changes fundamentally reflect an ontogenic brain development status and an index of neuronal function, both events finally related to neuronal activity and viability number (Sun et al., 2005). These features make 1H-MRS a promising technique to evaluate the

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biochemical component of in vivo brain tissue.1H-MRS preliminary studies in ADHD cases and controls have showed the presence of differences between ADHD cases and controls. Some studies have found differences in Glx-to-Cr ratio differences at the frontal-striatal region, and at the right dorsolateral frontal region (MacMaster, Carrey, Sparkes, & Kusumakar, 2003); lower NAA levels in the dorsolateral prefrontal cortex patients with ADHD (Hesslinger, Thiel, van Tebartz, Hennig, & Ebert, 2001); and a higher ratio of glutamate plus glutamine to myo-inositol-containing compounds in the anterior cingulate cortex (Moore et al., 2006). This is a promising technique that provides information about in vivo biochemistry changes in brain development and function. In the immediate future, it might provide opportunities to integrate our current knowledge in neuroanatomy, function, connectivity and metabolic changes that could explain the behavioral aspects observed in individuals with ADHD. Figure 53.4, summarizes some of the most important neurobiological aspects that have been implicated in the pathophysiology of ADHD. The combination of neuroimaging data, clinical data, neuropsychological evaluations, genetic results, and the distribution of neurotransmitters have contributed to this hypothetical “map for ADHD.”

Figure 53.4 Fronto-striatal-Cerebellar pathways have strongly implicated in the physiopathology of ADHD. Anatomical, neurotransmitters and functional correlations in this pathway play a fundamental role.

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Treatment

Neurodevelopmental Hypothesis in ADHD: Are Brain Developmental Pathways Different in ADHD? There is growing evidence that ADHD pathophysiology is strongly explained by changes and variations that primarily impact developmental trajectories (Arcos-Burgos & Acosta, 2007; Castellanos et al., 2002; Shaw et al., 2007; Sonuga-Barke, 2003). From the neuropsychological perspective, some researchers consider that ADHD should be better considered as a “developmental executive dysfunction.” The prefrontal hypotheses of ADHD have primarily involved the dorsolateral prefrontal cortex. Some of the behaviors associated with these structures are associated with organization, planning, working memory, and attentional dysfunctions and orbital lesions associated with social disinhibition and impulse control disorders (Bush et al., 2005; Seidman, Valera, & Makris, 2005; Valera, Faraone, Biederman, Poldrack, & Seidman, 2005). The frontosubcortical systems pathways associated with ADHD are rich in catecholamines, norepinephrine, and dopamine. A plausible model for the effects of medications in ADHD suggests that, through dopaminergic and/or noradrenergic pathways, these agents increase the inhibitory influences of frontal, cortical activity on subcortical structures (Pliszka, McCracken, & Maas, 1996; Zametkin & Rapoport, 1987). Furthermore, ADHD-associated dopaminergic deficits in the prefrontal cortex have been postulated (Sonuga-Barke, 2003). From this neurobiological evidence, it has been hypothesized that clinical manifestations and pharmacological responses to treatment could be explained by imbalances in dopaminergic and noradrenergic systems (Pliszka et al., 1996; Zametkin & Rapoport, 1987). During brain development, dopamine acts as a neuronal morphogen during frontal cortical development (Todd, 1992). Those findings together suggest that several components implied in the development of frontal cortex, including genetic markers, neurotransmitters, and formation of neural pathways could play an important role in the pathophysiology of ADHD. In addition, behavioral expressions of those variations in development are manifested as developmental components of executive functions. Interactions between genetic background and environmental factors may also play a role in the modification of these developmental neural trajectories. A recent paper proposes that a synaptic model for prenatal nicotine exposure interacting with ADHD genotypes may modulate the risk for severe ADHD (Neuman et al., 2007). ADHD symptoms are highly correlated with frontal lobe function and executive function manifestations. If ADHD is to be considered as a continuous trait in the population with a range of manifestation that goes from normal

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behavior to abnormal manifestations associated with social impairments, any advance in understanding the neurobiology of ADHD will advance our understanding of normal human behavior. Executive functions are a fundamental component of any cognitive process, and they can be the difference between failure and success for many individuals. It is also clear that they are impacted by many biological conditions. Advances in neuroimaging and genetics will help us to understand not only ADHD, but also the fundamental biological components of human behavior. Brain imaging studies strongly support the following anatomical and developmental differences between ADHD cases and control individuals: (a) the caudate nucleus and globus pallidus are smaller in the ADHD than in the control groups; (b) ADHD groups have larger posterior brain regions and smaller anterior brain regions (Castellanos et al., 2002); (c) areas coordinating multiple brain regions as the rostrum and splenium of the corpus callosum and the cerebellum vermis lobules VIII-X are smaller in ADHD than in control groups (Swanson et al., 2007); and (d) presence of delay in the age of attaining peak cortical thickness in ADHD children when compared with controls throughout most of the cerebrum (Shaw et al., 2007). These established structural variations suggest variable brain neuronal regulation and function as well as different patterns of brain ontogeny between ADHD cases and controls.

TREATMENT Pharmacological Treatment Pharmacological treatment is currently considered a fundamental part of any intervention plan for ADHD children and adults. Dysfunction of noradrenergic and/or dopaminergic neurotransmission has been widely implicated in the manifestation of ADHD (Pliszka et al., 1996; Zametkin & Rapoport, 1987). Noradrenaline (NA) and dopamine (DA) exert neuromodulatory influences over behavior and cognition via fronto-striato-cerebellar circuitry (Alexander et al., 1986) and pharmacotherapy is thought to target these systems to ameliorate problems with impulsivity, inattention, and hyperactivity (Spencer et al., 2005; Spencer, Biederman, Wilens, & Faraone, 2002). Psychostimulants are widely used for ADHD and act to increase free brain levels of noradrenaline and dopamine by blocking reuptake and triggering release (Spencer et al., 2002, 2005). A wealth of evidence shows these agents to be effective in the treatment of ADHD for up to 80% of patients. (Biederman & Spencer, 2008). The most commonly used compounds in this class include methylphenidate (Ritalin), d-methylphenidate (Focalin),

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d-amphetamine (Dexedrine), and a mixed-amphetamine product (Adderall). These drugs have been shown to enhance dopaminergic and noradrenergic transmission (Volkow et al., 2001). Treatment with stimulants improves not only abnormal behaviors of ADHD but also selfesteem, cognition, and social and family function. A new generation of highly sophisticated, well-developed, safe, and effective long-acting preparations of stimulant drugs has recently reached the market and revolutionized the treatment of ADHD (Biederman & Spencer, 2008). The most commonly reported side effects associated with stimulant medication are appetite suppression and sleep disturbances. Although less commonly reported, mood disturbances ranging from increased tearfulness to a full-blown major depression-like syndrome can be associated with stimulant treatment. Other infrequent side effects include headaches, abdominal discomfort, increased lethargy, and fatigue (Biederman & Spencer, 2008). For an extensive review on ADHD pharmacological treatment, review Pliszka (2007) and Biederman and Spencer (2008). In 1997, the Collaborative Multimodal Treatment Study of Children with ADHD (MTA) examined the long-term effectiveness of medication versus behavioral treatment versus both for treatment of ADHD and compared state-ofthe-art treatment with routine community care (Arnold et al., 1997). After 14 months of treatment, patients assigned to the medication group showed superior responses compared with the group that received behavioral treatment. The combined treatment group (medication 1 behavioral treatment) did not show greater benefits than medication alone (MTA Study, 1999). These results made clear that pharmacological intervention was a fundamental part in the treatment of ADHD. Recently, however, some concern has been shown about the long-term efficacy of methylphenidate based on the 36-month data from the Multimodal

TABLE 53.4

Treatment Study of Children with ADHD study (Jensen et al., 2007). Comparison between groups showed that for the long-term prognosis, the superior improvement for the medication treatment group is less evident, and behavioral treatment also improves ADHD-related symptoms and prognosis over time. Multiple nonstimulant compounds have been used as an alternative or complementary treatment in ADHD. Those more frequently used are presented in Table 53.4. Atomoxetine, approved in the United States for the treatment of ADHD in November 2002, is a nonstimulant that is thought to act presynaptically via the inhibition of norepinephrine reuptake. It is the most prescribed nonstimulant medication for treatment of ADHD. Atomoxetine has limited effect on the serotonin or dopamine transporters and has low affinity at dopaminergic, muscarinic-cholinergic, histaminic, serotonergic, and a1- or a2-adrenergic receptors. Multiple recent reports have provided evidence that this medication is safe and well tolerated. In addition to treating ADHD, atomoxetine was shown to improve depression or anxiety in a pediatric population (Kratochvil et al., 2005). Although a stimulant will be the first choice in most patients, atomoxetine may be preferred in some cases, particularly when substance abuse or comorbid tics are a problem, if a strong family preference exists for a nonstimulant, if 24-hour action is strongly required, or if comorbid anxiety is present. In the presence of adverse events while using stimulants, the next step often will be atomoxetine. ADHD is a heterogeneous disorder with strong neurobiological basis that affects millions of individuals of all ages worldwide. Although the stimulants remain the mainstay of treatment for this disorder, a new generation of no-stimulant drugs is emerging that provides a viable alternative for patients and families. When assessing patients

Most frequently used medications in ADHD

Medication Family

Brand and Generic name

Amphetamines (dextroanphetamine, mixed salts of amphetamine)

Short acting: Adderall (mixed salt amphetamines), Dexedrine, Dextrostat Long acting: Dexedrine Spansule, Adderall XR, Lisdexamfetamine

Methylphenidate

Short acting: Focalin (d-isomer), Methylin, Ritalin Intermediate acting: Metadate-ER, Methylin ER, Ritalin SR, Metadate CD, Ritalin LA Long acting: Concerta (OROS-MPH), Daytrana Patch (MPH Transdermal system), Focalin XR (d-isomer MPH)

Selective norepinephrine reuptake inhibitor

Strattera (Atomoxetine)

Antidepressants

Wellbutrin (Bupoprion), Tofranil (Imipramine), Pamelor (Nortriptyline)

α 2-Adrenergic agonist

Tenex (Guanfacine), Catapres (Clonidine)

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Summary

who have ADHD, a careful differential diagnosis must be applied that considers psychiatric, social, cognitive, educational, and medical/neurological factors that may contribute to the clinical presentation. Realistic expectations of intervention, careful definition of target symptoms, and careful assessment of the potential risks and benefits of each type of intervention for these patients are major ingredients for success. Genetic and neuroimaging studies are becoming more and more involved in the understanding of pharmacological and therapeutic responses. Medications with demonstrable efficacy in the treatment of ADHD act mainly on these systems and have been shown to exert beneficial effects on aspects of cognition (such as response inhibition) in proof-of-concept studies. Translational approaches are shedding light on the precise neurochemical mechanisms. The body of evidence also implicates subtle structural and functional abnormalities of fronto-striatal-cerebellar circuitry in the manifestation of the disorder. Pharmaco-fMRI should be used to investigate the effects of ADHD medications on neural activity during cognitive tests and to compare different agents. There is also a need for baseline factors influencing clinical outcomes to be explored. It will be critical to examine whether baseline cognitive function and the presence of different genetic polymorphisms modulate treatment outcomes. Research in these areas may contribute to the development of improved treatment algorithms for children and adults with ADHD, to reduce harm (side effects and abuse potential) and maximize clinical benefits. Nonpharmacological Treatment: Complementary and Alternative Medical Therapies (CAM) In the United States, an estimated 2.5 million children take stimulants (Nissen, 2006). Despite the evidence of the stimulant treatments’ efficacy, many parents seek alternatives for their children because of their concern about giving their child a controlled substance or because of the changes in personality some parents report. Some parents worry that their child will develop drug abuse problems after using stimulants for ADHD, despite evidence to the contrary (Dosreis et al., 2003; Volkow et al., 2008). Parents and the medical communities often express concern about the number of children prescribed these controlled substances and question the possibility of misdiagnosis or overdiagnosis of ADHD. Further studies are needed to better understand the long-term effects of stimulant medications on the developing brain and the neuronal imprinting effects of these medications. The frequency of use in children who have ADHD of complementary therapies ranges between 12% and 64%. Complementary alternative medical therapies include

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nutritional changes (Feingold diet, food sensitivities and sugar avoidance, essential fatty acid), herbal and natural health products, electroencephalographic feedback, massage and yoga, meditation, vitamins and minerals, homeopathy, and environmental issues (for an extensive review, see Weber & Newmark, 2007). A search on the Internet for ADHD treatment provides hundreds of links to over-the-counter products and treatments that are being enthusiastically advertised as a ‘‘definitive cure’’ for ADHD. Most of these products and treatments have little if any research documenting their safety let alone their efficacy. There is a significant gap between our current knowledge of CAM for ADHD and their use frequency. There is a large use rate compared with a very low number of well-controlled, large, randomized trials. It is essential for researchers to include a comparison group when studying natural treatments for these conditions, which often are based on parental or teacher reports. Without a control group, it is impossible to determine whether the improvement seen in a trial is the result of the natural course of the symptoms. Effective CAM treatments for ADHD are highly desired by parents who seek alternatives to stimulant medications. Clinical trials are needed to determine the safety and efficacy of these natural therapeutic options.

SUMMARY ADHD is the most common behavioral disorder of childhood. It is more complex than was thought previously and the underlying neurobiology is not well understood. However, key elements of the neurobiological bases of ADHD are starting to be uncovered, as new neuroimaging and molecular techniques allow research to aim at increasingly sophisticated goals. Studies have identified relevant genetic and environmental risk factors for ADHD, making genes and exposures to substances, necessary study variables for subsequent etiologic research. Brain studies are proving to be an essential transitional point of analysis, building the necessary link between disease susceptibility and clinical expression, as researchers discover how specific combinations of genes relate to various abnormalities in brain-based functions. A translational approach to ADHD decipherment is essential for putting together neuroscience and nosology. The recognition of biological bases of behavioral processes might reduce the stigma associated with mental disorders. Most importantly, insights from the neurobiology of ADHD may strengthen its validity as a clinical syndrome and improve diagnosis and treatment. A growing scientific movement considers that despite the high social impact, it is unclear if ADHD should be

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considered as a nosological entity or as a common variant of human behavior. Our understanding of “normal” human behavior would help to clarify those questions. A promising strategy for future research is to integrate information from multiple levels of analysis, by studying genetic, neurophysiological, and clinical processes across the developmental continuum. Most importantly, insights from the neurobiology of ADHD may strengthen its validity as a clinical syndrome, and improve diagnosis and treatment. Interesting questions regarding the validity of nonpharmacological medical interventions are increasingly arising from parents and care providers.

REFERENCES Achenbach, T., & Edelbrock, C. (1983). Manual for the child behavior checklist and revised child behavior profile. Burlington, VT: University of Vermont, Department of Psychiatry. Achenbach, T. M., & Ruffle, T. M. (2000). The child behavior checklist and related forms for assessing behavioral/emotional problems and competencies. Pediatrics in Review, 21, 265–271. Acosta, M. T., Castellanos, F. X., Bolton, K. L., Balog, J. Z., Eagan, P., Nee, L., et al. (2008). Latent class subtyping of attention-deficit/hyperactivity disorder and comorbid conditions. Journal of the American Academy of Child and Adolescent Psychiatry, 47,797–807. Acosta, M. T., Gioia, G. A., & Silva, A. J. (2006). Neurofibromatosis type 1: New insights into neurocognitive issues. Current Neurology and Neuroscience Reports, 6, 136–143. Acosta, M. T., Arcos-Burgos, M., & Muenke, M. (2004). Attention deficit/ hyperactivity disorder (ADHD): Complex phenotype, simple genotype? Genetics in Medicine, 6, 1–15. Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357–381. American Psychiatry Association. (1987). Diagnostic and statistical manual of mental disorders: DSM III-R (pp. 85–98). Washington DC, American Psychiatry Association. American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author. Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, 40, 57–87. Antshel, K. M., & Waisbren, S. E. (2003). Developmental timing of exposure to elevated levels of phenylalanine is associated with ADHD symptom expression. Journal of Abnormal Child Psychology, 31, 565–574. Arcos-Burgos, M., & Acosta, M. T. (2007). Tuning major gene variants conditioning human behavior: The anachronism of ADHD. Current Opinion in Genetics and Development, 17, 234–238. Arcos-Burgos, M., Castellanos, F. X., Pineda, D., Lopera, F., Palacio, J. D., Palacio, L. G., et al. (2004). Attention-deficit/hyperactivity disorder in a population isolate: Linkage to loci at 4q13.2, 5q33.3, 11q22, and 17p11. American Journal of Human Genetics, 75, 998–1014. Arnold, L. E. (1996). Sex differences in ADHD: Conference summary. Journal of Abnormal Child Psychology, 24, 555–569.

c53.indd Sec8:1038

Arnold, L. E., Abikoff, H. B., Cantwell, D. P., Conners, C. K., Elliott, G., Greenhill, L. L., et al. (1997). National institute of mental health collaborative multimodal treatment study of children with ADHD (the MTA): Design challenges and choices. Archives of General Psychiatry, 54, 865–870. August, G. J., Realmuto, G. M., Joyce, T., & Hektner, J. M. (1999). Persistence and desistance of oppositional defiant disorder in a community sample of children with ADHD. Journal of the American Academy of Child and Adolescent Psychiatry, 38, 1262–1270. Bakker, S. C., van der Meulen, E. M., Buitelaar, J. K., Sandkuijl, L. A., Pauls, D. L., Monsuur, A. J., et al. (2003). A whole-genome scan in 164 Dutch sib pairs with attention-deficit/hyperactivity disorder: Suggestive evidence for linkage on chromosomes 7p and 15q. American Journal of Human Genetics, 72, 1251–1260. Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychological Bulletin, 121, 65–94. Barkley, R. A., Edwards, G., Laneri, M., Fletcher, K., & Metevia, L. (2001). Executive functioning, temporal discounting, and sense of time in adolescents with attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD). Journal of Abnormal Child Psychology, 29, 541–556. Barkley, R. A., Fischer, M., Smallish, L., & Fletcher, K. (2002). The persistence of attention-deficit/hyperactivity disorder into young adulthood as a function of reporting source and definition of disorder. Journal of Abnormal Psychology, 111, 279–289. Becker, K., El-Faddagh, M., Schmidt, M. H., Esser, G., & Laucht, M. (2008). Interaction of dopamine transporter genotype with prenatal smoke exposure on ADHD symptoms. Journal of Pediatrics, 152, 263–269. Biederman, J. (1998). Attention-deficit/hyperactivity disorder: A life-span perspective. Journal of Clinical Psychiatry, 59(Suppl. 7), 4–16. Biederman, J. (2004). Impact of comorbidity in adults with attention-deficit/ hyperactivity disorder. Journal of Clinical Psychiatry, 65(Suppl. 3), 3–7. Biederman, J. (2005). Attention-deficit/hyperactivity disorder: A selective overview. Biological Psychiatry, 57, 1215–1220. Biederman, J., Kwon, A., Aleardi, M., Chouinard, V. A., Marino, T., Cole, H., et al. (2005). Absence of gender effects on attention-deficit/ hyperactivity disorder: Findings in nonreferred subjects. American Journal of Psychiatry, 162, 1083–1089. Biederman, J., Mick, E., Faraone, S. V., Braaten, E., Doyle, A., Spencer, T., et al. (2002). Influence of gender on attention-deficit/hyperactivity disorder in children referred to a psychiatric clinic. American Journal of Psychiatry, 159, 36–42. Biederman, J., & Spencer, T. J. (2008). Psychopharmacological interventions. Child and Adolescent Psychiatric Clinics of North America, 17, 439–458. Bilder, R. M. (2008). Phenomics: Building scaffolds for biological hypotheses in the post-genomic era. Biological Psychiatry, 63, 439–440. Brookes, K. J., Mill, J., Guindalini, C., Curran, S., Xu, X., Knight, J., et al. (2006). A common haplotype of the dopamine transporter gene associated with attention-deficit/hyperactivity disorder and interacting with maternal use of alcohol during pregnancy. Archives of General Psychiatry, 63, 74–81. Brown, R. T., Freeman, W. S., Perrin, J. M., Stein, M. T., Amler, R. W., Feldman, H. M., et al. (2001). Prevalence and assessment of attention-deficit/hyperactivity disorder in primary care settings. Pediatrics, 107, E43. Burke, J. D., Loeber, R., Lahey, B. B., & Rathouz, P. J. (2005). Developmental transitions among affective and behavioral disorders in adolescent boys. Journal of Child Psychology and Psychiatry, 46, 1200–1210. Bush, G., Valera, E. M., & Seidman, L. J. (2005). Functional neuroimaging of attention-deficit/hyperactivity disorder: A review and suggested future directions. Biological Psychiatry, 57, 1273–1284.

8/17/09 5:29:15 PM

References 1039 Castellanos, F. X., Lee, P. P., Sharp, W., Jeffries, N. O., Greenstein, D. K., Clasen, L. S., et al. (2002). Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. Journal of the American Medical Association, 288, 1740–1748. Castellanos, F. X., Margulies, D. S., Kelly, C., Uddin, L. Q., Ghaffari, M., Kirsch, A., et al. (2008). Cingulate-precuneus interactions: A new locus of dysfunction in adult attention-deficit/hyperactivity disorder. Biological Psychiatry, 63, 332–337.

Faraone, S. V., Biederman, J., Mick, E., Williamson, S., Wilens, T., Spencer, T., et al. (2000). Family study of girls with attention-deficit/hyperactivity disorder. American Journal of Psychiatry, 157, 1077–1083.

Castellanos, F. X., & Tannock, R. (2002). Neuroscience of attentiondeficit/hyperactivity disorder: The search for endophenotypes. Nature Reviews: Neuroscience, 3, 617–628.

Faraone, S. V., Biederman, J., & Monuteaux, M. C. (2000). Attention-deficit disorder and conduct disorder in girls: Evidence for a familial subtype. Biological Psychiatry, 48, 21–29.

Collett, B. R., Ohan, J. L., & Myers, K. M. (2003). Ten-year review of rating scales: Pt. V. Scales assessing attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 1015–1037.

Faraone, S. V., Biederman, J., & Monuteaux, M. C. (2001). Attention deficit hyperactivity disorder with bipolar disorder in girls: Further evidence for a familial subtype? Journal of Affective Disorders, 64, 19–26.

Conners, C. K., Wells, K. C., Parker, J. D., Sitarenios, G., Diamond, J. M., & Powell, J. W. (1997). A new self-report scale for assessment of adolescent psychopathology: Factor structure, reliability, validity, and diagnostic sensitivity. Journal of Abnormal Child Psychology, 25, 487–497. Costello, E. J., Egger, H. L., & Angold, A. (2005). The developmental epidemiology of anxiety disorders: Phenomenology, prevalence, and comorbidity 6. Child and Adolescent Psychiatric Clinics of North America, 14, 631–648. Curran, S., Rijsdijk, F., Martin, N., Marusic, K., Asherson, P., Taylor, E., et al. (2003). CHIP: Defining a dimension of the vulnerability to attention-deficit/hyperactivity disorder (ADHD) using sibling and individual data of children in a community-based sample. American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics, 119, 86–97. Dickstein, S. G., Bannon, K., Castellanos, F. X., & Milham, M. P. (2006). The neural correlates of attention-deficit/hyperactivity disorder: An ALE meta-analysis. Journal of Child Psychology and Psychiatry, 47, 1051–1062. Ding, Y. C., Chi, H. C., Grady, D. L., Morishima, A., Kidd, J. R., Kidd, K. K., et al. (2002). Evidence of positive selection acting at the human dopamine receptor D4 gene locus. Proceedings of the National Academy of Sciences, USA, 99, 309–314. Dosreis, S., Zito, J. M., Safer, D. J., Soeken, K. L., Mitchell, J. W., Jr., & Ellwood, L. C. (2003). Parental perceptions and satisfaction with stimulant medication for attention-deficit/hyperactivity disorder. Journal of Developmental and Behavioral Pediatrics, 24, 155–162.

Faraone, S. V., Biederman, J., Spencer, T., Wilens, T., Seidman, L. J., Mick, E., et al. (2000). Attention-deficit/hyperactivity disorder in adults: An overview. Biological Psychiatry, 48, 9–20. Faraone, S. V., Perlis, R. H., Doyle, A. E., Smoller, J. W., Goralnick, J. J., Holmgren, M. A., et al. (2005). Molecular genetics of attention-deficit/ hyperactivity disorder. Biological Psychiatry, 57, 1313–1323. Faraone, S. V., Sergeant, J., Gillberg, C., & Biederman, J. (2003). The worldwide prevalence of ADHD: Is it an American condition? World Psychiatry, 2, 104–113. Farzin, F., Perry, H., Hessl, D., Loesch, D., Cohen, J., Bacalman, S., et al. (2006). Autism spectrum disorders and attention-deficit/hyperactivity disorder in boys with the fragile X premutation. Journal of Developmental and Behavioral Pediatrics, 27, S137–S144. Fisher, S. E., Francks, C., McCracken, J. T., McGough, J. J., Marlow, A. J., MacPhie, I. L., et al. (2002). A genomewide scan for loci involved in attention-deficit/hyperactivity disorder. American Journal of Human Genetics, 70, 1183–1196. Gau, S. S., Shang, C. Y., Liu, S. K., Lin, C. H., Swanson, J. M., Liu, Y. C., et al. (2008). Psychometric properties of the Chinese version of the Swanson, Nolan, and Pelham, version IV Scale: Parent form. International Journal of Methods in Psychiatric Research, 17, 35–44. Gaub, M., & Carlson, C. L. (1997a). Behavioral characteristics of DSM-IV ADHD subtypes in a school-based population. Journal of Abnormal Child Psychology, 25, 103–111. Gaub, M., & Carlson, C. L. (1997b). Gender differences in ADHD: A metaanalysis and critical review. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 1036–1045.

Doyle, A. E., Faraone, S. V., Seidman, L. J., Willcutt, E. G., Nigg, J. T., Waldman, I. D., et al. (2005). Are endophenotypes based on measures of executive functions useful for molecular genetic studies of ADHD? Journal of Child Psychology and Psychiatry, 46, 774–803.

Giedd, J. N., Clasen, L. S., Wallace, G. L., Lenroot, R. K., Lerch, J. P., Wells, E. M., et al. (2007). XXY (Klinefelter syndrome): A pediatric quantitative brain magnetic resonance imaging case-control study. Pediatrics, 119, e232–e240.

DuPaul, G. J., Ervin, R. A., Hook, C. L., & McGoey, K. E. (1998). Peer tutoring for children with attention-deficit/hyperactivity disorder: Effects on classroom behavior and academic performance. Journal of Applied Behavior Analysis, 31, 579–592.

Gioia, G. A., Isquith, P. K., Kenworthy, L., & Barton, R. M. (2002). Profiles of everyday executive function in acquired and developmental disorders. Child Neuropsychology, 8, 121–137.

Eiraldi, R. B., Power, T. J., & Nezu, C. M. (1997). Patterns of comorbidity associated with subtypes of attention-deficit/hyperactivity disorder among 6- to 12-year-old children. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 503–514. Ernst, M., Heishman, S. J., Spurgeon, L., & London, E. D. (2001). Smoking history and nicotine effects on cognitive performance. Neuropsychopharmacology, 25, 313–319. Ezpeleta, L., de la Cruz, O. N., Domenech, J. M., Navarro, J. B., Losilla, J. M., & Judez, J. (1997). Diagnostic agreement between clinicians and the Diagnostic Interview for Children and Adolescents (DICA-R): In an outpatient sample. Journal of Child Psychology and Psychiatry, 38, 431–440. Faraone, S. V., & Biederman, J. (2005). What is the prevalence of adult ADHD? Results of a population screen of 966 adults. Journal of Attention Disorders, 9, 384–391.

c53.indd Sec9:1039

Faraone, S. V., Biederman, J., Mennin, D., Gershon, J., & Tsuang, M. T. (1996). A prospective four-year follow-up study of children at risk for ADHD: Psychiatric, neuropsychological, and psychosocial outcome. Journal of the American Academy of Child and Adolescent Psychiatry, 35, 1449–1459.

Gioia, G. A., Isquith, P. K., Retzlaff, P. D., & Espy, K. A. (2002). Confirmatory factor analysis of the Behavior Rating Inventory of Executive Function (BRIEF) in a clinical sample. Child Neuropsychology, 8, 249–257. Greene, R. W., Biederman, J., Faraone, S. V., Monuteaux, M. C., Mick, E., DuPre, E. P., et al. (2001). Social impairment in girls with ADHD: Patterns, gender comparisons, and correlates. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 704–710. Hartung, C. M., Willcutt, E. G., Lahey, B. B., Pelham, W. E., Loney, J., Stein, M. A., et al. (2002). Sex differences in young children who meet criteria for attention-deficit/hyperactivity disorder. Journal of Clinical Child and Adolescent Psychology, 31, 453–464. Hay, D. A., Bennett, K. S., Levy, F., Sergeant, J., & Swanson, J. (2007). A twin study of attention-deficit/hyperactivity disorder dimensions rated by the strengths and weaknesses of ADHD-symptoms and normal-behavior (SWAN) scale. Biological Psychiatry, 61, 700–705.

8/17/09 5:29:16 PM

1040 Attention-Deficit/Hyperactivity Disorder Hesslinger, B., Thiel, T., van Tebartz, E. L., Hennig, J., & Ebert, D. (2001). Attention-deficit disorder in adults with or without hyperactivity: Where is the difference? A study in humans using short echo (1)H-magnetic resonance spectroscopy. Neuroscience Letters, 304, 117–119.

Lahey, B. B., Pelham, W. E., Loney, J., Kipp, H., Ehrhardt, A., Lee, S. S., et al. (2004). Three-year predictive validity of DSM-IV attention-deficit/hyperactivity disorder in children diagnosed at 4–6 years of age. American Journal of Psychiatry, 161, 2014–2020.

Hill, S. Y., Lowers, L., Locke-Wellman, J., & Shen, S. A. (2000). Maternal smoking and drinking during pregnancy and the risk for child and adolescent psychiatric disorders. Journal of Studies on Alcohol, 61, 661–668.

Lahey, B. B., Schaughency, E. A., Hynd, G. W., Carlson, C. L., & Nieves, N. (1987). Attention deficit disorder with and without hyperactivity: Comparison of behavioral characteristics of clinic-referred children. Journal of the American Academy of Child and Adolescent Psychiatry, 26, 718–723.

Hinshaw, S. P. (2002). Preadolescent girls with attention-deficit/hyperactivity disorder: Pt. I. Background characteristics, comorbidity, cognitive and social functioning, and parenting practices. Journal of Consulting and Clinical Psychology, 70, 1086–1098. Hinshaw, S. P., Carte, E. T., Sami, N., Treuting, J. J., & Zupan, B. A. (2002). Preadolescent girls with attention-deficit/hyperactivity disorder: Pt. II. Neuropsychological performance in relation to subtypes and individual classification. Journal of Consulting and Clinical Psychology, 70, 1099–1111. Houghton, S., Douglas, G., West, J., Whiting, K., Wall, M., Langsford, S., et al. (1999). Differential patterns of executive function in children with attention-deficit/hyperactivity disorder according to gender and subtype. Journal of Child Neurology, 14, 801–805. Hudziak, J. J., Heath, A. C., Madden, P. F., Reich, W., Bucholz, K. K., Slutske, W., et al. (1998). Latent class and factor analysis of DSM-IV ADHD: A twin study of female adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 37, 848–857. Jain, M., Palacio, L. G., Castellanos, F. X., Palacio, J. D., Pineda, D., Restrepo, M. I., et al. (2007). Attention-deficit/hyperactivity disorder and comorbid disruptive behavior disorders: Evidence of pleiotropy and new susceptibility loci. Biological Psychiatry, 61, 1329–1339. Jensen, P. S., Arnold, L. E., Swanson, J. M., Vitiello, B., Abikoff, H. B., Greenhill, L. L., et al. (2007). 3-year follow-up of the NIMH MTA study. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 989–1002. Kahn, R. S., Khoury, J., Nichols, W. C., & Lanphear, B. P. (2003). Role of dopamine transporter genotype and maternal prenatal smoking in childhood hyperactive-impulsive, inattentive, and oppositional behaviors. Journal of Pediatrics, 143, 104–110. Keller, M. C., & Miller, G. (2006). Resolving the paradox of common, harmful, heritable mental disorders: Which evolutionary genetic models work best? Behavioral Brain Science, 29, 385–404. Kendler, K. S. (1996). Major depression and generalised anxiety disorder. Same genes, (partly) different environments: Revisited. British Journal of Psychiatry, 30 (Suppl.), 68–75. Kessler, R. C., Adler, L. A., Barkley, R., Biederman, J., Conners, C. K., Faraone, S. V., et al. (2005). Patterns and predictors of attentiondeficit/hyperactivity disorder persistence into adulthood: Results from the national comorbidity survey replication. Biological Psychiatry, 57, 1442–1451. Kieling, C., Goncalves, R. R., Tannock, R., & Castellanos, F. X. (2008). Neurobiology of attention-deficit/hyperactivity disorder. Child and Adolescent Psychiatric Clinics of North America, 17, 285–307. Knopik, V. S., Sparrow, E. P., Madden, P. A., Bucholz, K. K., Hudziak, J. J., Reich, W., et al. (2005). Contributions of parental alcoholism, prenatal substance exposure, and genetic transmission to child ADHD risk: A female twin study. Psychological Medicine, 35, 625–635. Kratochvil, C. J., Newcorn, J. H., Arnold, L. E., Duesenberg, D., Emslie, G. J., Quintana, H., et al. (2005). Atomoxetine alone or combined with fluoxetine for treating ADHD with comorbid depressive or anxiety symptoms. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 915–924. Lahey, B. B., Hartung, C. M., Loney, J., Pelham, W. E., Chronis, A. M., & Lee, S. S. (2007). Are there sex differences in the predictive validity of DSM-IV ADHD among younger children? Journal of Clinical Child and Adolescent Psychology, 36, 113–126.

c53.indd Sec9:1040

Lavigne, J. V., Cicchetti, C., Gibbons, R. D., Binns, H. J., Larsen, L., & DeVito, C. (2001). Oppositional defiant disorder with onset in preschool years: Longitudinal stability and pathways to other disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 1393–1400. Levy, F., Hay, D. A., McStephen, M., Wood, C., & Waldman, I. (1997). Attention-deficit/hyperactivity disorder: A category or a continuum? Genetic analysis of a large-scale twin study. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 737–744. Leyfer, O. T., Woodruff-Borden, J., Klein-Tasman, B. P., Fricke, J. S., & Mervis, C. B. (2006). Prevalence of psychiatric disorders in 4 to 16-year-olds with Williams syndrome. American Journal of Medical Genetics: Part B, Neuropsychiatric Genetics, 141, 615–622. Linnet, K. M., Dalsgaard, S., Obel, C., Wisborg, K., Henriksen, T. B., Rodriguez, A., et al. (2003). Maternal lifestyle factors in pregnancy risk of attention-deficit/hyperactivity disorder and associated behaviors: Review of the current evidence. American Journal of Psychiatry, 160, 1028–1040. Loeber, R., Green, S. M., Lahey, B. B., & Stouthamer-Loeber, M. (1991). Differences and similarities between children, mothers, and teachers as informants on disruptive child behavior. Journal of Abnormal Child Psychology, 19, 75–95. Loney, J., & Milich, R. (1982). Hyperactivity, inattention and aggression in clinical practice. In D. Routh (Ed.), Advances in behavioral pediatrics. New York: Plenum Press. Lopera, F., Palacio, L. G., Jimenez, I., Villegas, P., Puerta, I. C., Pineda, D., et al. (1999). Discrimination between genetic factors in attention deficit. Revue Neurologique, 28, 660–664. MacMaster, F. P., Carrey, N., Sparkes, S., & Kusumakar, V. (2003). Proton spectroscopy in medication-free pediatric attention-deficit/hyperactivity disorder. Biological Psychiatry, 53, 184–187. Magidson, J., & Vermunt, J. K. (2003). Latent class analysis . In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (Chapter 10 pp. 175–198). New burry Park, CA, Califorina: Sage Publications. Maher, B. S., Marazita, M. L., Moss, H. B., & Vanyukov, M. M. (1999). Segregation analysis of attention-deficit/hyperactivity disorder. American Journal of Medical Genetica, 88, 71–78. Mick, E., Biederman, J., Faraone, S. V., Sayer, J., & Kleinman, S. (2002). Case-control study of attention-deficit/hyperactivity disorder and maternal smoking, alcohol use, and drug use during pregnancy. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 378–385. Moore, C. M., Biederman, J., Wozniak, J., Mick, E., Aleardi, M., Wardrop, M., et al. (2006). Differences in brain chemistry in children and adolescents with attention-deficit/hyperactivity disorder with and without comorbid bipolar disorder: A proton magnetic resonance spectroscopy study. American Journal of Psychiatry, 163, 316–318. Nash, K., Rovet, J., Greenbaum, R., Fantus, E., Nulman, I., & Koren, G. (2006). Identifying the behavioural phenotype in fetal alcohol spectrum disorder: Sensitivity, specificity and screening potential. Archives of Women’s Mental Health, 9, 181–186. Neuman, R. J., Heath, A., Reich, W., Bucholz, K. K., Madden, P. A. F., Sun, L., et al. (2001). Latent class analysis of ADHD and comorbid symptoms in a population sample of adolescent female twins. Journal of Child Psychology and Psychiatry, 42, 933–942.

8/17/09 5:29:16 PM

References 1041 Neuman, R. J., Lobos, E., Reich, W., Henderson, C. A., Sun, L. W., & Todd, R. D. (2007). Prenatal smoking exposure and dopaminergic genotypes interact to cause a severe ADHD subtype. Biological Psychiatry, 61, 1320–1328.

Russell, H. F., Wallis, D., Mazzocco, M. M., Moshang, T., Zackai, E., Zinn, A. R., et al. (2006). Increased prevalence of ADHD in Turner syndrome with no evidence of imprinting effects. Journal of Pediatric Psychology, 31, 945–955.

Nissen, S. E. (2006). ADHD drugs and cardiovascular risk. New England Journal of Medicine, 354, 1445–1448.

Sanders, M., Arduca, Y., Karamitsios, M., Boots, M., & Vance, A. (2005). Characteristics of internalizing and externalizing disorders in medicationnaive, clinically referred children with attention-deficit/hyperactivity disorder, combined type and dysthymic disorder. Australian and New Zealand Journal of Psychiatry, 39, 359–365.

Ogdie, M. N., Bakker, S. C., Fisher, S. E., Francks, C., Yang, M. H., Cantor, R. M., et al. (2006). Pooled genome-wide linkage data on 424 ADHD ASPs suggests genetic heterogeneity and a common risk locus at 5p13. Molecular Psychiatry, 11, 5–8. Ogdie, M. N., MacPhie, I. L., Minassian, S. L., Yang, M., Fisher, S. E., Francks, C., et al. (2003). A genomewide scan for attention-deficit/ hyperactivity disorder in an extended sample: Suggestive linkage on 17p11. American Journal of Human Genetics, 72, 1268–1279. O’Malley, K. D., & Nanson, J. (2002). Clinical implications of a link between fetal alcohol spectrum disorder and attention-deficit/hyperactivity disorder. Canadian Journal of Psychiatry, 47, 349–354. Palacio, J. D., Castellanos, F. X., Pineda, D. A., Lopera, F., rcos-Burgos, M., Quiroz, Y. T., et al. (2004). Attention-deficit/ hyperactivity disorder and comorbidities in 18 Paisa Colombian multigenerational families. Journal of the American Academy of Child and Adolescent Psychiatry, 43, 1506–1515. Pelham, W. E., Foster, E. M., & Robb, J. A. (2007). The economic impact of attention-deficit/hyperactivity disorder in children and adolescents. Journal of Pediatric Psychology, 32, 711–727. Pliszka, S. R. (2000). Patterns of psychiatric comorbidity with attentiondeficit/hyperactivity disorder. Child and Adolescent Psychiatric Clinics of North America, 9, 525–540. Pliszka, S. R. (2007). Pharmacologic treatment of attention-deficit/ hyperactivity disorder: Efficacy, safety and mechanisms of action. Neuropsychology Review, 17, 61–72. Pliszka, S. R., McCracken, J. T., & Maas, J. W. (1996). Catecholamines in attention-deficit/hyperactivity disorder: Current perspectives. Journal of the American Academy of Child and Adolescent Psychiatry, 35, 264–272. Rasmussen, E. R., Neuman, R. J., Heath, A. C., Levy, F., Hay, D. A., & Todd, R. D. (2002). Replication of the latent class structure of attention-deficit/hyperactivity disorder (ADHD) subtypes in a sample of Australian twins. Journal of Child Psychology and Psychiatry, 43, 1018–1028. Reynolds, C. R., & Kamphaus, R. W. (1992). Behavior assessment system for children (BASC). Cicle Pines, MN: American Guidance Service. Rohde, L. A., Barbosa, G., Polanczyk, G., Eizirik, M., Rasmussen, E. R., Neuman, R. J., et al. (2001). Factor and latent class analysis of DSMIVADHD symptoms in a school sample of Brazilian adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 711–718. Rowland, A. S., Lesesne, C. A., & Abramowitz, A. J. (2002). The epidemiology of attention-deficit/hyperactivity disorder (ADHD): A public health view. Mental Retardation and Developmental Disabilities Research Reviews, 8, 162–170. Rowland, A. S., Umbach, D. M., Catoe, K. E., Stallone, L., Long, S., Rabiner, D., et al. (2001). Studying the epidemiology of attentiondeficit/hyperactivity disorder: Screening method and pilot results. Canadian Journal of Psychiatry, 46, 931–940. Rubia, K., Overmeyer, S., Taylor, E., Brammer, M., Williams, S. C., Simmons, A., et al. (1999). Hypofrontality in attention-deficit/ hyperactivity disorder during higher-order motor control: A study with functional MRI. American Journal of Psychiatry, 156, 891–896. Rucklidge, J. J., & Tannock, R. (2001). Psychiatric, psychosocial, and cognitive functioning of female adolescents with ADHD. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 530–540.

c53.indd Sec9:1041

Schwab-Stone, M. E., Shaffer, D., Dulcan, M. K., Jensen, P. S., Fisher, P., Bird, H. R., et al. (1996). Criterion validity of the NIMH Diagnostic Interview Schedule for Children Version 2.3 (DISC-2.3). Journal of the American Academy of Child and Adolescent Psychiatry, 35, 878–888. Seidman, L. J., Valera, E. M., & Makris, N. (2005). Structural brain imaging of attention-deficit/hyperactivity disorder. Biological Psychiatry, 57, 1263–1272. Shaffer, D., Fisher, P., Dulcan, M. K., Davies, M., Piacentini, J., SchwabStone, M. E., et al. (1996). The NIMH Diagnostic Interview Schedule for Children Version 2.3 (DISC-2.3): Description, acceptability, prevalence rates, and performance in the MECA study: Methods for the epidemiology of child and adolescent mental disorders study. Journal of the American Academy of Child and Adolescent Psychiatry, 35, 865–877. Shaw, P., Eckstrand, K., Sharp, W., Blumenthal, J., Lerch, J. P., Greenstein, D., et al. (2007). Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proceedings of the National Academy of Sciences, USA, 104, 19649–19654. Shekim, W. O., Asarnow, R. F., Hess, E., Zaucha, K., & Wheeler, N. (1990). A clinical and demographic profile of a sample of adults with attention-deficit/hyperactivity disorder, residual state. Comprehensive Psychiatry, 31, 416–425. Simpson, J. L., de la Cruz, C. F., Swerdloff, R. S., Samango-Sprouse, C., Skakkebaek, N. E., Graham, J. M., Jr., et al. (2003). Klinefelter syndrome: Expanding the phenotype and identifying new research directions. Genetics in Medicine, 5, 460–468. Smith, A. C., Dykens, E., & Greenberg, F. (1998). Behavioral phenotype of Smith-Magenis syndrome (del 17p11.2). American Journal of Medical Genetics, 81, 179–185. Sonuga-Barke, E. J. (2003). The dual pathway model of AD/HD: An elaboration of neuro-developmental characteristics. Neuroscience and Biobehavior Reviews, 27, 593–604. Spencer, T. J., Biederman, J., Madras, B. K., Faraone, S. V., Dougherty, D. D., Bonab, A. A., et al. (2005). In vivo neuroreceptor imaging in attention-deficit/hyperactivity disorder: A focus on the dopamine transporter. Biological Psychiatry, 57, 1293–1300. Spencer, T. J., Biederman, J., Wilens, T. E., & Faraone, S. V. (2002). Overview and neurobiology of attention-deficit/hyperactivity disorder. Journal of Clinical Psychiatry, 63(Suppl. 12), 3–9. Sprich, S., Biederman, J., Crawford, M. H., Mundy, E., & Faraone, S. V. (2000). Adoptive and biological families of children and adolescents with ADHD. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 1432–1437. Stevenson, J. (1992). Evidence for a genetic etiology in hyperactivity in children. Behavior Genetics, 22, 337–344. Sullivan, K., Hatton, D., Hammer, J., Sideris, J., Hooper, S., Ornstein, P., et al. (2006). ADHD symptoms in children with FXS. American Journal of Medical Genetics, Part A, 140, 2275–2288. Sun, L., Jin, Z., Zang, Y. F., Zeng, Y. W., Liu, G., Li, Y., et al. (2005). Differences between attention-deficit disorder with and without hyperactivity: A 1H-magnetic resonance spectroscopy study. Brain Development, 27, 340–344. Swanson, J. M., & Castellanos, F. X. (1998). Diagnosis and treatment of attention deficit hyperactivity disorder (ADHD). Biological bases of

8/17/09 5:29:17 PM

1042 Attention-Deficit/Hyperactivity Disorder ADHD: Neuroanatomy, genetics, and pathophysiology. In NIH consensus development conference, NIH consensus online. Nov 16–18; 16(2), 1–37

sample. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 768–775.

Swanson, J. M., Kinsbourne, M., Nigg, J., Lanphear, B., Stefanatos, G. A., Volkow, N., et al. (2007). Etiologic subtypes of attention-deficit/ hyperactivity disorder: Brain imaging, molecular genetic and environmental factors and the dopamine hypothesis. Neuropsychology Review, 17, 39–59.

Volkow, N. D., Wang, G., Fowler, J. S., Logan, J., Gerasimov, M., Maynard, L., et al. (2001). Therapeutic doses of oral methylphenidate significantly increase extracellular dopamine in the human brain. Journal of Neuroscience, 21, RC121.

Swanson, J. M., Kraemer, H. C., Hinshaw, S. P., Arnold, L. E., Conners, C. K., Abikoff, H. B., et al. (2001). Clinical relevance of the primary findings of the MTA: Success rates based on severity of ADHD and ODD symptoms at the end of treatment. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 168–179. Thapar, A., van den Bree, B. M., Fowler, T., Langley, K., & Whittinger, N. (2006). Predictors of antisocial behaviour in children with attentiondeficit/hyperactivity disorder. European Child and Adolescent Psychiatry, 15, 118–125. The MTA Study. (1999). A 14-month randomized clinical trial of treatment strategies for attention-deficit/hyperactivity disorder. The MTA cooperative group: Multimodal treatment study of children with ADHD. Archives of General Psychiatry, 56, 1073–1086. Todd, R. D. (1992). Neural development is regulated by classical neurotransmitters: Dopamine D2 receptor stimulation enhances neurite outgrowth. Biological Psychiatry, 31, 794–807. Todd, R. D., Rasmussen, E. R., Neuman, R. J., Reich, W., Hudziak, J. J., Bucholz, K. K., et al. (2001). Familiality and heritability of subtypes of attention-deficit/hyperactivity disorder in a population sample of adolescent female twins. American Journal of Psychiatry, 158, 1891–1898.

Volkow, N. D., Wang, G. J., Telang, F., Fowler, J. S., Logan, J., Childress, A. R., et al. (2008). Dopamine increases in striatum do not elicit craving in cocaine abusers unless they are coupled with cocaine cues. Neuroimage, 39, 1266–1273. Weber, W., & Newmark, S. (2007). Complementary and alternative medical therapies for attention-deficit/hyperactivity disorder and autism. Pediatric Clinics of North America, 54, 983–1006. Weiss, M., Murray, C., & Weiss, G. (2002). Adults with attentiondeficit/hyperactivity disorder: Current concepts. Journal of Psychiatric Practice, 8, 99–111. Wigal, S. B., Gupta, S., Guinta, D., & Swanson, J. M. (1998). Reliability and validity of the SKAMP rating scale in a laboratory school setting. Psychopharmacology Bulletin, 34, 47–53. Wilens, T. E., Kwon, A., Tanguay, S., Chase, R., Moore, H., Faraone, S. V., et al. (2005). Characteristics of adults with attention-deficit/hyperactivity disorder plus substance use disorder: The role of psychiatric comorbidity. American Journal of Addictions, 14, 319–327. Willcutt, E. G., Doyle, A. E., Nigg, J. T., Faraone, S. V., & Pennington, B. F. (2005). Validity of the executive function theory of attention-deficit/ hyperactivity disorder: A meta-analytic review. Biological Psychiatry, 57, 1336–1346.

Valera, E. M., Faraone, S. V., Biederman, J., Poldrack, R. A., & Seidman, L. J. (2005). Functional neuroanatomy of working memory in adults with attention-deficit/hyperactivity disorder. Biological Psychiatry, 57, 439–447.

Willcutt, E. G., Pennington, B. F., Smith, S. D., Cardon, L. R., Gayan, J., Knopik, V. S., et al. (2002). Quantitative trait locus for reading disability on chromosome 6p is pleiotropic for attention-deficit/ hyperactivity disorder. American Journal of Medical Genetics, 114, 260–268.

Vance, A., Sanders, M., & Arduca, Y. (2005). Dysthymic disorder contributes to oppositional defiant behaviour in children with attentiondeficit/hyperactivity disorder, combined type (ADHD-CT). Journal of Affective Disorders, 86, 329–333.

Wolraich, M. L., Lambert, W., Doffing, M. A., Bickman, L., Simmons, T., & Worley, K. (2003). Psychometric properties of the Vanderbilt ADHD diagnostic parent rating scale in a referred population. Journal of Pediatric Psychology, 28, 559–567.

Volk, H. E., Henderson, C., Neuman, R. J., & Todd, R. D. (2006). Validation of population-based ADHD subtypes and identification of three clinically impaired subtypes. American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics, 141, 312–318.

Wortis, J. (1988). Struwwelpeter Heinrich Hoffmann (1809–1894). Biological Psychiatry, 24, 615–618.

Volk, H. E., Neuman, R. J., & Todd, R. D. (2005). A systematic evaluation of ADHD and comorbid psychopathology in a population-based twin

c53.indd Sec9:1042

Zametkin, A. J., & Rapoport, J. L. (1987). Neurobiology of attention deficit disorder with hyperactivity: Where have we come in 50 years? Journal of the American Academy of Child and Adolescent Psychiatry, 26, 676–686.

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Chapter 54

Schizophrenia CAMERON S. CARTER, MICHAEL MINZENBERG, AND JONG H. Y. YOON

hospitalizations with premature discharge and insufficient postdischarge care, and those in jails and prisons, suggesting a pervasive failure in contemporary society to adequately meet the needs of these patients.

Schizophrenia is a serious, disabling lifelong mental disorder that affects men and women equally and 1% of the population worldwide. It is characterized by a range of striking disturbances in many aspects of mental life and behavior, including hallucinations and delusions, diminished emotional expression and motivation, cognitive deficits, and behavioral disorganization. Schizophrenia has its onset typically during adolescence or early adulthood. Historically, a substantial majority of people with the illness have been unable to maintain independent living or gainful employment for any significant period of time in their lives after the onset of illness. Once a chronic course is established, patients generally suffer relapsing periods of overt psychotic symptoms, characterized by disruptions in the capacity to properly perceive the environment, maintain coherent thinking processes, or derive meaning in a manner that can properly guide thoughts, plans, and behaviors. During quiescent periods of the illness, patients continue to have cognitive and social disturbances that sharply limit their capacity for true recovery and reintegration into the community. Schizophrenia also has profound disruptive and demoralizing effects on the families who often struggle to help the affected individual cope with the illness. Overall, the public health impact of schizophrenia is staggering. While the prevalence of the illness is approximately 1%, patients with schizophrenia occupy 25% of all inpatient hospital beds (Terkelsen & Menikoff, 1995) and represent 50% of all inpatient admissions (Geller, 1992). The total cost of the illness is estimated to be $44.9 billion in the United States for the year 1994 (Murray & Lopez, 1996). Schizophrenia is one of the top 10 causes of disability-adjusted life years (Murray & Lopez, 1996), representing 2.3% of the total burden of disease in developed countries (the fourth leading cause among persons ages 15 to 44) and 0.8% in developing countries (U.S. Institute of Medicine, 2001). Patients with schizophrenia are also disproportionately found among the chronically homeless, those who undergo the “revolving door” of repeated brief

OVERVIEW Haslam, and independently Pinel, both in the early nineteenth century, wrote the earliest modern descriptions of individuals afflicted with the illness that we now recognize as schizophrenia. Later in the eighteenth century, Morel first used the term dementia praecox to describe schizophrenia as a premature dementia, emphasizing the early onset and progressive clinical decline. Kahlbaum categorized the symptoms and derived subtypes of schizophrenia, such as catatonia, and hebetic paraphrenia, later termed hebephrenia by Hecker. The German psychiatrist Kraepelin adopted the term dementia praecox and provided a detailed account of the clinical course and outcome of patients. He noted that the age of onset, family history, premorbid personality, and a deteriorating clinical course were useful in the distinction of dementia praecox from manic-depressive illness. Kraepein also emphasized hereditary factors, obstetrical complications, and physical abnormalities as potentially important etiological factors in the illness and indicated that clinical improvement in these patients should be considered temporary, as residual symptoms were ubiquitous, and relapse inevitable. In his later writings, however, came to acknowledge that some patients experienced a relatively later onset of illness and/or a significant measure of recovery (Adityanjee, Aderibigbe, Theodoridis, & Vieweg, 1999). Eugen Bleuler, a Swiss psychiatrist introduced the term schizophrenia. He criticized the notion of dementia praecox, which he considered a heterogeneous group of disorders. Bleuler defined the primary features of schizophrenia as the “four As”: (1) looseness of associations, (2) affective flattening, (3) autism, and (4) ambivalence. This is essentially 1043

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1044

Schizophrenia

an emphasis on cognition, apparent in the link between the term schizophrenia, or split mind, and the formal thought disorder manifest in disturbed associations. Importantly, he also recognized disturbances in emotion and motivation that were largely neglected by earlier theorists. The other European figure whose work helped shape modern notions of schizophrenia is Schneider who outlined a set of “first-rank” symptoms that included many of the most extreme disruptions of reality, such as thought insertion and withdrawal, thought broadcasting, hallucinated voices in argument with each other, and some other more severe delusional and passivity experiences that patients with schizophrenia reported. This represented one of the first attempts at establishing a discrete criteria set for the diagnosis. This also had the effect of narrowing the diagnosis of schizophrenia, as the first-rank symptoms were clearly pathological, in comparison to some of Bleuler ’s symptoms, which appeared to be more continuously distributed in the general population. The ideas of Kraepelin, Bleuler, and Schneider remain highly influential to this day and helped shape the construct of schizophrenia as defined in the DSM, the diagnostic system of the American Psychiatric Association. Symptoms Schizophrenia is operationally defined by a large set of signs and symptoms cutting across diverse domains of behavior and mental processes. While there is still active debate on the relative merits and validity of the various symptom classification systems that have been proposed, in this chapter, we mostly rely on a scheme that segregates clinical findings into positive, negative, and disorganized symptoms or syndromes. This system is simple and has received empirical validation in factor analytic studies (Bilder, Mukherjee, Rieder, & Pandurangi, 1985; Liddle, 1987). The term positive symptom refers to the presence of abnormal mental processes. Positive symptoms include hallucinations, which may be experienced in any sensory modality and delusions, or false beliefs. Negative symptom refers to the absence of normal mental function, such as reduced emotional expression, decreased interest in social activities, and reduced motivation. The disorganized category refers to disturbances in language production (or thought disorder), gross distractibility, and odd or unusual behavior. Cognitive Deficits Kraeplin and Bleuler both emphasized cognitive impairments as a core aspect of this illness. With the emergence of modern cognitive science, it has become increasingly clear that patients with schizophrenia show a range of impaired

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higher cognitive functions including problems with attention, long-term memory, working memory, abstraction and planning, and language comprehension and production. These cognitive deficits present significant barriers to maintaining occupational and everyday function. Cognitive deficits may be the best predictor of functionality over and above other symptom clusters (Green, 1996). Attention and memory problems are common in schizophrenia. Working memory, the capacity to maintain information on line to rapidly guide thoughts and behavior, has been proposed as a fundamental cognitive deficit in schizophrenia. These theories suggest that many of the clinical features of schizophrenia are manifestations of working memory deficits. For example, thought disorder can be conceived as the inability to maintain a communication goal in mind. Problems with multitasking, distractibility, and planning may also result from working memory problems. Long-term memory is also an important disability in schizophrenia. Memory problems are not progressive or as profound as in amnestic syndromes such as Alzheimer ’s disease and qualitatively resemble those seen in patients with frontal lobe injury rather than dementias resulting from injury to the medial temporal lobe. Common and clinically relevant manifestations of this impairment include forgotten appointments or medication directions, which may directly impact the treatment and stability of the patient.

DIAGNOSING SCHIZOPHRENIA The DSM-IV-TR defines schizophrenia as an illness characterized by positive, negative, and/or disorganized symptoms that must be present for a significant portion of time during at least 1 month (unless the symptoms are successfully treated). These Criterion A symptoms are referred to as active-phase symptoms. There must be impairment in psychosocial function (work, interpersonal relationships, or self-care). To receive a diagnosis, some continuous signs of the disturbance must be evident for at least 6 months; this must include at least 1 month of active-phase symptoms but may include periods of prodromal or residual symptoms. At the present time, the diagnostic process rests solely on the history of illness and a thorough mental status examination. No reliable laboratory tests have yet been established for this illness. The major task in differential diagnosis is to distinguish schizophrenia from a range of other psychiatric disorders that may also involve psychotic symptoms. These include schizoaffective disorder; major mood disorders that can present with psychotic features, such as major depression and acute mania among bipolar affective disorder type I patients; delusional disorder; and personality disorders. To rule out the major mood disorders

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Long-Term Outcome of Schizophrenia

or schizoaffective disorder, the active phase of psychosis should occur in the absence of an acute mood disorder episode, or alternatively the mood episodes should be relatively brief in relation to the total duration of the psychotic episode. Most mood disorder patients also maintain or recover significant levels of psychosocial function in between episodes of illness, as they do not experience continuous psychotic symptoms or persistently severe mood disturbance. Delusional disorder is distinguished by the lack of other psychotic symptoms, and the content of delusions tend not to be the bizarre thoughts or beliefs often observed in schizophrenia, such as beliefs that monitoring devices are implanted in the patient’s body, or that the patient is communicating with other species. These individuals also tend to maintain a higher level of function because they largely experience only the circumscribed delusions that meet the criteria for the disorder. Schizophreniform disorder and brief psychotic disorder are also characterized by overt psychotic symptoms. If a clinician encounters the patient relatively early in the active psychotic phase of illness, one of these diagnoses (both with a briefer duration criterion then schizophrenia) is most appropriate to assign initially. However, if psychotic symptoms persist beyond 6 months, then the diagnosis of schizophrenia is most appropriate.

LONG-TERM OUTCOME OF SCHIZOPHRENIA Numerous studies have been conducted in an attempt to characterize the long-term outcome of patients with schizophrenia. Hegarty, Baldessarini, Tohen, Waternaux, and Oepen (1994) reviewed 320 studies that included a total of 51,800 patients with schizophrenia, conducted between 1895 and 1992. Patients were followed approximately 6 years on average, and 40% were considered to have improved, as measured by recovery, remission, or becoming clinically stable with minimal symptoms. Those studies where patients were diagnosed with a more narrow criteria set showed lower rates of improvement, 27% on average, probably reflecting the more “Kraepelinian” deteriorating course associated with narrower definitions of the illness. In addition, the rate of improvement was greater for those patients identified after the middle of the twentieth century than those followed earlier, likely reflecting treatment advances in this period. Studies reported more recently have indicated lower rates of improvement, possibly reflecting again-narrowed criteria for the diagnosis, as well as more stringent criteria for clinical improvement as well. Several studies have now been reported where patients with schizophrenia have been identified using contemporary diagnostic criteria, and follow-up obtained over at least

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1045

10 years (Jobe & Harrow, 2005). Most of these have been retrospective chart reviews, typically of patients who were identified initially upon hospitalization and then followed after discharge. The Iowa 500 study followed 500 psychiatric patients admitted to the Iowa State Psychiatric Hospital between 1934 and 1944, and used the Feighner (Feighner et al., 1972) criteria to identify schizophrenia patients, of whom 200 were followed an average of 35 years from the index hospitalization. These patients were followed in an era prior to modern antipsychotic medication or modern psychosocial treatment, therefore providing significant documentation of the untreated course of schizophrenia. In this study, the schizophrenia patients were observed to have poorer outcome on all measures, relative to other psychiatric patients and nonpsychiatric surgical patients. 54% had incapacitating symptoms, 67% never married, 18% were living in institutions, and over 10% had committed suicide (Tsuang & Winokur, 1975). The Chestnut Lodge study followed 532 patients discharged from this private hospital between 1950 and 1975, for an average of 15 years. Patients were diagnosed by less restrictive DSMIII criteria, yet the findings were broadly similar to those of the Iowa 500. The 163 schizophrenia patients as a group were found to have the following outcomes: 6% recovered, 8% good, 22% moderate, 23% marginal, and 41% continuously incapacitated (McGlashan, 1984). A study conducted at the New York State Psychiatric Institute included 552 patients who underwent treatment with psychoanalytically oriented psychotherapy, of whom 99 met DSM-III criteria for schizophrenia. With follow-up between 10 to 23 years, the schizophrenia patients showed poorer outcomes when compared with other psychiatric patients, had an average DSM GAF score of 39, and a completed suicide rate of 10% (Stone, 1986). While these studies largely emphasized the relatively poor prognosis of most schizophrenia patients, other studies identified subgroups with better outcomes. These studies include Vaillant’s study in Boston, where the patients identified as completely remitted from an earlier study were then followed prospectively for 4 to 16 years. He found that 61% of these patients remained in remission. A study in Alberta found 58% of 92 patients diagnosed with DSM-II schizophrenia to experience full recovery, despite 45% of the full sample having discontinued their psychiatric medication in the 10 months after the index hospitalization. When this same sample was narrowed by using stricter diagnostic criteria, the percentage of those considered fully recovered was halved (Bland, Parker, & Orn, 1978). Only two long-term follow-up studies were fully prospective in design. The Chicago Follow-up Study included 73 schizophrenia patients followed up to 20 years. This study found the schizophrenia patients to generally fluctuate between moderate and severe disability, though with over 40%

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Schizophrenia

showing periods of recovery, which often lasted for several years (Harrow & Jobe, 2005). Some of these patients were able to function without the benefit of continuous antipsychotic treatment and tended to have better premorbid function. In addition, a large percentage of the full sample of schizophrenia patients (65%) had also experienced at least one depressive syndrome at 20-year followup; the completed suicide rate was 10% at 10 years and over 12% at 20 years. The other long-term prospective study of schizophrenia patients, conducted by Carpenter and Strauss (1991), followed 55 DSM-III-identified schizophrenia patients for 11 years, finding no change in their relatively poorer outcome status at 5 versus 10 years. A number of long-term follow-up studies have been conducted outside of North America. These studies have typically used ICD criteria rather than DSM criteria to identify patients. Some of these studies have found women to have a relatively more benign course of illness compared to men (Angermeyer, Goldstein, & Kuehn, 1989). One particularly important study is the International Pilot Study of Schizophrenia sponsored by the WHO. A total of 1,633 subjects from 14 incidence cohorts and 4 prevalence cohorts, in 9 different nations were studied. The most dramatic finding was that outcome in schizophrenia was poorer in fully industrialized countries than in developing countries. Repeated psychotic episodes, for instance, was more common in the developed countries despite the greater availability of modern treatment. The range and severity of symptoms at initial enrollment was not significantly different between sites. This finding has been subject to a great deal of discussion. Some have suggested that a culture of tolerance and benevolence toward those with unusual thoughts and behaviors is more prevalent in developing countries, with a salutary effect of normalizing or “buffering” the patient’s psychopathology, and maintaining integration in the local community. However, this does not appear to fully account for the differences between these groups of nations (McGrath et al., 2004). Others have suggested that economies that are not fully market-oriented place fewer psychological and practical demands on schizophrenia patients, with less illness exacerbation and less downward social drift as a result. On the whole, the strongest predictors of poor outcome in the WHO study were social isolation, duration of index episode, history of psychiatric treatment, unmarried status, and history of childhood behavioral problems. It is possible that these factors all reflect a more severe form of illness at outset.

ETIOLOGY AND PATHOPHYSIOLOGY Modern psychiatric research has produced an abundance of evidence supporting the notion that schizophrenia is a

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disorder primarily related to brain dysfunction. The convergence of disparate modern investigative techniques into this question has revealed many important clues as to the neurobiological basis of this condition. However, despite these advances, the full understanding of the causes and the biological pathways leading to schizophrenia remains one of the most pressing challenges facing modern medicine. Two concepts describe the generally accepted framework reflecting our current understanding of the etiology and pathophysiology of schizophrenia. The first is the view that schizophrenia is a neurodevelopmental disorder—that disturbances in the growth and maturation of neurons and neural pathways give rise to schizophrenia. The other overarching framework is the stress-diathesis model of schizophrenia. This model posits a dynamic interplay between environmental (stress) and heritable (diathesis) factors in determining whether any individual develops this illness. This model is consistent with available data showing that while the risk of developing schizophrenia is strongly influenced by genetics, the eventual development of this illness is also strongly modulated by the environmental factors (D. A. Lewis & Levitt, 2002; Lieberman et al., 2001). Genetics That schizophrenia has a strong genetic component is a readily accepted notion (see Chapter 60). The degree of risk is proportional to the degree of shared genes and twin studies show concordance rates between 25% and 50% (McGue & Gottesman, 1991). Adoption studies show an elevated risk for schizophrenia among the offspring of mothers with schizophrenia (Kety, Rosenthal, Wender, & Schulsinger, 1971). The exact manner in which schizophrenia is heritable and the identity of the specific genes that may give rise to schizophrenia, however, remain topics of significant debate and uncertainty. It is very evident that schizophrenia does not follow simple Mendelian principles of inheritance (McGue & Gottesman, 1989). This conclusion follows from the logic that inheritance patterns of diseases following simple Mendelian genetics are relatively easy to detect and no such pedigree has ever been described for schizophrenia. A complex genetic model of transmission is much more likely to be the case for schizophrenia. Complex diseases involve several genes, each with a modest effect on heritability, acting in concert, either in a linear or synergistic manner, to confer an overall disease risk (Risch, 1990). Additional complexity may arise from partial penetrance of these genes, interactions between genes, and epigenetic neurodevelopmental or environmental factors. The potential complexity of genetic and nongenetic factors in schizophrenia is illustrated by twin adoption studies. Several have been published, and on

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Etiology and Pathophysiology 1047

the whole, they have been remarkably consistent in demonstrating approximately 50% concordance rate for monozygotic twins. This result accentuates the importance of both the genetic and nongenetic factors in conferring disease risk. Despite the fact that two individuals share identical genetic makeup, there is only approximately 50% chance that both will develop schizophrenia. Consequently, nongenetic causes must account for this lack of full concordance. This elevated risk may be mediated in part by a stressful environment (Tienari et al., 1994). Similar models of gene-environment interaction leading to disease expression has received empirical validation in other psychiatric disorders (Moffitt, Caspi, & Rutter, 2005). In the epidemiology section, some of the more commonly cited nongenetic factors thought to explain this concordance rate are reviewed. In the past 10 years, with the development of novel study designs and high throughput methods, we have witnessed a tremendous proliferation in the number of putative schizophrenia risk genes. An interesting aspect of this list is that many of these genes are related to neurodevelopmental processes involved in the establishment of neural networks, for example, neuronal migration and synapse formation and/or the regulation of synaptic transmission. One such gene that has received a lot of attention is dysbindin DTNBP1 (Straub et al., 2002). This gene product binds to components of the dystrophin complex, thought to be important in mediating neural synapse structure and function. Another putative schizophrenia gene is neuregulin (NGR1; Stefansson et al., 2002). It is located on 8p21–22 and it may exhibit a diverse range of roles in neural transmission, axonal development, and synaptogenesis (Corfas, Roy, & Buxbaum, 2004). Replications of findings from linkage studies have been relatively rare. However, this may be resolved by considering that several risk genes are involved, each with only modest effect. A recent metaanalysis of these linkage studies did show some support for the involvement of several regions (Badner & Gershon, 2002; C. M. Lewis et al., 2003). Follow-up association studies in many of these regions have been promising and they have identified several candidate schizophrenia risk genes (Owen, Craddock, & O’Donovan, 2005). Environmental Factors Because identical twins have a concordance rate of only 50%, it is eminently clear that nonheritable or environmental factors also play a significant role in the risk for developing schizophrenia. The idea that fetal neural development represents a vulnerable period for the genesis of schizophrenia is supported by observations of higher incidence of obstetric and perinatal complications in patients with schizophrenia in a number of studies. A recent meta-analytic review

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has categorized these events as (a) complications of pregnancy, (b) abnormal fetal growth and development, and (c) complications of delivery (Cannon, Jones, & Murray, 2002). The meta-analysis indicates that each of these categories was significantly associated with increased risk, but that the effect sizes were generally modest. Another line of studies has found an association between maternal nutritional status and schizophrenia in the offspring. The Dutch Famine study examined the prevalence of schizophrenia among a cohort of births that occurred during the winter of 1944/1945, a period of severe malnutrition for most citizens in a region of the Netherlands (Susser et al., 1996). The study showed a two-fold increased risk for schizophrenia associated with extreme prenatal malnutrition. Most epidemiologic studies investigating environmental risk factors for schizophrenia are limited by the retrospective manner in which data is collected. For example, in the case of maternal exposure to influenza, this information is usually obtained from participants’ recollection of influenza infection during pregnancy or the association of a known influenza outbreak in a particular community within the period of pregnancy. The Prenatal Determinants of Schizophrenia (PDS) study addresses this limitation by relying on prospectively gathered data, which included maternal serum obtained during prenatal visits and demographic information of the participants (Susser, Schaefer, Brown, Begg, & Wyatt, 2000). From the cohort of roughly 12,000 pregnant women, potential cases of schizophrenia were identified from medical and pharmacy records. Of these potential cases, face-to-face diagnostic evaluations by research psychiatrists resulted in the identification of 71 subjects with schizophrenia. This study concluded that there is a seven-fold increased risk of schizophrenia and related disorders associated with influenza infection in the 1st trimester (Brown et al., 2004). Other possible pathogens that have been identified in the PDS study include toxoplasmosis and lead. Another line of research has pointed to the importance of the physical environment and fetal exposures during gestation. Seasonal variation in the prevalence of births leading to schizophrenia has been identified, with an excess of births in winter and spring months (Davies, Welham, Chant, Torrey, & McGrath, 2003). A variety of theories attempting to account for this have been proposed—environmental factors that predisposed to schizophrenia development such as ambient temperature, exposure to infectious agents, and nutritional deficiencies; increased resistance to infections and other insults conferred by schizophrenia leading to increased survival in winter months. Although the worldwide prevalence is thought to be equivalent across nations (Jablensky, 2000; Sartorius, Jablensky, & Shapiro, 1977), there have been numerous

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Schizophrenia

findings and theories suggesting a direct relationship between specific social and cultural factors and the development or severity of schizophrenia. Some of these factors include immigration status, urbanicity, and socioeconomic status. However, the results of studies examining these factors have either been inconsistent or complicated by confounds that make it very difficult to ascertain whether these factors are causes or effects of illness, for example, downward drift in socioeconomic status due to mental illness. Neurochemical Abnormalities Dopamine Chlorpromazine was originally synthesized in the 1950s as an antihistamine for use as a preanesthetic agent. Upon noting its particularly striking calming effect on patients, the French surgeon Henri Laborit recommended chlorpromazine to his psychiatric colleagues for use with agitated patients. They quickly found it beneficial with patients with schizophrenia. They also noted Parkinsonian side effects with higher doses. They coined the term neuroleptic, literally translated from the French as “seizing the neuron,” to reflect their intuition that the mechanism of action somehow involved neural modulation. The serendipitous discovery of the usefulness of chlorpromazine in schizophrenia led ultimately to the development of the dopamine hypothesis, one of the most influential theories on the etiology of schizophrenia. It posits that the symptoms of this illness are the byproducts of dysfunction of dopamine neurotransmission. The main lines of evidence supporting this role for dopamine came from work in the 1960s and 1970s. It was shown, for example, that the administration of phenothiazines in animals blocks the behavioral effects of dopamine agonists (such as amphetamine) and results in increased turnover of dopamine. Conversely, the administration of amphetamine, which was known to increase

synaptic levels of dopamine, resulted in behavioral abnormalities and symptoms reminiscent of schizophrenia. Later work further specified that the most important dopamine receptor may be the D2 subtype in that clinical potency is best correlated with binding to this receptor subtype (Creese, Burt, & Snyder, 1976). Neuroimaging has made significant contributions to our evolving understanding of the neurochemical basis for schizophrenia. Imaging modalities such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) are allowing researchers to assess the functional status of neurotransmitter systems (Figure 54.1). One line of PET studies has led to a more refined hypothesis of dopamine dysregulation. These studies indicate that the dopaminergic tone associated with schizophrenia may be more complex than previously thought. This newer hypothesis proposes a hyperdopamineragic state in the striatal D2 system (Abi-Dargham et al., 2000) giving rise to positive symptoms and a hypodopaminergic state in the prefrontal D1 system associated with higher-order cognitive deficits (Abi-Dargham et al., 2002). As important as the dopamine hypothesis has been to schizophrenia research, modern psychiatry has appreciated the limitations of this theory. The challenge to the dopamine hypothesis comes from primarily two lines of evidence. First, the dopamine hypothesis does not account for negative symptoms, which are now acknowledged to be essential components of this illness. Dopamine blocking agents have not been shown to be effective in treating negative symptoms nor have dopaminergic agents been shown to induce negative symptoms. The second challenge to the dopamine hypothesis comes from the efficacy of the so-called atypical neuroleptics, medications that are thought to act through multiple neurotransmitter systems in addition to dopamine.

(A)

(B)

(C)

(D)

(E)

(F)

(G)

(H)

Figure 54.1 Evidence for increased dopamine release in schizophrenia Note: From Abi-Dargham et al. 2002.

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Etiology and Pathophysiology 1049

Other Monoamines The observations that the prototypical “atypical” neuroleptic, clozapine, is often effective in patients who have symptoms refractory to the traditional D2 blocking agents and possesses high affinity for diverse monoaminergic receptors including serotonin, histamine, muscarinic, and alpha-adrenergic receptors, in addition to the D2 receptor, have led to the hypothesis that other neurotransmitter systems may be involved in the pathophysiology of schizophrenia. One of the most important of these other neurotransmitters is serotonin. Serotonin has been implicated by the clinical efficacy of the many atypical agents with high affinity for its receptors. There are 14 known serotonin receptor subtypes but some of the most important for schizophrenia include the 5HT-2C, -2A, and -1A subtypes. The acetylcholine system has been implicated in the pathophysiology of schizophrenia based initially on the observation that patients with schizophrenia exhibit high rates of use of tobacco products. This led to the hypothesis that the nicotine in tobacco provides some amelioration of symptoms through its action on the acetylcholine system. This hypothesis has received some support by work examining the effects of nicotine on early sensory deficits that were well documented in schizophrenia: Nicotine normalizes measures of deficient auditory gating in schizophrenia (Adler, Hoffer, Griffith, Waldo, & Freedman, 1992). Glutamate/NMDA Glutamate is the most prevalent excitatory neurotransmitter in the brain. Consequently, the function of glutamate is fundamentally different from dopamine and the other monoaminergic neurotransmitters, which are primarily modulators of excitatory or inhibitory neurotransmission. The involvement of the glutamate system in the pathophysiology of schizophrenia is inferred primarily from the observation that people intoxicated with agents acting on the glutamate receptor, phencyclidine (PCP) and ketamine, often exhibit a behavioral syndrome mimicking schizophrenia. This syndrome can include both positive and negative symptoms of schizophrenia (Javitt & Zukin, 1991). PCP and ketamine bind to the N-methyl-D-aspartate (NMDA) class of glutamate receptors and, consequently, the main focus of glutamate research has been on this receptor. The NMDA receptor regulation is highly complex with numerous sites of allosteric modulation. One of the most important in terms of psychopathology appears to be the glycine site. There have been several clinical trials examining partial (D-cycloserine) and full agonists (glycine, D-serine, and D-alanine) of this site. The pharmacodynamics of cycloserine with the NMDA receptor is complex with cycloserine acting as an agonist at low and an antagonist

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at high concentrations. One of the main current uses of cycloserine is treatment for tuberculosis in high doses and a relatively common side effect in this setting is psychosis. The results of clinical studies investigating the effects of glycine agonists have been mixed with some studies showing benefit for both positive and negative symptoms. However, due to the limited number of studies, understanding the importance of the glutamate/NDMA system in schizophrenia will require further studies. GABA The potential role for GABA in the pathophysiology of schizophrenia follows two separate but related lines of research involving inhibitory interneurons. In the first line of research, it is thought that the psychotomimetic effects of NMDA antagonists, such as PCP, are mediated through their action on GABA release. NMDA receptors are found on GABAergic inhibitory interneurons. Activation of these NMDA receptor results in increased GABA release, which then causes suppression of glutamate release from glutamatergic cells. The binding of an antagonist on the NMDA receptor on the inhibitory neurons ultimately results in a hyperglutamatergic state, which is presumed to cause symptoms of psychosis. In the second line of research, it is thought that alterations in the neural circuitry of the prefrontal cortex, involving GABA, give rise to the higher-order cognitive deficits in schizophrenia. Theories on GABA dysfunction in schizophrenia center on the parvalbumin (PV) containing group of inhibitory interneurons. Studies showing reduction in the number of PV cells and under-expression of glutamic acid decarboxylase (GAD), a key enzyme in GABA synthesis (Akbarian et al., 1995; Volk, Austin, Pierri, Sampson, & Lewis, 2000) point to a functional deficit in GABA in the prefrontal cortex. PV cells can be further subdivided based on differences in histological and putative functional properties. Chandelier cell axons target the axonal initial segment (AIS) of pyramidal cells in the neocortex and show a limited coverage area of its axons. The wide arbor cells target the soma and proximal portions of the dendrites and, as its name implies, its axons cover a broad area. With the privileged position of its axonal cartridges, the chandelier cells are thought to potently regulate the timing of output of pyramidal cells within a column, while wide arbor cells are thought to inhibit pyramidal cells in neighboring columns (D. A. Lewis, 2000). Additionally, chandelier cells can terminate on several hundreds of pyramidal cells, setting the stage for the synchronization of many cells (Figure 54.2; Cobb, Buhl, Halasy, Paulsen, & Somogyi, 1995; Howard, Tamas, & Soltesz, 2005). Taken together, the chandelier and wide arbor cells are thought to coordinate the fine control of the synchrony and spatial extent of pyramidal cell

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GAT-1 Protein

GABAA Receptor α2 Subunit Protein GAT-1 mRNA GAD67 mRNA

Figure 54.2 Cortical microanatomy and schizophrenia Note: From “Impaired Prefrontal Inhibition in Schizophrenia. Relevance for Cognitive Dysfunction,” by D. Volk and D. A. Lewis, 2002, Physiology and Behavior, 77, p. 503. Reprinted with permission.

activity in the prefrontal cortex. The disruption of these functions in schizophrenia would be expected to lead to the loss of temporal and spatial organization in neuronal activity necessary for higher order cognitive processes. Anatomic and Histologic Studies The study of structural abnormalities in brains of individuals with schizophrenia was once considered a “graveyard” for neuropathologists. The emergence of modern neuroimaging and molecular techniques has led to a renewed interest in this field. Neuroimaging studies have shown robust evidence of whole brain volume deficits while modern neuropathology studies have uncovered provocative clues pointing to alterations in the microscopic neuroanatomy in schizophrenia (Table 54.1). The advent of modern neuroimaging techniques has allowed detailed analysis of brain structures and has significantly shaped our understanding of the neural basis of schizophrenia. Previously, the measurement of brain volumes could only be conducted in a reliable manner with postmortem samples. The relative ease of use has resulted in a proliferation of in vivo neuroimaging volumetric studies. Computed tomography (CT) studies documenting significant enlargement of cerebral ventricles and decrease in overall brain volume in subjects with schizophrenia (relative to healthy control subjects) have provided the first compelling neuroimaging results indicating that schizophrenia is a brain-based disorder (Johnstone, Crow, Frith,

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Husband, & Kreel, 1976). These results remain the most reliable and robust volumetric findings in schizophrenia, with a median reduction in ventricular volume estimated to be 40% (Lawrie & Abukmeil, 1998). However, despite the large difference between patients and controls, there is substantial overlap between groups and this measure cannot be used to reliably differentiate between patients and controls. In other words, we do not yet have a good biological diagnostic marker for schizophrenia. More recent MRI volumetric studies have confirmed the results of these earlier CT studies. They have also identified several specific regions of decreased volume including the prefrontal, medial temporal structures, lateral temporal cortex, and thalamus (Harrison, 1999). The magnitude of volume difference between subjects with schizophrenia and healthy controls is generally modest in these regions and these results have not been as consistent as the ventricular and whole brain findings. A recent meta-analysis of MRI studies involving first episode subjects showed highly significant reductions in total brain and increased ventricular volume (Steen, Mull, McClure, Hamer, & Lieberman, 2006), suggesting that these findings are not just the result of disease chronicity or medication exposure. Neuroimaging studies have strongly confirmed that brain abnormalities are indeed associated with schizophrenia. Consequently, there has been renewed interest in identifying microscopic neural abnormalities, with modern neuropathology studies revealing alterations not previously appreciated in the brains of individuals with schizophrenia. A review of the literature shows robust findings including reduction in cortical neuronal size, reduction in axonal and dendritic arborization, and reduction in the number of thalamic neurons. The latter study has shown highly significant loss in the number of neurons in the mediodorsal nucleus of the thalamus, particularly in the subnucleus that projects to the dorsolateral prefrontal cortex (Popken, Bunney, Potkin, & Jones, 2000). The development of diffusion tensor imaging (DTI), an MR based technique, is allowing researchers to measure white fiber integrity in the brain. DTI has been quickly adopted by schizophrenia researchers to examine white fiber pathology (Kanaan et al., 2005), thereby testing the hypothesis that schizophrenia is a result of diminished connectivity between brain regions. A growing number of studies have reported loss of white fiber integrity in many areas, such as in tracts connecting the prefrontal and temporal cortices. However, as expected with such a new technique applied to a complex illness, there has yet to be a large body of studies replicating these early results. Consequently, the field will have to await future studies using this promising technology before we can assess the importance of this line of research.

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Etiology and Pathophysiology 1051 TABLE 54.1

Summary of Structural Brain Abnormalities in Schizophrenia

Change in Brain Structure

Strength of Evidence

Macroscopic Findings Enlarged lateral and third ventricles



Decreased cortical volume



The above changes present in first-episode patients



Disproportionate volume loss from temporal lobe (incl. hippocampus)



Decreased thalamic volume



Cortical volume loss affects grey rather than white matter



Enlarged basal ganglia secondary to antipsychotic medication



Histological Findings Absence of gliosis as an intrinsic feature



Smaller cortical and hippocampal neurons



Fewer neurons in dorsal thalamus



Reduced synaptic and dendritic markers in hippocampus



Maldistribution of white matter neurons



Entorhinal cortex dysplasia



Cortical or hippocampal neuron loss



Disarray of hippocampal neurons



Miscellaneous Alzheimer ’s disease is not more common in schizophrenia



Pathology interacts with cerebral asymmetries



  Weak;   Moderate;   Good;   Strong;   Shown by meta-analysis. Note: From “The Neuropathology of Schizophrenia. A Critical Review of the Data and Their Interpretation,” by P. J. Harrison, 1999, Brain 122(Pt. 4), pp. 593–624.

Cognitive and Information Processing Deficits Cognitive deficits have been recognized as an important feature of schizophrenia since the beginning of efforts to systematically study this condition. About 100 years ago, Kraepelin referred to schizophrenia as dementia praecox, or premature dementia, to describe the prominent cognitive deficits that he thought formed the core of this condition. As noted earlier, the word schizophrenia, originally coined by Bleuler, is best translated from German as the “splitting of the mind,” a term intended to capture the loss of integration of mental processes. The interest in cognition waned in the intervening years as other aspects of the illness became the focal point of research interest. However, in the past 20 years, there has been renewed interest in studying cognitive dysfunction in schizophrenia as a way to understand its pathophysiology. The logic is that cognitive abnormalities represent core deficits of schizophrenia and that the study of core deficits may provide a better index

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of underlying neural dysfunction. Evidence that cognition is a core feature of schizophrenia comes from many fronts. First, studies have documented a fairly strong correlation between cognitive deficits and functional status. This is in distinction to psychotic symptoms, which generally do not correlate well with functional status. Second, cognitive deficits are very common among individuals suffering from schizophrenia. Third, cognitive deficits appear to be an essential aspect of this condition because they predate the onset of psychotic symptoms, and they are present in unaffected first-degree relatives and identical twins. The study of cognition has the additional practical benefit that there is an abundance of paradigms amenable to experimental controls and manipulation and it is now possible to image brain activity using fMRI and other non invasive methods during the course of cognitive processing. There is now an abundance of research indicating prominent deficits in higher-order cognition in schizophrenia.

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Disturbances in cognitive control (the coordination of thought and actions), attention, language, and memory have been documented by a number of researchers using diverse paradigms. Some investigators have attempted to develop comprehensive cognitive models of schizophrenia that could explain many of the behavioral deficits and symptoms of schizophrenia. Goldman-Rakic proposed that working memory, the maintenance of information “online” to guide behavior, is the fundamental disturbance in schizophrenia. She further proposed that the cognitive deficits and symptoms such as disorganization in speech and actions are manifestations of working memory deficits Cohen and colleagues have proposed the context processing deficit model for schizophrenia (Cohen et al., 1999). Here context is defined as the conjunction of items, rules, and goals required to guide behavior or decisions. A real life example of context processing is the ability of a tourist from the United States, while visiting England, to avoid being hit by a car while crossing a street. He does so by realizing that one needs to look right first and then left before crossing a street in England. In this example, the conjunction of seeing the crossing signal and the rules of the road in England constitutes the context with which actions (looking right then left) are decided on. According to the context processing models, much of the diverse cognitive deficits seen in schizophrenia can be reduced to this inability to hold diverse representations in mind. Andreasen (1997, 2000) proposed the cognitive dysmetria model of schizophrenia in which the primary deficit is in the inability of patients to rapidly and efficiently coordinate mental activity in a task appropriate manner. The first generation of cognitive neuroscience studies focused primarily on traditional areas of research in cognition, namely higher-order cognitive processes. More recently, the boundaries of inquiry have broadened to include virtually all domains of mental processes impaired in schizophrenia. Consequently, the term information processing deficits may be a more general and appropriate term to describe the diverse studies currently undertaken by schizophrenia researchers. These studies are revealing information processing deficits in early sensory, affective, and social domains. Early Sensory Processing Deficits While dysfunction in higher-order cognitive processes have now been firmly established, another line of research is investigating the hypothesis that deficits in early sensory processing is a fundamental aspect of schizophrenia. Some have proposed that these early sensory deficits may contribute to higher-order cognitive deficits and have significant impact on the functional status of the affected individuals (Brenner, Lysaker, Wilt, & O’Donnell,

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2002; Javitt, Strous, Grochowski, Ritter, & Cowan, 1997; Saccuzzo & Braff, 1981). The visual and auditory systems have been the best studied. In the visual domain, studies examining the earliest processes in visual perception have demonstrated deficits in schizophrenia. For example, visual masking is a procedure in which the perception of a briefly presented object (target) is reduced by the presentation of another object (mask) shortly before or after. Numerous studies have demonstrated that patients exhibit visual masking deficits, meaning they have more difficulty, compared to healthy subjects, accurately perceiving the target when a mask is presented (Green & Walker, 1986). The visual masking deficit has been shown to correlate with negative symptoms (Green & Walker, 1986) and formal thought disorder (Perry & Braff, 1994). Another line of research has demonstrated neural correlates of deficits in early visual processing. Using evoked response potentials (ERP), several groups have demonstrated abnormalities in the P1 component of visual evoked responses in schizophrenia. In the auditory domain, early sensory deficits have been found using auditory ERP. Patients exhibit abnormalities in the so-called P50 suppression. In healthy subjects, two sounds presented in rapid succession will produce a reduction in the amplitude of the P50 component of the auditory ERP elicited by the second sound (Adler et al., 1982). This can be viewed as a type of habituation in which the repetition of a sensory event results in a dampening of the neural response. It has been shown that patients do not exhibit this P50 suppression with the second auditory stimulus. This has been interpreted as the inability of patients to properly gate sensory information. Patients have also been shown to exhibit deficits in mismatch negativity (MMN; Shelley et al., 1991). In healthy subjects, the presentation of an oddball tone, a deviant tone within a train of brief repetitions of a standard tone, elicits an auditory ERP that is different from the response elicited by the standard tone. Like the P50 suppression, it is thought that MMN is preattentive in that the MMN can be elicited regardless of whether the subject is attending to the stimulus. Affect Processing With the recognition of the importance of negative symptoms in schizophrenia, increasing attention is being paid to the study of affect and related processes in schizophrenia. In the past 10 years, we have witnessed an exponential increase in the number of studies focusing on this aspect of the illness. These affect studies can be further categorized as those focusing on emotional expression, recognition of emotional signals, and the subjective experiencing of emotions. Deficits in the emotional expressivity of patients, for example, blunted or flat affect, is perhaps the single

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most visibly apparent symptom of schizophrenia. Other than the distressed expressions associated with psychosis, there is a marked decrease in the emotional expressivity and responsivity of the face in schizophrenia (Berenbaum & Oltmanns, 1992). Contrary to the belief that diminished expression of emotion reflects diminished experience of emotion, patients, in general, appear not to have a subjective, experiential deficit (Berenbaum & Oltmanns, 1992; Earnst & Kring, 1999). This is true even in patients with the deficit syndrome or a predominance of blunted affect. In addition to deficits in the ability to express emotions, individuals with schizophrenia experience difficulty recognizing affect in others. A number of studies have found that when presented with a series of pictures of faces depicting the basic emotions, patients have difficulty naming the expressed emotion (Kohler, Bilker, Hagendoorn, Gur, & Gur, 2000; Schneider et al., 2006). Some researchers have hypothesized that this deficit is one of the basis of social communication problems that patients face in everyday life. An important factor yet to be clarified in this line of work is the specificity of the affect recognition deficit above and beyond a generalized cognitive deficit because some studies have shown the absence (Kohler et al., 2000; Salem, Kring, & Kerr, 1996) while others have shown the presence of a differential deficit (Schneider et al., 2006). Social Cognition As is the case with affect, there has been a great expansion in the interest in examining deficits in social functioning in schizophrenia. A strong argument can be made that the social deficits of schizophrenia constitutes a core feature of this illness based on the observations that abnormalities in social functions often occur during the prodromal phase (Davidson et al., 1999), at the time of initial diagnosis, and throughout the course of illness (Addington & Addington, 2000). Studies on social cognition have identified two general areas of abnormality in schizophrenia: theory of mind and social perceptions (Pinkham, Penn, Perkins, & Lieberman, 2003). Theory of mind refers to the capacity to (a) understand that the mental state (beliefs, intentions, and perspectives) of others is separate and distinct from one’s own, and (b) the ability to make inferences about another ’s intentions. Theory-ofmind skills are higher-order cognitive processes requiring the integration of sensory inputs from multiple channels with contextual information. Studies have shown schizophrenia patients to be lacking in theory-of-mind skills (Corcoran, Mercer, & Frith, 1995; Frith & Corcoran, 1996). Social perception, the ability to recognize information governing appropriate social behavior, has also consistently been shown to be abnormal in schizophrenia patients. The facial affect recognition deficits previously discussed is

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an important example of a social perception dysfunction. It is thought that deficits in affect recognition is the cause of schizophrenia patients’ inability to decode the emotional state of the others. Deficits in social cue perception have been shown to be more acute for abstract compared to nonabstract information (Corrigan & Nelson, 1998). Functional Neuroimaging The discovery that the activity of specific brain regions could be imaged in awake and behaving subjects has been one of the most important developments in the history of psychiatric and schizophrenia research. Especially since the availability of functional magnetic resonance imaging (fMRI), functional neuroimaging has been widely adopted by researchers and is now a mainstream method in our search for the neurobiological basis of schizophrenia. By allowing researchers to assess the neural functional correlates of a given cognitive task, functional neuroimaging allows researchers to identify diseased brain regions and abnormal cognitive processes in schizophrenia. The identification of dysfunctional regions provides information that can inform and constrain hypotheses in studies utilizing other research methods. For example, the discovery of abnormal engagement of the DLPFC has been very important in guiding postmortem and genetic studies seeking the cellular and molecular basis of higher-order cognitive deficits in schizophrenia. Functional Imaging Studies of Higher-Order Cognitive Deficits Although modern functional neuroimaging studies are beginning to uncover the neural correlates of most clusters of clinical features of schizophrenia, including those associated with deficits in early sensory, affective, social processes mentioned previously, the majority of functional neuroimaging studies have historically focused on higherorder cognitive deficits. These studies point to abnormalities in several multimodal associative brain regions. These include deficits in the anterior cingulated cortex, superior temporal gyrus, and medial temporal cortex. Since the 1970s implementation of the earliest functional neuroimaging studies in schizophrenia (Ingvar & Franzen, 1974), there has been special interest in the DLPFC. The DLPFC is thought to be a key region subserving higher-order cognitive processing and, consequently, the DLPFC is hypothesized to be one of the most important sites of pathology in schizophrenia. Ingvar and Franzen, and later Weinberger, Berman, and colleagues, found that the DLPFC is hypoactive in schizophrenia (Berman, Zec, & Weinberger, 1986; Weinberger, Berman, & Zec, 1986). These results provide the basis for the “hypofrontality” hypothesis of

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temporal-parietal cortex during auditory hallucinations. Consequently, it would be logical to hypothesize that treatment of auditory hallucination could be effected through the deactivation of this region. Hoffman and colleagues (2005) have proposed to do this with repetitive transcranial magnetic stimulation (rTMS). RTMS is a procedure in which brief, repetitive pulses of a magnetic field is applied to a localized region of the cortex. It is thought that rTMS reduces excitability in the applied region. A large clinical study indicates that rTMS of the left temporalparietal region is a safe and effective method to reduce the severity of AH in medication-resistant subjects with schizophrenia.

schizophrenia. In the past 20 years, a large number of neuroimaging studies have generally supported the notion of a dysfunctional DLPFC in schizophrenia across different imaging modalities and cognitive paradigms (Callicott et al., 2000; Manoach et al., 2000; Perlstein, Carter, Noll, & Cohen, 2001). Neural Basis of Symptoms Although functional neuroimaging, particularly fMRI, is a relatively new investigative tool, it has already made significant contributions to our understanding of the neural basis of the clinical features of schizophrenia. Two such clinical features are cognitive disorganization and auditory hallucinations. Broadly following the theories set forth by GoldmanRakic and others that postulate that (a) the ability to maintain information “on-line” forms the basis for many higher-order cognitive processes and behaviors and (b) the DLPFC is the key brain region supporting the maintenance of information on-line, a series of functional imaging studies has demonstrated that the degree of activation of the DLPFC in schizophrenia is highly correlated with clinical measures of cognitive and behavioral disorganization (Figure 54.3). Another series of studies is elucidating the neural basis of auditory hallucinations and thereby providing a neurobiological rational for an effective treatment for this symptom. Auditory hallucinations appear to be the result of abnormal activation of the neural system serving auditory sensory processing. In one study involving patients with schizophrenia with auditory hallucinations, the onset and offset of the hallucinations correlated with the engagement and disengagement of the primary auditory cortex (Dierks et al., 1999). Functional neuroimaging studies, such as the one cited above, have provided support for a novel treatment strategy targeting auditory hallucinations refractory to medications. fMRI studies have shown over-activation in the

INTERVENTION AND MANAGEMENT Antipsychotic Medications Pharmacological agents have been the mainstay of schizophrenia treatment since the mid-twentieth century, though other medical approaches were in use prior to this time. Indeed, the modern history of approaches to schizophrenia treatment exemplifies the process of scientific discovery in clinical medicine, and the evolution of how this illness has been conceptualized (see Chapter 5). Early in the twentieth century, a variety of pharmacological interventions for schizophrenia were attempted and reported in the literature, including cocaine, manganese, castor oil, and sulfur oil. More widely known are the attempts to remediate symptoms of schizophrenia by induction of either sleep or insulin-induced coma, the latter of which dominated the treatment options for psychiatrists until the 1950s (Ban, 2004). As described earlier, the development of novel adjunct medication for anesthesia yielded the compound chlorpromazine, which was synthesized in 1950 and subsequently observed to induce conscious sedation in agitated patients. It was quickly adopted for use in

Activity (Partial r)

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6 8 10 Disorganization

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Figure 54.3 (Figure C.52 in color section) Decreased Prefrontal Cortical Function in First Episode Schizophrenia Note: From MacDonald et al. (2005).

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agitated patients with schizophrenia and found by inpatient clinicians to decrease the need for physical restraint. This phenothiazine compound was the first medication for schizophrenia to be tested in placebo-controlled studies, with the landmark report of its superiority in the treatment of schizophrenia emerging several years later. In addition, reserpine (isolated from the Rauwolfia plant) was introduced in 1954, though its propensity to induce or worsen depressive symptoms was noted, leading to the examination of its monoaminergic actions and the subsequent articulation of the biogenic amine hypothesis of depression, which paralleled the dopaminergic hypothesis of schizophrenia. Haloperidol was synthesized in 1958 and introduced the following year; it remains one of the most widely prescribed antipsychotic medications. Subsequent studies conducted in the 1960s and thereafter further specified the target symptoms responsive to these medications, rates of clinical response, and functional outcome of patients offered these treatments (detailed later). Basic science investigations established the neurochemical basis for the clinical efficacy of these medications. Initially, Carlsson and Lindqvist (1963) found that administration of these compounds to rodents led to increased levels of dopamine metabolites and antagonized the behavioral effects of dopamine agonists such as amphetamine and apomorphine. This led Snyder and colleagues to demonstrate that the clinical efficacy of existing antipsychotic medications was directly related to their potency in blocking dopamine receptors (Creese et al., 1976), thus refining the dopamine hypothesis of schizophrenia. Mechanism of Antipsychotic Medication Action To date, over 30 medications from 11 different chemical classes have been introduced worldwide for the treatment of schizophrenia (Ban, 2004). These are generally identified as first-generation (FGA) or second-generation antipsychotics (SGA), also commonly known as “atypical” antipsychotics. FGAs (typified by haloperidol) all have in common a high affinity for D2 receptors, and the clinical efficacy of these medications is strongly related to binding affinity for these receptors (Seeman, Lee, Chau-Wong, & Wong, 1976). PET studies have demonstrated that clinical antipsychotic effects occur at doses where striatal D2 receptor occupancy of 65% to 70%, whereas D2 receptor occupancy above 80% is associated with significantly increased incidence of extrapyramidal symptoms (EPS; Remington & Kapur, 1999). These studies have also found that at therapeutic doses, FGAs block D2-like receptors to an equal degree in limbic cortical areas and the striatum, which is also consistent with the relatively narrow range of antipsychotic efficacy in the absence of EPS (Xiberas

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et al., 2001). The precise cellular feature of altered dopaminergic activity that provides the basis for clinical efficacy remains an active area of investigation. A leading hypothesis suggests that acute administration of these medications is associated with antagonism of D2 autoreceptors on dopaminergic nerve terminals, leading to a depolarization inactivation of ion channels at those terminals and a resulting incapacity of propagating action potentials to further depolarize the terminal, thus chronically blocking dopamine release into the synapse (Grace, Bunney, Moore, & Todd, 1997). In contrast, the six SGAs that are currently available in the United States are more heterogeneous in their profile of dopamine receptor antagonism. Risperidone, for example, exhibits D2 antagonism that is within the range of that for FGAs, and consequently at therapeutic doses is associated with rates of EPS intermediate between FGAs and other SGAs. Other SGAs, such as clozapine and quetiapine, exhibit minimal D2 receptor binding at therapeutic doses (Miyamoto, Duncan, Marx, & Lieberman, 2005). These medications (including the other available SGAs, olanzapine, ziprasidone, and aripiprazole) show heterogeneous profiles of binding at other dopamine receptors as well. A leading current hypothesis (the “fastoff ” hypothesis) suggests that the relative lack of EPS stemming from the use of these medications may be due to the relatively faster rate of dissociation of these agents from D2 receptors. This faster dissociation rate would be expected to more optimally accommodate normal physiological dopamine transmission. In contrast, a competing hypothesis of what constitutes atypicality emphasizes the serotonergic receptor activity (5HT2A and 5HT2C antagonism and 5HT1A agonism) that is found among SGAs. These actions are associated with enhanced dopamine and glutamate in prefrontal relative to subcortical areas and, in particular, the ratio of 5HT2A to D2 blockade may prevent EPS and remediate negative symptoms of schizophrenia in a manner superior to the FGAs (Meltzer, Li, Kaneda, & Ichikawa, 2003). In addition, aripiprazole is unique as a D2 partial agonist, which may stabilize elevated rates of dopamine transmission while avoiding a degree of dopamine blockade necessary for EPS. It should also be emphasized here that all antipsychotics (FGAs and SGAs) exhibit high-affinity binding at a range of other monoamine receptors in the brain, which may be partly responsible for their efficacy but are well-established as the basis for many of their side effects. This includes antagonism at muscarinic, histaminergic, and adrenergic receptors, with predictable autonomic effects. In addition, the monoaminergic transporter blocking effects and 5HT1A receptor partial agonism or antagonism exhibited by some SGAs suggest that these medications may exert antidepressant and anxiolytic effects as well.

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Despite the substantial basic pharmacological differences between FGAs and SGAs noted previously, recent effectiveness studies such as the NIMH sponsored CATIE study have highlighted the fact that in the context of those trials, the advantages, in terms of patient adherence, effects on cognition and so on are modest at best. The high discontinuation rates in this study highlight the limited effectiveness of modern antipsychotic treatment, the high prevalence of unpleasant side effects, and the need for the development of more effective and better tolerated therapies as well as more integrative approaches that combine pharmacotherapy with psychosoc1al interventions.

worker) serves a role somewhat analogous to a primary care physician, assessing and prioritizing the needs of the patient, developing an integrated care plan, arranging for provision of this care, and serving as the patient’s primary point of contact in the mental health system. Case managers interact both with social service agencies and with clinicians, to achieve and maintain access to entitlements, social services, and clinical care. Case management aims to maintain the patient in the system of care, to permit the most efficacious treatment in the least restrictive setting, and to optimize outcome, particularly quality of life and social function. Psychotherapy

Importance of Integrated Schizophrenia Treatment Psychosocial treatment is an essential element of the treatment needs of all patients with schizophrenia. In general, all of the interventions described next are compatible not only with each other but with pharmacological treatment as well (Lauriello, Lenroot, & Bustillo, 2003). As a complex disorder that affects virtually every psychological and functional domain, a comprehensive treatment approach to schizophrenia must necessarily address a broad spectrum of problems. Lehman (1999) has proposed a framework for evaluating outcomes in schizophrenia that is based on the findings of an NIMH expert panel. Four domains are identified: clinical, rehabilitative, humanitarian, and public welfare. The clinical domain includes psychopathology and treatment issues. The rehabilitative domain includes social and vocational function. The humanitarian domain includes quality of life, subjective well-being, and other patient-centered measures, and the public safety domain includes optimizing and resolving the rights of the patients with the welfare of the community at large. It is increasingly recognized that integration of care is associated with maximal benefit for patients with schizophrenia, particularly those who are the sickest and are the highest users of services (Lenroot, Bustillo, Lauriello, & Keith, 2003). A cornerstone of this perspective is the establishment and maintenance of the alliance not only with the patient but with families and other care and service providers as well. This is also of increasing importance given the progressive shifting of the locus of care for the most severely chronically disabled schizophrenia patients, from the large state hospitals of an earlier era to the community today.

Effective psychotherapies for schizophrenia include psychoeducation, cognitive behavior therapy targeted at coping with and reducing positive symptoms, social skills training, and cognitive rehabilitation. Successful intervention requires a multidisciplinary approach that focuses on engaging, educating, and supporting the family as well as on addressing specific needs of the patient. This extends beyond positive symptom control and relapse prevention, for which antipsychotic medications are effective when adherence is adequate, to include social, occupational, and cognitive deficits in the illness.

SUMMARY Schizophrenia is a common, debilitating illness that presents a major burden for individuals, families, and society. Our understanding of schizophrenia has evolved significantly along with recent advances in neuroscience and genetics. This increased understanding includes significant refinement in how the illness is identified as well as a deeper understanding of its natural course, relationship to boundary conditions, the disturbances in brain structure and function that underlie cognitive and functional deficits, and the genetic and environmental factors that modify the appearance and clinical course of this illness. This advance in understanding is likely to accelerate in the coming years, with the promise of leading us toward more effective therapies and prevention strategies and improving the lives of patients and their families.

REFERENCES Case Management/Assertive Community Treatment Case management is fundamentally a method of coordinating services for the patient in the community. In this model, an individual case manager (typically a licensed social

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Abi-Dargham, A., Mawlawi, O., Lombardo, I., Gil, R., Martinez, D., Huang, Y., et al. (2002). Prefrontal dopamine D1 receptors and working memory in schizophrenia. Journal of Neuroscience, 22, 3708–3719. Abi-Dargham, A., Rodenhiser, J., Printz, D., Zea-Ponce, Y., Gil, R., Kegeles, L. S., et al. (2000). Increased baseline occupancy of D2

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References 1057 receptors by dopamine in schizophrenia. Proceedings of the National Academy of Sciences, USA, 97, 8104–8109. Addington, J., & Addington, D. (2000). Neurocognitive and social functioning in schizophrenia: A 2.5 year follow-up study. Schizophrenia Research, 44, 47–56. Adityanjee, Aderibigbe, Y. A., Theodoridis, D., & Vieweg, V. R. (1999). Dementia praecox to schizophrenia: The first 100 years. Psychiatry and Clinical Neurosciences, 53(4), 437–448. Adler, L. E., Hoffer, L. J., Griffith, J., Waldo, M. C., & Freedman, R. (1992). Normalization by nicotine of deficient auditory sensory gating in the relatives of schizophrenics. Biological Psychiatry, 32, 607–616. Adler, L. E., Pachtman, E., Franks, R. D., Pecevich, M., Waldo, M. C., & Freedman, R. (1982). Neurophysiological evidence for a defect in neuronal mechanisms involved in sensory gating in schizophrenia. Biological Psychiatry, 17, 639–654. Akbarian, S., Kim, J. J., Potkin, S. G., Hagman, J. O., Tafazzoli, A., Bunney, W. E., Jr., et al. (1995). Gene expression for glutamic acid decarboxylase is reduced without loss of neurons in prefrontal cortex of schizophrenics. Archives of General Psychiatry, 52, 258–266. Andreasen, N. C. (1997). The evolving concept of schizophrenia: From Kraepelin to the present and future. Schizophrenia Research, 28, 105–109. Andreasen, N. C. (2000). Schizophrenia: The fundamental questions. Brain Research: Brain Research Reviews, 31, 106–112. Angermeyer, M. C., Goldstein, J. M., & Kuehn, L. (1989). Gender differences in schizophrenia: Rehospitalization and community survival. Psychological Medicine, 19, 365–382. Badner, J. A., & Gershon, E. S. (2002). Meta-analysis of whole-genome linkage scans of bipolar disorder and schizophrenia. Molecular Psychiatry, 7, 405–411. Ban, T. A. (2004). Neuropsychopharmacology and the genetics of schizophrenia: A history of the diagnosis of schizophrenia. Progress in NeuroPsychopharmacology and Biological Psychiatry, 28, 753–762. Berenbaum, H., & Oltmanns, T. F. (1992). Emotional experience and expression in schizophrenia and depression. Journal of Abnormal Psychology, 101, 37–44. Berman, K. F., Zec, R. F., & Weinberger, D. R. (1986). Physiologic dysfunction of dorsolateral prefrontal cortex in schizophrenia: Pt. II. Role of neuroleptic treatment, attention, and mental effort. Archives of General Psychiatry, 43, 126–135. Bilder, R. M., Mukherjee, S., Rieder, R. O., & Pandurangi, A. K. (1985). Symptomatic and neuropsychological components of defect states. Schizophrenia Bulletin, 11, 409–419. Bland, R. C., Parker, J. H., & Orn, H. (1978). Prognosis in schizophrenia: Prognostic predictors and outcome. Archives of General Psychiatry, 35, 72–77. Brenner, C. A., Lysaker, P. H., Wilt, M. A., & O’Donnell, B. F. (2002). Visual processing and neuropsychological function in schizophrenia and schizoaffective disorder. Psychiatry Research, 111, 125–136. Brown, A. S., Begg, M. D., Gravenstein, S., Schaefer, C. A., Wyatt, R. J., Bresnahan, M., et al. (2004). Serologic evidence of prenatal influenza in the etiology of schizophrenia. Archives of General Psychiatry, 61, 774–780. Callicott, J. H., Bertolino, A., Mattay, V. S., Langheim, F. J., Duyn, J., Coppola, R., et al. (2000). Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited. Cerebral Cortex, 10, 1078–1092. Cannon, M., Jones, P. B., & Murray, R. M. (2002). Obstetric complications and schizophrenia: Historical and meta-analytic review. American Journal of Psychiatry, 159, 1080–1092. Carlsson, A., & Lindqvist, M. (1963). Effect of chlorpromazine or haloperidol on formation of 3methoxytyramine and normetanephrine in mouse brain. Acta Pharmacologica et Toxicologica, 20, 140–144.

c54.indd 1057

Carpenter, W. T., Jr., & Strauss, J. S. (1991). The prediction of outcome in schizophrenia: Pt. IV. Eleven-year follow-up of the Washington IPSS cohort. Journal of Nervous and Mental Disease, 179, 517–525. Cobb, S. R., Buhl, E. H., Halasy, K., Paulsen, O., & Somogyi, P. (1995, November 2). Synchronization of neuronal activity in hippocampus by individual GABAergic interneurons. Nature, 378, 75–78. Cohen, J.D., Barch, D.M., Carter, C.S. & Servan-Schreiber, D. (1999). Context processing deficits in schizophrenia: Converging evidence from three theoretically motivated cognitive tasks. Journal of Abnormal Psychology, 108, 120–133. Corcoran, R., Mercer, G., & Frith, C. D. (1995). Schizophrenia, symptomatology and social inference: Investigating “theory of mind” in people with schizophrenia. Schizophrenia Research, 17, 5–13. Corfas, G., Roy, K., & Buxbaum, J. D. (2004). Neuregulin 1-erbB signaling and the molecular/cellular basis of schizophrenia. Nature Neuroscience, 7, 575–580. Corrigan, P. W., & Nelson, D. R. (1998). Factors that affect social cue recognition in schizophrenia. Psychiatry Research, 78, 189–196. Creese, I., Burt, D. R., & Snyder, S. H. (1976, April 30). Dopamine receptor binding predicts clinical and pharmacological potencies of antischizophrenic drugs. Science, 192, 481–483. Davidson, M., Reichenberg, A., Rabinowitz, J., Weiser, M., Kaplan, Z., & Mark, M. (1999). Behavioral and intellectual markers for schizophrenia in apparently healthy male adolescents. American Journal of Psychiatry, 156, 1328–1335. Davies, G., Welham, J., Chant, D., Torrey, E. F., & McGrath, J. (2003). A systematic review and meta-analysis of northern hemisphere season of birth studies in schizophrenia. Schizophrenia Bulletin, 29, 587–593. Dierks, T., Linden, D. E., Jandl, M., Formisano, E., Goebel, R., Lanfermann, H., et al. (1999). Activation of Heschl’s gyrus during auditory hallucinations. Neuron, 22, 615–621. Earnst, K. S., & Kring, A. M. (1999). Emotional responding in deficit and non-deficit schizophrenia. Psychiatry Research, 88, 191–207. Feighner, J. P., Robins, E., Guze, S. B., Woodruff, R. A., Jr., Winokur, G., & Munoz, R. (1972). Diagnostic criteria for use in psychiatric research. Archives of General Psychiatry, 26, 57–63. Frith, C. D., & Corcoran, R. (1996). Exploring ‘theory of mind’ in people with schizophrenia. Psychological Medicine, 26, 521–530. Geller, J. L. (1992). A report on the “worst” state hospital recidivists in the US. Hospital and Community Psychiatry, 43, 904–908. Grace, A. A., Bunney, B. S., Moore, H., & Todd, C. L. (1997). Dopaminecell depolarization block as a model for the therapeutic actions of antipsychotic drugs. Trends in Neurosciences, 20, 31–37. Green, M., & Walker, E. (1986). Symptom correlates of vulnerability to backward masking in schizophrenia. American Journal of Psychiatry, 143, 181–186. Green, M. F. (1996). What are the functional consequences of neurocognitive deficits in schizophrenia? American Journal of Psychiatry, 153, 321–330. Harrison, P. J. (1999). The neuropathology of schizophrenia: A critical review of the data and their interpretation. Brain, 122(Pt. 4), 593–624. Harrow, M., & Jobe, T. H. (2005). Longitudinal studies of outcome and recovery in schizophrenia and early intervention: Can they make a difference? Canadian Journal of Psychiatry, 50, 879–880. Hegarty, J. D., Baldessarini, R. J., Tohen, M., Waternaux, C., & Oepen, G. (1994). One hundred years of schizophrenia: A metaanalysis of the outcome literature. American Journal of Psychiatry, 151, 1409–1416. Hoffman, R. E., Gueorguieva, R., Hawkins, K. A., Varanko, M., Boutros, N. N., Wu, Y. T., et al. (2005). Temporoparietal transcranial magnetic stimulation for auditory hallucinations: Safety, efficacy and moderators in a fifty patient sample. Biological Psychiatry, 58, 97–104.

8/17/09 5:29:39 PM

1058

Schizophrenia

Howard, A., Tamas, G., & Soltesz, I. (2005). Lighting the chandelier: New vistas for axo-axonic cells. Trends in Neurosciences, 28, 310–316. Ingvar, D. H., & Franzen, G. (1974). Abnormalities of cerebral blood flow distribution in patients with chronic schizophrenia. Acta Psychiatrica Scandinavica, 50, 425–462. Jablensky, A. (2000). Epidemiology of schizophrenia: The global burden of disease and disability. European Archives of Psychiatry and Clinical Neuroscience, 250, 274–285. Javitt, D. C., Strous, R. D., Grochowski, S., Ritter, W., & Cowan, N. (1997). Impaired precision, but normal retention, of auditory sensory (echoic) memory information in schizophrenia. Journal of Abnormal Psychology, 106, 315–324. Javitt, D. C., & Zukin, S. R. (1991). Recent advances in the phencyclidine model of schizophrenia. American Journal of Psychiatry, 148, 1301–1308. Jobe, T. H., & Harrow, M. (2005). Long-term outcome of patients with schizophrenia: A review. Canadian Journal of Psychiatry, 50, 892–900. Johnstone, E. C., Crow, T. J., Frith, C. D., Husband, J., & Kreel, L. (1976). Cerebral ventricular size and cognitive impairment in chronic schizophrenia. Lancet, 2, 924–926.

Manoach, D. S., Gollub, R. L., Benson, E. S., Searl, M. M., Goff, D. C., Halpern, E., et al. (2000). Schizophrenic subjects show aberrant fMRI activation of dorsolateral prefrontal cortex and basal ganglia during working memory performance. Biological Psychiatry, 48, 99–109. McGlashan, T. H. (1984). The Chestnut Lodge follow-up study: Pt. II. Long-term outcome of schizophrenia and the affective disorders. Archives of General Psychiatry, 41, 586–601. McGrath, J., Saha, S., Welham, J., El Saadi, O., MacCauley, C., & Chant, D. (2004). A systematic review of the incidence of schizophrenia: The distribution of rates and the influence of sex, urbanicity, migrant status and methodology. BMC Medicine, 2, 13. McGue, M., & Gottesman, I. I. (1989). A single dominant gene still cannot account for the transmission of schizophrenia. Archives of General Psychiatry, 46, 478–480. McGue, M., & Gottesman, I. I. (1991). The genetic epidemiology of schizophrenia and the design of linkage studies. Eur Arch Psychiatry Clin Neurosci., 240, 174–181. Meltzer, H. Y., Li, Z., Kaneda, Y., & Ichikawa, J. (2003). Serotonin receptors: Their key role in drugs to treat schizophrenia. Progress in NeuroPsychopharmacology and Biological Psychiatry, 27, 1159–1172.

Kanaan, R. A., Kim, J. S., Kaufmann, W. E., Pearlson, G. D., Barker, G. J., & McGuire, P. K. (2005). Diffusion tensor imaging in schizophrenia. Biological Psychiatry, 58, 921–929.

Miyamoto, S., Duncan, G. E., Marx, C. E., & Lieberman, J. A. (2005). Treatments for schizophrenia: A critical review of pharmacology and mechanisms of action of antipsychotic drugs. Molecular Psychiatry, 10, 79–104.

Kety, S. S., Rosenthal, D., Wender, P. H., & Schulsinger, F. (1971). Mental illness in the biological and adoptive families of adpoted schizophrenics. American Journal of Psychiatry, 128, 302–306.

Moffitt, T. E., Caspi, A., & Rutter, M. (2005). Strategy for investigating interactions between measured genes and measured environments. Archives of General Psychiatry, 62, 473–481.

Kohler, C. G., Bilker, W., Hagendoorn, M., Gur, R. E., & Gur, R. C. (2000). Emotion recognition deficit in schizophrenia: Association with symptomatology and cognition. Biological Psychiatry, 48, 127–136.

Murray, C. J., & Lopez, A. D. (1996, November 1). Evidence-based health policy: Lessons from the Global Burden of Disease Study. Science, 274, 740–743.

Lauriello, J., Lenroot, R., & Bustillo, J. R. (2003). Maximizing the synergy between pharmacotherapy and psychosocial therapies for schizophrenia. Psychiatric Clinics of North America, 26, 191–211.

Owen, M. J., Craddock, N., & O’Donovan, M. C. (2005). Schizophrenia: Genes at last? Trends Genet, 21, 518–525.

Lawrie, S. M., & Abukmeil, S. S. (1998). Brain abnormality in schizophrenia: A systematic and quantitative review of volumetric magnetic resonance imaging studies. British Journal of Psychiatry, 172, 110–120. Lehman, A. F. (1999). Developing an outcomes-oriented approach for the treatment of schizophrenia. Journal of Clinical Psychiatry, 60(Suppl, 19), 30–35; discussion 36–37.

Perlstein, W. M., Carter, C. S., Noll, D. C., & Cohen, J. D. (2001). Relation of prefrontal cortex dysfunction to working memory and symptoms in schizophrenia. American Journal of Psychiatry, 158, 1105–1113. Perry, W., & Braff, D. L. (1994). Information-processing deficits and thought disorder in schizophrenia. American Journal of Psychiatry, 151, 363–367.

Lenroot, R., Bustillo, J. R., Lauriello, J., & Keith, S. J. (2003). Integrated treatment of schizophrenia. Psychiatric Services, 54, 1499–1507.

Pinkham, A. E., Penn, D. L., Perkins, D. O., & Lieberman, J. (2003). Implications for the neural basis of social cognition for the study of schizophrenia. American Journal of Psychiatry, 160, 815–824.

Lewis, C. M., Levinson, D. F., Wise, L. H., DeLisi, L. E., Straub, R. E., Hovatta, I., et al. (2003). Genome scan meta-analysis of schizophrenia and bipolar disorder: Pt. II. Schizophrenia. American Journal of Human Genetics, 73, 34–48.

Popken, G. J., Bunney, W. E., Jr., Potkin, S. G., & Jones, E. G. (2000). Subnucleus-specific loss of neurons in medial thalamus of schizophrenics. Proceedings of the National Academy of Sciences, USA, 97, 9276–9280.

Lewis, D. A. (2000). GABAergic local circuit neurons and prefrontal cortical dysfunction in schizophrenia. Brain Research: Brain Research Reviews, 31, 270–276.

Remington, G., & Kapur, S. (1999). D2 and 5-HT2 receptor effects of antipsychotics: Bridging basic and clinical findings using PET. Journal of Clinical Psychiatry, 60(Suppl, 10), 15–19.

Lewis, D. A., & Levitt, P. (2002). Schizophrenia as a disorder of neurodevelopment. Annual Review of Neuroscience, 25, 409–432.

Risch, N. (1990). Genetic linkage and complex diseases, with special reference to psychiatric disorders. Genet Epidemiol, 7, 3–16; discussion 17–45.

Liddle, P. F. (1987). The symptoms of chronic schizophrenia: A reexamination of the positive-negative dichotomy. British Journal of Psychiatry, 151, 145–151.

Saccuzzo, D. P., & Braff, D. L. (1981). Early information processing deficit in schizophrenia: New findings using schizophrenic subgroups and manic control subjects. Archives of General Psychiatry, 38, 175–179.

Lieberman, J. A., Perkins, D., Belger, A., Chakos, M., Jarskog, F., Boteva, K., et al. (2001). The early stages of schizophrenia: Speculations on pathogenesis, pathophysiology, and therapeutic approaches. Biological Psychiatry, 50, 884–897.

Salem, J. E., Kring, A. M., & Kerr, S. L. (1996). More evidence for generalized poor performance in facial emotion perception in schizophrenia. Journal of Abnormal Psychology, 105, 480–483.

MacDonald, A. W., Carter, C. S., Kerns, J. G., Ursu, S., Barch, D. M., Holmes, A. J., Stenger, V. A., & Cohen, J. D. (2005). Specificity of prefrontal dysfunction and context processing deficits to schizophrenia in never-medicated patients with first-episode psychosis. American Journal of Psychiatry, 162, 475–484.

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Sartorius, N., Jablensky, A., & Shapiro, R. (1977). Two-year follow-up of the patients included in the WHO international pilot study of schizophrenia. Psychological Medicine, 7, 529–541. Schneider, F., Gur, R. C., Koch, K., Backes, V., Amunts, K., Shah, N. J., et al. (2006). Impairment in the specificity of emotion processing in schizophrenia. American Journal of Psychiatry, 163, 442–447.

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References 1059 Seeman, P., Lee, T., Chau-Wong, M., & Wong, K. (1976, June 24). Antipsychotic drug doses and neuroleptic/dopamine receptors. Nature, 261, 717–719.

Susser, E. S., Schaefer, C. A., Brown, A. S., Begg, M. D., & Wyatt, R. J. (2000). The design of the prenatal determinants of schizophrenia study. Schizophrenia Bulletin, 26, 257–273.

Shelley, A. M., Ward, P. B., Catts, S. V., Michie, P. T., Andrews, S., & McConaghy, N. (1991). Mismatch negativity: An index of a preattentive processing deficit in schizophrenia. Biological Psychiatry, 30, 1059–1062.

Terkelsen, K. G., & Menikoff, A. (1995). Measuring the costs of schizophrenia: Implications for the post-institutional era in the US. Pharmacoeconomics, 8, 199–222.

Steen, R. G., Mull, C., McClure, R., Hamer, R. M., & Lieberman, J. A. (2006). Brain volume in first-episode schizophrenia: Systematic review and meta-analysis of magnetic resonance imaging studies. British Journal of Psychiatry, 188, 510–518. Stefansson, H., Sigurdsson, E., Steinthorsdottir, V., Bjornsdottir, S., Sigmundsson, T., Ghosh, S., et al. (2002). Neuregulin 1 and susceptibility to schizophrenia. American Journal of Human Genetics, 71, 877–892. Stone, M. H. (1986). Exploratory psychotherapy in schizophrenia-spectrum patients: A reevaluation in the light of long-term follow-up of schizophrenic and borderline patients. Bulletin of the Menninger Clinic, 50, 287–306. Straub, R. E., Jiang, Y., MacLean, C. J., Ma, Y., Webb, B. T., Myakishev, M. V., et al. (2002). Genetic variation in the 6p22.3 gene DTNBP1, the human ortholog of the mouse dysbindin gene, is associated with schizophrenia. American Journal of Human Genetics, 71, 337–348. Susser, E., Neugebauer, R., Hoek, H. W., Brown, A. S., Lin, S., Labovitz, D., et al. (1996). Schizophrenia after prenatal famine: Further evidence. Archives of General Psychiatry, 53, 25–31.

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Tienari, P., Wynne, L. C., Moring, J., Lahti, I., Naarala, M., Sorri, A., et al. (1994). The Finnish adoptive family study of schizophrenia: Implications for family research. British Journal of Psychiatry, 23, 20–26. Tsuang, M. T., & Winokur, G. (1975). The Iowa 500: Field work in a 35year follow-up of depression, mania, and schizophrenia. Canadian Psychiatric Association Journal, 20, 359–365. Volk, D. W., Austin, M. C., Pierri, J. N., Sampson, A. R., & Lewis, D. A. (2000). Decreased glutamic acid decarboxylase67 messenger RNA expression in a subset of prefrontal cortical gamma-aminobutyric acid neurons in subjects with schizophrenia. Archives of General Psychiatry, 57, 237–245. Volk, D., & Lewis, D. A. (2002). Impaired prefrontal inhibition in schizophrenia: Relevance for cognitive dysfunction. Physiology and Behavior, 77, 510–505. Weinberger, D. R., Berman, K. F., & Zec, R. F. (1986). Physiologic dysfunction of dorsolateral prefrontal cortex in schizophrenia: Pt. I. Regional cerebral blood flow evidence. Archives of General Psychiatry, 43, 114–124. Xiberas, X., Martinot, J. L., Mallet, L., Artiges, E., Loc, H. C., Maziere, B., et al. (2001). Extrastriatal and striatal D(2) dopamine receptor blockade with haloperidol or new antipsychotic drugs in patients with schizophrenia. British Journal of Psychiatry, 179, 503–508.

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Chapter 55

Depression BOADIE W. DUNLOP AND CHARLES B. NEMEROFF

A clinically depressed mood is found in several psychiatric disorders, including major depressive disorder (MDD), bipolar disorder, posttraumatic stress disorder, and dysthymia, as well as in substance-induced mood disorders, and mood disorders associated with comorbid medical illnesses, such as Parkinson’s disease. These disorders all have distinguishing biological characteristics and treatment responsiveness, illustrating how the experience of a depressed mood may arise from a variety of biological sources. This chapter focuses on the findings of studies of MDD because it is the most common of these illnesses, has the greatest impact on public health, and has been the focus of the most extensive neuroscientific investigations. It is difficult to overstate the public health importance of MDD. The lifetime prevalence of MDD is 16%, and the 12-month prevalence is 6.6% (Kessler et al., 2003). The lifetime risk for the illness in women is approximately double the risk in men. A widely cited study, the Global Burden of Disease, conducted by the World Bank, the World Health Organization, and the Harvard School of Public Health, predicts that by the year 2020, MDD will be the second leading cause of disability worldwide, trailing only cardiovascular disease (Murray & Lopez, 1996). MDD is also a leading cause of premature death due to suicide. Depressive symptoms contribute to risk for several other important diseases, including coronary artery disease and stroke (Anda et al., 1993; Jonas & Mussolino, 2000). MDD follows a chronic course in about 20% of those affected, and of those who remit, approximately 85% will

experience another episode of depression within 15 years (Mueller et al., 1999). Finally, the economic burden of MDD is enormous, with conservatively estimated annual direct costs of $2.1 billion and indirect costs of $4.2 billion per year in the United States alone (Jones & Cockrum, 2000). MDD, also known as unipolar depression, to distinguish it from depression occurring in bipolar disorder (manicdepressive illness), is a multidimensional disorder. Only one major depressive episode (see Table 55.1) is required for the diagnosis of MDD, though major depressive episodes can also occur in other disorders. The primary clinical characteristics that distinguish these disorders from MDD are presented in Table 55.2. The clinical diagnosis of a major depressive episode refers to a syndrome in which there is a significant change in (a) mood state: either prominent feelings of sadness and/or anhedonia, along with the presence of several other symptoms. These other symptoms can be grouped into additional categories; (b) neurovegetative systems: disturbances in sleep and appetite, and reductions in energy; (c) cognitive functions: excessive thoughts of guilt or worthlessness, poor concentration or indecisiveness, and thoughts of suicide; and (d) psychomotor performance: either slowed (retarded) or agitated. The symptoms in each of these categories have their own specific neurobiological basis. Because the diagnosis of a major depressive episode can be made when all four categories are present, or when as few as two categories of symptoms are present, great heterogeneity between equivalently diagnosed patients exists, both phenomenologically and biologically.

Dr. Dunlop is supported by 5K12RR017643 and 1KL2RR025009. Dr. Nemeroff was supported by NIH MH-42088, MH-39415, MH-77083, MH-69056, and MH-58922. Disclosures of Possible Conflicts of Interest: Dr. Dunlop has received research support from AstraZeneca, Bristol-Myers Squibb, Cephalon, Forest, Janssen, Ono Pharmaceuticals, Novartis, and Takeda. He has served as a consultant to Cephalon, Shire, and Wyeth, and served on the speaker’s bureau of Bristol-Myers Squibb. Over the past 1.5 years, Dr. Nemeroff has served on the Scientific Advisory Board for Astra-Zeneca, Johnson & Johnson, Pharma Neuroboost, Forest Laboratories, Quintiles, and NARSAD. He is a grant recipient from NIH, NARSAD, and AFSP. He serves on the Board of Directors of AFSP, NovaDel Pharmaceuticals, Mt. Cook Pharma, Inc., and the George West Mental Health Foundation. He owns equity in CeNeRx and Reevax. He owns stock or stock options in Corcept and NovaDel. 1060

Handbook of Neuroscience for the Behavioral Science, edited by Gary G. Berntson and John T. Cacioppo. Copyright # 2009 John Wiley & Sons, Inc. c55.indd 1060

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Heterogeneity 1061 table 55.1

Diagnostic criteria for a major depressive episode

Symptom Category

Symptom

Mood change

1. Excessive sadness 2. Anhedonia/loss of interest

Neurovegetative

3. Insomnia or hypersomnia 4. Weight or appetite change 5. Diminished energy

Cognitive

6. Poor concentration or indecisiveness 7. Excessive guilt or worthlessness 8. Thoughts of own death or suicide 9. Psychomotor agitation or retardation

Psychomotor speed

Note: To diagnose a major depressive episode, at least five of the listed symptoms must be present for most of the day, nearly every day for the past 2 weeks and the symptoms must cause some level of impairment. At least 1 of the symptoms must be either excessive sadness or anhedonia. One major depressive episode justifies the diagnosis of major depressive disorder, as long as criteria for other disorders higher in the diagnostic hierarchy are not met. table 55.2 Other DSM-IV diagnoses with prominent depression without psychotic symptoms Diagnosis

Primary Characteristic Distinguishing from MDD

Bipolar disorder

If a major depressive episode is present, the patient has also experienced at least one episode of elevated, irritable, or expansive mood.

Dysthymia

Chronic ( 2 years) of depressed mood with less intensity and associated depressive symptoms than a major depressive episode.

Post-traumatic stress disorder (PTSD)

In addition to symptoms of depression, the patient also experiences re-experiencing symptoms (e.g., nightmares, flashbacks, intrusive memories) of a traumatic life event. Patient may have both major depressive disorder (MDD) and PTSD, concurrently.

Substance-induced mood disorder

Depressed mood stemming directly from a state of intoxication or withdrawal from a substance (e.g., alcohol or cocaine).

Mood disorder due to a medical condition

Depressed mood derived directly from the pathophysiologic processes of a medical disorder (e.g., hypothyroidism).

HETEROGENEITY Many efforts have been made to identify subtypes of MDD to address this heterogeneity. The first approaches attempted to distinguish depressed patients on clinical grounds. Although the categories have undergone revision over time, the clinical approach is still codified in the fourth edition of the Diagnostic and Statistical Manual (DSM-IV). The current DSM-IV clinical descriptors of a major depressive episode are: Melancholic features: Near complete loss of pleasure or reactivity to stimuli.

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In addition, three of the following 6 symptoms must also be present: distinct quality of depressed mood, anorexia or weight loss, early morning awakening, worse morning mood, psychomotor change, and excessive guilt. Atypical features: Preserved mood reactivity to stimuli. In addition, two of the following four symptoms must also be present: hypersomnia, heavy/leaden feeling in arms or legs, weight gain or increased appetite, and persistent sensitivity to feelings of rejection in relationships. Psychotic features: Presence of delusions or hallucinations during the depressive episode (not better explained by another disorder; e.g., schizophrenia). Catatonic features: Profound psychomotor disturbance during the depressive episode, characterized by severe motoric immobility or purposeless excessive activity, bizarre movements or posturing, echolalia or echopraxia, or profound negativism (i.e., mutism or resistance to being moved). Postpartum onset: Onset of current depressive episode within 4 weeks postpartum. Chronic: Full criteria for MDD met continuously for at least 2 years. The episode may also be described in terms of its overall severity: mild, moderate, or severe. There are precious few factors available today to guide clinician choices of treatments for MDD. Those that are used derive from clinical data, for example, a personal or family history of response to a specific treatment, presence of other medical or psychiatric disorders in addition to MDD, and a desire to avoid certain side effects. In the research arena, a renewed interest in identifying biologically based subtypes is emerging. This approach has not yet yielded any specific clinical benefits, but future progress toward developing more precise treatments will depend on gains in our understanding of the pathophysiology of the mood disorders. The concept of endophenotypes, that is, subtypes of depression with specific clinical or biological features, may have particular relevance in our efforts to determine the neurobiological underpinnings of MDD. The clinical endophenotypes with the strongest data are those of anhedonia, increased stress sensitivity, and depressive mood bias. Biological endophenotypes identified include those depressed patients sensitive to tryptophan depletion and those with abnormal hypothalamic-pituitary-adrenal (HPA) axis function (Hasler, Drevets, Manji, & Charney, 2004). Table 55.3 lists the current clinical and biological endophenotypes in order of the strength of existing evidence. This limitation of inadequately defined clinical and biological subtypes of major depression is a significant hindrance to identifying homogeneous samples of patients

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table 55.3

Putative Endophenotypes for Major Depressive Disorder Heritability

Description

Clinical Endophenotype Anhedonia Increased stress sensitivity Depressed mood bias

Yes Yes No

Diurnal variation Executive cognitive function

Yes Yes

Prominent deficits in reward processing Heightened susceptibility to depression after exposure to stressful life events Prominent biasing of information processing toward negative (sad or unpleasant) content Marked variability in symptoms related to disruptions in circadian rhythms Decreased speed of response on tasks of selecting strategies, planning, and monitoring performance

Biological Endophenotype Tryptophan depletion Dex/CRH test

Yes Yes

Catecholamine depletion REM sleep abnormalities Altered subgenual PFC function

No Yes No

Reduced 5HT1A receptor BP Increased cytokine activity

Yes No

Depressive symptoms emerging in state of reduced dietary tryptophan Elevated concentrations of ACTH and cortisol from CRH stimulation after dexamethasone dosing to suppress HPA axis AMPT-induced exacerbation of mood symptoms Reduced REM latency, higher REM density and increased overall REM sleep Volume loss and increased (volume corrected) metabolic processing in this component of the affective ACC Decreased expression or activity of 5HT1A receptors Increased concentrations of proinflammatory cytokines IL-1, IL-6, and TNF-

Note: “Heritability” refers to whether there are published studies suggesting that variance in the endophenotypes is associated with genetic variance. ACC  anterior cingulate cortex; AMPT  -methylparatyrosine; ACTH  adreoncorticotropin; BP  binding potential; Dex/CRH  dexamethasone/ corticotropin releasing hormone challenge test; 5HT  5-hydroxytryptophan; IL-1  interleukin-1; IL-6  interleukin-6; REM  rapid eye movement; TNF  tumor necrosis factor. From “Discovering Endophenotypes for Major Depression,” by G. Hasler, W. C. Drevets, H. K. Manji, and D. S. Charney, 2004, Neuropsychopharmacology, 29, pp. 1765–1781. Adapted with permission.

who may share the same pathobiology. Further heterogeneity is introduced by differences in family history (and thus genetic susceptibility) and presence of other psychiatric conditions that frequently co-occur with major depression, such as anxiety disorders, attention deficit hyperactivity disorder, substance abuse, and psychotic symptoms. Another ongoing challenge is separating state effects (i.e., those features of the biology present only during the depressive episode) from trait effects (i.e., the biological features that continue to be present both during an episode and in remission from the illness, perhaps representing some aspect of vulnerability to depression). The impact of age is an important confounder. Patients 50 years of age or older experiencing a first episode of depression are more likely to have cerebrovascular changes contributing to their depressive syndrome (Krishnan, Hays, & Blazer, 1997). Responsiveness to placebo treatment, and the naturally remitting nature of the illness further complicate the biological assessments of the specificity of treatments of MDD. Finally, adequately distinguishing between anxiety and depression is also challenging because these two forms of mental experience commonly co-occur and share some biological substrates. Anxiety is best conceived as a state of apprehension and hyperarousal related to perceived future threat or danger; depression is a state of reduced hedonic experience or excessive sadness. All these factors contribute to the substantial heterogeneity within MDD, and likely underlie the inconsistency of biological findings reported to date.

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METHODS OF INVESTIGATION The scientific study of major depression can be divided into four categories of approach: biochemistry, genetics, neuroimaging, and postmortem and animal studies. Biochemistry The first and most well-established approach, biochemistry, involves the analysis of various endogenous substances in the brain and body thought to be important in the regulation of mood and behavior. From the original discovery of the importance of monoamines in the regulation of mood, these neurochemistry approaches have broadened to include a large number of potentially important molecules, which will be discussed in the following section. Biochemical approaches include: (a) comparing the quantity or activity of a biological component or system in those with MDD and a group of healthy controls not afflicted with the illness; (b) challenging or stimulating a biological system through pharmacologic, psychologic, or other means, and comparing the results between MDD and control subjects, or within MDD subjects at two different time points; and (c) exploring the effects of treatment on biological systems of depressed patients. Measures that “normalize” to the levels of nonaffected individuals after remission from the episode identify state effects of the illness. Comparisons between subjects remitted from a depressive episode and never-depressed control groups

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Methods of Investigation 1063

are crucial for the identification of trait effects, which do not change significantly between ill and well phases of the illness. The argument that what works to treat an illness reflects the underlying pathophysiology of the illness is somewhat circular and not necessarily valid. Nevertheless, since the start of biological investigations of MDD, studies of the pathophysiology of MDD have been driven in part by studying the mechanisms of drugs found to provide relief from the illness. The first example was the catecholamine hypothesis of MDD, derived in large part from the finding that imipramine impeded norepinephrine reuptake from the synapse. Biochemical experiments in MDD have nearly always produced results for which the ranges of measurements overlap between the depressed and nondepressed groups, limiting their use as diagnostic tests, though insight into pathophysiology is possible.

Genetics The rapidly advancing field of genetics constitutes the second major approach to the study of MDD. Genetic studies aim to identify alleles of genes that convey vulnerability or resilience to developing MDD. The heritability of MDD (i.e., the proportion of the variation in major depression attributable to genetic factors) is approximately one-third, with an even greater percentage present in cases of depression with an early age of onset (i.e., before age 40) that are recurrent (P. F. Sullivan, Neale, & Kendler, 2000). Familybased studies have found the relative risk of developing MDD for first-degree relatives of depressed individuals is nearly three times greater than the general population (Gershon et al., 1982; Maier et al., 1992). MDD is almost certainly a complex polygenetic illness, with its phenotypic expression most often dependent on interactions between genes and the individual’s environmental experiences (i.e., gene by environment interactions). This model implies that inheritance of specific alleles produces a trait of vulnerability (or resilience) to developing depression, which can become manifest after the individual experiences certain negative life events. In polygenetic illnesses, the effect size of any individual gene is likely to be small, and the same gene may produce vulnerability for other illnesses in addition to major depression. For example, MDD shares about 55% of the genetic risk with neuroticism (i.e., a personality type characterized by high degrees of dysphoria, tension, anxiety, and emotional reactivity; Kendler, Neale, Kessler, Heath, & Eaves, 1993). Generalized anxiety disorder also shares similar genetic factors with MDD, and some evidence links the genetics of panic disorder and social phobia to MDD (Mineka, Watson, & Clark, 1998; Weissman et al., 2005).

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Association and linkage studies have identified many potential genetic contributors to MDD, though confirmatory studies often have failed to replicate the findings. Perhaps one of the most important reasons for this inconsistency is that the clinical syndrome of MDD can have significant symptomatic variation between individuals, suggesting that specific genes may exert variable effects depending on the form of depression under study. Incomplete penetrance of genes, such that individuals of the same genotype do not uniformly express the illness, is another complicating factor. For example, dysthymia and “subsyndromal” depression or “minor depression,” in which a patient meets some by not all criteria for MDD, may reflect incomplete penetrance of MDD-related genes. The concept of incomplete penetrance of genes in MDD is concordant with the concept of gene by environment interactions, because in this model the gene only becomes “penetrant” if the individual experiences certain environmental adverse events. One potential mediator for variable gene expression is the role of DNA methylation patterns, which regulate the transcription of genes in response to environmental events (Abdolmaleky et al., 2004).

Neuroimaging The third and most recent approach to the study of depression is the use of neuroimaging. The promise of neuroimaging approaches for depression is in elucidating specifically how brain function is disrupted in the diseased state. Neuroimaging approaches to depression fall into two main categories: (1) Structural imaging approaches identify how the morphology of the brain of depressed patients differs from that of healthy controls. (2) Functional neuroimaging examines patterns of brain activity of specific brain regions or circuits, either while the brain is “at rest” or engaged with the performance of a task. Structural neuroimaging, originally employing computerized tomography (CT) and now magnetic resonance imaging (MRI) techniques, is useful for identifying abnormalities of the gross morphology of living subjects, and in doing so, identify structures that warrant further examination through other approaches. The structural approach is, however, somewhat limited because the gross morphological changes in major depression are relatively small, in contrast to the morphological changes observed in schizophrenia. Functional imaging approaches offer the tantalizing promise of evaluating brain changes in real time. Three functional imaging approaches used in the study of depression are positron emission tomography (PET), single photon emission computerized tomography (SPECT), and functional MRI (fMRI). Due to its greater resolution, PET

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Depression

is superior to SPECT, though more expensive and difficult to conduct. PET and SPECT studies, using low levels of radioactive tracers, can be employed in two ways. First, by attaching a radioactive tag to molecules that bind to specific receptors or transporters (reuptake pumps) in the brain, the density of these proteins can be measured. This approach thus allows for the comparison of receptor densities between depressed and healthy control subjects, or in depressed subjects both before and after treatment or recovery from the episode of depression. Thus, biochemical theories of treatment-induced changes of receptor and transporter availability can be tested in the living patient. An important caveat of these studies is that binding does not necessarily reflect activity states of the protein. Thus, proteins (or components of proteins) that bind the tracer molecule may be present in the cytoplasm of the cell (i.e., internalized), and therefore not actively engaged in signal transduction on the cell surface. The other PET method is the use of radioisotopes of water (15O) or glucose (18FGlu) to measure cerebral blood flow and glucose metabolism, respectively. This approach thereby complements that of fMRI, which measures changes in blood flow over time. Functional imaging studies can be divided into resting state studies, in which subjects are imaged while simply lying down, relaxed, and not thinking of anything in particular, and activation studies, in which subjects are imaged prior, during, and after being prompted to engage in some observation or task.

Postmortem and Animal Studies Research into the neurobiology of depression also relies on two other important approaches. Postmortem studies complement living-subject studies in that they allow for analysis of central nervous system (CNS) tissue from individuals who suffered from depression during life. There are several limitations to postmortem studies, including incomplete or uncertain diagnoses of the deceased, variability in the state and clinical features of the depressive illness during life, uncertainty about previous treatment and substance abuse, difficulty of separating suicidality from depression, and variation in agonal states. Animal models of depression complement these human-subject study designs. Rodent models are by far the most commonly employed, but they present their own challenges. Most current models (e.g., learned helplessness, forced swim test, chronic mild stress, tail suspension test) focus on inducing a state of inescapable stress, whereas the bulbectomized rat model induces a state of dysregulated amygdala function and cortisol hypersecretion (O’Neill & Moore, 2003; Song & Leonard, 2005). The maternal

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deprivation model in rodents and nonhuman primates focuses on the now well-established observation that early life trauma is associated with a marked increase in risk for depression in adulthood (Kendler, Gardner, & Prescott, 2002). Due to the rodents’ markedly less-developed prefrontal cortex compared to humans’, rodent models cannot provide insight into the uniquely human cognitive experiences of guilt, suicidal thoughts, and poor concentration that occur in depression. Rather, these models have greater face validity with the more overt symptoms of depression, such as anhedonia, amotivation, and behavioral despair (helplessness). Despite their limitations, these models have been quite successful at identifying novel compounds that are effective antidepressants in humans.

Summary The results of these scientific investigations into MDD have produced four main systems of interest relevant to the pathophysiology of depression. These systems will be discussed separately, though it should be emphasized that all of these systems have complex interactions with each other. We start with the limbic-HPA axis because current evidence suggests that this is the system closest to being the core disruption in most individuals with MDD. Subsequently, the roles of cytokines, monoamines, and brain-derived neurotrophic factor (BDNF) and neurogenesis is discussed. Other systems that are perhaps less central to the core pathophysiologies are then described, including the findings regarding thyroid and other endocrine systems, the fast-acting neurotransmitters glutamate and -amino butyric acid (GABA), and sleep. We also include a separate section on the neuroimaging findings in MDD, before concluding with examples of how these various systems interact. The brain regions considered to be of greatest importance to the pathophysiology of MDD are identified in Figure 55.1.

ROLE OF THE HYPOTHALAMICPITUITARY-ADRENAL AXIS Particularly early in the course of MDD, depressive episodes often emerge in the wake of a significant life stressor. As the disease progresses, future episodes are less closely linked to adverse life experiences (Lewinsohn, Allen, Seeley, & Gotlib, 1999). This observation served as an important initial impetus for research on the HPA axis in MDD (Figure 55.2). One of the earliest biological findings in the study of the pathophysiology of depression were abnormalities in adrenocortical function in depressed

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Role of the Hypothalamic-Pituitary-Adrenal Axis 1065 Lateral View Dorsolateral Prefrontal Cortex

Hypothalamus (PVN)

Third Ventricle



Hippocampus

CRH/AVP

Median Eminence

Pituitary



Orbitofrontal Cortex

ACTH 

Cortisol

Sagittal View Adrenal Gland

Anterior Cingulate Cortex



Cortisol

Subgenual Cingulate Cortex Hypothalamus Brainstem Monoamine Nuclei Coronal View

Proinflammatory Cytokines (IL-1, IL-6, TNF-alpha)

Immune Cells

Glucocorticoid Signaling Cortisol

MDR Pump

New Protein (e.g., 1) D

HS

11

CBG

Striatum Amygdala

GR HSP

GR

GR GR

DNA

GR NF-

HSP

Hippocampus

Figure 55.2 HPA axis and cytokine activity. Figure 55.1 (Figure C.53 in color section) Brain regions important in the pathophysiology of major depressive disorder. Note: Three views of the brain, identifying regions considered to be important in the pathophysiology of depression. Brodmann area 25 (BA25) is part of the subgenual cingulate cortex. The brain stem monoamine nuclei of importance include the dorsal raphe (serotonin), the locus ceruleus (norepinephrine) and the ventral tegmental area (dopamine). The striatum is a component of the basal ganglia, and includes the caudate nucleus and putamen. From “Depression: Perspectives from Affective Neuroscience,” by R. J. Davidson, D. Pizzagalli, J. B. Nitschke, and K. M. Putnam, 2002, Annual Review of Psychology, 53, pp. 545–574. Reprinted, with permission, from the Annual Review of Psychology, Volume 53 ©2002 by Annual Reviews www.annualreviews.org.

patients (J. L. Gibbons & McHugh, 1962). These early findings were subsequently confirmed, with elevated cortisol concentrations in the plasma and urine of patients with MDD now among the most reproducible findings in biologic psychiatry (Sachar, Hellman, Fukushima, & Gallagher, 1970). Some components of the HPA axis also demonstrate structural changes in depressed patients, including enlargement of the pituitary and adrenal glands (Krishnan et al., 1991; Nemeroff et al., 1992). Adrenal gland enlargement (due to adrenocortical, not adrenomedullary hypertrophy) likely results from adrenocorticotropic hormone (ACTH)

c55.indd Sec3:1065

Note: Corticotropin releasing factor (hormone) (CRH) and vasopressin (AVP) secreted from the paraventricular nucleus of the hypothalamus control release of adrenocorticotropin (ACTH) from the pituitary. ACTH regulates cortisol release from the adrenal glands. Upon activation by cortisol, the glucocorticoid receptor (GR) dissociates from its chaperone heat shock protein (HSP) and translocates to the nucleus, where it interacts with other transcription factors or binds directly to DNA to influence gene transcription. Cortisol regulates its own production via negative feedback at the hippocampus, hypothalamus, and pituitary. CRH release can be stimulated by the proinflammatory cytokines, including interleukin-1 (IL-1), interleukin-6 (IL-6), and tumor necrosis factor (TNF). From “When Not Enough Is Too Much: The Role of Insufficient Glucocorticoid Signaling in the Pathophysiology of Stress-Related Disorders,” by C. L. Raison and A. H. Miller, 2003, American Journal of Psychiatry, 160, 1554–1565. Reprinted with permission.

hypersecretion in depressed patients; it appears to be statedependent because adrenal enlargement is correlated with symptomatic status of the patient (Nemeroff et al., 1992; Rubin, Phillips, Sadow, & McCracken, 1995). Corticotropin-releasing factor (CRF), a 41 amino acid peptide, is synthesized in the diencephalon, primarily in the parvocellular neurons of the paraventricular nucleus (PVN) of the hypothalamus. Brain regions thought to be important in emotion processing, including the brain stem, amygdala, and the bed nucleus of the stria terminalis all

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project to the PVN CRF neurons (Hauger & Dautzenberg, 2000). The PVN CRF neurons have short projections to the median eminence of the hypothalamus, where they release CRF into the hypothalamo-hypophyseal portal system under conditions of stress. The released CRF is transported to the anterior pituitary where it binds to CRF receptors on corticotrophs. These pituicytes then synthesize pro-opiomelanocortin, the precursor of ACTH and -endorphin, and release ACTH. Vasopressin (also known as antidiuretic hormone), which is secreted along with CRF from the hypothalamus, amplifies the effects of CRF at the pituitary. The ACTH secreted from the pituitary enters the systemic circulation and acts on the adrenal cortex to induce the production and release of cortisol. In the healthy state, cortisol then exerts negative feedback effects via binding to glucocorticoid (GC) receptors in the pituitary and hippocampus, resulting in a down-regulation of activity of the HPA axis. In many patients with depression, this negative feedback process does not occur, despite high levels of circulating cortisol. Challenging the Hypothalamic-Pituitary Adrenal Axis The dexamethasone suppression test (DST) was developed in the 1960s as a means of evaluating HPA axis function in patients with primary endocrine disorders such as Cushing’s disease, and then adapted for use in the study of depression (Carroll, Martin, & Davies, 1968). The DST is typically conducted by administering an oral dose of 1.0 mg of dexamethasone at 11 pm, followed by measurement of plasma cortisol concentrations at various times the following day. In healthy controls, administration of dexamethasone (a synthetic glucocorticoid) suppresses endogenous cortisol via its cortisol-like negative feedback effects, primarily by action on the pituitary. By contrast, many patients with MDD fail to exhibit suppression of cortisol production; such subjects are referred to as nonsuppressors. Escape from the suppressive effects of cortisol or dexamethasone in depressed patients may be explained by impaired glucocorticoid receptor signaling at the level of the pituitary. This hypothesis is supported by a transgenic mouse model, producing mice with diminished glucocorticoid receptor function (Pepin, Pothier, & Barden, 1992). These transgenic mice demonstrate escape from dexamethasone suppression, which can be normalized after 10 days of treatment with the antidepressant imipramine. Unfortunately, results of the DST have produced a sensitivity level too low for use as a screening test for major depression (Arana, Baldessarini, & Ornsteen, 1985). The test also has insufficient specificity for use as a confirmatory diagnostic test because several other medical conditions

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and about 7% of normal controls were found to be nonsuppressors (Janicak, Davis, Preskorn, & Ayd, 1997). In the 1980s, the amino acid sequence for CRF was identified, and a standardized CRF stimulation test was developed to assess HPA axis activity. In this test, a CRF dose of 1 g/kg, or a fixed dose of 100 g, is administered intravenously, and the concentrations of ACTH and cortisol are measured at 30-minute intervals over a 2- to 3-hour period (Holsboer, von Bardeleben, Wiedemann, Muller, & Stalla, 1987). Depressed patients demonstrate a blunted ACTH and -endorphin response to CRF compared with nondepressed controls (Kathol, Jaeckle, Lopez, & Meller, 1989; Young et al., 1990). The attenuated ACTH response to CRF observed in depressed patients may result from downregulation of CRF receptors in the anterior pituitary stemming from chronic CRF hypersecretion (Wynn, Harwood, Catt, & Aguilera, 1988). An alternative hypothesis is that chronically elevated cortisol concentrations result in diminished ACTH release via negative feedback. However, the cortisol response in the CRF stimulation test does not consistently differ between patients and controls. The state of the art in assessing HPA axis function has now evolved into the combination of these two tests, which overcomes the limitations of the DST. In the dexamethasone/CRF test, at 3 pm on the day following the 1 mg dexamethasone dose, 100 g of CRF is administered intravenously, with blood draws at 30-minute intervals for 2 hours to measure ACTH and cortisol plasma concentrations (Holsboer et al., 1987). This test has a diagnostic sensitivity in depression of up to 80% (Heuser, Yassouridis, & Holsboer, 1994). Depressed patients who are nonsuppressors in the dexamethasone/CRF test prior to treatment demonstrate a better response to antidepressant treatment than suppressors, that is, normal results on this test prior to treatment predict poor response to antidepressant medication. Additionally, failure to normalize HPA axis function from nonsuppression to suppression 2 to 3 weeks after starting antidepressant medication also predicts poor response, and a greater likelihood of depressive relapse among those who do clinically improve with treatment (Aubry et al., 2007). Another test to assess HPA axis responsivity in a more naturalistic manner is the Trier social stress test (TSST; Kirschbaum, Pirke, & Hellhammer, 1993). This test requires subjects to perform a simulated 10-minute public speech and a very challenging mental arithmetic task in front of a small unsupportive audience. Performing the TSST reliably activates the HPA axis, as assessed by increases in ACTH and cortisol concentrations during the test. In the TSST, subjects with a history of early life abuse or neglect show particularly marked elevations of ACTH, but only subjects who have experienced abuse or neglect and are

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Cytokines

actively depressed also exhibit increased cortisol concentrations (Heim, Newport, Bonsall, Miller, & Nemeroff, 2001). The fact that actively depressed subjects without a history of abuse or neglect do not show elevations of ACTH or cortisol on the TSST greater than controls suggests that stress reactivity may be one means through which the pathophysiology of depression can be subtyped. Corticotropin-Releasing Factor Considerable attention to the CRF receptor as a novel target for intervention in depression has occurred over the past decade. Two CRF receptor subtypes have been identified, CRF1 and CRF2, that have a distinct neuro-anatomic localization and receptor pharmacology, have been identified in rats and humans (Hauger & Dautzenberg, 2000). Both receptors are G-protein coupled receptors and are positively coupled to adenylyl cyclase via the protein Gs. The CRF1 subtype is considered to play a more central role in mediating depressive and anxious behaviors, and preclinical and clinical studies suggest that compounds that block this receptor may possess antidepressant and anxiolytic efficacy (Zobel et al., 2000). In addition to their location in the hypothalamus, CRFproducing neurons are found in a variety of brain regions that regulate stress response and emotion processing, including the amygdala, thalamus, hippocampus and prefrontal cortex (Sanchez, Young, Plotsky, & Insel, 1999; Van Pett et al., 2000). CRF directly injected into the CNS of laboratory animals produces behaviors similar to those of MDD, including decreased libido, reduced appetite, weight loss, sleep disturbances, and neophobia (Owens & Nemeroff, 1991). Some of the extrahypothalamic CRF neurons project to the spinal cord (Kiss, Martos, & Palkovits, 1991) and brain stem nuclei (Swanson & Kuypers, 1980), including the locus ceruleus (LC) and dorsal raphe (Reyes, Valentino, Xu, & Van Bockstaele, 2005). This close anatomic proximity between the monoamine systems and CRF suggests a likely regulatory interaction between these two systems, and may be a mechanism by which antidepressants act upon the CRF system. The potential role for extrahypothalamic CRF systems in the pathophysiology of MDD is supported by human studies demonstrating elevated CRF concentrations in the cerebrospinal fluid (CSF) of depressed patients (Banki, Bissette, Arato, O’Connor, & Nemeroff, 1987; France et al., 1988; Nemeroff et al., 1984), although discrepant results have been reported (Roy et al., 1987). Elevated CRF concentrations are also present in the CSF of depressed suicide victims (Arato, Banki, Bissette, & Nemeroff, 1989). Suicide victims also have elevated CRF concentrations and reduced CRF receptor binding density and expression in the frontal

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cortex compared with controls (Merali et al., 2006; Merali, Khan, Michaud, Shippy, & Anisman, 2004; Nemeroff, Owens, Bissette, Andorn, & Stanley, 1988). Reductions in CSF CRF concentrations occur in healthy volunteers administered the antidepressant desipramine (Veith et al., 1993) and in depressed patients treated with fluoxetine (De Bellis, Gold, Geracioti, Listwak, & Kling, 1993), amitriptyline (Heuser et al., 1998), or ECT (Nemeroff, Bissette, Akil, & Fink, 1991). These findings argue that elevated CSF CRF concentrations are a marker of the depressed state, rather than a trait marker, of many individuals with MDD. A strongly supported endophenotype of MDD is one characterized by exaggerated stress sensitivity. Exposure to severe stress early in life may result in sensitization of the HPA axis and extrahypothalamic CRF neurons, thereby increasing the risk for these individuals to develop MDD after exposure to additional stressors later in life. Animal studies employing models of early life stress, such as removal of rat pups from their mothers for various periods of time, have demonstrated both acute and sustained changes in neuroendocrine and behavioral systems. In particular, rat pups exposed to maternal deprivation display hypersecretion of CRF, increased CRF mRNA expression, and increased CRF signal transduction when exposed to psychological stressors as adults (Newport, Stowe, & Nemeroff, 2002). In primate models of stress, mothers rearing their young in an environment of variable foraging demand (in which the food supply was unpredictable) provided less maternal care to their infants than mothers in situations where food supply was predictably plentiful or scarce. The offspring reared in the variable foraging demand condition had significantly elevated CRF concentrations which persist for years and abnormal functioning of both norepinephrine (NE) and serotonin systems in adulthood (Coplan et al., 1996, 2005). Taken together, the neurochemical, postmortem, and pharmacological data is concordant with the hypothesis that CRF is hypersecreted in many patients with MDD. Conditions experienced during childhood may alter the set-point for the activity of CRF neurons and HPA axis activity when exposed to stress, which may form the basis for an endophenotype of MDD characterized by exaggerated stress sensitivity. It remains to be determined whether dysfunction of the CRF system is the primary pathophysiologic disturbance in MDD, or whether that dysfunction results from another dysregulated brain system.

CYTOKINES The breadth of evidence supporting a central role for glucocorticoid resistance in the pathophysiology of many patients

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with MDD leads to the question of what process is responsible for the development of insensitivity to glucocorticoid feedback. Emerging data suggest that the source of the defect may be impaired glucocorticoid receptor function resulting, perhaps, from sustained elevated concentrations of inflammatory cytokines during medical illness or chronic stress exposure (Pace, Hu, & Miller, 2007). Several medical disorders, such as asthma, rheumatoid arthritis, and inflammatory bowel disease share the feature of excessive inflammation, and have been associated with diminished inhibitory effects of glucocorticoids (Raison & Miller, 2003). Glucocorticoids exert their effects via binding to glucocorticoid receptors (GRs) located in the cytoplasm, where they are usually maintained in a nonactive state through association with a chaperone protein complex containing heat shock proteins. Once the GR is bound by a glucocorticoid, it activates and translocates to the nucleus where it dimerizes. The resultant homodimers alter cellular function via binding to the glucocorticoid responsive promoter element of particular genes or interacting with other nuclear transcription factors. Through these mechanisms, glucocorticoid binding to GRs inhibits inflammatory cytokine signaling, CRF release, and the sympathetic nervous system (Pace et al., 2007). The most well-replicated finding of immune system activation in MDD is increased plasma concentrations of interleukin (IL)-6 and its liver product, C-reactive protein (CRP). Other proinflammatory cytokines, including IL-1 and tumor necrosis factor (TNF)-, have also been observed to be elevated in MDD (Raison, Capuron, & Miller, 2006). These cytokines can inhibit GR function by reducing GR nuclear translocation and by impairing activation of GR-inducible enzymes (A. H. Miller, Pariante, & Pearce, 1999). There are a large number of inflammatory and immune-regulating signaling pathways (e.g., the mitogen activated protein kinase pathway) that regulate GR function, and which are susceptible to disruption by proinflammatory cytokines. Another important component of the inflammatory response is a nuclear transcription factor, nuclear factor- (NF-). This protein plays a key role in mediating inflammatory and immune responses to the proinflammatory cytokines IL-1, IL-6 and TNF-. Once activated, NF- translocates to the nucleus and induces the transcription of specific genes, including the proinflammatory cytokines. In the previously described TSST paradigm, depressed patients with a history of early life trauma demonstrate significantly higher levels of IL-6 and activation of NF-, than healthy controls (Pace et al., 2006). In the healthy state, glucocorticoids are potent inhibitors of NF-, and consequently suppress inflammatory activity. This model of the pathophysiology of MDD is particularly attractive as it identifies insufficient glucorticoid

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signaling as the biological link that connects the high incidence of depression in medical disorders associated with inflammation, including diabetes, cancer and cardiovascular disease (Raison et al., 2006). Furthermore, administration of proinflammatory cytokines induces “sickness behavior” in animals and humans, characterized by disturbances in mood, cognition and neurovegetative behaviors that mimic to a considerable extent the syndrome of MDD (Dantzer, 2004). Patients with hepatitis C who receive interferon- treatment frequently develop symptoms of MDD (Raison et al., 2006), which can be effectively treated with SSRI treatment (Musselman et al., 2001; Raison, Demetrashvili, Capuron, & Miller, 2005). Indeed, antidepressants have been found to exhibit anti-inflammatory activity, with clinical response correlated with reductions in cytokine levels (Raison, Marcin, & Miller, 2002). Although much work needs to be done to clarify the relevant intracellular GR pathways disrupted in MDD, several antidepressants increase dexamethasone-induced GR-mediated gene transcription and GR translocation (Pariante & Miller, 2001). Thus, although antidepressant medications exert their initial effects on the cell surface, the effects on intracellular signaling pathways stemming from signal transduction of monoamine receptor binding is likely to be a crucial component of their antidepressant effects.

MONOAMINES In the absence of a clear pathogenetic model of MDD, early clues about the biology of the illness were drawn from the study of the biological effects of treatments known to reduce the symptoms of the illness. The monoamine hypothesis of depression (also known as the biogenic amine hypothesis) derived from several observations in the 1950s. These included the depressogenic effects of reserpine, an antihypertensive agent that interferes with the vesicular storage of monoamines (Muller, Pryer, Gibbons, & Orgain, 1955; Shore, Silver, & Brodie, 1955); the antidepressant effects of iproniazid, an anti-tubercular drug that inhibits the monoamine oxidase activity (Bloch, Dooneief, Buchberg, & Spellman, 1954); and imipramine, an agent that blocks the reuptake of norepinephrine and serotonin (5-hydroxytryptophan, 5HT) developed as an anxiolytic to treat psychotic patients, enhanced the mood of depressed subjects (Glowinski & Axelrod, 1966; Glowinski, Snyder, & Axelrod, 1966; Sulser & Dingell, 1968). Imipramine was the first of the tricyclic antidepressants (TCAs, so named based on their three-ring hydrocarbon nucleus), which became the primary medication treatment for MDD until 1988, when the first of the selective serotonin reuptake inhibitors (SSRIs), fluoxetine, was introduced.

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Monoamines

As it became clear that all of these medications altered the concentrations of the monoamines NE, 5HT, and/or dopamine (DA), several hypotheses were proposed suggesting that MDD resulted from depletions in these monoamines, though theories differed in their emphasis on NE (Bunney & Davis, 1965; Schildkraut, 1965) or 5HT (Coppen, 1967; Lapin & Oxenkrug, 1969). These theories shared the basic concept that MDD resulted from a deficiency of one or more monoamines, and that antidepressant treatment corrected this imbalance. These original formulations of the monoamine hypothesis faced several significant challenges. Most important is the generally observed delay in the onset of antidepressant action, despite the increase in synaptic concentrations of monoamines that occur within hours of the ingestion of the antidepressant. In addition, some studies of NE concentrations in CSF and plasma demonstrated increased noradrenergic activity in MDD, rather than deficiencies of the neurotransmitter (Veith et al., 1994; Wong et al., 2000). Consequently, the original monoamine hypothesis of MDD has undergone revision, with a focus on the regulation of neurotransmitter release by feedback at autoreceptors of monoamine neurons, on both the cell body and axon terminal. It is now generally believed that disrupted signaling of no single neurotransmitter is the etiologic agent for MDD because the monoamine systems interact extensively, both in the brain stem at the level of the cell bodies and in the terminal projection regions. Thus, alterations in the activity of one neurotransmitter will have an impact on the activity of other neurotransmitters. The nature and degree of the neurotransmitter disruptions likely has an impact on the specific depressive symptoms that become manifest in a particular patient. Monoamine functioning undergoes regulation at several levels, as indicated in Table 55.4. The importance of monoamine degradation was recently revealed in a PET study using [11C]harmine [a radioligand specific for the measurement of monoamine oxidase A (MAO-A) activity]. Depressed subjects demonstrated a dramatic 34% elevation in MAO-A activity in numerous brain regions when compared with control subjects (Meyer et al., 2006). MAO-A is responsible for catabolizing 5HT, NE, and, to a lesser extent, DA in the CNS. This finding suggestes that elevated MAO-A levels may be an important contributor to reduced monoamine concentrations in MDD. Serotonin All 5HT in the CNS is synthesized in the raphe nuclei located in the brain stem. The serotonin hypothesis of depression posits that deficits in serotonergic signaling are either the proximate cause or represent a vulnerability factor for the development of MDD (Maes & Meltzer, 1995).

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table 55.4. function

1069

Sites of regulation of monoamine neurotransmitter

Site of Regulation

Examples

Precursor availability

Dietary tryptophan (5HT) Dietary tyrosine (NE, DA)

Synthesis of the monoamine

Tryptophan hydroxylase (5HT) Tyrosine hydroxylase (NE, DA)

Storage and release of the monoamine from synaptic vesicles

Vesicular transporters (5HT, NE, DA)

Postsynaptic signal transduction via receptor binding

5HT2 receptor (5HT) 1 receptor (NE) D2 receptor (DA)

Intracellular signal transduction

Adenylate cyclase; phopholipase C

Termination of monoamine signal via presynaptic reuptake

SERT (5HT) NET (NE) DAT (DA)

Feedback to monoamine cell firing via presynaptic receptors

5HT1a receptor (5HT) 2 receptor (NE) D2 receptor (DA)

Degradation of the monoamine after reuptake

Monoamine oxidase (5HT, NE, DA)

DA  dopamine; DAT  dopamine transporter; 5HT  5-hydroxytryptophan (Serotonin); NE  norepinephrine; NET  norepinephrine transporter; SERT  serotonin transporter.

It is not possible in this review to comprehensively cover all of the research studies conducted on this topic. Rather we focus on those investigations that provide the most compelling argument for the neurotransmitter ’s involvement. Keep in mind that serotonergic function in depression has been difficult to keep completely distinct from its role in suicidality and impulsivity. One of the most compelling pieces of evidence for the involvement of 5HT-containing circuits in the neurobiology of major depression arises from the efficacy of 5HTaltering agents in its treatment. SSRIs, including fluoxetine, fluvoxamine, sertraline, paroxetine, citalopram, and escitalopram, induce remission from depression in approximately one-third of patients, and significantly improve symptoms in another third (Trivedi et al., 2006). These agents act by blocking 5HT reuptake by the 5HT transporter (SERT) from the synapse into the presynaptic nerve terminal, thereby increasing intrasynaptic 5HT concentrations. Similarly, agents such as nefazodone and mirtazapine, that antagonize postsynaptic 5HT receptors, especially the 5-HT2 subtypes, are also effective antidepressants. The effort to develop 5HT-specific agents for the treatment of depression arose from several lines of research. The depletion of tryptophan (TRP), the aminoacid precursor in the synthesis of 5HT, was found to increase the rate of relapse in patients successfully treated with antidepressants. The risk of relapse was greatest for patients taking

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Depression

serotoninergic, as opposed to noradrenergic, antidepressants. Additionally, TRP depletion in first-degree relatives of depressed patients induces dysphoria, but not in relatives of nondepressed individuals (Benkelfat, Ellenbogen, Dean, Palmour, & Young, 1994). CSF concentrations of the primary serotonin metabolite, 5-hydroxyindoleacetic acid (5-HIAA) have not revealed consistent differences between depressed and control subjects, though it is reduced in patients who attempt or die by suicide, particularly those employing a violent method (Asberg, Traskman, & Thoren, 1976; R. D. Gibbons & Davis, 1986; Roy, De Jong, & Linnoila, 1989), regardless of psychiatric diagnosis (Traskman, Asberg, Bertilsson, & Sjostrand, 1981; Van Praag, 1982). Overall the strongest association of low CSF 5-HIAA concentrations is with violent and/or impulsive behavior (Linnoila & Virkkunen, 1992). MDD with low CNS serotonin availability may result in a subtype of depression with significant impulsivity and risk for lethal suicide attempts. There are a number of neuroendocrine challenge tests that have been used to assess activity of the 5HT system. The best replicated of these is the fenfluramine challenge test (Mitchell & Smythe, 1990). Fenfluramine, an appetite suppressant, induces a rapid release of 5HT from presynaptic terminals. In the anterior pituitary gland, 5HT binding to postsynaptic 5HT2a and -2c receptors induces the release of prolactin into the systemic circulation. Thus, in healthy individuals, fenfluramine administration generally results in an increase in serum prolactin concentrations, an effect that is blunted in depressed subjects (O’Keane & Dinan, 1991). Unfortunately, the effects of antidepressant treatment on the fenfluramine challenge test are highly conflicting, with increases, decreases, and no changes from baseline reported in the literature (Kavoussi, Hauger, & Coccaro, 1999; Shapira, Cohen, Newman, & Lerer, 1993). The results of this test suggest that the serotonergic dysfunction in MDD stems either from diminished 5HT release from the presynaptic neuron or diminished postsynaptic 5HTand 2c responsiveness. Results from another challenge 2a test, the m-chorophenylpiperazine (m-CPP) test suggests that the dysfunction results from presynaptic dysfunction (Anand et al., 1994) because m-CPP exerts mixed effects at postsynaptic 5HT receptors, most notably the 5-HT2 receptor family, and no differences in neuroendocrine responses have been found in depressed versus control subjects after m-CPP administration. However, it is uncertain the degree to which the activity of serotonin system in the hypothalamic-pituitary complex can be generalized to activity of 5HT-containing neurons elsewhere in the CNS. The SSRIs act by blocking the SERT, thereby increasing 5HT concentrations in the synapse. Thus, the density or availability of the SERT on the cell membrane may be

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a key component in the pathophysiology of major depression. As discussed later, the genetic polymorphism for the SERT has been strongly implicated in the pathogenesis of major depression. Additional evidence of the importance of 5HT reuptake has come from the study of platelets, which also express the SERT. SERT density can be measured with [3H]-imipramine binding and [3H]-paroxetine binding. The functional capacity of platelet and CNS SERT is highly correlated, supporting the use of platelets as a surrogate measure for CNS SERT function, though there may be differences in how the SERT gene and protein are regulated between the two tissue types (Rausch et al., 2005). The great majority of studies of platelet SERT activity demonstrate both reduced 5HT uptake and density of SERT binding sites in depressed versus control subjects (Ellis & Salmond, 1994). This reduced 5HT binding is likely a state marker for depression because it improves with treatment, though all of these studies were conducted prior to the discovery of the SERT genetic polymorphism, which affects SERT binding and function. The gene encoding the serotonin transporter (SLC6A4) was an obvious candidate gene for depression because it is the theorized site of action of SSRIs, and neuroimaging studies have demonstrated reduced SERT binding in depressed patients versus controls (see later discussion of findings from neuroimaging). Three functional genetic polymorphisms in the promoter region of the SERT gene have been identified (Heils et al., 1996; Hu, Zhu, Lipsky & Goldman, 2004). Initially only two alleles, the short (S) and long (L) forms of the promoter region of the gene were described, though subsequent work identified two functional variants of the long form: LA, which results in greater SERT expression than the S form, and LG, which expresses the SERT comparably to the S form. The S form of this polymorphism has been modestly associated with bipolar disorder, suicidal behavior, and depression-related trait scores, but association and linkage studies have reported mixed results for increased S form expression in patients with a diagnosis of MDD (Levinson, 2006). The inconsistency between studies may arise in part from variations in ethnicity between sampled populations because the effects of SERT promoter polymorphism on measures of CNS serotonergic function have been shown to vary by race in healthy subjects (Williams et al., 2003). The importance of the SERT promoter polymorphism is better established in studies exploring gene by environment interactions. Consistently, the S and LG forms of the SERT polymorphisms have been found to convey increased risk for the development of MDD following stressful life events (Caspi et al., 2003; Kendler, Kuhn, Vittum, Prescott, & Riley, 2005). Several published studies have now confirmed this finding (Zammit & Owen, 2006).

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Monoamines

PET imaging studies of SERT density have found a lower binding potential in depressed versus control subjects (Malison et al., 1998; Parsey et al., 2006), and individuals with the S form exhibit reduced SERT binding (Heinz et al., 2000). Discrepant SERT binding results have also been reported (Meyer et al., 2004). Postmortem brain tissue analyses of SERT binding in depressed and control brains have not been as consistent as the platelet binding data, likely due to the many confounding issues in postmortem tissue analysis (Bligh-Glover et al., 2000; Mann et al., 2000). The 5-HT1A receptors located on the cell body of serotonergic neurons control their rate of firing and consequently the availability of 5-HT in the synapse. Antidepressant treatment has been hypothesized to induce changes in the number or responsiveness of the 5-HT1A receptor, leading to an increase in the firing rate of the serotonergic neurons (Blier, deMontigny, & Chaput, 1990). Postmortem studies of 5-HT1A receptor density have not consistently found differences between the brains of depressed and control subjects (Lowther, De Paermentier, Crompton, Katona, & Horton, 1994; Mattsubara, Arora, & Meltzer, 1991). PET imaging using ligands specific to the 5-HT1A and 5-HT2A receptors now allow for in vivo studies (Fujita, Charney, & Innis, 2000). The ligand [11C] WAY100635 has been consistently used to image the 5-HT1A receptor in PET studies. Reduced 5-HT1A receptor binding in depressed patients compared to their control groups has been reported in limbic and midbrain raphe regions (Hasler, et al., 2007; Sargent et al., 2000). However, another study reported increased 5-HT1A receptor binding in antidepressant naïve depressed patients versus control subjects, and no difference between previously treated depressed subjects and controls (Parsey et al., 2006). A persistent 17% decrease versus controls in cortical 5-HT1A receptor binding in treated male patients remitted from MDD has also been reported (Bhagwagar, Rabiner, Sargent, Grasby, & Cowen, 2004). These data do not resolve the question of whether reduced 5-HT1A expression is a trait marker for major depression, but they do suggest that antidepressant use may exert long-term effects on the 5-HT1A receptor. The 5-HT2A receptors are located postsynaptically and are widely distributed with particularly high density in the frontal cortex, caudate nucleus, nucleus accumbens, and hippocampus. These receptors are involved in a variety of functions, including secretion of ACTH and cortisol and working memory and response execution. They are also suspected of being integral in response to antidepressant treatment, if not in the pathophysiology of MDD itself. Results from imaging studies of this receptor are also quite variable, probably due to methodological variability in the specific radioligand employed and inconsistent exposure of patients to psychotropic medications in relation

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to the time of scanning. Two studies have found significantly decreased 5-HT2A receptor binding in frontal cortical regions (Biver et al., 1997; Yatham et al., 2000). In the largest study to date, depressed subjects had 29% lower receptor binding in the hippocampus (Mintun et al., 2004). PET studies in which 5-HT2 receptors were measured both pre- and posttreatment have produced highly inconsistent results that do not allow conclusions about the effects of antidepressants on 5-HT2 receptor expression (Attar-Levy et al., 1999; Yatham et al., 1999). In a large study of antidepressant treatments, allelic variation in an intron of the gene for the 5-HT2A receptor was strongly correlated with antidepressant nonresponse. The allele predictive of nonresponse was six times more common among AfricanAmerican depressed subjects than Caucasians (McMahon et al., 2006). Tryptophan hydroxylase (TPH) is the rate-limiting enzyme in the synthetic pathway of 5HT. Despite numerous investigations, the TPH isoform 1 gene (TPH1) has not been consistently linked to the pathogenesis of depression. In 2003, a second isoform (TPH2) was discovered to be preferentially expressed in the brain, whereas TPH1 is mainly expressed in the periphery. A rare functional single nucleotide polymorphism (SNP) was initially identified in a sample of elderly, treatment-resistant subjects with major depression (Zhang et al., 2005). Although this polymorphism has not been replicated in other samples of depressed subjects, SNP and haplotype linkage analyses of the TPH2 gene have identified several polymorphisms and regions associated with increased risk for MDD, suicidality, and bipolar disorder (M. Harvey et al., 2004; van den Bogaert et al., 2006; Zill et al., 2004). A TPH2 polymorphism has also been correlated with lower CSF 5HIAA levels in depressed subjects (Zhou et al., 2005). In conclusion, there is a large database that implicates disrupted serotonergic signaling in the pathophysiology of MDD. The most impressive component of the data is that many antidepressants apparently act by modifying the activity of serotonergic circuits. The degree to which changes in serotonergic signaling impact other important systems, such as the CRF/HPA axis and neurotrophic factors, will be a major focus of future research. Norepinephrine Measuring the concentration of NE in bodily fluids is difficult because it is rapidly catabolized. However, the principal metabolite, 3-methoxy-4-hydroxyphenylglycol (MHPG), is stable, allowing its concentration to be used as a surrogate measure of NE levels. Approximately 20% of urinary MHPG derives from the CNS pool, leading to the assumption that changes in urinary MHPG concentration

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Depression

reflect changes in the activity of NE neurons in the CNS (Potter, Karoum, & Linnoila, 1984). Although early investigations found lower urinary MHPG in depressed versus control subjects, subsequent studies have not replicated these findings, especially in studies where care was taken to exclude subjects with bipolar depression (Maas, Fawcett, & Dekirmenjian, 1972; Schildkraut, 1973). Urinary MHPG levels do not differentiate patients with MDD from healthy controls. The utility of NE and MHPG concentrations may be greater for efforts to identify specific biological subtypes of MDD. CSF MHPG concentrations were found to be elevated in hospitalized depressed patients with high levels of anxiety, agitation, somatization, and sleep disturbance (Redmond et al., 1986). CSF NE concentrations measured hourly for 30 consecutive hours were significantly elevated in subjects with the melancholic subtype of depression compared to controls (Wong et al., 2000). A unique study of treatment-refractory MDD patients employing cannulas inserted into the internal jugular vein to measure venoarterial neurotransmitter gradients found reduced venoarterial NE, MHPG, and dopamine gradients in the patients compared to the controls (Lambert, Johansson, Agren, & Friberg, 2000). These studies suggest that the study of NE concentrations and its metabolites may reflect differences in subtypes of MDD. The activity of the CNS NE system can be reduced by the use of -methyl-para-tyrosine (AMPT), a tyrosine hydroxylase (TH) inhibitor that transiently depletes CNS NE and other catecholamine (i.e., DA and epinephrine) stores. Healthy subjects with no history of depression who are administered AMPT experience no change in their mood state (Salomon, Miller, Krystal, Heninger, & Charney, 1997). Untreated depressed patients receiving AMPT also do not experience any worsening of core mood symptoms, but do have worsening of anergia (H. L. Miller et al., 1996). However, depressed patients responsive to desipramine or mazindol, which specifically inhibit NE reuptake from the synapse, suffer a significant return of depressive symptoms upon AMPT administration (Berman et al., 1999; H. L. Miller et al., 1996). Using PET imaging techniques, this AMPT-induced return of depressive symptoms was found to correlate with reduced brain metabolism in the dorsolateral prefrontal cortex (PFC), orbitofrontal cortex, and thalamus (Bremner et al., 2003). In contrast, SSRI-treated patients do not relapse when treated with AMPT. These findings parallel the findings of tryptophan depletion in SSRI-treated subjects, arguing that, regardless of the underlying pathobiology of depression, existing medication treatments require adequate neurotransmitter concentrations in the systems they are thought to alter in order to provide efficacy (Heninger, Delgado, & Charney, 1996).

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The effects of NE are mediated at several different adrenergic receptor subtypes, which may contribute to the pathophysiology of depression and the mechanism of action of antidepressants (Duman & Nestler, 1995). Of particular importance is the 2 receptor subtype. Postmortem studies of patients with MDD have elevated 2 receptor densities in the locus ceruleus compared to controls (Ordway, Schenk, Stockmeier, May, & Klimek, 2003). The 2 receptors in the locus ceruleus likely function as autoreceptors, inhibiting NE cell firing. Postmortem studies of 2 receptor binding density in the cerebral cortex of depressed patients and healthy controls have had mixed results, with both elevations (Meana, Barturen, & Garcia-Sevilla, 1992), and unchanged levels reported (Arango, Ernsberger, Sved, & Mann, 1993; Klimek et al., 1999). Platelets also express 2 receptors, allowing their function to be studied as surrogates for CNS 2 receptors. An increased density of 2 receptors on the platelets of medication-free depressed patients have consistently been reported compared to control subjects (Garcia-Sevilla, Ulibarri, Ugedo, & Gutierrez, 1987; Garcia-Sevilla, Padro, Giralt, Guimon, & Areso, 1990; Gurguis, Vo, Griffith, & Rush, 1999), though discrepant reports have appeared (Maes, Gastel, Delmeire, & Meltzer, 1999). One of the functions of the platelet 2 receptor is to mediate platelet aggregation, a response that is exaggerated in depressed patients (Musselman et al., 1996). The functional state of signal transduction of CNS 2 receptors may be indirectly assessed via the clonidine challenge test. Clonidine is an 2 agonist that induces growth hormone (GH) release from the anterior pituitary gland, likely through a postsynaptic mechanism. The GH response to clonidine is blunted in acutely depressed patients versus controls (Amsterdam, Maislin, Skolnick, Berwish, & Winokur, 1989; Siever et al., 1984), as well as in patients treated with antidepressants, and in remitted patients (Mitchell, Bearn, Corn, & Checkley, 1988; Siever et al., 1992). Although tentative, these findings suggest that altered 2 receptor function may be a trait characteristic of some depressed subjects. One limitation of these studies was the potential confounding effects of anxiety. In a recent study using the clonidine challenge test, depressed patients without anxiety were compared with anxious patients without depression or mixed anxious/depressed patients. Only the anxious or mixed anxiety/depression patients demonstrated a reduced GH response to clonidine (Cameron, Abelson, & Young, 2004). -Adrenergic receptors may also contribute to the pathophysiology of MDD and response to antidepressants. Postmortem brain tissue studies of suicide victims have alternately found both increases or no difference in -adrenergic receptor density in depressed versus control

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Monoamines

subjects (Crow et al., 1984; de Paermentier, Cheetham, Crompton, Katona, & Horton, 1990; Mann, Stanley, McBride, & McEwen, 1986). Leukocytes also possess -adrenergic receptors and equally mixed results have been obtained (Extein, Tallman, Smith, & Goodwin, 1979; Healy, Carney, O’Halloran, & Leonard, 1985). Downregulation of -adrenergic receptors has been postulated to be integral to antidepressant action (Banarjee, Kung, Riggi, & Chanda, 1977). In animal models, chronic, but not acute, treatment with tricyclic antidepressants consistently induces down-regulation of -receptors and uncoupling of the receptors from their second messenger systems. This finding is specific for noradrenergic-acting antidepressants, because studies with primarily serotonergic agents have found no such effect (Nalepa & Vetulani, 1993; Ordway et al., 1991). In this way, the specificity of treatment effects is similar to the results of the monoamine depletion studies using tryptophan depletion and AMPT discussed earlier. Taken together, results evaluating the expression and function of - and -adrenergic receptors present a mixed picture that is difficult to interpret. This variability may result from differences in methodology between studies, or from heterogeneity of patient populations. Given the importance of NE signaling in anxiety disorders such as PTSD and panic disorder (Ressler & Nemeroff, 2000), the most likely explanation for the findings may be that depression co-occurring with significant anxiety is the subtype most likely to demonstrate elevated noradrenergic activity. Dopamine With the exception of depression with psychotic features, where it has a fundamental role, DA has historically received scant consideration in the pathophysiology of depression. There is now increasing interest in the contribution of DA to depressive symptomatology, at least in a subset of patients. DA systems are well-established to play a seminal role in normal motivation, pleasure, psychomotor speed, and cognitive ability, all systems that may be disrupted in MDD. The psychostimulants d-amphetamine and methylphenidate increase DA signaling, resulting in improvements in energy, activation, and mood. However, when used as monotherapy, these agents do not consistently produce an antidepressant effect, unlike the NE- and 5HTacting agents (Little, 1988). Nevertheless, many subjects with MDD do not achieve full remission with an SSRI or TCA, and may benefit from augmentation with DA-acting agents to achieve full recovery. The activity of DA systems can be assessed by measuring concentrations of homovanillic acid (HVA), the major metabolite of DA, in bodily fluids. Most studies exploring CSF HVA concentrations in MDD reported

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lower concentrations in depressed patients compared to controls, particularly in patients with psychomotor retardation (Kapur & Mann, 1992). However, low CSF HVA concentrations are also present in Parkinson’s (where there is a loss of DA neurons) and Alzheimer ’s diseases, both of which are characterized by psychomotor retardation (van Praag, Korf, Lakke, & Schut, 1975; Wolfe et al., 1990). Conversely, increased CSF HVA is present in agitated and manic patients further implicating CSF HVA levels and hence DA neurotransmission as a state marker for psychomotor activity more than for mood state (Willner, 1983). However, 5% to 10% of Parkinson’s disease patients develop a major depressive episode, with another 10% to 30% developing subsyndromal depressive symptoms (Tandberg, Larsen, Aarsland, & Cummings, 1996). In these patients, depressive symptoms often precede the development of the physical manifestations of the disorder and do not appear to be related to the severity of disability stemming from Parkinson’s disease itself (Guze & Barrio, 1991; van Praag et al., 1975). Secretion of growth hormone releasing hormone (GHRH) from the arcuate nucleus of the hypothalamus, which regulates the release of GH from the anterior pituitary is in part mediated by DA receptor function. Apomorphine, an agonist at D2/D3 DA receptors, acts to induce secretion of GH via binding to postsynaptic DA receptors. Studies employing this test to assess CNS DA receptor function have overall found no differences between depressed and healthy control subjects in GH concentrations after apomorphine administration (McPherson, Walsh, & Silverstone, 2003). There is some data, however, to suggest that the apomorphine challenge test may distinguish between depressed patients with and without significant suicidality (D’Haenen & Bossuyt, 1994; Pitchot, Hansenne, et al., 2001; Pitchot, Reggers, et al., 2001; Shah, Ogilvie, Goodwin, & Ebmeier, 1997). Neuroimaging studies of DA function in depressed subjects have produced inconclusive results, probably due to heterogeneity between patient samples. Elevated D2 receptor binding in neuroimaging studies may result from increased numbers of D2 receptors in MDD (perhaps reflecting a reduction in synaptic DA availability or increased affinity of the receptor for the ligand). In early studies, elevated striatal D2 expression binding density was reported in depressed inpatients (D’Haenen & Bossuyt, 1994; Shah et al., 1997), particularly in patients with psychomotor retardation (Ebert, Feistel, Loew, & Pirner, 1996). Later studies, employing nonhealthy controls or less ill outpatients identified no differences in D2 density between patients and the comparison group (Klimke et al., 1999; Parsey et al., 2001). A major confound across these studies is that most subjects were either treated with antidepressants or had only a 1-week washout prior to

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imaging procedure. Additionally, as is the case in the studies of NE, variability in the level of anxiety may confound the results; anxiety has been associated with reduced D2 receptor binding (Schneier et al., 2000). The density and function of the dopamine transporter (DAT) has also been studied in MDD, with similarly inconsistent results. The most comprehensive PET study of DAT function found reduced binding in MDD (Meyer et al., 2001). Another PET study using [18F]-fluoro-DOPA uptake in the striatum to assess DA neuronal function found depressed patients with psychomotor retardation to exhibit less striatal uptake of the radioligand compared to anxious depressed inpatients and healthy volunteers (Paillere-Martinot et al., 2001). Anhedonia is one of the two core symptoms of depression and has particular relevance to DA function because DA is critical to the processing of reward and pleasure experiences. Medication-free, severely depressed subjects experience greater reward from an oral dose of d-amphetamine (which increases DA transmission by a variety of mechanisms) than do controls and mildly depressed subjects (Tremblay, Naranjo, Cardenas, Herrmann, & Busto, 2002). The few postmortem studies exploring the DA system in depressed patients have provided conflicting results. DA or HVA concentrations in brains of suicide victims have been found to be elevated, reduced, or unchanged in depressed subjects versus controls (Beskow, Gottfries, Roos, & Winblad, 1976; Bowden, Cheetham, et al., 1997; Crow et al., 1984). In a postmortem study of the amygdala, DAT density was reduced, and D2/3 receptor binding density elevated in the brains of depressed subjects versus those of psychiatrically healthy controls (Klimek, Schenck, Han, Stockmeier, & Ordway, 2002), though a second study using different methods and focusing on the basal ganglia found no difference in D2 receptor number or affinity (Bowden, Theodorou, et al., 1997). An important persisting question regarding the role of DA in the pathogenesis of MDD is whether the psychomotor retardation is simply an epiphenomenon of more central pathophysiologic mechanisms that secondarily produce DA dysfunction, or whether there is a specific subtype of depression that derives in part due to a primary disruption in dopaminergic signaling (Dunlop & Nemeroff, 2007). Interactions between the Hypothalamic-PituitaryAdrenal Axis and Monoamine Systems There are many interactions between monoamine systems and components of the HPA axis. One example is the effect of glucocorticoids to selectively facilitate DA transmission in the nucleus accumbens (Marinelli & Piazza,

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2002). In healthy control subjects, cortisol concentrations are positively associated with d-amphetamine-induced DA release in the ventral striatum and dorsal putamen. Healthy individuals with higher circulating plasma cortisol concentrations report greater positive effects after administration of a stimulant drug (Oswald et al., 2005). In subjects who report poor early life maternal care, exposure to psychosocial stressors produces elevations in ventral striatal DA concentrations, and the DA increase is correlated with the increase in salivary cortisol concentrations (Pruessner, Champagne, Meaney, & Dagher, 2004). The high incidence of hypercortisolemia in MDD, particularly in severe depression, raises speculation that elevated cortisol concentrations alter dopaminergic reward systems, thereby altering hedonic responsiveness. One proposed model posits that over time, frequent bouts of stress associated with intermittent increased exposure to glucocorticoids sensitizes the mesolimbic DA system (Oswald et al., 2005). In a test of this model, dexamethasone added to the drinking water of maternal rats both pre- and postpartum resulted in a 50% greater survival rate of midbrain dopaminergic neurons in the adult offspring (McArthur, McHale, Dalley, Buckingham, & Gillies, 2005). Such a model also provides a potential explanatory framework for the high comorbidity rate between MDD and substance abuse. Depression with psychotic features, which is associated with markedly increased glucocorticoid secretion, exemplifies another link between DA and the HPA axis. Patients with psychotic depression exhibit increased plasma DA and HVA concentrations compared to patients with nonpsychotic depression (Devanand, Bowers, Hoffman, & Nelson, 1985; Schatzberg, Rothschild, & Langlais, 1985). The elevated glucocorticoid concentrations may drive the increase in DA activity, resulting in psychotic symptoms (Posener et al., 1999; Schatzberg, Rothschild, Langlais, Bird, & Cole, 1985). This hypothesis is supported by the established ability of high-dose synthetic glucocorticoids to induce psychosis in otherwise psychiatrically healthy individuals (Lewis & Smith, 1983).

NEUROTROPHINS AND NEUROGENESIS A particularly exciting area of current research in MDD is the study of neurotrophins and neurogenesis. The neurotrophins are a family of molecules, including brain-derived neurotrophic factor (BDNF) and vascular endothelial growth factor, involved in the maintenance, growth, and survival of neurons and their synapses. Cyclic adenosine monophosphate response element binding protein (CREB) is a protein activated via G-protein systems that increases the expression of neurotrophic and neuroprotective proteins.

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Neurotrophins and Neurogenesis

Specifically, CREB increases the levels of BDNF and its receptor, tropomyosin receptor-related kinase B (TrkB). BDNF is believed to regulate the survival of neurons via its interaction with the mitogen-activated protein kinases (MAPK), which in turn can increase the expression of Bcl-2, a protein that acts to inhibit the programmed cell death of neurons. BDNF also regulates synaptic plasticity through its effects on the NMDA receptor, thus significantly affecting how networks of neurons communicate (Manji, Drevets, & Charney, 2001). The neurotrophic hypothesis of depression proposes that deficient neurotrophic activity contributes to disrupted functioning of the hippocampus in depression, and that recovery with antidepressant treatment is mediated in part by reversal of this deficit (Duman, Heninger, & Nestler, 1997). Unlike the history of interest in the HPA axis and monoamines, the interest in the role of BDNF in MDD grew out of findings from animal research. Restraint stress in rats reduces BDNF expression in the hippocampus, implying a role for the HPA axis in suppressing BDNF levels (Smith, Makino, Kvetnansky, & Post, 1995). Direct injection of BDNF into the rat brain is efficacious in two animal models of depression (Shirayama, Chen, Nakagawa, Russell, & Duman, 2002; Siuciak, Lewis, Wiegand, & Lindsay, 1997). Many antidepressants increase CREB activity and BDNF levels in the hippocampus and prefrontal cortex of rats, which begins about 2 to 3 weeks after initiating the antidepressant, consistent with the usual time course for clinical improvement (Nestler, Terwilliger, & Duman, 1989; Nibuya, Nestler, & Duman, 1996). ECT also increases BDNF levels in the hippocampus (Vaidya, Siuciak, Du, & Duman, 1999). Surprisingly, the function of BDNF also seems required to manifest depressive states. In a mouse model of depression, BDNF was required for mice to develop social aversion in a social defeat paradigm; blockade of BDNF activity in the VTA and nucleus accumbens produced antidepressant effects in the same paradigm (Berton et al., 2006). Although CREB can be activated and regulated by G-protein linked neurotransmitter receptors, several other intracellular signaling cascades can alter CREB activity, including those associated with growth factors and inflammatory cytokines. Subsequent work has revealed considerable complexity in the effects of antidepressants on CREB activity and BDNF levels, with variability in different brain regions and between drugs, which will require further study (Tardito et al., 2006). The first evidence that hippocampal cell proliferation may be required for antidepressant effects was revealed in a study of mice treated with fluoxetine or imipramine, with or without prior irradiation of their hippocampus (Santarelli et al., 2003). Animals receiving irradiation prior to antidepressant administration did not develop expected antidepressant

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behaviors, in contrast with the control animals. Moreover, 5HT1a receptor knockout mice treated with fluoxetine did not show the expected increase in cell proliferation, whereas knockout mice treated with imipramine did. These findings suggest that 5HT1a receptor stimulation may be necessary for the behavioral and neurogenic effects of SSRIs. That antidepressants may increase neuronal proliferation is concordant with the finding that chronic antidepressant treatment increases hippocampal volume in humans (Vermetten, Vythilingam, Southwick, Charney, & Bremner, 2003). More recent work suggests that SSRI antidepressants affect hippocampal neurogenesis and development through two separate processes. First, they can increase the proliferation of early-stage progenitor cells, but not the newborn stem-like cells, in the dentate gyrus of the hippocampus (Encinas, Vaahtokari, & Enikolopov, 2006). Second, these drugs increase the efficient survival of young neurons after they complete mitosis and migrate to the granule cell layer. BDNF signaling may play a crucial role in the new cell’s formation of a dendritic tree and formation of synapses with cells in the CA3 region of the hippocampus (Sairanen, Lucas, Ernfors, Castren, & Castren, 2005; see Figure 55.3). The elimination of neurons through apoptosis (A) ML

hilus

GCL

(B) ML GCL mf

SGZ Survival Differentiation Proliferation

Figure 55.3 (Figure C.54 in color section) Neurogenesis in hippocampus. Note: (A) The dark cells indicated by arrowheads are bromodeoxy uridinelabeled newborn neurons in the rat adult hippocampus. (B) Newborn neurons in the subgranular zone (SGZ) undergo proliferation, an effect enhnaced by antidepressants. The new cells migrate to the granular cell layer (GCL), and mature to become granule cells, forming dendritic connections in the molecular layer (ML) and extending axons into the CA3 pyramidal cell layer through the mossy fiber pathway (mf). From “Hippocampal Neurogenesis: Opposing Effects of Stress and Antidepressant Treatment,” by J. L. Warner-Schmidt and R. S. Duman, 2006, Hippocampus, 16, p. 241. Reprinted with permission.

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increases concurrently with the increase in neurogenesis in the hippocampus in antidepressant-treated mice. Thus, antidepressants may act by facilitating functional neuronal connectivity and plasticity to improve information processing in neural networks involved in mood regulation (Castren, 2005). If these new synaptic networks represent a form of learning, then exposure to positive events during treatment may be necessary for recovery from depression. The few postmortem studies measuring BDNF concentrations in the brains of depressed patients have been inconclusive, with both increases and decreases reported (Slattery, Hudson, & Nutt, 2004). Although several groups have reported reduced BDNF levels in the peripheral circulation of subjects with MDD versus controls, the degree to which plasma BDNF levels reflect brain BDNF levels remains uncertain (Karege et al., 2005).

HYPOTHALAMIC-PITUITARYTHYROID AXIS Interest in the HPT axis in MDD emerged from observations that patients with either hyper- and hypothyroidism could develop profound depressive symptoms indistinguishable from MDD. The hypothalamic-pituitary-thyroid (HPT) axis is organized similarly to the HPA axis, starting with the release of thyrotropin-releasing hormone (TRH) from nerve terminals in the median eminence of the hypothalamus. TRH is transported in the hypothalamohypophoseal portal system to the anterior pituitary where it induces the release of thyroid stimulating hormone (TSH) into the peripheral circulation. TSH then acts on the thyroid gland to induce the synthesis and release of triiodothyronine (T3) and thyroxine (T4). Thyroid hormones provide negative feedback at the hypothalamus and pituitary to inhibit the release of TRH and TSH, respectively. In the brain, the enzyme type II 5’-deiodinase converts T4 to T3 (considered the active form of thyroid hormone in the CNS). The action of this enzyme can be inhibited by cortisol, thus linking the HPT and HPA axes in MDD (Hindal & Kaplan, 1988). Administration of antidepressants in rodents results in increased activity of this enzyme (Campos-Barros et al., 1994). Approximately 20% to 30% of patients with MDD have abnormal values on one or more laboratory measures of thyroid function. TRH concentrations in CSF have been found to be elevated in depressed versus control subjects in 2 of the 3 published studies on this topic (Banki, Bissette, Arato, & Nemeroff, 1988; Kirkegaard, Faber, Hummer, & Rogowski, 1979; Roy, Wolkowitz, Bissette, & Nemeroff, 1994). The TRH stimulation test involves administering a standard dose of TRH intravenously, then measuring

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plasma TSH levels every 30 minutes for 2 hours after dosing. Approximately 25% to 30% of depressed patients who have no detectable thyroid dysfunction demonstrate a blunted TSH response to TRH challenge (Kastin, Ehrensing, Schalch, & Anderson, 1972; Prange, Lara, Wilson, Alltop, & Breese, 1972). A possible explanation for this is that TRH receptors in the pituitary are downregulated in response to chronically elevated secretion of TRH into the hypophyseal-portal circulation during a depressive episode, though evidence for this hypothesis is inconclusive. Approximately 15% of patients with MDD display an enhanced TSH response to TRH stimulation (Extein, Pottash, & Gold, 1981). In two small studies, intrathecal TRH administration transiently improved mood state in patients with treatment refractory depression (Callahan et al., 1997; Marangell et al., 1997). Transthyretin is a protein that transports and distributes thyroid hormones in the CNS. In MDD, CSF transthyretin concentrations are decreased versus controls (G. M. Sullivan et al., 1999; M. Sullivan et al., 2006). Reduced levels of transthyretin could result in a functionally hypometabolic and hypothyroid state in the CNS of affected individuals, even in the presence of normal-range serum thyroid hormone concentrations. Such a scenario could explain the observed clinical benefit of the addition of thyroid hormone to the treatment regimen of depressed patients with an inadequate antidepressant response. Another consideration in HPT function is that, unlike the HPA axis, the HPT axis can also be disrupted by anti-thyroid antibodies, that is, antithyroglobulin and antithyroid microsomal (thyroid peroxidase) antibodies. These antibodies are found more frequently in depressed subjects than in the general population (M. S. Gold, Pottash, & Extein, 1982). Thyroid hormones may be involved in the pathophysiology of depression via their effect on serotonin function. T3, both alone and in combination with fluoxetine, reduces transcription of the 5-HT1A and 5-HT1B receptors (Lifschytz et al., 2006). As previously noted, down-regulation of the 5-HT1A autoreceptor may be an important mechanism of action of antidepressants. Results from treatment studies suggest that addition of thyroid hormones may enhance the speed and overall response to antidepressant treatment (Altshuler et al., 2001; Aronson, Offman, Joffe, & Naylor, 1996). Recent studies using SSRIs and large sample sizes have produced divergent results on these questions (Appelhof et al., 2004; Cooper-Kazaz et al., 2007). The effects of antidepressant treatment on thyroid hormone availability is also mixed. Severely depressed patients treated with paroxetine had a mean 11.2% reduction in circulating T4 levels, whereas 15 depressed female inpatients treated for 24 weeks with sertraline demonstrated a 24% increase in T3 levels, but no change in T4 levels (Konig,

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Fast-Acting Neurotransmitters

Hauger, von Hippel, Wolfersdorf, & Kaschka, 2000; Sagud et al., 2002). One study suggests that higher serum T3 concentrations at the time of achieving remission from MDD may be protective against relapse (Joffe & Marriott, 2000). In summary, the importance of HPT axis disruption in the pathophysiology of MDD remains unclear. It is possible that a subtype of depression exists in which abnormal HPT axis function revealed by the TRH challenge test could be separable from other forms of MDD, and warrant specific treatment with thyroid replacement. However, this remains speculative based on the current evidence.

GROWTH HORMONE AND SOMATOSTATIN Growth hormone (GH) is an important regulator of body fuel stores, and thereby may contribute to the pathophysiology of MDD. Secretion of GH from the anterior pituitary is controlled by two hypothalamic peptides: somatostatin, which inhibits, and GHRH, which stimulates, GH release. Dopamine, NE, and tryptophan also stimulate GH secretion. In healthy individuals, GH is secreted in a circadian pattern, with peak levels occurring in the first few hours of sleep. Depressed subjects, however, show lower nocturnal GH release, and higher daylight plasma GH concentrations (Mendlewicz et al., 1985; Schilkrut et al., 1975). Dysfunction of GH secretion may be a trait marker for MDD because adolescents demonstrating lower GH levels prior to sleep onset are at greater risk of developing MDD as adults (Coplan et al., 2000). The clonidine challenge test, which stimulates GH release through clonidine’s agonism at CNS 2 receptors, is blunted in depressed patients (Matussek et al., 1980). In addition to its effect on GH, somatostatin inhibits GABA activity and the release of CRF, ACTH, and TRH. This neuropeptide is therefore positioned to influence many of the neurotransmitters implicated in the pathophysiology of depression. CSF somatostatin concentrations have been found to be reduced in depressed patients versus controls, perhaps due to glucocorticoid inhibition of the activity of somatostatin neurons (Bissette et al., 1986; Wolkowitz et al., 1987). As is true for many hypothalamic peptides, much remains to be determined about the role of somatostatin in MDD.

FAST-ACTING NEUROTRANSMITTERS GABA Gamma ()-aminobutyric acid (GABA) is the predominant inhibitory neurotransmitter in the CNS. Approximately 20% to 40% of all neurons in the cortex and  75% of all

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striatal neurons are GABAergic (Hendry, Schwark, Jones, & Yan, 1987; Tepper, Koos, & Wilson, 2004). The vast majority of GABAergic cells in the brain are interneurons, characterized by short axons that form synapses within a few hundred microns of their cell body, and which connect different neurons together to coordinate neuronal activity within local brain regions. The serotonergic neurons of the raphe nuclei are under GABAergic tonic inhibition, and 5HT-containing neurons from the raphe project to the cortex and preferentially synapse on GABA interneurons (more so than pyramidal neurons). These 5HT inputs are stimulatory to GABA interneurons, increasing their firing rate. Indirect evidence of reduced GABAergic activity in MDD is suggested by findings of lower resting-state levels of cortical inhibition in patients undergoing transcranial magnetic stimulation (Bajbouj et al., 2006). Lower CSF and plasma GABA concentrations have been found in comparison to control patients, with the low plasma GABA levels persisting for up to 4 years after remission (B. I. Gold, Bowers, Roth, & Sweeney, 1980). Plasma GABA levels have also been reported to be lower in nondepressed individuals who have a first-degree relative with a history of major depression than in those without such a family history (Bjork et al., 2001). Although these findings suggest that low GABA concentrations may be a trait marker for MDD, the source of plasma GABA remains obscure, and it may not be derived from CNS activity. Evidence against GABA levels as a trait marker for depression emerged from recent magnetic resonance spectroscopy (MRS) studies that found that the reduced GABA concentrations in the occipital cortex in the acutely depressed state resolved after successful treatment with medication or ECT (Hasler et al., 2005; Sanacora et al., 2003; Sanacora, Mason, Rothman, & Krystal, 2002). Furthermore, reduced GABA concentrations are not specific to MDD, having also been demonstrated in alcohol dependence and mania (Petty, 1994). GABA signaling is influenced by the neurosteroids 3-alpha, 5-alpha-tetrahydroprogesterone (THP, allopregnanolone) and 3-alpha, 5-alpha-tetrahydrodeoxycorticosterone (THDOC). These metabolites of progesterone are produced by neurons and glia in the CNS and are thought to act in a paracrine manner as positive allosteric modulators at the GABA-A receptor, enhancing GABAergic transmission (Belelli & Lambert, 2005). Administration of neurosteroids in the mouse forced swim test model of depression has demonstrated efficacy (Khisti, Chopde, & Jain, 2000). In addition, allopregnanolone exerts negative feedback on the HPA axis, decreasing plasma ACTH concentrations and CRF release (Patchev, Hassan, Holsboer, & Almeida, 1996; Patchev, Shoaib, Holsboer, & Almeida, 1994). Significantly lower serum and CSF allopregnanolone levels have been found in patients with MDD

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compared to healthy controls, with normalization of these concentrations after successful treatment with antidepressants, though not with ECT or transcranial magnetic stimulation (Baghai et al., 2005; Uzunova et al., 1998). Glutamate The N-methyl-D-aspartate (NMDA) receptor may have significance in the pathophysiology of MDD, because NMDA signaling is crucial to many forms of learning, and in high concentrations glutamate can induce neurotoxicity. NMDA receptor antagonists possess antidepressant properties in an animal model of depression (Papp & Moryl, 1994). Chronic treatment with antidepressants has also been shown to modulate NMDA receptor function (Nowak, Trullas, Layer, Skolnick, & Paul, 1993). A recent clinical trial using the NMDA antagonist ketamine reported a rapid response in treatment-refractory patients with MDD (Zarate et al., 2006). Some work suggests that MDD is associated with excessive glial cell loss (Ongur, Drevets, & Price, 1998; Rajkowska et al., 1999), which could result in increased glutamatergic transmission because glial cells remove glutamate from the synapse via glutamate transporters (Slattery et al., 2004). Finally, elevated glutamate levels have been demonstrated in the same cortical regions where GABA levels are reduced, suggesting that both fast-acting neurotransmitter systems may contribute to the pathophysiology of MDD. It is also possible that a metabolic pathway common to both systems may be responsible for these results (Sanacora et al., 2004). FINDINGS FROM NEUROIMAGING Structural Imaging Studies Perhaps the most consistent structural imaging finding in MDD is the presence of smaller hippocampi in depressed versus nondepressed individuals (Neumeister et al., 2005). There is some evidence that duration of depressive illness is inversely correlated with hippocampal volume (MacQueen et al., 2003; Sheline, Sanghavi, Mintun, & Gado, 1999). To date, however, structural imaging studies have not been able to determine whether smaller hippocampal volume is a cause or consequence of MDD. To be more precise, are smaller hippocampi genetically inherited or environmentally induced, resulting in greater risk for depression, or is there shrinkage of the hippocampus as a result of a pathologic process active in the depressed patients? Smaller hippocampi are not unique to MDD; similar structural findings are present in patients with PTSD (Karl et al., 2006). Work from PTSD subjects in a twin cohort suggests that smaller hippocampi is a risk factor for the development

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of the illness, rather than a consequence of the disease (Gilbertson et al., 2002), though divergent data are available (J. Douglas Bremner, personal communication). Nevertheless, the finding of smaller hippocampal volume is intriguing because the hippocampus has important roles in regulation of the HPA axis, is a site of neurogenesis, and has close functional connections to other brain structures implicated in MDD, including the amygdala and prefrontal cortex. The subgenual region of the anterior cingulate cortex (Brodmann area 25, BA 25) has also been reported to have smaller volume in depressed subjects compared to control subjects. Functional Imaging Studies Functional imaging studies offer the potential to better delineate specific neural networks associated with specific symptom domains of MDD. As discussed earlier, the phenotypic expression of MDD is heterogeneous, with the various symptom domains disturbed in some individuals but not others. By selecting for depressed subjects with marked symptomatic disturbances (e.g., profound anhedonia, or obvious psychomotor slowing), the neural networks underlying these symptoms may be more specifically evaluated, and may thereby contribute to a more biologically relevant categorization of MDD. For example, reduced dorsolateral prefrontal activity is correlated with impairments of psychomotor speed and executive functions (Bench, Friston, Brown, Frackowiak, & Dolan, 1993; Mayberg, 1994). Activity in the anterior cingulate cortex and its antero-medial extensions are associated with cognitive performance, emotional bias, and emotion regulation (Dolan, Bench, Brown, Scott, & Frackowiak, 1994; Elliott, Rubinsztein, Sahakian, & Dolan, 2002), and the parietal cortex and parahippocampus with anxiety (Osuch et al., 2000). Resting state imaging studies have consistently identified differences in activity in ventral and dorsal prefrontal cortex, anterior cingulate, basal ganglia, amygdala, and hippocampal regions in depressed versus healthy controls. Across all resting state studies of depressed patients, the most consistent finding is that of reduced prefrontal cortex activity, often inversely correlated with depression severity (Ketter, George, Kimbrell, Benson, & Post, 1996). Subgenual Anterior Cingulate Cortex The anterior cingulate cortex (ACC), situated on the medial (mesial) wall of each cerebral hemisphere, is frequently divided into a dorsal component, involved extensively in cognitive functions, and the rostral/ventral component (Brodmann areas 25, 33, and 24), which is involved in affective processing. The affective component of the

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Findings from Neuroimaging

ACC has extensive connections with the amygdala and periaqueductal grey, and parts of it project to autonomic brain stem motor nuclei. A part of the affective ACC, the subgenual ACC (BA25), is commonly identified to have altered metabolic activity at rest in depressed versus control subjects. In most studies, this region is hyperactive in depressed subjects, with partial normalization of activity with effective treatment (Figure 55.4). Although some studies have reported reduced blood flow in this region in depressed subjects, correction for volume loss (see previous discussion) indicates that overall activity here is increased (Botterton, Raichle, Drevets, Heath, & Todd, 2002; Drevets, 2000; Drevets et al., 1997). BA 25 is a component of the affective section of the ACC and receives input from many structures implicated in the pathophysiology of MDD. Induction of sadness in healthy controls activates the ventral ACC, along with the insula and amygdala among other regions, often in conjunction with reductions in dorsolateral prefrontal cortex activity (Mayberg et al., 1999). Remarkably, juvenile monkeys undergoing stimulation of BA 25 make a cry of distress similar to those heard when an animal is separated from its mother (MacLean & Newman, 1988). In humans, greater pretreatment metabolic activity in BA 25 is associated with poorer response to treatment with medication or psychotherapy. Four of six subjects with treatment-resistant severe depression demonstrated significant improvement after chronic electronic deep brain stimulation (DBS) of the white matter tracts adjacent to the subgenual ACC (Mayberg et al., 2005). The clinical improvement was associated with reduction in blood flow to the subgenual cingulate, insula, and OFC, and increases in DLPFC and dorsal ACC. It is likely that the subgenual ACC plays a fundamental role in the pathophysiology of MDD. (A)

(B)

pCg31

Cg25 Cg25

P

Figure 55.4 (Figure C.55 in color section) Subgenual anterior cingulate cortex activation in depressive states. Note: (A) The subgenual cingulated cortex (Cg25) increases in activity (red) during the induction of transient sadness in healthy control subjects. (B) Chronic treatment with fluoxetine for depression induces a reduction in activity (green). From “Targeting Abnormal Neural Circuits in Mood and Anxiety Disorders: from the Laboratory to the Clinic,” by K. J. Ressler and H. S. Mayberg, 2007, Nature Neuroscience, 10, p. 1117. Reprinted with permission.

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1079

Emotional and Cognitive Processing So-called neuroimaging challenge paradigms involve presenting the subject with a stimulus or task designed to engage the brain in some specific manner. fMRI studies of depressed patients have identified heightened ACC and paralimbic region activity in response to negatively valenced emotional cues. Exposure to negative words results in greater and more prolonged activity of the amygdala and ventromedial prefrontal cortex in depressed patients versus controls (Elliott et al., 2002; Fossati et al., 2003). Similarly, viewing negatively valenced faces induces greater amygdala reactivity in depressed versus control subjects (Sheline et al., 2001). Through its output to the ventral PFC, this sustained amygdala response may be an important neural correlate of the common symptom of rumination in depression. The increased amygdala activity in these paradigms is reduced by effective treatment with either an SSRI (Fu et al., 2004; Sheline et al., 2001) or cognitive behavioral therapy (Siegle, Carter, & Thase, 2006). Healthy controls given an SSRI demonstrate reductions in amygdala activation in response to aversive faces (Del-Ben et al., 2005), and both SSRIs and NE reuptake inhibitors reduce the identification of negative stimuli in healthy controls (Harmer, Shelley, Cowen, & Goodwin, 2004). A common complaint of patients with MDD is diminished concentration, often resulting from the emergence of distracting negative thoughts and feelings. fMRI studies in healthy subjects have found that as the cognitive load required to complete a task is increased, metabolic activity in cortical cognitive areas increases, along with a concomitant decrease in activity in the limbic and paralimbic regions (Pochon et al., 2002). This pattern of activity likely reflects an emotional gating that acts to inhibit emotional interference while performing cognitive work. Patients with MDD demonstrate limited ability to modulate medial prefrontal regions in the face of increasing cognitive demand, implying that the normal cortical-limbic control systems are dysfunctional, resulting in poorer performance on effortful cognitive tasks in depression (P. O. Harvey et al., 2005). In processing information from their environment, patients with MDD demonstrate a process of automatic distortion of events in a way that emphasizes their negative aspects. These distortions can take the form of exaggerating the significance of failure, biased attention toward words that describe themselves negatively, and an exaggerated sensitivity to mistakes and negative feedback (Murphy, Michael, Robbins, & Sahakian, 2003; Wenzlaff & Grozier, 1988). The neurobiology underlying this enhanced processing of negative stimuli has been explored with fMRI and electroencephalographic monitoring (EEG). With EEG, so-called error-related negativity

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is detected via an electrical signal present in the midline frontocentral scalp electrodes 50 to 100 milliseconds after an incorrect response in binary choice tasks. This location is consistent with the dorsal regions of the ACC, which are thought to serve in part as error detectors. Patients with MDD demonstrate greater amplitude of the error-related negativity compared with controls (Chiu & Deldin, 2007).

SLEEP Sleep disruption is a very common, though nonspecific, symptom of MDD. Alteration of normal sleep can occur as part of the prodrome of a depressive episode, or as a symptom of MDD, and is the most common residual symptom after recovery from depression. In depression, the amount of sleep may be excessive (hypersomnia), inhibited (insomnia), or a combination of both. Sleep onset and offset is regulated by hypothalamic systems, influenced by circadian inputs and the homeostatic sleep drive. The structure of sleep is regulated in large part by monoamines, acetylcholine, and GABA signaling, which are also implicated in the pathophysiology of MDD. The structure of sleep is disrupted in many depressed patients, with prolonged sleep initiation times, more fragmentation of sleep, smaller percentage of total sleep time spent in stage III and IV deep sleep (slow-wave sleep), and greater percentage of time spent in REM sleep (Peterson & Benca, 2006). Increased REM sleep is specifically associated with unipolar, as opposed to bipolar depression (Rao et al., 2002), and increased REM density versus controls is present in the first-degree relatives of patients with MDD (Giles, Kupfer, Rush, & Roffwarg, 1998). Because sleep disturbance is so common in MDD, understanding the nature of disrupted sleep in depression may provide insight into the pathophysiology of the illness. Both REM and non-REM sleep are disrupted in patients with MDD versus healthy controls. Non-REM sleep is a state of reduced cortical and thalamic activity compared to the waking and REM sleep states. Depressed subjects show less of a decline in metabolic activity in the thalamus and in frontal and parietal cortical regions during the transition from waking to non-REM sleep than control subjects using PET imaging (Germain, Nofzinger, Kupfer, & Buysse, 2004) Although the function of non-REM sleep is unknown, it likely subserves a general neural restorative process, and may be important for consolidation of memories. Another PET study, this one examining REM sleep, found heightened metabolic activity in brain stem, limbic, and cortical regions in depressed patients versus controls, perhaps reflecting greater intensity of affective response to stimuli, such as dreams (Nofzinger et al., 2004). REM sleep

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occurs during periods of cholinergic activation, which is usually inhibited by serotonergic projections to the cholinergic nuclei in the pons. Therefore, an alteration in the balance between monoamine and cholinergic activity, such as decreased serotonergic transmission or increased cholinergic activity, could decrease the amount of slow-wave sleep and increase REM sleep. Most antidepressants (but not nefazodone or bupropion) suppress REM sleep (Winokur et al., 2001). Increased catecholamine transmission or increased postsynaptic 5HT1a receptor activity or may be responsible for this effect of medication (Seifritz, 2001). These alterations of sleep are consistent with the concept of depression as being a state of overarousal, and may underlie the subjective complaints of insomnia and nonrestorative sleep reported by depressed patients. Nocturnal levels of IL-6 and soluble intercellular adhesion molecule (sICAM, an endothelial activation marker) are elevated in depressed patients versus controls, and these increases are associated with difficulty initiating sleep (Motivala, Sarfatti, Olmos, & Irwin, 2005). Insufficient sleep may impact the stability networks involved in cognitive functions, particularly cortical regions involved in sustained attention, such as the medial and lateral prefrontal cortices.

SUMMARY Despite the tremendous public health importance of MDD, our knowledge of its pathophysiology remains remarkably limited. The recent additions of powerful neuroimaging and genetics techniques to the traditional biochemical approaches offer the promise of new advances in our ability to determine and manipulate the biological forces that shape MDD and its response to treatment. The greatest advances in the neuroscience of MDD are likely to occur through the thoughtful combination of techniques in carefully selected patient populations, attempting to control some of the heterogeneity captured by the current diagnostic nomenclature. There are several recent examples of the kind of work that can be done through the combination of investigative methods. One of the most intriguing is the emerging field of genetic neuroimaging. This paradigm uses brainimaging techniques to evaluate how genetic polymorphisms affect neuronal processing in response to stimuli. Perhaps the most impressive examples of this kind of study have explored the effects of the SERT promoter polymorphism in healthy individuals exposed to fearful or angry facial expressions using fMRI. Individuals free of psychiatric illness, but who carry the S form of the gene, demonstrate greater amygdala reactivity to these threatening stimuli than do healthy controls with the L form (Hariri

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References 1081

et al., 2005). This finding was subsequently extended to demonstrate that elevated amygdala reactivity in individuals with the S form was associated with impaired functional connectivity between the amygdala and the anterior cingulate regions that regulate amygdala reactivity, which may represent a risk factor for MDD in the face of stress (Pezawas et al., 2005). Another study in this paradigm using PET imaging found that subjects in remission from major depression who carry at least one copy of the LA allele for the SERT promoter polymorphism experienced greater worsening of depressive symptoms, and increased metabolism in the amygdala, subgenual cingulate cortex, and hippocampus, than did subjects with two copies of the S allele when undergoing tryptophan depletion (Neumeister et al., 2006). A similar study has been used linking the BDNF gene to findings on structural MRI. At coding position 66 in the BDNF gene, a single nucleotide polymorphism produces a switch in the amino acid sequence from a valine to a methionine. This switch has been inconsistently found to be associated with neuroticism and poorer cognitive performance (Sen et al., 2003). Carriers of the methionine allele for BDNF were found to have smaller hippocampal volumes than individuals homozygous for the Val allele (Frodl et al., 2007). Because smaller hippocampal volumes are associated with the development of MDD, it may be that the Met-BDNF allele conveys a risk factor for MDD via its effects on hippocampal development or plasticity. Another study explored hedonic responsiveness to d-amphetamine administration in depressed subjects, using fMRI to assess changes in neuronal processing after receipt of the stimulant. Untreated severely depressed subjects (but not mildly depressed subjects) demonstrated reduced reward experience to stimuli prior to receiving the stimulant, with subsequent hyper-responsivity to reward after ingesting the stimulant. This increase in responsivity was associated with greater activity in the VLPFC, OFC, and basal ganglia by fMRI than healthy controls (Tremblay et al., 2005). This study helps delineate a specific reward circuit deficit that may be present in severely ill, anhedonic depressed patients, versus nonseverely ill, depressed patients and healthy controls. The few studies cited may point the way to developing a more specific and biologically based diagnostic nomenclature for the catch-all diagnosis of MDD. The outcome of this work will be improved treatment, and perhaps prevention, of depressive syndromes. REFERENCES Abdolmaleky, H. M., Smith, C. L., Faraone, S. V., Shafa, R., Stone, W., Glatt, S. J., et al. (2004). Methylomics in psychiatry: Modulation of gene-environment interactions may be through DNA methylation.

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American Journal of Medical Genetics, Pt. B: Neuropsychiatric Genetics, 127, 51–59. Altshuler, L. L., Bauer, M., Frye, M., Gitlin, M. J., Mintz, J., Szuba, M. P., et al. (2001). Does thyroid supplementation accelerate tricyclic antidepressant response? A review and meta-analysis of the literature. American Journal of Psychiatry, 158, 1617–1622. Amsterdam, J. D., Maislin, G., Skolnick, B., Berwish, N., & Winokur, A. (1989). Multiple hormone responses to clonidine administration in depressed patients and healthy volunteers. Biological Psychiatry, 26, 265–278. Anand, A., Charney, D. S., Delgado, P. L., McDougle, C. J., Heninger, G. R., & Price, L. H. (1994). Neuroendocrine and behavioral responses to intravenous m-chlorophenylpiperazine (mCPP) in depressed patients and healthy comparison subjects. American Journal of Psychiatry, 151, 1626–1630. Anda, R., Williamson, D., Jones, D., Macera, C., Eaker, E., Glassman, A., et al. (1993). Depressed affect, hopelessness, and the risk of ischemic heart disease in a cohort of US adults. Epidemiology, 4, 285–294. Appelhof, B. C., Brouwer, J. P., van Dyck, R., Fliers, E., Hoogendijk, W. J., Huyser, J., et al. (2004). Triiodothyronine addition to paroxetine in the treatment of major depressive disorder. Journal of Clinical Endocrinology and Metabolism, 89, 6271–6276. Arana, G. W., Baldessarini, R. J., & Ornsteen, M. (1985). The dexamethasone suppression test for diagnosis and prognosis in psychiatry. Archives of General Psychiatry, 42, 1193–1204. Arango, V., Ernsberger, P., Sved, A. F., & Mann, J. (1993). Quantitative autoradiography of ·1- and ·2-adrenergic receptors in the cerebral cortex of controls and suicide victims. Brain Research, 630, 271–282. Arato, M., Banki, C. M., Bissette, G., & Nemeroff, C. B. (1989). Elevated CSF CRF in suicide victims. Biological Psychiatry, 25, 355–359. Aronson, R., Offman, H. J., Joffe, R. T., & Naylor, C. D. (1996). Triiodothyronine augmentation in the treatment of refractory depression: A meta-analysis. Archives of General Psychiatry, 53, 842–848. Asberg, M., Traskman, L., & Thoren, P. (1976). 5-HIAA in the cerebrospinal fluid: A biochemical suicide predictor? Archives of General Psychiatry, 33, 1193–1197. Attar-Levy, D., Martinot, J. L., Blin, J., Dao-Castellana, M. H., Crouzel, C., Mazoyer, B., et al. (1999). The cortical serotonin2 receptors studied with positron-emission tomography. Biological Psychiatry, 45, 180–186. Aubry, J.-M., Gervasoni, N., Osiek, C., Perret, G., Rossier, M. F., Bertschy, G., et al. (2007). The DEX/CRH neuroendocrine test and the prediction of depressive relapse in remitted depressed outpatients. A Journal of Psychiatric Research, 41, 290–294. Baghai, T. C., di Michele, F., Schule, C., Eser, D., Zwanzger, P., Pasini, A., et al. (2005). Plasma concentrations of neuroactive steroids before and after electroconvulsive therapy in major depression. Neuropsychopharmacology, 30, 1181–1186. Bajbouj, M., Lisanby, S. H., Lang, U., Danker-Hopfe, H., Heuser, I., & Neu, P. (2006). Evidence for impaired cortical inhibition in patients with unipolar major depression. Biological Psychiatry, 59, 395–400. Banarjee, S. P., Kung, L. S., Riggi, S. J., & Chanda, S. K. (1977, August 4). Development of B adrenergic receptor subsensitivity by antidepressants. Nature, 268, 455–456. Banki, C. M., Bissette, G., Arato, M., & Nemeroff, C. B. (1988). Elevation of immunoreactive CSF TRH in depressed patients. American Journal of Psychiatry, 145, 1526–1531. Banki, C. M., Bissette, G., Arato, M., O’Connor, L., & Nemeroff, C. B. (1987). CSF corticotropin-releasing factor-like immunoreactivity in depression and schizophrenia. American Journal of Psychiatry, 144, 873–877. Belelli, D., & Lambert, J. J. (2005). Neurosteroids: Endogenous regulators of the GABA(A) receptor. Nature Reviews Neuroscience, 6, 565–575.

8/17/09 5:30:14 PM

1082

Depression

Bench, C. J., Friston, K., Brown, R., Frackowiak, R., & Dolan, R. J. (1993). Regional cerebral blood flow in depression measured by positron emission tomography: The relationship with clinical dimensions. Psychological Medicine, 23, 579–590. Benkelfat, C., Ellenbogen, M. A., Dean, P., Palmour, R. M., & Young, S. N. (1994). Mood-lowering effect of tryptophan depletion: Enhanced susceptibility in young men at genetic risk for major affective disorders. Archives of General Psychiatry, 51, 687–697. Berman, R. M., Narasimhan, M., Miller, H. L., Anand, A., Cappiello, A., Oren, D. A., et al. (1999). Transient depressive relapse induced by catecholamine depletion. Archives of General Psychiatry, 56, 395–403.

effects of tolerance and implications for therapeutics. Biological Psychiatry, 41, 264–272. Cameron, O. G., Abelson, J. L., & Young, E. A. (2004). Anxious and depressive disorders and their comorbidity: Effect on central nervous system noradrenergic function. Biological Psychiatry, 56, 875–883. Campos-Barros, A., Meinhold, H., Stula, M., Muller, F., Kohler, R., Eravci, M., et al. (1994). The influence of desipramine on thyroid hormone metabolism in rat brain. Journal of Pharmacology and Experimental Therapeutics, 268, 1143–1152. Carroll, B. J., Martin, F. I., & Davies, B. (1968). Pituitary-adrenal function in depression. Lancet, 1(7556), 1373–1374.

Berton, O., McClung, C. A., Dileone, R. J., Krishnan, V., Renthal, W., Russo, S. J., et al. (2006, February 10). Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress. Science, 311, 864–868.

Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., Harrington, H., et al. (2003, July 18). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 301, 386–389.

Beskow, J., Gottfries, C. G., Roos, B. E., & Winblad, B. (1976). Determination of monoamine and monoamine metabolites in the human brain: Post mortem studies in a group of suicides and in a control group. Acta Psychiatrica Scandinavica, 53, 7–20.

Castren, E. (2005). Is mood chemistry? Nature Reviews Neuroscience, 6, 241–246.

Bhagwagar, Z., Rabiner, E. A., Sargent, P. A., Grasby, P. M., & Cowen, P. J. (2004). Persistent reduction in brain serotonin1A receptor binding in recovered depressed men measured by positron emission tomography with. Molecular Psychiatry, 9, 386–392. Bissette, G., Widerlov, E., Walleus, H., Karlsson, I., Eklund, K., Forsman, A., et al. (1986). Alterations in cerebrospinal fluid concentrations of somatostatinlike immunoreactivity in neuropsychiatric disorders. Archives of General Psychiatry, 43, 1148–1151. Biver, F., Wikler, D., Lotstra, F., Damhaut, P., Goldman, S., & Mendlewicz, J. (1997). Serotonin 5-HT2 receptor imaging in major depression: Focal changes in orbito-insular cortex. British Journal of Psychiatry, 171, 444–448. Bjork, J. M., Moeller, F. G., Kramer, G. L., Kram, M., Suris, A., Rush, A. J., et al. (2001). Plasma GABA levels correlate with aggressiveness in relatives of patients with unipolar depressive disorder. Psychiatry Research, 101, 131–136. Blier, P., deMontigny, C., & Chaput, Y. (1990). A role for the serotonin system in the mechanism of acion of antidepressant treatment: Preclinical evidence. Journal of Clinical Psychiatry, 51, 14–21. Bligh-Glover, W., Kolli, T. N., Shapiro-Kulnane, L., Dilley, G. E., Friedman, L., Balraj, E., et al. (2000). The serotonin transporter in the midbrain of suicide victims with major depression. Biological Psychiatry, 47, 1015–1024. Bloch, R. G., Dooneief, A. S., Buchberg, A. S., & Spellman, S. (1954). The clinical effect of isoniazid and iproniazid in the treatment of pulmonary tuberculosis. Annals of Internal Medicine, 40, 881–900. Botterton, K., Raichle, M. E., Drevets, W. C., Heath, A., & Todd, R. D. (2002). Volumetric reduction in left subgenual prefrontal cortex in early onset depression. Biological Psychiatry, 51, 342–344. Bowden, C., Cheetham, S. C., Lowther, S., Katona, C. L., Crompton, M. R., & Horton, R. W. (1997). Reduced dopamine turnover in the basal ganglia of depressed suicides. Brain Research, 769, 135–140. Bowden, C., Theodorou, A. E., Cheetham, S. C., Lowther, S., Katona, C. L., Crompton, M. R., et al. (1997). Dopamine D1 and D2 receptor binding sites in brain samples from depressed suicides and controls. Brain Research, 752, 227–233. Bremner, J., Vythilingam, M., Ng, C. K., Vermetten, E., Nazeer, A., Oren, D. A., et al. (2003). Regional brain metabolic correlates of alphamethylparatyrosine-induced depressive symptoms: Implications for the neural circuitry of depression. Journal of the American Medical Association, 289, 3125–3134. Bunney, W. E., & Davis, J. M. (1965). Norepinephrine in depressive reactions: A review. Archives of General Psychiatry, 13, 483–493. Callahan, A. M., Frye, M. A., Marangell, L. B., George, M. S., Ketter, T. A., L’Herrou, T., et al. (1997). Comparative antidepressant effects of intravenous and intrathecal thyrotropin-releasing hormone: Confounding

c55.indd Sec9:1082

Chiu, P. H., & Deldin, P. J. (2007). Neural evidence for enhanced error detection in major depressive disorder. American Journal of Psychiatry, 164, 608–616. Cooper-Kazaz, R., Apter, J. T., Cohen, R., Karagichev, L., MuhammedMoussa, S., Grupper, D., et al. (2007). Combined treatment with sertraline and liothyronine in major depression: A randomized, double-blind, placebo-controlled trial. Archives of General Psychiatry, 64, 679–688. Coplan, J. D., Altemus, M., Mathew, S. J., Smith, E. L. P., Scharf, B., Coplan, P. M., et al. (2005). Synchronized maternal-infant elevations of primate CSF CRF concentrations in response to variable foraging demand. CNS Spectrums, 10, 530–536. Coplan, J. D., Andrews, M. W., Rosenblum, L. A., Owens, M. J., Friedman, S., Gorman, J. M., et al. (1996). Persistent elevations of cerebrospinal fluid concentrations of corticotropin-releasing factor in adult nonhuman primates exposed to early-life stressors: Implications for the pathophysiology of mood and anxiety disorders. Proceedings of the National Academy of Sciences, USA, 93, 1619–1623. Coplan, J. D., Wolk, S. I., Goetz, R. R., Ryan, N. D., Dahl, R. E., Mann, J. J., et al. (2000). Nocturnal growth hormone secretion studies in adolescents with or without major depression re-examined: Integration of adult clinical follow-up data. Biological Psychiatry, 47, 594–604. Coppen, A. (1967). The biochemistry of affective disorders. British Journal of Psychiatry, 113, 1237–1264. Crow, T. J., Cross, A. J., Cooper, S. J., Deakin, J. F., Ferrier, I. N., Johnson, J. A., et al. (1984). Neurotransmitter receptors and monoamine metabolites in the brains of patients with Alzheimer-type dementia and depression, and suicides. Neuropharmacology, 23, 1561–1569. Dantzer, R. (2004). Cytokine-induced sickness behaviour: A neuroimmune response to activation of innate immunity. European Journal of Pharmacology, 500, 399–411. Davidson, R. J., Pizzagalli, D., Nitschke, J. B., & Putnam, K. M. (2002). Depression: Perspectives from affective neuroscience. Annual Review of Psychology, 53, 545–574. De Bellis, M. D., Gold, P. W., Geracioti, T. D., Jr., Listwak, S. J., & Kling, M. A. (1993). Association of fluoxetine treatment with reductions in CSF concentrations of corticotropin-releasing hormone and arginine vasopressin in patients with major depression. American Journal of Psychiatry, 150, 656–657. Del-Ben, C. M., Deakin, J., McKie, S., Delvai, N. A., Williams, S. R., Elliott, R., et al. (2005). The effect of citalopram pretreatment on neuronal responses to neuropsychological tasks in normal volunteers: An fMRI study. Neuropsychopharmacology, 30, 1724–1734. de Paermentier, F., Cheetham, S. C., Crompton, M., Katona, C. L., & Horton, R. W. (1990). Brain -adrenoceptor binding sites in antidepressant-free depressed suicide victims. Brain Research, 525, 71–77. Devanand, D. P., Bowers, M. B., Hoffman, F. J., & Nelson, J. C. (1985). Elevated plasma homovanillic acid levels in depressed females with melancholia and psychosis. Psychiatry Research, 15, 1–4.

8/17/09 5:30:14 PM

References 1083 D’Haenen, H. A., & Bossuyt, A. (1994). Dopamine D2 receptors in depression measured with single photon emission computed tomography. Biological Psychiatry, 35, 128–132. Dolan, R. J., Bench, C. J., Brown, R. G., Scott, L. C., & Frackowiak, R. S. (1994). Neuropsychological dysfunction in depression: The relationship to regional cerebral blood flow. Psychological Medicine, 24, 849–857. Drevets, W. C. (2000). Neuroimaging studies of mood disorders. Biological Psychiatry, 48, 813–829. Drevets, W. C., Price, J. L., Simpson, J. R., Todd, R. D., Reich, T., Vannier, M., et al. (1997, April 24). Subgenual prefrontal cortex abnormalities in mood disorders. Nature, 386, 824–827. Duman, R. S., Heninger, G. R., & Nestler, E. J. (1997). A molecular and cellular theory of depression. Archives of General Psychiatry, 54, 597–606. Duman, R. S., & Nestler, E. J. (1995). Signal transduction pathways for catecholamine receptors. In F. E. Bloom & D. J. Kupfer (Eds.), Psychopharmacology: The fourth generation of progress (pp. 303– 320). New York: Raven Press.

Germain, A., Nofzinger, E. A., Kupfer, D. J., & Buysse, D. J. (2004). Neurobiology of non-REM sleep in depression: Further evidence for hypofrontality and thalamic dysregulation. American Journal of Psychiatry, 161, 1856–1863. Gershon, E. S., Hamovit, J., Guroff, J. J., Dibble, E., Leckman, J. F., Sceery, W., et al. (1982). A family study of schizoaffective bipolar, I, bipolar II, unipolar, and normal control probands. Archives of General Psychiatry, 39, 1157–1167. Gibbons, J. L., & McHugh, P. R. (1962). Plasma cortisol in depressive illness. Journal of Psychiatric Research, 1, 162–171. Gibbons, R. D., & Davis, J. M. (1986). Consistent evidence for a biological subtype of depression characterized by low CSF monoamine levels. Acta Psychiatrica Scandinavica, 74, 8–12.

Dunlop, B. W., & Nemeroff, C. B. (2007). The role of dopamine in the pathophysiology of depression. Archives of General Psychiatry, 64, 327–337.

Gilbertson, M. W., Shenton, M. E., Ciszewski, A., Kasai, K., Lasko, N. B., Orr, S. P., et al. (2002). Smaller hippocampal volume predicts pathologic vulnerability to psychological trauma. Nature Neuroscience, 5, 1242–1247.

Ebert, D., Feistel, H., Loew, T., & Pirner, A. (1996). Dopamine and depression: Striatal dopamine D2 receptor SPECT before and after antidepressant therapy. Psychopharmacology, 126, 91–94.

Giles, D. E., Kupfer, D. J., Rush, A., & Roffwarg, H. P. (1998). Controlled comparison of electrophysiological sleep in families of probands with unipolar depression. American Journal of Psychiatry, 155, 192–199.

Elliott, R., Rubinsztein, J. S., Sahakian, B. J., & Dolan, R. J. (2002). The neural basis of mood-congruent processing biases in depression. Archives of General Psychiatry, 59, 597–604.

Glowinski, J., & Axelrod, J. (1966). Effects of drugs on the disposition of H-3-norepinephrine in the rat brain. Pharmacological Reviews, 18, 775–785.

Ellis, P. M., & Salmond, C. (1994). Is platelet imipramine binding reduced in depression? A meta-analysis. Biological Psychiatry, 36, 292–299.

Glowinski, J., Snyder, S., & Axelrod, J. (1966). Subcellular localization of H3-norepinephrine in the rat brain and the effect of drugs. Journal of Pharmacology and Experimental Therapeutics, 152, 282–292.

Encinas, J. M., Vaahtokari, A., & Enikolopov, G. (2006). Fluoxetine targets early progenitor cells in the adult brain. Proceedings of the National Academy of Sciences, USA, 103, 8233–8238. Extein, I., Pottash, A. L., & Gold, M. S. (1981). The thyrotropin-releasing hormone test in the diagnosis of unipolar depression. Psychiatry Research, 5, 311–316. Extein, I., Tallman, J., Smith, C. C., & Goodwin, F. K. (1979). Changes in lymphocyte beta-adrenergic receptors in depression and mania. Psychiatry Research, 1, 191–197. Fossati, P., Hevenor, S. J., Graham, S. J., Grady, C., Keightley, M. L., Craik, F., et al. (2003). In search of the emotional self: An fMRI study using positive and negative emotional words. American Journal of Psychiatry, 160, 1938–1945. France, R. D., Urban, B., Krishnan, K. R., Bissett, G., Banki, C. M., Nemeroff, C., et al. (1988). CSF corticotropin-releasing factor-like immunoactivity in chronic pain patients with and without major depression. Biological Psychiatry, 23, 86–88. Frodl, T., Schule, C., Schmitt, G., Born, C., Baghai, T., Zill, P., et al. (2007). Association of the brain-derived neurotrophic factor Val66Met polymorphism with reduced hippocampal volumes in major depression. Archives of General Psychiatry, 64, 410–416. Fu, C. H., Williams, S. C., Cleare, A. J., Brammer, M. J., Walsh, N. D., Kim, J., et al. (2004). Attenuation of the neural response to sad faces in major depression by antidepressant treatment: A prospective, eventrelated functional magnetic resonance imaging study. Archives of General Psychiatry, 61, 877–889. Fujita, M., Charney, D. S., & Innis, R. B. (2000). Imaging serotonergic neurotransmission in depression: Hippocampal pathophysiology may mirror global brain alterations. Biological Psychiatry, 48, 801–812. Garcia-Sevilla, J., Padro, D., Giralt, T., Guimon, J., & Areso, P. (1990). 2-Adrenoceptor-mediated inhibition of platelet adenylate cyclase and induction of aggregation in major depression: Effect of long-term cyclic antidepressant drug treatment. Archives of General Psychiatry, 47, 125–132.

c55.indd Sec9:1083

Garcia-Sevilla, J., Ulibarri, I., Ugedo, L., & Gutierrez, M. (1987). 2-Adrenoceptor-mediated inhibition of platelet adenylate cyclase activity in heroin addicts in abstinence. Psychopharmacology, 92, 320–323.

Gold, B. I., Bowers, M. B., Roth, R. H., & Sweeney, D. W. (1980). GABA levels in CSF of patients with psychiatric disorders. American Journal of Psychiatry, 137, 362–364. Gold, M. S., Pottash, A. C., & Extein, I. (1982). Symptomless autoimmune thyroiditis in depression. Psychiatry Research, 6, 261–269. Gurguis, G. N., Vo, S. P., Griffith, J. M., & Rush, A. (1999). Platelet alphasub(2A)-adrenoceptor function in major depression: G-sub(i) coupling, effects of imipramine and relationship to treatment outcome. Psychiatry Research, 89, 73–95. Guze, B. H., & Barrio, J. C. (1991). The etiology of depression in Parkinson’s disease patients. Psychosomatics, 32, 390–394. Hariri, A. R., Drabant, E. M., Munoz, K. E., Kolachana, B. S., Mattay, V. S., Egan, M. F., et al. (2005). A susceptibility gene for affective disorders and the response of the human amygdala. Archives of General Psychiatry, 62, 146–152. Harmer, C. J., Shelley, N. C., Cowen, P. J., & Goodwin, G. M. (2004). Increased positive versus negative affective perception and memory in healthy volunteers following selective serotonin and norepinephrine reuptake inhibition. American Journal of Psychiatry, 161, 1256–1263. Harvey, M., Shink, E., Tremblay, M., Gagne, B., Raymond, C., Labbe, M., et al. (2004). Support for the involvement of the TPH2 gene in affective disorders. Molecular Psychiatry, 9, 980–981. Harvey, P. O., Fossati, P., Pochon, J. B., Levy, R., Le Bastard, G., Lehericy, S., et al. (2005). Brain resources and cognitive effort in depression: A fMRI study using the n-back task. NeuroImage, 26, 860–869. Hasler, G., Bonwetsch, R., Giovacchini, G., Toczek, M. T., Bagic, A., Luckenbaugh, D. A., et al. (2007). 5-HT1A receptor binding in temporal lobe epilepsy patients with and without major depression. Biological Psychiatry, 62, 1258–1264. Hasler, G., Drevets, W. C., Manji, H. K., & Charney, D. S. (2004). Discovering endophenotypes for major depression. Neuropsychopharmacology, 29, 1765–1781.

8/17/09 5:30:15 PM

1084

Depression

Hasler, G., Neumeister, A., van der Teen, T., Tumonis, E., Bain, J., Shen, J., et al. (2005). Normal prefrontal gamma-aminobutryric acid levels in remitted depressed subjects determined by proton magnetic resonance spectroscopy. Biological Psychiatry, 58, 969–973. Hauger, R. L., & Dautzenberg, F. M. (2000). Regulation of the stress response by corticotropin-releasing factor receptors. In P. M. Conn & M. E. Freedman (Eds.), Neuroendocrinology in physiology and medicine (pp. 261–286). Totowa, NJ: Humana Press. Healy, D., Carney, P., O’Halloran, A., & Leonard, B. (1985). Peripheral adrenoceptors and serotonin receptors in depression: Changes associated with response to treatment with trazodone or amitriptyline. Journal of Affective Disorders, 9, 285–296. Heils, A., Teufel, A., Petri, S., Stober, G., Riederer, P., Bengel, D., et al. (1996). Allelic variation of human serotonin transporter gene expression. Journal of Neurochemistry, 66, 2621–2624. Heim, C., Newport, D. J., Bonsall, R., Miller, A. H., & Nemeroff, C. B. (2001). Altered pituitary-adrenal axis responses to provocative challenge tests in adult survivors of childhood abuse. American Journal of Psychiatry, 158, 575–581. Heinz, A., Jones, D. W., Mazzanti, C., Goldman, D., Ragan, P., Hommer, D., et al. (2000). A relationship between serotonin transporter genotype and in vivo protein expression and alcohol neurotoxocity. Biological Psychiatry, 47, 643–649. Hendry, S. H., Schwark, H. D., Jones, E. G., & Yan, J. (1987). Numbers and proportions of GABA-immunoreactive neurons in different areas of monkey cerebral cortex. Journal of Neuroscience, 7, 1503–1509. Heninger, G., Delgado, P., & Charney, D. (1996). The revised monoamine theory of depression: A modulatory role for monoamines, based on new findings from monoamine depletion experiments in humans. Pharmacopsychiatry, 29, 2–11. Heuser, I., Bissette, G., Dettling, M., Schweiger, U., Gotthardt, U., Schmider, J., et al. (1998). Cerebrospinal fluid concentrations of corticotropinreleasing hormone, vasopressin and somatostatin in depressed patients and healthy controls: Response to amitriptyline treatment. Depression and Anxiety, 8, 71–79. Heuser, I., Yassouridis, A., & Holsboer, F. (1994). The combined dexamethasone/CRH test: A refined laboratory test for psychiatric disorders. Journal of Psychiatric Research, 28, 341–356. Hindal, J. T., & Kaplan, M. M. (1988). Inhibition of thyroxine 5'-deiodination type II in cultured human placental cells by cortisol, insulin, 3', 5'-cyclic adenosine monophosphate and butyrate. Metabolism, 37, 664–668. Holsboer, F., von Bardeleben, U., Wiedemann, K., Muller, O. A., & Stalla, G. K. (1987). Serial assessment of corticotropin-releasing hormone response after dexamethasone in depression: Implications for pathophysiology of DST nonsuppression. Biological Psychiatry, 22, 228–234. Hu, X. Z., Zhu, G. S., Lipsky, R. H., & Goldman, D. (2004). HTTLPR allele expression is codominant, correlating with gene effects on fMRI and SPECT imaging intermediate phenotypes, and behavior. Biological Psychiatry, 55, S191. Janicak, P. G., Davis, J. M., Preskorn, S. H., & Ayd, F. J. (1997). Principles and practice of psychopharmacotherapy (2nd ed.). Philadelphia: Lippincott, Williams, & Wilkins. Joffe, R. T., & Marriott, M. (2000). Thyroid hormone levels and recurrence of major depression. American Journal of Psychiatry, 157, 1689–1691. Jonas, B. S., & Mussolino, M. E. (2000). Symptoms of depression as a prospective risk factor for stroke. Psychosomatic Medicine, 62, 463–471. Jones, M. E., & Cockrum, P. C. (2000). A critical review of published economic modeling studies in depression. Pharmacoeconomics, 17, 555–583. Kapur, S., & Mann, J. J. (1992). Role of the dopaminergic system in depression. Biological Psychiatry, 32, 1–17. Karege, F., Bondolfi, G., Gervasoni, N., Schwald, M., Aubry, J. M., & Bertschy, G. (2005). Low brain-derived neurotrophic factor (BDNF)

c55.indd Sec9:1084

levels in serum of depressed patients probably results from lowered platelet BDNF release unrelated to platelet reactivity. Biological Psychiatry, 57, 1068–1772. Karl, A., Schaefer, M., Malta, L. S., Dorfel, D., Rohleder, N., & Werner, A. (2006). A meta-analysis of structural brain abnormalities in PTSD. Neuroscience and Biobehavioral Reviews, 30, 1004–1031. Kastin, A. J., Ehrensing, R. H., Schalch, D. S., & Anderson, M. S. (1972). Improvement in mental depression with decreased thyrotropin response after administration of thyrotropin-releasing hormone. Lancet, 2, 740–742. Kathol, R. G., Jaeckle, R. S., Lopez, J. F., & Meller, W. H. (1989). Consistent reduction of ACTH responses to stimulation with CRH, vasopressin and hypoglycaemia in patients with major depression. British Journal of Psychiatry, 155, 468–478. Kavoussi, R. J., Hauger, R. L., & Coccaro, E. F. (1999). Prolactin response to d-fenfluramine in major depression before and after treatment with serotonin reuptake inhibitors. Biological Psychiatry, 45, 295–299. Kendler, K. S., Gardner, C. O., & Prescott, C. A. (2002). Toward a comprehensive developmental model for major depression in women. American Journal of Psychiatry, 159, 1133–1145. Kendler, K. S., Kuhn, J. W., Vittum, J., Prescott, C. A., & Riley, B. (2005). The interaction of stressful life events and a serotonin transporter polymorphism in the prediction of episodes of major depression: A replication. Archives of General Psychiatry, 62, 529–535. Kendler, K. S., Neale, M. C., Kessler, R. C., Heath, A. C., & Eaves, L. J. (1993). A longitudinal twin study of personality and major depression in women. Archives of General Psychiatry, 50, 853–862. Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., et al. (2003). The epidemiology of major depressive disorder: Results from the National Comorbidity Survey Replication (NCS-R). Journal of the American Medical Association, 289, 3095–3105. Ketter, T. A., George, M. S., Kimbrell, T. A., Benson, B. A., & Post, R. M. (1996). Functional brain imaging, limbic function, and affective disorders. Neuroscientist, 2, 55–65. Khisti, R. T., Chopde, C. T., & Jain, S. P. (2000). Antidepressant-like effect of the neurosteroid 3alpha-hydroxy-5alpha-pregnan-20-one in mice forced swim test. Pharmacology, Biochemistry and Behavior, 67, 137–143. Kirkegaard, C., Faber, J., Hummer, L., & Rogowski, P. (1979). Increased levels of TRH in cerebrospinal fluid from patients with endogenous depression. Psychoneuroendocrinology, 4, 227–235. Kirschbaum, C., Pirke, K. M., & Hellhammer, D. H. (1993). The ‘Trier Social Stress Test’: A tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology, 28, 76–81. Kiss, J. Z., Martos, J., & Palkovits, M. (1991). Hypothalamic paraventricular nucleus: A quantitative analysis of cytoarchitectonic subdivisions in the rat. Journal of Comparative Neurology, 313, 563–573. Klimek, V., Rajkowska, G., Luker, S. N., Dilley, G., Meltzer, H. Y., Overholser, J. C., et al. (1999). Brain noradrenergic receptors in major depression and schizophrenia. Neuropsychopharmacology, 21, 69–81. Klimek, V., Schenck, J. E., Han, H., Stockmeier, C. A., & Ordway, G. A. (2002). Dopaminergic abnormalities in amygdaloid nuclei in major depression: A postmortem study. Biological Psychiatry, 52, 740–748. Klimke, A., Larisch, R., Janz, A., Vosberg, H., Muller-Gartner, H. W., & Gaebel, W. (1999). Dopamine D2 receptor binding before and after treatment of major depression measured by. Psychiatry Research, 90, 91–101. Konig, F., Hauger, B., von Hippel, C., Wolfersdorf, M., & Kaschka, W. P. (2000). Effect of paroxetine on thyroid hormone levels in severely depressed patients. Neuropsychobiology, 42, 135–138. Krishnan, K. R., Doraiswamy, P. M., Lurie, S. N., Figiel, G. S., Husain, M. M., Boyko, O. B., et al. (1991). Pituitary size in depression. Journal of Clinical Endocrinology and Metabolism, 72, 256–259.

8/17/09 5:30:15 PM

References 1085 Krishnan, K. R., Hays, J. C., & Blazer, D. G. (1997). MRI-defined vascular depression. American Journal of Psychiatry, 154, 497–501.

polymorphism (5-HTTLPR) and prefrontal cortical binding in major depression and suicide. Archives of General Psychiatry, 57, 729–738.

Lambert, G., Johansson, M., Agren, H., & Friberg, P. (2000). Reduced brain norepinephrine and dopamine release in treatment-refractory depressive illness: Evidence in support of the catecholamine hypothesis of mood disorders. Archives of General Psychiatry, 57, 787–793.

Marangell, L. B., George, M. S., Callahan, A. M., Ketter, T. A., Pazzaglia, P. J., L’Herrou, T. A., et al. (1997). Effects of intrathecal thyrotropin-releasing hormone (protirelin) in refractory depressed patients. Archives of General Psychiatry, 54, 214–222.

Lapin, I. P., & Oxenkrug, G. F. (1969). Intensification of central serotonergic process as a possible determinant of thymoleptic effect. Lancet, 1(7586), 132–136.

Marinelli, M., & Piazza, P. V. (2002). Interaction between glucocorticoid hormones, stress and psychostimulant drugs. European Journal of Neuroscience, 16, 387–394.

Levinson, D. F. (2006). The genetics of depression: A review. Biological Psychiatry, 60, 84–92.

Mattsubara, S., Arora, R., & Meltzer, H. (1991). Serotonergic measures in suicide brain: 5HT1a binding sites in frontal cortex of suicide victims. Journal of Neural Transmission, 85, 181–194.

Lewinsohn, P. M., Allen, N. B., Seeley, J. R., & Gotlib, I. H. (1999). First onset versus recurrence of depression: Differential processes of psychosocial risk. Journal of Abnormal Psychology, 108, 483–489. Lewis, D. A., & Smith, R. E. (1983). Steroid-induced psychiatric syndromes. Journal of Affective Disorders, 5, 319–332. Lifschytz, T., Segman, R., Shalom, G., Lerer, B., Gur, E., Golzer, T., et al. (2006). Basic mechanisms of augmentation of antidepressant effects with thyroid hormone. Current Drug Targets, 7, 203–210. Linnoila, M., & Virkkunen, M. (1992). Aggression, suicidality and serotonin. Journal of Clinical Psychiatry, 53(10, Suppl), 46–51. Little, K. Y. (1988). Amphetamine, but not methylphenidate, predicts antidepressant efficacy. Journal of Clinical Psychopharmacology, 8, 177–183. Lowther, S., De Paermentier, F., Crompton, M. R., Katona, C. L., & Horton, R. W. (1994). Brain 5-HT2 receptors in suicide victims: Violence of death, depression and effects of antidepressant treatment. Brain Research, 642, 281–289. Maas, J. W., Fawcett, J. A., & Dekirmenjian, H. (1972). Catecholamine metabolism, depressive illness and drug response. Archives of General Psychiatry, 26, 252–262. MacLean, P. D., & Newman, J. D. (1988). Role of midline frontolimbic cortex in production of the isolation call of squirrel monkeys. Brain Research, 450, 111–123. MacQueen, G. M., Campbell, S., McEwen, B. S., Macdonald, K., Amano, S., Joffe, R. T., et al. (2003). Course of illness, hippocampal function, and hippocampal volume in major depression. Proceedings of the National Academy of Sciences, USA, 100, 1387–1392. Maes, M., Gastel, A. V., Delmeire, L., & Meltzer, H. Y. (1999). Decreased platelet alpha-2 adrenoceptor density in major depression: Effects of tricyclic antidepressants and fluoxetine. Biological Psychiatry, 45, 278–284. Maes, M., & Meltzer, H. Y. (1995). The serotonin hypothesis of major depression. In F. E. Bloom & D. J. Kupfer (Eds.), Psychopharmacology: The fourth generation of progress (pp. 933–944). New York: Raven Press. Maier, W., Lichtermann, D., Minges, J., Heun, R., Hallmayer, J., & Benkert, O. (1992). Schizoaffective disorder and affective disorders with mood-incongruent psychotic features: Keep separate or combine? Evidence from a family study. American Journal of Psychiatry, 149, 1666–1673. Malison, R. T., Price, L. H., Berman, R., van Dyck, C. H., Pelton, G. H., Carpenter, L., et al. (1998). Reduced brain serotonin transporter availability in major depression as measured by 123-I-2 Beta-carbomethoxy3 beta-(4-iodophenyl)tropane and single photon emission computed tomography. Biological Psychiatry, 44, 1090–1098. Manji, H. K., Drevets, W. C., & Charney, D. S. (2001). The cellular neurobiology of depression. Nature Medicine, 7, 541–547. Mann, J., Stanley, M., McBride, P., & McEwen, B. S. (1986). Increased serotonin-sub-2 and -adrenergic receptor binding in the frontal cortices of suicide victims. Archives of General Psychiatry, 43, 954–959. Mann, J. J., Huang, Y. Y., Underwood, M. D., Kassir, S. A., Oppenheim, S., Kelly, T. M., et al. (2000). A serotonin transporter gene promoter

c55.indd Sec9:1085

Matussek, N., Ackenheil, M., Hippius, H., Muller, F., Schroder, H. T., Schultes, H., et al. (1980). Effects of clonidine on growth hormone release in psychiatric patients and controls. Psychiatry Research, 2, 25–36. Mayberg, H. S. (1994). Frontal lobe dysfunction in secondary depression. Journal of Neuropsychiatry and Clinical Neurosciences, 6, 428–442. Mayberg, H. S., Liotti, M., Brannan, S. K., McGinnis, S., Mahurin, R. K., Jerabek, P. A., et al. (1999). Reciprocal limbic-cortical function and negative mood: Converging PET findings in depression and normal sadness. American Journal of Psychiatry, 156, 675–682. Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C., et al. (2005). Deep brain stimulation for treatment-resistant depression. Neuron, 45, 651–660. McArthur, S., McHale, E., Dalley, J. W., Buckingham, C., & Gillies, G. E. (2005). Altered mesencephalic dopaminergic populations in adulthood as a consequence of brief perinatal glucocorticoid exposure. Journal of Neuroendocrinology, 17, 475–482. McMahon, F. J., Buervenich, S., Charney, D., Lipsky, R., Rush, A. J., Wilson, A. F., et al. (2006). Variation in the gene encoding the serotonin 2A receptor is associated with outcome of antidepressant treatment. American Journal of Human Genetics, 78, 804–814. McPherson, H., Walsh, A., & Silverstone, T. (2003). Growth hormone and prolactin response to apomorphine in bipolar and unipolar depression. Journal of Affective Disorders, 76, 121–125. Meana, J., Barturen, F., & Garcia-Sevilla, J. A. (1992). A-sub-2Adrenoceptors in the brain of suicide victims: Increased receptor density associated with major depression. Biological Psychiatry, 31, 471–490. Mendlewicz, J., Linkowski, P., Kerkhofs, M., Desmedt, D., Golstein, J., Copinschi, G., et al. (1985). Diurnal hypersecretion of growth hormone in depression. Journal of Clinical Endocrinology and Metabolism, 60, 505–512. Merali, Z., Kent, P., Du, L., Hrdina, P., Palkovits, M., Faludi, G., et al. (2006). Corticotropin-releasing hormone, arginine vasopressin, gastrin-releasing peptide, and neuromedin B alterations in stress-relevant brain regions of suicides and control subjects. Biological Psychiatry, 59, 594–602. Merali, Z., Khan, S., Michaud, D. S., Shippy, S. A., & Anisman, H. (2004). Does amygdaloid corticotropin-releasing hormone (CRH) mediate anxiety-like behaviors? Dissociation of anxiogenic effects and CRH release. European Journal of Neuroscience, 20, 229–239. Meyer, J. H., Ginovart, N., Boovariwala, A., Sagrati, S., Hussey, D., Garcia, A., et al. (2006). Elevated monoamine oxidase A levels in the brain: An explanation for the monoamine imbalance of major depression. Archives of General Psychiatry, 63, 1209–1216. Meyer, J. H., Houle, S., Sagrati, S., Carella, A., Hussey, D. F., Ginovart, N., et al. (2004). Brain serotonin transporter binding potential measured with carbon 11-labeled DASB positron emission tomography: Effects of major depressive episodes and severity of dysfunctional attitudes. Archives of General Psychiatry, 61, 1271–1279. Meyer, J. H., Kruger, S., Wilson, A. A., Christensen, B. K., Goulding, V. S., Schaffer, A., et al. (2001). Lower dopamine transporter binding potential in striatum during depression. NeuroReport, 12, 4121–4125.

8/17/09 5:30:16 PM

1086

Depression

Miller, A. H., Pariante, C. M., & Pearce, B. D. (1999). Effects of cytokines on glucocorticoid receptor expression and function: Glucocorticoid resistance and relevance to depression. Advances in Experimental Medicine and Biology, , 461, 107–116.

Nestler, E. J., Terwilliger, R. Z., & Duman, R. S. (1989). Chronic antidepressant administration alters the subcellular distribution of cyclic AMP-dependent protein kinase in rat frontal cortex. Journal of Neurochemistry, 53, 1644–1647.

Miller, H. L., Delgado, P. L., Salomon, R. M., Berman, R., Krystal, J. H., Heninger, G. R., et al. (1996). Clinical and biochemical effects of catecholamine depletion on antidepressant-induced remission of depression. Archives of General Psychiatry, 53, 117–128.

Neumeister, A., Hu, X.-Z., Luckenbaugh, D. A., Schwarz, M., Nugent, A. C., Bonne, O., et al. (2006). Differential effects of 5-HTTLPR genotypes on the behavioral and neural responses to tryptophan depletion in patients with major depression and controls. Archives of General Psychiatry, 63, 978–986.

Mineka, S., Watson, D., & Clark, L. A. (1998). Comorbidity of anxiety and unipolar mood disorders. Annual Review of Psychology, 49, 377–412. Mintun, M. A., Sheline, Y. I., Moerlein, S. M., Vlassenko, A. G., Huang, Y., & Snyder, A. Z. (2004). Decreased hippocampal 5-HT2A receptor binding in major depressive disorder: In vivo measurement with. Biological Psychiatry, 55, 217–224. Mitchell, P., Bearn, J., Corn, T., & Checkley, S. A. (1988). Growth hormone response to clonidine after recovery in patients with endogenous depression. British Journal of Psychiatry, 152, 34–38. Mitchell, P. B., & Smythe, G. (1990). Hormonal responses to fenfluramine in depressed and control subjects. Journal of Affective Disorders, 19, 43–51. Motivala, S. J., Sarfatti, A., Olmos, L., & Irwin, M. R. (2005). Inflammatory markers and sleep disturbance in major depression. Psychosomatic Medicine, 67, 187–194. Mueller, I. M., Leon, A. C., Keller, M. B., Solomon, D. A., Endicott, J., Coryell, W., et al. (1999). Recurrence after recovery from major depressive disorder during 15 years of observational follow-up. American Journal of Psychiatry, 156, 1000–1006. Muller, J. C., Pryer, W. W., Gibbons, J. E., & Orgain, E. (1955). Depression and anxiety occurring during Rauwolfia therapy. Journal of the American Medical Association, 159, 836–839. Murphy, F., Michael, A., Robbins, T., & Sahakian, B. (2003). Neuropsychological impairment in patients with major depressive disorder: The effects of feedback on task performance. Psychological Medicine, 33, 455–467. Murray, C. J. L., & Lopez, A. D. (1996). The global burden of disease: A comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020. Cambridge, MA: Harvard University Press.

Neumeister, A., Wood, S., Bonne, O., Nugent, A. C., Luckenbaugh, D. A., Young, T., et al. (2005). Reduced hippocampal volume in unmedicated, remitted patients with major depression versus control subjects. Biological Psychiatry, 57, 935–937. Newport, D., Stowe, Z. N., & Nemeroff, C. B. (2002). Parental depression: Animal models of an adverse life event. American Journal of Psychiatry, 159, 1265–1283. Nibuya, M., Nestler, E. J., & Duman, R. S. (1996). Chronic antidepressant administration increases the expression of cAMP response element binding protein (CREB) in rat hippocampus. Journal of Neuroscience, 6, 2365–2372. Nofzinger, E. A., Buysse, D. J., Germain, A., Carter, C., Luna, B., Price, J. C., et al. (2004). Increased activation of anterior paralimbic and executive cortex from waking to rapid eye movement sleep in depression. Archives of General Psychiatry, 61, 695–702. Nowak, G., Trullas, R., Layer, R. T., Skolnick, P., & Paul, I. A. (1993). Adaptive changes in the N-methyl-D-aspartate receptor complex after chronic treatment with imipramine and 1-aminocyclopropanecarboxylic acid. Journal of Pharmacology and Experimental Therapeutics, 265, 1380–1386. O’Keane, V., & Dinan, T. G. (1991). Prolactin and cortisol responses to dfenfluramine in major depression: Evidence of diminished responsivity of central serotonergic function. American Journal of Psychiatry, 148, 1009–1015. O’Neill, M. F., & Moore, N. A. (2003). Animal models of depression: Are there any? Human Psychopharmacology, 18, 239–254. Ongur, D., Drevets, W. C., & Price, J. L. (1998). Glial reduction in the subgenual prefrontal cortex in mood disorders. Proceedings of the National Academy of Sciences, USA, 95, 13290–13295.

Musselman, D. L., Lawson, D. H., Gumnick, J. F., Manatunga, A. K., Penna, S., Goodkin, R. S., et al. (2001). Paroxetine for the prevention of depression induced by high-dose interferon alfa [See comment]. New England Journal of Medicine, 344, 961–966.

Ordway, G. A., Gambarana, C., Tejani-Butt, S. M., Areso, P., Hauptmann, M., & Frazer, A. (1991). Preferential reduction of binding of 125Iiodopindolol to beta-1 adrenoceptors in the amygdala of rat after antidepressant treatments. Journal of Pharmacology and Experimental Therapeutics, 257, 681–690.

Musselman, D. L., Tomer, A., Manatunga, A. K., Knight, B. T., Porter, M. R., Kasey, S., et al. (1996). Exaggerated platelet reactivity in major depression. American Journal of Psychiatry, 153, 1313–1317.

Ordway, G. A., Schenk, J., Stockmeier, C. A., May, W., & Klimek, V. (2003). Elevated agonist binding to alpha2-adrenoceptors in the locus coeruleus in major depression. Biological Psychiatry, 53, 315–323.

Nalepa, I., & Vetulani, J. (1993). The effect of calcium channel blockade on the action of chronic ECT and imipramine on responses of 1- and -adrenoreceptors in the rate cerebral cortex. Polish Journal of Pharmacology, 45, 201–205.

Osuch, E. A., Ketter, T. A., Kimbrell, T. A., George, M. S., Benson, B. E., Willis, M. W., et al. (2000). Regional cerebral metabolism associated with anxiety symptoms in affective disorder patients. Biological Psychiatry, 48, 1020–1030.

Nemeroff, C. B., Bissette, G., Akil, H., & Fink, M. (1991). Neuropeptide concentrations in the cerebrospinal fluid of depressed patients treated with electroconvulsive therapy: Corticotrophin-releasing factor, betaendorphin and somatostatin. British Journal of Psychiatry, 158, 59–63.

Oswald, L. M., Wong, D. F., McCaul, M., Zhou, Y., Kuwabara, H., Choi, L., et al. (2005). Relationships among ventral striatal dopamine release, cortisol secretion, and subjective responses to amphetamine. Neuropsychopharmacology, 30, 821–832.

Nemeroff, C. B., Krishnan, K. R., Reed, D., Leder, R., Beam, C., & Dunnick, N. R. (1992). Adrenal gland enlargement in major depression: A computed tomographic study. Archives of General Psychiatry, 49, 384–387.

Owens, M. J., & Nemeroff, C. B. (1991). Physiology and pharmacology of corticotropin releasing factor. Pharmacological Reviews, 43, 425–473.

Nemeroff, C. B., Owens, M. J., Bissette, G., Andorn, A. C., & Stanley, M. (1988). Reduced corticotropin releasing factor binding sites in the frontal cortex of suicide victims. Archives of General Psychiatry, 45, 577–579.

Pace, T. W., Hu, F., & Miller, A. H. (2007). Cytokine-effects on glucocorticoid receptor function: Relevance to glucocorticoid resistance and the pathophysiology and treatment of major depression. Brain, Behavior, and Immunity, 21, 9–19.

Nemeroff, C. B., Widerlov, E., Bissette, G., Walleus, H., Karlsson, I., Eklund, K., et al. (1984, December 14). Elevated concentrations of CSF corticotropin-releasing factor-like immunoreactivity in depressed patients. Science, 226, 1342–1344.

Pace, T. W., Mletzko, T. C., Alagbe, O., Musselman, D. L., Nemeroff, C. B., Miller, A. H., et al. (2006). Increased stress-induced inflammatory responses in male patients with major depression and increased early life stress. American Journal of Psychiatry, 163, 1630–1633.

c55.indd Sec9:1086

8/17/09 5:30:16 PM

References 1087 Palliere-Martinot, M., Bragulat, V., Artiges, E., Dolle, F., Hinnen, F., Jouvent, R., et al. (2001). Decreased presynaptic dopamine function in the left caudate of depressed patients with affective flattening and psychomotor retardation. American Journal of Psychiatry, 158, 314–316. Papp, M., & Moryl, E. (1994). Antidepressant activity of non-competitive and competitive NMDA receptor antagonists in a chronic mild stress model of depression. European Journal of Pharmacology, 263, 1–7. Pariante, C. M., & Miller, A. H. (2001). Glucocorticoid receptors in major depression: Relevance to pathophysiology and treatment. Biological Psychiatry, 49, 391–404. Parsey, R. V., Hastings, R. S., Oquendo, M. A., Huang, Y. Y., Simpson, N., Arcement, J., et al. (2006). Lower serotonin transporter binding potential in the human brain during major depressive episodes. American Journal of Psychiatry, 163, 52–58. Parsey, R. V., Oquendo, M. A., Zea-Ponce, Y., Rodenhiser, J., Kegeles, L. S., Pratap, M., et al. (2001). Dopamine D2 receptor availability and amphetamine-induced dopamine release in unipolar depression. Biological Psychiatry, 50, 313–322. Patchev, V. K., Hassan, A. H. S., Holsboer, F., & Almeida, O. F. X. (1996). The neurosteroid tetrahydroprogesterone attenuates the endocrine response to stress and exerts glucocorticoid-like effects on vasopressin gene transcription in the rat hypothalamus. Neuropsychopharmacology, 15, 533–540. Patchev, V. K., Shoaib, M., Holsboer, F., & Almeida, O. F. (1994). The neurosteroid tetrahydroprogesterone counteracts corticotropin-releasing hormone-induced anxiety and alters the release and gene expression of corticotropin-releasing hormone in the rat hypothalamus. Neuroscience, 62, 265–271. Pepin, M. C., Pothier, F., & Barden, N. (1992, February 20). Impaired type II glucocorticoid-receptor function in mice bearing antisense RNA transgene. Nature, 355, 725–728. Peterson, M. J., & Benca, R. M. (2006). Sleep in mood disorders. Psychiatric Clinics of North America, 29, 1009–1032. Petty, F. (1994). Plasma concentrations of GABA and mood disorders: A blood test for manic depressive disease? Clinical Chemistry, 40, 296–302. Pezawas, L., Meyer-Lindenberg, A., Drabant, E. M., Verchinski, B. A., Munoz, K. E., Kolachana, B. S., et al. (2005). 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: A genetic susceptibility mechanism for depression. Nature Neuroscience, 8, 828–834. Pitchot, W., Hansenne, M., Moreno, A. G., Pinto, E., Reggers, J., Fuchs, S., et al. (2001). Reduced dopamine function in depressed patients is related to suicidal behavior but not its lethality. Psychoneuroendocrinology, 26, 689–696. Pitchot, W., Reggers, J., Pinto, E., Hansenne, M., Fuchs, S., Pirard, S., et al. (2001). Reduced dopaminergic activity in depressed suicides. Psychoneuroendocrinology, 26, 331–335. Pochon, J. B., Levy, R., Fossati, P., Lehericy, S., Poline, J. B., Pillon, B., et al. (2002). The neural system that bridges reward and cognition in humans: An fMRI study. Proceedings of the National Academy of Sciences, USA, 99, 5669–5674. Posener, J. A., Schatzberg, A. F., Williams, G. H., Samson, J. A., McHale, N. L., Bessette, M. P., et al. (1999). Hypothalamic-pituitary-adrenal axis effects on plasma homovanillic acid in man. Biological Psychiatry, 45, 222–228. Potter, W., Karoum, F., & Linnoila, M. (1984). Common mechanisms of action of biochemically specific antidepressants. Progress in Neuropsychopharmacology and Biologial Psychiatry, 8, 153–161.

c55.indd Sec9:1087

and its relationship to early life maternal care: A positron emission tomography study using [11C] raclopride. Journal of Neuroscience, 24, 2825–2831. Raison, C. L., Capuron, L., & Miller, A. H. (2006). Cytokines sing the blues: Inflammation and the pathogenesis of depression. Trends in Immunology, 27, 24–31. Raison, C. L., Demetrashvili, M., Capuron, L., & Miller, A. H. (2005). Neuropsychiatric side effects of interferon-alpha: Recognition and management. CNS Drugs, 19, 1–19. Raison, C. L., Marcin, M., & Miller, A. H. (2002). Antidepressant treatment of cytokine-induced depression. Acta Neuropsychiatrica, 4, 18–25. Raison, C. L., & Miller, A. H. (2003). When not enough is too much: The role of insufficient glucocorticoid signaling in the pathophysiology of stress-related disorders. [Review] [114 refs]. American Journal of Psychiatry, 160, 1554–1565. Rajkowska, G., Miguel-Hidalgo, J. J., Wei, J., Dilley, G., Pittman, S. D., Meltzer, H., et al. (1999). Morphometric evidence for neuronal and glial prefrontal cell pathology in major depression. Biological Psychiatry, 48, 486–504. Rao, U., Dahl, R. E., Ryan, N. D., Birmaher, B., Williamson, D. E., Rao, R., et al. (2002). Heterogeneity in EEG sleep findings in adolescent depression: Unipolar versus bipolar clinical course. Journal of Affective Disorders, 70, 273–280. Rausch, J. L., Johnson, M. E., Li, J., Hutcheson, J., Carr, B. M., Corley, K. M., et al. (2005). Serotonin transport kinetics correlated between human platelets and brain synaptosomes. Psychopharmacology, 180, 391–398. Redmond, D., Katz, M. M., Maas, J. W., Swann, A., Casper, R., & Davis, J. M. (1986). Cerebrospinal fluid amine metabolites: Relationships with behavioral measurements in depressed, manic, and healthy control subjects. Archives of General Psychiatry, 43, 938–947. Ressler, K. J., & Mayberg, H. S. (2007). Targeting abnormal neural circuits in mood and anxiety disorders: From the laboratory to the clinic. Nature Neuroscience, 10, 1116–1124. Ressler, K. J., & Nemeroff, C. B. (2000). Role of serotonergic and noradrenergic systems in the pathophysiology of depression and anxiety disorders. Depression and Anxiety, 12(Suppl, 1), 2–19. Reyes, B. A., Valentino, R. J., Xu, G., & Van Bockstaele, E. J. (2005). Hypothalamic projections to locus coeruleus neurons in rat brain. European Journal of Neuroscience, 22, 93–106. Roy, A., De Jong, J., & Linnoila, M. (1989). Cerebrospinal fluid monoamine metabolites and suicidal behavior in depressed patients: A 5year follow-up study. Archives of General Psychiatry, 46, 609–612. Roy, A., Pickar, D., Paul, S., Doran, A., Chrousos, G. P., & Gold, P. W. (1987). CSF corticotropin-releasing hormone in depressed patients and normal control subjects. American Journal of Psychiatry, 144, 641–645. Roy, A., Wolkowitz, O. M., Bissette, G., & Nemeroff, C. B. (1994). Differences in CSF concentrations of thyrotropin-releasing hormone in depressed patients and normal subjects: Negative findings. American Journal of Psychiatry, 151, 600–602. Rubin, R. T., Phillips, J. J., Sadow, T. F., & McCracken, J. T. (1995). Adrenal gland volume in major depression: Increase during the depressive episode and decrease with successful treatment. Archives of General Psychiatry, 52, 213–218. Sachar, E. J., Hellman, L., Fukushima, D. K., & Gallagher, T. F. (1970). Cortisol production in depressive illness. A clinical and biochemical clarification. Archives of General Psychiatry, 23, 289–298.

Prange, A. J., Lara, P. P., Wilson, I. C., Alltop, L. B., & Breese, G. R. (1972). Effects of thyrotropin-releasing hormone in depression. Lancet, 2, 999–1002.

Sagud, M., Pivac, N., Muck-Seler, D., Jakovljevic, M., Mihaljevic-Peles, A., & Korsic, M. (2002). Effects of sertraline treatment on plasma cortisol, prolactin and thyroid hormones in female depressed patients. Neuropsychobiology, 45, 139–143.

Pruessner, J. C., Champagne, F., Meaney, M. J., & Dagher, A. (2004). Dopamine release in response to a psychological stress in humans

Sairanen, M., Lucas, G., Ernfors, P., Castren, M., & Castren, E. (2005). Brain-derived neurotrophic factor and antidepressant drugs have

8/17/09 5:30:17 PM

1088

Depression

different but coordinated effects on neuronal turnover, proliferation, and survival in the adult dentate gyrus. Journal of Neuroscience, 25, 1089–1094.

emotional faces in depressed subjects resolves with antidepressant treatment: An fMRI study. Biological Psychiatry, 50, 651–658.

Salomon, R. M., Miller, H. L., Krystal, J. H., Heninger, G. R., & Charney, D. S. (1997). Lack of behavioral effects of monoamine depletion in healthy subjects. Biological Psychiatry, 41, 58–64.

Sheline, Y. I., Sanghavi, M., Mintun, M. A., & Gado, M. H. (1999). Depression duration but not age predicts hippocampal volume loss in medically healthy women with recurrent major depression. Journal of Neuroscience, 19, 5034–5043.

Sanacora, G., Gueorguieva, R., Epperson, N., Wu, Y.-T., Appel, M., Rothman, D. L., et al. (2004). Subtype-specific alterations of gammaaminobutyric acid and glutamate in patients with major depression. Archives of General Psychiatry, 61, 705–713.

Shirayama, Y., Chen, A. C., Nakagawa, S., Russell, D. S., & Duman, R. S. (2002). Brain-derived neurotrophic factor produces antidepressant effects in behavioral models of depression. Journal of Neuroscience, 22, 3251–3261.

Sanacora, G., Mason, G. F., Rothman, D. L., Hyder, F., Ciarcia, J. J., Ostroff, R. B., et al. (2003). Increased cortical GABA concentrations in depressed patients receiving ECT. American Journal of Psychiatry, 160, 577–579.

Shore, P. A., Silver, S. L., & Brodie, B. B. (1955, August 12). Interaction of reserpine, serotonin, and lysergic acid diethylamide in brain. Science, 122, 284–285.

Sanacora, G., Mason, G. F., Rothman, D. L., & Krystal, J. H. (2002). Increased occipital cortex GABA concentrations in depressed patients after therapy with selective serotonin reuptake inhibitors. American Journal of Psychiatry, 159, 663–665. Sanchez, M. M., Young, L. J., Plotsky, P. M., & Insel, T. R. (1999). Autoradiographic and in situ hybridization localization of corticotropinreleasing factor 1 and 2 receptors in nonhuman primate brain. Journal of Comparative Neurology, 408, 365–377. Santarelli, L., Saxe, M., Gross, C., Surget, A., Battaglia, F., Dulawa, S., et al. (2003, August 8). Requirement of hippocampal neurogenesis for the behavioral effects of antidepressants. Science, 301, 805–809. Sargent, P. A., Kjaer, K. H., Bench, C. J., Rabiner, E. A., Messa, C., Meyer, J., et al. (2000). Brain serotonin1A receptor binding measured by positron emission tomography with. Archives of General Psychiatry, 57, 174–180. Schatzberg, A. F., Rothschild, A. J., & Langlais, P. J. (1985). A corticosteroid/dopamine hypothesis for psychotic depression and related states. Journal of Psychiatric Research, 19, 57–64. Schatzberg, A. F., Rothschild, A. J., Langlais, P. J., Bird, E. D., & Cole, E. J. (1985). A corticosteroid/dopamine hypothesis for psychotic depression and related states. Journal of Psychiatric Research, 19, 57–64. Schildkraut, J. J. (1965). The catecholamine hypothesis of affective disorders: A review of supporting evidence. American Journal of Psychiatry, 122, 509–522. Schildkraut, J. J. (1973). Norepinephrine metabolites as biochemical criteria for classifying depressive disorders and predicting response to treatment: Preliminary findings. American Journal of Psychiatry, 130, 695–699.

Siegle, G. J., Carter, C. S., & Thase, M. R. (2006). FMRI predicts recovery in cognitive behavioral therapy for unipolar depression. American Journal of Psychiatry, 163, 735–738. Siever, L. J., Trestman, R. L., Coccaro, E. F., Bernstein, D., Gabriel, S. M., Owen, K., et al. (1992). The growth hormone response to clonidine in acute and remitted depressed male patients. Neuropsychopharmacology, 6, 165–177. Siever, L. J., Uhde, T. W., Jimerson, D. C., Lake, C., Silberman, E. R., Post, R. M., et al. (1984). Differential inhibitory noradrenergic responses to clonidine in 25 depressed patients and 25 normal control subjects. American Journal of Psychiatry, 141, 733–741. Siuciak, J. A., Lewis, D. R., Wiegand, S. J., & Lindsay, R. M. (1997). Antidepressant-like effect of brain-derived neurotrophic factor (BDNF). Pharmacology, Biochemistry and Behavior, 56, 131–137. Slattery, D. A., Hudson, A. L., & Nutt, D. J. (2004). The evolution of antidepressant mechanisms. Fundamental and Clinical Pharmacology, 18, 1–21. Smith, M. A., Makino, S., Kvetnansky, R., & Post, R. M. (1995). Stress and glucocorticoids affect the expression of brain-derived neurotrophic factor and neurotrophin-3 mRNAs in the hippocampus. Journal of Neuroscience, 15, 1768–1777. Song, C., & Leonard, B. E. (2005). The olfactory bulbectomised rat as a model of depression. Neuroscience and Biobehavioral Reviews, 29, 627–647. Sullivan, G. M., Hatterer, J. A., Herbert, J., Chen, X., Roose, S. P., Attia, E., et al. (1999). Low levels of transthyretin in CSF of depressed patients. American Journal of Psychiatry, 156, 710–715.

Schilkrut, R., Chandra, O., Osswald, M., Ruther, E., Baafusser, B., & Matussek, N. (1975). Growth hormone release during sleep and with thermal stimulation in depressed patients. Neuropsychobiology, 1, 70–79.

Sullivan, M., Mann, J. J., Oquendo, M., Lo, E., Cooper, T., & Gorman, J. (2006). Low cerebral spinal fluid transthyretin levels in depression: Correlations with suicidal ideation and low serotonin function. Biological Psychiatry, 60, 500–506.

Schneier, F. R., Liebowitz, M. R., Abi-Dargham, A., Zea-Ponce, Y., Lin, S. H., & Laruelle, M. (2000). Low dopamine D(2) receptor binding potential in social phobia. American Journal of Psychiatry, 157, 457–459.

Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000). Genetic epidemiology of major depression: Review and meta-analysis. American Journal of Psychiatry, 157, 1552–1662.

Seifritz, E. (2001). Contribution of sleep physiology to depressive pathophysiology. Neuropsychopharmacology, 25, 85–S88.

Sulser, F., & Dingell, J. V. (1968). Adrenergic mechanisms in the central action of tricyclic antidepressants and substituted phenothiazines. Agressologie, 9, 281–287.

Sen, S., Nesse, R. M., Stoltenberg, S. F., Li, S., Gleiberman, L., Chakravarti, A., et al. (2003). A BDNF coding variant associated with the NEO personality inventory domain neuroticism: A risk factor for depression. Neuropsychopharmacology, 28, 397–401. Shah, P. J., Ogilvie, A. D., Goodwin, G. M., & Ebmeier, K. P. (1997). Clinical and psychometric correlates of dopamine D2 binding in depression. Psychological Medicine, 27, 1247–1256. Shapira, B., Cohen, J., Newman, M. E., & Lerer, B. (1993). Prolactin response to fenfluramine and placebo challenge following maintenance pharmacotherapy withdrawal in remitted depressed patients. Biological Psychiatry, 33, 531–535. Sheline, Y. I., Barch, D. M., Donnelly, J. M., Ollinger, J. M., Snyder, A. Z., & Mintun, M. A. (2001). Increased amygdala response to masked

c55.indd Sec9:1088

Swanson, L. W., & Kuypers, H. G. (1980). The paraventricular nucleus of the hypothalamus: Cytoarchitectonic subdivisions and organization of projections to the pituitary, dorsal vagal complex, and spinal cord as demonstrated by retrograde fluorescence double-labeling methods. Journal of Comparative Neurology, 194, 555–570. Tandberg, E., Larsen, J. P., Aarsland, D., & Cummings, J. L. (1996). The occurrence of depression in Parkinson’s disease. Archives of Neurology, 53, 175–179. Tardito, D., Perez, J., Tiraboschi, E., Musazzi, L., Racagni, G., & Popoli, M. (2006). Signaling pathways regulating gene expression, neuroplasticity, and neurotrophic mechanisms in the action of antidepressants: A critical overview. Pharmacological Reviews, 58, 115–134.

8/17/09 5:30:17 PM

References 1089 Tepper, J. M., Koos, T., & Wilson, C. J. (2004). GABAergic microcircuits in the neostriatum. Trends in Neuroscience, 11, 662–669.

central nervous system serotonin function. Neuropsychopharmacology, 28, 533–541.

Traskman, L., Asberg, M., Bertilsson, L., & Sjostrand, L. (1981). Monoamine metabolites in CSF and suicidal behavior. Archives of General Psychiatry, 38, 631–636.

Willner, P. (1983). Dopamine and depression: A review of recent evidence. Brain Research Reviews, 6, 211–246.

Tremblay, L. K., Naranjo, C. A., Cardenas, L., Herrmann, N., & Busto, U. E. (2002). Probing brain reward system function in major depression. Archives of General Psychiatry, 59, 409–416. Tremblay, L. K., Naranjo, C. A., Graham, S. J., Herrmann, N., Mayberg, H. S., Hevenor, S., et al. (2005). Functional neuroanatomical substrates of altered reward processing in major depressive disorder revealed by a dopaminergic probe. Archives of General Psychiatry, 62, 1228–1236. Trivedi, M. H., Rush, A., Wisniewski, S. R., Nierenberg, A. A., Warden, D., Ritz, L., et al. (2006). Evaluation of outcomes with citalopram for depression using measurement-based care in STARD: Implications for clinical practice. American Journal of Psychiatry, 163, 28–40. Uzunova, V., Sheline, Y., Davis, J. M., Rasmusson, A., Uzunov, D. P., Costa, E., et al. (1998). Increase in the cerebrospinal fluid content of neurosteroids in patients with unipolar major depression who are receiving fluoxetine or fluvoxamine. Proceedings of the National Academy of Sciences, USA, 95, 3239–3244. Vaidya, V. A., Siuciak, J. A., Du, F., & Duman, R. S. (1999). Hippocampal mossy fiber sprouting induced by chronic electroconvulsive seizures. Neuroscience, 89, 157–166. van den Bogaert, A., Sleegers, K., de Zutter, S., Heyrman, L., Norrback, K.-F., Adolfsson, R., et al. (2006). Association of brain-specific tryptophan hydroxylase, TPH2, with unipolar and bipolar disorder in a northern Swedish, isolated population. Archives of General Psychiatry, 63, 1103–1110. Van Pett, K., Viau, V., Bittencourt, J. C., Chan, R. K., Li, H. Y., Arias, C., et al. (2000). Distribution of mRNAs encoding CRF receptors in brain and pituitary of rat and mouse. Journal of Comparative Neurology, 428, 191–212. Van Praag, H. M. (1982). Depression, suicide, and the metabolites of serotonin in the brain. Journal of Affective Disorders, 4, 21–29. van Praag, H. M., Korf, J., Lakke, J. P. W. F., & Schut, T. (1975). Dopamine metabolism in depressions, psychoses and Parkinson’s disease: The problem of the specificity of biological variables in behavior disorders. Psychological Medicine, 5, 138–146. Veith, R. C., Lewis, N., Langohr, J. I., Murburg, M. M., Ashleigh, E. A., Castillo, S., et al. (1993). Effect of desipramine on cerebrospinal fluid concentrations of corticotropin-releasing factor in human subjects. Psychiatry Research, 46, 1–8. Veith, R. C., Lewis, N., Linares, O. A., Barnes, R. F., Raskind, M. A., Villacres, E. C., et al. (1994). Sympathetic nervous system activity in major depression: Basal and desipramine-induced alterations in plasma norepinephrine kinetics. Archives of General Psychiatry, 51, 411–422. Vermetten, E., Vythilingam, M., Southwick, S. M., Charney, D. S., & Bremner, J. (2003). Long-term treatment with paroxetine increases verbal declarative memory and hippocampal volume in posttraumatic stress disorder. Biological Psychiatry, 54, 693–702. Weissman, M. M., Wickramaratne, P., Nomura, Y., Warner, V., Verdeli, H., Pilowsky, D. J., et al. (2005). Families at high and low risk for depression: A 3-generation study. Archives of General Psychiatry, 62. Wenzlaff, R. M., & Grozier, S. A. (1988). Depression and the magnification of failure. Journal of Abnormal Psychology, 97, 90–93. Williams, R. B., Marchuk, D. A., Gadde, K. M., Barefoot, J. C., Grichnik, K., Helms, M. J., et al. (2003). Serotonin-related gene polymorphisms and

c55.indd Sec9:1089

Winokur, A., Gary, K. A., Rodner, S., Rae-Red, C., Fernando, A. T., & Szuba, M. P. (2001). Depression, sleep physiology, and antidepressant drugs. Depression and Anxiety, 14, 19–28. Wolfe, N., Katz, D. I., Albert, M. L., Almozlino, A., Durso, R., Smith, M. C., et al. (1990). Neuropsychological profile linked to low dopamine: In Alzheimer ’s disease, major depression, and Parkinson’s disease. Journal of Neurology, Neurosurgery, and Psychiatry, 53, 915–917. Wolkowitz, O. M., Rubinow, D. R., Breier, A., Doran, A. R., Davis, C., & Pickar, D. (1987). Prednisone decreases CSF somatostatin in healthy humans: Implications for neuropsychiatric illness. Life Sciences, 41, 1929–1933. Wong, M. L., Kling, M. A., Munson, P. J., Listwak, S., Licinio, J., Prolo, P., et al. (2000). Pronounced and sustained central hypernoradrenergic function in major depression with melancholic features: Relation to hypercortisolism and corticotropin-releasing hormone. Proceedings of the National Academy of Sciences, USA, 97, 325–330. Wynn, P. C., Harwood, J. P., Catt, K. J., & Aguilera, G. (1988). Corticotropin-releasing factor (CRF) induces desensitization of the rat pituitary CRF receptor-adenylate cyclase complex. Endocrinology, 122, 351–358. Yatham, L. N., Liddle, P. F., Dennie, J., Shiah, I. S., Adam, M. J., Lane, C. J., et al. (1999). Decrease in brain serotonin 2 receptor binding in patients with major depression following desipramine treatment: A positron emission tomography study with fluorine-18-labeled setoperone. Archives of General Psychiatry, 56, 705–711. Yatham, L. N., Liddle, P. F., Shiah, I. S., Scarrow, G., Lam, R. W., Adam, M. J., et al. (2000). Brain serotonin2 receptors in major depression: A positron emission tomography study. Archives of General Psychiatry, 57, 850–858. Young, E. A., Watson, S. J., Kotun, J., Haskett, R. F., Grunhaus, L., MurphyWeinberg, V., et al. (1990). Beta-lipotropin-beta-endorphin response to low-dose ovine corticotropin releasing factor in endogenous depression: Preliminary studies. Archives of General Psychiatry, 47, 449–457. Zammit, S., & Owen, M. J. (2006). Stressful life events, 5-HTT genotype and risk of depression. British Journal of Psychiatry, 188, 199–201. Zarate, C. A., Jr., Singh, J. B., Carlson, P. J., Brutsche, N. E., Ameli, R., Luckenbaugh, D. A., et al. (2006). A randomized trial of an N-methylD-aspartate antagonist in treatment-resistant major depression. Archives of General Psychiatry, 63, 856–864. Zhang, X., Gainetdinov, R. R., Beaulieu, J.-M., Sotinkova, T. D., Burch, L. J., Williams, R. B., et al. (2005). Loss-of-function mutation in tryptophan hydroxylase-2 identified in unipolar major depression. Neuron, 45, 11–16. Zhou, Z., Roy, A., Lipsky, R., Kuchipudi, K., Zhu, G., Taubman, J., et al. (2005). Haplotype-based linkage of tryptophan hydroxylase 2 to suicide attempt, major depression, and cerebrospinal fluid 5-hydroxyindoleacetic acid in 4 populations. Archives of General Psychiatry, 62, 1109–1118. Zill, P., Baghai, T. C., Zwanzger, P., Schule, C., Eser, D., Rupprecht, R., et al. (2004). SNP and haplotype analysis of a novel tryptophan hydroxylase isoform (TPH2) gene provide evidence for association with major depression. Molecular Psychiatry, 9, 1030–1036. Zobel, A. W., Nickel, T., Kunzel, H. E., Ackl, N., Sonntag, A., Ising, M., et al. (2000). Effects of high-affinity corticotropin-releasing hormone receptor 1 antagonist R121919 in major depression: The first 20 patients treated. Journal of Psychiatric Research, 34, 171–181.

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Chapter 56

The Genetics of Anxiety KIARA R. TIMPANO, GREGOR HASLER, CHRISTINA RICCARDI, DENNIS L. MURPHY, AND NORMAN B. SCHMIDT

genetic epidemiology of the anxiety disorders found that odds ratios across panic isorder (PD), generalized anxiety disorder (GAD), phobias, and obsessive compulsive disorder (OCD) ranged from 4 to 6 (Hettema, Neale, & Kendler, 2001). This same review identified estimated heritabilities for these disorders ranging from 30% to 40%. Although this estimate is rather moderate and indicates that environmental factors play a large role in the liability for the various anxiety syndromes, it does, nonetheless, point to the important role genetics may play in the pathogenesis of these conditions. These findings from genetic epidemiology have subsequently opened the road for an increasing number of investigations attempting to identify specific genetic risk factors. We first provide a brief overview of each of the anxiety disorders, consisting of an introduction to the “landmarks” of each syndrome, including symptom presentation and associated features, followed by a review of the respective heritability and genetics research conducted to date. These summaries are not exhaustive, but rather more illustrative with regard to the most pertinent issues and research of the phenomenon being considered. PD, social anxiety disorder (SAD) and other phobias, GAD, and OCD are highlighted. We then discuss a number of broader issues relevant to interpreting past genetics research and conducting future investigations. These considerations include the importance of clearly defined phenotypes, the role of other factors such as environmental variables, and the utilization of alternative approaches, specifically the examination of endophenotypes (intermediate phenotypes more closely linked to action of genes), to studying the relationship between a disease entity and associated genetics.

Anxiety-related psychopathology represents one of the most prevalent and debilitating forms of mental illness (Kessler, Berglund, et al., 2005; Weissman, 1990). Extrapolating from epidemiological studies, it may be conservatively estimated that 25% of the population will suffer from clinically significant anxiety at some point in their lives with a 12-month prevalence rate of approximately 18% (Kessler, Chiu, Demler, Merikangas, & Walters, 2005). Anxiety disorders generally maintain a chronic course when untreated (Pine, Cohen, Gurley, Brook, & Ma, 1998) and result in substantial impairment across the life span (Ferdinand, van der Reijden, Verhulst, Nienhuis, & Giel, 1995). In addition to the immense personal suffering created by clinically significant anxiety syndromes, these disorders create a considerable public expense that includes treatment costs, lost work time, and mortality. One study estimated the annual cost in the United States associated with anxiety disorders to be more than $42 billion, which is one-third of the total cost linked with the economic burden of all psychiatric disorders (Greenberg et al., 1999). Anxiety psychopathology is also associated with increased utilization of nonpsychiatric medical services (Greenberg et al., 1999), further amplifying the associated public health burden. Despite considerable advances in elucidating the phenomenology of anxiety-related syndromes, the development of concise models of pathogenesis and the identification of definitive risk factors has remained relatively more elusive. Like most other neuropsychiatric diseases, the anxiety disorders have long been suspected of being influenced by an interaction between environmental factors and a heritable component (e.g., Cohen, 1951; Marks, 1986). Research amassed over the past few decades now provides considerable evidence for the familial aggregation of anxiety disorders. Utilizing a combination of twin and family studies, researchers have found that there is a significant association between an anxiety disorder in an individual and the occurrence of that same or genetically related disorders in their first-degree relatives (Shih, Belmonte, & Zandi, 2004). A meta-analysis of the

GENERAL CONSIDERATIONS Linking Neurobiology and Genetics In searching for the origins of psychiatric disorders, it is critical to consider the different points of organization that 1090

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shape a given syndrome, spanning from the macro to the micro level (Figure 56.1). That is, in conducting etiological research on vulnerability and risk factors, one should be cognizant of the different layers constituting and influencing a behavior, and how these layers work in concert. A disease phenomenon can entail neurobiological problems at the system, organ, or cellular level, which in turn can be formed from differences in the molecular underpinnings. At the same time, the disease entity is also influenced by a multitude of factors including developmental, environmental, and social/cultural variables. There is a constant bidirectional flow between these layers in the sense that at every level of the model, downstream features (e.g., environmental experiences) can subsequently influence upstream features (e.g., gene expression or neurological reactivity), or vice versa. While a specific research project may only target one organizational aspect, such as the genetics of a psychiatric disorder, it is vital to consider the consequences (e.g., neurobiology) and influencing factors (e.g., life stress). The connection between neurobiology and genetics is important. The relationship between these two layers of

organization clearly demonstrates the bidirectional flow outlined in Figure 56.1. Treatment response has often been one of the methods available to researchers who wish to pinpoint the specific neurobiological systems that may play a role in the etiology of a disorder. A specific example is the hypothesis that a disruption in the serotonin system is related to anxiety, based on the efficacy of serotonin-focused drugs in treating anxiety disorders. This knowledge has consequently led researchers to investigate genetic variations in the serotonin transporter. These genetic variations have reciprocally been associated with differential neuronal reactivity to specific stimuli. Brain regions implicated in the anxiety disorders include the prefrontal cortex, subcortical hippocampus, subgenual cingulated cortex, basal ganglia, and amygdala. Specific anxietyrelated systems that cross multiple areas in the brain include the serotonergic, noradrenergic, gamma-aminobutyric acid (GABA), and dopaminergic pathways (see Figure 56.2). Given our focus on the genetics of anxiety disorders, we refer the reader to several excellent reviews on the neurology of anxiety for a more in-depth discussion (Ressler & Mayberg, 2007).

Macro Level Developmental

Environment

Social/Cultural

Narrow Definition

Psychiatric Syndrome

Broad Definition

Endophenotype

Neurophysiology

Molecular Underpinnings Micro Level

Figure 56.1 The behavior genetics viewpoint of the organizational schema of psychiatric disorders.

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1092 The Genetics of Anxiety Fornix Septal region

Cortex

Thalamus

Striatum

Substantia Nigra Thalamus Prefrontal Cortex

Hypothalamus

Cerebellum

Globus Pallidus

Raphe Nuclei

Hippocampus

Hippocampus Amygdala

Cerebellar Cortex

Superior Colliculus

Locus Ceruleus

GABA pathways Serotonetgic pathways

Dopaminergic pathways

Noradrenergic pathways Concomitant serotonergic/noradrenergic pathways

Figure 56.2 The neurobiology of anxiety disorders.

General Factors Relevant to Behavior Genetics It may be helpful to first consider a number of general concepts relevant to studying the genetics of psychiatric disorders. Kendler and Greenspan (2006), as well as others (Lander & Schork, 1994), have identified several key aspects of the nature of genetic influence on behavior, which are reflected across species and appear to be fundamental. First of all, most behaviors, including those linked with anxiety psychopathology, represent complex phenomena with both clinical and genetic heterogeneity (Fanous, Gardner, Prescott, Cancro, & Kendler, 2002; Murphy et al., 2003). In the absence of Mendelian inheritance patterns, it is assumed that many common genetic variants contribute small effects to the macro-psychiatric phenotype. A second consideration, and one that has been supported extensively in investigations with animal models (e.g., murine), is the nonspecificity of genes as risk factors for particular traits. That is, most genes realistically influence any number of diverse phenotypes. Animal models and an increasing number of investigations with psychiatric populations have also revealed that genes interact with the environment and each other (Murphy et al., 2003). It has been found that the environment can modify genetic effects (e.g., Caspi et al., 2003), and that genes, by determining behaviors, can manipulate the environment (e.g., Lyons

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et al., 1993). All of these aspects serve to complicate any attempt to tie a specific syndrome or behavior with a specific gene, and although research is making advances in that endeavor, it is important to remember that factors such as these may obscure progress.

REVIEW OF THE ANXIETY DISORDERS AND RELEVANT GENETIC FINDINGS Panic Disorder A central component to PD is the experience of recurrent panic attacks, which are an acute fear reaction that appears at inappropriate or unexpected times with no apparent stimulus. These attacks are accompanied by a tremendous arousal of the autonomic system, along with cognitive fears about the consequences of the attack (e.g., fear of dying or going crazy). PD is diagnosed if there is a consistent and distressing concern about experiencing panic attacks, which may be accompanied by drastic changes in behavior (e.g., agoraphobia). PD with or without agoraphobia is a highly debilitating condition with an approximately 4.7% lifetime prevalence (Kessler et al., 2006). Millions more experience panic attacks and other subsyndromal anxiety symptoms that markedly diminish quality of life. Those

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affected by PD suffer from negative personal, social, and economic consequences of a magnitude equal to or greater than that evidenced in major depression, alcoholism, and serious medical conditions. Other serious sequelae include increased risk for various medical conditions such as cardiovascular disease, and marked elevations in suicide attempts, as well as extremely high utilization of health services. PD is the leading cause of emergency department consultation (Weissman, 1991) and also the leading cause for seeking mental health services, ahead of both schizophrenia and mood disorders (Boyd, 1986). Evidence from epidemiological data suggests that PD is associated with significant social and health consequences including perceptions of poor physical and emotional health, obesity, alcohol and drug abuse, increased rate of suicide attempts, increased usage of psychoactive medications, and increased marital, social, and financial problems (Markowitz, Weissman, Ouellette, Lish, & Klerman, 1989; Simon et al., 2006; Weissman, 1991). Indeed, Markowitz et al. (1989) concluded that PD confers negative social and health consequences of a magnitude equal to or greater than that for major depression. In discussing PD, it is important to consider the distinction between the disorder and the experience of panic attacks because the two are not isomorphic. An estimated 28% of people will experience a panic attack in their lifetime, yet less than 5% develop PD (Kessler et al., 2006). The onset of panic attacks occur adolescence or early adulthood (Regier, Rae, Narrow, Kaelber, & Schatzberg, 1998) and are classified into three types: unexpected, situationally bound, and situationally predisposed. Unexpected panic attacks seem to occur out of the blue and are not associated with a particular situation or internal cue. In contrast, cued or situationally bound attacks almost always occur after exposure to or in anticipation of a particular situation. Similarly, situationally predisposed panic attacks are linked to a particular situation but do not always occur. For example, an individual may be more likely to have a panic attack when he drives, but there are times when he drives without having an attack or he may experience a panic attack only when he drive mores than 50 miles from his home. Spontaneous or uncued panic attacks are often considered to be central to the experience of PD. In fact, spontaneous panic is required for this diagnosis. However, many patients with PD experience situationally bound and predisposed panic attacks as well. Situationally bound and predisposed attacks seem to be tied to other anxiety disorders such as specific or social phobia. Agoraphobia is one other diagnosis associated with PD. Consistent with Marks’ (1970) contentions, the DSM-III (American Psychiatric Association, 1980) classified agoraphobia as a phobic disorder that could occur with or

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without panic attacks, whereas PD was considered to be a separate class of anxiety disorders called anxiety states or anxiety neuroses. In the DSM-III, agoraphobia received primary consideration because PD could not be diagnosed if the patient met criteria for agoraphobia. Over time, as researchers increasingly recognized that agoraphobia is often a consequence of experiencing panic attacks, the DSM reversed the relationship between these conditions such that in the DSM-III-R (American Psychiatric Association, 1987) and DSM-IV (American Psychiatric Association, 1994), agoraphobia is typically considered secondary to PD. In fact, in the DSM-IV, agoraphobia is coded only in the context of either PD or limited-symptom panic attacks (agoraphobia without history of PD). Thus, agoraphobic behaviors are now more commonly conceptualized as panicrelated sequelae (Frances et al., 1993; A. J. Goldstein & Chambless, 1978). Of all of the anxiety disorders, PD has received the most attention with regard to genetics research (for detailed review, refer to Gratacos et al., 2007). There is considerable evidence for the heritability of PD. Family aggregation studies have consistently shown a significant risk for PD in first-degree relatives of PD probands. A meta-analysis of the extant family studies revealed a summary odds ratio of 5.0 and an unadjusted aggregate risk of 10.0% for first-degree relatives, versus 2.1% in comparison relatives of probands (Hettema et al., 2001). Further support for heritability has emerged from twin studies, which have found concordance rates of 0% to 17% in dizygotic twins, in contrast to 24% to 73% concordance in monozygotic twins. That is, the concordance rates for identical twins are 2- to 3-fold higher than for fraternal twins (see Shih et al., 2004; van den Heuvel, van de Wetering, Veltman, & Pauls, 2000, for reviews). The meta-analysis conducted by Hettema et al. (2001) calculated the variance attributable to additive genetics for PD at about 30% to 40%, with a heritability estimate of .48. The data from twin studies have also revealed information relevant to factors influencing the liability for PD. Data have failed to produce any evidence for the role of common familial environmental factors in the development of PD because it appears all variance is attributable to either genetic or individual-specific environmental factors (Hettema et al., 2001). With regard to the specific mode of genetic transmission, a series of complex segregation analyses have identified a number of best-fitting models (see van den Heuvel et al., 2000, for a review). Although consensus on specific models is lacking, the conclusions are the same: genetic liability for PD most likely is polygenetic in nature, in that any number of genes with small effects might interact with one another and environmental events (Gratacos et al., 2007). Further support for this theory comes from the findings of various linkage studies.

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1094 The Genetics of Anxiety

These studies are reviewed in detail by Gratacos et al. (2007), but briefly, suggestive linkage has been found for chromosome 22q (Weissman et al., 2004), 13q (Hamilton et al., 2003; Weissman et al., 2004), 7p (Crowe et al., 2001; Knowles et al., 1998; Logue, Vieland, Goedken, & Crowe, 2003), 9q (Thorgeirsson, 2003), 1q, 11p (Gelernter et al., 2001), and 15q (Fyer et al., 2006). As Gratacos et al. (2007) discuss, the next step will be for investigators to perform fine-mapping in an attempt to identify more specific regions and/or genes. One problem is that linkage studies typically need a strong gene effect to identify a significant LOD score (Risch & Merikangas, 1996) and, based on the findings from complex segregation analyses, this is not the case for PD. A considerable number of candidate genes have been investigated with regard to PD. They are summarized in greater detail in Gratacos et al. (2007). Broadly speaking, candidate genes for PD can be classified into two neurobiological groups. First, there are genes relevant to the receptors and transporters of neurotransmitter pathways. Specific pathways have been targeted given pharmacological agents that are either used to treat PD (i.e., panicolytic agents) or are utilized to induce panic attacks (i.e., panicogenic agents). Polymorphisms in the dopaminergic, serotonergic, noradrenergic, and GABAergic, and CCKergic systems have been examined in relation to PD (Gratacos et al., 2007). The second class of genes falls within the neurodevelopmental and synaptic plasticity domain, and include genes for neurotrophic factors and their receptors. Across all studies, very few replications exist, and no gene seems to be the definitive “PD-gene”—in line with expectations from segregation and linkage analyses.

Social Anxiety Disorder and Specific Phobias Compared to other disorders within the anxiety spectrum, social anxiety disorder (SAD) and the specific phobias (SP) have received dramatically less research on the associated genetics. As a consequence of the paucity of extant investigations, many previous reports and reviews have examined these syndromes collectively (Kendler, Neale, Kessler, Heath, & Eaves, 1992). It should be noted that although in many cases the distinction is made among the various phobic disorders (i.e., social phobia, specific phobias, agoraphobia), there are instances in the literature where they are fully combined within a general “phobia” condition (e.g., Hettema et al., 2001). From a clinical and phenomenological perspective, the latter does not seem justified because these conditions present as discrete entities (Hettema, Prescott, Myers, Neale, & Kendler, 2005; Ogliari et al., 2006), and investigations that have combined them should be considered with this caveat in mind.

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Social Anxiety Disorder Social anxiety disorder (SAD), also known as social phobia, is characterized by an intense and often debilitating fear of social situations and inappropriate fear of negative evaluations. SAD is the second most common psychological disorder after depression (Kessler, Chiu, et al., 2005), with an estimated lifetime prevalence of 12.1% (Kessler, Berglund, et al., 2005). The negative impact of this disorder is startling. One epidemiological study found that 50% of people with SAD failed to complete high school, 70% fell within the bottom half of socioeconomic status, and 22% received welfare benefits (Schneier, Johnson, Hornig, Liebowitz, & Weissman, 1992). SAD has also been associated with increased public health costs (Greenberg et al., 1999); impairment in occupational, school, and social endeavors; and a lower rate of marriage (Schneier et al., 1994; Stein & Kean, 2000). SAD may be classified via two subtypes. In the case of nongeneralized SAD, social fears are specific to situations (e.g., public speaking or writing), whereas with generalized SAD fear is pervasive across most social situations (Heimberg, 1993). Onset of SAD typically occurs in childhood or early adolescence (Robins & Regier, 1991) and affects men and women equally (Kessler, Berglund, et al., 2005). Individuals with SAD have low rates of treatment presentation (Magee, Eaton, Wittchen, McGonagle, & Kessler, 1996), which is surprising given the level of impairment and chronic course associated with the disorder (Furmark, 2002). Both behavioral inhibition and shyness in children have been linked with SAD in adulthood. As is the case with other anxiety conditions, rates of comorbidity are high, being most pronounced with other anxiety disorders, depression, and substance use disorders (Buckner et al., in press; Regier et al., 1998). Early etiological research on phobias and fear focused primarily on fear conditioning and learning, with particular emphasis on social learning and classical conditioning models. More recent etiological research has focused on a potential genetic liability in the acquisition of fear and phobias, and, although the data are limited, research indicates that genes most likely also play a role in the pathogenesis of SAD (Hettema et al., 2005). A small, but growing, literature points to the familial aggregation of SAD (Fyer, Mannuzza, Chapman, Liebowitz, & Klein, 1993). A consistent finding has been that heritability appears to play a larger role for generalized SAD and that discrete SAD does not appear to be familial (Mannuzza et al., 1995; Stein, Chartier, Hazen, et al., 1998). For example, one investigation found that only relatives of generalized SAD probands, versus those of nongeneralized SAD probands, were at a 10-fold greater risk of also being diagnosed with SAD (Stein, Chartier, Hazen, et al., 1998). Family studies

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have furthermore revealed that SAD in probands increases the risk in first-degree relatives only for SAD and not that of other phobias (Fyer, Mannuzza, Chapman, Martin, & Klein, 1995). Twin studies have suggested a moderate role of genes for SAD, with one odds ratio estimate at 2.3 (Kendler, Myers, Prescott, & Neale, 2001). Of note, this study also demonstrated that monozygotic twins’ SAD concordance rates were much lower than those reported for other disorders. This has led some to suggest that perhaps (a) the environment may play a larger role in determining SAD, and (b) it may be that a susceptibility to SAD is inherited, rather than the disorder per se (Mathew, Coplan, & Gorman, 2001). With regard to the importance of environmental factors for SAD, there are several lines of additional research supporting this notion. First, SAD is one of the anxiety disorders that evidence varying prevalence rates across cultures. Furmark (2002) has discussed the possibility that cultural variation may be explained by cultural factors influencing socializing practices. For example, Asian cultures that are more collectivist in nature also have the lowest rates of reported SAD (e.g., 0.5% to 0.6% lifetime prevalence in Korea; Lee et al., 1990), and variation can also exist within one general culture (e.g., Europe) depending on geographical and climatic regions (Furmark et al., 1999; Lecrubier et al., 2000). Although these findings do not rule out the role of genetics, they do emphasize the influence of culture as one environmental factor. Second, animal models of SAD have demonstrated that specific contexts (i.e., environment) can lead to neuroplastic changes in brain-cell production that in turn increases socially anxious behaviors (Mathew et al., 2001). Finally, research has revealed that certain types of parenting styles may influence the development of SAD (Rapee & Melville, 1997), though it is important to consider that this may be an instance through which genes can influence the environment (i.e., genes most likely contribute to parenting styles) (Perusse, Rice, Despres, Rao, & Bouchard, 1997). With regard to the second possibility, that the genetics of SAD might confer a susceptibility for the disorder rather than the syndrome itself, a number of family studies have considered quantitative traits related to SAD. These include primarily harm avoidance and behavioral inhibition (Mathew et al., 2001). Preliminary data are in support of this hypothesis. For example, one family study demonstrated that the personality construct of harm avoidance is heritable in populations of SAD probands (Stein, Chartier, Lizak, & Jang, 2001). Similarly, a twin study within the general population revealed that one of the cardinal diagnostic criteria for SAD, the fear of negative evaluations, provided a heritability estimate of .42 (Stein, Jang, & Livesley, 2002). The authors concluded that perhaps these

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quantitative traits may reflect a more robust genetic phenotype for SAD than the disorder itself. Only one genome scan has been conducted with SAD populations, and no complex segregation analyses could be identified. The linkage analysis of SAD found evidence of suggestive linkage on chromosome 16, which contains the gene for the norepinephrine transporter, a possible candidate gene for SAD (Gelernter, Page, Stein, & Woods, 2004). Given the general paucity of neurological work on SAD, it is perhaps not surprising that only a handful of candidate gene association studies have been completed. Pharmacological studies (Blanco, Raza, Schneier, & Liebowitz, 2003) and more imaging studies (Tiihonen et al., 1997) suggest that genes in the serotonin and dopamine neurotransmitter systems are two plausible candidates that may be involved in the etiology of SAD. In three earlier reports, genes for the dopamine transporter and receptors, as well as the serotonin system genes for 5-HT, 5-HT2A, and 5-HT1B, were investigated, with few positive results (Kennedy et al., 2001; Schneier et al., 2000; Stein, Chartier, Kozak, King, & Kennedy, 1998). An investigation by Lochner et al. (2007) suggested the 5-HT2A T102C polymorphism may play a role in the etiology of SAD, though future studies are needed to replicate this finding. Specific Phobias An intense and irrational fear of something that possesses little actual threat is known as a specific phobia (SP). As many as 19.2 million American adults suffer from specific phobias (Kessler et al., 1994), and females are two to three times more likely to be affected than males (Magee et al., 1996). The onset of SP typically occurs in childhood or adolescence and has a persistent and chronic course. Three SP subtypes have been identified: animal, blood/injury, and situational (Muris, Schmidt, & Merckelbach, 1999). These subgroups are differentiated by symptom response, age of onset, and heritability (Fyer, 1998). Despite the availability of effective psychological and pharmacological treatments, less than 20% of individuals suffering from SPs present for treatment (Fyer, 1998). Low rates of treatment presentation most likely result from variable levels of impairment among those suffering from SP—if a feared situation can be avoided with minimal disruption of daily routine (e.g., snakes in an urban setting), it is unlikely a person will seek help. Very little research on the potential involvement of genes in the etiology of SPs has been conducted. No candidate association studies or linkage analyses could be identified. Family and twin investigations have revealed that there appears to be a modest genetic vulnerability for SPs (Kendler et al., 1992), and furthermore, that any noted familial aggregation is largely accounted for by genetic

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1096 The Genetics of Anxiety

factors (Kendler, Karkowski, & Prescott, 1999). One twin investigation with adolescence calculated a heritability estimate of approximately 0.6, though it should be noted that this is the highest heritability estimate to date (Ogliari et al., 2006). No sizeable age or gender differences have been found in the majority of family and/or twin studies (Kendler et al., 1992, 2001; Ogliari et al., 2006). Across the twin studies investigating SPs, data indicate that in addition to genetic influences, environmental factors also play a role in the etiology. These data also provide direct evidence against classical fear conditioning and learning etiological models for SPs. It appears that while genes are related to familial aggregation, familial learning is not (e.g., Sundet, Skre, Okkenhaug, & Tambs, 2003). That is, environmental experiences that influence the acquisition of fears are largely independent from familial experiences (Hettema et al., 2001; Sundet et al., 2003). Instead, the findings from both twin and family studies point to the theory of prepared conditionability (Ohman, 1986), which states that there are certain fears hardwired (i.e., genetic predisposition) that makes conditioning more probable. For example, in their classic experiment, Mineka, Davidson, Cook, and Keir (1984) found that in response to watching individuals from their group, rhesus monkeys learned to fear snakes but not flowers. This theory is also consistent with evolutionary models of SP. One final comment reflected in this literature is that the SPs (particularly those of an animal nature, such as fears of snakes, spiders, etc.), although related to other anxiety disorders, appear to be more distinct phenomena. Specifically, a large twin study revealed that SPs were influenced by a genetic factor largely uncorrelated with a second factor that was associated with all other anxiety disorders (Hettema et al., 2005). Moreover, that same study found that animal and situational type phobias appear to be more etiologically distinct from other anxiety disorders than blood injection phobia. Genetics of General Phobic Conditions Although we presented data on the separate genetic findings for SAD and SPs, it should also be noted that evidence is suggestive of common genetic underpinnings for all phobic conditions. Much of this data has emerged from a series of twin studies conducted by Kendler and colleagues (1992, 2001). By assessing a range of common fears and phobias in samples of male and female twins, it was found that although there are specific genetic factors involved with each type of phobic condition (e.g., social versus animal phobia), there is also a common genetic factor. Shared environmental factors appeared to play a relatively substantial role for only agoraphobia and social phobia, while individual/unique environmental factors played a role in all other types (see discussion on genetics of SPs).

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Generalized Anxiety Disorder Generalized anxiety disorder (GAD) is characterized by chronic worry that is both excessive and unrealistic across a variety of domains. Somatic symptoms, including headaches, chest pain, muscle tenderness, and fatigue, are another hallmark feature of GAD. The onset of GAD is gradual, beginning when an individual is in his or her 20s (Rickels & Schweizer, 1990), and although the condition is chronic, symptoms fluctuate and can be exacerbated by levels of current life stress (Hidalgo & Davidson, 2001). Epidemiological investigations report a global lifetime prevalence of GAD between 4.1% and 6.6% (e.g., Kessler, Davis, & Kendler, 1997), with women approximately twice as likely than men to be diagnosed with the condition (Kessler et al., 1994; Wittchen, 2002). In addition to experiencing somatic tension, individuals with GAD are also significantly more likely to be diagnosed with somatic disorders such as osteoporosis, asthma, and diabetes (Hidalgo & Davidson, 2001). This latter fact partially explains why individuals suffering from GAD are much more likely to present for treatment in a primary care setting (Schweizer & Rickels, 1997). Indeed, after PD, GAD populations evidence the second highest utilization of medical resources among the anxiety disorders (Kessler et al., 1999). Although comorbidity with major depressive disorder (MDD) is an important consideration across the anxiety syndromes, the specific comorbidity between GAD and depression is noteworthy. Evidence suggests that this comorbidity is particularly strong and that the genetic underpinnings for these two disorders are almost identical. Gorwood (2004) outlined three factors that support the theory that the same genes may be at play within MDD and GAD. First, there is reliable comorbidity between the two conditions. Findings from epidemiological studies demonstrate that MDD and GAD are more often comorbid with one another, than either disorder occurring individually, and that the co-occurrence of these two disorders is the most frequent “combination” of mood and anxiety disorders (Ballenger, 1999; Kessler, Foster, Saunders, & Stang, 1995). Approximately 80% of individuals diagnosed with GAD also qualify for a lifetime diagnosis of MDD (Judd et al., 1998). Similarly, an individual diagnosed with MDD has a significant odds ratio of 9.4 for a lifetime diagnosis of GAD and 17.8 for 6-month prevalence (Kessler et al., 1995). These data suggest that the comorbidity between GAD and MDD is not a matter of chance alone. Second, the two disorders have considerable joint heritability. Family studies have found elevated rates of morbidity among first-degree relatives of individuals with GAD, and the reported aggregate risk for GAD was 19.5% contrasted with 3.5% in control subjects (Mendlewicz,

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Review of the Anxiety Disorders and Relevant Genetic Findings 1097

Papadimitriou, & Wilmotte, 1993). A meta-analysis of available twin studies attributed 31.6% of the variance for liability to GAD to additive genetics for both men and women (Hettema et al., 2001). Importantly and with regard to MDD, several studies have found that the genetic factors for GAD and MDD are substantial and almost completely shared (Kendler, Gardner, Gatz, & Pedersen, 2007; Kendler et al., 1992). That is, the genes at play for MDD are most likely the same genes that influence GAD. Gorwood (2004) also notes that the two disorders must evidence similar mechanisms. For example, both GAD and MDD respond to similar treatments, whether psychological or pharmacological in nature, and similar temperamental characteristics are associated with the two conditions. Both of these factors provide an avenue for the identification of candidate genes. Genes linked with the transmission and regulation of serotonin have been the primary system investigated. In addition to polymorphisms in the serotonin transporter gene (5-HTT; O’Hara et al., 1999; You, Hu, Chen, & Zhang, 2005), researchers have considered genes coding for tryptophan hydroxylase (Fehr et al., 2001; Zhang et al., 2005), the 5-HT2A (Fehr et al., 2001), 5-HT2C (Fehr, Szegedi, et al., 2000), and 5-HTR1B (Fehr, Grintschuk, et al., 2000) receptors and MAOA (Samochowiec et al., 2004). As is the case with other disorders, findings across these candidate gene association studies are marred by few replications and many inconsistencies. Another area of genetics research relevant to GAD and MDD is the examination of genes linked with associated temperamental characteristics. Both behavioral inhibition and neuroticism have been investigated from a genetic perspective and represent traits related to GAD/MDD. Particularly those investigations focused on neuroticism are pertinent because a recent study revealed that this factor contributes around 25% to the genetic risk for MDD and GAD (Kendler et al., 2007; Pedersen, 2007). Several linkage studies on neuroticism have found significant linkage to the following chromosomes: 1q, 4q, 7p, 8p, 12q, and 13q (Cloninger et al., 1998; Fullerton et al., 2003; Zohar et al., 2003). Candidate association studies in turn have investigated the serotonin system genes, as well as variants in the GABA, DRD4, COMT, MAOA, and BDNF genes (see Arnold, Zai, & Richter, 2004). Although investigations utilizing a continuous trait score are characterized by increased power, results generally mirror those for the disorder (i.e., no conclusive findings). Obsessive Compulsive Disorder Obsessive compulsive disorder (OCD) is a debilitating neuropsychiatric disease characterized by intrusive and persistent thoughts (obsessions) that evoke anxiety, followed

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by urges or ritualized behaviors (compulsions) meant to neutralize and relieve the distress associated with obsessions. Recent epidemiological findings indicate that OCD has a lifetime, global prevalence of approximately 2% to 3% (Angst et al., 2004; Kessler, Berglund, et al., 2005), with the greatest period of risk occurring prior to the age of 18 (Nestadt, Bienvenu, Cai, Samuels, & Eaton, 1998). This disorder represents a serious global, public health concern, due to the chronic nature and extensive personal and societal costs associated with its symptoms (Dupont, 1993). The World Health Organization included OCD as one of the top 10 leading causes of disability (Lopez & Murray, 1998). Although obsessions and compulsions represent the hallmark features of OCD and have been consistently identified across diverse cultures (Horwath & Weissman, 2000; Rasmussen & Eisen, 1990; Weissman et al., 1994), further examination of the phenomenological, etiological, and treatment traits of OCD reveals marked heterogeneity (Miguel et al., 2005). Although the cardinal features of OCD may be consistent, the scope of symptoms classified as obsessions and compulsions diverse and includes a wide range of intrusive thoughts and preoccupations, rituals, and compulsions. Other phenomenological aspects of the disorder also reflect heterogeneity, including differences in insight, gender, age of onset, and comorbidity patterns (Leckman et al., 1997; Miguel et al., 2005; Minichiello, Baer, Jenike, & Holland, 1990). Comorbidity seems particularly relevant in any discussion of OCD. Investigations have consistently demonstrated that around 90% of individuals with OCD qualify for at least one additional, comorbid lifetime diagnosis (LaSalle et al., 2004; Pinto, Mancebo, Eisen, Pagano, & Rasmussen, 2006), and about half meet criteria for at least one additional current disorder (Denys, Tenney, van Megen, de Geus, & Westenberg, 2004; Pinto et al., 2006). Although major depressive disorder represents the most frequently comorbid lifetime diagnosis, other Axis I disorders, including anxiety disorders, Tourette’s disorder, eating disorders, and substance use disorders, also have elevated comorbidity rates in samples of individuals with OCD (LaSalle et al., 2004). One development within the OCD literature has been the emphasis and exploration of symptom dimensions. Extrapolating from the historical classification of patients based on phenomenology (e.g., washers; Khanna & Mukherjee, 1992), a series of exploratory factor analyses of symptom rating scales has delineated specific OCD factors. Since initial reports, over 14 factor analytic studies have been conducted on more than 2,000 patients, and similar factors have been consistently identified across investigations (Mataix-Cols, Rosario-Campos, & Leckman, 2005). The majority of analyses have suggested

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1098 The Genetics of Anxiety

the existence of either four or five dimensions, including: cleaning/contamination, checking/obsessions, symmetry/ ordering, and hoarding (e.g., Hasler et al., 2007). Of note, emerging research indicates that these dimensions exhibit clinically meaningful differences with regard to etiology, neurobiology, comorbidity patterns, and treatment response (Miguel et al., 2005). For example, some of the derived dimensions have been used successfully in familial analyses (Alsobrook, Leckman, Goodman, Rasmussen, & Pauls, 1999), neuroimaging experiments (Mataix-Cols et al., 2004; Rauch et al., 1998; Saxena & Maidment, 2004), treatment response (Mataix-Cols, Rauch, Manzo, Jenike, & Baer, 1999), and genetic studies (Cavallini, Di Bella, Siliprandi, Malchiodi, & Bellodi, 2002). Given the phenotypic complexity of OCD, it should be no surprise that etiological factors, particularly those that are biological in nature, appear to be no less multifaceted. That being said, there is now growing empirical research providing substantial evidence for a heritable component for OCD and the role of genes in the pathogenesis of this syndrome (for a more detailed review, see Grados & Wilcox, 2007). Family studies have consistently found that first-degree relatives of individuals with OCD have significantly elevated morbidity rates, such that the aggregate risk for OCD is 8.2% to 12% in the first-degree relatives, compared to less than 2% in controls (Hanna, Fischer, Chadha, Himle, & Van Etten, 2005; Hettema et al., 2001; Nestadt, Samuels, et al., 2000). One study provided evidence of specificity, in that relatives of OCD probands were significantly more likely to have OCD but not other anxiety disorders (Fyer, Lipsitz, Mannuzza, Aronowitz, & Chapman, 2005). Differences in estimates arise primarily from phenotypic considerations, such as comorbidity and age of onset. Specifically, it has been reported that both early onset and comorbidity with tics is linked with greater risk for OCD (Hanna et al., 2005; Leckman et al., 1995). Twin studies, although few in number and marked by small sample sizes, have provided further support for the notion that OCD has a hereditary element (Rasmussen & Tsuang, 1984). Twin concordance rates for OCD have ranged from 55% to 87% for monozygotic twins, and 22% to 47% for dizygotic twins. A recent cross-cultural twin study estimated that the additive genetic influence was approximately 45% to 58%, indicating that both genes and unique environmental factors play a role in the pathogenesis of OCD (Hudziak et al., 2004). A handful of complex segregation analyses have been conducted to investigate the specific nature of OCD transmission patterns (Alsobrook et al., 1999; Cavallini, Pasquale, Bellodi, & Smeraldi, 1999; Nestadt, Lan, et al., 2000). The collective evidence is suggestive of a complex pattern of inheritance, with some genes of major effect

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interacting with other genes in a mixed mode of transmission (Hemmings & Stein, 2006; Wendland, Kruse, Cromer, & Murphy, 2007). This is in keeping with early results from two genome-wide scans of OCD probands that revealed regions of potential involvement on six chromosomes (Hanna et al., 2002; Shugart et al., 2006). Hanna and colleagues (2002) reported suggestive linkage on chromosome 9p24, and Shugart et al. (2006) found suggestive linkage peaks at 3q27, 1q, 6q, 7q, and 11p. The former was conducted with only seven early-onset OCD proband families, whereas the latter was based on a sample of over 200 extended sib-pair OCD families. A third linkage study utilizing the Shugart et al. sample focused on one specific phenotype, that of compulsive hoarding. This analysis found a significant linkage peak on 14q23 (Samuels et al., 2007). Association studies are by far the primary methodology used in investigating the molecular genetics of OCD, as is true across all the anxiety disorders. It has been increasingly realized that association studies are more powerful than linkage studies for disorders with many genes and small gene effects (Risch & Merikangas, 1996). Candidate genes for OCD have revolved primarily around the serotonergic neurotransmission genes, given the therapeutic effect of SSRIs in the pharmacotherapy of OCD. The most researched gene is the serotonin transporter gene (5-HTT; SLC6A4), and an insertion-deletion polymorphism in the promoter region (5-HTTLPR). Other SERT variants, including Stin2 and the rare I425, have also been investigated (Ozaki et al., 2003). Additional serotonin system genes that have been examined are the 5-HT2A, the 5-HT1DB, and the 5-HT2C receptor genes. Across all studies investigating serotonin system genes, little consensus exists and conclusions are difficult given the number of null findings. In addition to the serotonin system, dopaminergic neurotransmission, glutaminergic neurotransmission, catechol-O-methyl transferase enzyme, monoamine oxidase A, and neurodevelopmental genes have also been examined. Candidates that have at least some positive findings include a Va1158Met polymorphism in the COMT gene, a 7-repeat allele in the VNTR of the dopamine-4 receptor gene, a disruption of the Hoxb8 gene, and a Val66Met variation of the proBDNF protein gene (for a more detailed review, refer to Grados & Wilcox, 2007). As is the case with the other anxiety disorders, findings are mixed with no definitive candidate gene linked—as of yet—to the manifestation of OCD. Conclusions across investigations indicate that further analyses with increased sample sizes and more homogeneous patient samples will be necessary to identify the role a given polymorphism may play as a genetic component of OCD. Other factors that most likely need to be taken into consideration are age of

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Future Genetics Research on Anxiety Psychopathology 1099

onset, gender, and comorbidity patterns—particularly with regard to Tourette’s disorder.

FUTURE GENETICS RESEARCH ON ANXIETY PSYCHOPATHOLOGY Over the past several decades, improvements in molecular genetic techniques and the increasingly fine-tuned mapping of the human genome have dramatically bolstered the feasibility of identifying molecular mechanisms and gene-candidates involved in psychopathology. Given these advances, it may be surprising that the specific genes believed to function as markers for particular disorders are relatively few in number (Smoller & Tsuang, 1998). This is certainly reflected in the findings reviewed in the first part of this chapter. Despite a substantial number of hypothesized etiological candidates, definitive genes for the anxiety disorders are still not known. As discussed briefly in the introduction to this chapter and as highlighted in the reviews of each syndrome, anxiety disorders represent complex traits. Across studies there is little to no evidence for relatively simple Mendelian inheritance patterns, and the intricacies of gene  gene  environment interactions inherent in the polygenetic nature of these disorders obscures the identification of disease and single-gene associations (Lander & Schork, 1994; Murphy et al., 2003). From a molecular perspective, additional challenges facing the successful isolation of risk genes include incomplete penetrence and the occurrence of phenocopies (Lander & Schork, 1994). Incomplete penetrance is a situation in which an individual may be a disease-causing gene carrier, but is not (at least outwardly) displaying the disorder. In this situation, investigations may be more likely to overlook a true association. Phenocopy, on the other hand, is when the disorder occurs without that individual’s being a carrier for the diseasecausing gene. In this instance the “copy” may be caused by random environmental factors. One overarching challenge that influences and can interact with these molecular complications (e.g., phenocopies, gene  gene interactions) is the necessity of clearly identifying the most accurate and definitive phenotype. Tsuang, Faraone, and Lyons (1993) have identified this problem as the “rate-limiting” step of genetic investigations. Over all other impediments, this one challenge is perhaps the most central because one will never detect the association between A and C, if B is being considered rather than A. In reviewing the literature for the anxiety disorders, it may have become evident that the definition of A is still open to debate. That is, the field as a whole still lacks consensus on what the precise phenotypic definitions for each of

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the anxiety syndromes should be (Shih et al., 2004). As such, many genetic investigations have been complicated by trying to tie a known marker (i.e., genetic variant) to a shifting and ambiguous phenotype. The lack of consensus on definitive phenotypes is a natural by-product of the tremendous heterogeneity inherent in each of the anxiety disorders (R. Freedman, Adler, & Leonard, 1999). Several examples of phenotypic heterogeneity reviewed in the first part of the chapter include PD with or without agoraphobia, or the various manifestations of SAD (i.e., generalized versus nongeneralized, situation specific). The anxiety disorder with perhaps the most diverse presentation is OCD, if one takes into consideration symptom dimensions, comorbidity patterns, and familiality. In all of these examples, mixed genetic findings fail to provide credence to the hypothesis that the examined constructs are homogeneous phenotypes (Pato, Pato, & Pauls, 2002). Adding another layer of complexity is the fact that in some instances researchers are still attempting to identify the most optimal means of measuring a phenomenon. For example, a study by our group found that depending on which measure was utilized, strikingly different results emerged in the association between trauma and hoarding symptoms (Cromer, Schmidt, & Murphy, 2007). Relying on a classical instrument used to tap OCD-related symptoms (i.e., the Yale-Brown Obsessive Compulsive Scale Checklist; Goodman et al., 1989), no association with trauma was revealed; yet when a more recent and empirically validated questionnaire was used to measure hoarding behaviors (i.e., Saving Inventory Revised; Frost, Steketee, & Grisham, 2004), a significant relationship emerged. There thus appear to be both conceptual and methodological obstacles, making it a challenge to settle on the “correct” phenotype, and moreover assuring that all individuals in a given sample are representatives of that “correct” phenotype. Several frameworks have been put forth in an attempt to address the issue of phenotypic heterogeneity, with the aim of subsequently increasing the power of genetic investigations. An established approach has been to consider broad versus narrow diagnostic definitions. One classic example of this classification method can be seen in the evolution of the phenotype for schizophrenia. More recently, researchers have considered other means by which to define phenotypes, including comorbidity, the possibility of endophenotypes, and the consideration of additional, nongenetic factors such as environmental dynamics. We explore each of these approaches and how they relate to the anxiety disorders in the following sections. One additional note in regard to the interaction between phenotypes and genetics research is that a multilevel integrative approach to the study of psychiatric phenomena

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1100 The Genetics of Anxiety

key in fostering the identification of definitive etiological theories (Cacioppo & Berntson, 1992). Only by analyzing a given syndrome from various perspectives, and then integrating any gained knowledge across macro (e.g., phenotype) and micro (e.g., genetics) levels can we hope to ascertain causative pathways and outcomes (see Figure 56.1). This notion is based on the corollary that there is a bidirectional influence between the developmental, environmental, and social cultural factors associated with a psychiatric syndrome, and the underlying neurophysiological and molecular processes of that phenomenon. That is, problems in genetic research can be largely alleviated by the careful examination of a given phenotype, and by the same token, genetic investigations can provide supportive evidence for a plausible phenotype (R. Freedman et al., 1999). It becomes increasingly clear that a necessity across research domains is for the phenotypes to be theoretically valid. In other words, for true and accurate classification (i.e., phenotypic definition), one needs an explicit theoretical foundation (Follette & Houts, 1996). Although research on the anxiety disorders in large part strives to be rooted in theory, a number of the classic definitions currently employed appear to lack a valid empirical basis. One example is the DSM’s characterization of OCD, with an emphasis on the distinction between obsessions and compulsions; yet research has provided clear support against this notion (Mataix-Cols et al., 2005; Summerfeldt, Richter, Antony, & Swinson, 1999). In addition to the approaches discussed in the following sections, taxometrics is a method for identifying theoretically based phenotypes. Taxometrics represents a data analytic tool for discerning categories from continua and establishes definitive indicators of identified categories (Schmidt, Kotov, & Joiner, 2004). We do not provide a detailed discussion of taxometrics in this chapter (for a thorough explanation of the methods and potential research applications please see Schmidt et al., 2004), but propose that this analytical approach may be extremely useful in the identification of “true” anxiety phenotypes, which in turn may be explored in genetic studies. Narrow versus Broad Phenotypic Definitions The heterogeneity inherent in complex psychiatric traits may be addressed by relying on two separate classification categories on opposite ends of a spectrum: those relying on discrete definitions, typically in relation to symptom presentation, and those focusing on more expansive traits that could potentially cross diagnostic boundaries. Narrow Phenotypic Definitions The vast majority of psychiatric genetic studies have relied on discrete diagnostic entities to define the phenotype

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of interest, and this is certainly true for research on the anxiety disorders (Arnold et al., 2004; Smoller & Tsuang, 1998). These definitions most often are based on the syndromal classification of a given condition as outlined by the DSM-IV-TR or the ICD-10. Although this approach has been at least partially helpful, in that twin and family studies have identified a familial and heritable component of anxiety disorders, candidate gene studies utilizing these phenotypes are highly inconsistent. One problem is that it is unclear whether the symptoms outlined in the diagnostic manuals represent the disease process under investigation (Hayes, Wilson, Gifford, Follette, & Strosahl, 1996). A second difficulty is that our current diagnostic categories are based on consensus definitions, resulting in a certain level of arbitrariness, with obvious consequences for definitively including a given individual in a class. We address these possibilities and potential solutions in the section that follows this one. A third problem is that diagnostic categories, as supposedly homogeneous groupings, entail marked heterogeneity. That is, even within the classically narrow definitions of each diagnostic entity, any number of finer groupings is feasible. A growing number of family and candidate gene association studies have examined applying a subgrouping method in an effort to identify even more homogeneous phenotypes. The most salient example of a factor used to create subphenotypes of a specific phenomenon is symptom presentation. Earlier we discussed the recent shift in our understanding of the symptomatic features of OCD, specifically the relevance of symptom dimensions. Research on the genetics, including candidate gene investigations, have been more promising, examining the possibility that these symptom dimensions may represent more homogeneous subphenotypes of OCD (Hasler, Drevets, Gould, Gottesman, & Manji, 2006; Hasler et al., 2007; Lochner et al., 2004). Another example would be the focus on generalized SAD, rather than considering all presentations of the disorder (i.e., exclusion of nongeneralized SAD), and again, genetic findings with this phenotype have had more relative success (Lochner et al., 2007). Family studies have also found that the specific genetic contribution to many of these subphenotypes is relatively small, as is the case, for example, with the checking subtype of OCD (Hasler et al., 2007). Thus, although much time and energy has been allotted to identifying subtypes of psychiatric conditions based on clinical symptoms, the overall success of tying these subtypes to specific genes has been limited. Additional groupings that have been proposed to lessen the heterogeneity of diagnostic categories include severity, age of onset, gender, and familiality. Smoller and Tsuang (1998) have proposed ascertaining phenotypic extremes,

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Future Genetics Research on Anxiety Psychopathology 1101

including identifying those probands with early onset only, or those who evidence particularly severe forms of a disorder. This approach has been utilized with some success for both OCD (Leckman et al., 1995) and PD (R. B. Goldstein, Wickramaratne, Horwath, & Weissman, 1997). Identifying those individuals with a familial form of a given disorder, may furthermore be a fruitful subtyping strategy. This approach has been employed successfully in the investigation of nonpsychiatric disorders (e.g., colon and breast cancer; Lander & Schork, 1994) and is increasingly being applied to mood (Drevets et al., 1997) and anxiety disorders (Hasler et al., 2007). Broad Phenotypic Definitions In direct contrast to narrow phenotypic definitions, another response to the heterogeneity of psychiatric disorders has been to broaden the definitions of phenotypes. This approach is based on (a) the possibility that anxiety disorders may represent an extreme end of a continuum of a given phenomenon, and (b) that different syndromes may share certain genetic vulnerabilities in common. We will briefly outline means of broadening a phenotype relevant to both perspectives in following paragraphs. As was discussed with regard to narrow phenotypes, the diagnostic entities in the DSM are both relatively arbitrary and atheoretical (Clark, Watson, & Reynolds, 1995). For example, if an individual meets all the criteria for GAD with the exception of endorsing two rather than three of the associated symptoms (e.g., muscle tension, fatigue, restlessness), they technically would not receive a diagnosis of GAD and would most likely be excluded from genetic investigations utilizing a narrow definition. Yet, can it be definitively concluded that this individual does not suffer from a GAD-like syndrome? The answer is no, and moreover, it is very plausible that the genetics at play for the narrow definition of GAD may also be present in this individual. Utilizing a broad definition, subclinical or subsyndromal cases may be included for analysis. Both studies for PD and OCD, as well as those for SPs have relied on this broadening technique (e.g., Crowe, Noyes, Pauls, & Slymen, 1983; Kendler et al., 2001; Shugart et al., 2006; Weissman et al., 1993). In addition to relaxing the phenotypic definition to include subsyndromal cases, there is a growing interest in considering not only the disorders per se, but also quantifiable traits relevant to the larger diagnostic categories (Hettema et al., 2001). Assessing dimensional phenotypes can either focus on a trait relevant to a specific disorder or on a dimension that cuts across diagnostic entities. Investigations looking at disorder-specific traits are increasing in frequency, though still relatively few in number. One example would be the study on fear of negative evaluations, as

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a trait relevant to SAD, conducted by Stein and colleagues (Stein et al., 2002). Other possibilities might include various cognitive vulnerabilities (e.g., anxiety sensitivity for PD, thought-action-fusion for OCD) or biological risk factors (e.g., response to CO2 inhalation for PD). Many of these traits may be plausible endophenotypes, which will be discussed in a later section. Focus on Comorbidity The second approach to examining quantifiable traits—assessing dimensional phenotypes that cut across diagnostic entities—is based on the findings that different syndromes may share genetic vulnerabilities in common. As highlighted throughout the review of the anxiety disorders, comorbidity is pervasive. Some researchers have noted that a lack of comorbidity may constitute the exception, and that empirically exploring patterns of co-occurring disorders will be necessary for clarifying phenotypes for genetic investigations (Clark et al., 1995; Shih et al., 2004). One of the key considerations with regard to comorbidity is to determine the reasons for the co-occurrence of two or more disorders. That is, the comorbidity may be causal in nature (i.e., one disorder causes the other disorder) or an instance where the two disorders may reflect different clinical aspects of the same underlying disease or diathesis (Merikangas et al., 2003). This notion is based on the theory that commonly comorbid disorders may be comprised of independently heritable components that make up the more complex syndromes and that are shared across diagnostic entities (Grados, Walkup, & Walford, 2003). By looking across conditions at constellations of common traits, investigators may be able to identify promising and more parsimonious phenotypes for genetic analyses. Support for pleitropy among the anxiety disorders has emerged from a series of twin and family studies. Krueger (1999) proposed grouping psychiatric disorders into internalizing and externalizing classes, and Kendler, Prescott, Myers, and Neale (2003) have also found that patterns of comorbidity (i.e., externalizing versus internalizing) were largely accounted for by genetics. In analyses conducted for both men and women, support was found for common genetic factors that play a role in the etiology of all anxiety disorders. In a series of subsequent investigations, more fine-grained investigations of potentially shared genetic factors for the anxiety disorders were conducted. Across studies, it seems that there are common shared genetic factors, along with both shared and unique environmental factors, and unique genetic factors. SPs were less strongly linked, and SAD more intermediately linked to other anxiety disorders (Hettema et al., 2005; Kendler et al., 1992, 2001). One limitation to these investigations was the noninclusion of OCD.

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1102 The Genetics of Anxiety

The next step emerging from these findings is to directly investigate potential phenotypes that might be associated with the common genetic factors. Two plausible dimensional phenotypes that cut across diagnostic entities within the anxiety spectrum are trait anxiety, or neuroticism, and behavioral inhibition (Arnold et al., 2004; Smoller & Tsuang, 1998). Genetic association studies have begun to emerge for both neuroticism and behavioral inhibition (Arnold et al., 2004). Neuroticism is of particular interest given that it may help explain the pervasive comorbidity between anxiety and depression, by representing one common characteristic that is part of a general diathesis for these disorders (Fanous et al., 2002; Hettema, Prescott, & Kendler, 2004; Jardine, Martin, & Henderson, 1984). A population-based twin study found that although the genetic correlations between each anxiety disorder and neuroticism were high, there may also be additional shared genetic factors, independent of neuroticism, between depression, GAD, and PD (Hettema, Neale, Myers, Prescott, & Kendler, 2006).

narrow endophenotypic diagnostic definitions (e.g., pure schizophrenia; Farmer, McGuffin, & Gottesman, 1987). Likewise, in longitudinal studies, broader diagnostic categories (e.g., mood plus anxiety disorders) showed greater stability over time than narrow diagnostic definitions (e.g., pure PD; Angst, Vollrath, Merikangas, & Ernst, 1990). This might lead one to conclude that relatively broad endophenotypes such as brain function endophenotypes (e.g., cognitive performance) are the most heritable and most appropriate for genetic studies. However, one further consideration is that although broad phenotypic constructs may show high familial transmission in twin and family studies, they may not represent alternative manifestations of a single liability distribution (McGue, Gottesman, & Rao, 1983). Genetic factors for intermediate traits that are closer to the genotype in the developmental scheme, such as biological markers, may generally be easier to identify because of the improved signal-to-noise ratio in the fraction of variance explained by any single factor (Carlson, Eberle, Kruglyak, & Nickerson, 2004). Evaluation of Biological Endophenotypes

Endophenotypes The exploration of endophenotypes is an additional strategy that has been proposed for overcoming the methodological difficulties discussed throughout this chapter with respect to elucidating the genetic basis of any given anxiety disorder. The term endophenotype is described as an internal phenotype (i.e., not obvious to the unaided eye) that fills the gap between available descriptors and between the gene and the elusive disease process (Gottesman & Shields, 1973), and therefore may help to resolve questions about etiological models. The endophenotype concept was based on the assumption that the number of genes involved in the variations of endophenotypes representing relatively straightforward and putatively more elementary phenomena (as opposed to behavioral macros) are fewer than those involved in producing a psychiatric diagnostic entity (Gottesman & Gould, 2003). Endophenotypes provide a means for identifying the downstream traits of clinical phenotypes, as well as the upstream consequence of genes. The methods available to identify endophenotypes include neuropsychological, cognitive, neurophysiological, neuroanatomical, and biochemical measures. Advantages of Biological Endophenotypes Within the broad class endophenotypes there is a gradient of proximity to the gene and gene products (geno) versus closer to the symptoms and the disease itself (pheno; Hasler et al., 2006). In twin studies, broader diagnostic definitions (e.g., schizophrenia plus mood-incongruent affective disorders plus schizotypal personality disorder plus atypical psychosis) may provide higher heritability estimates than

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The evaluation of biological endophenotypes is based on the following criteria, which were structured on the seminal work by Gottesman and Shields (Hasler, Drevets, Manji, & Charney, 2004): • Specificity: The endophenotype is more strongly associated with the disease of interest than with other psychiatric conditions. • State-independence: The endophenotype is stable over time and not an epiphenomenon of the illness or its treatment. • Heritability: Variance in the endophenotype is associated with genetic variance. • Familial association: The endophenotype is more prevalent among the relatives of ill probands compared with an appropriate control group. • Co-segregation: The endophenotype is more prevalent among the ill relatives of ill probands compared with the well relatives of the ill probands. • Biological and clinical plausibility: The endophenotype bears some conceptual relationship to the disease. We next discuss in detail one example of an endophenotype for the anxiety disorders, specifically focusing on PD, followed by a more general discussion of potential endophenotypes for OCD. All endophenotypes that have been proposed for PD must be seen as putative endophenotypes because whether they consistently meet all required endophenotype criteria is yet to be determined. In this section, we discuss the response to caffeine as a putative endophenotype

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Future Genetics Research on Anxiety Psychopathology 1103

because it meets many of the endophenotype criteria and has been successfully used to identify risk genes for PD. Sensitivity to Caffeine as a Putative Endophenotype for Panic Disorder Because of its psychostimulant effects, caffeine is the most widely consumed psychotropic agent. In healthy subjects with no risk for anxiety disorders, caffeine in small and moderate doses is a mild stimulant when taken orally, although it can have marked effects when administered intravenously, including producing olfactory hallucinations. The oral intake of large amounts of caffeine in healthy subjects has been shown to produce anxiety, insomnia, and a variety of somatic symptoms, including increased blood pressure (Charney, Heninger, & Jatlow, 1985). Considerable individual differences in the effects of caffeine (Evans & Griffiths, 1991) may point to genetic factors underlying caffeine sensitivity. Although there is little evidence for the heritability of the behavioral response to caffeine, there is increasing evidence that genetic factors influence caffeine consumption (Cornelis, El-Sohemy, & Campos, 2007), caffeine-induced anxiety (Alsene, Deckert, Sand, & de Wit, 2003), and caffeine-related reductions in sleep quality (Retey et al., 2007). There are several lines of evidence for an increased sensitivity to caffeine in subjects with PD. A study using a caffeine consumption inventory (Boulenger, Uhde, Wolff, & Post, 1984) indicated that subjects with PD, but not depressed patients or normal controls, experienced levels of anxiety that correlated with their degree of caffeine consumption. The sensitivity to caffeine was confirmed by the observation that more subjects with PD than healthy controls reported the discontinuation of coffee intake due to unwanted effects. Caffeine challenge experiments also confirmed an association between increased sensitivity to caffeine and PD (Charney et al., 1985). In the study by Charney et al. (1985), caffeine produced significantly greater increases in subject-rated anxiety, nervousness, fear, nausea, heart palpitations, restlessness, and tremors in subjects with PD than in healthy controls. In the patients, these symptoms were significantly correlated with plasma caffeine levels. In sum, there is reasonable evidence for a relatively specific elevation of caffeine sensitivity associated with PD. A pilot study in subjects with remitted PD (NIMH, unpublished data) suggests state-independence of increased caffeine sensitivity in panic disorder. Although caffeine binding in the brain is mostly nonspecific, the stimulatory action of caffeine seems to be brought about by an inhibition of transmission via adenosine A2a receptors. The central role of adenosine receptors in mediating the behavioral effects of caffeine has motivated a search for variation in genes encoding these receptors that could influence interindividual differences in caffeine response. A gene association study (Alsene et al.,

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2003) found that polymorphisms in the A1 and A2a adenosine receptor genes were related to the variation in caffeine-induced anxiety. Healthy, infrequent caffeine users received either placebo or caffeine citrate (300 mg) in random order, under double-blind conditions. To determine whether genetic variations in the A1 and the A2a receptor were related to subjective or behavioral measurements, individuals were assigned to one of three genotypic groups (e.g., TT, CT, or CC) at each of the four loci of the receptor genes (716T > G, 263C > T, 1976T > C, and 2592C > Tins). The 1976T > C and 2592C > Tins polymorphisms were in nearly complete linkage disequilibrium and therefore these two linked polymorphisms formed only three genotypic groups (e.g., TT, CT, or CC). Individuals with the linked 1976 T/T and the 2592 Tins/Tins variants in the A2a adenosine receptor gene reported greater increases in anxiety after caffeine challenge than did individuals in either of the other two genotypic groups. A subsequent study examined associations between the A1 and A2a adenosine receptor genes and PD. They found that subjects with PD had a significantly greater frequency of the 1976T allele and the 1976T/T genotype of the A2a adenosine receptor gene than matched controls (Deckert et al., 1998). The increased sensitivity to caffeine intake in healthy first-degree relatives of panic patients (Nardi et al., 2007) suggests sheared genetic risk for caffeine sensitivity and PD. Taken together, these studies suggest heritability, familial association, and biological plausibility for caffeine sensitivity as putative endophenotpye for PD. While there is considerable evidence for caffeine sensitivity as an endophenotype in panic, data for other biological endophenotypes are lacking. Structural imaging studies have not yielded consistent brain volumetric abnormalities in anxiety disorders. Further endophenotypes may be derived from the dysfunctions of the central serotonergic, noradrenergic, and dopaminergic systems and the hypothalamic-pituitary-adrenal axis found in patients with anxiety disorders although these dysfunctions seem to be nonspecific. Reduced binding to the benzodiazepine receptor in the prefrontal cortex has been consistently found in patients with PD (Cameron et al., 2007; Malizia et al., 1998). A recent study by Hasler et al. (unpublished data) showed specificity of this finding for PD. This literature encourages the evaluation of other endophenotype criteria, including temporal stability and familial association of this abnormality. Another putative endophenotype for PD may be derived from the respiratory system dysfunction that has been proposed as an important etiological factor in panic (Klein, 1993). Alterations of the central systems using cholecystokinin, neuropeptide Y, and dysfunctions of the thyroid axis may provide other promising leads in the discovery of endophenotypes for anxiety disorders.

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1104 The Genetics of Anxiety

Putative Endophenoyptes for Obsessive Compulsive Disorder While the amygdala is considered as the key brain structure in the pathophysiology of anxiety disorders including PD, PTSD, and phobias, the epidemiology and the neuroanatomical model for OCD seem to be different (Fontenelle & Hasler, in press). Aberrant functioning within the corticostriato-thalamo-cortical circuitry is the foundation of current models of OCD (de Mathis et al., 2006). Promising candidate brain function endophenotypes for OCD include (a) nonverbal recall impairments that are possibly due to the use of inappropriate organizational strategies as measured with the Rey complex figure test (Savage et al., 1999), (b) failures in decision making as measured by the Iowa Gambling Task (Bechara, Damasio, Damasio, & Anderson, 1994), (c) impaired set-shifting as measured by the object alternation test (M. Freedman, 1990), and (d) responseinhibition deficits as measured by oculomotor tests (Rosenberg et al., 1997). Chamberlain, Blackwell, Fineberg, Robbins, and Sahakian (2005) concluded that the neurobiology of the relatively specific neuropsychological deficits in OCD may be conceptualized in terms of lateral orbitofrontal loop dysfunction suggesting that functional imaging endophenotypes may be derived from such a dysfunction. Brain structure endophenotypes may be based on anatomical alterations in the orbitofrontal cortex and the striatum frequently reported in OCD (Brambilla, Barale, Caverzasi, & Soares, 2002). Given the increasing recognition of the importance of epigenetic transformations and developmental factors in the expression of psychiatric phenotypes (Gottesman & Hanson, 2004; Hasler et al., 2005), the symptom provocation method may be necessary to achieve the endophenotype criterion state-independence. Exposure to provocative stimuli has successfully been used as a symptom provocation paradigm for neurobiological studies in OCD (Mataix-Cols et al., 2004). Studies aimed at evaluating candidate endophenotypes for OCD with respect to specificity, temporal stability, and prevalence in unaffected relatives are clearly warranted. Given the lack of well-designed twin, family, and prospective studies evaluating putative endophenotypes in anxiety disorders, future research has the potential to considerably improve the phenotypic definition of these possibly heterogeneous entities.

ological model for anxiety disorders (e.g., Hettema et al., 2006). These factors most likely help shape the trajectory a particular genotype may take, particularly in cases where disorders share common diatheses. For example, environmental factors most likely largely account for the differentiation between MDD and GAD. This type of relationship is directly in line with traditional diathesis-stress theories for psychiatric disorders (Zuckerman, 1999). A growing number of studies have found that experiences of life stress interact with genes. A seminal investigation conducted by Caspi and colleagues (2003) found that the life stress interacted with the serotonin transporter polymorphism 5-HTTLPR in the expression of depression. Furthermore, there is also evidence for gene  gene  environment interactions with psychiatric populations, involving the serotonin transporter and BDNF genes, as well as the experience of childhood abuse (Kaufman et al., 2006). To effectively study the influence of environmental factors and gene-by-environment interactions, large samples will be needed.

SUMMARY Considering that genetic investigations of anxiety-related phenomena began less than 3 decades ago, the findings to date are quite remarkable. Across the anxiety disorders, we have extensive support for a heritable component and multiple association studies have suggested potential candidate genes. Furthermore, advances in molecular genetic techniques are empowering association, linkage, and segregation analyses, particularly with the increasing feasibility of genome-wide scans. That being said, when analyzing the data collectively, it becomes evident that the genetic picture of the anxiety construct is far from complete. In this chapter, we set out to provide a snapshot of the anxiety-related disorders, and to discuss factors that may be contributing to the challenge of identifying specific gene and disorder (or trait) associations. Taken together, reducing the phenotypic heterogeneity is crucial for the identification of vulnerability genes. We also outlined possible alternative approaches that may provide more fruitful avenues for future investigations. These include dissecting the behavioral phenotypes into key components and simultaneously considering specific environmental risk factors. The hope is that by fine-tuning a multilevel integrative approach, we may ultimately improve phenotypic definitions and unearth the genetic diathesis of anxiety.

Environmental Factors Environmental factors also play an important role in genetic etiological mechanisms (Cacioppo & Berntson, 1992). Across all twin studies, a consistent finding has been the inclusion of both shared and unique environmental factors in the eti-

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REFERENCES Alsene, K., Deckert, J., Sand, P., & de Wit, H. (2003). Association between A2a receptor gene polymorphisms and caffeine-induced anxiety. Neuropsychopharmacology, 28, 1694–1702.

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References 1105 Alsobrook, I. J., Leckman, J. F., Goodman, W. K., Rasmussen, S. A., & Pauls, D. L. (1999). Segregation analysis of obsessive-compulsive disorder using symptom-based factor scores. American Journal of Medical Genetics, 88, 669–675. American Psychiatric Association. (1980). Diagnostic and Statistical Manual of Mental Disorders (3rd ed.). Washington, DC: Author. American Psychiatric Association. (1987). Diagnostic and Statistical Manual of Mental Disorders (3rd ed., revised). Washington, DC: Author. American Psychiatric Association. (1994). Diagnostic and Statistical Manual of Mental Disorders (4th ed.). Washington, DC: Author. Angst, J., Gamma, A., Endrass, J., Goodwin, R., Ajdacic, V., Eich, D., et al. (2004). Obsessive-compulsive severity spectrum in the community: Prevalence, comorbidity, and course. European Archives of Psychiatry and Clinical Neuroscience, 254, 156–164. Angst, J., Vollrath, M., Merikangas, K., & Ernst, C. (1990). Comorbidity of anxiety and depression in the Zurich cohort study of young adults. In J. D. Maser & C. R. Cloninger (Eds.), Comorbidity of mood and anxiety disorders (pp. 123–137). Washington, DC: American Psychiatric Press. Arnold, P. D., Zai, G., & Richter, M. A. (2004). Genetics of anxiety disorders. Current Psychiatry Reports, 6, 243–254. Ballenger, J. C. (1999). Current treatments of the anxiety disorders in adults. Biological Psychiatry, 46, 1579–1594. Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1–3), 7–15. Blanco, C., Raza, M. S., Schneier, F. R., & Liebowitz, M. R. (2003). The evidence-based pharmacological treatment of social anxiety disorder. International Journal of Neuropsychopharmacology, 6, 427–442. Boulenger, J. P., Uhde, T. W., Wolff, E. A., III, & Post, R. M. (1984). Increased sensitivity to caffeine in patients with panic disorders: Preliminary evidence. Archives of General Psychiatry, 41, 1067–1071. Boyd, J. H. (1986). Use of mental health services for the treatment of panic disorder. American Journal of Psychiatry, 143, 1569–1574. Brambilla, P., Barale, F., Caverzasi, E., & Soares, J. C. (2002). Anatomical MRI findings in mood and anxiety disorders. Epidemiologia e Psichiatria Sociale, 11(2), 88–99. Buckner, J. D., Schmidt, N. B., Lang, A. R., Small, J., Schlauch, R. C., & Lewinsohn, P. M. (in press). Specificity of social anxiety disorder as a risk factor for alcohol and cannabis dependence. Journal of Psychiatric Research. Cacioppo, J. T., & Berntson, G. G. (1992). Social psychological contributions to the decade of the brain: Doctrine of multilevel analysis. American Psychologist, 47, 1019–1028. Cameron, O. G., Huang, G. C., Nichols, T., Koeppe, R. A., Minoshima, S., Rose, D., et al. (2007). Reduced [gamma]-aminobutyric acidabenzodiazepine binding sites in insular cortex of individuals with panic disorder. Archives of General Psychiatry, 64, 793–800. Carlson, C. S., Eberle, M. A., Kruglyak, L., & Nickerson, D. A. (2004, May 27). Mapping complex disease loci in whole-genome association studies. Nature, 429, 446–452. Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., Harrington, H., et al. (2003, July 18). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 301, 386–389. Cavallini, M. C., Di Bella, D., Siliprandi, F., Malchiodi, F., & Bellodi, L. (2002). Exploratory factor analysis of obsessive-compulsive patients and association with 5-HTTLPR polymorphism. American Journal of Medical Genetics, 114, 347–353.

c56.indd Sec4:1105

compulsive disorder: The importance of failures in cognitive and behavioural inhibition as candidate endophenotypic markers. Neuroscience and Biobehavioral Reviews, 29, 399–419. Charney, D. S., Heninger, G. R., & Jatlow, P. I. (1985). Increased anxiogenic effects of caffeine in panic disorders. Archives of General Psychiatry, 42, 233–243. Clark, L. A., Watson, D., & Reynolds, S. (1995). Diagnosis and classification of psychopathology: Challenges to the current system and future directions. Annual Review of Psychology, 46, 121–153. Cloninger, C. R., Van Eerdewegh, P., Goate, A., Edenberg, H. J., Blangero, J., Hesselbrock, V., et al. (1998). Anxiety proneness linked to epistatic loci in genome scan of human personality traits. American Journal of Medical Genetics, 81, 313–317. Cohen, D. D. (1951). Psychotherapy and its application to anxiety states. McGill Medical Journal, 20(1), 28–35. Cornelis, M. C., El-Sohemy, A., & Campos, H. (2007). Genetic polymorphism of the adenosine A2A receptor is associated with habitual caffeine consumption. American Journal of Clinical Nutrition, 86, 240–244. Cromer, K. R., Schmidt, N. B., & Murphy, D. L. (2007). Do traumatic events influence the clinical expression of compulsive hoarding? Behaviour Research and Therapy, 45, 2581–2592. Crowe, R. R., Goedken, R., Samuelson, S., Wilson, R., Nelson, J., & Noyes, R., Jr. (2001). Genomewide survey of panic disorder. American Journal of Medical Genetics, 105, 105–109. Crowe, R. R., Noyes, R., Pauls, D. L., & Slymen, D. (1983). A family study of panic disorder. Archives of General Psychiatry, 40, 1065–1069. Deckert, J., Nothen, M. M., Franke, P., Delmo, C., Fritze, J., Knapp, M., et al. (1998). Systematic mutation screening and association study of the A1 and A2a adenosine receptor genes in panic disorder suggest a contribution of the A2a gene to the development of disease. Molecular Psychiatry, 3, 81–85. de Mathis, M. A., Diniz, J. B., do Rosario, M. C., Torres, A. R., Hoexter, M., Hasler, G., et al. (2006). What is the optimal way to subdivide obsessive-compulsive disorder? CNS Spectrums, 11, 762–776. Denys, D., Tenney, N., van Megen, H. J., de Geus, F., & Westenberg, H. G. (2004). Axis I and II comorbidity in a large sample of patients with obsessive-compulsive disorder. Journal of Affective Disorders, 80(2–3), 155–162. Drevets, W. C., Price, J. L., Simpson, J. R., Jr., Todd, R. D., Reich, T., Vannier, M., et al. (1997, April 24). Subgenual prefrontal cortex abnormalities in mood disorders. Nature, 386, 824–827. Dupont, R. L. (1993). “Town Hall” panel presentation. Paper presented at the Anxiety Disorders of America Conference. Evans, S. M., & Griffiths, R. R. (1991). Dose-related caffeine discrimination in normal volunteers: Individual differences in subjective effects and self-reported cues. Behavioural Pharmacology, 2(4/5), 345–356. Fanous, A. H., Gardner, C. O., Prescott, C. A., Cancro, R., & Kendler, K. S. (2002). Neuroticism, major depression and gender: A population-based twin study. Psychological Medicine, 32, 719–728. Farmer, A. E., McGuffin, P., & Gottesman, I. I. (1987). Twin concordance for DSM-III schizophrenia: Scrutinizing the validity of the definition. Archives of General Psychiatry, 44, 634–641. Fehr, C., Grintschuk, N., Szegedi, A., Anghelescu, I., Klawe, C., Singer, P., et al. (2000). The HTR1B 861G>C receptor polymorphism among patients suffering from alcoholism, major depression, anxiety disorders and narcolepsy. Psychiatry Research, 97, 1–10.

Cavallini, M. C., Pasquale, L., Bellodi, L., & Smeraldi, E. (1999). Complex segregation analysis for obsessive compulsive disorder and related disorders. American Journal of Medical Genetics, 88, 38–43.

Fehr, C., Schleicher, A., Szegedi, A., Anghelescu, I., Klawe, C., Hiemke, C., et al. (2001). Serotonergic polymorphisms in patients suffering from alcoholism, anxiety disorders and narcolepsy. Progress in NeuroPsychopharmacology and Biological Psychiatry, 25, 965–982.

Chamberlain, S. R., Blackwell, A. D., Fineberg, N. A., Robbins, T. W., & Sahakian, B. J. (2005). The neuropsychology of obsessive

Fehr, C., Szegedi, A., Anghelescu, I., Klawe, C., Hiemke, C., & Dahmen, N. (2000). Sex differences in allelic frequencies of the 5-HT2C Cys23Ser

8/19/09 4:14:18 PM

1106 The Genetics of Anxiety polymorphism in psychiatric patients and healthy volunteers: Findings from an association study. Psychiatric Genetics, 10, 59–65. Ferdinand, R. F., van der Reijden, M., Verhulst, F. C., Nienhuis, J., & Giel, R. (1995). Assessment of the prevalence of psychiatric disorder in young adults. British Journal of Psychiatry: Journal of Mental Science, 166, 480–488. Follette, W. C., & Houts, A. C. (1996). Models of scientific progress and the role of theory in taxonomy development: A case study of the DSM. Journal of Consulting and Clinical Psychology, 64, 1120–1132. Fontenelle, L. F., & Hasler, G. (in press). The analytical epidemiology of obsessive-compulsive disorder: Risk factors and correlates. Progress in Neuro-Psychopharmacology and Biological Psychiatry.

Goodman, W. K., Price, L. H., Rasmussen, S. A., Mazure, C., Fleischmann, R. L., Hill, C. L., et al. (1989). The Yale-Brown Obsessive Compulsive Scale: Pt. I. Development, use, and reliability. Archives of General Psychiatry, 46, 1006–1011. Gorwood, P. (2004). Generalized anxiety disorder and major depressive disorder comorbidity: An example of genetic pleiotropy? European Psychiatry, 19(1), 27–33. Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry: Etymology and strategic intentions. American Journal of Psychiatry, 160, 636–645. Gottesman, I. I., & Hanson, D. R. (2004). Human development: Biological and genetic processes. Annual Review of Psychology, 56, 10.11–10.24.

Frances, A., Miele, G. M., Widiger, T. A., Pincus, H. A., Manning, D., & Davis, W. W. (1993). The classification of panic disorders: From Freud to DSM-IV. Journal of Psychiatric Research, 27(Suppl, 1), 3–10.

Gottesman, I. I., & Shields, J. (1973). Genetic theorizing and schizophrenia. British Journal of Psychiatry: Journal of Mental Science, 122(566), 15–30.

Freedman, M. (1990). Object alternation and orbitofrontal system dysfunction in Alzheimer ’s and Parkinson’s disease. Brain and Cognition, 14, 134–143.

Grados, M. A., Walkup, J., & Walford, S. (2003). Genetics of obsessivecompulsive disorders: New findings and challenges. Brain and Development, 25(Suppl, 1), S55–S61.

Freedman, R., Adler, L. E., & Leonard, S. (1999). Alternative phenotypes for the complex genetics of schizophrenia. Biological Psychiatry, 45, 551–558.

Grados, M. A., & Wilcox, H. C. (2007). Genetics of obsessive-compulsive disorder: A research update. Expert Review of Neurotherapeutics, 7, 967–980.

Frost, R. O., Steketee, G., & Grisham, J. (2004). Measurement of compulsive hoarding: Saving inventory-revised. Behaviour Research and Therapy, 42, 1163–1182.

Gratacos, M., Sahun, I., Gallego, X., Amador-Arjona, A., Estivill, X., & Dierssen, M. (2007). Candidate genes for panic disorder: Insight from human and mouse genetic studies. Genes, Brain, and Behavior, 6(Suppl, 1), 2–23.

Fullerton, J., Cubin, M., Tiwari, H., Wang, C., Bomhra, A., Davidson, S., et al. (2003). Linkage analysis of extremely discordant and concordant sibling pairs identifies quantitative-trait loci that influence variation in the human personality trait neuroticism. American Journal of Human Genetics, 72, 879–890. Furmark, T. (2002). Social phobia: Overview of community surveys. Acta Psychiatrica Scandinavica, 105(2), 84–93. Furmark, T., Tillfors, M., Everz, P., Marteinsdottir, I., Gefvert, O., & Fredrikson, M. (1999). Social phobia in the general population: Prevalence and sociodemographic profile. Social Psychiatry and Psychiatric Epidemiology, 34, 416–424. Fyer, A. J. (1998). Current approaches to etiology and pathophysiology of specific phobia. Biological Psychiatry, 44, 1295–1304. Fyer, A. J., Hamilton, S. P., Durner, M., Haghighi, F., Heiman, G. A., Costa, R., et al. (2006). A third-pass genome scan in panic disorder: Evidence for multiple susceptibility loci. Biological Psychiatry, 60, 388–401. Fyer, A. J., Lipsitz, J. D., Mannuzza, S., Aronowitz, B., & Chapman, T. F. (2005). A direct interview family study of obsessive-compulsive disorder Pt. I. Psychological Medicine, 35, 1611–1621. Fyer, A. J., Mannuzza, S., Chapman, T. F., Liebowitz, M. R., & Klein, D. F. (1993). A direct interview family study of social phobia. Archives of General Psychiatry, 50, 286–293.

Greenberg, P. E., Sisitsky, T., Kessler, R. C., Finkelstein, S. N., Berndt, E. R., Davidson, J. R., et al. (1999). The economic burden of anxiety disorders in the 1990s. Journal of Clinical Psychiatry, 60, 427–435. Hamilton, S. P., Fyer, A. J., Durner, M., Heiman, G. A., Baisre de Leon, A., Hodge, S. E., et al. (2003). Further genetic evidence for a panic disorder syndrome mapping to chromosome 13q. Proceedings of the National Academy of Sciences, USA, 100, 2550–2555. Hanna, G. L., Fischer, D. J., Chadha, K. R., Himle, J. A., & Van Etten, M. (2005). Familial and sporadic subtypes of early-onset obsessivecompulsive disorder. Biological Psychiatry, 57, 895–900. Hanna, G. L., Veenstra-VanderWeele, J., Cox, N. J., Boehnke, M., Himle, J. A., Curtis, G. C., et al. (2002). Genome-wide linkage analysis of families with obsessive-compulsive disorder ascertained through pediatric probands. American Journal of Medical Genetics, 114, 541–552. Hasler, G., Drevets, W. C., Gould, T. D., Gottesman, II, & Manji, H. K. (2006). Toward constructing an endophenotype strategy for bipolar disorders. Biological Psychiatry. Hasler, G., Drevets, W. C., Manji, H. K., & Charney, D. S. (2004). Discovering endophenotypes for major depression. Neuropsychopharmacology, 29, 1765–1781.

Fyer, A. J., Mannuzza, S., Chapman, T. F., Martin, L. Y., & Klein, D. F. (1995). Specificity in familial aggregation of phobic disorders. Archives of General Psychiatry, 52, 564–573.

Hasler, G., Pine, D. S., Kleinbaum, D. G., Gamma, A., Luckenbaugh, D., Ajdacic, V., et al. (2005). Depressive symptoms during childhood and adult obesity: The Zurich Cohort Study. Molecular Psychiatry, 10, 842–850.

Gelernter, J., Bonvicini, K., Page, G., Woods, S. W., Goddard, A. W., Kruger, S., et al. (2001). Linkage genome scan for loci predisposing to panic disorder or agoraphobia. American Journal of Medical Genetics, 105, 548–557.

Hasler, G., Pinto, A., Greenberg, B. D., Samuels, J., Fyer, A. J., Pauls, D., et al. (2007). Familiality of factor analysis-derived YBOCS dimensions in OCD-affected sibling pairs from the OCD Collaborative Genetics Study. Biological Psychiatry, 61, 617–625.

Gelernter, J., Page, G. P., Stein, M. B., & Woods, S. W. (2004). Genome-wide linkage scan for loci predisposing to social phobia: Evidence for a chromosome 16 risk locus. American Journal of Psychiatry, 161, 59–66.

Hayes, S. C., Wilson, K. G., Gifford, E. V., Follette, V. M., & Strosahl, K. (1996). Experimental avoidance and behavioral disorders: A functional dimensional approach to diagnosis and treatment. Journal of Consulting and Clinical Psychology, 64, 1152–1168.

Goldstein, A. J., & Chambless, D. L. (1978). Reanalysis of agoraphobia. Behavior Therapy, 9, 47–59. Goldstein, R. B., Wickramaratne, P. J., Horwath, E., & Weissman, M. M. (1997). Familial aggregation and phenomenology of “early”-onset (at or before age 20 years) panic disorder. Archives of General Psychiatry, 54, 271–278.

c56.indd Sec4:1106

Heimberg, R. G. (1993). Specific issues in the cognitive-behavioral treatment of social phobia. Journal of Clinical Psychiatry, 54(Suppl), 36–45. Hemmings, S. M., & Stein, D. J. (2006). The current status of association studies in obsessive-compulsive disorder. Psychiatric Clinics of North America, 29, 411–444.

8/19/09 4:14:18 PM

References 1107 Hettema, J. M., Neale, M. C., & Kendler, K. S. (2001). A review and metaanalysis of the genetic epidemiology of anxiety disorders. American Journal of Psychiatry, 158, 1568–1578. Hettema, J. M., Neale, M. C., Myers, J. M., Prescott, C. A., & Kendler, K. S. (2006). A population-based twin study of the relationship between neuroticism and internalizing disorders. American Journal of Psychiatry, 163, 857–864. Hettema, J. M., Prescott, C. A., & Kendler, K. S. (2004). Genetic and environmental sources of covariation between generalized anxiety disorder and neuroticism. American Journal of Psychiatry, 161, 1581–1587. Hettema, J. M., Prescott, C. A., Myers, J. M., Neale, M. C., & Kendler, K. S. (2005). The structure of genetic and environmental risk factors for anxiety disorders in men and women. Archives of General Psychiatry, 62, 182–189. Hidalgo, R. B., & Davidson, J. R. (2001). Generalized anxiety disorder: An important clinical concern. Medical Clinics of North America, 85, 691–710. Horwath, E., & Weissman, M. M. (2000). The epidemiology and crossnational presentation of obsessive-compulsive disorder. Psychiatric Clinics of North America, 23, 493–507. Hudziak, J. J., Van Beijsterveldt, C. E., Althoff, R. R., Stanger, C., Rettew, D. C., Nelson, E. C., et al. (2004). Genetic and environmental contributions to the Child Behavior Checklist ObsessiveCompulsive Scale: A cross-cultural twin study. Archives of General Psychiatry, 61, 608–616.

Kessler, R. C., Chiu, W. T., Jin, R., Ruscio, A. M., Shear, K., & Walters, E. E. (2006). The epidemiology of panic attacks, panic disorder, and agoraphobia in the National Comorbidity Survey Replication. Archives of General Psychiatry, 63, 415–424. Kessler, R. C., Davis, C. G., & Kendler, K. S. (1997). Childhood adversity and adult psychiatric disorder in the US National Comorbidity Survey. Psychological Medicine, 27, 1101–1119. Kessler, R. C., Foster, C. L., Saunders, W. B., & Stang, P. E. (1995). Social consequences of psychiatric disorders: Pt. I. Educational attainment. American Journal of Psychiatry, 152, 1026–1032. Kessler, R. C., McGonagle, K. A., Zhao, S., Nelson, C. B., Hughes, M., Eshleman, S., et al. (1994). Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: Results from the National Comorbidity Survey. Archive of General Psychiatry, 51, 8–19. Kessler, R. C., Zhao, S., Katz, S. J., Kouzis, A. C., Frank, R. G., Edlund, M., et al. (1999). Past-year use of outpatient services for psychiatric problems in the National Comorbidity Survey. American Journal of Psychiatry, 156, 115–123. Khanna, S., & Mukherjee, D. (1992). Checkers and washers: Valid subtypes of obsessive compulsive disorder. Psychopathology, 25, 283–288.

Jardine, R., Martin, N. G., & Henderson, A. S. (1984). Genetic covariation between neuroticism and the symptoms of anxiety and depression. Genetic Epidemiology, 1(2), 89–107.

Klein, D. F. (1993). False suffocation alarms, spontaneous panics, and related conditions: An integrative hypothesis. Archives of General Psychiatry, 50, 306–317.

Judd, L. L., Kessler, R. C., Paulus, M. P., Zeller, P. V., Wittchen, H. U., & Kunovac, J. L. (1998). Comorbidity as a fundamental feature of generalized anxiety disorders: Results from the National Comorbidity Study (NCS). Acta Psychiatrica Scandinavica, Supplementum, 393, 6–11.

Knowles, J. A., Fyer, A. J., Vieland, V. J., Weissman, M. M., Hodge, S. E., Heiman, G. A., et al. (1998). Results of a genome-wide genetic screen for panic disorder. American Journal of Medical Genetics, 81, 139–147.

Kaufman, J., Yang, B. Z., Douglas-Palumberi, H., Grasso, D., Lipschitz, D., Houshyar, S., et al. (2006). Brain-derived neurotrophic factor-5-HTTLPR gene interactions and environmental modifiers of depression in children. Biological Psychiatry, 59, 673–680. Kendler, K. S., Gardner, C. O., Gatz, M., & Pedersen, N. L. (2007). The sources of co-morbidity between major depression and generalized anxiety disorder in a Swedish national twin sample. Psychological Medicine, 37, 453–462.

Krueger, R. F. (1999). The structure of common mental disorders. Archives of General Psychiatry, 56, 921–926. Lander, E. S., & Schork, N. J. (1994, September 30). Genetic dissection of complex traits. Science, 265, 2037–2048. LaSalle, V. H., Cromer, K. R., Nelson, K. N., Kazuba, D., Justement, L., & Murphy, D. L. (2004). Diagnostic interview assessed neuropsychiatric disorder comorbidity in 334 individuals with obsessive-compulsive disorder. Depression and Anxiety, 19, 163–173.

Kendler, K. S., & Greenspan, R. J. (2006). The nature of genetic influences on behavior: Lessons from “simpler” organisms. American Journal of Psychiatry, 163, 1683–1694.

Leckman, J. F., Goodman, W. K., Anderson, G. M., Riddle, M. A., Chappell, P. B., McSwiggan-Hardin, M. T., et al. (1995). Cerebrospinal fluid biogenic amines in obsessive compulsive disorder, Tourette’s syndrome, and healthy controls. Neuropsychopharmacology, 12(1), 73–86.

Kendler, K. S., Karkowski, L. M., & Prescott, C. A. (1999). Fears and phobias: Reliability and heritability. Psychological Medicine, 29, 539–553.

Leckman, J. F., Grice, D. E., Boardman, J., Zhang, H., Vitale, A., Bondi, C., et al. (1997). Symptoms of obsessive-compulsive disorder. American Journal of Psychiatry, 154(7), 911–917.

Kendler, K. S., Myers, J., Prescott, C. A., & Neale, M. C. (2001). The genetic epidemiology of irrational fears and phobias in men. Archives of General Psychiatry, 58, 257–265.

Lecrubier, Y., Wittchen, H. U., Faravelli, C., Bobes, J., Patel, A., & Knapp, M. (2000). A European perspective on social anxiety disorder. European Psychiatry, 15(1), 5–16.

Kendler, K. S., Neale, M. C., Kessler, R. C., Heath, A. C., & Eaves, L. J. (1992). The genetic epidemiology of phobias in women: The interrelationship of agoraphobia, social phobia, situational phobia, and simple phobia. Archives of General Psychiatry, 49, 273–281.

Lee, C. K., Kwak, Y. S., Yamamoto, J., Rhee, H., Kim, Y. S., Han, J. H., et al. (1990). Psychiatric epidemiology in Korea: Pt. I. Gender and age differences in Seoul. Journal of Nervous and Mental Disease, 178(4), 242–246.

Kendler, K. S., Prescott, C. A., Myers, J., & Neale, M. C. (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of General Psychiatry, 60, 929–937.

Lochner, C., Hemmings, S., Seedat, S., Kinnear, C., Schoeman, R., Annerbrink, K., et al. (2007). Genetics and personality traits in patients with social anxiety disorder: A case-control study in South Africa. European Neuropsychopharmacology, 17, 321–327.

Kennedy, J. L., Neves-Pereira, M., King, N., Lizak, M. V., Basile, V. S., Chartier, M. J., et al. (2001). Dopamine system genes not linked to social phobia. Psychiatric Genetics, 11, 213–217.

Lochner, C., Hemmings, S. M., Kinnear, C. J., Moolman-Smook, J. C., Corfield, V. A., Knowles, J. A., et al. (2004). Gender in obsessivecompulsive disorder: Clinical and genetic findings. European Neuropsychopharmacology, 14, 105–113.

Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., & Walters, E. E. (2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 593–602.

c56.indd Sec4:1107

Kessler, R. C., Chiu, W. T., Demler, O., Merikangas, K. R., & Walters, E. E. (2005). Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 617–627.

Logue, M. W., Vieland, V. J., Goedken, R. J., & Crowe, R. R. (2003). Bayesian analysis of a previously published genome screen for panic disorder reveals new and compelling evidence for linkage

8/19/09 4:14:18 PM

1108 The Genetics of Anxiety to chromosome 7. American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics, 121, 95–99.

mutant mice as models for human polygenic and epistatic traits and disorders. Genes, Brain, and Behavior, 2, 350–364.

Lopez, A. D., & Murray, C. C. (1998). The global burden of disease, 1990–2020. Nature Medicine, 4, 1241–1243.

Nardi, A. E., Valenca, A. M., Nascimento, I., Freire, R. C., Veras, A. B., de-Melo-Neto, V. L., et al. (2007). A caffeine challenge test in panic disorder patients, their healthy first-degree relatives, and healthy controls. Depression and Anxiety.

Lyons, M. J., Goldberg, J., Eisen, S. A., True, W., Tsuang, M. T., Meyer, J. M., et al. (1993). Do genes influence exposure to trauma? A twin study of combat. American Journal of Medical Genetics, 48, 22–27. Magee, W. J., Eaton, W. W., Wittchen, H. U., McGonagle, K. A., & Kessler, R. C. (1996). Agoraphobia, simple phobia, and social phobia in the National Comorbidity Survey. Archives of General Psychiatry, 53, 159–168. Malizia, A. L., Cunningham, V. J., Bell, C. J., Liddle, P. F., Jones, T., & Nutt, D. J. (1998). Decreased brain GABA(A)-benzodiazepine receptor binding in panic disorder: Preliminary results from a quantitative PET study. Archives of General Psychiatry, 55, 715–720. Mannuzza, S., Schneier, F. R., Chapman, T. F., Liebowitz, M. R., Klein, D. F., & Fyer, A. J. (1995). Generalized social phobia: Reliability and validity. Archives of General Psychiatry, 52, 230–237. Markowitz, J. S., Weissman, M. M., Ouellette, R., Lish, J. D., & Klerman, G. L. (1989). Quality of life in panic disorder. Archive of General Psychiatry, 46, 984–992. Marks, I. M. (1970). The classification of phobic disorders. British Journal of Psychiatry: Journal of Mental Science, 116, 377–386. Marks, I. M. (1986). Genetics of fear and anxiety disorders. British Journal of Psychiatry: Journal of Mental Science, 149, 406–418. Mataix-Cols, D., Rauch, S. L., Manzo, P. A., Jenike, M. A., & Baer, L. (1999). Use of factor-analyzed symptom dimensions to predict outcome with serotonin reuptake inhibitors and placebo in the treatment of obsessivecompulsive disorder. American Journal of Psychiatry, 156, 1409–1416. Mataix-Cols, D., Rosario-Campos, M. C., & Leckman, J. F. (2005). A multidimensional model of obsessive-compulsive disorder. American Journal of Psychiatry, 162, 228–238. Mataix-Cols, D., Wooderson, S., Lawrence, N., Brammer, M. J., Speckens, A., & Phillips, M. L. (2004). Distinct neural correlates of washing, checking, and hoarding symptom dimensions in obsessive-compulsive disorder. Archive of General Psychiatry, 61, 564–576. Mathew, S. J., Coplan, J. D., & Gorman, J. M. (2001). Neurobiological mechanisms of social anxiety disorder. American Journal of Psychiatry, 158, 1558–1567. McGue, M., Gottesman, II., & Rao, D. C. (1983). The transmission of schizophrenia under a multifactorial threshold model. American Journal of Human Genetics, 35, 1161–1178. Mendlewicz, J., Papadimitriou, G. N., & Wilmotte, J. (1993). Family study of panic disorder: Comparison with generalized anxiety disorder, major depression and normal subjects. Psychiatric Genetics, 3, 73–78. Merikangas, K. R., Zhang, H., Avenevoli, S., Acharyya, S., Neuenschwander, M., & Angst, J. (2003). Longitudinal trajectories of depression and anxiety in a prospective community study: The Zurich Cohort Study. Archives of General Psychiatry, 60, 993–1000.

Nestadt, G., Bienvenu, O. J., Cai, G., Samuels, J., & Eaton, W. W. (1998). Incidence of obsessive-compulsive disorder in adults. Journal of Nervous and Mental Disease, 186, 401–406. Nestadt, G., Lan, T., Samuels, J., Riddle, M. A., Bienvenu, O. J., III, Liang, K. Y., et al. (2000). Complex segregation analysis provides compelling evidence for a major gene underlying obsessive-compulsive disorder and for heterogeneity by sex. American Journal of Medical Genetics, 67, 1611–1616. Nestadt, G., Samuels, J., Riddle, M., Bienvenu, O. J., III, Liang, K. Y., LaBuda, M., et al. (2000). A family study of obsessive-compulsive disorder. Archives of General Psychiatry, 57, 358–363. Ogliari, A., Citterio, A., Zanoni, A., Fagnani, C., Patriarca, V., Cirrincione, R., et al. (2006). Genetic and environmental influences on anxiety dimensions in Italian twins evaluated with the SCARED questionnaire. Journal of Anxiety Disorders, 20, 760–777. Ohara, K., Suzuki, Y., Ochiai, M., Tsukamoto, T., Tani, K., & Ohara, K. (1999). A variable-number-tandem-repeat of the serotonin transporter gene and anxiety disorders. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 23, 55–65. Ohman, A. (1986). Face the beast and fear the face: Animal and social fears as prototypes for evolutionary analyses of emotion. Psychophysiology, 23, 123–145. Ozaki, N., Goldman, D., Kaye, W. H., Plotnicov, K., Greenberg, B. D., Lappalainen, J., et al. (2003). Serotonin transporter missense mutation associated with a complex neuropsychiatric phenotype. Molecular Psychiatry, 8, 933–936. Pato, M. T., Pato, C. N., & Pauls, D. L. (2002). Recent findings in the genetics of OCD. Journal of Clinical Psychiatry, 63(Suppl, 6), 30–33. Perusse, L., Rice, T., Despres, J. P., Rao, D. C., & Bouchard, C. (1997). Cross-trait familial resemblance for body fat and blood lipids: Familial correlations in the Quebec Family Study. Arteriosclerosis, Thrombosis, and Vascular Biology, 17, 3270–3277. Pine, D. S., Cohen, P., Gurley, D., Brook, J., & Ma, Y. (1998). The risk for early-adulthood anxiety and depressive disorders in adolescents with anxiety and depressive disorders. Archives of General Psychiatry, 55, 56–64. Pinto, A., Mancebo, M. C., Eisen, J. L., Pagano, M. E., & Rasmussen, S. A. (2006). The Brown Longitudinal Obsessive Compulsive Study: Clinical features and symptoms of the sample at intake. Journal of Clinical Psychiatry, 67, 703–711. Rapee, R. M., & Melville, L. F. (1997). Recall of family factors in social phobia and panic disorder: Comparison of mother and offspring reports. Depression and Anxiety, 5, 7–11. Rasmussen, S. A., & Eisen, J. L. (1990). Epidemiology of obsessive compulsive disorder. Journal of Clinical Psychiatry, 51(Suppl), 10–13; discussion 14.

Miguel, E. C., Leckman, J. F., Rauch, S. L., do Rosario-Campos, M. C., Hounie, A. G., Mercadante, M. T., et al. (2005). Obsessive-compulsive disorder phenotypes: Implications for genetic studies. Molecular Psychiatry, 10, 258–275.

Rasmussen, S. A., & Tsuang, M. T. (1984). The epidemiology of obsessive compulsive disorder. Journal of Clinical Psychiatry, 45, 450–457.

Mineka, S., Davidson, M., Cook, M., & Keir, R. (1984). Observational conditioning of snake fear in rhesus monkeys. Journal of Abnormal Psychology, 93, 355–372.

Rauch, S. L., Dougherty, D. D., Shin, L. M., Alpert, N. M., Manzo, P., Leahy, L., et al. (1998). Neural correlates of factoranalyzed OCD symptom dimensions: A PET study. CNS Spectrum, 3, 37–43.

Minichiello, W. E., Baer, L., Jenike, M. A., & Holland, A. (1990). Age of onset of major subtypes of obsessive-compulsive disorder. Journal of Anxiety Disorders, 4, 147–150.

Regier, D. A., Rae, D. S., Narrow, W. E., Kaelber, C. T., & Schatzberg, A. F. (1998). Prevalence of anxiety disorders and their comorbidity with mood and addictive disorders. British Journal of Psychiatry, (Suppl, 34), 24–28.

Muris, P., Schmidt, H., & Merckelbach, H. (1999). The structure of specific phobia symptoms among children and adolescents. Behaviour Research and Therapy, 37, 863–868.

Ressler, K. J., & Mayberg, H. S. (2007). Targeting abnormal neural circuits in mood and anxiety disorders: From the laboratory to the clinic. Nature Neuroscience, 10, 1116–1124.

Murphy, D. L., Uhl, G. R., Holmes, A., Ren-Patterson, R., Hall, F. S., Sora, I., et al. (2003). Experimental gene interaction studies with SERT

Retey, J. V., Adam, M., Khatami, R., Luhmann, U. F., Jung, H. H., Berger, W., et al. (2007). A genetic variation in the adenosine A2A receptor gene

c56.indd Sec4:1108

8/19/09 4:14:19 PM

References 1109 (ADORA2A) contributes to individual sensitivity to caffeine effects on sleep. Clinical Pharmacology and Therapeutics, 81, 692–698. Rickels, K., & Schweizer, E. (1990). Clinical overview of serotonin reuptake inhibitors. Journal of Clinical Psychiatry, 51(Suppl, B), 9–12. Risch, N., & Merikangas, K. (1996, September 13). The future of genetic studies of complex human diseases. Science, 273, 1516–1517. Robins, L. R., & Regier, D. (1991). Psychiatric disorders in America: The epidemiologic catchment area study. New York: Free Press. Rosenberg, D. R., Averbach, D. H., O’Hearn, K. M., Seymour, A. B., Birmaher, B., & Sweeney, J. A. (1997). Oculomotor response inhibition abnormalities in pediatric obsessive-compulsive disorder. Archives of General Psychiatry, 54, 831–838. Samochowiec, J., Syrek, S., Michal, P., Ryzewska-Wodecka, A., Samochowiec, A., Horodnicki, J., et al. (2004). Polymorphisms in the serotonin transporter and monoamine oxidase A genes and their relationship to personality traits measured by the Temperament and Character Inventory and NEO Five-Factor Inventory in healthy volunteers. Neuropsychobiology, 50, 174–181. Samuels, J., Shugart, Y. Y., Grados, M. A., Willour, V. L., Bienvenu, O. J., Greenberg, B. D., et al. (2007). Significant linkage to compulsive hoarding on chromosome 14 in families with obsessive-compulsive disorder: Results from the OCD Collaborative Genetics Study. American Journal of Psychiatry, 164, 493–499. Savage, C. R., Baer, L., Keuthen, N. J., Brown, H. D., Rauch, S. L., & Jenike, M. A. (1999). Organizational strategies mediate nonverbal memory impairment in obsessive-compulsive disorder. Biological Psychiatry, 45, 905–916.

Stein, M. B., Jang, K. L., & Livesley, W. J. (2002). Heritability of social anxiety-related concerns and personality characteristics: A twin study. Journal of Nervous and Mental Disease, 190, 219–224. Stein, M. B., & Kean, Y. M. (2000). Disability and quality of life in social phobia: Epidemiologic findings. American Journal of Psychiatry, 157, 1606–1613. Summerfeldt, L. J., Richter, M. A., Antony, M. M., & Swinson, R. P. (1999). Symptom structure in obsessive-compulsive disorder: A confirmatory factor-analytic study. Behaviour Research and Therapy, 37, 297–311. Sundet, J. M., Skre, I., Okkenhaug, J. J., & Tambs, K. (2003). Genetic and environmental causes of the interrelationships between self-reported fears: A study of a non-clinical sample of Norwegian identical twins and their families. Scandinavian Journal of Psychology, 44, 97–106. Tiihonen, J., Kuikka, J., Bergstrom, K., Lepola, U., Koponen, H., & Leinonen, E. (1997). Dopamine reuptake site densities in patients with social phobia. American Journal of Psychiatry, 154, 239–242. Tsuang, M. T., Faraone, S. V., & Lyons, M. J. (1993). Identification of the phenotype in psychiatric genetics. European Archives of Psychiatry and Clinical Neuroscience, 243(3–4), 131–142. van den Heuvel, O. A., van de Wetering, B. J., Veltman, D. J., & Pauls, D. L. (2000). Genetic studies of panic disorder: A review. Journal of Clinical Psychiatry, 61, 756–766.

Saxena, S., & Maidment, K. M. (2004). Treatment of compulsive hoarding. Journal of Clinical Psychology, 60, 1143–1154.

Weissman, M. M. (1990). Epidemiology of panic disorder and agoraphobia. Psychiatric Medicine, 8(2), 3–13.

Schmidt, N. B., Kotov, R., & Joiner, T. E. (2004). Taxometrics: Toward a new diagnostic scheme for psychopathology (1st ed.). Washington, DC: American Psychological Association.

Weissman, M. M. (1991). Panic disorder: Impact on quality of life. Journal of Clinical Psychiatry, 52(Suppl), 6–8, 9.

Schneier, F. R., Heckelman, L. R., Garfinkel, R., Campeas, R., Fallon, B. A., Gitow, A., et al. (1994). Functional impairment in social phobia. Journal of Clinical Psychiatry, 55, 322–331. Schneier, F. R., Johnson, J., Hornig, C. D., Liebowitz, M. R., & Weissman, M.M. (1992). Social phobia: Comorbidity and morbidity in an epidemiologic sample. Archives of General Psychiatry, 49, 282–288. Schneier, F. R., Liebowitz, M. R., Abi-Dargham, A., Zea-Ponce, Y., Lin, S. H., & Laruelle, M. (2000). Low dopamine D(2) receptor binding potential in social phobia. American Journal of Psychiatry, 157, 457–459. Schweizer, E., & Rickels, K. (1997). Strategies for treatment of generalized anxiety in the primary care setting. Journal of Clinical Psychiatry, 58(Suppl, 3), 27–31; discussion 32–23. Shih, R. A., Belmonte, P. L., & Zandi, P. P. (2004). A review of the evidence from family, twin and adoption studies for a genetic contribution to adult psychiatric disorders. International Review of Psychiatry, 16, 260–283. Shugart, Y. Y., Samuels, J., Willour, V. L., Grados, M. A., Greenberg, B. D., Knowles, J. A., et al. (2006). Genomewide linkage scan for obsessivecompulsive disorder: Evidence for susceptibility loci on chromosomes 3q, 7p, 1q, 15q, and 6q. Molecular Psychiatry, 11, 763–770. Simon, G. E., Von Korff, M., Saunders, K., Miglioretti, D. L., Crane, P. K., van Belle, G., et al. (2006). Association between obesity and psychiatric disorders in the US adult population. Archives of General Psychiatry, 63, 824–830. Smoller, J. W., & Tsuang, M. T. (1998). Panic and phobic anxiety: Defining phenotypes for genetic studies. American Journal of Psychiatry, 155, 1152–1162. Stein, M. B., Chartier, M. J., Hazen, A. L., Kozak, M. V., Tancer, M. E., Lander, S., et al. (1998). A direct-interview family study of generalized social phobia. American Journal of Psychiatry, 155, 90–97. Stein, M. B., Chartier, M. J., Kozak, M. V., King, N., & Kennedy, J. L. (1998). Genetic linkage to the serotonin transporter protein and 5HT2A receptor genes excluded in generalized social phobia. Psychiatry Research, 81, 283–291.

c56.indd Sec4:1109

Stein, M. B., Chartier, M. J., Lizak, M. V., & Jang, K. L. (2001). Familial aggregation of anxiety-related quantitative traits in generalized social phobia: Clues to understanding “disorder” heritability? American Journal of Medical Genetics, 105, 79–83.

Weissman, M. M., Bland, R. C., Canino, G. J., Greenwald, S., Hwu, H. G., Lee, C. K., et al. (1994). The cross national epidemiology of obsessive compulsive disorder: The Cross National Collaborative Group. Journal of Clinical Psychiatry, 55(Suppl), 5–10. Weissman, M. M., Gross, R., Fyer, A., Heiman, G. A., Gameroff, M. J., Hodge, S. E., et al. (2004). Interstitial cystitis and panic disorder: A potential genetic syndrome. Archives of General Psychiatry, 61, 273–279. Weissman, M. M., Wickramaratne, P., Adams, P. B., Lish, J. D., Horwath, E., Charney, D., et al. (1993). The relationship between panic disorder and major depression: A new family study. Archives of General Psychiatry, 50, 767–780. Wendland, J. R., Kruse, M. R., Cromer, K. R., & Murphy, D. L. (2007). A large case-control study of common functional SLC6A4 and BDNF variants in obsessive-compulsive disorder. Neuropsychopharmacology. Wittchen, H. U. (2002). Generalized anxiety disorder: Prevalence, burden, and cost to society. Depression and Anxiety, 16, 162–171. You, J. S., Hu, S. Y., Chen, B., & Zhang, H. G. (2005). Serotonin transporter and tryptophan hydroxylase gene polymorphisms in Chinese patients with generalized anxiety disorder. Psychiatric Genetics, 15, 7–11. Zhang, J. S., Jin, X. M., Zhou, X. D., Shen, L. X., Huang, H., & Shen, X. M. (2005). Anxiety state and its related factors in Shanghai high school students. Zhonghua Yu Fang Yi Xue Za Zhi, 39, 348–351. Zohar, A. H., Dina, C., Rosolio, N., Osher, Y., Gritsenko, I., BachnerMelman, R., et al. (2003). Tridimensional personality questionnaire trait of harm avoidance (anxiety proneness) is linked to a locus on chromosome 8p21. American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics, 117, 66–69. Zuckerman, M. (1999). Diathesis-stress models. In Vulnerability to psychopathology: A biosocial model (pp. 3–23). Washington, DC: American Psychological Association.

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Chapter 57

Neurobiology of Psychopathy CHRISTOPHER J. PATRICK AND EDWARD M. BERNAT

The goal of basic human neuroscience research is to understand psychological processes in terms of the structure and functioning of underlying neurobiological systems. In parallel with this, the aim of applied clinical neuroscience is to understand problem behaviors and syndromes in terms of core processes and their neurobiological substrates. A number of challenges exist to understanding traditional mental disorders in neuroscientific terms. One of the most significant is that mental disorder syndromes represent crude targets for neurobiological study: They manifest in diverse ways clinically (phenotypically) and they show frequent overlap (comorbidity) rather than occurring in isolation from one another. Here, using the syndrome of psychopathy (psychopathic personality) as an example, we argue that progress toward a neuroscientific understanding of mental disorders may in some cases require us to reconceptualize these disorders in terms of constituent constructs with purer neurobiological referents. The foundation for modern empirical research on psychopathy was Cleckley’s classic clinical portrayal, set forth in his book The Mask of Sanity (1941/1976). As Cleckley described it, psychopathy is an unusual psychiatric condition that entails an inherent dualism. On one hand, psychopathic individuals present as personable, carefree, and psychologically well adjusted. They do not exhibit the salient perceptual or thought disturbance of psychotic patients or the mood or anxiety disturbance evident among neurotic patients. On the other hand, psychopathic individuals exhibit severe behavioral problems that bring them into repeated conflict with others in society—and which result in adverse personal consequences ranging from job or relationship loss to long-term institutionalization. Along

Preparation of this chapter was supported by grants MH52384, MH65137, and MH072850 from the National Institute of Mental Health, Grant R01 AA12164 from the National Institute on Alcohol Abuse and Alcoholism, and funds from the Hathaway endowment at the University of Minnesota.

with these behavioral problems, psychopathic individuals exhibit characteristic affective features included a striking absence of guilt, remorse, or empathic concern for others. For many years, the dominant theoretic perspective on psychopathy has been that it is a unitary syndrome that arises from a core underlying pathology or deficit. Some etiologic models of psychopathy have proposed that a basic deficit in emotional reactivity accounts for the characteristic affective, interpersonal, and behavioral features of the disorder; other models have focused on disturbances in cognitive and attentional processing as the primary underlying cause. Here, we argue that progress in understanding this somewhat paradoxical syndrome—and, in particular, progress toward understanding its etiologic foundations— can be advanced by considering psychopathy in terms of separable dispositions with distinctive neurobiological substrates. The two dispositional constructs we focus on are trait fearlessness (theorized to reflect under-reactivity of the brain’s defensive motivational system) and externalizing vulnerability (presumed to reflect impairments in frontocortical systems that mediate anticipation, planfulness, and affective/behavioral control). We present evidence in support of this model from studies employing personality trait and clinical diagnostic measures, and from studies employing peripheral and electrocortical physiological measures. We argue that a two-process perspective on psychopathy can help to advance our understanding of the causal bases of the disorder, as well as clarify our thinking about phenomena such as psychopathy subtypes and “successful” psychopathy. CONCEPTUALIZATION AND ASSESSMENT OF PSYCHOPATHY The dominant assessment instrument in contemporary psychopathy research is the Psychopathy Checklist—Revised (PCL-R; Hare, 1991, 2003). The PCL-R was developed to identify individuals in correctional or forensic settings who

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qualify as psychopathic according to the conceptualization set forth by Cleckley (1941/1976). Cleckley described psychopathy as a severe underlying pathology masked by an outward appearance of good psychological health. In contrast with other psychiatric patients who present as disoriented, anxious, dysphoric, interpersonally withdrawn, or otherwise disturbed, psychopaths appear on initial impression to be alert, poised, socially adept, and generally well adjusted. It is only through repeated observation and experience across a range of settings that the severe interpersonal and behavioral maladjustment of the psychopath becomes apparent. Cleckley formulated a list of 16 formal criteria for diagnosing psychopathic individuals. These criteria can be grouped into three categories (Patrick, 2006): (1) The first consists of indicators of positive psychological functioning (i.e., good intelligence and social charm, absence of delusions or other irrational thinking, lack of anxiousness, and low rate of suicide). The narrative descriptions of these indicators refer not just to the absence of typical mental disorder symptoms (e.g., delusions, anxiousness, depression), but the presence of psychological hardiness and adjustment: “The surface of the psychopath . . . shows up as equal to or better than normal and gives no hint at all of a disorder within. . . . His mask is that of robust mental health.” (p. 383). (2) However, in contrast with this outward social appearance, the psychopath’s day-to-day behavior is marked by obvious and severe maladjustment. This behavioral maladjustment component is captured by criteria reflecting impulsive antisocial actions, unreliability (i.e., reckless irresponsibility), promiscuous sexual relations, and a general failure to plan or learn from experience. (3) And third, Cleckley’s diagnostic criteria included a set of items reflecting deficient emotional experience (e.g., general poverty of affect, absence of remorse, incapacity for love) and a lack of genuine interpersonal relationships (e.g., untruthfulness, disloyalty). Cleckley maintained that individuals with the essential disposition of a psychopath could be found in the upper echelons of society as well as among society’s outcasts. He presented case illustrations of “successful psychopaths” who attained high levels of accomplishment in occupations such as medicine, academia, or business. These individuals resembled unsuccessful (persistently criminal) psychopaths in terms of their blunted capacity for emotion, lack of close personal attachments, and whimsical behavioral tendencies—but were distinguished by their ability to focus for extended periods of time on activities crucial to advancement and success. In contrast with Cleckley’s criteria for psychopathy, the 20 diagnostic items of the PCL-R focus more uniformly on

deviant tendencies. Specifically, the behavioral maladjustment and affective-interpersonal features included among Cleckley’s criteria are well represented in the PCL-R, but the positive psychological indicators emphasized by Cleckley are not. Even the “glibness and superficial charm” item of the PCL-R (item 1), which ostensibly resembles Cleckley’s “superficial charm and good intelligence” criterion, is defined in a more deviant manner—that is, reflecting an excessively talkative, slick, and insincere demeanor. Patrick (2006) attributed the absence of positive adjustment indicators in the PCL-R to the strategy that was used to select items for the original PCL. Specifically, items were chosen from a larger pool of candidate indicators to index psychopathy as a unitary construct, using global ratings of psychopathy based on Cleckley’s description as the criterion. Because the majority of Cleckley’s criteria (12 of 16) reflect deviancy as opposed to adjustment, it is likely that the initial pool of candidate items for the PCL included more deviance-related indicators, and that positive adjustment indicators dropped out in the process of selecting items with desirable psychometric properties (i.e., adequate item-total correlations, high overall internal consistency). However, despite the fact that the items of the PCL (and PCL-R) were selected to index psychopathy as a unitary entity, evidence from factor analytic and correlational studies indicates that its items nonetheless tap differentiable component constructs, or factors. In addition, recent cluster analytic studies have revealed that distinctive subgroups of high PCLR psychopaths exist with markedly different personality profiles. The next section summarizes these findings together with evidence from other relevant literatures that challenges the view that the syndrome of psychopathy is unitary.

EMPIRICAL EVIDENCE THAT PSYCHOPATHY IS NOT UNITARY Distinctive Factors of the PCL-R Although the PCL/PCL-R was developed to measure psychopathy as a unitary construct, factor analytic studies have revealed that it contains distinctive subgroups of items (i.e., factors) that, while correlated, nonetheless show diverging relations with external criterion variables. Most published research on the criterion-related validity of subcomponents of the PCL-R has focused on the two factors of the original PCL structural model (Hare et al., 1990; Harpur, Hakstian, & Hare, 1988). In this model, Factor 1 encompasses the interpersonal (charm, grandiosity, and deceitfulness/conning) and affective features of psychopathy (absence of remorse,

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empathy, and emotional depth, and blame externalization), whereas Factor 2 encompasses the antisocial deviance features (e.g., child behavior problems, impulsivity, irresponsibility, and a lack of long-term goals). Elevations on PCL-R Factor 1 are associated with higher scores on measures reflecting selfishness and exploitativeness, such as narcissistic personality and Machiavellianism (Hare, 1991; Harpur, Hare, & Hakstian, 1989; Verona, Patrick, & Joiner, 2001), and lower scores on measures of empathy (Hare, 2003). At the same time, Factor 1—in particular, its variance that is nonredundant with Factor 2—is positively correlated with measures of social dominance (Verona et al., 2001; see also Hare, 1991; Harpur et al., 1989), and in some studies, with achievement (Verona et al., 2001) and trait positive affect (Patrick, 1994). Thus, scores on Factor 1 show some relationship with adaptive personality traits. In contrast, Factor 2 of the PCL-R shows associations mainly with indicators of deviancy and maladaptive behavior, including selective positive relations with personality traits of aggression, impulsivity, and general sensation seeking (Hare, 1991; Harpur et al., 1989) and child symptoms of DSM antisocial personality disorder (APD); markedly stronger correlations than Factor 1 with adult APD symptoms and criminal history variables such as onset and frequency of offending (Hare, 2003; Verona et al., 2001); and robust positive associations with measures of alcohol and drug dependence (versus null associations for Factor 1; cf. Hare, 2003). These diverging relations with other measures are highly unusual for two variables considered to be elements of a unitary construct. Especially striking are occurrences of cooperative suppression (Paulhus, Robins, Trzesniewski, & Tracy, 2004) in which opposing relations of the two PCL-R factors with criterion measures become stronger once their overlapping variance is removed. For example, Hicks and Patrick (2006) reported evidence of cooperative suppressor effects in the associations of the two PCL-R factors with self-report measures of fear, anxiety, and depression. In each case, after removing overlap between the PCL-R factors, the association for Factor 1 emerged as negative whereas the association for Factor 2 was positive. Cooperative suppressor effects of this kind are notable because they signify that components of a putatively onedimensional measurement device are actually indexing separable and distinct underlying constructs (Paulhus et al., 2004). In the case of the PCL-R, the finding of suppressor effects for its two factors fits with Cleckley’s original idea that the syndrome of psychopathy reflects the atypical co-occurrence in the same individual of tendencies toward psychological resiliency on one hand, and behavioral maladjustment on the other. In particular, available evidence indicates that the items of Factor 1 contain variance that is associated with adjustment-related tendencies—including

agency (dominance and achievement); positive affectivity; and low levels of fearfulness, distress, and depression. Alternatives to the two-factor model of the PCL-R have been proposed. Cooke and Michie (2001) advanced a three-factor model in which Factor 1 is divided into separate affective and interpersonal factors and Factor 2 is limited to items reflecting impulsive-irresponsible behavioral tendencies. Hare (2003) proposed a four-factor model in which Factor 1 is parsed into Cooke and Michie’s interpersonal and affective factors, and Factor 2 is parsed into one-factor mirroring Cooke and Michie’s impulsiveirresponsible factor and another reflecting overt antisocial behaviors. Research examining associations separately for the interpersonal and affective components of Factor 1 indicates that it is the interpersonal component that accounts mostly for correlations with adjustment-related constructs (Hall, Benning, & Patrick, 2004). PCL-R Psychopathy Subtypes The unitary conceptualization of psychopathy implies that individuals scoring high on the PCL-R should comprise a relatively homogeneous group. The dominant research strategy to date in studies of psychological and neurobiological processes in psychopathy (cf. Patrick, 2006) has been to contrast extreme high and low overall PCL-R scorers under the assumption that differences in performance or reactivity on laboratory tasks will reflect some mechanism in common among high scorers. The effort to identify etiologic mechanisms underlying PCL-R psychopathy has been characterized as a search for the “core psychopathic deficit” (Lynam & Derefinko, 2006). However, empirical evidence challenges the view that high PCL-R scorers comprise a unitary group. Using model-based cluster analysis to classify the personality profiles of male offenders with high overall scores on the PCL-R, Hicks, Markon, Patrick, Krueger, and Newman (2004) identified two subgroups with markedly different profiles: (1) an “aggressive” subgroup with high scores on negative affective traits (especially aggression and alienation) and low scores on traits reflecting planfulness and restraint, and (2) a “stable” subgroup low in stress reactivity (anxiousness) and high on traits reflecting agency (well-being, social dominance, and achievement). The ratio of aggressive to stable psychopaths in the sample was over 2:1, reflecting the fact that the PCL-R as a whole is weighted toward the detection of high impulsive-antisocial (“externalizing”) individuals. Nonetheless, individuals resembling Cleckley’s psychologically well-adjusted prototype were also represented among high PCL-R scorers in this offender sample. Extrapolating beyond the prison setting, we would expect individuals with this stable profile

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to be even more strongly represented among “successful” psychopaths who achieve positions of stature within the community (e.g., corporate executives, political leaders; see Hall & Benning, 2006; Hare, 1993; Lykken, 1995).1 Distinctive Factors of Psychopathy in Noncriminals: The Psychopathic Personality Inventory An alternative, self-report based method for assessing psychopathy, developed for use in nonincarcerated populations, is the Psychopathic Personality Inventory (PPI; Lilienfeld, 1990; Lilienfeld & Andrews, 1996; Lilienfeld & Widows, 2005). In contrast with the PCL-R, the strategy used to select items for the PPI did not rely on the assumption that psychopathy is a unitary construct. Rather, a comprehensive personality-based approach was taken with the aim of assessing the full range of trait constructs embodied in Cleckley’s description of psychopathy. A review of the literature was undertaken to identify all constructs with potential relevance to psychopathy and separate unidimensional subscales were developed to assess these varying constructs. Although the PPI was developed with no specific structural model of psychopathy in mind, factor analyses of its eight subscales (Benning, Patrick, Hicks, Blonigen, & Krueger, 2003; Benning, Patrick, Salekin, & Leistico, 2005; Ross, Benning, Patrick, Thompson, & Thurston, 2009) have nonetheless revealed a clear two-factor structure in which Social Potency, Stress Immunity, and Fearlessness subscales load preferentially on one factor (PPI-I) and Impulsive Nonconformity, Blame Externalization, Machiavellian Egocentricity, and Carefree Nonplanfulness subscales load on a second factor (PPI-II). Benning, Patrick, Blonigen, Hicks, and Iacono (2005) labeled these two factors Fearless Dominance and Impulsive Antisociality. Unlike PCL-R Factors 1 and 2, which are moderately correlated, the two factors of the PPI are uncorrelated. The eighth subscale of the PPI, Coldheartedness, does not load appreciably on either PPI factor.

1

Skeem, Johansson, Andershed, Kerr, and Eno Louden (2007) also found evidence for two distinct subgroups of high PCL-R violent offenders from a model-based cluster analysis of PCL-R facet scores (Interpersonal, Affective, Lifestyle, Antisocial; Hare, 2003) along with scores on a self-report measure of trait anxiety. Although comparable in PCL-R facet score elevations, the two subgroups differed dramatically in average anxiety scale scores. The authors labeled the low- and high-anxious subgroups “primary” and “secondary,” respectively, reflecting longstanding terminology in the literature (cf. Poythress & Skeem, 2006)—and made note of their resemblance to the stable and aggressive subgroups identified by Hicks et al. (2004).

Validation studies have reported meaningful, diverging associations for the two factors of the PPI with a variety of external criterion measures (Benning, Patrick, Blonigen, et al., 2005; Benning et al., 2003; Benning, Patrick, Salekin, et al., 2005; Blonigen et al., 2005; Patrick, Edens, Poythress, & Lilienfeld, 2006; Ross et al., 2009). In these studies, the correlates of PPI Factors 1 and 2 have largely mirrored those of the unique variance in PCL-R Factor 1 (its interpersonal component, in particular) and of Factor 2, respectively. Specifically, high scores on PPI-I are associated with better psychological and social adaptation as well as with tendencies toward narcissism, thrillseeking, and low empathy, whereas scores on PPI-II are more generally indicative of psychological and behavioral maladjustment—including impulsiveness and aggression, child and adult antisocial deviance, alcohol and drug problems, heightened anxiousness and somatic complaints, and suicidal ideation. However, the two factors of the PPI are by no means identical to the two PCL-R factors (cf. Benning, Patrick, Blonigen, et al., 2005). The PPI factors are derived from self-report, whereas the PCL-R factors are derived from clinical diagnostic assessment. The PPI factors are independent of one another, whereas the PCL-R factors are moderately intercorrelated. Associations with adjustmentrelated variables (e.g., social dominance, resiliency) tend to be markedly stronger for PPI-I than for PCL-R Factor 1, and emerge clearly in simple (zero-order) correlations. In sum, the two factors of the PPI index major components of the psychopathy construct in a more clearly differentiated way than the two correlated factors of the PCL-R. This is probably due to differences in how the two instruments were developed: Whereas the PCL-R was developed to index a putatively unidimensional target construct, the PPI was developed to comprehensively assess the spectrum of psychopathy-related traits. In particular, PPI-I taps the positive adjustment features of psychopathy highlighted by Cleckley more robustly and distinctively than Factor 1 of the PCL-R. Atypical Relations between Externalizing and Internalizing Tendencies in Psychopathy An additional source of evidence regarding the nonunitary nature of the psychopathy construct comes from research examining patterns of comorbidity among diagnostic syndromes in the DSM. It is well known that behavior problems in children cluster around two correlated factors, labeled internalizing and externalizing (Achenbach & Edelbrock, 1978). Internalizing problems in childhood and adolescence are marked by symptoms of anxiety, dysphoria, and social withdrawal, whereas externalizing problems

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are expressed in terms of impulsiveness, aggression, and delinquent acts. In parallel with this, factor analytic studies of adult mental disorders (e.g., Krueger, 1999; Vollebergh et al., 2001) have yielded evidence of internalizing and externalizing factors underlying the most prevalent disorders within the DSM—with the internalizing factor reflecting systematic comorbidity among mood and anxiety disorders, and the externalizing factor reflecting systematic comorbidity among APD, alcohol dependence, and drug dependence. Notably, these two broad dimensions of psychopathology—whether derived from child or adult diagnostic data—show a moderate positive relationship with one another, rather than being uncorrelated (orthogonal) or inversely correlated (bipolar). That is, as a general rule, children and adults who exhibit more externalizing symptomatology (rebelliousness, rule-breaking, aggression, substance use/abuse) tend also to exhibit more internalizing symptomatology (mood and anxiety disorder symptoms). However, psychopathic individuals present a salient exception to this rule: They exhibit severe impulsiveexternalizing behavior (including, in Cleckley’s [1976] words, “inadequately motivated antisocial behavior” [p. 343] and “fantastic and uninviting behavior under the influence of alcohol and sometimes without” [p. 355]) without accompanying internalizing symptomatology (i.e., “absence of nervousness or psychoneurotic manifestations” [p. 340]). Scores on the PCL-R as a whole are generally uncorrelated with measures of anxiety and depression (Hare, 2003) and scores on its two factors show opposing relations with such measures, particularly after the overlapping variance between the two factors is removed (Hicks & Patrick, 2006): Consistent with Cleckley’s portrayal, relations for Factor 1 are negative; in contrast, consistent with evidence that PCL-R Factor 2 indexes the broad externalizing factor of DSM psychopathology (Patrick, Hicks, Krueger, & Lang, 2005), relations for this factor are positive. Scores on the two corresponding factors of the PPI likewise show opposing relations with measures of anxiety and depression (Benning, Patrick, Blonigen, et al., 2005; Blonigen et al., 2005). Why are externalizing and internalizing tendencies, in contrast with the norm, uncoupled in psychopathy? Our theoretical perspective is that the classic syndrome of psychopathy as described by Cleckley reflects the confluence within the same individual of two distinctive etiologic processes—one involving a lack of normal defensive (fear) reactivity that confers an immunity to internalizing problems, and the other a dispositional weakness in impulse control that confers a vulnerability to externalizing problems. The next section articulates this two-process conceptualization in greater detail.

TWO-PROCESS THEORY OF PSYCHOPATHY The two-process theory of psychopathy proposes that the disorder can be understood in terms of separable etiologic processes that reflect deviations in distinct underlying neurobiological systems. The processes on which the theory focuses are trait fearlessness and externalizing vulnerability. Our position contrasts with the unitary-syndrome perspective, which posits that a single underlying deficit or impairment can account for the features of the disorder as a whole. Examples of the unitary-syndrome perspective include Lykken’s low fear hypothesis (Lykken, 1957, 1995) and Newman’s response modulation hypothesis (Newman, 1998; Patterson & Newman, 1993). The two-process conceptualization of psychopathy emerged out of research investigating emotional deficits in psychopathy using picture viewing and image processing paradigms (Patrick, Bradley, & Lang, 1993; Patrick, Cuthbert, & Lang, 1994).2 The findings of these studies suggested that the core affective-interpersonal features of psychopathy were associated with an absence of normal defensive activation in the face of explicit (visual) aversive cues, whereas the behavioral deviance features were associated with a lack of normal arousal during processing of internal (imaginal) affective representations. Subsequently, Patrick (1994) reported further evidence tying deficits in aversive startle potentiation specifically to Factor 1 of the PCL-R. Patrick and Lang (1999) posited that the affective and interpersonal features of psychopathy associated with PCL-R Factor 1 reflect an underlying weakness in the brain’s core defensive motivational system—akin to the fear deficit postulated by Lykken (1995)—whereas the behavioral dyscontrol associated with PCL-R factor 2 reflects an impairment in higher representational systems that interact with primary motivational systems—akin to the impairment in higher-level processing (and affiliated behavioral disinhibition) that occurs with acute alcohol intoxication. Levenston, Patrick, Bradley, and Lang (2000) presented evidence that the aberrant startle pattern exhibited by criminal psychopaths in the Patrick et al. (1993) study and replicated subsequently by other investigators (e.g., Herpertz et al., 2001; Pastor, Molto, Vila, & Lang, 2003; Sutton, Vitale, & Newman, 2002) arises from a heightened threshold for shifting from attentional engagement (orienting) to defensive activation. Vanman, Mejia, Dawson, Schell, and Raine (2003) reported an absence of startle potentiation specifically in relation to high scores on Factor 1 of the 2

A complementary perspective, drawing on findings from the psychophysiological literature as well as concepts from the child temperament literature, was advanced by Fowles and Dindo (2006).

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PCL-R in a sample of nonincarcerated individuals from the community. Similarly, Benning, Patrick, and Iacono (2005) reported a deviant pattern of startle modulation resembling that of incarcerated psychopaths in participants from the community who scored at the high extreme on the Fearless Dominance factor of the PPI (PPI-I). In contrast, individuals with extreme elevations on the Impulsive Antisociality factor (PPI-II) showed a normal affect-startle pattern. With regard to Factor 2 of the PCL-R, Patrick, Hicks, Krueger, and Lang (2005) demonstrated a close association between this component of psychopathy and externalizing factor scores estimated from child and adult symptoms of APD, measures of alcohol and drug abuse/dependence, and scores on a broad self-report index of impulsivity and sensation seeking. The association between scores on the externalizing factor and scores on PCL-R Factor 2, controlling for variance shared with Factor 1, was extremely high, whereas the unique variance in Factor 1 was unrelated to externalizing. Paralleling this, Blonigen et al. (2005) reported a robust positive association between externalizing factor scores computed from DSM diagnostic variables and scores on the Impulsive Antisociality factor of the PPI in both women and men. Corresponding associations for the Fearless Dominance factor of the PPI were low, and significant only for men. As described more fully in subsequent sections, the general propensity toward externalizing psychopathology is presumed to reflect impairments in anterior brain systems that function to regulate affect and behavior in situations involving complex or competing stimulus contingencies. Patrick (2007) postulated that the antisocial deviance features embodied in PCL-R Factor 2 reflect similar underlying impairments. Consistent with this perspective, antisocial deviance in childhood and adulthood is reliably associated with deficits on neuropsychological measures of frontal lobe function (Morgan & Lilienfeld, 2000). In addition, Molto, Poy, Segarra, Pastor, and Montanes (2007) recently demonstrated that Factor 2 of the PCL-R accounts for enhanced errors of commission in a conflict (reward-punishment) task that Newman and colleagues had used previously to infer response modulation deficits in PCL-R defined psychopaths. Our position, emerging from the evidence reviewed in the foregoing sections, is that scientific understanding of the etiology and development of psychopathy can be advanced by focusing directly on dispositional fear and externalizing vulnerability as targets of study. The two broad factors of Hare’s PCL-R, and the two somewhat parallel factors of Lilienfeld’s PPI, can be viewed as imperfect manifest (phenotypic) indicators of these two underlying etiologic (genotypic) dispositions. From this perspective, a clearer understanding of etiological mechanisms underlying psychopathy can be gained by directly assessing

individuals (from community as well as clinical/forensic settings) on dimensions of fear/fearlessness and externalizing, and investigating differences in psychological processing associated with varying positions along these dimensions using physiological measures. We believe this approach will also prove valuable in elucidating phenomena such as psychopathy subtypes and noncriminal (“successful”) psychopaths. The remainder of this chapter reviews research work done to: (a) refine measurement of the constructs of dispositional fear and externalizing vulnerability, and (b) identify physiological response indicators of these constructs as a step toward understanding the brain processes that underlie them.

REFINING THE MEASUREMENT OF TRAIT FEAR AND EXTERNALIZING CONSTRUCTS Evidence for a Continuous Bipolar Dimension Underlying Psychometric Measures of Fear and Fearlessness As noted earlier, available data indicate that the two factors of Lilienfeld’s (1990) PPI tap distinctive facets of the psychopathy construct. PPI-I (aka “Fearless Dominance”) is negatively associated with self-report measures of fearfulness and distress (Benning, Patrick, Blonigen, et al., 2005), with diagnostic symptoms of internalizing disorders (Blonigen et al., 2005), and with fear-potentiated startle (Benning, Patrick, & Iacono, 2005). To further clarify the construct underlying PPI-I, we used principal components analysis to evaluate whether this factor of the PPI would load with other established indicators of fear and fearlessness on a common trait dimension. We utilized data from two separate samples (male and female college students, N ⫽ 346; male prisoners, N ⫽ 218) in which scores on PPI-I (estimated from scores on Tellegen’s [1982] Multidimensional Personality Questionnaire; cf. Benning, Patrick, Blonigen, et al., 2005) were available along with another measure of fearlessness, the thrill and adventure seeking subscale of the Sensation Seeking Scale (SSS-tas; Zuckerman, 1979), and two measures of fearfulness, the Fear Survey Schedule (FSS; Arrindell, Emmelkamp, & van der Ende, 1984) and the fearfulness subscale of the Emotionality-Activity-Sociability Temperament Inventory (EAS-fear; Buss & Plomin, 1984). In each participant sample, an exploratory principal components analysis of these four indicators revealed a single dominant factor on which all indicators loaded substantially (from ⫺.63 to ⫹.80) and which accounted for over 50% of the variance across indicators. The implication is that the PPI-I construct operates as a low pole indicator of a broad individual differences

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dimension ranging from extreme fearlessness at one end to extreme fearfulness at the other. Given that the findings were so similar in these two distinct participant samples, we expected that this trait fear factor would likewise emerge from a structural analysis of similar indicators in other participant samples. To address this possibility, and also to assess etiologic contributions to the broad trait fear factor, we collected data for various fear and fearlessness measures in a large, mixedgender sample (N ⫽ 2,572) of identical and fraternal twins recruited from the community (Kramer, Patrick, Bayevsky, & Krueger, 2008). The following scales were administered: FSS, EAS-fear, SSS-tas, the three subscales of PPI-I (Social Potency, Stress Immunity, Fearlessness), and the four subscales (anticipatory worry; fear of uncertainty; shyness with strangers; fatigability) comprising the Harmavoidance (ha) scale of the Tridimensional Personality Questionnaire (TPQ; Cloninger, 1987). The TPQ-ha subscales were included as indicators because they are described as indexing fearfulness and sensitivity to cues for danger. Confirmatory factor analyses of these various scale measures revealed the best fit for a model of the data in which all scales loaded substantially on a general, overarching factor (trait fear), with some scales loading additionally on one of two subordinate factors (stimulation seeking, assertiveness in social situations). Consistent with expectation, fearfulness measures (FSS, EAS-fear, four TPQ-ha subscales) loaded positively (M ⫽ ⫹.69) on this broad factor whereas fearlessness measures (three PPI-I scales, SSS-tas) loaded negatively (M ⫽ ⫺.58). In addition, because the participant sample for this study consisted of monozygotic (Mz) and dizygotic (Dz) twins, it was possible to estimate the proportion of variance in the broad trait fear factor that was attributable to genetic compared with environmental influences. Using standard formulae for estimating genetic and environmental contributions to a target phenotype within an Mz/Dz twin dataset (Falconer, 1989), the heritability of the broad trait fear factor (i.e., percentage of variance in scores attributable to genetic influence) was 74%, with the remaining 16% of the variance in scores attributable to nonshared environmental influence. Thus, the general disposition toward fear versus fearlessness as indexed by the broad factor underlying these various psychometric measures (which included the three subscales comprising PPI-I) appears to be a highly heritable phenotype. Hierarchical Model of Externalizing Disorders and Traits As noted earlier, impulse control problems of various kinds co-occur in a systematic manner such that these disorders operate as indicators of a common “externalizing” factor. Krueger et al. (2002) evaluated genetic and environmental

contributions to the externalizing factor—defined as the covariance among symptoms of various DSM disorders (child conduct disorder, adult antisocial behavior, alcohol dependence, and drug dependence) along with a self-report measure of disinhibitory personality—in a large, mixedgender sample of twins. More than 80% of the variance in the common externalizing factor was found to be attributable to additive genetic influence (see also: Kendler, Prescott, Myers, & Neale, 2003; Young, Stallings, Corley, Krauter, & Hewitt, 2000). The remaining variance in each primary variable not accounted for by the broad externalizing factor was attributable primarily to nonshared environmental influence—although for conduct disorder there was also a significant contribution of shared environment. Based on these findings, Krueger et al. (2002) proposed a hierarchical model in which the general externalizing factor is conceptualized as a predominantly heritable vulnerability that contributes to the development of various traits and problem behaviors, with the precise phenotypic expression of this vulnerability (i.e., as subclinical disinhibitory tendencies, antisocial deviance of different sorts, or alcohol or drug problems) determined by other specific etiologic influences. Krueger, Markon, Patrick, Benning, and Kramer (2007) extended this work by developing scales to comprehensively assess the domain of externalizing problems and traits in terms of elemental constructs. Unidimensional scales were developed to measure 23 separate constructs including varying forms of impulsivity; differing types of aggression (physical, relational, and destructive); irresponsibility; rebelliousness; excitement seeking; blame externalization; and alcohol, drug, and marijuana use/problems. Confirmatory factor analyses of these 23 scales yielded evidence of a superordinate factor (externalizing) on which all subscales loaded substantially (.45 or higher), and two subordinate factors (callous aggression, addictions) that accounted for residual variance in some subscales. These findings provide further support for the idea that a common dispositional factor (externalizing) contributes to a broad spectrum of impulse control problems and affiliated traits. In addition, they suggest that separate dispositional factors shape the expression of externalizing tendencies toward callous aggression on one hand, and addictive behaviors on the other.

PHYSIOLOGICAL RESPONSE INDICATORS OF TRAIT FEAR AND EXTERNALIZING Startle Reflex Potentiation as an Index of Trait Fear A key method for indexing fear reactivity in both animals and humans, mentioned earlier in relation to studies of

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emotion in PCL-R psychopathy, is startle reflex potentiation. There is a rich literature on startle potentiation as an index of defensive reactivity in animals and humans. Davis and colleagues (e.g., Davis, 1989; Davis, Falls, Campeau, & Kim, 1993) mapped the neural circuitry of fear-potentiated startle, defined as increased magnitude of the whole-body reaction to an abrupt noise probe in the presence of a fear cue, in animals. These investigators showed that the mechanism for this effect is a pathway from the central nucleus of the amygdala to the pontine reticular node of the basic startle circuit. In unselected human participants, the startle blink response to sudden noise is reliably enhanced during viewing of aversive pictures compared with neutral pictures (Lang, 1995; Lang, Bradley, & Cuthbert, 1990). Blink potentiation is strongest for directly threatening images (e.g., aimed weapons; menacing attackers), although it also occurs to a less reliable degree for vicarious aversive scenes involving physical injury or aggression (Bernat, Patrick, & Benning, 2006; Bradley, Codispoti, Cuthbert, & Lang, 2001; Levenston et al., 2000). This effect in humans is blocked by diazepam (Patrick, Berthot, & Moore, 1996), a drug that inhibits activity in the amygdala, and which has also been shown to block fear-potentiated startle in animals (Davis, 1979). Startle reflex potentiation has also been demonstrated in other aversive cuing contexts in humans, including fear conditioning (Grillon & Davis, 1997; Hamm, Greenwald, Bradley, & Lang, 1993), stressor anticipation (Patrick & Berthot, 1995), and imagery of fearful situations (Vrana & Lang, 1990). There is also evidence for the specificity of aversive startle potentiation as an index of fear. Davis, Walker, and Lee (1997; see also Davis, 1998) argued for the existence of two distinct systems underlying defensive reactivity: a phasic activation system, associated with the central nucleus of the amygdala, and a tonic activation system, associated with the extended amygdala—in particular, the bed nucleus of the stria terminalis (BNST). Davis et al. (1997) presented evidence that fear-potentiated startle, which involves a time-limited increase in defensive activation tied to an explicit aversive cue, is mediated by the former, whereas startle sensitization, involving a more persistent increase in negative emotional activation, is mediated by the latter. While acknowledging some interrelationship between the two systems (e.g., intense or repeated activation of the amygdala by stressful events may lead to longerterm activation of the BNST; cf. Rosen & Schulkin, 1998), Davis et al. posited that these systems play differing roles in fear and anxiety states—with the amygdala more important for cue-specific fear, and the BNST more important for nonspecific anxiety. From this perspective, startle reflex potentiation during discrete aversive cuing holds potential as an indicator of

individual differences in fear reactivity in humans. As described earlier, research by Patrick et al. (1993) and others has demonstrated deficient startle potentiation during aversive cuing in PCL-R defined psychopaths; this deviation has been linked in particular to the affectiveinterpersonal (Factor 1) component of the PCL-R (Patrick, 2007), which shows negative associations with trait measures of fear and negative affectivity (Hicks & Patrick, 2006). As an illustration of this finding, subplot A of Figure 57.1 depicts average startle blink magnitude for aversive versus neutral pictures in two prisoner subgroups from the Patrick et al. (1993) study with comparably high scores on PCL-R Factor 2, but differing on PCL-R Factor 1 (i.e., low versus high). In follow-up investigations with incarcerated male offenders, we have examined startle potentiation effects for psychopathy in relation to specific categories of aversive pictures. As noted, studies with nonclinical participants have demonstrated stronger startle potentiation effects for scenes of imminent threat or attack (e.g., guns pointed at the viewer, menacing attackers; Bernat et al., 2006; Bradley et al., 2001) than for other types of aversive scenes (e.g., snakes/spiders, physical injury, victimization). In a study of offenders, Levenston et al. (2000) reported differences between high and low PCL-R scorers in startle modulation effects for both victim scenes and threat scenes: For victim scenes, psychopaths showed startle inhibition as opposed to modest potentiation, and for threat scenes, they showed weak potentiation compared with very strong potentiation among nonpsychopaths. However, this study did not examine modulation effects in relation to the two distinct factors of the PCL-R. Our more recent research with offenders at a state medium security prison in Minnesota (Bernat et al., 2004) has examined modulatory effects for different picture contents separately in relation to PCL-R Factor 1 and Factor 2. Test subjects were selected to represent the entire range of scores on the PCL-R, so that associations for the two PCL-R factors with startle modulation could be examined continuously and in terms of extreme groups. Participants viewed aversive pictures consisting of threat scenes and victim (physical injury, other-attack) scenes, along with neutral pictures and differing categories of pleasurable scenes. Startle potentiation scores were computed consisting of average blink magnitude for each aversive picture category (threat, victim) minus average magnitude for neutral pictures. Within the sample as a whole (N ⫽ 107), a significant negative relationship was found between scores on PCL-R Factor 1 and startle potentiation, for threat scenes in particular; this association was not significant for PCLR Factor 2. Subplot B of Figure 57.1 depicts mean blink response magnitude for threat versus neutral pictures in

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Neurobiology of Psychopathy Prisoners

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Figure 57.1 Aversive startle potentiation effects in two prisoner and two nonprisoner samples using psychopathy factor scores and trait fear scores as selection criteria for participant subgroups. Note: A: Mean magnitude of startle blink responses to noise probes presented during viewing of neutral and aversive pictures in two male prisoner groups: (1) prisoners high on PCL-R Factor 2 but low on PCL-R Factor 1 (n ⫽ 18), and (2) prisoners high on both factors of the PCL-R (n ⫽ 17). Aversive pictures are of varying types (snakes, angry faces, direct threat, sickness, physical injury). Blink magnitude means are presented in T-score units (M ⫽ 50, SD ⫽ 10), based on standardization of raw magnitude scores across trials for each individual subject. Data are From “Emotion in the Criminal Psychopath: Startle Reflex Modulation,” by C. J. Patrick, M. M. Bradley, and P. J. Lang, 1993, Journal of Abnormal Psychology, 102, 82–92. B: Mean blink response magnitude (in standardized T-score units) to noise probes during viewing of neutral and direct-threat (i.e., aimed weapon, menacing attacker) scenes in two male prisoner groups:

offender groups comprising the lowest and highest 25% of scorers on PCL-R Factor 1 (n ⫽ 25 and 23, respectively). These findings provide further evidence of reductions in aversive startle potentiation specifically in relation to the affective-interpersonal (Factor 1) features of psychopathy. In addition, the fact that this association was evident especially for threat scenes supports the idea that the affectiveinterpersonal component of psychopathy is associated with a deficiency in cue-specific fear reactivity. Comparable results have been reported for nonprisoner groups differing on the first (Fearless Dominance)

High-Trait Fear

Low-Trait Fear

(1) prisoners low on PCL-R Factor 1, and (2) prisoners high on PCL-R Factor 1. Data are From “Exploring Mechanisms of Deviant Affect-Modulated Startle in Psychopathy,” by E. M. Bernat, C. J. Patrick, B. V. Steffen, J. R. Hall, & M. Ward, 2004, Psychophysiology, 41, p. S40. C: Mean blink magnitude (in T-score units) to noise probes during viewing of neutral pictures and aversive pictures of varying types in two selected subgroups of males from the community: (1) individuals low on PPI-I, and (2) individuals high on PPI-I. Data are From “Psychopathy, Startle Blink Modulation, and Electrodermal Reactivity in Twin Men,” by S. D. Benning, C. J. Patrick, and W. G. Iacono, 2005, Psychophysiology, 42, 753–762. D: Mean blink magnitude (in T-score units) to noise probes during viewing of neutral and direct-threat (weapons, attackers) scenes in two selected subgroups of college students: (1) individuals high in trait fear, and (2) individuals low in trait fear. Data are From Startle Reflex Potentiation during Aversive Picture Viewing as an Indicator of Trait Fear, by U. Vaidyanathan, C. J. Patrick, and E. M. Bernat, 2009, Psychophysiology, 46, 75–85.

factor of Lilienfeld’s (1990) PPI. Benning, Patrick, and Iacono (2005) examined patterns of blink reflex modulation during viewing of pleasant, neutral, and unpleasant pictures in relation to PPI factor scores in adult male participants (N ⫽ 307) recruited from the general community. Unpleasant pictures comprised an assortment of contents, with greater representation of victim (physical injury, disease) than threat scenes. Participants with very high scores on PPI-I showed a deviant startle pattern resembling that of offenders with high scores on PCL-R Factor 1 (i.e., an absence of fear-potentiated startle), whereas participants

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Empirical Evidence that Psychopathy Is not Unitary 1119

with very high scores on PPI-II showed no such deviation. Subplot C of Figure 57.1 shows mean startle blink magnitude for aversive versus neutral pictures in subgroups from this study comprising the lowest and highest 10% of PPI-I scorers (n ⫽ 31 per group). Along with psychopathy, the other individual difference construct that has been associated most reliably with variations in aversive startle potentiation is fearfulness, as indexed by the Fear Survey Schedule (FSS; Arrindell et al., 1984; Wolpe & Lang, 1964). The FSS assesses reported levels of fear in relation to various specific objects and situations (e.g., insects, small animals, blood, crowds, public speaking), with total scores reflecting general fearfulness. Cook (1999) reviewed findings from six separate studies with college samples demonstrating enhanced aversive startle potentiation among individuals selected to be high on the FSS, compared with low-FSS individuals. In five of these six studies, fear-potentiated startle was assessed in a picture-viewing procedure, with the remaining study involving an imagery procedure. A related trait measure that has been examined in relation to aversive startle potentiation is the Harm Avoidance (ha) Scale of the TPQ. In two studies employing neutral and affective picture stimuli, Corr and colleagues (Corr, Kamuri, Wilson, Checkley, & Gray, 1997; Corr et al., 1995) reported that participants high on TPQ-ha showed robust startle potentiation during aversive picture viewing whereas low TPQ-ha individuals did not. This finding is especially noteworthy in light of evidence that high scores on TPQ-ha reflect tendencies opposite to those associated with high scores on PPI-I (i.e., low social dominance, high stress reactivity, and high risk aversion (Waller, Lilienfeld, Tellegen, & Lykken, 1991)). As noted earlier, there is evidence that measures of dispositional fear and fearlessness including the FSS, TPQha, and PPI-I comprise indicators of a common, bipolar trait dimension (Kramer et al., 2008). Considering this along with the aforementioned findings from startle/individual differences studies, a tenable hypothesis is that aversive startle potentiation (which has been interpreted as an index of amygdala reactivity to explicit fear cues; Davis et al., 1997; Lang et al., 1990) is a continuous physiological indicator of this bipolar trait dimension. Vaidyanathan et al. (2008) evaluated this hypothesis in a sample of undergraduate participants (N ⫽ 88) who were assessed with multiple trait fear and fearlessness measures (FSS, TPQ-ha subscales, EAS-fear, PPI-1 subscales, SSStas) and tested in an affect-startle procedure that included varying categories of aversive (threat, mutilation, victim) and pleasant pictures (erotic, action, nurturant) along with neutral pictures. Consistent with the findings of Kramer et al. (2008), a principal components analysis of these varying trait scales yielded evidence of a dominant first factor

on which all scales loaded appreciably. An omnibus index of trait fear was computed for each participant consisting of scores on the first principal component from this analysis. A robust linear association was found in the sample as a whole between trait fear and startle modulation for threat pictures in particular—the picture category, as noted earlier, that is most directly fear-relevant and yields the most reliable startle potentiation effects. Subplot D of Figure 57.1 shows mean startle blink magnitude for threat versus neutral pictures in subgroups comprising the lowest and highest 20% of trait fear scorers (n ⫽ 18 per group) from this study. The findings of this study confirm that aversive startle potentiation represents a physiological indicator of the trait fear continuum. Brain Response Markers of Externalizing Vulnerability Reduced P3 Amplitude A brain response measure that has been shown to be associated with various forms of externalizing psychopathology is the P300 (or P3; see discussion that follows) component of the event-related potential (ERP). The P300 is a positive brain potential response, maximal over parietal scalp regions, that follows the occurrence of infrequent, attended targets in a stimulus sequence. It is well established that reduced amplitude of the P300 response is associated with alcohol problems and alcoholism risk. This link was first noted in work comparing abstinent alcoholics with controls (Porjesz, Begleiter, & Garozzo, 1980). Subsequent studies revealed that reduced P300 amplitude (typically assessed at parietal scalp sites) was associated not just with active symptoms, but also with risk for the development of alcohol problems. For example, children and adolescents with a paternal history of alcoholism show reliably reduced P300 compared with family negative controls (Begleiter, Porjesz, Bihari, & Kissin, 1984; Elmasian, Neville, Woods, Schuckit, & Bloom, 1982; Hill & Shen, 2002; for review, see Polich, Pollock, & Bloom, 1994). Additionally, smaller P300 amplitude prospectively predicts the later emergence of alcohol problems (Berman, Whipple, Fitch, & Noble, 1993; Hill, Steinhauer, Lowers, & Locke, 1995; Iacono, Carlson, Malone, & McGue, 2002). These results led theorists to postulate that reduced P300 response is an indicator of brain-based impairments in cognitive-executive function that confer a risk for alcohol dependence (e.g., Begleiter & Porjesz, 1999; Giancola & Tarter, 1999). However, subsequent investigations revealed links between reduced parietal P300 response and other disorders in the externalizing spectrum besides alcohol dependence, including: drug dependence (Attou, Figiel, & Timsit-Berthier, 2001; Biggins, MacKay, Clark, & Fein,

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1997; Branchey, Buydens-Branchey, & Horvath, 1993), nicotine dependence (Anokhin et al., 2000; Iacono et al., 2002), child conduct disorder (Bauer & Hesselbrock, 1999; Kim, Kim, & Kwon, 2001), and adult antisocial personality (Bauer, O’Connor, & Hesselbrock, 1994; Costa et al., 2000). In addition, reduced P300 is known to be associated with risk for these other disorders as well as with active symptoms (Brigham, Herning, & Moss, 1995; Iacono et al., 2002). Taken together, findings suggest that reduced P300 amplitude reflects an underlying vulnerability not just to alcohol problems—but vulnerability to externalizing problems more generally. Patrick, Bernat et al. (2006) evaluated this hypothesis in a large sample of male twin participants (N ⫽ 969; median age ⫽ 17.7 years). Scores on the externalizing vulnerability factor were derived from a principal components analysis of symptoms of conduct disorder, adult antisocial behavior, alcohol dependence, drug dependence, and nicotine dependence. P300 was assessed at parietal scalp sites in a “rotated heads” visual oddball task (Begleiter et al., 1984) in which target stimuli (consisting of schematic heads) occurred on one-third of trials and nontarget stimuli (simple ovals) occurred on two-thirds of trials. Participants responded with a button press each time a head appeared and disregarded more frequently occurring ovals. Behavioral performance on the oddball task (i.e., accuracy or latency of responses to target stimuli) did not vary as a function of externalizing. However, higher scores on the externalizing factor were robustly associated with reduced amplitude of P300 to the task stimuli (see Figure 57.2). In addition, mediational analyses demonstrated that scores on the externalizing factor completely accounted for associations between individual diagnostic variables and reduced P300 amplitude. These results indicate that P300 is an indicator of the common vulnerability that underlies antisocial syndromes and substance use disorders (i.e., the externalizing factor), rather than a marker of alcohol problems per se. Moreover, the fact that P300 amplitude, like externalizing vulnerability, is highly heritable (Katsanis, Iacono, McGue, & Carlson, 1997; O’Connor, Morzorati, Christian, & Li, 1994) raises the possibility that P300 amplitude may represent a quantitative endophenotype of externalizing vulnerability. An endophenotype is a biological characteristic that arises from, and thus directly reflects, an underlying genotypic predisposition (Gottesman & Shields, 1972; Iacono, 1998; John & Lewis, 1966). If reduced P300 is an endophenotype for externalizing vulnerability, it should occur at higher rates among asymptomatic individuals who are at risk for developing externalizing problems by virtue of a positive parental history of such problems (cf. Elmasian et al., 1982). Consistent with this, Iacono et al. (2002) reported

Low externalizing High externalizing 15 P300 Amplitude (µV)

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Figure 57.2 Average P300 response to visual stimuli in a two-stimulus oddball task for participants high versus low in externalizing scores. Note: P300 waveform data are for the midline parietal (Pz) scalp site, High and low externalizing groups represent top and bottom 25% of scorers (from a sample of 969 community participants) on the first principal component derived from a PCA of DSM-III-R symptoms of conduct disorder, adult antisocial behavior, alcohol dependence, drug dependence, and nicotine dependence. Data are From “P300 Amplitude as an Indicator of Externalizing in Adolescent Males,” by C. J. Patrick, E. M. Bernat, S. M. Malone, W. G. Iacono, R. F. Krueger, and M. K. McGue, 2006, Psychophysiology, 43, 84–92.

reduced P300 in the adolescent sons of fathers who met criteria for alcohol dependence, drug abuse/dependence, or antisocial personality, whether or not the offspring themselves met criteria for a diagnosis. In addition, they found that reduced P300 at age 17 predicted the development of externalizing problems of various kinds at age 20, even among individuals who were free from disorder at the time of P300 assessment. Hicks et al. (2007) tested the hypothesis that reduced P300 in relation to externalizing reflects an underlying biological-genetic association by undertaking biometric (Mz/Dz twin) analyses of this association in the sample of twin participants examined by Patrick et al. (2006). The Cholesky decomposition method was used to partition the variance and covariance of P300 amplitude and externalizing scores into three sources: additive genetic (A), shared or common environment (C), and nonshared or unique environment (E). Alternative models were fit to the data using full information maximum likelihood estimation. The model that yielded the best fit to the data for each individual variable (P300, externalizing) was the AE model, indicating no shared environmental contribution to either variable. Genetic and environmental contributions to the covariance between P300 amplitude and externalizing were examined by comparing the fit of the AE model to that of more restrictive models. A model that required nonshared environmental factors alone to account for the association between P300

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Empirical Evidence that Psychopathy Is not Unitary 1121

amplitude and externalizing yielded a significant reduction in fit—confirming an essential contributory role of genetic influences in the covariance between P300 amplitude and externalizing. In contrast, a model in which the covariance between P300 and externalizing was accounted for solely by genetic factors showed a slight increase in fit compared with the AE model. The results of this study demonstrate that the association between P300 amplitude and externalizing is primarily a genetic association—implying that reduced P300 directly reflects some alteration in brain function associated with the broad, predominantly genetic vulnerability to disorders within the externalizing spectrum. What might the finding of reduced P300 response tell us about the brain mechanisms underlying this broad vulnerability? Although the P300 has been characterized for some time as a distributed brain response reflecting activity in multiple brain regions, more recent research on the neutral generators underlying P300 response supports the idea that prefrontal brain regions play a particularly important role (see, for example, Dien, Spencer, & Donchin, 2003, and Nieuwenhuis, Aston-Jones, & Cohen, 2005). In addition, follow-up research by our laboratory group has produced evidence of an enhanced relationship between externalizing scores and P300 amplitude at fronto-central compared with parietal scalp sites, particularly for novel task stimuli that are known to preferentially activate anterior brain regions. The task procedure we have used to further investigate reduced P300 in relation to externalizing is a three-stimulus variant of the rotated heads oddball task (Bernat, Patrick, Cadwallader, van Mersbergen, & Seo, 2003). In addition to nontarget oval (70% of trials) and target “head” stimuli (15% of trials), the task includes infrequent novel stimuli (15% of trials), requiring no response. The novel stimuli consist of pleasant, neutral, and unpleasant picture stimuli selected from the International Affective Picture System (IAPS; Center for the Study of Emotion and Attention, 1999). All task stimuli occur for 100 ms, and because the primary task is to detect and respond to the target heads, the novel picture stimuli are processed incidentally. Target stimuli in a task of this kind are known to elicit a P300 response that is maximal at parietal scalp sites. In contrast, novel stimuli evoke a P300 response, termed the “novelty P3” (Courchesne, Hillyard, & Galambos, 1975) or P3a response (Squires, Squires, & Hillyard, 1975) to distinguish it from the target P300 (P3) response, that is maximal at fronto-central scalp sites. With regard to generators, evidence exists for a role of lateral prefrontal cortex in the processing of novel stimuli (see Nieuwenhuis et al., 2005) and ERP source localization work has additionally identified the anterior cingulate cortex (ACC) as contributing to the novelty P3 response (Dien et al., 2003).

Functional neuro-imaging research also implicates these two regions in novel stimulus processing, consistent with the idea that frontal brain regions are involved in the allocation of attentional resources to novel stimuli, as well as the processing of emotional cues (Fichtenholtz et al., 2004; Yamaski, LaBar, & McCarthy, 2002). The use of affective and neutral pictures as incidental, novel stimuli in this task procedure therefore provided us with an opportunity to examine automatic affective processing in relation to scores on the externalizing factor. The strategy we have used to select subjects for recent studies of externalizing using this task procedure is to administer a 100-item screening version of the Externalizing Spectrum Inventory (Krueger et al., 2007) to students in large undergraduate classes. Scores on this screening version correlate very highly (r ⫽ .98) with scores on the full, 415-item Externalizing Spectrum Inventory. Individuals in the lowest and highest quartiles of the overall score distribution are over-sampled to provide low externalizing and high externalizing groups of adequate size, and in addition, individuals among the middle 50% of scorers are included to provide for supplementary correlational analyses in which the full distribution of externalizing scores is represented. Using this strategy, a total of 149 participants were selected and tested in the three-stimulus oddball task: 34 (21 female) in the low-externalizing group, 61 (34 female) in the high-externalizing group, and 54 (34 female) intermediate scorers. Brain activity was recorded from 64 scalp sites, including frontal and central as well as parietal sites. This work has yielded a number of important findings. For ease of presentation, we focus here on results for midline anterior (FCz) and posterior (Pz) scalp sites. First, we replicated the finding of reduced P3 amplitude to target (head) stimuli in this task, both in the extreme groups analysis, and in the correlational analysis involving the full participant sample (N ⫽ 149). High externalizing participants showed significantly smaller P3 amplitude across scalp sites than low externalizing participants, and a significant negative correlation between externalizing scores and P3 amplitude across scalp sites was evident for the sample as a whole. This finding is important because it confirms that high scores on the Externalizing Spectrum Inventory, like high scores on the externalizing factor derived from DSM diagnostic symptoms, are associated with reduced P3 brain response. A second key finding was that a significant association of this kind was evident also for novel (picture) stimuli, both in the extreme (high versus low externalizing) groups analysis and in the correlational analysis employing all subjects. Also notable was the fact that the negative relationship between externalizing and P3 was stronger at anterior than at posterior sites—particularly in the case of the novel picture stimuli (see topographic

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Figure 57.3 Average P3 response to target and novel stimuli in a three-stimulus visual oddball task for participants high versus low in externalizing scores. Note: P3 waveform data are depicted for anterior (FCz; upper plots) and posterior (Pz; lower plots) scalp sites; waveforms for target (schematic ‘head’) and novel (affective and neutral picture) stimulus trials appear in left plots and right plots, respectively. High and low externalizing groups (ns ⫽ 61 and 34, respectively) were formed by oversampling from the top and bottom 25% of scorers on a 100-item version of the Externalizing

maps in Figure 57.3). The enhanced magnitude of effect at anterior versus parietal scalp sites is consistent with the hypothesis that the association between reduced P3 and externalizing reflects a deviation of some kind in frontal brain processing. No moderating effects of gender were found for the association between P3 and externalizing in these analyses—indicating that the association was present for women as well as men. A further notable finding of this study had to do with comparative brain responses to novel picture stimuli that were affective (pleasant or unpleasant) compared with neutral. Analyses of brain potential responses to affective pictures in relation to neutral pictures have consistently revealed a positive slow-wave component extending far beyond the P3 in time (e.g., Cuthbert, Schupp, Bradley, Birbaumer, & Lang, 2000; Schupp, Cuthbert, Bradley, C acioppo, Ito, & Lang., 2000). To examine effects for this affective slow-wave component separately from the P3

Spectrum Inventory (Krueger et al., 2007) in an undergraduate screening pool. Statistical maps at the bottom depict the scalp topography distribution of the negative association between externalizing factor scores and P3 amplitude for target ‘head’ stimuli (left map) and novel picture stimuli (right map) in the test sample as a whole (N ⫽ 149). Data are from “Neurophysiological Correlates of Behavioral Disinhibition: Separable Contributions of Distinct Personality Traits,” by N. C. Venables, E. M. Bernat, J. R. Hall, B. V. Steffen, M. Cadwallader, R. F. Krueger, et al., 2005, Psychophysiology, 42, p. S126.

response, we undertook a time-frequency decomposition of brain response data in the undergraduate sample (N ⫽ 149) tested in the three-stimulus oddball task. Time-frequency decomposition (cf. Bernat, Williams, & Gehring, 2005) is a statistical technique that isolates ERP components through concurrent consideration of activity in both time and frequency domains; the technique provides an effective way to separate brain signals that overlap in time but have distinctive frequency characteristics. The decomposition was undertaken separately for affective picture trials and neutral picture trials. Results for the sample as a whole are presented in Figure 57.4. A slow-wave (subdelta frequency) component is evident that overlaps with the P3/delta-frequency response, but extends beyond it in time. As expected from prior work (Cuthbert et al., 2000; Schupp et al., 2000), the slow-wave component accounts for most of the positive amplitude difference between affective and neutral pictures. In Figure 57.5, these P3/delta and slow-wave/subdelta

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Empirical Evidence that Psychopathy Is not Unitary 1123

ERPs to Novel Stimuli: Affective versus Neutral Slow Wave Difference Time Domain Amplitude (µV)

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Note: The data are for a sample of 149 undergraduate participants. Waveform plots depict results for electrode site FCz. The plot at the top depicts unfiltered ERP averages for the affective and neutral picture conditions. The waveform plots immediately below this show time-domain averages for affective versus neutral conditions that have been frequencyfiltered using third-order Butterworth filters to approximate the TF components (discussed below) in a more familiar waveform representation. Activity in the delta (0.5 to 3 Hz bandpass) range corresponding to P3

response is depicted in the left plot, and activity in the subdelta (0.5 Hz lowpass) range corresponding to the ERP slow-wave response is depicted in the right plot. The two-color surface plots depict principal component scores reflecting the P3/delta and slow-wave/subdelta activity contained in the ERP signal, derived from a TF decomposition of the EEG data across all picture trials. Statistical maps at the bottom depict the scalp topography distribution of the difference in amplitude of ERP response to affective versus neutral picture stimuli in the P3/delta TF component (left map) and in the slow-wave subdelta component (right map). Data are from “Neurophysiological Correlates of Behavioral Disinhibition: Separable Contributions of Distinct Personality Traits,” by N. C. Venables, E. M. Bernat, J. R. Hall, B. V. Steffen, M. Cadwallader, R. F. Krueger, et al., 2005, Psychophysiology, 42, p. S126.

time-frequency components are shown, but now comparing low versus high externalizing participant subgroups across all picture trials. The effect of externalizing is restricted to the P3/delta component; the slow-wave component shows no significant difference in relation to externalizing. In Figure 57.6, the affective versus neutral picture difference in the slow-wave/subdelta component is depicted for low and high externalizing groups. It can be seen that both

groups demonstrate robust slow-wave response differentiation between affective and neutral pictures. In summary, high externalizing participants showed no reduction in brain response differentiation between pictures that were affective compared with pictures that were neutral, despite showing an overall reduction in amplitude of P3 response to novel picture stimuli (Figure 57.5). This result indicates that even though high externalizing

Figure 57.4 (Figure C.56 in color section) Time-frequency (TF) decomposition of average ERP responses to affective versus neutral pictures presented as novel nontargets in a three-stimulus visual oddball task.

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Figure 57.5 (Figure C.57 in color section) Results from a time-frequency (TF) decomposition of average ERP responses to pictures presented as novel stimuli in a visual oddball task, displayed separately for subgroups of high versus low externalizing participants. Note: High and low externalizing participant groups are as described in Figure 57.3. Waveform plots depict results for electrode site FCz. The plot at the top depicts unfiltered ERP averages across all novel picture trials for these high and low externalizing subgroups. The waveform plots immediately below this show time-domain averages for the two subgroups that have been frequency-filtered using third-order Butterworth

individuals showed a generally attenuated brain response to the novel picture stimuli, these individuals nonetheless showed normal processing of the affective content of the pleasant and unpleasant pictures—suggesting an intact subcortical-affect processing system. Diminished Error-Related Negativity In other work employing undergraduate participants preselected according to scores on the Externalizing

filters. Activity in the delta (0.5 to 3 Hz bandpass) range corresponding to P3 response is depicted in the left plot, and activity in the subdelta (0.5 Hz low pass) range corresponding to the ERP slow-wave response is depicted in the right plot. The two color surface plots depict principal component scores reflecting the P3/delta and slow-wave/subdelta activity contained in the ERP signal, derived from a TF decomposition of the EEG data across all picture trials. Statistical maps at the bottom depict, for the overall test sample (N ⫽ 149) that included these extreme subgroups, the scalp topography distribution of the negative association between externalizing scores and amplitude of ERP response to novel pictures in the P3/delta TF component (left map) and in the slow-wave subdelta component (right map).

Spectrum Inventory, we have examined negative-polarity scalp potentials associated with errors in responding. Hall, Bernat, and Patrick (2007) tested for an association between externalizing scores and amplitude of the response-locked error-related negativity (ERN), a negative deflection of the ERP that is observed following errors in laboratory performance tasks. The peak of the ERN typically occurs within 100 ms of the commission of an error and is maximal at fronto-central scalp sites. The ERN has

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Empirical Evidence that Psychopathy Is not Unitary 1125 ERPs to Novel Stimuli: Affective-Neutral Slow Wave Difference Unrelated to Externalizing

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Figure 57.6 ERP slow-wave response for affective versus neutral pictures occurring as novel stimuli in a three-stimulus visual oddball task, displayed separately for subgroups of high versus low in externalizing scores. Note: High and low externalizing groups are as described in Figure 57.3. Average time-domain ERP waveforms, depicted for electrode site FCz, are frequency (third-order Butterworth) filtered to reflect activity in the subdelta (0.5 Hz lowpass) range corresponding to the ERP slow-wave

been characterized as a neurophysiological index of endogenous action monitoring—that is, the brain’s automatic capacity to monitor behavioral performance on-line and to initiate corrective action as needed, either through detection of errors (Gehring, Coles, Meyers, & Donchin, 1995) or detection of conflict among competing neural response pathways (Carter et al., 1998). Consistent with this interpretation, evidence from ERP source localization studies indicates that the primary neural generator of the ERN is the anterior cingulate cortex (ACC; Dehaene, Posner, & Tucker, 1994), a brain structure that is widely believed to be involved in self-monitoring and behavioral regulation (Bush, Luu, & Posner, 2000). The fact that externalizing psychopathology is characterized by an apparent failure to learn from experience (i.e., maladaptive behaviors are repeated despite an awareness of negative consequences for self or others) suggests the possibility of an impairment in this action monitoring capacity. In line with this hypothesis, research findings indicate that both states and traits related to behavioral disinhibition are associated with reduced amplitude of ERN responding. For instance, ERN amplitude is reduced following ingestion of moderate doses of alcohol

response. The left and middle statistical topographic maps at the bottom depict the scalp topography distribution of the difference in amplitude of slow-wave ERP response to affective versus neutral picture stimuli for the low and high externalizing subgroups, respectively; the bottom-right map depicts the lack of significant association at any scalp site between continuous scores on the externalizing factor within the test sample as a whole (N ⫽ 149) and the degree of affective versus neutral slow wave differentiation.

(Ridderinkhoff et al., 2002), a state manipulation known to produce behavioral disinhibition. In the domain of traits, Dikman and Allen (2000) found that participants low in socialization (a construct reflecting impulsivity, rebelliousness, and aggression; Gough, 1960) showed reduced ERN response within the punishment condition of a speeded reaction time task, compared with the reward condition. In a related vein, Pailing and Segalowitz (2004) reported that participants low in conscientiousness (a trait dimension reflecting tendencies toward dependability, dutifulness, and persistence) showed reduced ERN amplitude in particular when explicit performance incentives were absent. Reduced ERN amplitude has also been demonstrated in relation to trait impulsivity (Pailing, Segalowitz, Dywan, & Davies, 2002; Potts, George, Martin, & Barratt, 2006) and Eysenck’s psychoticism dimension (Santesso, Segalowitz, & Schmidt, 2005). On other hand, enhanced ERN response has been demonstrated in participants with obsessive-compulsive disorder, a syndrome marked by excessive rumination and self-evaluation (Gehring, Himle, & Nisenson, 2000). Collectively, these studies point to a relationship between impaired self-monitoring, as evidenced by reduced ERN

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response amplitude, and a variety of constructs related to externalizing. Evidence of an association with the ERN would be especially informative regarding brain mechanisms underlying externalizing vulnerability because the ERN indexes a distinctive psychological process with clear functional relevance to externalizing (i.e., online, endogenous action monitoring). Moreover, the ERN has been localized to a specific region of the brain (the ACC) that has been shown to play a key role in response monitoring processes. Using the 100-item screening version of the Externalizing Spectrum Inventory as a basis for subject selection, and oversampling high and low scorers from a large undergraduate screening pool, Hall et al. (2007) examined the association between ERN and externalizing in a sample of 92 undergraduate participants comprising 22 low-externalizing individuals, 38 high-externalizing individuals, and 32 intermediate scorers. Based on recent evidence that increases in anterior theta (4 to 7 Hz) activity account for much of the oscillatory signal comprising the ERN response (Bernat, Williams, & Gehring, 2005; Luu, Tucker, & Makieg, 2004), Hall et al. also examined the specific relationship between externalizing and brain activity within this frequency band following the commission of errors (i.e., during the ERN window). Considering these prior research findings, these investigators hypothesized that individuals high in externalizing would show a significant reduction in ERN, and a specific reduction in the anterior theta response associated with the ERN, relative to individuals low in externalizing. The experimental task was a modified version of the Eriksen flanker task (Eriksen & Eriksen, 1974) in which participants made right- or left-hand button-press responses to indicate the middle letter in a 5-letter stimulus array. Each stimulus array was presented for 150 ms, followed by a 1,000 ms response window. Participants were instructed to respond as quickly and accurately as possible to each target array. The main task comprised a total of 600 total trials. The ERN was computed from the average responselocked ERP on trials in which performance errors occurred. There were no significant differences between high- and low-externalizing groups in indices of behavioral performance (accuracy, reaction time) on the task. However, as predicted, ERN amplitude was significantly reduced in the high-externalizing group. Figure 57.7 depicts average response-locked ERP waveforms for error trials at electrode site Cz (where ERN amplitude was maximal) for high- versus low-externalizing groups. The ERN is evident as a sharp negative deflection in the error waveform that peaks around 50 ms postresponse. The Externalizing Group ⫻ Electrode Site interaction was not significant in this analysis, indicating that group differences were evident across the three midline scalp sites. No moderating effect of gender was found for the relationship between

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Figure 57.7 Average error-related negativity (ERN) response following performance errors in a flanker task for participants high versus low in externalizing scores Note: ERN waveform data are for the midline central (Cz) scalp site. High and low externalizing groups (ns ⫽ 38 and 22, respectively) were selected from the top and bottom 25 percent of scorers on a 100-item version of the Externalizing Spectrum Inventory (Krueger et al., 2007) in a large undergraduate screening sample. Data are from “Externalizing Psychopathology and the Error-Related Negativity,” by J. R. Hall, E. M. Bernat, and C. J. Patrick, 2007, Psychological Science,18, 326–333.

externalizing and ERN in these analyses—indicating (as for P300) that the association was present among women as well as men. Time-frequency analysis (Bernat et al., 2005) was used to isolate distinctive components of the response-locked ERP, taking into account variations in oscillatory (frequency) elements of the ERP signal across time. A principal components decomposition of time-frequency brain response data within the ERN response window yielded a dominant first component reflecting oscillatory activity within the theta frequency band (4 to 7 Hz). The peak of this responselocked increase in theta energy coincided in time with the peak of the ERN, and had a similar midline-central scalp distribution, encouraging an interpretation of this component as the time-frequency representation of the ERN. Moreover, a source localization analysis placed the source of this component squarely within the region of the ACC. Paralleling results for the ERN, a main effect of Electrode Site indicated that the theta response to errors was maximal at electrode Cz, and theta activity was significantly attenuated in the high-externalizing group. In addition, for this specific index of brain activity, a significant Externalizing Group ⫻ Electrode Site interaction was found, indicating that group differences in theta response were maximal at electrode Cz. Consistent with this, correlational analyses revealed a significant negative association between continuous externalizing scores and theta activity at electrode site Cz, but not at sites Fz or Pz. These findings demonstrate a relationship between reduced ERN response and the externalizing vulnerability factor.

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This finding is important because it points to impairment in endogenous action monitoring processes associated with the ACC as a potential mechanism for externalizing vulnerability.3 Despite showing reduced ERN response, highexternalizing participants nonetheless maintained a level of task performance equivalent to of the low-externalizing group—implying that within the simple processing context of the flanker task these participants were able to compensate for a lack of normal internal response monitoring to achieve adequate performance. Given a more complex experimental task, or under real world conditions, deficits in self-monitoring are likely to have more substantial consequences for performance.

SUMMARY The two-process theory postulates that the classic syndrome of psychopathy as described by Cleckley in his book The Mask of Sanity can be understood in terms of two separable individual difference constructs with distinctive neurobiological underpinnings. One of these constructs is a trait dimension of fear and fearlessness that is associated with varying levels of defensive reactivity to discrete aversive cues. Fear-potentiated startle, defined as enhanced blink startle reactivity during viewing of aversive (in particular, directly threatening) visual stimuli in relation to neutral stimuli, represents one physiological indicator of this underlying trait fear dimension. The other construct is a dispositional dimension of externalizing vulnerability reflecting disinhibitory personality tendencies and liability to impulse control problems of various kinds. Variations in stimulus-locked P300 and response-locked ERN brain potential amplitude represent physiological indicators of this externalizing dimension. Available data indicate that both of these individual difference factors, trait fear, and externalizing, are substantially heritable, and biometric modeling research has established reduced P300 amplitude as an endophenotype marker of externalizing vulnerability. The two-process theory articulated here emerged out of efforts to clarify associations between physiological response indicators of underlying neurobiological processes and phenotypic elements of the psychopathy construct as indexed by Hare’s PCL-R. In studies of fear reactivity using the startle modulation paradigm, associations with PCL-R psychopathy emerged most clearly in relation to

3

However, in a separate study (Bernat, Nelson, Steele, & Patrick, 2008), we found no evidence of reduced ACC-localized negativity to externally presented loss feedback—suggesting that reduced ERN reactivity may reflect impairment in monitoring circuitry involving the ACC, rather than ACC impairment per se.

the affective-interpersonal (Factor 1) component, particularly after controlling for the behavioral deviance (Factor 2) component. Findings regarding electrocortical correlates of psychopathy have historically been mixed (cf. Raine, 1989, 1993; but see more recent work by Kiehl, Hare, Liddle, & McDonald, 1999; Kiehl, Smith, Hare, & Liddle, 2000), whereas general psychopathology studies across many years have yielded reliable associations between externalizing syndromes and brain response measures such as P300. Recent structural modeling work demonstrating a close, selective association between the behavioral deviance (Factor 2) component of the PCL-R and the broad externalizing factor of psychopathology suggests that relations between psychopathy and electrocortical response can likewise be clarified by considering the two components of the PCL-R separately. It is notable that our two-process conceptualization, derived from findings of physiological studies of psychopathy with adult participants, dovetails with recent developmental models that postulate alternative pathways to conscience formation and psychopathic tendencies in youth (Fowles & Dindo, 2006; Frick & Marsee, 2006). A limitation of the PCL-R operationalization of psychopathy is that its affective-interpersonal and behavioral deviance components are moderately correlated, such that differential associations of these components with criterion measures are in some cases obscured (suppressed) due to their overlapping variance (cf. Hicks & Patrick, 2006). We hypothesize that differentiable facets of the psychopathy construct became partially fused in the PCL-R because the approach that was used to select items for the PCL-R favored measurement of a unidimensional construct. Lilienfeld’s self-report based PPI, which was developed to assess the spectrum of psychopathy-related traits with no a priori assumptions regarding their structure, provides an alternative operationalization in which narrower content subscales map onto two broad orthogonal dimensions—one reflecting dominance, stress immunity, and fearlessness (PPI-I), and the other reflecting impulsivity, rebelliousness, alienation, and aggression (PPI-II). The available evidence indicates that these PPI dimensions, which are related to but distinct from the PCL-R factors, represent more clearly differentiated phenotypic indicators (in the domain of self-report) of the aforementioned trait fear and externalizing constructs. However, our point in this chapter is not to advocate one approach to operationalizing psychopathy over another. Rather, our aim is to encourage a movement toward operationalizing and studying trait fear and externalizing as constructs in their own right. We believe these two constructs are important to study because they are crucial to an understanding of psychopathy, and also to an understanding of general psychopathology spectra encompassing, respectively, disorders of anxiety/mood and disorders

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of impulse control. In addition, we believe these constructs are important to study because they represent purer phenotypic targets for neurobiological research. They constitute multivariate behavioral phenotypes (cf. Iacono, 1998), reflecting the overlapping variance among multiple interrelated indicators. As such, they appear to be more substantially heritable than individual phenotypic indicators—whether modeled as latent variables (e.g., Krueger et al., 2002) or extracted as manifest composite variables (e.g., Kramer et al., 2008). In addition, they account for associations between individual phenotypic indicators and physiological response measures such as fear-potentiated startle (in the case of trait fear; Vaidyanathan et al., 2008) and P300 brain potential amplitude (in the case of externalizing; Patrick et al., 2006). As such, they represent important targets in the search for endophenotype markers of psychopathology (e.g., Hicks et al., 2007) and potentially valuable referents for a neurobiologically based science of individual differences (cf. Patrick & Bernat, 2006). The two-process model draws on two longstanding theoretical perspectives in the psychopathy literature, one emphasizing deficits in basic emotional reactivity (e.g., Blair, 2006; Lykken, 1995) and the other emphasizing deficits in inhibitory control of behavior (e.g., Gorenstein & Newman, 1980; Morgan & Lilienfeld, 2000; Newman, 1998). Our model hypothesizes that both of these processes contribute in differing ways to the phenotypic expression of the syndrome. We believe that systematic investigation of the constructs of trait fear and externalizing can help to elucidate a number of longstanding issues in the psychopathy literature, including inconsistencies in findings for cognitive and physiological measures, the role of constructs such as anxiety and aggression in psychopathy, the issue of psychopathy subtypes, and the question of how to conceptualize and identify “successful” psychopaths. More generally, we propose that to understand psychopathologic syndromes in terms of underlying brain processes, it may be necessary in some cases to reconceptualize these syndromes in alternative terms—that is, in terms of basic individual difference constructs that map more directly onto underlying neurobiological processes. We anticipate that in the future, neurobiologically based individual difference constructs will gain increasing ascendance as organizing dimensions in the domains of personality and psychopathology.

Anokhin, A. P., Vedeniapin, A. B., Sirevaag, E. J., Bauer, L. O., O’Connor, S. J., Kuperman, S., et al. (2000). The P300 brain potential is reduced in smokers. Psychopharmacology, 149, 409–413. Arrindell, W. A., Emmelkamp, P. M. G., & van der Ende, J. (1984). Phobic dimensions: Pt. I. Reliability and generalizability across samples, gender, and nations. Advances in Behavior Research and Therapy, 6, 207–254. Attou, A., Figiel, C., & Timsit-Berthier, M. (2001). ERP assessment of heroin detoxification and methadone treatment in chronic heroin users. Clinical Neurophysiology, 31, 171–180. Bauer, L. O., & Hesselbrock, V. M. (1999). P300 decrements in teenagers with conduct problems: Implications for substance abuse risk and brain development. Biological Psychiatry, 46, 263–272. Bauer, L. O., O’Connor, S., & Hesselbrock, V. M. (1994). Frontal P300 decrements in antisocial personality disorder. Alcoholism: Clinical and Experimental Research, 18, 1300–1305. Begleiter, H., & Porjesz, B. (1999). What is inherited in the predisposition toward alcoholism? A proposed model. Alcholism: Clinical and Experimental Research, 23, 1125–1135. Begleiter, H., Porjesz, B., Bihari, B., & Kissin, B. (1984, September 28). Event-related brain potentials in boys at risk for alcoholism. Science, 225, 1493–1496. Benning, S. D., Patrick, C. J., Blonigen, D. M., Hicks, B. M., & Iacono, W. G. (2005). Estimating facets of psychopathy from normal personality traits: A step toward community-epidemiological investigations. Assessment, 12, 3–18. Benning, S. D., Patrick, C. J., Hicks, B. M., Blonigen, D. M., & Krueger, R. F. (2003). Factor structure of the Psychopathic Personality Inventory: Validity and implications for clinical assessment. Psychological Assessment, 15, 340–350. Benning, S. D., Patrick, C. J., & Iacono, W. G. (2005). Psychopathy, startle blink modulation, and electrodermal reactivity in twin men. Psychophysiology, 42, 753–762. Benning, S. D., Patrick, C. J., Salekin, R. T., & Leistico, A. R. (2005). Convergent and discriminant validity of psychopathy factors assessed via self-report: A comparison of three instruments. Assessment, 12, 270–289. Berman, S. M., Whipple, S. C., Fitch, R. J., & Noble, E. P. (1993). P3 in young boys as a predictor of adolescent substance use. Alcohol, 10, 69–76. Bernat, E. M., Nelson, L. D., Steele, V. R., & Patrick, C. J. (2008). Externalizing psychopathology and gain/loss feedback in a simulated gambling task: Dissociable components of brain response revealed by time-frequency analysis. Manuscript submitted for publication. Bernat, E. M., Patrick, C. J., & Benning, S. D. (2006). Effects of picture content and intensity on affective physiological response. Psychophysiology, 43, 93–103. Bernat, E. M., Patrick, C. J., Cadwallader, M., van Mersbergen, M., & Seo, D. (2003). Affective orienting responses: A novelty P3 study with emotional pictures. Psychophysiology, 40, S25. Bernat, E. M., Patrick, C. J., Steffen, B. V., Hall, J. R., & Ward, M. (2004). Exploring mechanisms of deviant affect-modulated startle in psychopathy. Psychophysiology, 41, S40. Bernat, E. M., Williams, W. J., & Gehring, W. J. (2005). Decomposing ERP time-frequency energy using PCA. Clinical Neurophysiology, 116, 1314–1334. Biggins, C. A., MacKay, S., Clark, W., & Fein, G. (1997). Event-related potential evidence for frontal cortex effects of chronic cocaine dependence. Biological Psychiatry, 42, 472–485.

REFERENCES Achenbach, T. M., & Edelbrock, C. S. (1978). The classification of child psychopathology: A review and analysis of empirical efforts. Psychological Bulletin, 85, 1275–1301.

Blair, R. J. R. (2006). Subcortical brain systems in psychopathy: The amygdala and associated structures. In C. J. Patrick (Ed.), Handbook of psychopathy (pp. 296–312). New York: Guilford Press. Blonigen, D., Hicks, B., Patrick, C., Krueger, R., Iacono, W., & McGue, M. (2005). Psychopathic personality traits: Heritability and genetic overlap

Handbook of Adolescent Psychology, edited by Richard M. Lerner and Laurence Steinberg. Copyright # 2009 John Wiley & Sons, Inc. c57.indd 1128

7/13/09 7:01:22 PM

Empirical Evidence that Psychopathy Is not Unitary 1129 with internalizing and externalizing pathology. Psychological Medicine, 35, 637–648.

acoustic startle reflex. Annals of the New York Academy of Sciences, 831, 305–331.

Bradley, M. M., Codispoti, M., Cuthbert, B. N., & Lang, P. J. (2001). Emotion and motivation: Pt. I. Defensive and appetitive reactions in picture processing. Emotion, 1, 276–298.

Dehaene, S., Posner, M. I., & Tucker, D. M. (1994). Localization of a neural system for error detection and compensation. Psychological Science, 5, 303–305.

Branchey, M. H., Buydens-Branchey, L., & Horvath, T. B. (1993). Eventrelated potentials in substance-abusing individuals after long-term abstinence. American Journal of Addictions, 2, 141–148.

Dien, J., Spencer, K. M., & Donchin, E. (2003). Localization of the eventrelated potential novelty response as defined by principal components analysis. Cognitive Brain Research, 17(3), 637–650.

Brigham, J., Herning, R. I., & Moss, H. B. (1995). Event-related potentials and alpha synchronization in preadolescent boys at risk for psychoactive substance use. Biological Psychiatry, 37, 834–846.

Dikman, Z. V., & Allen, J. J. B. (2000). Error monitoring during reward and avoidance learning in high- and low-socialized individuals. Psychophysiology, 37, 43–54.

Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences, 4, 215–222. Buss, A. H., & Plomin, R. (1984). Temperament: Early developing personality traits. Hillsdale, NJ: Erlbaum.

Elmasian, R., Neville, H., Woods, D., Schuckit, M., & Bloom, F. (1982). Event-related brain potentials are different in individuals at high and low-risk for developing alcoholism. Proceedings of the National Academy of Sciences, USA, 79, 7900–7903.

Carter, C. S., Braver, T. S., Barch, D. M., Botvinick, M. M., Noll, D. N., & Cohen, J. D. (1998, May 1). Anterior cingulate cortex, error detection, and the online monitoring of performance. Science, 280, 747–749.

Eriksen, B. A., & Ericksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception and Psychophysics, 16, 143–149.

Center for the Study of Emotion and Attention. (1999). The international affective picture system: Digitized photographs. Gainesville, FL: University of Florida, Center for Research in Psychophysiology.

Falconer, D. S. (1989). Introduction to quantitative genetics (3rd ed.). New York: Wiley.

Cleckley, H. (1976). The mask of sanity (5th ed.). St. Louis, MO: Mosby. (Original edition published in 1941.)

Fichtenholtz, H. M., Dean, H. L., Dillon, D. G., Yamasaki, H., McCarthy, G., & LaBar, K. S. (2004). Emotion-attention network interactions during a visual oddball task. Cognitive Brain Research, 20, 67–80.

Cloninger, C. R. (1987). A systematic method for clinical description and classification of personality variants. Archives of General Psychiatry, 44, 573–588.

Fowles, D. C., & Dindo, L. (2006). A dual deficit model of psychopathy. In C. J. Patrick (Ed.), Handbook of psychopathy (pp. 14–34). New York: Guilford Press.

Cook, E. W., III. (1999). Affective individual differences, psychopathology, and startle reflex modulation. In M. E. Dawson, A. M. Schell, & A. H. Boehmelt (Eds.), Startle modification: Implications for clinical science, cognitive science, and neuroscience (pp. 187–208). New York: Cambridge University Press.

Gehring, W. J., Coles, M. G. H., Meyer, D. E., & Donchin, E. (1995). A brain potential manifestation of error-related processing. In G. Karmos, M. Molnar, V. Csepe, I. Czigler, & J. E. Desmedt (Eds.), Perspectives of event-related potentials research (pp. 267–272). Amsterdam: Elsevier.

Cooke, D. J., & Michie, C. (2001). Refining the construct of psychopathy: Towards a hierarchical model. Psychological Assessment, 13, 171–188. Corr, P. J., Kumari, V., Wilson, G. D., Checkley, S., & Gray, J. A. (1997). Harm avoidance and affective modulation of the startle reflex: A replication. Personality and Individual Differences, 22, 591–593.

Gehring, W. J., Himle, J., & Nisenson, L. G. (2000). Action-monitoring dysfunction in obsessive-compulsive disorder. Psychological Science, 11, 1–6. Giancola, P. R., & Tarter, R. E. (1999). Executive cognitive functioning and risk for substance abuse. Psychological Science, 10, 203–205. Gottesman, I. I., & Shields, J. (1972). Schizophrenia and genetics: A twin study vantage point. New York: Academic Press.

Corr, P. J., Wilson, G. D., Fotiadou, M., Kumari, V., Gray, N. S., Checkley, S., et al. (1995). Personality and affective modulation of the startle reflex. Personality and Individual Differences, 19, 543–553.

Gorenstein, E. E., & Newman, J. P. (1980). Disinhibitory psychopathology: A new perspective and a model for research. Psychological Review, 87, 301–315.

Costa, L., Bauer, L., Kuperman, S., Porjesz, B., O’Connor, S., Hesselbrock, V., et al. (2000). Frontal P300 decrements, alcohol dependence, and antisocial personality disorder. Biological Psychiatry, 47, 1064–1071.

Gough, H. G. (1960). Theory and measurement of socialization. Journal of Consulting Psychology, 24, 23–30.

Courchesne, E., Hillyard, S. A., & Galambos, R. (1975). Stimulus novelty, task relevance and the visual evoked potential in man. Electroencephalography and Clinical Neurophysiology, 39, 131–143. Cuthbert, B. N., Schupp, H. T., Bradley, M. M., Birbaumer, N., & Lang, P. J. (2000). Brain potentials in affective picture processing: Covariation with autonomic arousal and affective report. Biological Psychology, 52, 95–111. Davis, M. (1979). Diazepam and flurazepam: Effects on conditioned fear as measured with the potentiated startle paradigm. Psychopharmacology, 62, 1–7. Davis, M. (1989). Neural systems involved in fear-potentiated startle. In M. Davis, B. L. Jacobs, & R. I. Schoenfeld (Eds.), Annals of the New York Academy of Sciences, Vol. 563: Modulation of defined neural vertebrate circuits (pp. 165–183). New York: Author. Davis, M. (1998). Are different parts of the extended amygdala involved in fear versus anxiety? Biological Psychiatry, 44, 1239–1247. Davis, M., Falls, W. A., Campeau, S., & Kim, M. (1993). Fear-potentiated startle: A neural and pharmacological analysis. Behavioral and Brain Research, 58, 175–198. Davis, M., Walker, D. L., & Lee, Y. (1997). Roles of the amygdala and bed nucleus of the stria terminalis in fear and anxiety measured with the

Grillon, C., & Davis, M. (1997). Fear-potentiated startle conditioning in humans: Explicit and contextual cue conditioning following paired versus unpaired training. Psychophysiology, 34, 451–458. Hall, J. R., & Benning, S. D. (2006). The “successful” psychopath: Adaptive and subclinical manifestations of psychopathy in the general population. In C. J. Patrick (Ed.), Handbook of psychopathy (pp. 459–478). New York: Guilford Press. Hall, J. R., Benning, S. D., & Patrick, C. J. (2004). Criterion-related validity of the three-factor model of psychopathy: Personality, behavior, and adaptive functioning. Assessment, 11, 4–16. Hall, J. R., Bernat, E. M., & Patrick, C. J. (2007). Externalizing psychopathology and the error-related negativity. Psychological Science, 18, 326–333. Hamm, A. O., Greenwald, M. K., Bradley, M. M., & Lang, P. J. (1993). Emotional learning, hedonic change, and the startle reflex. Journal of Abnormal Psychology, 102, 453–465. Hare, R. D. (1991). The Hare Psychopathy Checklist-revised. Toronto, Ontario: Multi-Health Systems. Hare, R. D. (1993). Without conscience: The disturbing world of the psychopaths among us. New York: Pocket Books. Hare, R. D. (2003). The Hare Psychopathy Checklist-revised, (2nd ed.). Toronto, Ontario: Multi-Health Systems.

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Hare, R. D., Harpur, T. J., Hakstian, A. R., Forth, A. E., Hart, S. D., & Newman, J. P. (1990). The Revised Psychopathy Checklist: Reliability and factor structure. Psychological Assessment, 2, 338–341. Harpur, T. J., Hakstian, A. R., & Hare, R. D. (1988). Factor structure of the psychopathy checklist. Journal of Consulting and Clinical Psychology, 56, 741–747. Harpur, T. J., Hare, R. D., & Hakstian, A. R. (1989). Two-factor conceptualization of psychopathy: Construct validity and assessment implications. Psychological Assessment, 1, 6–17. Herpertz, S. C., Werth, U., Lukas, G., Qunaibi, M., Schuerkens, A., Kunert, H., et al. (2001). Emotion in criminal offenders with psychopathy and borderline personality disorder. Archives of General Psychiatry, 58, 737–744. Hicks, B. M., Bernat, E. M., Malone, S. M., Iacono, W. G., Patrick, C. J., Krueger, R. F., et al. (2007). Genes mediate the association between P3 amplitude and externalizing disorders. Psychophysiology, 44, 98–105. Hicks, B. M., Markon, K. E., Patrick, C. J., Krueger, R. F., & Newman, J. P. (2004). Identifying psychopathy subtypes on the basis of personality structure. Psychological Assessment, 16, 276–288. Hicks, B. M., & Patrick, C. J. (2006). Psychopathy and negative affectivity: Analyses of suppressor effects reveal distinct relations with trait anxiety, depression, fearfulness, and anger-hostility. Journal of Abnormal Psychology, 115, 276–287. Hill, S. Y., & Shen, S. (2002). Neurodevelopmental patterns of visual P3b in association with familial risk for alcohol dependence and childhood diagnosis. Biological Psychiatry, 51, 621–631. Hill, S. Y., Steinhauer, S., Lowers, L., & Locke, J. (1995). Eight-year longitudinal follow-up of P300 and clinical outcome in children from highrisk for alcoholism families. Biological Psychiatry, 37, 823–827. Iacono, W. G. (1998). Identifying psychophysiological risk for psychopathology: Examples from substance abuse and schizophrenia research. Psychophysiology, 35, 621–637. Iacono, W. G., Carlson, S. R., Malone, S. M., & McGue, M. (2002). P3 event-related potential amplitude and risk for disinhibitory disorders in adolescent boys. Archives of General Psychiatry, 59, 750–757. John, B., & Lewis, K. (1966, May 6). Chromosome variability and geographic distribution in insects. Science, 152, 711–721. Katsanis, J., Iacono, W. G., McGue, M., & Carlson, S. R. (1997). P300 event-related potential heritability in monozygotic and dizygotic twins. Psychophysiology, 34, 47–58. Kendler, K. S., Prescott, C. A., Myers J., & Neale, M. C. (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of General Psychiatry, 60, 929–937. Kiehl, K. A., Hare, R. D., Liddle, P. F., & McDonald, J. J. (1999). Reduced P300 responses in criminal psychopaths during a visual oddball task. Biological Psychiatry, 45, 1498–1507.

antisocial behavior, and personality: Modeling the externalizing spectrum. Journal of Abnormal Psychology, 111, 411–424. Krueger, R. F., Markon, K. E., Patrick, C. J., Benning, S. D., & Kramer, M. (2007). Linking antisocial behavior, substance use, and personality: An integrative quantitative model of the adult externalizing spectrum. Journal of Abnormal Psychology, 116, 645–666. Lang, P. J. (1995). The emotion probe: Studies of motivation and attention. American Psychologist, 50, 372–385. Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1990). Emotion, attention, and the startle reflex. Psychological Review, 97, 377–398. Levenston, G. K., Patrick, C. J., Bradley, M. M., & Lang, P. J. (2000). The psychopath as observer: Emotion and attention in picture processing. Journal of Abnormal Psychology, 109, 373–385. Lilienfeld, S. O. (1990). Development and preliminary validation of a selfreport measure of psychopathic personality. Unpublished doctoral dissertation, University of Minnesota. Lilienfeld, S. O., & Andrews, B. P. (1996). Development and preliminary validation of a self-report measure of psychopathic personality traits in noncriminal populations. Journal of Personality Assessment, 66, 488–524. Lilienfeld, S. O., & Widows, M. R. (2005). Psychopathic Personality Inventory- revised (PPI-R) professional manual. Odessa, FL: Psychological Assessment Resources. Luu, P., Tucker, D. M., & Makieg, S. (2004). Frontal-midline theta and the error-related negativity: Neurophysiological mechanisms of action regulation. Clinical Neurophysiology, 115, 1821–1835. Lykken, D. T. (1957). A study of anxiety in the sociopathic personality. Journal of Abnormal and Clinical Psychology, 55, 6–10. Lykken, D. T. (1995). The antisocial personalities. Hillsdale, NJ: Erlbaum. Lynam, D. R., & Derefinko, K. J. (2006). Psychopathy and personality. In C. J. Patrick (Ed.), Handbook of psychopathy (pp. 133–155). New York: Guilford Press. Molto, J., Poy, R., Segarra, P., Pastor, M., & Montanes, S. (2007). Response perseveration in psychopaths: Interpersonal/affective or social deviance traits? Journal of Abnormal Psychology, 3, 632–637. Morgan, A. B., & Lilienfeld, S. O. (2000). A meta-analytic review of the relation between antisocial behavior and neuropsychological measures of executive function. Clinical Psychology Review, 20, 113–136. Nieuwenhuis, S., Aston-Jones, G., & Cohen, J. D. (2005). Decision making, the P3, and the locus coeruleus-norepinephrine system. Psychological Bulletin, 131, 510–532. Newman, J. P. (1998). Psychopathic behavior: An information processing perspective. In D. J. Cooke, R. D. Hare, & A. Forth (Eds.), Psychopathy: Theory, research and implications for society (pp. 81–104). The Netherlands: Kluwer Academic.

Kiehl, K. A., Smith, A. M., Hare, R. D., & Liddle, P. F. (2000). An eventrelated potential investigation of response inhibition in schizophrenia and psychopathy. Biological Psychiatry, 48, 210–221.

O’Connor, S., Morzorati, S., Christian, J. C., & Li, T. K. (1994). Heritable features of the auditory oddball event-related potential: Peaks, latencies, morphology and topography. Electroencephalography and Clinical Neurophysiology, 92, 115–125.

Kim, M. S., Kim, J. J., & Kwon, J. S. (2001). Frontal P300 decrement and executive dysfunction in adolescents with conduct problems. Child Psychiatry and Human Development, 32, 93–106.

Pailing, P. E., & Segalowitz, S. J. (2004). The error-related negativity as a state and trait measure: Motivation, personality, and ERPs in response to errors. Psychophysiology, 41, 84–95.

Knight, R. T. (1984). Decreased response to novel stimuli after prefrontal lesions in man. Electroencephalography and Clinical Neurophysiology, 59, 9–20.

Pailing, P. E., Segalowitz, S. J., Dywan, J., & Davies, P. L. (2002). Error negativity and response control. Psychophysiology, 39, 198–206.

Kramer, M. D., Patrick, C. J., Bayevsky, M., & Krueger, R. F. (2008). Phenotypic and etiologic parallels in the structure of fear and fearlessness. Manuscript submitted for publication. Krueger, R. F. (1999). The structure of common mental disorders. Archives of General Psychiatry, 56, 921–926. Krueger, R. F., Hicks, B., Patrick, C. J., Carlson, S., Iacono, W. G., & McGue, M. (2002). Etiologic connections among substance dependence,

Pastor, M. C., Molto, J., Vila, J., & Lang, P. J. (2003). Startle reflex modulation, affective ratings and autonomic reactivity in Spanish incarcerated psychopaths. Psychophysiology, 40, 934–938. Patrick, C. J. (1994). Emotion and psychopathy: Startling new insights. Psychophysiology, 31, 319–330. Patrick, C. J. (2006). Back to the future: Cleckley as a guide to the next generation of psychopathy research. In C. J. Patrick (Ed.), Handbook of psychopathy (pp. 605–617). New York: Guilford Press.

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Empirical Evidence that Psychopathy Is not Unitary 1131 Patrick, C. J. (2007). Getting to the heart of psychopathy. In H. Hervé & J. C. Yuille (Eds.), The psychopath: Theory, research, and social implications (pp. 207–252). Hillsdale, NJ: Erlbaum. Patrick, C. J., & Bernat, E. M. (2006). The construct of emotion as a bridge between personality and psychopathology. In R. F. Krueger & J. Tackett (Eds.), Personality and psychopathology (pp. 174–209). New York: Guilford Press. Patrick, C. J., Bernat, E. M., Malone, S. M., Iacono, W. G., Krueger, R. F., & McGue, M. K. (2006). P300 amplitude as an indicator of externalizing in adolescent males. Psychophysiology, 43, 84–92. Patrick, C. J., & Berthot, B. D. (1995). Startle potentiation during anticipation of a noxious stimulus: Active versus passive response sets. Psychophysiology, 32, 72–80. Patrick, C. J., Berthot, B. D., & Moore, J. D. (1996). Diazepam blocks fearpotentiated startle in humans. Journal of Abnormal Psychology, 105, 89–96. Patrick, C. J., Bradley, M. M., & Lang, P. J. (1993). Emotion in the criminal psychopath: Startle reflex modulation. Journal of Abnormal Psychology, 102, 82–92. Patrick, C. J., Cuthbert, B. N., & Lang, P. J. (1994). Emotion in the criminal psychopath: Fear image processing. Journal of Abnormal Psychology, 103, 523–534. Patrick, C. J., Edens, J. F., Poythress, N., & Lilienfeld, S. O. (2006). Construct validity of the PPI two-factor model with offenders. Psychological Assessment, 18, 204–208. Patrick, C. J., Hicks, B. M., Krueger, R. F., & Lang, A. R. (2005). Relations between psychopathy facets and externalizing in a criminal offender sample. Journal of Personality Disorders, 19, 339–356. Patrick, C. J., & Lang, A. R. (1999). Psychopathic traits and intoxicated states: Affective concomitants and conceptual links. In M. E. Dawson, A. M. Schell, & A. H. Boehmelt (Eds.), Startle modification: Implications for clinical science, cognitive science, and neuroscience (pp. 209–230). New York: Cambridge University Press. Patterson, C. M., & Newman, J. P. (1993). Reflectivity and learning from aversive events: Toward a psychological mechanism for the syndromes of disinhibition. Psychological Review, 100, 716–736. Paulhus, D. L., Robins, R. W., Trzesniewski, K. H., & Tracy, J. L. (2004). Two replicable suppressor situations in personality research. Multivariate Behavioral Research, 39, 303–328. Polich, J., Pollock, V. E., & Bloom, F. E. (1994). Meta-analysis of P300 amplitude from males at risk for alcoholism. Psychological Bulletin, 115, 55–73. Porjesz, B., Begleiter, H., & Garozzo, R. (1980). Visual evoked potential correlates of information processing deficits in chronic alcoholics. In H. Begleiter (Ed.), Biological effects of alcohol (pp. 603–623). New York: Plenum Press. Potts, G. F., George, M. R. M., Martin, L. E., & Barratt, E. S. (2006). Reduced punishment sensitivity in neural systems of behavior monitoring in impulsive individuals. Neuroscience Letters, 397, 130–134. Poythress, N. G., & Skeem, J. L. (2006). Disaggregating psychopathy subtypes: Where and how to look for subtypes. In C. J. Patrick (Ed.), Handbook of psychopathy (pp. 172–192). New York: Guilford Press. Raine, A. (1989). Evoked potentials and psychopathy. International Journal of Psychophysiology, 8, 1–16. Raine, A. (1993). The psychopathology of crime. San Diego, CA: Academic Press.

Ross, S. R., Benning, S. D., Patrick, C. J., Thompson, A., & Thurston, A. (2009). Factors of the Psychopathic Personality Inventory: Criterionrelated validity and relationship to the BIS/BAS and five-factor models of personality. Assessment, 16, 71–78. Santesso, D. L., Segalowitz, S. J., & Schmidt, L. A. (2005). ERP correlates of error monitoring in 10 year-olds are related to socialization. Biological Psychology, 70, 79–87. Schoeppner, W. L., Bernat, E. M., Scott, H., Hall, J. R., Venables, N. C., Steffen, B. V., et al. (2005). Externalizing psychopathology and brain responses to gain and loss feedback in a simulated gambling task. Psychophysiology, 42, S131. Schupp, H. T., Cuthbert, B. N., Bradley, M. M., Cacioppo, J. T., Ito, T., & Lang, P. J. (2000). Affective picture processing: The late positive potential is modulated by motivational relevance. Psychophysiology, 37, 257–261. Skeem, J. L., Johansson, P., Andershed, H., Kerr, M., & Eno Louden, J. (2007). Two subtypes of psychopathic violent offenders that parallel primary and secondary variants. Journal of Abnormal Psychology, 116, 395–409. Squires, N. K., Squires, K. C., & Hillyard, S. A. (1975). Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man. Electroencephalography Clinical Neurophysiology, 38, 387–410. Sutton, S. K., Vitale, J. E., & Newman, J. P. (2002). Emotion among females with psychopathy during picture perception. Journal of Abnormal Psychology, 111, 610–619. Tellegen, A. (1982). Manual for the multidimensional personality questionnaire. Unpublished manuscript. Minneapolis: University of Minnesota. Vaidyanathan, U., Patrick, C. J., & Bernat, E. M. (2009). Startle reflex potentiation during aversive picture viewing as an indicator of trait fear. Psychophysiology, 46, 75–85. Vanman, E. J., Mejia, V. Y., Dawson, M. E., Schell, A. M., & Raine, A. (2003). Modification of the startle reflex in a community sample: Do one or two dimensions of psychopathy underlie emotional processing? Personality and Individual Differences, 35, 2007–2021. Venables, N. C., Bernat, E. M., Hall, J. R., Steffen, B. V., Cadwallader, M., Krueger, R. F., et al. (2005). Neurophysiological correlates of behavioral disinhibition: Separable contributions of distinct personality traits. Psychophysiology, 42, S126. Verona, E., Patrick, C. J., & Joiner, T. E. (2001). Psychopathy, antisocial personality, and suicide risk. Journal of Abnormal Psychology, 110, 462–470. Vollebergh, W. A. M., Iedema, J., Bijl, R. V., de Graaf, R., Smit, F., & Ormel., J. (2001). The structure and stability of common mental disorders: The NEMESIS study. Archives of General Psychiatry, 58, 597–603. Vrana, S. R., & Lang, P. J. (1990). Fear imagery and the startle probe reflex. Journal of Abnormal Psychology, 99, 181–189. Waller, N. G., Lilienfeld, S. O., Tellegen, A., & Lykken, D. T. (1991). The tridimensional personality questionnaire: Structural validity and comparison with the multidimensional personality questionnaire. Multivariate Behavioral Research, 26, 1–23. Wolpe, J., & Lang, P. J. (1964). A fear survey schedule for use in behavior therapy. Behaviour Research and Therapy, 2, 27–30.

Ridderinkhoff, K. R., de Vlugt, Y., Bramlage, A., Spaan, M., Elton, M., Snel, J., & Band, G. P. H. (2002). Alcohol consumption impairs detection of performance errors in mediofrontal cortex. Science, 298, 2209–2211.

Young, S. E., Stallings, M. C., Corley, R. P., Krauter, K. S., & Hewitt, J. K. (2000). Genetic and environmental influences on behavioral disinhibition. American Journal of Medical Genetics (Neuropsychiatric Genetics), 96, 684–695.

Rosen, J. B., & Schulkin, J. B. (1998). From normal fear to pathological anxiety. Psychological Review, 105, 325–350.

Zuckerman, M. (1979). Sensation seeking: Beyond the optimal level of arousal. Hillsdale, NJ: Erlbaum.

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Chapter 58

Addictive Processes MARY M. TORREGROSSA AND PETER W. KALIVAS

thought of as abuse of a drug or chemical such as alcohol, nicotine, cocaine, or heroin; addiction can develop to food and certain activities, including gambling, sex, shopping, work, and exercise. It is generally believed that overlapping neural systems are involved in the development of all addictions, but there may also be some important differences (Goodman, 2007). Most neuroscience research into the neural mechanisms underlying addiction has focused on drugs; therefore, this chapter focuses on the study of addiction to drugs of abuse. In addition, it should be noted that not all drug users are addicts. There are people who control and limit drug intake, and these people are often referred to as social users. There are also people who are considered drug abusers, but not addicts from a clinical diagnostic perspective. Such an individual generally has difficulty controlling the amount of use, but can stop using when necessary, does not let his or her drug use interfere with daily activities, and adjusts his or her behavior in the face of adverse consequences. It is believed that continued exposure to a drug of abuse in a person with certain genetic predispositions and in certain psychosocial situations will eventually lead to addiction if there is no intervention. The changes in the brain that occur with chronic drug use that lead from casual use to addiction is an area of active research (Koob et al., 2004; Koob & Kreek, 2007). Addiction is also often characterized as a cyclical disorder consisting of periods of excessive use or binges and periods of abstinence or withdrawal that can last from days to years; however, withdrawal usually refers to a specific syndrome of negative symptoms that lasts only for a few days after cessation of drug use. Unfortunately, the addict often cannot maintain abstinence from the drug and will relapse to drug taking, often repeating this cycle over and over again, often to the point where drug use becomes compulsive (Foy, 2007; Koob & LeMoal, 2001). The factors that trigger relapse and the neural mechanisms necessary for relapse are being actively investigated because preventing relapse could help many people recover from their

The study of addiction involves integrating the principles and techniques of behavioral analysis, molecular biology, genetics, pharmacology, cell biology, and electrophysiology. Understanding the process of addiction also requires a close coordination between preclinical research and clinical studies of human addicts. The objective of this chapter is to familiarize the reader with the methods used to study addiction, the major findings in the field that have led to our current understanding of how addiction develops and persists, and the current state of emerging pharmacotherapeutics to treat addiction. First, we define addiction and describe the acute mechanism of action of drugs of abuse. Then, we describe how addiction is studied behaviorally, followed by descriptions of studies using neurochemical, pharmacological, cellular, and molecular techniques that have elucidated the neural changes that are thought to be critical for the development of addictive behaviors. Next, we describe the relevance of addiction research in animals to human addiction based on human imaging studies. Last, we describe how these studies have led to the discovery of new targets for the treatment of addiction that are still in the early stages of investigation.

DEFINITION OF ADDICTION: CLINICAL PERSPECTIVE Addiction is a progressive, chronic disease that involves both biological and environmental factors. An addict generally displays several characteristic behaviors including preoccupation with the abused substance between uses, inability to control the amount of use, use despite adverse consequences, tolerance to the substance, withdrawal symptoms after use, use to control withdrawal symptoms, continued effort and failure to discontinue use, and a reduction in the participation in normal social and occupational activities in favor of use of the substance (American Psychiatric Association, DSM-IV-TR, 2000; Morse & Flavin, 1992). While addiction is most often 1132

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Behavioral Studies of Addiction

addiction and regain a normal productive life (Kalivas & Volkow, 2005).

DEFINITION OF ADDICTION: NEUROSCIENCE PERSPECTIVE From a neuroscience perspective, addiction can be thought of as a disorder of biological learning and reward processes (Hyman, 2005; Jones & Bonci, 2005; Kelley, 2004). Drugs usurp the neural systems responsible for normal reward learning, and after repeated exposure, can cause long-term changes in brain functioning. These long-term changes in the brain are referred to as neuroplasticity. Drug-induced, enduring neuroplasticity in learning processes is thought to underlie the loss of control over behavior that occurs in addiction, such that even after long periods of abstinence addicts continue to desire the drug and will often relapse. These concepts are discussed in more detail later in this chapter. Drugs of abuse alter normal brain functioning both acutely and over the long term, resulting in the symptoms of addiction. Therefore, a primary focus of addiction research is to understand the neural changes resulting from long-term exposure to drugs of abuse, and thereby identify interventions that will reverse or countermand druginduced neuroplasticity in order to prevent craving and relapse (Hyman, Malenka, & Nestler, 2006; Kalivas & O’Brien, 2008).

PHARMACOLOGY OF DRUGS OF ABUSE The most commonly studied drugs of abuse include alcohol; nicotine; stimulants such as cocaine, amphetamine, and MDMA; opiates such as heroin and morphine; sedative hypnotics including barbiturates; dissociative anesthetics such as ketamine and phencyclidine (PCP); hallucinogens like LSD and psilocybin; and cannabinoids (marijuana). While all of these substances bind to neurotransmitter receptors or other proteins in the brain to produce their effects, the types of receptors and proteins they bind to are very different from one another. Alcohol and the sedative hypnotics bind to different parts of the GABAA receptor to potentiate GABA-induced inhibition. Nicotine activates nicotinic acetylcholine receptors, the stimulants enhance the synaptic concentration of dopamine by preventing reuptake of dopamine by the dopamine transporter, and, in the case of amphetamine and MDMA, also enhance the release of dopamine into the synapse. MDMA also enhances the release of serotonin and has direct effects on serotonin receptors. The opiates are preferential mu opioid receptor agonists, while the dissociative anesthetics are

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glutamatergic NMDA receptor antagonists. The hallucinogens are believed to produce their effects by activating serotonin (5-HT) 2A receptors, and the active ingredients in marijuana, the cannabinoids, bind and activate endogenous cannabinoid receptors. The diverse action of drugs of abuse explains some of the reported differences in the experiences these drugs produce, and the degree to which they are abused and addiction develops, but they also share downstream effects on brain systems regulating reward and learning (Johnson & North, 1992; Nestler, 2001; Ritz, Lamb, Goldberg, & Kuhar, 1987). All drugs produce some sort of euphoric effect or “high,” which is thought to reinforce repeated drug use. In addition, neurochemical studies have found that almost every drug of abuse and many natural rewards increase the release or the metabolism of the neurotransmitter dopamine in certain brain regions (DiChiara & Bassareo, 2007), and dopamine receptor antagonists can reduce the reinforcing effects of drugs of abuse (Wise, 1996). These data have led to the “dopamine hypothesis” of addiction (Adinoff, 2004; Goodman, 2007), which is discussed later in this chapter, along with caveats to this hypothesis. In summary, abused drugs have diverse actions in the brain, but they may ultimately have similar effects on specific neural systems that lead to the development of addiction.

BEHAVIORAL STUDIES OF ADDICTION Although the primary objective of this chapter is to explain the neuroscience of drug addiction, it is impossible to explain the neural changes without first explaining the behavioral methods used to study addiction. The majority of studies in the addiction field utilize behavioral methods in combination with other techniques so that neural changes that occur with chronic exposure to drugs of abuse can be correlated with the behavioral state of the animal. In this section, we explain some of the most common behavioral assays used in the study of addiction and discuss how experiments are generally designed using these methods. In later sections of this chapter, addiction-related neurobiological changes will often be described in relation to the behavioral methods used. The behaviors described in this section are certainly not an exhaustive list, but are often used in combination with biochemical or genetic studies. Psychomotor Sensitization Several animal models have been developed to study different aspects of addiction from the very simple to the very complex. The simplest and possibly most common behavioral effect studied in the field of addiction is psychomotor

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1134 Addictive Processes

or locomotor sensitization. In pharmacological terms, sensitization is any increase in drug effect that occurs after repeated exposure. Psychomotor sensitization is the phenomena that repeated, intermittent exposure to most drugs of abuse produces a progressive increase in the pyschomotor activation of an animal, which is measured as an increase in locomotor activity and/or stereotyped motor behavior. While a progressive increase in locomotor activity does not necessarily occur in humans, people do experience a progressive increase in arousal, attention, approach, and an increase in the salience of drug-related stimuli. Therefore, psychomotor sensitization provides a fairly easy way to study potentially important changes in neural circuitry that occur with chronic drug exposure. Using sensitization to study addiction has several advantages. Sensitization occurs to several drugs of abuse, including cocaine, amphetamine, morphine, nicotine, PCP, and alcohol (Kalivas & Stewart, 1991; Robinson & Berridge, 2003). Sensitization occurs after just a few days of experimenter administered drug, and the behavioral measures of locomotor activity and stereotypy can be quantified using automated systems, making sensitization studies relatively efficient to conduct. Environmental factors and stress also affect sensitization, allowing researchers to study the interaction of drugs, stress, and the environment (Badiani & Robinson, 2004; Marinelli & Piazza, 2002). Moreover, once an animal is sensitized to one type of drug, it will often show a sensitized response to a different class of drug, a phenomena known as cross-sensitization, which is another indication that drugs act on similar neural systems despite different primary mechanisms of action (Vezina, Giovino, Wise, & Stewart, 1989; Vezina & Stewart, 1990). Sensitization studies can be coupled to neurochemical studies using microdialysis, voltammetry, and electrophysiology, so that changes in the response of neurotransmitters or neuron firing in different brain regions can be measured alongside changes in behavior. Conditioned Place Preference Conditioned place preference is a relatively simple way to study some of the learning aspects of addiction and to confirm the reinforcing efficacy of a drug. The paradigm consists of exposing animals to a chamber that contains two compartments with distinct visual and tactile characteristics. Typically, an animal is first allowed to freely explore the chamber to determine if there is any bias for one compartment or the other. Then, the animal is given a reinforcer (sucrose, injection of drug) and is placed in one compartment without access to the other; on alternate trials the animal is given a control nonrewarding stimulus and is confined to the other compartment. After a few of

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these training sessions, if the animal is given free access to both compartments again, it will show a preference for the side paired with the reward, as measured by time spent in the reward-paired compartment. Drugs abused by humans produce a preference in this paradigm, while noxious stimuli produce a place aversion. If a compartment is paired with drug withdrawal, such as naloxone precipitated opiate withdrawal, the animal will develop conditioned aversion for that compartment. Manipulations that inhibit the development or expression of a place preference can be inferred to be interfering with the rewarding effect of the drug or the animal’s ability to learn an association between reward and context, respectively. In addition, animals can have the association between drug and context extinguished by pairing the previously rewarding compartment with a neutral stimulus, and then reinstatement of place preference can be tested by re-exposing the animal to the drug (see later discussion for a description of reinstatement; Sanchis-Segura & Spanagel, 2006; Tzschentke, 2007). Conditioned place preference is an easier and more technically viable means of testing drug reward in mice, and is therefore, often the paradigm of choice to study drug reward in genetically mutated mice. Drug Self-Administration A more sophisticated method of studying addiction is the drug self-administration model. In self-administration studies, an animal performs a behavior that produces an administration of a drug. Usually an animal is implanted with an intravenous catheter allowing for intravenous administration of a set amount of drug after the animal performs an instrumental response. However, animals will also self-administer drugs orally, by intramuscular, intraperitoneal, intragastric, or intracranial injection, or by inhalation. All drugs known to support self-administration in laboratory animals have been abused by humans, and substances that are not abused by humans, do not support self-administration in laboratory animals. During selfadministration, an animal presses a lever (or performs another instrumental response) a set number of times to receive an injection or other access to the drug. The number of times the animal has to respond to receive the drug and the drug dose can be manipulated to study different aspects of addiction. Self-administration has the advantage that the animal takes as much drug as it desires during the time it has access to the drug, and patterns of drug taking across classes of drug are similar to the pattern of intake in humans (see review by Gardner, 2000, for a detailed description of drug self-administration). Therefore, selfadministration has a great deal of face validity because the animal controls its intake, as do humans. In many cases,

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Behavioral Studies of Addiction

the abuse liability of a drug is defined by its ability to support self-administration in laboratory animals. Self-administration can be used to study many aspects of the addiction process. The acquisition of drug-taking behavior is the process of learning the association between performing an operant behavior and receiving the reinforcer. The neural substrates underlying acquisition of drug-taking behavior can be studied by comparing the amount of time (e.g., number of sessions) required for an animal to achieve stable drug intake with or without a pharmacological, genetic, or other manipulations. Acquisition studies have also been used to study the vulnerability to addiction. For example, human females have been reported to be more vulnerable to addiction and have been shown to become addicted to psychostimulants faster than males. Acquisition studies in animals have also found that a greater percentage of female rats and monkeys acquire stimulant self-administration than males, and often at a faster rate than males (Roth, Cosgrove, & Carroll, 2004). The maintenance of self-administration is the period of time after the behavior has been acquired when the animal is taking a stable amount of drug during each session. Experimental manipulations can be made before a session to determine what factors inhibit or enhance self-administration when the behavior is already well learned. Generally, something that reduces the rewarding or reinforcing quality of a drug (e.g., a dopamine or opioid receptor antagonist for cocaine or heroin, respectively) will increase the number of responses for the drug as the animal tries to achieve the same level of drug reinforcement. An intervention that increases the reinforcing quality of the drug or that mimics the drug itself will generally reduce the amount of responding because the animal does not need as much drug to achieve the same amount of reinforcement (Koob & Weiss, 1990). We have primarily been discussing self-administration procedures in terms of continuous reinforcement, that is, using a fixed-ratio 1 (FR1) schedule of reinforcement, where one response results in one reinforcer. Other aspects of addiction can be studied by using different schedules of reinforcement. A number of different schedules have been used, but we focus on the most common here. One of these is the progressive ratio schedule where the number of responses required to receive a single reinforcer increases progressively (e.g., 1, 2, 4, 8, 16, 32). In this paradigm, the maximum number of responses an animal is willing to make for a single reinforcer, called the breakpoint, is taken as a measure of the reinforcing efficacy of the drug. In other words, the more an animal is willing to work for a drug, the more reinforcing that drug must be. This procedure allows different drug classes to be compared across doses for their reinforcing efficacy, and interventions that reduce the reinforcing effect of a drug will reduce the

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breakpoint, and vice versa. Alternatively, progressive ratio can be used as a measure of the motivation of the animal to seek a drug, and genetic or pharmacological manipulations that alter the breakpoint for the same dose of drug as controls, can be interpreted as altering the motivation to seek the drug (Roberts, Morgan, & Liu, 2007). Another schedule of reinforcement that is commonly studied is the second-order schedule. Second-order schedules generally require an animal to respond under a compound schedule where a fixed ratio of responses results in the presentation of a cue previously associated with the drug, and after a fixed interval has elapsed, the animal can complete the FR for presentation of the drug. In these studies, the first interval of responding for the cue is in the drug-free state and can be used as an indication of drugseeking behavior that is not influenced by potential ratelimiting effects of the drug. This procedure is also valuable for studying the ability of drug-associated stimuli (cues) to control behavior and the motivation for drugs. Brain lesioning and pharmacological studies have been conducted using this procedure to determine the neural substrates necessary for drug associated cues to support learning of a second-order schedule, for drug-seeking behavior itself, reinstatement (discussed next), and cue-independent drugseeking (Everitt & Robbins, 2000). Reinstatement The reinstatement procedure is a model of relapse to drug taking. The procedure involves training an animal to selfadminister a drug in the presence of a cue or context. After the animals have maintained stable self-administration for several days, they go into extinction, where they are placed in the chamber as they would be for self-administration, but pressing the lever no longer results in the infusion of drug or presentation of the cue. After several days of extinction training, the animals press the lever much less than they did during self-administration. However, if an animal receives a noncontingent administration of drug, if the animal is re-exposed to a cue previously associated with drug delivery, or if the animal is stressed (most commonly by foot-shock), they will reinstate lever pressing, even in the absence of any drug reinforcement. This model is very compelling because the triggers that cause reinstatement in animals, such as stress, cues, and situations previously associated with drug use, or the drug itself, are very similar to those that cause relapse in humans (de Wit & Stewart, 1981, 1983; Shaham, Shalev, Lu, de Wit, & Stewart, 2003; Shalev, Grimm, & Shaham, 2002). For example, humans often report relapsing to drug use after sampling the drug again (e.g., “just one drink”), being exposed to environmental stimuli (cues) associated with their drug use, and

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1136 Addictive Processes

after experiencing stressful life events. The reinstatement model has been used to determine the neural mechanisms responsible for drug craving and relapse, and has led to the discovery of new targets for the prevention of relapse that have shown some efficacy in early clinical studies to be discussed in detail later in this chapter. As mentioned, the conditioned place preference paradigm and second-order schedules can also be used to measure reinstatement.

NEUROCIRCUITRY OF ADDICTION Experimental Techniques The development of animal models of addiction has allowed researchers to study the neural mechanisms underlying the development and expression of addictive behaviors. Two techniques that have been used extensively to determine the basic brain regions and neurotransmitter systems affected by addictive drugs are lesion studies and microdialysis (Torregrossa & Kalivas, 2007). Lesions can be either irreversible or reversible. An irreversible lesion involves injecting a neurotoxic chemical into a brain region, killing the neurons in that region, therefore preventing any activity or involvement of that brain region in behavior. Reversible lesions consist of injecting an inhibitory agent into a brain region such that the region is temporarily inactivated, but the neurons are not destroyed and regain function after the inhibitory agent is eliminated. GABA receptor agonists like muscimol and baclofen and voltage-dependent sodium channel blockers such as tetrodotoxin (TTX) can be used to transiently inhibit the activity of a brain region. All of these methods have advantages and disadvantages depending on the aim of the experiment (Examples of experiments using these techniques include Fuchs & See, 2002; McFarland & Kalivas, 2001; Moller, Wiklund, Hyytia, Thorsell, & Heilig, 1997). Irreversible lesions have the advantage of eliminating the activity of a brain region throughout a behavioral experiment, such that the necessity of a brain region for the acquisition of a behavior can be examined without giving repeated daily infusions of a transient inactivation agent. On the other hand, transient inactivation of a region using GABA agonists or TTX has the advantage of allowing the activity of brain region to remain intact during all phases of training except for during a specific test (e.g., reinstatement). In addition, transient inactivation can allow for within subjects analysis of behavior with or without an inactivation infusion. The advantages and disadvantages between using a voltage-dependent sodium channel blocker, such as TTX or a local anesthetic, versus GABA agonists revolves primarily around two factors. GABA agonists will not inactivate fibers of passage like

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TTX or local anesthetics. Thus, GABA agonists avoid the possibility that the effect of inactivation may result from inhibiting axons passing through the nucleus in question. In contrast, GABA agonists can produce differential effects on different neuronal populations depending on the density of GABA receptors and the signaling induced by stimulating GABA receptors. While the dose of GABA agonists employed is presumed to inhibit all neurons at the injection site, it is possible that on the periphery of the injection differential neuronal sensitivity to GABA could influence the outcome of the experiment. Microdialysis is a technique that allows a researcher to measure the amount of extracellular neurotransmitter present in a specific brain region in a behaving animal. Microdialysis consists of inserting a semi-permeable membrane into a brain region via a probe that is connected to a syringe pump perfusing artificial cerebral spinal fluid (aCSF) into the brain. Neurotransmitters present in that brain region will travel across the concentration gradient through the membrane and out tubing to be collected for later analysis. Microdialysis can be used to determine basal concentrations of neurotransmitters, and how transmitter levels change in response to drugs or during different behavioral states (Parent et al., 2001). Neurochemical Mechanisms of Sensitization We mentioned that despite the variety of mechanisms of action of drugs of abuse, they have been shown to have common effects on dopaminergic signaling in the brain. Some of the earliest studies using microdialysis to understand the neurochemical changes that occur in response to drugs of abuse found that almost all drugs and natural rewards increase the release of dopamine into a brain region known as the nucleus accumbens (NAc; DiChiara & Bassareo, 2007). The NAc is part of the mesocorticolimbic dopamine system, which consists of dopamine cell bodies located in a part of the midbrain known as the ventral tegemental area (VTA) that project to cortical brain regions like the medial prefrontal cortex, and limbic brain regions involved in emotional processing and learning including the NAc, hippocampus, and amygdala. In addition, the cortex, amygdala, and hippocampus communicate with the NAc via glutamatergic projections (Hyman et al., 2006). See Figure 58.1 for a diagram of dopaminergic neurocircuitry and related connections between cortical, limbic, and striatal structures. When investigating the neurochemical mechanisms mediating psychomotor sensitization, particularly to stimulants, researchers using microdialysis techniques found that not only do drugs of abuse acutely increase dopamine release in the NAc, but after repeated administration and

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Neurocircuitry of Addiction

PFC

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Hippocampus CP

NAc VP Dopamine

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SN

Amygdala

Glutamate GABA/peptides

Figure 58.1 The approximate location of brain regions involved in addictive processes. Note: The view is from a sagittal section of rat brain. The ventral tegmental area (VTA) sends dopaminergic projections to the prefrontal cortex (PFC), nucleus accumbens (NAc), amygdala, and hippocampus; and the substania

withdrawal, a subsequent drug administration will increase dopamine release in the NAc to an even greater extent than was observed after the first administration (Kalivas & Duffy, 1993; Robinson, Jurson, Bennett, & Bentgen, 1988; Wolf, White, Nassar, Brooderson, & Khansa, 1993). These data reinforced the dopamine hypothesis of addiction (Wise, 1996); stating that reinforcement is mediated by increased dopamine signaling in the NAc, and that locomotor sensitization and addiction develop from repeated and enhanced dopamine release (Goodman, 2007). Subsequent research has found that animals expressing locomotor sensitization also show enhanced dopamine release in the medial prefrontal cortex (mPFC) and in the VTA. However, sensitization also produces an enhancement in glutamate release in the NAc and in the VTA (Kalivas & Duffy, 1998; Pierce, Bell, Duffy, & Kalivas, 1996). The enhancement of glutamate release in the NAc may be very important for sensitization as an AMPA glutamate receptor antagonist injected into the NAc prevents locomotor sensitization, and injection of AMPA itself into the NAc increases locomotor activity more effectively in cocaine-sensitized rats (Vanderschuren & Kalivas, 2000). In addition, other brain regions and/or other neurotransmitter systems have shown enhanced activity after a sensitizing regimen of drug administration, but the relative contribution of all these changes to the expression of locomotor sensitization and other addictive behaviors has not been fully elucidated. One caveat in many of the experiments mentioned here and to the “dopamine hypothesis of addicition” is that the enhanced neurotransmitter release is sometimes only seen within in a certain time period of withdrawal after the last drug administration, despite observation of behavioral sensitization at

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nigra (SN) sends dopaminergic projections to the caudate putamen (CP), also known as the dorsal striatum. The cortical, limbic, and striatal brain regions are interconnected through glutamatergic projections, and much of the output of corticolimbicstriatal circuitry is mediated via GABA/peptide projections from the NAc to the ventral pallidum (VP).

all time points. For example, some studies report that after short withdrawal times (1 to 5 days) using certain dosing regimens, there is no change in dopamine release relative to the first drug administration, but after longer withdrawal (1 to 3 weeks), sensitized dopamine release is observed, while increased locomotor activity is observed after both short and long withdrawal (Kalivas & Duffy, 1993; Wolf et al., 1993; Zhang, Loonam, Noailles, & Angulo, 2001). Therefore, enhanced dopamine release in the NAc does not fully explain the enhancement of locomotor activity after repeated drug exposure, suggesting that other mechanisms, such as increased glutamate release, are also important. Self-Administration Although it is useful to determine the acute and chronic effects of drugs of abuse on neurotransmission, the studies mentioned all examined the effects of experimenteradministered drug. However, a common issue in the study of addiction is that drugs can have different effects in the brain and on behavior depending on whether the drug is experimenter administered or self-administered. Therefore, some microdialysis studies have been conducted in animals self-administering a drug of abuse to verify some of the effects that have been observed with passive drug administration. A variety of neurotransmitters and brain regions have been examined. The most consistent finding has been that self-administered drugs, including cocaine, heroin, and nicotine, cause an increase in dopamine efflux preferentially in the shell subregion of the NAc rather than the core subregion, and the amount of dopamine efflux progressively increases across self-administration sessions

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1138 Addictive Processes

(Lecca et al., 2006; Lecca, Cacciapaglia, Valentini, Acquas, & Di Chiara, 2007; Lecca, Valentini, Cacciapaglia, Acquas, & Di Chiara, 2007). The importance of dopamine activity in the NAc for the development and maintenance of selfadministration has been supported by studies showing that selective lesions of dopamine neurons in the NAc inhibits self-administration (Caine & Koob, 1994; Sizemore, Co, Koves, Martin, & Smith, 2004) and dopamine antagonists injected into the NAc alters self-administration behavior in a manner consistent with reduced drug reward (Bari & Pierce, 2005; Caine, Heinrichs, Coffin, & Koob, 1995). A variety of other studies have shown changes in the release of several different neurotransmitters in several brain regions during self-administration. There is evidence for increased endogenous opioid and cannabinoid signaling that may be important for drug reward and self-administration behavior (Caille, Alvarez-Jaimes, Polis, Stouffer, & Parsons, 2007; Olive, Koenig, Nannini, & Hodge, 2001), but an exhaustive list of these effects is beyond the scope of this chapter. Reinstatement The reinstatement of drug-seeking behavior is one model of addictive behavior where the neurocircuitry has been well elucidated. Microdialysis and reversible lesion studies have been combined to determine the brain regions and neurotransmitters critical for reinstatement induced by drug re-exposure, cues, and stress (cocaine is the only drug that has been tested across all of these modes of reinstatement). These studies have found that the mesolimbic dopamine pathway is critical for reinstatement in addition to glutamatergic projections to the NAc. Specifically, each of several brain regions was individually inactivated by a combination of the GABAa agonist muscimol and GABAb agonist baclofen before the animal was tested for reinstatement. Cocaine-primed reinstatement is prevented by inhibition of the VTA, dorsal mPFC, NAc core subregion, and the ventral pallidum, which is a region that receives GABAergic and peptide projections from the NAc and coordinates limbic and motor activity. Cocaineprimed reinstatement was not affected by inhibition of the ventral mPFC, NAc shell subregion, or the basolateral amygdala (BLA; McFarland & Kalivas, 2001). In contrast, cue-induced reinstatement behavior does require activity of the BLA, indicating the importance of the amygdala for encoding associations between cues in the environment and rewards, and inhibition of the lateral orbitofrontal cortex inhibits cue- but not cocaine-primed reinstatement (Fuchs, Evans, Parker, & See, 2004; McLaughlin & See, 2003). Stress-induced reinstatement (induced by foot-shock), on the other hand, is blocked by inhibition of the central nucleus of the amygdala (CeA), ventral bed nucleus of the

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stria terminalis (vBNST), and the NAc shell. It is believed that this CeA complex activates motor circuitry through the VTA, as inhibition of the VTA, dmPFC, NAc core, and the ventral pallidum also blocks stress-induced reinstatement (McFarland, Davidge, Lapish, & Kalivas, 2004). Therefore, stress and drug-associated cues engage a richer neurocircuitry to induce reinstatement than drug priming alone, but all of these forms of reinstatement engage a common motor circuitry to produce the behavioral output (see Figure 58.2). The neurotransmitters necessary for mediating reinstatement in this circuitry have also been determined to a great extent (see Figure 58.2). In the previous section, we mentioned that sensitization to drugs of abuse and self-administration of drugs produces increased dopamine efflux in the NAc; however, a dopamine receptor antagonist infused in the NAc core does not inhibit cocaine-primed or stress-induced reinstatement. On the other hand, a dopamine receptor antagonist infused into the dmPFC inhibits cocaine-primed reinstatement, indicating that the dopamine projection from the VTA to the dmPFC rather than the NAc mediates reinstatement (McFarland, Lapish, & Kalivas, 2003; McFarland et al., 2004). The dmPFC sends a glutamatergic projection to the NAc core, and AMPA glutamate receptor antagonists infused in the core block cocaineprimed reinstatement, while AMPA infused into the NAc produces reinstatement on its own (Cornish, Duffy, &

AMY

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NA

NA

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GABA Glutamate VTA Drug Reward Dopamine release into the NA

VTA

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Transition to Addiction

Chronic Relapsing Disorder

Recruitment of PFC and AMY glutamate by repeated dopamine D1 stimulation

Dopamine trigger to drive PFC and AMY glutamate

Dopamine

Figure 58.2 Changes in neurotransmitter regulation of corticolimbic circuitry regulating the development of addiction. Note: Drug reward is regulated primarily by dopamine release in the projection from the ventral tegmental area (VTA) to the nucleus accumbens (NA). Repeated stimulation of this pathway by addictive drugs results in the recruitment of glutamatergic projections from the prefrontal cortex (PFC) and amygdala (AMY) to the NA. This transition corresponds to the progressive recruitment of environmental associations with drug reward. When drug use becomes a chronically relapsing disorder, it can be triggered by dopamine release as a result of a drug-associated cue, stress, or drug administration, but is ultimately driven by pathological glutamate release in the NA.

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Neurocircuitry of Addiction

Kalivas, 1999; Cornish & Kalivas, 2000). In addition, microdialysis studies have shown after chronic cocaine administration and abstinence or extinction, the basal concentration of glutamate in the NAc core is reduced compared to controls. In addition, cocaine-primed reinstatement results in increased dopamine and glutamate efflux in the NAc, but by selectively preventing glutamate release by inhibition of the dmPFC, reinstatement behavior is blocked. Moreover, animals exhibiting reinstatement show an increase in NAc glutamate and dopamine, while yoked controls who are not reinstating, only show an increase in dopamine, indicating that glutamate in the NAc is the critical factor mediating drug seeking (McFarland et al., 2003). The NAc not only receives glutamatergic projections from cortex and the amygdala, but also from the hippocampus, which is a structure known to be critical for learning involving spatial and contextual cues. A few studies have examined the role of the hippocampus in reinforcement, and lesions of dorsal hippocampus impair reinstatement induced by contextual, but not discrete, cues, while lesions of the ventral hippocampus impair reinstatement induced by discrete cues or a cocaine prime (Everitt & Robbins, 2005; Rogers & See, 2007). These studies highlight the importance of glutamatergic signaling in addiction, and how chronic drug use may cause enduring changes in basal glutamate activity that underlies the compulsion to relapse to drug use after extended periods of abstinence. Abstinence itself in human drug addicts may not be the same as extinction in the animal model of reinstatement. Extinction is a learning process that involves performing a behavior that no longer results in the presentation of a drug reward. On the other hand, in abstinence the activities and cues associated with drug use are still present, and continue to accurately predict drug reward. In humans, some forms of extinction learning can occur in a treatment program for recovering addicts, while some addicts may be placed in forced abstinence through incarceration or other means. Researchers have acknowledged this reality, and in addition to the extinction model, an abstinence model of reinstatement is also employed. In this model, animals go through self-administration, but rather than entering extinction training, the animal is kept away from the operant chamber for 2 to 3 weeks, often being placed in an alternate environment each day to control for the animal handling that would normally take place during extinction training. As in the extinction model, reinstatement testing includes pharmacological manipulations or reversible lesions immediately prior to the animal being placed in the operant chamber again, where the number of active lever presses is measured. This form of reinstatement is referred to as context-induced reinstatement after abstinence. These studies have revealed that the dorsal

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lateral caudate putamen (dlCP), which is also known as the dorsal lateral striatum, is critical for context reinstatement, but none of the other brain regions tested (including the BLA and dmPFC) affected context reinstatement (Fuchs, Branham, & See, 2006). The dlCP receives dopaminergic projections like the NAc (also known as the ventral striatum), but this dopamine primarily originates from the substantia nigra rather than the VTA. In addition, the dlCP receives glutamatergic inputs from the frontal cortex. The dlCP is known to be important for habit formation (Jog, Kubota, Connolly, Hillegaart, & Graybiel, 1999), suggesting that contextinduced reinstatement after abstinence may represent habitual responding that does not require cortical control, whereas, after extinction the cortex is acting to initiate behavior. This is further evidenced by the fact that animals respond more vigorously during the reinstatement test after abstinence than when tested after extinction. Several researchers have suggested that addiction develops because of pathological habit formation, to the point where behavior becomes compulsive (Everitt & Robbins, 2005). Second-Order Schedules Second-order schedules of reinforcement have also been used to determine the neurocircuitry underlying drug “wanting” versus drug “taking.” The advantage of the second-order schedule is that responding in the first component is drug-free, representing drug wanting, while responding in the second component and later sessions can be conceived of as drug taking. These studies have helped delineate the brain regions mediating the acute rewarding effects of a drug versus those driving drug seeking or wanting. For example, inhibition of the NAc core or BLA impairs the acquisition of a second-order schedule of reinforcement for cocaine or heroin, but inhibition of these regions does not affect nondelayed (continuous reinforcement) self-administration behavior (Alderson, Robbins, & Everitt, 2000; Hutcheson, Parkinson, Robbins, & Everitt, 2001; Ito, Robbins, & Everitt, 2004; Whitelaw, Markou, Robbins, & Everitt, 1996). In addition, pharmacological studies have found that dopamine, not glutamate, antagonists in the BLA inhibit responding for cocaine on a second-order schedule, whereas glutamate, not dopamine, antagonists in the NAc core inhibit second-order responding (DiCiano & Everitt, 2001, 2004). Therefore, much like reinstatement behavior, responding on a second-order schedule requires increased glutamatergic input into the NAc that is mediated by dopamine activation in another structure (in this case the amygdala). In addition, the orbitofrontal cortex (OFC), like the NAc core, has been shown to be necessary for the acquisition of cocaine seeking on

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1140 Addictive Processes

a second order schedule, but not under continuous reinforcement conditions (Everitt & Robbins, 2005). These studies provide evidence that interconnections between the OFC, BLA, and NAc are important for learning about and working for a delayed reinforcer. The OFC-BLA-NAc circuit is important for providing representations of the rewarding outcome of a series of behaviors. However, these structures are not necessary to respond for the drug when every response produces a reinforcer, indicating that these brain regions do not encode the rewarding or reinforcing effect of the drug itself. Research into the normal mechanisms and neurocircuitry of reward learning have aided addiction researchers in determining the structures involved in the reinforcing and motivational effects of drugs and have provided clues to how drugs of abuse may “hijack” normal reward learning systems, such that drugs become favored over natural rewards. Future research may determine ways of reverting this circuit level plasticity such that the pathological changes in reward learning are reversed, and addicts can regain control over their behavior.

MOLECULAR CHANGES IN ADDICTION Several molecular changes have been described after both acute and chronic exposure to drugs of abuse. Some of these changes are transient while others are long lasting. The transient changes may represent mechanisms involved in the basic pharmacological or rewarding effects of a drug, or initiation factors for the development of addiction. The long-lasting changes that persist during abstinence represent potential neuroplastic effects that may be responsible for craving and relapse. Experimental Techniques Molecular biological, genetic, and biochemical techniques provide a rich resource of methods for determining first, what molecules might be regulated by drugs of abuse, and second, which of these molecules is relevant for the development of addiction and/or is a compensatory response to drug administration. The studies outlined previously determined the neurocircuits important for addiction have provided molecular biologists a map of where in the brain they might find relevant molecular neuroplasticity. Thus, not only have the NAc and VTA been studied extensively, but in recent years the importance of other cortical and limbic brain regions has been realized, and they are beginning to receive more attention. The techniques used to study the molecular effects of addictive drugs depend on whether one is interested in

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changes in gene expression, protein levels, protein modifications, or activity of entire signaling cascades. To determine changes in gene expression, scientists can utilize several different methods, including in situ hybridization, reverse transcription–polymerase chain reaction RT-PCR), northern blots, or even gene arrays. In situ hybridization is a technique that involves labeling a small piece of cDNA that is antisense to a gene of interest with a radioactive or fluorescent molecule and hybridizing the cDNA to tissue from an animal (often brain slices). The cDNA will bind to the RNA of interest and the amount of radioactivity or fluorescence can be visualized and quantified to compare treatment effects and differences between brain regions on the expression of certain genes. RT-PCR, northern blots, and gene arrays also allows for the quantification of mRNA expression, but do not allow for visualization of expression in a whole brain section. One example of the use of in situ hybridization to better understand addiction has been to determine areas of brain activation and plasticity after exposure to cocaine-associated cues. Thomas, Arroyo, and Everitt (2003) found that mRNA expression of the neural activity/plasticity related immediate early gene zif268 is up-regulated after exposure to a cocaineassociated cue in mesocorticolimbic brain regions including the VTA, NAc core and shell, and basal nucleus of the amygdala. In addition, the plasticity-related gene gamma protein kinase C was found to have increased expression in the amygdala after rats were re-exposed to a cocaine-associated cue (Thomas & Everitt, 2001), therefore, the amygdala and other parts of the mesolimbic dopamine system are likely involved in learning about drug-related stimuli. These molecular results provide further evidence for the importance of the amygdala in learning associations between environmental stimuli and rewards, as was indicated by the neurocircuitry studies discussed earlier. In situ hybridization is fairly labor-intensive, especially for determining how drug use affects several different genes or families of genes; therefore, gene arrays and RT-PCR are often used to determine differences in the expression of multiple genes. In RT-PCR, mRNA from a brain region (or other tissue) of interest is isolated and purified. The RNA is then reverse transcribed using a polymerase chain reaction (PCR) to create complementary DNA (cDNA). RNA present in high concentrations will create more cDNA than RNA for a gene that is in low concentration. The amount of cDNA for a specific gene can then be determined using primers specific for that gene and performing real-time PCR using fluorescent dyes that increase in fluorescence when double-stranded DNA is formed. Thus, the PCR cycle where fluorescence is detected above background can be used as a measure of gene quantity. A gene in high concentration will reach threshold at an earlier cycle than a

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Molecular Changes in Addiction

gene in lower concentration. PCR is a convenient way to measure multiple genes, but gene arrays allow thousands of genes to be examined at once. Arrays consist of potentially thousands of “spots” of nucleic acids, where each spot is antisense to a specific gene. Purified RNA obtained from tissue is labeled (usually with a fluorescent marker) and allowed to hybridize to these spots producing a signal that increases with increased concentration of the gene. Gene array studies of human addicts have shown patterns of gene expression changes including decreases in genes encoding proteins involved in presynaptic neurotransmitter release in heroin abusers and decreases in myelin associated genes in cocaine abusers (Albertson, Schmidt, Kapatos, & Bannon, 2006). Gene array studies have several drawbacks, particularly with the technology that is currently available, including the high volume and complexity of data obtained and poor reproducibility. Results from gene array studies must be verified by secondary means, usually RT-PCR or in situ hybridization, and ultimately proven to be physiologically relevant through verification that the amount of protein is indeed altered by the treatment used for the array study. Quantification of protein changes or protein modifications (e.g., phosphorylation) are most often conducted by western blotting, though techniques like immunohistochemisitry can be used to determine cell specific protein localization, and the new field of proteomics is being used to determine how multiple proteins change, including entire signaling cascades in response to drugs. Western blotting involves separating proteins from homogenized tissue on a gel into separate bands based on molecular weight, these bands of proteins are then transferred to a blot, the blot is then incubated with an antibody specific to the protein of interest, and finally incubated with a secondary antibody that allows visualization of the bands where the antibody has bound. The amount of protein can then be quantified by comparing the intensity of the band in treated animals to controls. Immunohistochemistry also uses protein-specific antibodies to visualize proteins and can be used on slices of brain to determine the cellular localization and brain region specificity of proteins. Quantification of proteins is more difficult with immunohistochemistry, but in some cases the number of cells expressing the protein can be counted and used as a measure of protein up- or down-regulation. Proteomics assays are much like gene arrays in that the presence or absence of several proteins can be determined at once. Proteomics is also used to determine if certain conditions (e.g., exposure to drug) alter the amount and type of protein modifications in a system (e.g., phosphorylation, acetylation, ubiquitination). The field of proteomics utilizes a variety of techniques, but in general proteins are separated on gels and/or by high-performance liquid chromatography

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(HPLC) and the proteins and/or protein modifications present are identified by mass spectrometry. Like gene arrays, proteomics experiments generate a lot of complicated data that is not always reproducible, so other techniques, like western blotting, should be used to verify proteomics results. The advantage of using proteomics is that changes in entire categories of proteins, such as specific signaling cascades, can be determined at once, providing information on possible new targets for the treatment of addiction (see Li, Jiminez, van der Schors, Hornshaw, Schoffelmeer, & Smit, 2006, for an example of addiction-related proteomics research and Williams, Wu, Colangelo, & Nairn, 2004, for review). In all of these assays, the animal can be euthanized after acute or chronic drug exposure, differing periods of withdrawal, or after learning or performing addiction-related behaviors, allowing the researcher to determine which molecular events are relevant to which aspects of addiction. Some of the most elegant experiments take findings from the basic biochemistry and bring it back to the animal to determine how the molecule affects addiction-related behaviors. Examples of this type of research include using transgenic mice where a candidate gene is deleted (termed a “knock-out” mouse) or mutated and the response of an animal to drug exposure in an addiction-related behavioral paradigm is determined. Alternatively, the relevance of a specific protein to addiction-related behaviors can be determined by delivering a cDNA encoding an active or dominant negative protein by viral transfection, or by transfection of a small interfering RNA (siRNA) that inhibits synthesis of the protein of interest. The cDNA or interfering RNA can be administered to an animal in a specific brain region and in different stages of the addiction cycle. In particular, these studies have increased our understanding of the effects of short-term and long-term exposure to drugs of abuse and of how the brain responds to adapt to these drug effects. One example of a protein whose role in addiction was determined using the techniques discussed here is cyclindependent kinase 5 (Cdk5). First, Bibb and colleagues (2001) used in situ hybridization and western blotting to determine that chronic cocaine exposure increases Cdk5 gene expression and protein levels, respectively. In this study, inhibitors of Cdk5 were shown to enhance locomotor sensitization to cocaine, suggesting that normal Cdk5 activity is needed to restore homeostasis after chronic cocaine exposure. In a follow-up study, researchers induced specific forebrain deletion of Cdk5 using conditional knock-out mice, which also resulted in an enhancement of cocaineinduced locomotor activity, and increased progressive ratio responding for food, which further implicated Cdk5 in the effects of cocaine and reward-related processes (Benavides et al., 2007). Likewise, these researchers found that reducing

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Cdk5 expression specifically in the NAc using a viral transfection technique enhanced the locomotor activating effects of cocaine and promoted conditioned place preference to cocaine (Benavides et al., 2007). This series of studies provides a powerful example of how a combination of genetic, biochemical, and behavioral techniques can be used to increase our understanding of complicated disorders like addiction. To date, fewer studies have utilized siRNA technology to study addiction in vivo; however, there are some interesting examples that highlight the potential of future siRNA experiments. In one study, researchers specifically reduced the expression of the dopamine D3 receptor in the nucleus accumbens shell and determined the resulting locomotor response to cocaine. The siRNA mediated reduction in D3 receptor expression resulted in an increase in locomotor activity in response to cocaine compared to animals that received an infusion of a control virus (Bahi, Boyer, Bussard, & Dreyer, 2005). Therefore, increased activity of D3 receptors may inhibit the effects of cocaine, and may be a potential pharmacotherapeutic target for the treatment of addiction. The following sections describe in more detail the molecular effects of acute and long-term drug exposure, and list additional studies that utilize the techniques discussed earlier. Acute Molecular Effects Drugs of abuse have a variety of acute molecular effects depending on their mechanism of action. For example, the psychostimulants cocaine and amphetamine bind to dopamine transporters preventing dopamine from being transported back into the cell for degradation, in addition, amphetamines reverse the transporters, such that more dopamine is released into the synapse. The increase in extracellular dopamine thus results in increased stimulation of dopamine receptors, which include members of the D1 family of receptors (D1 and D5), which are coupled to Gs G-proteins, and the D2 family of receptors (D2, D3, and D4), which are coupled to Gi G-proteins (Neve, Seamans, & Trantham-Davidson, 2004; Ron & Jurd, 2005; Sibley, Monsma, & Shen, 1993; see later discussion for a more detailed explanation of G-protein signaling). Opiates like heroin and morphine directly activate Gi coupled opioid receptors. The reinforcing effects of opiate drugs are primarily attributed to their activation of the mu opioid receptor (Contet, Kieffer, & Befort, 2004). However, because opioids indirectly increase dopaminergic activity, opioids can indirectly stimulate Gs G-proteins in certain cells, particularly in the NAc. Other drugs of abuse like alcohol and nicotine act on ligand-gated ion channels in the VTA and other brain regions, but also ultimately increase dopamine release and activation of dopamine

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receptors in the NAc (Nestler, 2004). It is believed that activity at both D1 and D2 receptors in the NAc is important for the acute reinforcing effects of drugs of abuse, though the exact mechanism of reinforcement is not known (Nakajima, 1989). One primary action of G-proteins is to regulate the activity of cyclic adenosine monophosphate (cAMP). The cAMP signaling cascade is known to be involved in many aspects of addiction and other disorders, and will be reviewed briefly here. First, cAMP is regulated by the activation of G-protein coupled receptors by neurotransmitters. Stimulatory G-proteins are called Gs proteins, while inhibitory G-proteins are called Gi proteins. The G-protein complex consists of a three subunits, ␣, ␤, and ␥. The ␣ subunit is bound to GDP and the ␤␥ complex. When a ligand activates a Gs-coupled receptor it alters the conformation of the G-protein complex such that the ␣-subunit is exposed to the cytosol, and the GDP is exchanged for a GTP, and it then dissociates from the ␤␥ complex. The GTP-bound ␣-subunit can then activate the enzyme adenylyl cyclase, which then catalyzes the production of cAMP from ATP (Siegelbaum, Schwartz, & Kandel, 2000). Increased cAMP production results in increased activity of the cAMP-dependent protein kinase (PKA), which results in increased phosphorylation of several different proteins including several known to be involved in addictive processes such as DARPP-32 and CREB (Greengard, Allen, & Nairn, 1999; Hyman et al., 2006; Kalivas and O’Brien, 2008), which will be discussed below. Unlike Gs-coupled receptors, the ␣-subunit of Gi G proteins inhibits the activity of adenylyl cyclase when it is bound to GTP, thus decreasing the production of cAMP and the phosphorylation of proteins by PKA (Figure 58.3). However, it should be noted that Gi-coupled receptors can activate other signaling cascades, and the ␤␥ complex of both types of G-proteins can have their own independent effects on cell signaling (Siegelbaum et al., 2000). Therefore, the acute molecular effects of drugs of abuse can be very complicated due to their activation of multiple types of receptors and intracellular signaling cascades. One example of a possible direct effect of drug use on cAMP signaling is seen during acute withdrawal syndromes, particularly from opiate use. Opiates dampen cAMP signaling through Gi-coupled mu opioid receptors; however, during withdrawal when drug is absent, there is an up-regulation of cAMP signaling as the cellular systems “overshoot” when attempting to reestablish homeostasis (Nestler, 2001). Therefore, the negative effects of drug withdrawal may be mediated by an overproduction of cAMP. On the other hand, activation of dopamine D1 receptors in response to acute exposure to psychostimulant drugs leads to the activation of cAMP and does not

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Molecular Changes in Addiction Adenylyl Cyclase

Extracellular

D1R Gs

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Figure 58.3 Many effects of drugs of abuse are mediated through the activation of G-protein coupled receptors. Note: The cartoon illustrates G-protein regulation of adenylyl cyclase and the subsequent effects of cAMP activation. Arrows indicate activation or up-regulation while a straight line indicates inhibition. The D1 receptor (and many other receptors) is Gs-coupled and stimulates adenylyl cyclase and the PKA signaling cascade, while D2 receptors (and others, such as opioid receptors) are Gi-coupled and inhibit adenylyl cyclase and PKA signaling. Activation of PKA leads to phosphorylation of CREB to pCREB, which is then translocated into the nucleus to activate the transcription of several genes. The cartoon is meant to be illustrative only because D1 and D2 receptors are not necessarily located on the same cell and the genes depicted can be regulated differentially depending on the brain region, cell type, and receptors activated. Please refer to the text for abbreviation definitions.

produce a withdrawal syndrome, indicating that the acute effects of drugs on this signaling cascade is not sufficient to explain the development of addiction. However, activation of these signaling cascades in certain brain regions may be a common effect of most drugs. Moreover, the consequence of increased cAMP signaling is an up-regulation of the expression of transcription factors and other gene products that may initiate the long-term neuroplasticity that is common to all drugs of abuse (Figure 58.3). Therefore, while determining the acute effects of drugs is informative for determining the initial mechanisms of reinforcement or “reward,” these effects do not necessarily explain how drug-taking becomes compulsive and impervious to negative consequences. Consequently, more recent research has examined the chronic effects of drugs. Chronic Molecular Effects As we described, drugs of abuse stimulate the neurocircuitry involved in natural reward learning, but do so to a greater magnitude and duration than is caused by natural rewards, like food, water, and sex. The repeated overstimulation of these circuits by drugs is thought to cause many long-term changes and adaptations in this circuitry at the

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molecular level. Chronic drug use causes continued stimulation of dopamine D1 receptors. Moreover, chronic drug use up-regulates the G-protein binding protein Activator of G-protein Signaling 3 (AGS3; Bowers, McFarland, Lake, Peterson, Lapish, Gregory, et al., 2004). AGS3 selectively binds to and inhibits signaling through Gi␣ (Blumer and Lanier, 2003). In this way, after chronic drug administration dopamine release preferentially activates D1 signaling through Gs while D2 signaling through Gi is attenuated by elevated levels of AGS3 (Kalivas, Volkow, & Seamans, 2005). Accordingly, after chronic drug use there is a marked increase in cAMP activity and phosphorylation of protein kinase A (PKA). PKA phosphorylates and thus activates several other signaling molecules as described previously. One of these molecules is cAMP response element-binding protein (CREB). Phosphorylation of CREB leads to increased transcription of several genes including the immediate early genes cFos, Arc, brain-derived neurotrophic factor (BDNF) and zif/268, and other genes like Homer, dynorphin, and Narp, which have all been implicated in addictive processes (Hyman, 2005; Kalivas & O’Brien, 2008; Kelley, 2004). One particularly interesting gene up-regulated by drugs of abuse is deltaFosB. Unlike cFos, which rapidly increases for a short duration after acute drug exposure and diminishes after repeated drug administration, deltaFosB increases slowly after each drug administration and accumulates in dopamine terminal fields in the cortex and striatum upon repeated drug administration (McClung et al., 2004; Nestler, 2001; Nestler, Kelz, & Chen, 1999). The concentration of deltaFosB normalizes during abstinence, indicating that although increased deltaFosB is not an example of long-term neuroplasticity induced by addictive drugs, it may be critical in the transition from casual social use to compulsive drug use (Kalivas & O’Brien, 2008). The role many of these proteins play in the development of addiction has been determined using transgenic mice, pharmacological, or viral overexpression studies. Activating PKA in the NAc pharmacologically diminishes the rewarding effects of cocaine, while inhibiting PKA enhances cocaine’s rewarding effects as determined by progressive ratio responding, self-administration, and reinstatement (Lynch & Taylor, 2005; Self et al., 1998). Likewise, overexpression of CREB in the NAc inhibits cocaine reward, while overexpression of a dominant negative (inactive) form of CREB increases reward (as measured by conditioned place preference) (Carlezon et al., 1998). In addition, administration of an antisense oligonucleotide to CREB in the NAc reduces cocaine self-administration (Choi, Whisler, Graham, & Self, 2006). Therefore, enhancement of the PKA signaling pathway in the NAc after chronic drug exposure may be a homeostatic response. This homeostasis may be mediated by up-regulation of the preprodynorphin

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1144 Addictive Processes

gene, which codes for the peptide dynorphin, an endogenous agonist at kappa opioid receptors. Kappa opioid receptor activation in the VTA inhibits dopamine neuron activity, and likely reduces reward (Margolis, Hjelmstad, Bonci, & Fields, 2003). Kappa opioid receptor agonists are known to cause dysphoric effects in humans, and therefore, increased dynorphin expression may mediate the dysphoria addicts experience during drug withdrawal (Nestler, 2001). Increasing deltaFosB specifically in dynorphin containing neurons of the NAc using transgenic mice, increases the locomotor activating and sensitizing effects of cocaine (Colby, Whisler, Steffen, Nestler, & Self, 2003). Increased deltaFosB also increases cocaine self-administration and reinstatement behavior, while overexpression of a protein that inhibits deltaFosB in the NAc or striatum inhibits the effects of cocaine. Therefore, deltaFosB may be one of the molecular mediators for initiating the long-term effects of drugs, including sensitization and relapse (Nestler, 2001). In addition to CREB and deltaFosB, activation of PKA results in phosphorylation of the dopamine and cAMPdependent phosphoprotein of 32 kD (DARPP-32), specifically at threonine 34 (Thr34). Phosphorylation at this site makes DARPP-32 an inhibitor of protein-phosphatase 1 (PP-1), thus inhibiting the dephosphorylation of several proteins (Svenningsson, Nairn, & Greengard, 2005). Due to the acute ability of psychostimulants to stimulate the PKA/DARPP-32 signaling cascade the effects of cocaine in DARPP-32 knockout mice has been examined. Mice lacking the DARPP-32 gene and mice with a specific mutation to inactivate the Thr34 phosphorylation site show reduced sensitivity to the rewarding effects of cocaine in the conditioned place preference paradigm, and reduced sensitivity to the acute locomotor effects of cocaine; however, these mice displayed enhanced locomotor sensitization to cocaine (Hiroi et al., 1999; Zachariou et al., 2002, 2006). In addition, Thr34 mutated mice self-administer significantly more cocaine at lower doses compared to wild-types (Zhang et al., 2006). These results indicate that activation of DARPP-32 by PKA is important for some behavioral effects of cocaine, but that DARPP-32 activity may differentially affect cocaine-mediated behavior depending on the duration of cocaine exposure and may undergo longterm plasticity in function. Another interesting family of proteins regulated by drugs of abuse are the Homer proteins. There are multiple Homer proteins and isoforms that are known to be involved in regulating glutamate receptor activity, calcium signaling, and synaptic remodeling. In addition, Homer genes are dynamically regulated by cocaine and environmental stimuli; specifically, abstinence from chronic cocaine decreases Homer 1b/c expression in the NAc. When a synthetic nucleic acid sequence antisense to the Homer 1b/c

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gene is infused into the NAc, thus preventing the translation of the gene into protein (termed an antisense oligonucleotide), which recapitulates the cocaine effect, drug naïve rats have a sensitized locomotor response to cocaine (Ghasemzadeh, Permenter, Lake, Worley, & Kalivas, 2003). In addition, mice genetically modified such that the Homer1 or Homer 2 gene is knocked out (i.e., the mice no longer express the Homer proteins encoded by these genes) also display behaviors consistent with those observed after chronic cocaine and abstinence. Homer knockout mice display conditioned place preference and increased locomotor activity at lower doses of cocaine than wild-type controls. In addition, in wild-type animals exposed to chronic cocaine and abstinence, the basal extracellular concentration of glutamate in the NAc is decreased, but there is enhanced glutamate release in response to a cocaine challenge. Likewise, Homer knockout mice have a lower basal concentration of glutamate in the NAc, and an augmented glutamate (but not dopamine) release in response to cocaine. These cocaine-like effects in knock-out mice can be reversed by intra-NAc infusion of an adeno-associated virus (AAV) transfection of the Homer 2b splice variant. In other words, if Homer 2 expression is restored in the NAc of knockout mice, the sensitized effects of conditioned place preference, locomotor activity, and cocaine-induced glutamate release are reversed (Szumlinski et al., 2004). Therefore, Homer proteins are candidates for mediating the neuroplastic and behavioral effects of chronic drug use. Another long-term neuroadaptation produced by chronic cocaine exposure and abstinence is a persistent reduction in the basal concentration of glutamate in the NAc (Baker et al., 2003; McFarland et al., 2003). The basal concentrations of amino acid neurotransmitters like glutamate are primarily controlled by activity of transporters located on glial cells, rather than by neural activity. Therefore, researchers determined whether the activity of glutamate transporters was altered after chronic cocaine. One of these transporters is the cystine/glutamate exchanger, which exchanges one extracellular cystine for one intracellular glutamate, providing cystine to the cell for glutathione synthesis, and modulating excitatory neurotransmission by providing glutamatergic tone on extrasynaptic metabotropic glutamate receptors (mGluRs). The cystine/glutamate exchanger has been shown to be down-regulated during abstinence from chronic cocaine, in which reduces extracellular glutamatergic tone on mGluRs. This glutamatergic tone normally inhibits synaptic glutamate release, and in the absence of this inhibitory regulation a relevant environmental stimulus such as an injection of cocaine or a cocaine associated cue enhances synaptic glutamate release. It is believed that this adaptation in glutamate signaling mediates the propensity to relapse during abstinence, and this hypothesis has been

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Molecular Changes in Addiction

supported by several pieces of evidence. First, inhibitors of cystine/glutamate exchange reduce the basal concentration of glutamate similar to chronic cocaine, in drug naïve animals. Secondly, restoration of cystine/glutamate exchange by direct administration of cystine or systemic administration of the cysteine prodrug N-acetylcysteine (NAC) increases basal glutamate in cocaine abstinent animals to levels observed in controls. Finally, NAC administration prevents cocaine-priming from increasing glutamate in the NAc and prevents cocaine-primed reinstatement (Baker et al., 2003). Therefore, altered cystine/glutamate exchange is one of the enduring neuroplastic events that occurs in response to chronic cocaine. Moreover, it was recently shown that NAC blocks reinstatement in rats extinguished from heroin self-administration (Zhou & Kalivas, 2007), making altered glutamatergic signaling through the cystine/glutamate exchanger a possible mechanism for the high rates of craving and relapse in both cocaine and heroin addicts. Another molecule that is regulated by drugs of abuse and has received a lot of attention in terms of the neuroplasticity of addiction is brain-derived neurotrophic factor (BDNF). BDNF is in the nerve growth factor family of growth factors that are important for normal neural development, but have also been shown to be important in learning processes and psychiatric disorders in adults. BDNF gene expression is regulated by CREB, and acute cocaine increases BDNF mRNA expression in the NAc (Filip et al., 2006). In addition, withdrawal from cocaine self-administration results in a progressive increase in BDNF protein in the VTA, NAc, and amygdala (Grimm et al., 2003), and a single BDNF infusion into the VTA enhances cocaineseeking in extinction, and during reinstatement to cocaine cues for at least 30 days after infusion (Bossert, Ghitza, Lu, Epstein, & Shaham, 2005). Moreover, infusion of BDNF directly into the NAc or VTA enhances the development of sensitization to cocaine, and BDNF infused in the NAc increases the ability of a cocaine-associated stimulus to act as a conditioned reinforcer, even a month after the BDNF infusions had ceased (Corominas, Roncero, Ribases, Castells, & Casas, 2007; Horger et al., 1999). Repeated BDNF infusions into the NAc shell have been shown to increase cocaine self-administration and later reinstatement of cocaine seeking, while infusion of a neutralizing antibody to BDNF reduces cocaine self-administration and reinstatement. Likewise, inducible BDNF knock-out mice that have BDNF gene expression transiently knockedout in the NAc show reduced cocaine self-administration (Graham et al., 2007). Heterozygous BDNF knock-out mice have also been shown to have reduced reactivity to cocaine reward in the conditioned place preference paradigm (Hall, Drgonova, Goeb, & Uhl, 2003). All of these

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studies imply that BDNF is necessary for the rewarding and sensitizing effects of cocaine; however, BDNF infusion into the dorsal mPFC has been shown to inhibit all forms of reinstatement tested, suggesting that BDNF is an important regulator of addictive behavior, but that the direction of regulation is brain region dependent (Berglind et al., 2007). Morphological Adaptations in Addiction To this point we have been discussing changes in the expression or activity of genes and proteins that occurs with chronic drug use, but the actual structure of neurons has also been shown to change in response to drugs. The structure of neurons can be visualized by staining or filling neurons with Golgi stain or diI, respectively. Golgi staining has revealed that repeated administration of psychostimulants increases the density of dendritic spines on neurons in the PFC and NAc, while repeated morphine administration reduces spine density, suggesting that drugs can cause enduring changes in the structure of neurons. Dendritic spines are structures that protrude from neurons to receive synaptic inputs from other cells. There is some evidence of a positive correlation between the density of spines (and possibly in the shapes of spines and the degree of branching) and the number of synapses. The psychostimulant-induced increase in spine density has been shown to endure for up to several months after discontinuation of drug use. The increase in spine density may also correlate with the time period of expression of locomotor sensitization (Robinson & Kolb, 2004). While it may seem at odds to any “unified” theory of addiction that morphine has an opposite effect on spine density, there is no way to tell from these studies exactly what type of synapses (excitatory or inhibitory, cell type, etc.) are altered. Therefore, it is possible that the changes observed have the same net effect on behavioral output despite differences in the structural plasticity required to achieve it. The molecular mechanisms underlying this druginduced structural plasticity are poorly understood, but there have been a few studies exploring this question. One interesting finding is that chronic cocaine and abstinence results in an increase in actin cycling after a cocaine challenge (Toda, Shen, Peters, Cagle, & Kalivas, 2006). Actin cycling is the process where actin is assembled and disassembled, thereby maintaining a homeostasis between globular (G) actin and filamentous (F) actin. Actin cycling controls dendritic morphology and regulates protein insertion into the postsynaptic density. Acute cocaine increases F-actin in the NAc, as does withdrawal from chronic cocaine, amphetamine, and morphine. The increase in actin cycling can be disrupted by pharmacological means,

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either by preventing the polymerization of actin into filopodia-like structures using Latrunculin A or by promoting polymerization and the formation of lamellipodia-like structures with the LIM kinase inhibitor tat-cofilin. When either of these treatments is administered to the NAc prior to a cocaine-priming injection, reinstatement is enhanced. Therefore, increased actin cycling in response to cocaine appears to be an adaptation that may help the organism consume less drugs and maintain behavioral homeostasis (Toda et al., 2006). Summary Chronic drug exposure causes very long-lasting and possibly permanent changes in neurotransmission in neural circuits known to be important for normal reward-learning and motivated behavior. Several molecules have been identified that are modulated by chronic exposure to drugs, some of which have been shown to be adaptive effects, while others may mediate the long-term propensity to relapse observed in addicts. Current research is endeavoring to further characterize the molecular mechanisms of relapse, with an understanding that this is the most likely point of intervention for treating addiction.

HUMAN STUDIES Neuroimaging The advent of neuroimaging techniques has allowed researchers to directly study brain structure and function in addicts. Magnetic resonance imaging (MRI) and positron emission tomography (PET) allow researchers to visualize which brain regions are activated or inactivated when an addict receives drug or is exposed to drug-associated cues, and to determine how addicts perform on cognitive tasks. In these studies, the brains of addicts are compared to nonaddicted controls or the brain activity in drug situations is compared to a neutral situation. Functional MRI or fMRI measures the disruption in the magnetic properties of brain tissue that only occurs in areas of increased activation. The temporal and spatial resolution of fMRI allows activation patterns to be determined over short time intervals (seconds) and in very specific brain regions. PET studies can be used to measure general brain activation, but also allow for neurochemical studies to be done by determining how the binding of a radioactively labeled ligand for certain proteins (e.g., receptors, transporters) changes in response to a specific manipulation. For example, a radiolabeled ligand for dopamine D2 receptors shows a strong signal at baseline conditions as it binds to dopamine receptors in

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the striatum. However, if endogenous dopamine release is increased, such as when dopamine transport is blocked by cocaine, the radioactive signal will decrease as the ligand is displaced from the receptor by endogenous dopamine. Many neuroimaging studies using fMRI, PET, or their combination have been conducted in addicts, but only the most consistent findings are discussed here. One of the most common findings in human imaging studies is that addicts have deficits in frontal cortical function. The anterior cingulate cortex of many addicts is hypoactive (Goldstein & Volkow, 2002; Volkow et al., 1992). However, when a drug associated cue or the drug itself is presented, the anterior cingulate cortex shows greater activation in addicts than in control subjects (Childress et al., 1999; Maas et al., 1998; Wexler et al., 2001). These findings provide a possible explanation for the inability of addicts to attribute the appropriate salience properties to rewards, providing a mechanism for the enhanced salience attributed to drug associated stimuli over natural rewards. The OFC region of frontal cortex is also often shown to be hypoactive in addicts, which may explain the loss of inhibitory control observed in addiction (Volkow et al., 1992). PET studies also show that addicts have lower basal binding at D2 receptors in frontal cortex, suggesting that the cortical dysfunction observed in addiction may be mediated in part by reduced signaling through D2 receptors (Volkow et al., 1993). One caveat of these studies is that in humans it is impossible to determine if the person had fewer D2 receptors before or after they became addicted, allowing the possibility that D2 abnormalities are a risk factor for addiction rather than a result of chronic drug use (Volkow, Fowler, & Wang, 2004; Volkow & Li, 2004). Recent animal studies have used imaging to determine dopamine receptor binding before drug use. Rats with reduced D2/D3 receptor binding prior to any drug exposure showed increased impulsivity and enhanced cocaine reinforcement, suggesting that dopamine receptor dysfunction increases the likelihood of becoming addicted (Dalley et al., 2007). In addition, addicts have reduced D2 receptor binding (receptor availability) in both the dorsal and ventral striatum compared to controls, and this deficiency in striatal D2 receptors persists even after long periods of abstinence (Martinez et al., 2004; Volkow & Li, 2004). Addicts also show diminished dopamine release in response to methylphenidate exposure (a dopamine transport blocker), suggesting that addicts have reduced dopamine signaling as measured by changes in D2 receptor binding; however, the activity of D1 receptors has not been determined (Volkow, Fowler, & Wang, 1999). A few other studies have observed increased activation of the hippocampus and amygdala in response to drug-associated cues and increased activity of the thalamus based on the expectation of drug (Volkow

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Summary

et al., 2004). Overall, addicts show disruptions in the same mesocorticolimbic dopaminergic neurocircuitry that is observed in animal models of addiction. Several animal studies have shown that chronic drug use alters dopaminergic signaling, inhibits the ability to attribute proper salience to stimuli, and increases impulsivity while losing executive cortical control (Jentsch & Taylor, 1999). Therefore, while animal studies place a lot of emphasis on neuroplasticity in the NAc and human studies show more consistent alterations in frontal cortical function, there is significant concordance between the clinical studies of addiction and the basic science, suggesting that animal studies may lead to the development of better therapeutics to treat addiction. Treatment of Addiction Currently, the primary forms of treatment for addiction are agonist-based replacement therapies. These include methadone or buprenorphine for heroin addiction and the nicotine patch or gum for nicotine addiction. These therapies replace the abused drug with a drug that has the same mechanism of action (activates the same receptor), but has a less reinforcing pharmacokinetic profile, generally having a slower onset and longer duration of action. There are also antagonist therapies, such as using the mu opioid receptor antagonist naltrexone to treat opiate addiction. Long-lasting formulations of antagonist treatment seem to have lower recidivism rates than short-acting formulations. Naltrexone is also used to treat alcoholism because mu opioid receptor blockade appears to reduce multiple types of reinforcement. Nicotine addiction has also been treated successfully with the dopamine transport blocker bupropion, which has a similar mechanism of action as cocaine, but with less reinforcing pharmacokinetics. Bupropion has not been reported to be a successful treatment for cocaine or amphetamine addiction despite being pharmacologically akin to an agonist-based therapy for these drugs. In fact, few treatments have had any reported success in treating cocaine or amphetamine addiction, but there are several compounds under investigation (Volkow & Li, 2004). One potential treatment has emerged from the type of basic neurobiological research outlined here. Chronic cocaine administration causes enduring changes in glutamatergic neurotransmission in the NAc. In rats, this dysfunction was rescued by systemic administration of the cysteine pro-drug NAC. A limited double-blind clinical trial with NAC in cocaine addicts reported less cocaine craving, desire to use, and interest in cocaine. In addition, the treated addicts spent less time viewing cocaine related cues, indicating reduced cue reactivity to cocaine. Further research is needed to determine if NAC can produce

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an enduring decrease in relapse to drug taking (LaRowe et al., 2007). In addition, GABA agonists have shown promise as a treatment for multiple types of addiction. Neuroscientists have administered GABA agonists to specific brain regions to determine whether activity of that brain region is necessary for the behavior being examined, and the ventral pallidum (VP) was among the many regions where GABA agonists inhibit the reinstatement of drug seeking. Moreover, cocaineprimed reinstatement decreases GABA release into the VP (Tang, McFarland, Cagle, & Kalivas, 2005). Therefore, increasing GABAergic activity in this region should inhibit multiple types of reinstatement behavior or relapse. Several types of compounds that increase GABAergic activity by different mechanisms are currently under investigation, but the long-term success of these treatments has not been determined (Roberts, 2005). Several other interesting targets for the treatment of addiction have been suggested and are currently under investigation, including cannabinoid receptor (CB1) and corticotrophin-releasing factor (CRF) antagonists. CB1 antagonists block the effects of the active ingredient in marijuana and of the endogenous cannabinoids. There is some evidence that multiple drugs of abuse release endogenous cannabinoids, suggesting that CB1 antagonists may help prevent the rewarding effects of multiple drugs, and a CB1 antagonist has been shown to prevent cocaine-primed reinstatement in rats (Xi et al., 2006). In addition, the CB1 antagonist rimonabant is being investigated as a treatment for obesity, suggesting that cannabinoid receptor activity may mediate the drive to seek food in addition to drug reward. Corticotropin-releasing factor (CRF) is released when an organism experiences stress, and, in part, mediates the autonomic and central effects of stress. CRF antagonists prevent stress-induced reinstatement in animal models and are also being investigated for the treatment of depression with some success (Zoumakis, Rice, Gold, & Chrousos, 2006). Overall, the development of new treatments for addiction has been relatively slow. However, in the past few years, basic neurobiological research has discovered new targets for the treatment of addiction, and the first clinical trials determining the efficacy of these treatments in humans are underway.

SUMMARY Addiction is a complex, multifaceted disease that requires an interdisciplinary scientific approach and cooperation between clinical and basic science researchers. The past decade has witnessed a marked evolution in neurobiological information that has translated into lay perspectives on

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addiction. With this improved understanding of addiction as a pathology of neuroplasticity in neural circuits important for reward learning and as a dysfunction in cortical circuits necessary for executive control of behavior, we can better strategize treatments for addiction. While behavioral therapies are necessary to help people gain cognitive control over behavior and to rebuild adaptive relationships to the environment, pharmaceutical agents are needed to reverse the abnormal plasticity that leads to craving and compulsive drug seeking. Increased understanding of the molecular mechanisms underlying circuit level changes in the brain will continue to identify new targets for treatment. The study of addiction has also led to a better understanding of basic mechanisms of reinforcement, learning and memory, and habit formation. This has led to exciting new findings and perspectives in of the study of neuropsychiatric disorders other than addiction, including the cognitive symptoms associated with many disorders, including eating disorders, obsessive-compulsive disorder, and attention deficit disorder. Future research will undoubtedly lead to the discovery of additional neuroplasticity induced by drugs of abuse in neurocircuits, and these new mechanisms of neuroplasticity can be expected to expand our understanding of the physiology of brain function and how neuropathologies dysregulate behavioral adaptation and response to the environment.

Benavides, D. R., Quinn, J. J., Zhong, P., Hawasli, A. H., DiLeone, R. J., Kansy, J. W., et al. (2007). Cdk5 modulates cocaine reward, motivation, and striatal neuron excitability. Journal of Neuroscience, 27, 12967–12976. Berglind, W. J., See, R. E., Fuchs, R. A., Ghee, S. M., Whitfield, T. W., Miller, S. W., et al. (2007). A BDNF infusion into the medial prefrontal cortex suppresses cocaine seeking in rats. European Journal of Neuroscience, 26, 757–766. Bibb, J. A., Chen, J., Taylor, J. R., Svenningsson, P., Nishi, A., Snyder, G. L., et al. (2001, March 15). Effects of chronic exposure to cocaine are regulated by the neuronal protein Cdk5. Nature, 410, 376–380. Blumer, J. B., & Lanier, S. M. (2003). Accessory proteins for G protein signaling systems: Activators of G protein signaling and other nonreceptor proteins influencing the activation state of G proteins. Receptors and Channels, 9, 195–204. Bossert, J. M., Ghitza, U. E., Lu, L., Epstein, D. H., & Shaham, Y. (2005). Neurobiology of relapse to heroin and cocaine seeking: An update and clinical implications. European Journal of Pharmacology, 526, 36–50. Bowers, M. S., McFarland, K., Lake, R. W., Peterson, Y. K., Lapish, C. C., Gregory, M. L., et al. (2004). Activator of G-protein signaling 3: A gatekeeper of cocaine sensitization and drug-seeking. Neuron, 42, 269–281. Caille, S., Alvarez-Jaimes, L., Polis, I., Stouffer, D. G., & Parsons, L. H. (2007). Specific alterations of extracellular endocannabinoid levels in the nucleus accumbens by ethanol, heroin, and cocaine self-administration. Journal of Neuroscience, 27, 3695–3702. Caine, S. B., Heinrichs, S. C., Coffin, V. L., & Koob, G. F. (1995). Effects of dopamine D-1 antagonist SCH 23390 microinjected into the accumbens, amygdala, or striatum on cocaine self-administration in the rat. Brain Research, 692, 47–56. Caine, S. B., & Koob, G. F. (1994). Effects of mesolimbic dopamine depletion on responding maintained by cocaine and food. Journal of the Experimental Analysis of Behavior, 61, 213–221.

REFERENCES

Carlezon, W. A., Thome, J., Olson, V. G., Lane-Ladd, S. B., Brodkin, E. S., Hiroi, N., et al. (1998, December 18). Regulation of cocaine reward by CREB. Science, 282, 2272–2275.

Adinoff, B. (2004). Neurobiologic processes in drug reward and addiction. Harvard Reviews Psychiatry, 12, 305–320.

Childress, A. R., Mozley, P. D., McElgin, W., Fitzgerald, J., Reivich, M., & O’Brien, C. P. (1999). Limbic activation during cue-induced cocaine craving. American Journal of Psychiatry, 156, 11–18.

Albertson, D. N., Schmidt, C. J., Kapatos, G., & Bannon, M. J. (2006). Distinctive profiles of gene expression in the human nucleus accumbens associated with cocaine and heroin abuse. Neuropsychopharmacology, 31, 2304–2312.

Choi, K. H., Whisler, K., Graham, D. L., & Self, D. W. (2006). Antisenseinduced reduction in nucleus accumbens cyclic AMP response element binding protein attenuates cocaine reinforcement. Neuroscience, 137, 373–383.

Alderson, H. L., Robbins, T. W., & Everitt, B. J. (2000). The effects of excitotoxic lesions of the basolateral amygdala on the acquisition of heroinseeking behavior in rats. Psychopharmacology, 153, 111–119.

Colby, C. R., Whisler, K., Steffen, C., Nestler, E. J., & Self, D. W. (2003). Striatal cell type-specific overexpression of DeltaFosB enhances incentive for cocaine. Journal of Neuroscience, 23, 2488–2493.

American Psychiatric Association. (2000). Diagnostic and Statistical Manual of Mental Disorders (4th ed. text revision). Washington, DC: American Psychiatric Press.

Contet, C., Kieffer, B. L., & Befort, K. (2004). Mu opioid receptor: A gateway to drug addiction. Current Opinion in Neurobiology, 14, 370–378.

Badiani, A., & Robinson, T. E. (2004). Drug-induced neurobehavioral plasticity: The role of environmental context. Behavioral Pharmacology, 15, 373–376. Bahi, A., Boyer, F., Bussard, G., & Dreyer, J. L. (2005). Silencing dopamine D3-receptors in the nucleus accumbens shell in vivo induces changes in cocaine-induced hyperlocomotion. European Journal of Neuroscience, 21, 3415–3426. Baker, D. A., McFarland, K., Lake, R. W., Shen, H., Tang, X.-C., Toda, S., et al. (2003). Neuroadaptations in cystine-glutamate exchange underlie cocaine relapse. Nature Neuroscience, 6, 743–749. Bari, A. A., & Pierce, R. C. (2005). D1-like and D2 dopamine receptor antagonists administered into the shell subregion of the rat nucleus accumbens decrease cocaine, but not food, reinforcement. Neuroscience, 135, 959–968.

c58.indd Sec4:1148

Cornish, J. L., Duffy, P., & Kalivas, P. W. (1999). A role for nucleus accumbens glutamate transmission in the relapse to cocaine-seeking behavior. Neuroscience, 93, 1359–1367. Cornish, J. L., & Kalivas, P. W. (2000). Glutamate transmission in the nucleus accumbens mediates relapse in cocaine addiction. Journal of Neuroscience, 20, RC89. Corominas, M., Roncero, C., Ribases, M., Castells, X., & Casas, M. (2007). Brain-derived neurotrophic factor and its intracellular signaling pathways in cocaine addiction. Neuropsychobiology, 55, 2–13. Dalley, J. W., Fryer, T. D., Brichard, L., Robinson, E. S., Theobald, D. E., Laane, K., et al. (2007, August 24). Nucleus accumbens D2/3 receptors predict trait impulsivity and cocaine reinforcement. Science, 317, 1033–1035. de Wit, H., & Stewart, J. (1981). Reinstatement of cocaine-reinforced responding in the rat. Psychopharmacology, 75, 134–143.

8/17/09 5:31:47 PM

References 1149 de Wit, H., & Stewart, J. (1983). Drug reinstatement of heroin-reinforced responding in the rat. Psychopharmacology, 79, 29–31. DiCiano, P., & Everitt, B. J. (2001). Dissociable effects of antagonism of NMDA and AMPA/KA receptors in the nucleus accumbens core and shell on cocaine-seeking behavior. Neuropsychopharmacology, 25, 341–360. DiCiano, P., & Everitt, B. J. (2004). Direct interactions between the basolateral amygdala and nucleus accumbens core underlie cocaine-seeking behavior by rats. Journal of Neuroscience, 24, 7167–7173. DiChiara, G., & Bassareo, V. (2007). Reward system and addiction: What dopamine does and doesn’t do. Current Opinion in Pharmacology, 7, 69–76.

Horger, B. A., Iyasere, C. A., Berhow, M. T., Messer, C. J., Nestler, E. J., & Taylor, J. R. (1999). Enhancement of locomotor activity and conditioned reward to cocaine by brain-derived neurotrophic factor. Journal of Neuroscience, 19, 4110–4122. Hutcheson, D. M., Parkinson, J. A., Robbins, T. W., & Everitt, B. J. (2001). The effects of nucleus accumbens core and shell lesions on intravenous heroin self-administration and the acquisition of drug-seeking behavior under a second-order schedule of heroin reinforcement. Psychopharmacology, 153, 464–472.

Everitt, B. J., & Robbins, T. W. (2000). Second-order schedules of drug reinforcement in rats and monkeys: Measurement of reinforcing efficacy and drug-seeking behaviour. Psychopharmacology, 153, 17–30.

Hyman, S. E. (2005). Addiction: A disease of learning and memory. American Journal of Psychiatry, 162, 1414–1422.

Everitt, B. J., & Robbins, T. W. (2005). Neural systems of reinforcement for drug addiction: From actions to habits to compulsion. Nature Neuroscience, 8, 1481–1489.

Hyman, S. E., Malenka, R. C., & Nestler, E. J. (2006). Neural mechanisms of addiction: The role of reward-related learning and memory. Annual Reviews Neuroscience, 29, 565–598.

Filip, M., Faron-Gorecka, A., Kusmider, M., Golda, A., Frankowska, M., & Dziedzicka-Wasylewska, M. (2006). Alterations in BDNF and trkB mRNAs following acute or sensitizting cocaine treatments and withdrawal. Brain Research, 1071, 218–225.

Ito, R., Robbins, T. W., & Everitt, B. J. (2004). Differential control over cocaine-seeking behavior by nucleus accumbens core and shell. Nature Neuroscience, 7, 389–397.

Foy, A. (2007). Circuit breakers for addiction. Internal Medicine Journal, 37, 320–325. Fuchs, R. A., Branham, R. K., & See, R. E. (2006). Different neural substrates mediate cocaine seeking after abstinence versus extinction training: A critical role for the dorsolateral caudate-putamen. Journal of Neuroscience, 26, 3584–3588. Fuchs, R. A., Evans, K. A., Parker, M. P., & See, R. E. (2004). Differential involvement of orbitofrontal cortex subregions in conditioned cueinduced and cocaine-primed reinstatement of cocaine seeking in rats. Journal of Neuroscience, 24, 6600–6610. Fuchs, R. A., & See, R. E. (2002). Basolateral amygdala inactivation abolishes conditioned stimulus- and heroin-induced reinstatement of extinguished heroin-seeking behavior in rats. Psychopharmacology, 160, 425–433. Gardner, E. L. (2000). What we have learned about addiction from animal models of drug self-administration. American Journal of Addiction, 9, 285–313. Ghasemzadeh, M. B., Permenter, L. K., Lake, R., Worley, P. F., & Kalivas, P. W. (2003). Homer1 proteins and AMPA receptors modulate cocaineinduced behavioural plasticity. European Journal of Neuroscience, 18, 1645–1651. Goldstein, R. Z., & Volkow, N. D. (2002). Drug addiction and its underlying neurobiological basis: Neuroimaging evidence for the involvement of the frontal cortex. American Journal of Psychiatry, 159, 1642–1652. Goodman, A. (2007). Neurobiology of addiction: An integrative review. Biochemistry and Pharmacology, 75(1), 266–322. Graham, D. L., Edwards, S., Bachtell, R. K., DiLeone, R. J., Rios, M., & Self, D. W. (2007). Dynamic BDNF activity in nucleus accumbens with cocaine use increases self-administration and relapse. Nature Neuroscience, 10, 1029–1037. Greengard, P., Allen, P. B., & Nairn, A. C. (1999). Beyond the dopamine receptor: The DARPP-32/protein phosphatase 1 cascade. Neuron, 23, 435–447. Grimm, J. W., Lu, L., Hayashi, T., Hope, B. T., Su, T. P., & Shaham, Y. (2003). Time-dependent increases in brain-derived neurotrophic factor protein levels within the mesolimbic dopamine system after withdrawal from cocaine: Implications for incubation of cocaine craving. Journal of Neuroscience, 23, 742–747. Hall, F. S., Drgonova, J., Goeb, M., & Uhl, G. R. (2003). Reduced behavioral effects of cocaine in heterozygous brain-derived neurotrophic factor (BDNF) knockout mice. Neuropsychopharmacology, 28, 1485–1490.

c58.indd Sec5:1149

Hiroi, N., Fienberg, A. A., Haile, C. N., Alburges, M., Hanson, G. R., Greengard, P., et al. (1999). Neuronal and behavioural abnormalities in striatal function in DARPP-32-mutant mice. European Journal of Neuroscience, 11, 1114–1118.

Jentsch, J. D., & Taylor, J. R. (1999). Impulsivity resulting from frontostriatal dysfunction in drug abuse: Implications for the control of behavior by reward-related stimuli. Psychopharmacology, 146, 373–390. Jog, M. S., Kubota, Y., Connolly, C. I., Hillegaart, V., & Graybiel, A. M. (1999, November 26). Building neural representations of habits. Science, 286, 1745–1749. Johnson, S. W., & North, R. A. (1992). Opioids excite dopamine neurons by hyperpolarization of local interneurons. Journal of Neuroscience, 12, 483–488. Jones, S., & Bonci, A. (2005). Synaptic plasticity and drug addiction. Current Opinion in Pharmacology, 5, 20–25. Kalivas, P. W., & Duffy, P. (1993). Time course of extracellular dopamine and behavioral sensitization to cocaine: Pt. I. Dopamine axon terminals. Journal of Neuroscience, 13, 266–275. Kalivas, P. W., & Duffy, P. (1998). Repeated cocaine administration alters extracellular glutamate in the ventral tegmental area. Journal of Neurochemistry, 70, 1497–1502. Kalivas, P. W., & O’Brien, C. (2008). Drug addiction as a pathology of staged neuroplasticity. Neuropsychopharmacology, 33, 166–180. Kalivas, P. W., & Stewart, J. (1991). Dopamine transmission in the initiation and expression of drug- and stress-induced sensitization of motor activity. Brain Research Brain Research Reviews, 16, 233–244. Kalivas, P. W., & Volkow, N. D. (2005). The neural basis of addiction: A pathology of motivation and choice. American Journal of Psychiatry, 162, 1403–1413. Kalivas, P. W., Volkow, N., & Seamans, J. (2005). Unmanageable motivation in addiction: A pathology in prefrontal-accumbens glutamate transmission. Neuron, 45, 647–650. Kelley, A. E. (2004). Memory and addiction: Shared neural circuitry and molecular mechanisms. Neuron, 44, 161–179. Koob, G. F., Ahmed, S. H., Boutrel, B., Chen, S. A., Kenny, P. J., Markou, A., et al. (2004). Neurobiological mechanisms in the transition from drug use to drug dependence. Neuroscience and Biobehavioral Reviews, 27, 739–749. Koob, G. F., & Kreek, M. J. (2007). Stress, dysregulation of drug reward pathways, and the transition to drug dependence. American Journal of Psychiatry, 164, 1149–1159. Koob, G. F., & LeMoal, M. (2001). Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology, 24, 97–129. Koob, G. F., & Weiss, F. (1990). Pharmacology of drug self-administration. Alcohol, 7, 193–197.

8/17/09 5:31:48 PM

1150 Addictive Processes LaRowe, S. D., Myrick, H., Hedden, S., Mardikian, P., Saladin, M., McRae, A., et al. (2007). Is cocaine desire reduced by N-acetylcysteine? American Journal of Psychiatry, 164, 1115–1117.

and the American society of addiction medicine to study the definition and criteria for the diagnosis of alcoholism. Journal of the American Medical Association, 268, 1012–1014.

Lecca, D., Cacciapaglia, F., Valentini, V., Acquas, E., & Di Chiara, G. (2007). Differential neurochemical and behavioral adaptation to cocaine after response contingent and noncontingent exposure in the rat. Psychopharmacology, 191, 653–667.

Nakajima, S. (1989). Subtypes of dopamine receptors involved in the mechanism of reinforcement. Neuroscience and Biobehavioral Reviews, 13, 123–128.

Lecca, D., Cacciapaglia, F., Valentini, V., Gronli, J., Spiga, S., & Di Chiara, G. (2006). Preferential increase of extracellular dopamine in the rat nucleus accumbens shell as compared to that in the core during acquisition and maintenance of intravenous nicotine self-administration. Psychopharmacology, 184, 435–446. Lecca, D., Valentini, V., Cacciapaglia, F., Acquas, E., & Di Chiara, G. (2007). Reciprocal effects of response contingent and noncontingent intravenous heroin on in vivo nucleus accumbens shell versus core dopamine in the rat: A repeated sampling microdialysis study. Psychopharmacology, 194, 103–116. Li, K. W., Jiminez, C. R., van der Schors, R. C., Hornshaw, M. P., Schoffelmeer, A. N., & Smit, A. B. (2006). Intermittent administration of morphine alters protein expression in rat nucleus accumbens. Proteomics, 6, 2003–2008. Lynch, W. J., & Taylor, J. R. (2005). Persistent changes in motivation to self-administer cocaine following modulation of cyclic AMP-dependent protein kinase A (PKA) activity in the nucleus accumbens. European Journal of Neuroscience, 22, 1214–1220. Maas, L. C., Lukas, S. E., Kaufman, M. J., Weiss, R. D., Daniels, S. L., Rogers, V. W., et al. (1998). Functional magnetic resonance imaging of human brain activation during cue-induced cocaine craving. American Journal of Psychiatry, 155, 124–126. Margolis, E. B., Hjelmstad, G. O., Bonci, A., & Fields, H. L. (2003). Kappa-opioid agonists directly inhibit midbrain dopaminergic neurons. Journal of Neuroscience, 23, 9981–9986. Marinelli, M., & Piazza, P. V. (2002). Interaction between glucocorticoid hormones, stress, and psychostimulant drugs. European Journal of Neuroscience, 16, 387–394. Martinez, D., Broft, A., Foltin, R. W., Slifstein, M., Hwang, D. R., Huang, Y., et al. (2004). Cocaine dependence and D2 receptor availability in the functional subdivisions of the striatum: Relationship with cocaineseeking behavior. Neuropsychopharmacology, 29, 1190–1202. McClung, C. A., Ulery, P. G., Perrotti, L. I., Zachariou, V., Berton, O., & Nestler, E. J. (2004). DeltaFosB: A molecular switch for long-term adaptation in the brain. Brain Research Molecular Brain Research, 132, 146–154. McFarland, K., Davidge, S. B., Lapish, C. C., & Kalivas, P. W. (2004). Limbic and motor circuitry underlying footshock-induced reinstatement of cocaine-seeking behavior. Journal of Neuroscience, 24, 1551–1560. McFarland, K., & Kalivas, P. W. (2001). The circuitry mediating cocaineinduced reinstatement of drug-seeking behavior. Journal of Neuroscience, 21, 8655–8663. McFarland, K., Lapish, C. C., & Kalivas, P. W. (2003). Prefrontal glutamate release into the core of the nucleus accumbens mediates cocaine-induced reinstatement of drug-seeking behavior. Journal of Neuroscience, 23, 3531–3537. McLaughlin, J., & See, R. E. (2003). Selective inactivation of the dorsomedial prefrontal cortex and the basolateral amygdala attenuates conditioned-cued reinstatement of extinguished cocaine-seeking behavior in rats. Psychopharmacology, 168, 57–65. Moller, C., Wiklund, L., Thorsell, A., Hyytia, P., & Heilig, M. (1997). Decreased experimental anxiety and voluntary ethanol consumption in rats following central but not basolateral amygdala lesions. Brain Research, 760, 94–101. Morse, R. M., & Flavin, D. K. (1992). The definition of alcoholism: The joint committee of the national council on alcoholism and drug dependence

c58.indd Sec5:1150

Nestler, E. J. (2001). Molecular basis of long-term plasticity underlying addiction. Nature Reviews Neuroscience, 2, 119–128. Nestler, E. J. (2004). Historical review: Molecular and cellular mechanisms of opiate and cocaine addiction. Trends in Pharmacological Sciences, 7, 462–466. Nestler, E. J., Kelz, M. B., & Chen, J. (1999). DeltaFosB: A molecular mediator of long-term neural and behavioral plasticity. Brain Research, 835, 10–17. Neve, K. A., Seamans, J. K., & Trantham-Davidson, H. (2004). Dopamine receptor signaling. Journal of Receptor and Signal Transduction Research, 24, 165–205. Olive, M. F., Koenig, H. N., Nannini, M. A., & Hodge, C. W. (2001). Stimulation of endorphin neurotransmission in the nucleus accumbens by ethanol, cocaine, and amphetamine. Journal of Neuroscience, 21, RC184. Parent, M., Bush, D., Rauw, G., Master, S., Vaccarino, F., & Baker, G. (2001). Analysis of amino acids and catecholamines, 5-hydroxytryptamine, and their metabolites in brain areas in the rat using in vivo microdialysis. Methods, 23, 11–20. Pierce, R. C., Bell, K., Duffy, P., & Kalivas, P. W. (1996). Repeated cocaine augments excitatory amino acid transmission in the nucleus accumbens only in rats having developed behavioral sensitization. Journal of Neuroscience, 16, 1550–1560. Ritz, M. C., Lamb, R. J., Goldberg, S. R., & Kuhar, M. J. (1987, September 4). Cocaine receptors on dopamine transporters are related to self-administration of cocaine. Science, 237, 1219–1223. Roberts, D. C. S. (2005). Preclinical evidence for GABAB agonists as a pharmacotherapy for cocaine addiction. Physiology and Behavior, 86, 18–20. Roberts, D. C. S., Morgan, D., & Liu, Y. (2007). How to make a rat addicted to cocaine. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 31(8), 1614–1624. Robinson, T. E., & Berridge, K. C. (2003). Addiction. Annual Reviews of Psychology, 54, 25–53. Robinson, T. E., Jurson, P. A., Bennett, J. A., & Bentgen, K. M. (1988). Persistent sensitization of dopamine neurotransmission in ventral striatum (nucleus accumbens) produced by prior experience with (⫹)-amphetamine: A microdialysis study in freely moving rats. Brain Research, 462, 211–222. Robinson, T. E., & Kolb, B. (2004). Structural plasticity associated with exposure to drugs of abuse. Neuropharmacology, 47, 33–46. Rogers, J. L., & See, R. E. (2007). Selective inactivation of the ventral hippocampus attenuates cue-induced and cocaine-primed reinstatement of drug-seeking in rats. Neurobiology of Learning and Memory, 87, 688–692. Ron, D., & Jurd, R. (2005). The “ups and downs” of signaling cascades in addiction. Science’s Signal Transduction Knowledge Environment, 2005, re14. Roth, M. E., Cosgrove, K. P., & Carroll, M. E. (2004). Sex differences in the vulnerability to drug abuse: A review of preclinical studies. Neuroscience and Biobehavioral Reviews, 28, 533–546. Sanchis-Segura, C., & Spanagel, R. (2006). Behavioural assessment of drug reinforcement and addictive features in rodents: An overview. Addiction Biology, 11, 2–38. Self, D. W., Genova, L. M., Hope, B. T., Barnhart, W. J., Spencer, J. J., & Nestler, E. J. (1998). Involvement of cAMP-dependent protein kinase

8/17/09 5:31:48 PM

References 1151 in the nucleus accumbens in cocaine self-administration and relapse of cocaine-seeking behavior. Journal of Neuroscience, 18, 1848–1859. Shaham, Y., Shalev, U., Lu, L., de Wit, H., & Stewart, J. (2003). The reinstatement model of drug relapse: History, methodology and major findings. Psychopharmacology, 168, 3–20. Shalev, U., Grimm, J. W., & Shaham, Y. (2002). Neurobiology of relapse to heroin and cocaine seeking: A review. Pharmacology Review, 54, 1–42. Sibley, D. R., Monsma, F. J., & Shen, Y. (1993). Molecular neurobiology of dopaminergic receptors. International Review of Neurobiology, 35, 391–415. Siegelbaum, S. A., Schwartz, J. H., & Kandel, E. R. (2000). Modulation of synaptic transmission: Second messengers. In E. R. Kandel, J. H. Schwartz, & T. M. Jessell (Eds.), Principles of neural science (pp. 229–252). New York: McGraw-Hill. Sizemore, G. M., Co, C., Koves, T. R., Martin, T. J., & Smith, J. E. (2004). Time-dependent recovery from the effects of 6-hydroxydopamine lesions of the rat nucleus accumbens on cocaine self-administration and the levels of dopamine in microdialysates. Psychopharmacology, 171, 413–420.

Volkow, N. D., Fowler, J. S., & Wang, G. J. (2004). The addicted human brain viewed in light of imaging studies: Brain circuits and treatment strategies. Neuropharmacology, 47, 3–13. Volkow, N. D., Fowler, J. S., Wang, G. J., Hitzemann, R., Logan, J., Schlyer, D. J., et al. (1993). Decreased dopamine D2 receptor availability is associated with reduced frontal metabolism in cocaine abusers. Synapse, 14, 169–177. Volkow, N. D., Hitzemann, R., Wang, G. J., Fowler, J. S., Burr, G., Pascani, K., et al. (1992). Long-term frontal brain metabolic changes in cocaine abusers. Synapse, 11, 184–190. Volkow, N. D., & Li, T.-K. (2004). Drug addiction: The neurobiology of behaviour gone awry. Nature Reviews Neuroscience, 5, 963–970. Wexler, B. E., Gottschalk, C. H., Fulbright, R. K., Prohovnik, I., Lacadie, C. M., Rounsaville, B. J., et al. (2001). Functional magnetic resonance imaging of cocaine craving. American Journal of Psychiatry, 158, 86–95.

Sveninngsson P., Nairn, A. C., & Greengard, P. (2005). DARPP-32 mediates the actions of multiple drugs of abuse. Journal of the American Association of Pharmaceutical Scientists, 7, E353–E360.

Whitelaw, R. B., Markou, A., Robbins, T. W., & Everitt, B. J. (1996). Excitotoxic lesions of the basolateral amygdala impair the acquisition of cocaine-seeking behavior under a second-order schedule of reinforcement. Psychopharmacology, 127, 213–224.

Szumlinski, K. K., Dehoff, M. H., Kang, S. H., Frys, K. A., Lominac, K. D., Klugmann, M., et al. (2004). Homer proteins regulate sensitivity to cocaine. Neuron, 43, 401–413.

Williams, K., Wu, T., Colangelo, C., & Nairn, A. C. (2004). Recent advances in neuroproteomics and potential application to studies of drug addiction. Neuropharmacology, 47(Suppl. 1), 148–166.

Tang, X.-C., McFarland, K., Cagle, S., & Kalivas, P. W. (2005). Cocaineinduced reinstatement requires endogenous stimulation of mu-opioid receptors in the ventral pallidum. Journal of Neuroscience, 25, 4512–4520.

Wise, R. A. (1996). Neurobiology of addiction. Current Opinion Neurobiology, 6, 243–251.

Thomas, K. L., Arroyo, M., & Everitt, B. J. (2003). Induction of the learning and plasticity-associated gene Zif268 following exposure to a discrete cocaine-associated stimulus. European Journal of Neuroscience, 17, 1964–1972. Thomas, K. L., & Everitt, B. J. (2001). Limbic-cortical-ventral striatal activation during retrieval of a discrete cocaine-associated stimulus: A cellular imaging study with gamma protein kinase C expression. Journal of Neuroscience, 21, 2526–2535. Toda, S., Shen, H.-W., Peters, J., Cagle, S., & Kalivas, P. W. (2006). Cocaine increases actin cycling: Effects in the reinstatement model of drug seeking. Journal of Neuroscience, 26, 1579–1587. Torregrossa, M. M., & Kalivas, P. W. (2007). Microdialysis and the neurochemistry of addiction. Pharmacology Biochemistry and Behavior, 90(2), 261–272. Tzschentke, T. M. (2007). Measuring reward with the conditioned place preference (CPP) paradigm: Update of the last decade. Addiction Biology, 12, 227–462. Vanderschuren, L. J., & Kalivas, P. W. (2000). Alterations in dopaminergic and glutamatergic transmission in the induction and expression of behavioral sensitization: A critical review of preclinical studies. Psychopharmacology, 151, 99–120. Vezina, P., Giovino, A. A., Wise, R. A., & Stewart, J. (1989). Environmentspecific cross-sensitization between the locomotor activating effects of morphine and amphetamine. Pharmacology Biochemistry and Behavior, 32, 581–584. Vezina, P., & Stewart, J. (1990). Amphetamine administered to the ventral tegmental area but not to the nucleus accumbens sensitizes rats to systemic morphine: Lack of conditioned effects. Brain Research, 516, 99–106.

c58.indd Sec5:1151

Volkow, N. D., Fowler, J. S., & Wang, G. J. (1999). Imaging studies on the role of dopamine in cocaine reinforcement and addiction in humans. Journal of Psychopharmacology, 13, 337–345.

Wolf, M. E., White, F. J., Nassar, R., Brooderson, R. J., & Khansa, M. R. (1993). Differential development of autoreceptor subsensitivity and enhanced dopamine release during amphetamine sensitization. Journal of Pharmacology and Experimental Therapeutics, 264, 249–255. Xi, Z.-X., Gilbert, J. G., Peng, X.-Q., Pak, A. C., Li, X., & Gardner, E. L. (2006). Cannabinoid CB1 receptor antagonist AM251 inhibits cocaineprimed relapse in rats: Role of glutamate in the nucleus accumbens. Journal of Neuroscience, 26, 8531–8536. Zachariou, V., Benoit-Marand, M., Allen, P. B., Ingrassia, P., Fienberg, A. A., Gonon, F., et al. (2002). Reduction of cocaine place preference in mice lacking the protein phosphatase 1 inhibitors DARPP 32 or Inhibitor 1. Biological Psychiatry, 51, 612–620. Zachariou, V., Sgambato-Faure, V., Sasaki, T., Sveninngsson, P., Berton, O., Fienberg, A. A., et al. (2006). Phosphorylation of DARPP-32 at Threonine-34 is required for cocaine action. Neuropsychopharmacology, 31, 555–562. Zhang, Y., Loonam, T. M., Noailles, P. A., & Angulo, J. A. (2001). Comparison of cocaine- and methamphetamine-evoked dopamine and glutamate overflow in somatodendritic and terminal field regions of the rat brain during acute, chronic, and early withdrawal conditions. Annals of the New York Academy of Science, 937, 93–120. Zhang, Y., Sveninngsson, P., Picetti, R., Schlussman, S. D., Nairn, A. C., Ho, A., et al. (2006). Cocaine self-administration in mice is inversely related to phosphorylation at Thr34 (protein kinase A site) and Ser130 (kinase CK1 site) of DARPP-32. Journal of Neuroscience, 26, 2645–2651. Zhou, W., & Kalivas, P. W. (2007). N-Acetylcysteine reduces extinction responding and induces enduring reductions in cue- and heroin-induced drug-seeking. Biological Psychiatry, 63(3), 338–340. Zoumakis, E., Rice, K. C., Gold, P. W., & Chrousos, G. P. (2006). Potential uses of corticotropin-releasing hormone antagonists. Annals of the New York Academy of Science, 1083, 239–251.

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Chapter 59

Cognitive Neurology VAUGHAN BELL AND PETER W. HALLIGAN

point of view, cognitive systems are best viewed as a series of related functional systems (e.g., language, memory, attention executive) all of which can be impaired differentially (depending on age, location, and extent) following acquired brain damage. Although extensive neurological damage following stroke or head injury typically impairs several interacting cognitive systems, relatively discrete neuropathologies produce more selective impairments. The observed associations and dissociations in the patterns of impairments are subsequently used to infer the functional architecture of the brain, together with converging evidence from other behavioral and anatomical studies from normal participants and neurological patients. Cognitive neurology is characterized by the twin focus on clinical and basic (cognitive) research questions. By considering traditional clinical and neurological syndromes in terms of damage to known cognitive systems, investigators can move beyond mere description of symptomatology and meaningfully link cognitive deficits to impaired neural processes. This was well described by Basso and Marangolo (2000, p. 228) who wrote:

Cognitive neurology is a discipline that draws on cognitive neuroscience to reveal the nature of both clinical disorders and the functional architecture of the mind in an experimentally testable way (Cappa, Abutalebi, Demonet, Fletcher, & Garrard, 2008). It is informed by theories of neural and cognitive mechanisms that underlie mental processes and their behavioral manifestations and, like all bridging disciplines, comprises a broad range of methods and approaches. Central to this approach, however, is the reciprocal and ongoing relationship whereby cognitive neuroscience can inform neurology and clinical findings can enlighten cognitive research. Moreover, as a recent addition to the cognitive neurosciences, cognitive neurology is well placed to exert a powerful influence on basic science, clinical research, and rehabilitation by integrating complimentary strengths and methods from a number of key cognitive fields. As a testament to its interdisciplinary nature, cognitive neurology connects knowledge and methods from cognitive neuropsychology, where patterns of performance produced by brain damage are used to develop and evaluate theories of normal function (Caramazza & Coltheart, 2006); behavioral neurology, a subspecialty of neurology concerned with understanding the phenomenology, pathophysiology, diagnosis, and treatment of cognitive, emotional, and behavioral disturbances in individuals with recognized neurological disorders (Silver, 2006; see Chapter 66); and cognitive neuroscience, including localization methods, which harness the powerful spatial and temporal resolution afforded by modern technologies such as event-related potentials (ERP), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) and transcranial magnetic stimulation (TMS) (Gazzaniga, Ivry, & Mangun, 2002).

The most important contribution of cognitive neuropsychology . . . lies in the massive reduction of the theoretically motivated choices left open to the therapist. Clearly articulated and detailed hypotheses about representations and processing of cognitive functions allow rejection of all those strategies for treatment that are not theoretically justified. The more detailed the cognitive model, the narrower the spectrum of rationally motivated treatments; whereas the less fine-grained the cognitive model, the greater the number of theoretically justifiable therapeutic interventions.

Consequently, clinical terms such as dyslexia, dysphasia, amnesia, or visual neglect are not explanations in themselves but rather shorthand descriptions for different types of behavior that stand in need of a cognitive explanation. A major focus of cognitive neurology is the development of theories of how healthy systems break down with the intention of using the observed impairments to inform mechanisms,

Like its related subfields, cognitive neurology has its roots in the cognitive revolution of the 1960s and 1970s, where the success of information processing theories of the mind provided a framework for linking behavior and psychology to identifiable brain networks. From a conceptual 1152

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diagnosis, assessment, and potential interventions. This approach holds considerable promise for advancing the fields of functional cognition (Donovan et al., 2008), clinical diagnosis (Cappa et al., 2008), and selectively targeted interventions (Halligan & Wade, 2005). Given that several areas related to cognitive neurology are covered elsewhere in the handbook (e.g., see chapters covering language/language disorders, stroke and recovery, memory, attention, spatial perception and consciousness) the aim of this chapter is provide a broad conceptual overview interspersed with several selective and in-depth considerations of common clinical conditions. For a comprehensive review the interested reader is directed to Cappa et al. (2008); Hodges (2007); Halligan, Kischka, and Marshall (2004); and Mesulam (2000).

LEARNING FROM NEUROLOGICAL DISSOCIATIONS Cognitive neuropsychology has achieved considerable understanding of the functional architecture of cognitive systems by charting dissociations between cognitive tasks in patients with selective brain damage (Shallice, 1988). When attempting to understand complex cognitive systems, examples of robust dissociations (where neurological damage A affects cognitive process X but not cognitive process Y) provide a useful tool. Examples of double dissociation are considered evidence of two functionally independent processes. Naturally occurring dissociations between task performances in neurological patients have provided valuable insights into the intact and damaged mechanisms in language (Margolin, 1991), amnesia (Cermak, 1982), dyslexia (Coslett & Saffran, 1989), prosopagnosia (Young, 1994), and neglect (Halligan & Marshall, 1994), to name but a few. Some of the most striking and theoretically important dissociations in neuropsychology result from disconnections between conscious or explicit awareness (e.g., what the patient reports) and nonconscious or implicit processing (e.g., how the patient performs), and these merit particular consideration. Recording patient reports is a relatively straightforward process in most cases, but evidence for implicit psychological processing has traditionally been ascertained using a combination of at least three different methods: (1) forced choice methods—where the patient is requested to guess or indicate a preference, (2) evaluating the extent to which selective primes or cues in the affected modality modulate or interact with responses on the nonaffected side, and (3) by directly measuring the physiological or autonomic responses.

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Traditionally, the method most commonly used to demonstrate dissociations involves comparing a patient’s subjective report with their behavioral or physiological performance. For example, in the case of prosopagnosia (a disorder of face perception where the ability to recognize faces is impaired), some patients demonstrate differential electrical skin conductance or evoked potentials to familiar faces despite being unable to explicitly identify them (Bauer, 1984; Tranel & Damasio, 1985). In the case of memory, amnesic patients may show significant improvements in overall accuracy when a test is repeated (practice effects), despite failing to explicitly recall the test or its content. Such distinctions make it necessary to qualify amnesia as an impairment of conscious recollection rather than as a global failure to retain it (Moscovitch, Winocur, & McLachlan, 1986). In aphasia, patients who fail tests of comprehension may show normal semantic priming and semantic context effects on lexical decision tasks (Milberg, Blumstein, & Dworetsky, 1987); in dyslexia, patients who cannot read when tested explicitly can nevertheless guess correctly what the words denote using drawings (Shallice & Saffran, 1986). Blindsight is one of the better known dissociations of consciousness reported in a small number of patients who show impressive intact visual processing in their blind visual field (at levels significantly above chance) despite a lack of phenomenological awareness for the location of stimuli when requested to guess (Stoerig, 1996; Weiskrantz, 1986). Until the 1970s, it was typically assumed that brain injury involved damage to the primary visual areas and consequently produced permanent loss of vision for selective parts of the visual field. Assessing visual field deficits involved asking the patient to report with eyes fixated on a central target, what they could see when stimuli at different locations were presented in the peripheral fields. Although demonstrably unaware of targets in their affected field, some patients were able to indicate by pointing or moving their eyes (and when requested to guess in a forced choice experiment), the location of targets in their blind field (Weiskrantz, Warrington, Sanders, & Marshall, 1974). Although such patients clearly perceive more than might be expected, blindsight does not appear to confer any functional benefit for the patient (Weiskrantz, 1991). Evidence of blindsight has also been found using skin conductance performance (Zihl, Tretter, & Singer, 1980) and altered pupil size (Weiskrantz, 1990). Rafal, Smith, Krantz, Cohen, and Brennan (1990) demonstrated that unseen stimuli presented to the blind hemifield had the effect of inhibiting the latency of saccades to the seen stimulus in the intact field. Studies of blindsight indicate that the processing of visual stimuli can take place even though there is no phenomenological awareness by the

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subject. Anatomical and physiological evidence suggest that some forms of blindsight may rely on intact residual visual ability that is mediated subcortically (Stoerig, 1996; Weiskrantz, 1986). Other forms may be explained in terms of a disconnect between specialized areas in the visual cortex (Zeki, 1993).

ASSESSMENT, NEUROPSYCHOLOGICAL TESTING AND NEUROIMAGING Central to cognitive neurology is the assessment and quantification in terms of the impact to known cognitive structures of neurological disturbance. This requires understanding how neuropsychological testing and clinical neuroimaging complement each other to inform a comprehensive clinical picture. Importantly, there is no fixed contribution that each method makes because the scope of each procedure changes with the arrival of new conceptual and technological developments. For example, neuropsychological testing is no longer the primary method for localizing brain lesions owing to the wide availability of structural and functional brain imaging, although the advent of the clinical applications of functional neuroimaging has meant that well-designed psychometric tasks are now key to uncovering meaningful functional brain networks in the latest brain scanners. Similarly, the fact that pathology can be characterized entirely at the cognitive level (e.g., dysexecutive syndrome), the neurological level (e.g., glioma), or a mixture of both (e.g., vascular dementia) means that the contribution of each approach to the diagnosis or conceptual formulation of the disorder depends partly on the presenting clinical problem and reasons for assessment. One of the key functions of assessment is to formulate a working hypothesis regarding the areas of intact strength and weakness in functioning that in turn provides for setting appropriate goals for effective intervention (Byng, Kay, Edmundson, & Scott, 1990; Howard & Hatfield, 1987). While this is not the sole role of assessment, the adequacy of assessments for characterizing the underlying condition in cognitive terms and informing the rehabilitation process is clearly critical. The clinical aims of both neuropsychological testing and neuroimaging typically focus on three main areas: 1. Diagnosis to determine the nature and extent of the underlying problem in both clinical and cognitive terms. 2. Impact to gauge the effect of the impairment on everyday functioning and cognitive ability.

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3. Course/outcome to measure and predict change over time, either from premorbid levels or throughout the progression of the recovery. When attempting to answer these questions, the clinical team will typically integrate the patient’s history and presentation with the results of neuropsychology and relevant neuroimaging assessments to arrive at a well-rounded formulation based on a set of well-defined clinical questions, although clinical reality dictates that the assessment might need to be based on the best available evidence and often proceeds in an iterative manner. Key aspects of a patient’s history include their social and medical history, paying particular attention to any personal or family history concerning educational attainment, employment, developmental or idiopathic neurological disorder, dementia, or psychiatric illness. Similarly, the onset and course of the problem in an individual patient is noted alongside any results of earlier assessments and previous experience of the tests, assessments, or environment. Key aspects of presentation include the signs (behavioral indicators suggestive of underlying disease) and symptoms (subjective reports of ill health by patient), mental state, insight, other medical problems, understanding of the purpose and possible outcomes of the assessment, comprehension of the test instructions and expressive language, level of concentration, level of motivation during each test, current mood, and ongoing pain. Simple cognitive screening tests (such as the Minnesota Mental State Examination, MMSE) or simple bedside tests may be conducted by most suitably trained clinicians but more thorough cognitive testing requires the involvement of a clinical neuropsychologist. A general overview of the types of clinical assessment employed by cognitive neurologists is provided in Cappa (Chapter 2; 2001) in addition, excellent guides to neuropsychological testing are available (Hodges, 2007; Snyder, Nussbaum, & Robins, 2005) as well as more comprehensive handbooks (Lezak, Howieson, Loring, Hannay, & Fischer, 2004; Strauss, Sherman, & Spreen, 2006). In combination with neuropsychological test results, structural scans are important for inferring links between cognitive deficits and detectable lesions in individuals (see Figure 59.1), and can also be useful for constraining test interpretation (e.g., an individual who performs poorly on an executive test but has only posterior lesions might suggest that the deficit is one that involves early vision rather than the executive system). Functional neuroimaging offers accessible reliable measures of hemodynamic changes— blood flow in the case of positron emission tomography (PET) and blood oxygenation in the case of functional magnetic resonance imaging (fMRI)—in response to selective cognitive task engagement. Both fMRI and PET

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Figure 59.1 Axial A: and coronal B: slices from the MRI scans of a professional musician who suffered a stroke and experienced a selective loss in musical ability. Note: The patient lost the ability to discriminate or reproduce rhythms but showed preserved metric judgment and normal performance in all aspects of melodic processing. The scan shows a left temporo-parietal infarct in the territory of the superior temporal gyrus, the posterior part of the middle temporal gyrus and the inferior parietal lobe. From “Receptive Amusia: Temporal Auditory Processing Deficit in a Professional Musician Following a Left Temporo-Parietal Lesion,” by M. Di Pietro, M. Laganaro, B. Leemann, and A. Schnider, 2004, Neuropsychologia, 42, pp. 868–877. Reprinted with permission.

provide indirect measures of synaptic activity and neural firing and are extensively used to characterize the neural bases of intact and impaired neural systems underlying different sensory and cognition tasks (see Figure 59.1). Resting state functional scans (typically using positron emission tomography (PET) or single photon emission computed tomography (SPECT), and more recently perfusion MRI) can similarly provide information on whether there are disturbances of cerebral perfusion, suggesting areas which might be consistently under- or overactive. Due to individual variation in the neuroanatomical areas that support particular cognitive functions and the fact that not all neurocognitive impairments are detectable on standard clinical scans, there has been an increased interest in applying task-based functional neuroimaging, more typically used in research on normal neurocognition, for addressing clinical problems in individual patients. Much research has focused on replacing the Wada test for establishing language lateralization and postoperative outcome assessment. The Wada test, or the intracarotid sodium amobarbital procedure (ISAP), involves neuropsychological testing of specific hemispheric functions while one, and subsequently the other, hemisphere of the brain is functionally impaired by the injection of a barbiturate into the ipsilateral carotid artery. The Wada test is also commonly used to determine likely memory impairment after a proposed unilateral temporal lobectomy in cases of intractable epilepsy. Although effective, the procedure is expensive and carries an approximate 1% morbidity risk

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(Baxendale, 2000) so the development of noninvasive alternatives offers significant advantage. The application of neuroimaging methods has been promising but, so far, none are in a position to replace the Wada test. This is partly because of a lack of research data, but partly because activation patterns can be influenced by task, analysis technique, and noise in the data (Abou-Khalil, 2007). Related difficulties affect all such attempts to apply functional neuroimaging to individual patients, which has largely been developed to determine average activation over a group of people. Data acquisition artifacts that are likely to be of minor influence when data is averaged across participants (such as head movement or minor anatomical differences) have a much larger impact when only one person is being scanned. Similarly, analyses used for groups of young healthy participants, such as the reliance on a constant blood oxygenation level dependent (BOLD) signal response, may not apply so readily to children or older patients, for whom cerebral blow flow rate is known to be significantly related to age (Ackerstaff, Keunen, van Pelt, Montauban van Swijndregt, & Stijnen, 1990; Schöning & Hartig, 1996). To overcome similar sorts of issues, neuropsychological tests commonly employ standardized scoring and normreferenced performance comparisons, so that an individual’s performance can be seen alongside a relevant age, education, ethnicity, and/or gender matched comparison group. However, similar data for clinical function neuroimaging assessments is still rare and clinicians are encouraged to make their data available to others so these essential data sets can be created. Although neuropsychological tests typically provide an estimate of the performance level in different cognitive domains, equally important is the process by which individuals complete the task. For example, patients with differing pathologies may not differ in their final test score, but may show remarkable differences in the way they complete the test (Kaplan, 1988). A related pattern has also been observed in functional neuroimaging studies, where a difference in behavioral measures but not activation, or vice versa, has been found (Wilkinson & Halligan, 2004). Some tests may have a measure of process builtin, and others do not. Careful observation during testing may be the key to uncovering relevant cognitive deficits in these cases. When considering in-scanner clinical assessments, Desmond and Chen (2002) make several recommendations: (a) experimental tasks designed for research participants may be too taxing for patients, so recommend that a middle-ground compromise between collecting relevant data points and creating a valid task needs to be reached; (b) cognitive tasks may need practice outside the scanner;

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(c) to draw valid clinical conclusions, the tasks need to be norm referenced; and (d) standardization of image analysis methods need to be adopted.

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Attention—the mental process of selectively focusing on aspects of our environment including one’s body while ignoring or disattending from other things—is probably one of the most important cognitive processes, given that it pervades all aspects of cognitive life and, when compromised, provides for a wide range of debilitating consequences. The range of deficits stems from the fact that attention is not a single process but rather a set of interacting, albeit relatively autonomous, subprocesses vulnerable to damage with differing consequences. As assessments have been refined over the past decade, the negative impact of impaired attention for recovery and outcome has become increasingly clear. Consequently, the development of interventions designed to enhance natural recovery in these systems remains a pressing clinical goal (Robertson & Halligan, 1999). Clearly, a framework for understanding the functional organization of attention is vital for both the clinic and research laboratory. One influential model proposed by Posner and Petersen (1990) suggests three key specific functions of attention: 1. Spatial attention: The capacity to distinguish incoming signals from one spatial location. 2. Selective or focused attention: The ability to prioritize some types of information and to restrain others on the basis of an existing planned goal or a stored representation of a target. 3. Arousal/sustained attention: The ability to maintain an alert, ready state. Although these and similar taxonomies (e.g., Mirsky, Anthony, Duncan, Ahearn, & Kellam, 1991; Raz & Buhle, 2006; Van Zomeren, Brouwer, & Deelman, 1984) are capable of further fractionation, it is clear that one of the most important functional consequences is that attention modulates or “gates” activity in primary sensory areas of the brain (Desimone & Duncan, 1995) including vision (Moran & Desimone, 1985), audition (Woldorff et al., 1993), and somatoensory perception (Drevets et al., 1995). Attention as a cognitive process cannot be observed directly but rather its presence is detected by monitoring the systematic variation in performance of different attentiondemanding tasks. Posner (1980), using visual cueing paradigms, (see Figure 59.2) employed a simple but highly

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Figure 59.2 Posner ’s attentional cueing paradigm Note: Participants were instructed using a symbolic central cue (the arrow) that a target stimulus (a filled circle) would occur with 80% of chance in a given spatial location. Subjects were required to fixate a central mark, not move their eyes, and to press a key as fast as possible when the filled circle appeared. Covert attention was taken as the reaction time benefit when responding to a target that appeared in the attended location (valid trials) as opposed to a response to targets appearing in unattended locations (invalid trials) or to neutral trials. From Bottini and Paulesu (2003). Adapted with permission.

influential example of this approach. Here subjects were asked to maintain their gaze at the center of a screen and only press a button when they observed a designated target to appear. Subjects had no information where on the screen this target would be, although on a number of informative trials they received a directional or spatial cue as to probable location. These attentional cues, when accurate, produced significant reductions in overall reaction time, but when inaccurate, produced significant increases. Given that all other aspects of the task remained constant, the temporal differences were attributed to the movement or allocation of spatial attention. Attention has also been shown to operate on stimuli at differential levels of analysis depending on processing demands (Lavie, 1995). Such methods have led to clearer accounts of the capacities and limitations of normal human attention. When combined with the study of brain damaged patients, neuroimaging or neurophysiological techniques provided a working hypothesis regarding the neural basis of some cognitive abilities. Hemi-Inattention (Visuo-spatial Neglect) The most common and striking neurological condition to follow brain damage involving intentional processes is visuo-spatial or hemispatial neglect (Karnath, Miller, & Vallar, 2002). The neglect syndrome has become an established clinical entity that features prominently in most current texts of behavioral neurology and cognitive neuropsychology (Heilman & Valenstein, 2003). Not surprisingly,

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Acquired Disorders of Attention

it is also the area where rehabilitation is also well developed (Luaute, Halligan, Rode, Rossetti, & Boisson, 2006). Visual neglect refers to a person’s difficulty in detecting, acting on, or even thinking about information on one side (see Robertson & Halligan, 1999). People with visual neglect often fail to notice food on the left side of their plate, fail to dress or wash the left side of their body, have difficulty in imagining the left side of familiar objects, and, in some cases, even deny ownership of their own left limbs (Figure 59.3). A number of basic clinical observations have been established that inform our understanding of the brain’s representation of space, attention, and action (Buxbaum, 2006). The condition has been reported in the visual, auditory, tactile, and olfactory modalities (Halligan & Marshall, 1993), although the most extensive investigations typically concern visuospatial neglect (Figure 59.4). Left neglect after right hemisphere lesions are more frequent, severe and long lasting than right neglect after left hemisphere lesions. Neglect can affect personal (or body) space, peripersonal space (stimuli within reaching and grasping distance), and extrapersonal space (stimuli within walking distance). Although lateralized (left-right) visual neglect has attracted most research interest, comparable phenomena have been observed for the other two dimensions of space (radial and altitudinal neglect). Although classically associated with lesions to the right posterior parietal cortex (Heilman & Watson, 1977; Vallar & Perani, 1986), neglect has been observed following damage to a variety of brain structures including the right prefrontal cortex and subcortical areas (Damasio, Damasio, & Chui, 1980; Mesulam, 1981; Karnath, Ferber, & Himmelbach, 2001; Samuelsson, Jensen, Ekholm, Naver, & Blomstrand, 1997).

Figure 59.3 Illustration of left-sided visual neglect when copying.

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Figure 59.4 Cortical anatomical correlates of visuospatial neglect. Note: Cortical anatomical correlates of unilateral visuospatial neglect. Most anatomo-clinical correlation studies show that the lesions responsible involve right inferior parietal lobule (Brodmann areas BA [regions of the cortex defined based on their cytoarchitecture] 39 and 40, highlighted in black) and in particular the supramarginal gyrus, at the temporoparietal junction (black–grey area). Neglect after right frontal damage although less common is usually associated with lesions to the frontal premotor cortex, particularly BA 44 and ventral BA 6. Neglect has also been associated with damage to the more dorsal and medial regions of the frontal premotor cortex, and to the superior temporal gyrus. (From Halligan, Fink, Marshall, & Vallar, 2003, reprinted with permission).

Visuospatial neglect is often diagnosed on the basis of simple (bedside) tests such as cancellation, line bisection, copying, spontaneous drawing, reading, and writing; in many patients, the verbal description of complex visual images and topographic routes (generated from longterm memory) can also show lateralized neglect (Bisiach, Brouchon, Poncet, & Rusconi, 1993). Even with such relatively simple tasks as copying or spontaneous drawing, many qualitatively distinct patterns of impairment can present as lateralized neglect (Halligan & Marshall, 2001). Research over the past 30 years has convincingly shown that neglect is a protean disorder whose symptoms can selectively affect different sensory modalities, cognitive processes, spatial domains, and coordinated systems (Buxbaum, 2006; Halligan, Fink, Marshall, & Vallar, 2003). Deficits of attention, intention, global-local processing, spatial memory, and mental representation make it unlikely that this clinical syndrome can be traced back to the disruption of a single supramodal cognitive process (Vallar, 1998). Many of these clinical findings have been used to better understand the anatomical and functional architecture of the premorbid subsystems of spatial cognition, in particular: (a) neuropsychological structure of space, (b) relevant spatial frames of reference used prior to recognition, and (c) selective preservation of preattentive processes.

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Neuropsychological Structure of Space Although space extends seamlessly in three dimensions, it does not appear to be homogeneously represented in the brain. Embodied space can be behaviorally divided into at least three different regions: personal space, peripersonal space, and extrapersonal space (see Figure 59.5; Robertson & Halligan, 1999). Personal space involves the body and body surface: the space in and on where one can feel, touch, and within which one can comb one’s hair or scratch an itch. Peripersonal space is the working space beyond the torso but within arm’s reach. Extrapersonal space is beyond arm’s reach but one can obviously bring objects within peripersonal space by moving there or by deploying a tool. One can orient the eyes toward an object in extrapersonal space, point to it, or throw something at it. Evidence for the neurobiological distinction between peripersonal (near) and extrapersonal (far) space comes from a wide range of animal and human studies (Caramazza & Hillis, 1990; Previc, 1990) but some of the clearest evidence comes from patients with left neglect after right hemisphere lesions (Buxbaum, 2006; Rizzolatti, Berti, & Gallese, 2000). Personal Space Neglect of left personal space can occur without neglect of left peripersonal space (Guariglia & Antonucci, 1992) and vice versa (Beschin & Robertson, 1997). One of the first demonstrations of dissociation was reported by Brain (1941) in a case of right hemisphere glioblastoma where the patient was impaired in pointing to objects in near space without comparable difficulty for objects in

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Figure 59.5 Multiple representations of space demonstrated by clinical dissociations between different spatial domains (from Robertson & Halligan, 1999).

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far space. Typical manifestations of left personal neglect include failure to shave or groom the left side of the face, failure to adjust spectacles on the left side, and failure to notice the position of the left limbs and use them appropriately even when no significant motor weakness is present. By contrast, the ability to use left personal space without difficulty can be seen in the context of severe left neglect of peripersonal space as assessed by visual search tasks where the targets are displayed within arm’s reach (Beschin & Robertson, 1997). This double dissociation of personal and peripersonal neglect suggested that distinct neuronal circuits underlie how the two spaces are represented in the human brain. Performance difference in different space has also been shown to include imaginal or representational space (Beschin, Basso, & Della Sala, 2000; Ortigue, Mégevand, Perren, Landis, & Blanke, 2006). Peripersonal and Extrapersonal Space Similar double dissociations have been discovered between left neglect in peripersonal and in extrapersonal space. When lines of constant visual angle are bisected by a laser pen in near versus far space, some patients show accurate performance in far space but a significant rightward deviation in near space (Halligan & Marshall, 1991), while other patients show the reverse dissociation: far left neglect without near left neglect (Vuilleumier, Valenza, Mayer, Reverdin, & Landis, 1998). A study by ViaudDelmon, Brugger, and Landis (2007) shows how back space is also represented in patients suffering from spatial neglect and further underscores the distinction between motor and nonmotor space. It appears that acting in a particular spatial domain involves distinct neuronal representations of near or far space to become active. Nevertheless, a study by Pitzalis, Di Russo, Spinelli, and Zoccolotti (2001) employing both perceptual and motor versions of line bisection in near and far space argues against this view. The same patients were tested in all conditions. In both the perceptual and the motor tasks, some patients showed near left neglect without far left neglect and others the reverse dissociation. Thus, different accuracy of performance between spatial domains can be revealed by purely perceptual tasks. Furthermore, the patients showed similar degrees of impairment on the motor and the perceptual versions of line bisection. The coding of space as extrapersonal and peripersonal is not solely determined by the hand-reaching distance and can depend on how the brain represents action capabilities. Berti and Frassinetti (2000) showed that in a patient with demonstrable peripersonal space neglect, previously intact far space bisection (using a laser light pen) became as severe as neglect in the near space when the patient

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Acquired Disorders of Attention

performed the task using a stick as an artificial extension of the patient’s body and appeared to undergo a remapping of extrapersonal space as peripersonal space. Weiss et al. (2000) used PET to determine the functional anatomy involved when volunteers were requested to bisect lines and point to dots in peripersonal or extrapersonal space (see Figure 59.6). Twelve healthy right-handed male volunteers bisected lines or pointed to dots in near or far space using a laser pen. When performing either task in near space, subjects showed neural activity in the left dorsal occipital cortex, left intraparietal cortex, left ventral premotor cortex, and left thalamus. In far space, subjects showed activation of the ventral occipital cortex bilaterally

and the right medial temporal cortex. These findings provide physiological support for the clinically observed dissociations even when the motor components of the tasks were identical when performed in both spaces. Spatial Frames of Reference Systematic analysis of visual neglect over the past two decades has revealed significant insights into how attention can be allocated to object- and space-based representation in terms of differential spatial coordinate frames used in normal cognition. There is compelling evidence that egocentric space can be coded in different viewer-centered frames

Figure 59.6 Differential neural activity when performing tasks in NEAR space (B) and FAR space (C) . From Weiss et al, Brain 2000. Reprinted with permission. A: Experimental participant set-up. A computer monitor located within reaching range was used for visual stimulation in NEAR space .A second screen beyond reaching range was used for FAR space presentation. B: Sagittal and transverse views of relative rCBF increases associated with movement in NEAR space ( p  0.001 uncorrected). The lower rows are transverse SPM{Z} maps which have been superimposed upon the group mean MRI which was spatially normalized into the same stereotaxic space. C: Sagittal and transverse views of relative rCBF increases associated with movement in FAR space ( p  0.001 uncorrected). Actions involving FAR space resulted in activations in the ventral visuoperceptual processing stream, right medial temporal cortex (8) and bilateral ventral occipital cortex (6&7).

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of reference, including eye, head, torso, shoulder, arm- and hand-centered coordinates (Beschin, Cubelli, Della Sala, & Spinazzola, 1997). The terms left or right are consequently relative, since they can be defined with respect to different reference points (Buxbaum, 2006). Visuospatial attention can also operate in coordinate frameworks independent of the position of the observer. In object-based neglect, the left side of an object is ignored (Umilta, 2000). Some of the most convincing evidence for selective damage to object-based attention deployment can be found in the drawing and copying performance of neglect patients (see Figure 59.7). By contrast, object-centered coding (Driver & Halligan, 1991) of left and right concerns the intrinsic laterality of an object (e.g., English words have an intrinsic left to right sequence of letters). Examples of this form of coding in neglect have been elegantly demonstrated by Caramazza and Hillis (1990) in a left brain damaged patient with right neglect dyslexia. When reading, her errors were always located on the right side of the word irrespective of whether the words were presented horizontally, vertically, or even mirror-reversed. Finally, neglect findings have been used to both support and question features of Kosslyn’s (1994) analogue theory of visual mental images; that is, representations that produce the experience of seeing in the absence of sensory input. Within cognitive science, the debate about the depictive representational format of visual mental imagery clearly differentiates analogue (picture-like images with intrinsically spatial representational properties) from the propositional account (linguistic descriptions without inherently spatial properties) championed by Pylyshyn (1981). Moreover, a defining assumption of the former account is that perceptual and imagery processes share the same mental

Figure 59.7 copying.

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Illustrations of object-centered visual neglect in

operations and neural structures (Kosslyn & Thomson, 2003). Clinical accounts of visual neglect such as those by Bisiach and Luzzatti (1978) and others involving neglect of visual images in parallel with deficits in perception (Marshall & Halligan, 2002) have been used to support the analog claims. However, subsequent case reports describing selective lateralized breakdown of imaginal representation or imagery without corresponding deficits in perceptuo-motor performance question the close functional overlap of imagery and perception processes (Bartolomeo, 2002; Behrman, Winocur, & Moscovitch, 1992). Knowing without Knowing Investigations of neglect have contributed to the fascinating debate regarding the processing locus of attentional selection (Kanwisher & Wojciulik, 2000). Several studies of visual neglect have shown that different levels of preattentive processing up to the level of meaning can take place in the neglected field without conscious awareness (Driver & Vuilleumier, 2001). Even on line bisection (a traditional clinical measure), patients with left neglect show implicit sensitivity to manipulations of both stimulus and the visual background (Shulman, Alexander, McGlinchey-Berroth, & Milberg, 2002) confirming that preattentive visual capacities of figure ground and stimulus can influence explicit visual motor performance. Informally, many students of visuospatial neglect consider the condition to be a classic disorder of visual awareness— where awareness is equated to the psychological construct of attention (Posner, 1978). Unlike blindsight, which is elicited experimentally, left neglect occurs spontaneously and remains a major negative prognostic factor associated with poor performance on most functional recovery measures (Halligan & Robertson, 1992). However, with blindsight, there is considerable evidence that when tested indirectly, many patients can show some degree of information processing for the stimulus of the affected side (Berti & Rizzolatti, 1992; Marshall & Halligan, 1988; McGlinchey-Berroth, Milberg, Verfaellie, Alexander, & Kilduff, 1993; McIntosh et al., 2004). Evidence for this possibility of nonconscious perception in the case of neglect can be traced back to Kinsbourne and Warrington (1962) who reported a length effect in neglect dyslexia; reading errors maintained the length of words presented. Moreover, A. W. Ellis, Flude, and Young (1987) who replicated this finding suggested that although neglect selectively affected the coding of the identity of the left most letters, patients are still capable of coding letter position and overall length. A clinically similar but less well known phenomenon occurs in line bisection. When requested to bisect a line located in the center of a page

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Acquired Disorders of Attention

most patients with neglect show a displacement of absolute magnitude that is linearly related to line length (Halligan, 1995; Halligan & Marshall, 1988). This linear performance may be explained in terms of implicit processing of the visual information on the neglected side. Thus, the neglected end of the stimulus line may covertly influence the patient’s performance in deciding the subjective center of the line. In one of the first clinical cases reported by Marshall and Halligan (1988), patient PS, who had sustained a right hemisphere stroke, was presented with two line drawings of a house (see Figure 59.8) simultaneously, one of which had red flames emitting from the left side window. Requested to make same/different judgments between the two simultaneously presented pictures, PS reliably judged the two drawings identical. When asked several minutes later to select the house she would prefer to live in, she reliably chose the nonburning house with a high level of statistical significance, commenting that it was a “silly question” since both houses were identical. In other words, although PS was unable to perceive the crucial differences between the two houses (despite free movement of the head and eyes), she nevertheless appeared able to process some information in the hemispace contralateral to lesion that influenced her preference judgment (cf. Manning & Kartsounis, 1993). Later, more detailed studies by Berti and Rizzolatti (1992) and McGlinchey-Berroth et al. (1993) using cross field matching and priming experiments

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showed that implicit perception, up to the level of meaning, was possible in some patients with neglect. In the case of Berti and Rizzolatti (1992), patients who denied seeing anything in the left visual field nevertheless showed significantly shorter reaction times to the right field stimulus for the congruent rather than the noncongruent conditions. Marshall and Halligan (1994) showed evidence of a further type of dissociation between two forms of conscious perceptual awareness—again in a free vision task (Figure 59.9). In a series of experiments, they showed that a patient with neglect had a selective inability to analyze and copy accurately the left contours of geometric nonsense figures. These results were present even when there was a single vertical contour (to be copied) that divided a rectangle or a circle into two subfigures. A physically identical boundary was copied more accurately when it was cued as the right edge of the left subfigure than when it was cued as the left edge of the right subfigure. The results were interpreted in terms of demonstrating the presence of intact preattentive (global) figure-ground parsing despite gross impairment when focal attention was demanded and the right side of an object was only coded as a figure. In most cases, however, global processing of the visual world can no longer be used to direct automatic focal attention to spatial locals that require further focal analysis. Without this ability, local attention which is usually biased to the right will always represent too little of the visual

C

A

B

Figure 59.9 Preserved figure-ground segregation in visual neglect.

Figure 59.8 Illustration of covert processing in visual neglect (see Marshall & Halligan, 1988).

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Note: When asked to copy the top display, the patient only drew the right side of the black figure (C). However if requested to copy the left side of the same object when cued as the right side of the left white subfigure (previously ground), the left side could be accurately copied (A). Copies of the left side for the same right white subfigure (previously ground) always showed neglect of the details of the contour (B) (see Halligan, Fink, Marshall, & Vallar, 2003).

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world. However, once attention has been focused, the panoramic or global view is lost to conscious awareness. When focal attention is biased to the right as in the case of neglect, the patient is in no position to observe the absence of left-sided input. Even if selective attention can be voluntarily moved leftward, the necessary guiding framework provided by the global scale is no longer available. Consequently, patients no longer have any reason to continue to explore leftward. In these and other examples (Marshall & Halligan, 1995) where performance within an individual patient can be normal on one aspect and grossly impaired on another involving the same stimulus seconds later, left neglect may be regarded as a partial disconnection of conscious visual awareness where residual processes of the impaired right hemisphere cannot be used to constrain the performance of the intact left hemisphere in performing the designated task. Collectively, these findings from visual neglect highlight the danger of equating phenomenological conscious experience with the operation of the perceptual mechanisms involved. In the absence of apparent phenomenological awareness, there is evidence that many patients, when tested indirectly, may show some degree of information processing for the stimulus of the affected side in the case of neglect or blindsight. Although adequate cognitive accounts of awareness still remain to be developed (see Clare & Halligan, 2006; Dehaene & Naccache, 2001; Farah & Feinberg, 1997), productive contributions toward the emerging cognitive neuroscience of consciousness rely on pathologies of awareness and the tasks used to reveal them (Babinski, 1914; Bisiach & Berti, 1987; Forstl, Owen, & David, 1993; Prigatano & Schacter, 1991). These have also included examples from nonvisual modalities. For example, in the tactile modality, reports of blind touch (Lahav, 1993; Paillard, Michel, & Stelmach, 1983; Rossetti, Rode, & Boisson, 1995) or “numbsense” (Perenin & Rossetti, 1996) and “deaf hearing” (Michel, Peronnet, & Schott, 1980) have also been recorded. These reports lend further support to the concept of multichanneling sensory information already well established in the visual system and the realization that perception is not a unitary process but one subserved by several separable modules. While most theoretical studies have been concerned with showing what a patient can do without explicit awareness of their clinical condition using experimental task performance (Berti & Rizzolatti, 1992; Bisiach & Rusconi, 1990; Marshall & Halligan, 1988), many other clinical studies are primarily concerned with diagnostic issues (Cutting, 1978; S. J. Ellis & Small, 1994; Levine, Calvanio, & Rinn, 1991; Nathanson, Bergman, & Gordon, 1952) and characterizing the anatomical and functional consequences

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of their reported and behavioral unawareness (Pia, NeppiModona, Ricci, & Berti, 2004; Samuelsson et al., 1997; Stone, Halligan, & Greenwood, 1993). However, it is clear that a patient does not need to be explicitly unaware of their cognitive or neurological deficit at the level of verbal reporting to continue to demonstrate significant pathologies of awareness on formal testing. Several patients with intractable chronic neglect show what appears to be considerable conceptual and experiential insight into their deficit and its consequences while continuing to demonstrate neglect on selective tasks (Cantagallo & Della Sala, 1998). Moreover, stroke patients with anosognosia may verbally admit to being hemiplegic yet appear to ignore the consequences of such statements when planning and programming their functional motor activities (House & Hodges, 1988; Marcel, Tegnér, & Nimmo-Smith, 2004).

DISORDERS OF READING AND WRITING Acquired Reading Disorders Reading is a complex process that involves visual processing, access to semantics and phonology, and control of articulation. Since Marshall and Newcombe’s (1966, 1973) landmark studies on two patients with reading disorders after brain injury, knowledge of both the diversity of reading deficits and of how reading occurs in the normal brain has grown exponentially. Notably, while the literature makes clear distinctions between different reading deficits, they are rarely observed as totally distinct syndromes in individual patients and are often accompanied by other language or visual processing difficulties (Patterson & Lambon Ralph, 1999). This section focuses on acquired reading disorders because they have been the focus of most research in cognitive neurology, although developmental dyslexias are now being increasingly studied in the same context (Temple, 2006). Two neuropsychological models of normal reading currently form the basis of acquired dyslexia theories. The dual route model and its variations (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Rapcsak, Henry, Teague, Carnahan, & Beeson, 2007; Figure 59.10a) are largely based on observed post-brain injury dissociations between reading regular words (that follow the standard rules of pronunciation—such as ‘drink’), irregular words (that are exceptions to the normal rules of pronunciation—such as ‘chord’), and nonwords (pronounceable but meaningless letter strings—such as ‘lart’). These models suggest that there are two main routes for determining the identity of a word or letter string. The first lexical route is where a word

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Disorders of Reading and Writing

is recognized by matching the letter string to a directory of familiar words stored in memory, called the visual word form system. Once the visual word form is activated, this can activate the phonology (pronunciation) of the word either via the word’s meaning (the lexical-semantic route) or direct link to its phonological representation (the directlexical route). Alternatively, the nonlexical route derives the pronunciation of words by working out letter-sound associations. The triangle model of reading (Plaut, 1997; Seidenberg & McClelland, 1989; Figure 59.10B) takes a radically different approach using a connectionist model with three interconnected systems that represent the recognition of orthography (vision), phonology (pronunciation), and semantics (meaning). This model suggests that both nonwords and regular words are read aloud via the vision to phonology route, while reading aloud irregular words requires both the vision to semantics route and the semantics to phonology route. This latter account is notable because it suggests that disorders of reading can be understood in terms of the disruption to one or more of these processes without the need for procedures specific to reading itself. In other words, it suggests that acquired dyslexia is not a disorder of reading per se, but the result of damage to more general cognitive processes. Acquired dyslexia is often classified into peripheral or central types. Peripheral dyslexias are characterized by perceptual deficits that prevent the affected person from matching the visual representation of the word to the stored visual word form. Central dyslexias are where the impairment affects access to meaning or speech production after the point where the visual word form is activated. Peripheral Dyslexias Patients with peripheral dyslexia are impaired in reading text but have intact writing, speaking, spelling, listening comprehension, and recognition of orally spelled words. Pure alexia (also called letter-by-letter dyslexia, alexia without agraphia, spelling dyslexia, verbal dyslexia, word blindness, or letter confusability dyslexia) is a visual perceptual impairment in the processing of word and letter shapes and is often considered with the agnosias (Farah, 2004). It is typically associated with lesions centered on the left occipito-temporal junction and is often accompanied by a contralesional visual field impairment (homonymous hemianopia; Leff, Spitsyna, Plant, & Wise, 2006). It may result in letter-by-letter reading, where patients are required to identify each letter individually before comprehending the word, although even this process can be impaired in severe cases (Shallice & Rosazza, 2001). Neglect dyslexia can result from hemispatial neglect (see section on Attention) where patients miss the leftmost letters of a word, and is particularly apparent when

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attempting to read nonwords (di Pellegrino, Ladavas, & Galletti, 2002). Attentional dyslexia (sometimes called letter position dyslexia) is where the reading of single isolated words may be relatively well preserved, but with impaired reading of words in the context of other words or letters (Friedmann & Gvion, 2001). Central Dyslexias Deep dyslexia is, perhaps, the most studied of the acquired reading impairments and is often the most profound and complex (Coltheart, Patterson, & Marshall, 1987). The most striking feature is the tendency to make frequent semantic errors (reading uncle as cousin, for example) although visual errors (e.g., reading crowd as crown) are also typically present, and combined visual-semantic errors have been reported (e.g., reading earl as deaf). Patients are often also impaired in reading nonwords, function words, and are worse at reading less imageable words compared with more imageable words (e.g., trust is more difficult than tree). Furthermore, nouns are typically read better than adjectives, and adjectives better than verbs. In terms of the dual route model, deep dyslexia is likely to result from reading that is reliant on the lexical-semantic route because the other pathways are impaired (Coltheart, 2006). Deep dyslexia is commonly associated with large left hemisphere lesions that cover the frontotemperoparietal area (Lambon Ralph & Graham, 2000). Surface dyslexia is an impairment in the ability to read phonologically irregular words (such as chord or ache) while the reading of regular words (such as book or tree) and nonwords is relatively well preserved (A. W. Ellis, Lambon Ralph, Morris, & Hunter, 2000). It is most common in the dementias and particularly characteristic of semantic dementia (Hodges et al., 1999) although it is linked to left temporal damage even in acquired impairments (Vanier & Caplan, 1985). Phonological dyslexia is a selective impairment in reading nonwords (such as wux or lart) compared with relatively intact reading of both regular and irregular real words, which suggests disruption to the nonlexical reading route that relies on working out letter-sound associations (Tree & Kay, 2006). In a review of lesion studies, Lambon, Ralph, and Graham (2000) noted that phonological dyslexia was most associated with damage focused on the anterior perisylvian regions with significant variation in size and extent. Comparisons of patients with deep and phonological dyslexia have tended to show a continuum of impairment in phonology and semantics with no clear dividing line between the two, supporting the triangle model of reading (Crisp & Lambon Ralph, 2006). However, the model would predict that if dyslexia arises from damage to one

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of the three general processes (vision, semantics, or phonology), similar impairments would also be apparent in other areas that draw on the same function. In rare cases, this does not seem to be the case, as with a patient reported by Tree and Kay (2006) who presented with a clear phonological dyslexia (nonword reading impairment) despite having intact good performance on a variety of other phonological tasks. These types of cases make it unlikely that reading is based on purely general processes, although the extent to which the brain has become specialized for these relatively recently developed skills (either through selection or developmental plasticity) is still undecided. Much of the recent research is an attempt to settle this issue.

(A) Written Word Visual Analysis Orthographic Lexicon

Letters

Writing

PG/GP Conversion

Semantic System Phonological Lexicon

Speech

Phonemes

Acoustic Analysis

Writing Disorders Writing is a similarly complex process involving several cognitive, linguistic, and sensorimotor processes and, although agraphia has been less studied than dyslexia, similar principles apply. Although incorporating a wide range of language processes, writing and spelling have been similarly explained using a dual route model (Rapcsak et al., 2007; Figure 59.10A) broken down into peripheral and central components (Beeson & Rapcsak, 2003): Peripheral writing processes include allographic conversion (letter representations converted to letter shapes), graphic motor programs (spatial sequences for specific letters), and graphic innervatory patterns (motor commands to control relevant muscles); central writing processes include semantic representation of word meaning, orthographical output lexicon (learned spellings), and phoneme-grapheme (sound to letter) conversion, all of which are thought to converge on a common output mechanism, termed the graphemic buffer (working memory for written letter output). Furthermore, a nonlexical and lexical-semantic route has been suggested to account for nonlexical phonetic spelling (creating plausible spellings from sound-to-letter conversion) and lexicalsemantic retrieval of orthographic information via the activation of word meaning (Rapcsak et al., 2007). The triangle model (Plaut, 1997) has similarly been applied to disorders of writing, suggesting that they largely arise from damage to a general three-component system of recognition of orthography (vision), phonology (pronunciation), and semantics (meaning). Recent comparisons of patients with deep dyslexia, dysgraphia, and dysphasia suggest a level of common impairment in general processes, rather than solely with task-specific abilities (Jefferies, Sage, & Ralph, 2007). Recent theories that are not so strictly tied to models of reading and which attempt more explicitly to integrate spelling processes have become more prominent (Glasspool, Shallice, & Cipolotti, 2006) partly influenced by the fact that patients

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Spoken Word (B) Context

Meaning

Orthography

Phonology

MAKE

/mAk/

Figure 59.10 A: The dual-route model of reading and spelling and B: the triangle model of reading. Note: (A) From “Do Dual-Route Models Accurately Predict Reading and Spelling Performance in Individuals with Acquired Alexia and Agraphia?” by S. Z. Rapcsak, M. L. Henry, S. L. Teague, S. D. Carnahan, and P. M. Beeson, 2007, Neuropsychologia, 45, 2519–2524. Reprinted with permission. (B) “A Distributed, Developmental Model of Word Recognition and Naming,” by M. S. Seidenberg and J. L. McClelland, 1989, Psychological Review, 96, pp. 523–568. Reprinted with permission.

with both general semantic deficits and writing specific impairments (e.g., in the graphemic buffer) have been reported, which are not adequately accounted for by the existing three-factor models of central processes (e.g., Cipolotti, Bird, Glasspool, & Shallice, 2004). Peripheral Agraphias Impairments in the sensorimotor aspect of writing can produce peripheral agraphias that have much in common with

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Object Perception and Face Recognition 1165

apraxia. Apraxic agraphia with normal praxis is a selective impairment in writing while other motor functions, sensorimotor processes, and oral spelling and typing are preserved (Roeltgen & Heilman, 1983). Visuospatial agraphia (also known as constructional or afferent agraphia) is characterized by impairment to spatial orientation during writing and can result in inability to maintain writing on a line, insertion of blank space between letters, and stroke perseveration (Ardila & Rosselli, 1993). Patients with allographic writing impairment have difficulty selecting letter shapes, often selective for one case, or sometimes producing mixed case words (e.g., HelLo), despite having relatively intact oral spelling, praxis, and visuospatial ability (Forbes & Venneri, 2003). Writing can also be impaired following executive impairments that impact general motor planning (Ardila & Surloff, 2006) and after damage to neuromuscular control processes affected by disorders such as Parkinson’s disease (Van Gemmert, Teulings, & Stelmach, 2001). Central Agraphias Lexical or surface agraphia is where patients are not able to access whole word spelling information and so have to rely on phoneme-to-grapheme conversion and spell words as they sound. It is most commonly linked to left posterior inferior temporal cortex damage (Rapcsak & Beeson, 2004) and is thought to arise when there is damage to the lexical-semantic route leaving spelling to rely on the relatively intact nonlexical route (Macoir & Bernier, 2002). In phonological agraphia, patients have difficulty writing nonwords in response to dictation, while writing words to dictation and oral repetition of the words and nonwords are relatively preserved. Spelling errors are often based on visual similarity reflecting the presumed deficit in the phonological system (Roeltgen, 2003) and, despite, considerable variability, the disorder is most commonly linked to lesions in the anterior-inferior part of the left supramarginal gyrus, although superior temporal damage has been reported (Kim, Chu, Lee, Kim, & Park, 2002). Patients with deep agraphia have similar trouble spelling nonwords, but also have a tendency to make semantic errors (e.g., writing flight instead of propeller), have more trouble with function words than with nouns, and words of low imageability (e.g., love) compared with words of high imageability (e.g., lamb). The syndrome is typically associated with large left hemisphere lesions (Rapcsak, Beeson, & Rubens, 1991).

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field deficits such as scotoma; acquired color-blindness (achromatopsia), motion-blindness (akinetopsia), or impairments in shape, form, or size discrimination, which can occur after visual cortex damage; or a range of visual agnosias, including object and face recognition difficulties, alexia (see earlier section on Disorders of Reading and Writing), and vision-for-action problems, usually caused by damage to either one or both of the ventral or dorsal streams (Pisella, Binkofski, Lasek, Toni, & Rossetti, 2006). Less common, although admittedly, less studied, are perceptual disorders of other modalities, including auditory disorders that can impair specific frequency ranges or word or music comprehension, tactile disorders that can impair perception of simple sensations or semantic recognition through touch. In contrast to these disorders of functional deficit, frank hallucinations of varying complexity can be an equally distressing result of neurological disturbance that can occur in any of the perceptual modalities. The visual system is heavily integrated with and reliant on other cognitive functions, which means that damage to the attentional system can lead to a similar behavioral syndrome, but with a markedly different cause (e.g., hemispatial neglect). Care must be taken to distinguish these using appropriate tests and clinicians must bear in mind that disorders of both attention and perception may co-exist. Possibly owing to the influence of Marr ’s (1982) sequential computational approach to visual perception, cognitive neuroscience has been better at outlining the sequential bottom-up stages, rather than the functional neuroanatomy of top-down processing (Bly & Kosslyn, 1997). However, it has been clear from lesion studies that even early perception is heavily influenced by feedback from higher-level brain areas, as illustrated by the fact that, for example, patients with left-sided lesions typically have problems perceiving detail, while patients with right-sided lesions are more likely to have problems with perceiving perceptual wholes, even when lesions are not primarily located in the visual cortex (Robertson & Lamb, 1991). Many of the syndromes detailed in the following section have been key to understanding the components of the visual perceptual system, but have been less useful in understanding the dynamics of perception. Neuroimaging methods have been particularly useful in this regard, helping to uncover the time course of perception-related brain activity (Hopfinger, Woldorff, Fletcher, & Mangun, 2001) and how bottom-up and top-down processes interact during perceptual tasks (Mechelli, Price, Friston, & Ishai, 2004).

OBJECT PERCEPTION AND FACE RECOGNITION

Disorders of the Early Visual System

Disorders of visual perception are a relatively common result of neurological disturbance. They can include frank visual

Because the visual pathway from the retina via the lateral geniculate nucleus to the primary visual cortex is

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retinotopically organized, selective damage to any part of this pathway will lead to corresponding visual scotomas or, in severe cases, cortical blindness. The actual extent of the subjective visual field deficit may seem significantly smaller than the objective deficit, owing to the effects of visual completion (filling-in) and nystagmus (Valmaggia & Gottlob, 2002; Zur & Ullman, 2003). As well as selective impairment, visual cortex damage may also cause more diffuse problems of visual acuity. The specialization of the primary visual cortex for the processing of color (V4) and motion (V5) means that lesions to this area can lead to selective deficits in these abilities. Color perception deficits most commonly occur after lesions to the ventral occipital cortex, although rarely affect color vision in its entirety and are typically accompanied by other perceptual difficulties including prosopagnosia, alexia, object agnosia, and spatial perception impairments (Bouvier & Engel, 2006). Reports of pure motion blindness are much rarer in the literature, although are more apparent if syndromes are included that present in only one part of the visual field (Vaina, Cowey, Eskew, LeMay, & Kemper, 2001), or are selective for a particular direction of motion (Blanke, Landis, Mermoud, Spinelli, & Safran, 2003). Visual Agnosia Visual agnosia is the loss of object recognition and identification in the absence of any significant damage to the early visual system and without intellectual impairment (alexia is sometimes considered among the agnosias, but is discussed earlier in the section on Disorders of Reading and Writing). Farah (2004) provides an excellent guide to the whole range of visual agnosias. Following Lissauer (1890; translated in Shallice & Jackson, 1988) agnosia is typically divided into an apperceptive type (increasingly called visual form agnosia), where the problem concerns assembling a unified perceptual impression from its component parts—largely attributed to impairments in visual grouping, and an associative type, where the difficulty lies in attributing meaning to a correctly perceived object. Classically, the distinction between these two subtypes is made on the basis that although neither can identify objects, patients with apperceptive agnosia are additionally unable to match, copy, or distinguish between simple objects. The clinical syndromes are often indistinct, however, and agnosias with features of each major subtype have been reported (De Renzi & Lucchelli, 1993; Farah, 1990), suggesting that these are ends of spectrum rather than discrete disorders. Although a recent case has been reported (Anaki, Kaufman, Freedman, & Moscovitch, 2007), agnosia rarely presents without some

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form of basic visual impairment, although these are not considered sufficient to account for the wider syndrome. More specific syndromes have also been reported, such as impaired recognition when viewed as a mirror reflection (Priftis, Rusconi, Umilta, & Zorzi, 2003), when viewed from unusual angles (Warrington & James, 1986), or intact object recognition but impaired identification of its orientation (Turnbull, Della Sala, & Beschin, 2002). Associative visual agnosia is where an object is seemingly perceived correctly but the patient cannot name, describe, explain, or categorize the object (even in nonverbal grouping tasks), although it is possible to do so through other senses (e.g., touch). In contrast to apperceptive agnosias, visual spatial processing tends to be is relatively intact. Humphreys and Riddoch (1987) proposed that associative agnosia results from selective impairment in the integration of the largely intact high-level visual processes with memory. However, this has been challenged by Farah (1990) who reviewed the literature and found significant evidence of perceptual deficits in these cases, suggesting that the role of perception and memory in object recognition is not clearly distinct. An illustration of an informational processing model for visual object recognition is outlined in Figure 59.11. This model charts the hypothetical information processing routes from preattentive extraction of simple structural properties, to more advanced post attentional integration of local or global processing, and finally to assignment of relevant spatial frames of reference prior to recognition and naming. Simultanagnosia Simultanagnosia is a related condition and involves the inability to perceive complex visual scenes, or two or more objects simultaneously, despite being able to perceive single objects without significant impairment. Farah (2004) divides the syndrome into dorsal and ventral simultanagnosia based on in which of the postoccipital visual pathways the lesion occurs. Dorsal simutanagnosia typically occurs in the context of Balint’s syndrome that entails an additional inability to direct eye or hand movements to visual targets. Ventral simutanagnosia differs in that patients are somewhat less impaired and can often see multiple objects simultaneously (although do not necessarily recognize them simultaneously), can manipulate objects, and can navigate without bumping into obstacles. Nevertheless, both types typically involve grossly impaired reading ability. The condition is typically explained as a form of pathological local attentional capture, although recent studies have suggested that global scene structures or unseen objects are processed implicitly (Dalrymple, Kingstone, & Barton, 2007; Jackson, Shepherd, Mueller, Husain, & Jackson, 2006).

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Object Perception and Face Recognition 1167

Automatic coding of basic visual elements into separate ‘feature maps’ Colour

Line orientation

Size

Direction of movement

Localization and integration of visual elements via processes of selective attention Global shape processing

Local feature processing

Visual buffer Viewer-centered description

Object-centered description

Object form knowledge Semantic knowledge

Object name

Figure 59.11 An informational processing model for visual object recognition. Note: From p125 of “Spatial Cognition: Evidence from Visual Neglect,” by P. W. Halligan, G. R. Fink, J. C. Marshall, and G. Vallar, 2003, Trends in Cognitive Science, 7, pp. 125–133. Reprinted with permission.

Prosopagnosia Prosopagnosia is a form of visual agnosia that is selective or relatively selective for the recognition of faces (Figure 59.12). (See Figure 59.8 for a cognitive model of face recognition.) The condition has been classified into (1) an apperceptive type, where patients are not able to construct coherent face perceptions, and is typically detected by the inability to distinguish between faces and non-face configurations of facial pictures; and (2) an associative type involving an inability to recognize famous or previously familiar faces. Early research focused almost exclusively on the acquired type, occurring after right or bilateral fusiform gyrus lesions. It has been increasingly recognized, however that there is an idiopathic form (variously called congenital or developmental prosopagnosia) that more closely matches the associative type. There is some evidence of the syndrome running in families in an autosomal dominant pattern (Grueter et al., 2007; Kennerknecht, Plumpe,

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Edwards, & Raman, 2007) and affected individuals may not be significantly impaired in everyday life because they learn to rely on non-face cues or external face features (hair, glasses etc.) for recognition. As this is an adaptive developmental strategy, people with this form of the condition often do not realize until quite late in life that they recognize people differently from others. There remains a considerable debate over whether prosopagnosia is the result of a facespecific deficit (McKone, Kanwisher, & Duchaine, 2007) or whether it is simply the most common result of damage to domain-general perceptual expertise system (Gauthier & Bukach, 2007).

Other Perceptual Disturbances Although visual agnosias are mainly studied, agnosias are also found in other sensory modalities. Auditory agnosia has been reported—with evidence for a dissociation between

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View-centred descriptions

Expression analysis

Structural encoding

Facial speech analysis

Directed visual processing

Expressionindependent descriptions

Face recognition units

Person identity nodes

Cognitive system

Name generation

Figure 59.12 Cognitive model of face recognition. Note: From p. 151 of “Capgras Delusion: A Window on Face Recognition,” by H. D. Ellis and M. B. Lewis, 2001, Trends in Cognitive Science, 5, pp. 149–156. Reprinted with permission.

auditory and musical recognition deficits (Vignolo, 2003), as have gustatory (Small, Bernasconi, Bernasconi, Sziklas, & Jones-Gotman, 2005) and tactile agnosias (Caselli, 1991). Hallucinations with preserved insight into the false nature of the perceptions arise in a number of neurological conditions, including macular degeneration, migraine, epilepsy, and dementia (Manford & Andermann, 1998). Auditory verbal hallucinations (hearing voices) seem a relatively rare result of acquired brain injuries in the absence of psychosis (Lampl, Lorberboym, Gilad, Boaz, & Sadeh, 2005).

LEARNING AND MEMORY Memory is one of the most commonly affected cognitive abilities after neurological disturbance (cross reference—v 3-8) and can be the source of the most disabling long-term effects. Learning occurs at all levels throughout the brain, and from this perspective, virtually all cognitive neurology is the study of how learned patterns are disrupted by neuropathology. However, decades of careful experimental

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studies on both healthy participants and neurological patients have shown us that the brain has become specialized to encode, consolidate, store, and retrieve information for certain types of tasks. Current theories of memory make functional divisions that are not necessarily mutually exclusive, although some have been shown to rely on largely independent neural systems. Sensory memory is thought to last only a matter of milliseconds and provides a lingering impression to the senses. Working memory is considered to be a limited capacity memory system where temporarily stored information (usually less than 30 s) can be manipulated by the executive system during cognitive tasks. Longterm memory is considered to represent semi-permanent or permanent storage and has been divided into semantic memory (for general knowledge, facts and words that can be recalled without access to the context in which they were learned), and episodic memory (the remembrance of episodes from our personally experienced past). Memory can also be divided into declarative or explicit memory, which we can consciously recall, reflect upon, and describe; and procedural memory, which is the ability to learn skills and actions. Implicit memory is not consciously accessible and includes skill learning (as per procedural memory), but also conditioning, associative learning, priming, and, in fact, anything down to Hebbian learning at the neural level. Memory can also be distinguished by the content that is being remembered, such as verbal, visual, or spatial memory. Disorders of memory are usually categorized in a similar fashion and only the briefest outline will be given here. Brief reviews are available in Budson and Price (2005) and Kopelman (2002), or for a more in-depth treatment, Baddeley, Kopelman, and Wilson (2004) and Baddeley, Kopelman, and Wilson (2002) are excellent resources. The fact that the classification of memory disorders tends to pragmatically follow traditional classifications of normal memory is worth bearing in mind, particularly when data challenging the traditional models appears. For example, electrophysiological studies during tasks that are classically described as working memory tasks have been used as a basis to argue that there is no separable working memory system, only temporary activation of longterm memory stores (Ruchkin, Grafman, Cameron, & Berndt, 2003) and that short- and long-term memory stores are not separate (Cameron, Haarmann, Grafman, & Ruchkin, 2005). While this debate continues in the literature, both the clinician and researcher must be aware that there is a temptation to fit the complexity of clinical disorders into the traditional categories, rather than the more difficult task of adjusting the traditional categories to allow for the intricacy of impairment.

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Learning and Memory 1169

Working Memory Impairments Working memory is a short-term, explicit, declarative memory system that is commonly conceptualized as having both a visual-spatial and an auditory short-term store. It uses a network of cortical and subcortical areas, although it is particularly reliant on the prefrontal cortex (Postle, 2006). It is increasingly thought that working memory is not a separate memory system, but the emergent property of a number of cognitive processes working together (D’Esposito, 2007). This means working memory is particularly sensitive to impairment after neurological disturbance that is largely caused by disruption to the executive system, rather than the short-term stores themselves (Muller & Knight, 2006). Disorders of working memory are likely to present as difficulties with concentration, following instructions, or general forgetting, with the impairment also impacting on encoding into long-term memory (Blumenfeld & Ranganath, 2007).

Long-Term Memory Impairments Amnesia is the general name given to a range of long-term memory impairments although the classic antereograde amnesic syndrome consists of the impaired encoding of new declarative memories, intact recall of premorbid information, and intact implicit memory. Pathologies that cause anterograde amnesia typically result in a limited retrograde amnesia that follows a temporal gradient (Ribot’s law), in that memories are more likely to be intact because the memories are more distant in time. Differences between antereograde amnesia caused by medial temporal lobe and diencephalon disruption have been reported in the literature but are likely to be negligible in practice, while both have faster rates of forgetfulness and benefit less well from category prompts than when the syndrome is caused by prefrontal pathology (Kopelman, 2002). Because the prefrontal cortex is involved in both encoding and retrieval of memory, pathology in this area may also lead to increased memory distortion, including false memory recall, loss of context (source amnesia), and frank confabulation (Johnson, O’Connor, & Cantor, 1997). Posttraumatic amnesia is an amnesic syndrome that occurs in the acute stage after brain trauma. The length of the amnesia is known to reflect the severity of the brain injury and typical resolution times stretch from 1 day to several weeks (McMillan, Jongen, & Greenwood, 1996). Transient global amnesia is a dense amnesic syndrome with a sudden onset that resolves within a matter of hours. It can be triggered by physical or emotional stress, and, with the exception of headaches in affected younger people, is not reliably associated with other neurological signs

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(Quinette et al., 2006). Transient epileptic amnesia may present in a similar fashion, although it typically lasts for a shorter duration (less than 1 hour) and patients may experience clear seizure-related sensory and motor disturbance for the duration of the amnesia. Specific impairments in existing semantic memory take a number of forms and are particularly common after pathology of the anterolateral temporal lobes (Levy, Bayley, & Squire, 2004). Patients may present with object identification and language difficulties and so they can sometimes erroneously be assumed to have agnosia or aphasia. A presentation of a selective semantic deficit will be in the context of normal perception and intact nonsemantic language skills such as repetition, reading aloud, and writing to dictation. Alzheimer ’s disease is perhaps the most common form of semantic impairment that arises from both degradation of semantic memory owing to temporal atrophy, and impairment in executive control processes due to frontal pathology (Grossman et al., 2003). Semantic dementia is a variant of frontotemporal dementia that involves focal lateral temporal atrophy and a progressive decline in semantic knowledge with little or no distortion of the phonological and syntactic aspects of language, and relative sparing of other aspects of cognition, such as episodic memory, nonverbal problem solving, and perceptual and visuospatial skills (Garrard & Hodges, 2000). Impairments in semantic memory may be category specific, affecting knowledge of particular classes of objects. Dissociations between knowledge of animate and inanimate, living versus nonliving, and animals and plants (to name but a few) have been reported in the neurological literature (Gainotti, 2005). However, it is not clear whether these distinctions reflect the organization of knowledge within the semantic system, or whether there are other higher-level or emergent properties that could better explain the apparent category-specific effect (Borgo & Shallice, 2001). Impairments of Procedural Memory Procedural memory deficits can either take the form of marked impairments in acquiring new motor skills, or a loss of existing abilities. It can be spared in even the densest declarative memory amnesias allowing a considerable degree of implicit memory function and skill learning (Spiers, Maguire, & Burgess, 2001). Neuroimaging studies have indicated that procedural learning is associated with activation in the supplementary motor area, basal ganglia, and cerebellum (Daselaar, Rombouts, Veltman, Raaijmakers, & Jonker, 2003) and clinical studies have shown that damage to these areas can cause selective impairments in motor learning (Halsband & Lange, 2006). Unsurprisingly, particular impairments can be seen in degenerative disorders

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such as Parkinson’s and Huntingdon’s disease that involve circumscribe pathology to these circuits (Heindel, Salmon, Shults, Walicke, & Butters, 1989).

EXECUTIVE SYSTEM DISORDERS The idea of an executive system as a manager of other cognitive processes is a relatively late addition to neuropsychological theory and has largely stemmed from the discovery that neurological patients often have problems with coordinating their thoughts and actions over and above any deficits that directly impact on perception, stored memories, or motor control. One of the core ideas behind the concept of an executive system is that it is primarily involved in representing and manipulating abstract concepts, an ability that supports functions such as cognitive and emotional control, initiation and inhibition of actions, behavioral flexibility, planning, introspection, perspective taking, and social cognition. Burgess (1997) has described the executive system as lacking “process-behavior correspondence” meaning, behaviorally, the operation of this system can only be measured through other cognitive processes that are managed (or mismanaged) by the system. Lesion and neuroimaging studies have consistently indicated that the executive system relies heavily on the prefrontal cortex and its major pathways (Duncan & Owen, 2000) and damage to this system can cause a surprisingly diverse range of behavioral abnormalities, collectively labeled the dysexecutive syndrome. The executive system is thought to be more fully engaged in nonroutine, effortful, and online (real life) situations and so the extent of executive impairment as measured by neuropsychological testing may not always predict day-to-day disability (Burgess et al., 2006). Executive function is closely linked to the concept of attention, although has traditionally been distinguished from perceptual and spatial attention that is more closely linked to the function of the parietal lobe However, recent work has begun to question the strict distinction between these systems and the importance of the frontoparietal network in the interaction of both is now being increasingly highlighted (Collette, Hogge, Salmon, & Van der Linden, 2006; Hon, Epstein, Owen, & Duncan, 2006). Theories of Executive Function Duncan, Emslie, Williams, Johnson, and Freer (1996) have argued that the executive system is involved in constructing task plans by representing and maintaining the relevant goals and requirements, and is largely synonymous with general intelligence. Executive system impairment is described

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as arising from goal neglect where task requirements are disregarded despite the fact that they have been remembered and understood. More recently, Duncan et al. (2008) elaborated on their theory to suggest that goal neglect reflects a limit in working memory capacity. Notably, the limiting factor is suggested to be different from the bottleneck theories of traditional capacity models of attention that concern the limits of how much perceptual or spatial information can be attended to at any one time. In this model, the limiting factor is the capacity of working memory to retain a task model—a working-memory description of relevant facts, rules, and requirements used to control current behavior. According to the theory, dysexecutive problems arise when individual task representations are lost through competition between processes that update the global task model, particularly when capacity has been limited through neurological impairment. Norman and Shallice (1986) proposed the hugely influential supervisory attentional system (SAS) model that has two pivotal components. The contention scheduler is considered to mediate the effect of the environment (which may trigger certain actions) on the selection of automatic or routine actions. When triggered, the contention scheduling component controls the mutual inhibition of competing actions (since many actions may be triggered at once) to select the most appropriate course of action. The SAS (synonymous with the executive system in most accounts) is considered to intervene in nonroutine situations when actions have to be altered or inhibited because of a novel encounter or decision-making process. Increasingly, the supervisory system is not considered to be a single function, and there is a general consensus that it comprises of a number of anatomically and functionally independent but interrelated processes. Descriptions of the how these processes are fractionated differ and include shifting, updating, and inhibition (Miyake et al., 2000); energization, task setting, and monitoring (Stuss & Alexander, 2007) and schema selection, monitoring, memory specification, and intention setting (Shallice, 2002). Theories of the role of reward processing in executive function have traditionally focused on the ability to respond differently to changing contingencies in the social environment (Rolls, 1996). However, more recent approaches have widened the scope of reward processing theories based on evidence that the fronto-polar cortex is involved in maintaining and prioritizing the competing demands of behavioral plans or mental tasks based on representations of reward expectations (Koechlin & Hyafil, 2007), consistent with lesion data showing that patients display decision-making impairments in open-ended situations (Burgess, Gilbert, & Dumontheil, 2007).

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Summary

Problems of Affect and Social Judgment The medial frontal cortex is known to be particularly important for social cognition (Amodio & Frith, 2006) although damage to a range of prefrontal cortex areas is known to affect the ability to perceive and make judgments on social, moral, and emotional information. Two of the most popular theories of frontally related social disability include an impairment in understanding when reward contingencies for particular social behaviors have altered (Kringelbach & Rolls, 2004); and an impairment in the perception of affect-related arousal and signaling (the somatic marker hypothesis; Bechara, 2004). Common socially relevant dysexecutive symptoms include disinhibition, inappropriate social behavior, and even mania-like states (Starkstein & Robinson, 1997). At the more serious end of the spectrum, acquired sociopathy can result from damage to the orbitofrontal cortex and involves an inability to control reactive aggression and violent impulses (Blair, 2001). Similarly, impairments in the ability to reason about the acceptability of personal moral violations have been found in patients with ventromedial lesions (Ciaramelli, Muccioli, Ladavas, and Di Pellegrino, 2007). Deficits in understanding others’ emotions and mental states (Siegal & Varley, 2002) and impairments in understanding nonverbal social cues (Mah, Arnold, & Grafman, 2004) may also promote difficulties in social interaction after frontal pathology.

Deficits of Executive Memory The executive system is most closely linked to working memory—a limited capacity memory system responsible for the temporary storage and processing of information while cognitive tasks are performed (see Learning and Memory Impairments section). However, the executive system is also involved in the retrieval and encoding of long-term memories and memory for intended actions in the future (prospective memory). As a result, executive impairment can have a potentially wide and varied impact. Distractibility undoubtedly impacts all parts of the memory process and is likely a significant factor in the impaired use of efficient organization strategies during both encoding and recall (Mangels, 1997; Parkin, Ward, Bindschaedler, Squires, & Powell, 1999). Although it is traditionally thought that executive impairment leads to a pattern of impaired recall with intact recognition, it is now well established that recognition difficulties are common, although less pronounced (Bastin, Van der Linden, Lekeu, Andres, & Salmon, 2006). One of the most dramatic pathologies of memory associated with executive impairment is confabulation, where patients produce fictitious stories without the apparent intent to deceive and are seemingly unaware

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that they are inaccurate. While not all confabulation can be explained by executive impairment alone, they commonly present together (Schnider, 2003). Problems of Evaluation, Judgment, and Action Planning Despite scoring well on measures of general cognitive function, executive dysfunction is particularly associated with the inability to make competent real world decisions. Such impairments are only fully apparent in everyday situations (Goel & Grafman, 2000). Cases have been reported of patients who seem to score within the normal range during neuropsychological testing but who make obviously unwise decisions in their everyday life (Blair & Cipolotti, 2000; Elsinger & Damasio, 1985). On the level of completing specific short-term goals, impairment to the executive system has been shown to impact on action planning, so that actions sequences may be disjointed, perseverative, or contain unhelpful or irrelevant steps (Owen, 1997). Difficulties in both judgment and action planning can arise owing to problems with acquiring rule sets (Burgess & Shallice, 1996), self-monitoring and insight, making reasonable estimates (Brand, Kalbe, Fujiwara, Huber, & Markowitsch, 2003), or multitasking (Burgess, Veitch, de Lacy Costello, & Shallice, 2000). Neuroimaging evidence suggests that control implementation and performance or conflict monitoring are linked to dissociable processes in the dorsolateral prefrontal and anterior cingulate cortices, respectively (MacDonald, Cohen, Stenger, & Carter, 2000). However, lesion maps (see Figure 59.13) of cognitive control impairment after brain injury further suggest that these processes may fractionate further (Alexander, Stuss, Picton, Shallice, & Gillingham, 2007).

SUMMARY There is little doubt that cognitive neurology continues to contribute to our understanding of established neurological, psychiatric, affective, and related pseudoneurological and or functional disorders. There is an increasing prevalence of seemingly neurological conditions for which no organic disorder can be found to explain the patient’s hemiparesis, somatosensory loss, visual field constriction, and so on (Halligan, Bass, & Marshall, 2001). Functional neuroimaging techniques and cognitive analysis procedures have still to make a major clinical and theoretical contribution in the domain of neuropsychiatric and neurogenic disorders (Frith, 2008; Halligan & David, 2001). The rich data source of the Human Genome Project will increasingly add to our knowledge about neurological and psychiatric conditions

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Figure 59.13 Cortical lesions associated with prolonged reaction times on Stroop task. Note: From “Regional Frontal Injuries Cause Distinct Impairments in Cognitive Control,” by M. P. Alexander, D. T. Stuss, T. Picton, T. Shallice, and S. Gillingham, 2007, Neurology, 68, pp. 1515–1523. Reprinted with permission.

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References 1173

where the onset is multiply determined by genetic susceptibility, developmental conditions, and environmental stressors. We can expect an increase in the prevalence of neurodegenerative and vascular related diseases and their associated future impact on cognitive functions and quality of life due to a growing elderly population. New evidence about experience-dependent plasticity of the adult brain allows cautious optimism about the possibility of restitution of brain function following damage (Robertson, 2004). In 1989, Sohlberg and Mateer ’s book Introduction to Cognitive Rehabilitation: Theory and Practice helped locate cognitive rehabilitation alongside more established rehabilitation approaches. They reported an assembly of therapies that attempt to retrain, alleviate or compensate for the deficits caused by selective cognitive impairments (e.g., Basso, Cappa, & Gainotti, 2000; D. W. Ellis & Christensen, 1989; Fleminger & Powell, 1999; Ponsford; Sloan & Snow, 1995; Prigatano, 1999; Riddoch & Humphreys, 1994; Wood & Fussey, 1999). Recent progress in cognitive neuroscience provides a theoretical framework to link behaviorally mediated treatments with knowledge of underlying neurophysiological processes, where rehabilitation strategies can be tested and improved. Future developments will no doubt consider the mechanisms for neuroplasticity in the adult brain. The challenge for cognitive neurology (and sister subspecialties such as cognitive neuropsychology and neuroscience) is whether collectively they are capable of harnessing the wealth of new multidisciplinary findings offered by functional imaging to delineate the neural connectivity involved in many apparently simple cognitive functions.

REFERENCES Abou-Khalil, B. (2007). An update on determination of language dominance in screening for epilepsy surgery: The Wada test and newer noninvasive alternatives. Epilepsia, 48, 442–455. Ackerstaff, R. G., Keunen, R. W., van Pelt, W., Montauban van Swijndregt, A. D., & Stijnen, T. (1990). Influence of biological factors on changes in mean cerebral blood flow velocity in normal ageing: A transcranial Doppler study. Neurological Research, 12, 187–191. Alexander, M. P., Stuss, D. T., Picton, T., Shallice, T., & Gillingham, S. (2007). Regional frontal injuries cause distinct impairments in cognitive control. Neurology, 68, 1515–1523. Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: The medial frontal cortex and social cognition. Nature Reviews Neuroscience, 7, 268–277. Anaki, D., Kaufman, Y., Freedman, M., & Moscovitch, M. (2007). Associative (prosop)agnosia without (apparent) perceptual deficits: A case-study. Neuropsychologia, 45, 1658–1671. Ardila, A., & Rosselli, M. (1993). Spatial agraphia. Brain and Cognition, 22, 137–147. Ardila, A., & Surloff, C. (2006). Dysexecutive agraphia: A major executive dysfunction sign. The International Journal of Neuroscience, 116, 653–663.

c59.indd Sec8:1173

Babinski, J. (1914). Contribution a l’étude des troubles mentaux dans l’hémiplégie organique (anosognosie). Revue Neurologique, 27, 845–848. Baddeley, A. D., Kopelman, M., & Wilson, B. A. (2002). The Handbook of Memory Disorders (2nd ed.). London: Wiley. Baddeley, A. D., Kopelman, M., & Wilson, B. A. (2004). The Essential Handbook of Memory Disorders for Clinicians. London: Wiley Ltd. Bartolomeo, P. (2002). The relationship between visual perception and visual memtal imagery: A reappraisal of the neuropsychological evidence. Cortex, 38, 357–378. Basso, A., Cappa, A., & Gainotti, G. (Eds.). (2000). Cognitive neuropsychology and Language Rehabilitation. Hove, England: Psychology Press. Basso, A., & Marangolo, P. (2000). Cognitive neuropsychological rehabilitation: The emperor ’s new clothes? Neuropsychological Rehabilitation, 10, 219–230. Bastin, C., Van der Linden, M., Lekeu, F., Andres, P., & Salmon, E. (2006). Variability in the impairment of recognition memory in patients with frontal lobe lesions. Cortex, 42, 983–994. Bauer, R. M. (1984). Autonomic recognition of names and faces in prosopagnosia: A neuropsychological application of the Guilty Knowledge Test. Neuropsychologia, 22, 457–469. Baxendale, S. (2000). Carotid amobarbital testing and other amobarbital procedures. In J. Oxbury, C. Polkey, & M. Duchowny (Eds.), Intractable focal epilepsy. London: Bailliere Tindall. Bechara, A. (2004). The role of emotion in decision-making: Evidence from neurological patients with orbitofrontal damage. Brain and Cognition, 55, 30–40. Beeson, P. M., & Rapcsak, S. Z. (2003). Neuropsychological assessment and rehabilitation of writing disorders. In P. Halligan, U. Kischka, & J. C. Marshall (Eds.), Handbook of Clinical Neuropsychology. Oxford: Oxford University Press. Behrmann, M., Winocur, G., & Moscovitch, M. (1992). Dissociation between mental imagery and object recognition in a brain–damaged patient. Nature, 359, 636–637. Berti, A., & Frassinetti, F. (2000). When far becomes near: Remapping of space by tool use. Journal of Cognitive Neuroscience, 12, 415–420. Berti, A., & Rizzolatti, G. (1992). Visual processing without awareness: Evidence from unilateral neglect. Journal of Cognitive Neuroscience, 4, 345–351. Beschin, N., Basso, A., & Della Sala S. (2000). Perceiving left and imagining right: Dissociation in neglect. Cortex, 36, 401–414. Beschin, N., Cubelli, R., Della Sala, S., & Spinazzola, L. (1997). Left of what? The role of egocentric coordinates in neglect. Journal of Neurology, Neurosurgery, and Psychiatry, 63, 483–489. Beschin, N., & Robertson, I. H. (1997). Personal versus extrapersonal neglect: A group study of their dissociation using a reliable clinical test. Cortex, 33, 379–384. Bisiach, E. and Luzzatti, C. (1978): Unilateral neglect of representational space. Cortex, 14, 129–33. Bisiach, E., & Berti, A. (1987). Dyschiria. An attempt at its systemic explanation. In M. Jeannerod (Ed.), Neurophysiological and neuropsychological aspects of spatial neglect (pp. 183–201). Amsterdam: Elsevier. Bisiach, E., Brouchon, M., Poncet, M., & Rusconi, M. L. (1993). Unilateral neglect in route description. Neuropsychologia, 31, 1255–1262. Bisiach, E., &. Rusconi, M. L. (1990). Break-down of perceptual awareness in unilateral neglect. Cortex, 24, 643–649. Blair, R. J. R. (2001). Neurocognitive models of aggression, the antisocial personality disorders, and psychopathy. Journal of Neurology, Neurosurgery, and Psychiatry, 71, 727–731. Blair, R. J. R., & Cipolotti, L. (2000). Impaired social response reversal: A case of acquired sociopathy. Brain, 123, 1122.

8/19/09 5:12:23 PM

1174

Cognitive Neurology

Blanke, O., Landis, T., Mermoud, C., Spinelli, L., & Safran, A. B. (2003). Direction-selective motion blindness after unilateral posterior brain damage. European Journal of Neuroscience, 18, 709–722.

Caramazza, A., & Hillis, A. E. (1990). Spatial representation of words in the brain implied by studies of a unilateral neglect patient. Nature, 346, 267–9.

Blumenfeld, R. S., & Ranganath, C. (2007). Prefrontal cortex and long-term memory encoding: An integrative review of findings from neuropsychology and neuroimaging. Neuroscientist, 13, 280–291.

Caselli, R. J. (1991). Rediscovering tactile agnosia. Mayo Clinic Proceedings, 66, 129–42.

Bly, B. M., & Kosslyn, S. M. (1997). Functional anatomy of object recognition in humans: Evidence from positron emission tomography and functional magnetic resonance imaging. Current Opinion in Neurology, 10, 5–9.

Ciaramelli, E., Muccioli, M., Ladavas, E., & Di Pellegrino, G. (2007). Selective deficit in personal moral judgment following damage to ventromedial prefrontal cortex. Social Cognitive and Affective Neuroscience, 2, 84–92.

Borgo, F., & Shallice, T. (2001). When living things and other ‘sensory quality’ categories behave in the same fashion: A novel category specificity effect. Neurocase, 7, 201–220. Bottini, G., & Paulesu, E. (2003). Functional neuroanatomy of spatial perception, spatial processes, and attention. In P. W. Halligan, U. Kischka, & J. C. Marshall (Eds.), Handbook of clinical neuropsychology (pp. 607–723). Oxford: Oxford University Press. Bouvier, S. E., & Engel, S. A. (2006). Behavioral deficits and cortical damage loci in cerebral achromatopsia. Cerebral Cortex, 16, 183–191. Brain, W. R. (1941). Visual disorientation with special reference to lesions of the right cerebral hemisphere. Brain, 64, 244–272. Brand, M., Kalbe, E., Fujiwara, E., Huber, M., & Markowitsch, H. J. (2003). Cognitive estimation in patients with probable Alzheimer ’s disease and alcoholic Korsakoff patients. Neuropsychologia, 41, 575–584.

Cermak, L. S. (1982). Human memory and amnesia. Hillsdale, NJ: Erlbaum.

Cipolotti, L., Bird, C. M., Glasspool, D. W., & Shallice, T. (2004). The impact of deep dysgraphia on graphemic buffer disorders. Neurocase, 10, 405–419. Clare, L., & Halligan, P. W. (Editorial) (2006) Neuropsychological Rehabilitation, 16, 353–355. Collette, F., Hogge, M., Salmon, E., & Van der Linden, M. (2006). Exploration of the neural substrates of executive functioning by functional neuroimaging. Neuroscience, 139, 209–221. Coltheart, M. (2006). Acquired dyslexias and the computational modelling of reading. Cognitive Neuropsychology, 23, 96–109. Coltheart, M., Patterson, K., & Marshall, J. C. (Eds.). (1987). Deep dyslexia (2nd ed.). London: Routledge.

Bruce, V., & Young, A. W. (1986). Understanding face recognition. British Journal of Psychology, 77, 305–327.

Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204–256.

Budson, A. E., & Price, B. H. (2005). Memory dysfunction. New England Journal of Medicine, 352, 692–699.

Coslett, H. B., & Saffran, E. M. (1989). Evidence for preserved reading in ‘pure alexia’. Brain, 112, 327–359.

Burgess, P. W. (1997). Theory and methodology in executive function research. In P. Rabbit (Ed.), Methodology of Frontal and Executive Function. Hove, England: Psychology Press.

Crisp, J., & Lambon Ralph, M. A. (2006). Unlocking the nature of the phonological-deep dyslexia continuum: The keys to reading aloud are in phonology and semantics. Journal of Cognitive Neuroscience, 18, 348–362.

Burgess, P. W., Alderman, N., Forbes, C., Costello, A., Coates, L. M., Dawson, D. R., et al. (2006). The case for the development and use of “ecologically valid” measures of executive function in experimental and clinical neuropsychology. Journal of the International Neuropsychological Society, 12, 194–209. Burgess, P. W., Gilbert, S. J., & Dumontheil, I. (2007). Function and localization within rostral prefrontal cortex (area 10). Philosophical Transactions of the Royal Society of London, Series B, Biological Sciences, 362, 887–899. Burgess, P. W., & Shallice, T. (1996). Bizarre responses, rule detection and frontal lobe lesions. Cortex, 32, 241–259. Burgess, P. W., Veitch, E., de Lacy Costello, A., & Shallice, T. (2000). The cognitive and neuroanatomical correlates of multitasking. Neuropsychologia, 38, 848–863.

Cutting, J. (1978). Study of anosognosia. Journal of Neurology, Neurosurgery, and Psychiatry, 41, 548–555. Dalrymple, K. A., Kingstone, A., & Barton, J. J. (2007). Seeing trees OR seeing forests in simultanagnosia: Attentional capture can be local or global. Neuropsychologia, 45, 871–875. Damasio, A. R., Damasio, H., & Chui, H. C. (1980). Neglect following damage to frontal lobe or basal ganglia. Neuropsychologia, 18, 123–32. Daselaar, S. M., Rombouts, S. A, Veltman, D. J., Raaijmakers, J. G., & Jonker, C. (2003). Similar network activated by young and old adults during the acquisition of a motor sequence. Neurobiology of Aging, 24, 1013–1019. Dehaene, S., & Naccache, L. (2001). Towards a cognitive neuroscience of consciousness: Basic evidence and a workspace framework. Cognition, 79, 1–37.

Buxbaum, L. (2006). On the right (and left) track: Twenty years if progress in studying hemispatial neglect. Cognitive Neuropsychology, 23, 184–201.

De Renzi, E., & Lucchelli, F. (1993). The fuzzy boundaries of apperceptive agnosia. Cortex, 29, 187–215.

Byng, S., Kay, J., Edmundson, A., & Scott, C. (1990). Aphasia tests reconsidered. Aphasiology, 4, 67–92.

Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222.

Cameron, K. A., Haarmann, H. J., Grafman, J., & Ruchkin, D. S. (2005). Long-term memory is the representational basis for semantic verbal short-term memory. Psychophysiology, 42, 643–653.

Desmond, J. E., & Chen, S. H. A. (2002) Ethical issues in the clinical application of fMRI: Factors affecting the validity and interpretation of activations. Brain and Cognition, 50, 482–497.

Cantagallo, A., & Della Sala, S. (1998). Preserved insight in an artist with extrapersonal spatial neglect. Cortex, 34, 163–189.

D’Esposito, M. (2007). From cognitive to neural models of working memory. Philosophical Transactions of the Royal Society of London. Series, B., Biological Sciences, 362, 761–772.

Cappa, S. F. (2001). Cognitive Neurology: An Introduction. London: Imperial College Press. Cappa, S. F., Abutalebi, J., Demonet, J., Fletcher, P., & Garrard, P. (Eds.). (2008). Cognitive Neurology: A Clinical Textbook. Oxford: Oxford University Press. Caramazza, A., & Coltheart, M. (2006). Cognitive Neuropsychology Twenty Years on. Cognitive Neuropsychology, 21, 3–12.

c59.indd Sec8:1174

di Pellegrino, G., Ladavas, E., & Galletti, C. (2002). Lexical processes and eye movements in neglect dyslexia. Behavioral Neurology, 13(1–2), 61–74. Di Pietro, M., Laganaro, M., Leemann, B., & Schnider, A. (2004). Receptive amusia: Temporal auditory processing deficit in a professional musician following a left temporo-parietal lesion. Neuropsychologia, 42, 868–877.

8/19/09 5:12:23 PM

References 1175 Donovan, N. J., Kendall, D. L., Heaton, S. C., Kwon, S., Velozo, C. A., & Duncan, P. W. (2008). Conceptualizing functional cognition in stroke. Neurorehabilitation and Neural Repair, 22, 122–135. Drevets, W. C., Burton, H., Videen, T. O., Snyder, A. Z., Simpson, J. R., Jr, & Raichle, M. E. (1995). Blood flow changes in human somatosensory cortex during anticipated stimulation. Nature, 373, 249–252. Driver, J., & Halligan, P. W. (1991). Can visual neglect operate in objectcentered co-ordinates? An affirmative single case study. Cognitive Neuropsychology, 8, 475–496. Driver, J., & Vuilleumier, P. (2001). Perceptual awareness and its loss in unilateral neglect and extinction. Cognition, 79, 39–88. Duncan, J., Emslie, H., Williams, P., Johnson, R., & Freer, C. (1996). Intelligence and the frontal lobe: The organization of goal-directed behavior. Cognitive Psychology, 30, 257–303. Duncan, J., & Owen, A. M. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences, 23, 475–483. Duncan, J., Parr, A., Woolgar, A., Thompson, R., Bright, P., Cox, S., et al. (2008). Goal neglect and Spearman’s g: Competing parts of a complex task. Journal of Experimental Psychology: General, 137, 131–148. Ellis, A. W., Flude, B. M., & Young, A. W. (1987). “Neglect dyslexia” and the early visual processing of letters in words and nonwords. Cognitive Neuropsychology, 4, 439–463. Ellis, A. W., Lambon Ralph, M. A., Morris, J., & Hunter, A. (2000). Surface dyslexia: Description, treatment, and interpretation. In E. Funnell (Ed.), Case Studies in the Neuropsychology of Reading (pp. 85–122). Hove, East Sussex, England: Psychology Press. Ellis, D. W., & Christensen, A. L. (Eds.). (1989) Neuropsychological Treatment after Brain Injury. Boston: Kluwer Academic. Ellis, H. D., & Lewis, M. B. (2001). Capgras delusion: A window on face recognition. Trends in Cognitive Science, 5, 149–156. Ellis, S. J., & Small, M. (1994). Denial of eye closure in acute stroke. Stroke, 25, 1958–1962. Elsinger, P. J., & Damasio, A. R. (1985). Severe disturbance of higher cognition after bilateral frontal lobe ablation: Patient EVR. Neurology, 35, 1731–1741. Farah, M. J. (1990). Visual Agnosia: Disorders of Object Recognition and What They Tell Us About Normal Vision. Cambridge, MA: MIT Press. Farah, M. J. (2004). Visual Agnosia (2nd ed.). Cambridge, MA: MIT Press. Farah, M. J., & Feinberg, T.E. (1997). Perception and awareness. In T. E. Feinberg & M. J. Farah (Eds.), Behavioral neurology and neuropsychology. New York: McGraw-Hill. Fleminger, S., & Powell, J. (1999). Evaluation of outcomes in brain injury rehabilitation. Neuropsychological Rehabilitation, 9, 225–230. Forbes, K. E., & Venneri, A. (2003). A case for case: Handling letter case selection in written spelling. Neuropsychologia, 41, 16–24. Forstl, H., Owen, A. M., & David, A. (1993). Gabriel Anton and “Anton’s symptom”: “On focal diseases of the brain which are not perceived by the patient” (1898). Neuropsychiatry, Neuropsychology and Behavioral Neurology, 6, 1–6. Friedmann, N., & Gvion, A. (2001). Letter position dyslexia. Cognitive Neuropsychology, 18, 637–696. Frith, C. (2008). Editorial: In praise of cognitive neuropsychiatry. Cognitive Neuropsychiatry, 13, 1–7. Gainotti, G. (2005). The influence of gender and lesion location on naming disorders for animals, plants and artefacts. Neuropsychologia, 43, 1633–1644. Garrard, P., & Hodges, J. R. (2000). Semantic dementia: Clinical, radiological and pathological perspectives. Journal of Neurology, 247, 409–422. Gauthier, I., & Bukach, C. (2007). Should we reject the expertise hypothesis? Cognition, 103, 322–330.

c59.indd Sec8:1175

Gazzaniga, M. S., Ivry, R., & Mangun, G. R. (2002). Cognitive Neuroscience: The Biology of the Mind (2nd ed.). New York: Norton. Glasspool, D. W., Shallice, T., & Cipolotti, L. (2006). Towards a unified process model for graphemic buffer disorder and deep dysgraphia. Cognitive Neuropsychology, 23, 479–512. Goel, V., & Grafman, J. (2000). The role of the right prefrontal cortex in illstructured problem solving. Cognitive Neuropsychology, 17, 415–436. Grossman, M., Koenig, P., Glosser, G., DeVita, C., Moore, P., Rhee, J., et al. (2003). Neural basis for semantic memory difficulty in Alzheimer ’s disease: An fMRI study. Brain, 126, 292–311. Grueter, M., Grueter, T., Bell, V., Horst, J., Laskowski, W., Sperling, K., et al. (2007). Hereditary prosopagnosia: The first case series. Cortex, 43, 734–749. Guariglia, C., & Antonucci, G. (1992). Personal and extrapersonal space: A case of neglect dissociation. Neuropsychologia, 30, 1001–1009. Halligan, P. W. (1995). Drawing attention to neglect. The Psychologist, 8, 257–264. Halligan, P. W., Bass, C., & Marshall, J. C. (Eds.). (2001). Contemporary Approaches to the Study of Hysteria. Oxford: Oxford University Press. Halligan, P. W., & David, A. S. (2001). Cognitive neuropsychiatry: Towards a scientific psychopathology. Nature Reviews Neuroscience, 2, 209–215. Halligan, P. W., Fink, G. R., Marshall, J. C., & Vallar, G. (2003). Spatial cognition: Evidence from visual neglect. Trends in Cognitive Science, 7, 125–133. Halligan, P. W., Kischka, U., & Marshall, J. C. (Eds.). (2004). Handbook of clinical neuropsychology. Oxford University Press, UK. Halligan, P. W., & Marshall, J. C. (1988). How long is a piece of string? A study of line bisection in a case of visual neglect. Cortex, 24, 321–328. Halligan, P. W., & Marshall, J. C. (1991). Left neglect for near but not far space in man. Nature, 350, 498–500. Halligan, P. W., & Marshall, J. C. (1993). When two is one: A case study of spatial parsing in visual neglect. Perception, 22, 309–312. Halligan, P. W., & Marshall, J. C. (1994). Completion in visuo-spatial neglect: A case study. Cortex, 30, 685–694. Halligan, P. W., & Marshall, J. C. (2001). Graphic neglect-more than the sum of the parts. NeuroImage, 14, S91–7. Halligan, P. W., & Robertson, I. H. (1992). The assessment of unilateral neglect. Hove: Erlbaum. Halligan, P. W., & Wade, D. T. (Eds.). (2005). The effectiveness of rehabilitation for cognitive deficits, Oxford University Press, UK. Halsband, U., & Lange, R. K. (2006). Motor learning in man: A review of functional and clinical studies. Journal of Physiology Paris, 99, 414–424. Heilman, K. M., & Valenstein, E. (Eds.). (2003). Clinical neuropsychology (4th ed.). Oxford: Oxford University Press. Heilman, K. M., & Watson, R. T. (1977). Mechanisms underlying the unilateral neglect syndrome. Advances in Neurology, 18, 93–106. Heindel, W. C., Salmon, D. P., Shults, C. W., Walicke, P. A., & Butters, N. (1989). Neuropsychological evidence for multiple implicit memory systems: A comparison of Alzheimer ’s, Huntington’s, and Parkinson’s disease patients. Journal of Neuroscience, 9, 582–587. Hodges, J. R. (2007). Cognitive assessment for clinicians (2nd ed.). Oxford: Oxford University Press. Hodges, J. R., Patterson, K., Ward, R., Garrard, P., Bak, T., Perry, R., et al. (1999). The differentiation of semantic dementia and frontal lobe dementia (temporal and frontal variants of frontotemporal dementia) from early Alzheimer ’s disease: a comparative neuropsychological study. Neuropsychology, 13, 31–40. Hon, N., Epstein, R. A., Owen, A. M., & Duncan, J. (2006). Frontoparietal activity with minimal decision and control. Journal of Neuroscience, 26, 9805–9809.

8/19/09 5:12:24 PM

1176

Cognitive Neurology

Hopfinger, J. B., Woldorff, M. G., Fletcher, E. M., & Mangun, G. R. (2001). Dissociating top-down attentional control from selective perception and action. Neuropsychologia, 39, 1277–1291. House, A., & Hodges, J. (1988). Persistent denial of handicap after infarction of the right basal ganglia: A case study. Journal of neurology, neurosurgery, and psychiatry, 51, 112–115. Howard, D., & Hatfield, F. (1987). Aphasia therapy: Historical and contemporary issues. Hillsdale: Erlbaum. Humphreys, G. W., & Riddoch, M. J. (1987). To see but not to see: A case study of visual agnosia. Hove: Laurence ErlbaumLtd. Jackson, G. M., Shepherd, T., Mueller, S. C., Husain, M., & Jackson, S. R. (2006). Dorsal simultanagnosia: An impairment of visual processing or visual awareness? Cortex, 42, 740–749. Jefferies, E., Sage, K., & Ralph, M. A. (2007). Do deep dyslexia, dysphasia and dysgraphia share a common phonological impairment? Neuropsychologia, 45, 1553–1570. Johnson, M., O’Connor, M., & Cantor, J. (1997). Confabulation, memory deficits and frontal dysfunction. Brain and Cognition, 34, 189–206. Kanwisher, N., & Wojciulik, E. (2000). Visual attention: Insights from brain imaging. Nature Reviews Neuroscience, 1, 91–100. Kaplan, E. (1988). The process approach to neuropsychological assessment. Aphasiology, 2, 309–312. Karnath, H. O., Ferber, S., & Himmelbach, M. (2001). Spatial awareness is a function of the temporal not the posterior parietal lobe. Nature, 411, 950–953. Karnath, H. O., Miller, D., & Valar, G. (Eds.). (2002). The cognitive and neural bases of spatial neglect. Oxford: Oxford University Press. Kennerknecht, I., Plumpe, N., Edwards, S., & Raman, R. (2007). Hereditary prosopagnosia (HPA): The first report outside the Caucasian population. Journal of Human Genetics, 52, 230–236. Kim, H. J., Chu, K., Lee, K. M., Kim, D. W., & Park, S. H. (2002). Phonological agraphia after superior temporal gyrus infarction. Archives of Neurology, 59, 1314–1316. Kinsbourne, M., & Warrington, E. K. (1962). A study of finger agnosia. Brain, 85, 47–66. Koechlin, E., & Hyafil, A. (2007). Anterior prefrontal function and the limits of human decision-making. Science, 318, 594–598. Kopelman, M. D. (2000). Focal retrograde amnesia and the attribution of causality: An exceptionally critical view. Cognitive Neuropsychology, 17, 585–621. Kopelman, M. D. (2002). Disorders of memory. Brain, 125, 2152–2190. Kosslyn, S. M. (1994). Image and brain: The resolution of the imagery debate. MIT: MIT Press. Kosslyn, S. M. & Thomson, W. L. (2003). When is early visual cortex activated during visual mental imagery? Psychological Bulletin, 129, 723–746. Kringelbach, M. L., & Rolls, E. T. (2004). The functional neuroanatomy of the human orbitofrontal cortex: Evidence from neuroimaging and neuropsychology. Progress in Neurobiology, 72, 341–372. Lahav, H. (1993). What neuropsychology tell us about consciousness. Philosophy of Science, 60, 67–85. Lambon Ralph, M. A & Graham, N. L. (2000). Previous cases: Acquired phonological and deep dyslexia. Neurocase, 6, 141–178. Lampl, Y., Lorberboym, M., Gilad, R., Boaz, M., & Sadeh M. (2005). Auditory hallucinations in acute stroke. Behavioral Neurology, 16, 211–216.

Levine, D. N., Calvanio, R., & Rinn, W. E. (1991). The pathogenesis of anosognosia for hemiplegia. Neurology, 41, 1770–1781. Levy, D. A., Bayley, P. J., & Squire, L. R. (2004). The anatomy of semantic knowledge: Medial vs. lateral temporal lobe. Proceedings of the National Academy of Sciences USA, 101, 6710–6715. Lezak, M. D., Howieson, D. B., Loring, D. W., Hannay, H. J., & Fischer, J. S. (2004). Neuropsychological assessment (4th ed.). Oxford: Oxford University Press. Lissauer, H. (1890). Ein fall von seelenblindheit nebst einem beitrage zur theorie derselben. Archiv Fur Psychaitrie und Nervenkrankheiten, 21, 222–270. Luaute, J., Halligan, P., Rode, G., Rossetti, Y., & Boisson, D. (2006). Visuospatial neglect: A systematic review of current interventions and their effectiveness. Neuroscience and Biobehavioral Reviews, 30, 961–982. MacDonald, A. W. III., Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science, 288, 1835–1838. Macoir, J., & Bernier, J. (2002). Is surface dysgraphia tied to semantic impairment? Evidence from a case of semantic dementia. Brain and Cognition, 48, 452–457. Mah, L., Arnold, M. C., & Grafman, J. (2004). Impairment of social perception associated with lesions of the prefrontal cortex. American Journal of Psychiatry, 161, 1247–1255. Manford, M., & Andermann, F. (1998). Complex visual hallucinations: Clinical and neurobiological insights. Brain, 121, 1819–1840. Mangels, J. A. (1997). Strategic processing and memory for temporal order in patients with frontal lobe lesions. Neuropsychology, 11, 207–221. Manning, L., & Kartsounis, L. D. (1993). Confabulations related to tacit awareness in visual neglect. Behavioural Neurology, 6, 211–213. Marcel, A. J., Tegnér, R., & Nimmo-Smith, I. (2004). Anosognosia for plegia: Specificity, extension, partiality and disunity of bodily unawareness. Cortex, 40, 19–40. Margolin, D. I. (1991). Cognitive neuropsychology. Resolving enigmas about Wernicke’s aphasia and other higher cortical disorders. Archives of Neurology, 48, 751–765. Marr, D. (1982). Vision. San Francisco: Freeman. Marshall, J. C., & Halligan, P. W. (1988). Blindsight and insight in visuospatial neglect. Nature, 336, 766–767. Marshall, J. C., & Halligan, P. W. (1994). The Yin and the Yang of visuospatial neglect: A case study. Neuropsychologia, 32, 1037–1057. Marshall, J. C., & Halligan, P. W. (1995). Seeing the forest but only half the trees. Nature, 373, 521–523. Marshall, J. C., & Halligan, P. W. (2002). Whoever would have imagined it? Bisiach and Luzzatti. (1978). On representational neglect in patients, I. G., & NV. In C. Code, C. W. Wallesch, Y. Joanette & A. Riche-Lecours (Eds.), Classic Cases in Neuropsychology Vol. II (pp. 272–274). Hove: Psychology Press. Marshall, J. C., & Newcombe, F. (1966). Syntactic and semantic errors in paralexia. Neuropsychologia, 4, 181–188. Marshall, J. C., & Newcombe, F. (1973). Patterns of paralexia: A psycholinguistic approach. Journal of Psycholinguistic Research, 2, 175–199. McGlinchey-Berroth, R., Milberg, W. P., Verfaellie, M., Alexander, M., & Kilduff, P. T. (1993). Semantic processing in the neglected visual field: Evidence from a lexical decision task. Cognitive Neuropsychology, 10, 79–108.

Lavie, N. (1995). Perceptual load as a necessary condition for selective attention. Journal of Experimental Psychology: Human Perception and Performance 21, 451–468.

McIntosh, R. D., McClements, K. I., Schindler, I., Cassidy, T. P., Birchall, D., & Milner, A.D. (2004). Avoidance of obstacles in the absence of visual awareness. Proceedings of the Royal Society B: Biological Sciences, 271, 15–20.

Leff, A. P., Spitsyna, G., Plant, G. T., & Wise, R. J. (2006). Structural anatomy of pure and hemianopic alexia. Journal of Neurology, Neurosurgery, and Psychiatry, 77, 1004–1007.

McKone, E., Kanwisher, N., & Duchaine, B. C. (2007). Can generic expertise explain special processing for faces? Trends in Cognitive Sciences, 11, 8–15.

c59.indd Sec8:1176

8/19/09 5:12:24 PM

References 1177 McMillan, T. M., Jongen, E. L., & Greenwood, R. J. (1996). Assessment of post-traumatic amnesia after severe closed head injury: Retrospective or prospective? Journal of Neurology, Neurosurgery, and Psychiatry, 60, 422–427. Mechelli, A., Price, C. J., Friston, K. J., & Ishai, A. (2004). Where bottom-up meets top-down: Neuronal interactions during perception and imagery. Cerebal Cortex, 14, 1256–1265.

Pitzalis, S., Di Russo, F., Spinelli, D., & Zoccolotti, P. (2001). Influence of the radial and vertical dimensions on lateral neglect. Experimental Brain Research, 136, 281–294.

Mesulam, M. M. (1981). A cortical network for directed attention and unilateral neglect. Annals of Neurology, 10, 309–325.

Plaut, D. C. (1997). Structure and function in the lexical system: Insights from distributed models of word reading and lexical decision. Language and Cognitive Processes, 12, 767–808.

Mesulam, M. M. (2000). Principles of behavioral and cognitive neurology (2nd ed.). Oxford: Oxford University Press.

Ponsford, J. L., Sloan, S., & Snow, P. (1995). Traumatic brain injury: Rehabilitation for everyday adaptive living. Hove: Erlbaum Ltd.

Michel, F., Peronnet, F., & Schott, B. (1980). A case of cortical deafness: Clinical and electrophysiological data. Brain and Language, 10, 367–377.

Posner, M. I. (1978). Chronometric Explorations of Mind. Hillsdale, NJ: Erlbaum.

Milberg, W., Blumstein, S. E., & Dworetsky, B. (1987). Phonological processing and lexical access in aphasia. Brain and Language, 34, 279–293. Mirsky, A. F., Anthony, B. J., Duncan, C. C., Ahearn, M. B., & Kellam, S. G. (1991). Analysis of the elements of attention: A neuropsychological approach. Neuropsychology Review, 2, 109–145. Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100. Moran, J., & Desimone, R. (1985). Selective attention gates visual processing in the extrastriate cortex. Science, 229, 782–784. Moscovitch, M., Winocur, G., & McLachlan, D. (1986). Memory as assessed by recognition and reading time in normal and memoryimpaired people with Alzheimer ’s disease and other neurological disorders. Journal of Experimental Psychology: Learning Memory and Cognition, 115, 331–347. Muller, N. G., & Knight, R. T. (2006). The functional neuroanatomy of working memory: Contributions of human brain lesion studies. Neuroscience, 139, 51–58. Nathanson, M., Bergman, P., & Gordon, G. (1952). Denial of illness. Its occurrence in one hundred consecutive cases of hemiplegia. Archives of Neurology and Psychiatry, 68, 380–387.Neuropsychologia, 44, 2734–2748. Norman, D. A., & Shallice, T. (1986). Attention to action: Willed and automatic control of behavior. In Davidson, R. J., Schwartz, G. E., & Shapiro, D. (Eds.), Consciousness and self-regulation: Advances in research and theory (pp. 1–18). New York: Plenum Press. Ortigue, S., Mégevand, P., Perren, F., Landis, T., & Blanke, O. (2006). Double dissociation between representational personal and extrapersonal neglect. Neurology, 66, 1414–1417. Owen, A. M. (1997). Cognitive planning in humans: Neuropsychological, neuroanatomical and neuropharmacological perspectives. Progress in Neurobiology, 53, 431–450. Paillard, J., Michel, F., & Stelmach, G. (1983). Localization without content: A tactile analogue of ‘blind sight’. Archives of Neurology, 40, 548–551. Parkin, A. J., Ward, J., Bindschaedler, C., Squires, E. J., & Powell, G. (1999). False recognition following frontal lobe damage: The role of encoding factors. Cognitive Neuropsychology, 16, 243–265.

c59.indd Sec8:1177

Pisella, L., Binkofski, F., Lasek, K., Toni, I., & Rossetti, Y. (2006). No double-dissociation between optic ataxia and visual agnosia: Multiple sub-streams for multiple visuo-manual integrations.

Posner, M. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology 32, 3–25. Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25–42. Postle, B. R. (2006). Working memory as an emergent property of the mind and brain. Neuroscience, 139, 23–38. Previc, F. H. (1990). Functional specialization in the lower and upper visual fields in humans: Its ecological origins and neurophysiological implications. Behavioral and Brain Sciences, 13, 519–575. Priftis, K., Rusconi, E., Umilta, C., & Zorzi, M. (2003). Pure agnosia for mirror stimuli after right inferior parietal lesion. Brain, 126, 908–919. Prigatano, G. P. (1999). Principles of Neuropsychological Rehabilitation. Oxford: Oxford University Press. Prigatano, G. P., & Schacter, D. L. (Eds.). (1991). Awareness of Deficit after Brain Injury. Oxford, Oxford University Press. Pylyshyn, Z. W. (1981). The imagery debate: Analogue media versus tacit knowledge. Psychological Review, 87, 16–45. Quinette, P., Guillery-Girard, B., Dayan, J., de la Sayette, V., Marquis, S., Viader, F., et al. (2006). What does transient global amnesia really mean? Review of the literature and thorough study of 142 cases. Brain, 129, 1640–58. Rafal, R., Smith, J., Krantz, J., Cohen, A., & Brennan, C. (1990). Extrageniculate vision in hemianopic humans: Saccade inhibition by signals in the blind field. Science, 250, 118–121. Rapcsak, S. Z., & Beeson, P. M. (2004). The role of left posterior inferior temporal cortex in spelling. Neurology, 62, 2221–2229. Rapcsak, S. Z., Beeson, P. M., & Rubens, A. B. (1991). Writing with the right hemisphere. Brain and Language, 41, 510–530. Rapcsak, S. Z., Henry, M. L., Teague, S. L., Carnahan, S. D., & Beeson, P. M. (2007). Do dual-route models accurately predict reading and spelling performance in individuals with acquired alexia and agraphia? Neuropsychologia, 45, 2519–2524. Raz, A., & Buhle, J. (2006). Typologies of attentional networks. Nature Reviews Neuroscience, 7, 367–379. Riddoch, M. J., & Humphreys, G. W. (1994). Cognitive neuropsychology and cognitive rehabilitation: A marriage of equal partners. In M. J. Riddoch & G. W. Humphreys (Eds.), Cognitive Neuropsychology and Cognitive Rehabilitation. London: Erlbaum.

Patterson, K., & Lambon Ralph, M. A. (1999). Selective disorders of reading? Current Opinion in Neurobiology, 9, 235–239.

Rizzolatti, G., Berti, A., & Gallese, V. (2000). Spatial neglect: Neurophysiological bases, cortical circuits and theories. In F. Boller & J. Grafman (Eds.), Handbook of neuropsychology (pp. pp. 503–537). London: Elsevier.

Perenin, M.-T., & Rossetti, Y. (1996). Grasping in an hemianopic field. Another instance of dissociation between perception and action. NeuroReport, 7, 793–897.

Robertson, R. M. (2004). Modulation of neural circuit operation by prior environmental stress. Integrative and Comparative Biology, 44, 21–27.

Pia, L., Neppi-Modona, M., Ricci, R., & Berti, A. (2004). The anatomy of anosognosia for hemiplegia: A meta-analysis. Cortex, 40, 367–377.

Robertson, I. H., & Halligan, P. W. (1999). Spatial neglect: A clinical handbook for diagnosis and treatment. London: Erlbaum.

8/19/09 5:12:25 PM

1178

Cognitive Neurology

Robertson, L. C., & Lamb, M. R. (1991). Neuropsychological contributions to theories of part/whole organization. Cognitive Psychology, 23, 299–330. Roeltgen, D. P. (2003). Agraphia. In K. M. Heilman & E. Valenstein (Eds.), Clinical Neuropsychology (4th ed.) (pp. 63–89). Oxford: Oxford University Press. Roeltgen, D. P., & Heilman, K. M. (1983). Apraxic agraphia in a patient with normal praxis. Brain and Language, 18, 35–46. Rolls, E. T. (1996). The orbitofrontal cortex. Philosophic Transactions of the Royal Society of London, 351, 1433–1444. Rossetti, Y., Rode, G., & Boisson, D. (1995). Implicit processing of somaesthetic information: A dissociation between where and how? NeuroReport, 6, 506–510.

Stoerig, P. (1996). Varieties of vision: From blind responses to conscious recognition. Trends in Neurosciences, 19, 401–406. Stone, S. P., Halligan, P. W., & Greenwood, R. J. (1993). The incidence of neglect phenomena and related disorders in patients with an acute right or left hemisphere stroke. Age and Ageing, 22, 46–52. Strauss, E., Sherman, E. M. S., & Spreen, O. (2006). A compendium of Neuropsychological tests administration, norms, and commentary (3rd ed.). Oxford: Oxford University Press. Stuss, D. T., & Alexander, M. P. (2007). Is there a dysexecutive syndrome? Philosophical Transactions of the Royal Society of London. Series, B., Biological Sciences, 362, 901–915. Temple, C. M. (2006). Developmental and acquired dyslexias. Cortex, 42, 898–910.

Ruchkin, D. S., Grafman, J., Cameron, K., & Berndt, R. S. (2003). Working memory retention systems: A state of activated long-term memory. Behavioral and Brain Sciences, 26, 709–777.

Tranel, D., & Damasio, A. R. (1985). Knowledge without awareness: An autonomic index of facial recognition by prosopagnosics. Science, 228, 1453–1454.

Samuelsson, H., Jensen, C., Ekholm, S., Naver, H., & Blomstrand, C. (1997). Anatomical and neurological correlates of acute and chronic visuospatial neglect following right hemisphere stroke. Cortex, 33, 271–285.

Tree, J. J., & Kay, J. (2006). Phonological dyslexia and phonological impairment: An exception to the rule? Neuropsychologia, 44, 2861–2873.

Schnider, A. (2003). Spontaneous confabulation and the adaptation of thought to ongoing reality. Nature Reviews Neuroscience, 4, 662–671. Schöning, M., & Hartig, B. (1996). Age Dependence of Total Cerebral Blood Flow Volume from Childhood to Adulthood. Journal of Cerebral Blood Flow and Metabolism, 16, 827–833. Seidenberg, M. S., & McClelland, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96, 523–568. Shallice, T. (1988). From Neuropsychology to Mental Structure. Cambridge: Cambridge University Press. Shallice, T. (2002). Fractionation of the Supervisory System. In D. T. Stuss & R. T. Knight (Eds.), Principles of Frontal Lobe Function (pp. 261–277). Oxford: Oxford University Press. Shallice, T., & Jackson, M. (1988). Lissauer on agnosia. Cognitive Neuropsychology, 5, 153–192. Shallice, T., & Rosazza, C. (2001). Patterns of peripheral paralexia: Pure alexia and the forgotten visual dyslexia? Cortex, 42, 892–897. Shallice, T., & Saffran, E. (1986). Lexical processing in the absence of explicit word identification: Evidence from a letter-by-letter reader. Cognitive Neuropsychology, 3, 429–458. Shulman, M. B., Alexander, M. P., McGlinchey-Berroth, R., & Milberg, W. (2002). Triangular backgrounds shift the bias of line bisection performance in hemispatial neglect. Journal of Neurology, Neurosurgery, and Psychiatry, 72, 68–72. Siegal, M., & Varley, R. (2002). Neural systems involved in “theory of mind.” Nature Reviews Neuroscience, 3, 463–471. Silver, J. M. (2006). Behavioral neurology and neuropsychiatry is a subspecialty. Journal of Neuropsychiatry and Clinical Neurosciences, 18, 146–148. Small, D. M., Bernasconi, N., Bernasconi, A., Sziklas, V., & Jones-Gotman, M. (2005). Gustatory agnosia. Neurology, 64, 311–317. Snyder, P. J., Nussbaum, P. D., & Robins, D. L. (2005). Clinical Neuropsychology: A Pocket Handbook for Assessment (2nd ed.). American Psychological Association.

Turnbull, O. H., Della Sala, S., & Beschin, N. (2002). Agnosia for object orientation: Naming and mental rotation evidence. Neurocase, 8, 296–305. Umilta, C. (2000). Mechanisms of attention. In B. Rapp (Ed.), Handbook of Cognitive Neuropsychology (pp. 135–158). Hove: Psychology Press. Vaina, L. M., Cowey, A., Eskew, R. T., Jr, LeMay, M., & Kemper, T. (2001). Regional cerebral correlates of global motion perception: Evidence from unilateral cerebral brain damage. Brain, 124, 310–321. Vallar, G. (1998). Spatial hemineglect in humans. Trends in Cognitive Science, 2, 87–97. Vallar, G., & Perani, D. (1986). The anatomy of unilateral neglect after right-hemisphere stroke lesions. A clinical/CT-scan correlation study in man. Neuropsychologia, 24, 609–622. Valmaggia, C., & Gottlob, I. (2002). Optokinetic nystagmus elicited by filling-in in adults with central scotoma. Investigative Ophthalmology and Visual Science, 43, 1804–1808. Van Gemmert, A. W., Teulings, H. L., & Stelmach, G. E. (2001). Parkinsonian patients reduce their stroke size with increased processing demands. Brain and Cognition, 47, 504–512. Vanier, M., & Caplan, D. (1985). CT scan correlates of surface dyslexia. In KE Patterson (Ed.), Surface dyslexia: Neuropsychological and cognitive analyses of phonological reading. Hove: Psychology Press Ltd. Van Zomeren, A. H., Brouwer, W. H., & Deelman, B. G. (1984). Attentional deficits: The riddles of selectivity, speed, and alertness. In N. Brooks (Ed.), Closed head injury: Psychological, social and family consequences (pp. 74–107). Oxford: Oxford University Press. Viaud-Delmon, I., Brugger, P., & Landis, T. (2007). Hemineglect: Take a look at the back space. Annals of Neurology, 62, 418–422. Vignolo, L. A. (2003). Music agnosia and auditory agnosia: Dissociations in stroke patients. Annals of the New York Academy of Sciences, 999, 50–57. Vuilleumier, P., Valenza, N., Mayer, E., Reverdin, A., & Landis, T. (1998). Near and far visual space in unilateral neglect. Annals of Neurology, 43, 406–410.

Sohlberg, M., & Mateer, C. (1989). Introduction to Cognitive Rehabilitation: Theory and Practice. New York: Guilford Press.

Warrington, E. K., & James, M. (1986). Visual object recognition in patients with right hemisphere lesions: Axes or features? Perception, 15, 355–366.

Spiers, H. J., Maguire, E. A., & Burgess, N. (2001). Hippocampal amnesia. Neurocase, 7, 357–382.

Weiskrantz, L. (1986). Blindsight: A case study and implications. Oxford: Clarendon Press.

Starkstein, S. E., & Robinson, R. G. (1997). Mechanism of Disinhibition After Brain Lesions. Journal of Nervous and Mental Diseases, 185, 108–114.

Weiskrantz, L. (1990). The Ferrier lecture, 1989. Outlooks for blindsight: explicit methodologies for implicit processes. Proceedings of the Royal Society of London, Series B (Biology), 239, 247–278.

c59.indd Sec8:1178

8/19/09 5:12:25 PM

References 1179 Weiskrantz L. (1991). Disconnected awareness for detecting, processing, and remembering in neurological patients. Journal of the Royal Society of Medicine, 84, 466–470. Weiskrantz, L., Harlow, A., & Barbur, J. L. (1991). Factors affecting visual sensitivity in a hemianopic subject. Brain, 114, 2269–2282. Weiskrantz, L., Warrington, E. K., Sanders, M. D., & Marshall, J. (1974). Visual capacity in the hemianopic field following a restricted occipital ablation. Brain, 97, 709–728. Weiss, P. H., Marshall, J. C., Wunderlich, G., Tellmann, L., Halligan, P. W., Freund, H. J., et al. (2000). Neural consequences of acting in near versus far space: A physiological basis for clinical dissociations. Brain, 123, 2531–2541. Wilkinson, D., & Halligan, P. (2004). The relevance of behavioral measures for functional-imaging studies of cognition. Nature Reviews Neuroscience, 5, 67–73.

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Woldorff, M. G., Gallen, C. C., Hampson, S. A., Hillyard, S. A., Pantev, C., Sobel, D., et al. (1993). Modulation of early sensory processing in human auditory cortex during auditory selective attention. Proceedings of the National Academy of Sciences USA, 90, 8722–6. Wood, R. L. L., & Fussey, I. (2000). Cognitive Rehabilitation in Perspective. Hove, England: Psychology Press. Young, A. (1994). Covert recognition. In M. Farah & G. Ratcliff (Eds.), The neuropsychology of high-level vision. Hillsdale, NJ: Erlbaum. Zeki, S. (1993). A vision of the brain. Oxford: Blackwell. Zihl, J., Tretter, F., & Singer, W. (1980). Phasic electrodermal responses after visual stimulation in the cortically blind hemifield. Behavioural Brain Research, 1, 197–203. Zur, D., & Ullman, S. (2003). Filling-in of retinal scotomas. Vision Research, 43, 971–982.

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Chapter 60

Genetics and Psychopathology CHRISTEL M. MIDDELDORP AND DORRET I. BOOMSMA

In this chapter, we introduce the major principles of genetic research in psychopathology. To illustrate these principles, an overview of genetic studies of depression and anxiety is given. We first introduce the background of genetic epidemiology that focuses on the question to what extent a trait or disorder clusters in families and if this clustering has a genetic basis. The methods to investigate issues going beyond the question of heritability are described, including sex and age differences in the genetic architecture, gene-environment correlation, gene-environment interaction, and multivariate genetic approaches to examine the etiology of comorbidity between traits or disorders. This section is followed by a discussion of the results of these types of studies on anxiety and depression. Next, the methods of gene findings studies are introduced again followed by a discussion of the results of linkage and association studies that aim at localizing and identifying the genes underlying the genetic component in anxiety and depression. Finally, we briefly touch on some issues complicating genetics in psychopathology.

on variation in normal and abnormal human behavior may be additive or may manifest itself through more complex pathways in which the influences of genes and environment interact (Falconer & Mackay, 1996; Lynch & Walsh, 1998; Plomin, DeFries, McClearn, & McGuffin, 2008). Genetic factors represent the effects of one or many unidentified genes. For quantitative, complex traits these effects are due to a possibly large, but unknown, number of genes (polygenes). Genes can only influence variation if they are polymorphic, that is, occur in two or more variants in the population, called alleles. The effect of alleles can be additive (their effects sum up) or alleles at the same or different loci can interact. Interaction, or genetic nonadditivity, between alleles within the same locus is referred to as genetic dominance; interaction between alleles across different loci is referred to as epistasis. In data from humans, genetic dominance and epistasis are difficult to distinguish. Moreover, relatives will not show a high degree of resemblance for traits that result from genetic nonadditivity. There is one exception: Identical twins will resemble each other also for traits that show dominance or epistasis. A large difference in the degree of resemblance between identical twins and first-degree relatives thus gives an indication that genetic nonadditivity plays a role. The relative influence of genetic factors on phenotypic variation (where the phenotype stands for any observable trait), the heritability, is commonly defined as the proportion of total phenotypic variance that can be attributed to genetic variance. Broad-sense heritability includes all sources of genetic variance (additive and nonadditive); narrow-sense heritability only includes additive genetic variance. All nongenetic influences on phenotypic variation are referred to as environmental influences and include the early influences of prenatal environment, the influence of the (early) home environment, the influence of the neighborhood, and many other, usually unidentified nongenetic effects. Environmental influences are often distinguished into two broad classes: common environmental influences that are shared among family members (e.g., siblings) who

ASSESSMENT OF THE IMPORTANCE OF GENETIC VARIATION IN COMPLEX TRAITS Individual differences in complex traits (like psychiatric disorders, intelligence, height, or blood pressure) may be due to genetic or environmental factors. These traits are called complex because their genetic architecture most likely is complex. They are influenced by multiple genetic as well as environmental effects and do not show a simple pattern of Mendelian inheritance. The influence of these factors

We would like to thank Jouke-Jan Hottenga for his critical reading of this chapter. The study was supported by the Netherlands Organization for Scientific Research NWO/ZonMW (SPI 56-464-14192, 940g-37024, 400-05-717), CMSB (Center for Medical Systems Biology): NWO Genomics. CM was supported by the Hersenstichting Nederland (13F05(2).47). 1180

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Assessment of the Importance of Genetic Variation in Complex Traits

grow up in the same family and that tend to make them alike, and unique environmental influences, that is, environmental influences that are unique to an individual and are not shared among family members. Measurement error and other sources of unreliability in a trait contribute to the unique environmental influences. To estimate the influences of genotype and environment on phenotypic variation, it is not necessary to collect genetic material (DNA) or to measure the environment. The relative importance of both sources of variation may be estimated by statistically analyzing data that have been collected in groups of individuals who are genetically related or who do not share their genes, but who share their environment (Boomsma, Busjahn, & Peltonen, 2002; Martin, Boomsma, & Machin, 1997). For example, data from adopted children may be compared with data from their biological and adoptive parents. The resemblance between adopted children and their biological parents reinforces the importance of genetic inheritance, the resemblance of adoptive parents and their adopted children relates to the importance of cultural inheritance and shared home environment. There are some famous adoption studies on the inheritance of schizophrenia. For example, Heston (1966) looked at adopted children whose biological parents suffered from schizophrenia versus adopted children whose biological parents did not suffer from schizophrenia. All of the children studied were given up for adoption immediately after birth. Those children with a much higher chance to get the disorder had a biological parent who suffered from schizophrenia. These results clearly indicate a role for genetic factors in the development of schizophrenia. However, adoptions are relatively rare and often neither the adopted child nor the adoptive parents are entirely representative of the general population. Therefore, the majority of studies that estimate heritability of complex traits make use of the classical twin design to unravel sources of variance. An introduction to this methodology can be found, for example, in Boomsma et al. (2002), Kendler and Eaves (2005), Plomin et al. (2008), and Posthuma et al. (2003). In the classical twin design, data from monozygotic and dizygotic twins are used to decompose the variation of a trait into genetic and environmental contributions by comparing within pair resemblance for both types of twins. Monozygotic (MZ) twins share their common environment and (nearly always) 100% of their genes. Dizygotic (DZ) twins also share their common environment and on average 50% of their segregating genes (Hall, 2003). If MZ within twin pair resemblance for a certain trait is higher than DZ within twin pair resemblance, this suggests the presence of genetic influences on that trait. A first impression of the narrow-sense heritability (a2) of a phenotype can be calculated as twice the difference between the MZ and DZ correlations: a2  2(rMZ  rDZ). The expectation of

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the correlation in MZ twins equals: rMZ  a2  c2 (where c2 represents the proportion of the total variance attributable to common environment, that is, the environment shared by children raised in the same family). The expectation of the correlation in DZ twins equals: rDZ  ½a2  c2. If there is, in addition to additive genetic influences, a contribution of genetic dominance, the expectations for the MZ correlations is: rMZ  a2  d2  c2 and for the DZ correlation it is: rDZ  ½a2  ¼d2  c2. This is the situation in which the MZ correlation can be substantially higher than the DZ correlation and where the simple approach of doubling the difference between the two correlations is no longer appropriate. In this situation, the broad-sense heritability (h2) must be estimated using different approaches, outlined below, and represents the influence of additive and nonadditive genetic factors. Although it is possible that both genetic dominance and common environment are of importance, these effects cannot be simultaneously estimated in the classical twin design if only data from MZ and DZ twins reared together are available. If MZ within twin pair resemblance for a certain trait is similar to DZ within twin pair resemblance, or if rMZ  2rDZ this suggests the presence of common environmental influences on that trait. A first impression of the effect of common environmental influences can be calculated as c2  rMZ  a2 (or c2  2rDZ – rMZ). It may be important to emphasize that it is unknown what the common environment includes. One can think of parenting or socioeconomic status, but this needs to be investigated if a common environmental effect is found. A first impression of the importance of unique environmental influences can be calculated as e2  1 – rMZ. Finally, if genetic dominance plays a role, its importance can be estimated as: d2  4rDZ – rMZ. If, for example, the correlation for a certain trait equals 0.60 in MZ twins and 0.45 in DZ twins, then the estimate for a2  2(0.60–0.45)  0.30, for c2  0.60 – 0.30  0.30, and for e2  1 – 0.60  0.40. If, on the other hand, the correlation in MZ twins equals 0.8 and the correlation in DZ twin pairs equals 0.3, nonadditive effects are probably present. Then, the estimate for d2 is 1.2 – 0.8  0.4 and the estimate for a2 is also 0.4; giving a total heritability for the trait of 0.8 (note that in this case the total heritability is equal to the MZ correlation). The effect of e2 is 0.20. The effect of c2, if it were present, cannot be estimated. Structural Equation Modeling in the Classical Twin Design To test how well the expectations for the resemblance of relatives describe the actual data and to test which model (e.g., AE, ACE, CE, or ADE) describes the data best, parameters can be estimated by maximum likelihood or other

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approaches. Structural equation modeling (SEM) or genetic covariance structure modeling (GCSM) provides a general and flexible approach to analyze data gathered in genetically informative samples such as in the classical twin study. In applying GCSM to data from relatives, genotypic and environmental effects are modeled as the contribution of latent (unmeasured) variables to the (possibly multivariate) phenotypic individual differences. These latent factors represent the effects of many unidentified influences, for example, polygenes and environment. For a more detailed overview of GCSM, see Boomsma and Molenaar (1986), Boomsma and Dolan (2000), Neale (2000), and Posthuma et al. (2003). Structural relations between measured variables (phenotypes) and unmeasured variables are often graphically represented in a path diagram, which is a mathematically complete description of a structural equation model. An example of such a model for a single trait in a twin pair is shown in Figure 60.1. The variables in squares are the observed phenotypes in twin 1 and in twin 2. The variables in circles are latent (unobserved). Their influence on the phenotype is given by path coefficients a, c, and e. Identification of structural equation models in genetics is achieved from a design that includes relatives at different degrees of relatedness, for example, by inclusion of monozygotic (MZ) and dizygotic (DZ) twins into the study. Knowledge about Mendelian inheritance patterns defines the correlations among the latent factors in Figure 60.1. The path coefficients in Figure 60.1 define the relative importance of A, C, and E on the phenotype (P): P  aA ACE Model for Twin Data 1 MZ⫽1.0 / DZ⫽0.5 E

C

e

c

PT1

A

a

A

C

a

E

c

e

PT2

Figure 60.1 Path diagram for a single phenotype (P) assessed in twin 1 and twin 2. Note: P is influenced by latent factors A (additive genetic influences), C (common environment shared by twins), and E (unique environment). Parameters a, c, and e represent the nonstandardized factor loadings. The latent factors are standardized. The correlation between the latent A factors depends on zygosity, and is 1 for MZ (monozygotic) and 0.5 for DZ (dizygotic) twins. The correlation between C factors is independent of zygosity as MZ and DZ twins share the same amount of environment. The correlation between A factors and between C factors may depend on sex of the twins. Especially in the presence of qualitative sex differences, in dizygotic twins of opposite sex the correlation between A factors can be lower than 0.5 or the correlation between C factors can be lower than 1 (indicating that different genes are expressed in men and women; or that different common environmental factors are of importance in the two sexes).

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 cC  eE. Expressed in variance components, the phenotypic variance can be written as: var(P)  a2 var(A)  c2 var(C)  e2 var(E), under the assumption that A, C, and E are uncorrelated and do not interact. If var(A)  var(C)  var(E)  1, the expression for the population variance reduces to: var(P)  a2  c2  e2. Please note a possible source of confusion: In the above expression a2, c2, and e2 represent variance components; often, as we did ourselves in the introduction above, they represent standardized components (i.e., the variance components divided by the variance of P). The contributions of the latent variables are estimated as regression coefficients in the linear regression of the observed variables on the latent variables. Given an appropriate design providing sufficient information to identify these regression coefficients actual estimates may be obtained using a number of well-disseminated computer programs, such as LISREL (Jöreskog & Sörbom, 1989) or Mx (Neale, Boker, Xie, & Maes, 2006). These programs allow estimation of parameters by means of a number of estimators including normal theory maximum likelihood (ML) and weighted least squares (WLS). These estimators can also be applied to estimate and analyze correlations among family members for discrete variables or variables that show a nonnormal distribution (Derks, Dolan, & Boomsma, 2004). Data from relatives on categorical traits (e.g., presence or absence of disorder) are analyzed within this framework by making use of a threshold model that assumes there is a continuously and normally distributed liability underlying a disorder in the populations. One or more thresholds divide the continuous distribution into discrete classes, for example, affected and unaffected (Figure 60.2; Falconer & Mackay, 1996). The tetrachoric (for dichotomous traits) or polychoric (for ordered-category data with more than two categories) correlations represent the resemblance between relatives on the unobserved liability dimension. The significance of parameters a, c, (or d) in Figure 60.2 is tested with a likelihood ratio test. The test involves constraining the parameter of interest at zero and then testing whether the constraint leads to a significant decrease in goodness-of-fit of the model. Twice the difference between the log-likelihood of two models (e.g., an ACE model and an AE model in which the influence of C is constrained at zero) is distributed asymptotically as 2. The degrees of freedom for the test are equal to the difference in parameters being estimated. Utilizing the principle of parsimony, the most restrictive model is accepted as the best fitting one in case the difference between a nested and a more comprehensive model is not significant (Neale & Cardon, 1992). The e parameter cannot be dropped from the model because this also includes the measurement error.

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Assessment of the Importance of Genetic Variation in Complex Traits Multifactorial Threshold Model of Disease Single threshold Multiple thresholds

Unaffected

Affected

Normal

Disease liability

Mild Mod Severe

Disease liability

Figure 60.2 A threshold model assumes a normally distributed liability (or vulnerability) underlying a disorder or an orderedcategory trait. Note: The left figure shows a single threshold model with subjects scoring below the threshold on the, unobserved, liability scale being unaffected and subjects scoring above the threshold being affected. The right figure shows a model with multiple thresholds, for example mild, moderate, or severe depression. The tetrachoric correlation (for binary data) and the polychoric correlation (for ordered-category data) estimate what the correlation between family members would be if ratings for these traits were made on a continuous scale.

Beyond the Question of Heritability Sex and Age Differences in the Genetic Architecture The contributions of genetic factors to phenotypic variance may differ between men and women, as may the contribution of environmental factors. The expression of the genotype may also change with age. If we again denote the influence of A, C, and E on the phenotype by parameters a, c, and e, and the proportion of variance due to each of these factors as the square of these parameters, then three different models can be examined for quantitative sex differences in genetic architecture: 1. A full model in which estimates for a, c, and e are allowed to differ in magnitude between males and females. The outcome of the model can be, for example, that the genetic variance is the same in both sexes and the environmental variance larger in men than in women. 2. A scalar model in which heritabilities are constrained to be equal across sexes, but in which the total trait variance may differ in men and women. In the scalar model, all variance components for females, for example, are constrained to be equal to a scalar multiple i, of the male variance components, that is, af  iam, cf  icm, and ef  iem. As a result, the standardized variance components (such as heritabilities) are equal across sexes, even though the unstandardized components differ (Neale et al., 1992).

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3. A constrained model in which parameter estimates for a, c, and e are constrained to be equal in magnitude across sexes. If data from male and female twins are available, these quantitative sex differences models can be evaluated with standard likelihood ratio tests comparing the fit of the different models. If, for example, in males the correlation in MZ twins equals 0.60 and in DZ twins 0.30, while in females the correlation in MZ twins equals 0.70 and in DZ twins 0.40, the estimate for a2  0.60 in males and females, but the estimate for c2  0 in males and 0.10 in females. The estimate for e2 is 0.40 in males and 0.30 in females. The likelihood ratio test will show whether these differences are significant. Likewise, if data are available for twins of different ages, for example, adolescent and adult twin pairs, then the significance of age differences in heritability can be tested. If data are available for dizygotic opposite-sex (DOS) twins, a model for qualitative sex differences can be evaluated. Within this model, the test of interest is whether the same genes are expressed in men and women. This model is tested by estimating the correlation between genetic factors in the DOS pairs instead of fixing it at 0.5. If the correlation is significantly lower than 0.5, this indicates that different genes are expressed in the two sexes. A large difference between the correlation in same-sex DZ twins and DOS twins points to qualitative sex differences. This qualitative sex differences model can also be applied in the context of an environmental hypothesis: instead of fixing the correlations between C factors at 1 in DOS twins, it can be estimated as a free parameter. If it is significantly lower than 1.0, this indicates that the influence of the shared environment differs in the two sexes. Note, however, because there is only one group of opposite-sex twins (there are no monozygotic twins of opposite sex, xcept in extremely rare cases) that a choice needs to be made to test either the genetic or the common environment correlation, but they cannot be estimated simultaneously.

Multivariate and Longitudinal Analyses The decomposition into genetic and environmental variances for a single trait can be generalized to longitudinal and multivariate data where the variation and covariation of traits is decomposed into genetic and nongenetic sources (Boomsma et al., 2002; Boomsma & Molenaar, 1986; Martin & Eaves, 1977). In such data, the cross trait-cross twin correlations indicate how the value of twin 1 for trait A (e.g., depression) predicts the value of twin 2 for trait B (e.g., anxiety), and vice versa. The pattern of cross trait-cross

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e11

C

c11 a11 Phenotype 1

A a c21 21 e21

A

C

a22

E

c22

e22

Phenotype 2

Figure 60.3 Genetic bivariate model, represented for one individual who is measured on two phenotypes. Note: A, C, and E are the latent additive genetic, common environmental, and unique environmental factors, respectively, that influence the first phenotype (with factor loadings a11, c11, e11), and/or the second phenotype (with factor loadings a21, c21, e21), The second phenotype can also be influenced by a second set of independent latent factors (with factor loadings a22, c22, e22). This model leads to the following equations for the estimates of the value of an individual’s phenotype (P), the total variance (VP), and the heritability (h2) for phenotype 1 and phenotype 2: equations

twin correlations for MZ twins and DZ twins indicates (as described previously) to what extent the covariance between traits is influenced by genetic or environmental factors. Thus, if the cross-trait cross-twin correlation is larger in MZ twins than in DZ twins, genetic effects are likely to explain the covariance between traits. Multivariate and longitudinal studies thus offer insight into the etiology of associations between traits, the comorbidity between disorders, and the stability of traits across time. If, for example, the same set of genes influences multiple traits, this constitutes evidence for genetic pleiotropy. If longitudinal stability is due to genetic factors, this indicates that the same set of genes is expressed across the life span. For two variables (for a single individual), the correlation between traits can be decomposed into parts caused by correlated genetic and correlated environmental factors (Figure 60.3). Genotype Environment Correlation and Interaction The designs discussed all assumed that genetic factors act independently from environmental factors. However, this assumption might be false. Gene-environment correlation or gene-environment interaction might play a role (Eaves, 1987; Kendler & Eaves, 1986; Rutter & Plomin, 1997). In passive gene-environment correlation the environment of an offspring depends on the genotype of parents (Eaves, 1987). For example, children who inherit the risk for depression may also grow up in a suboptimal environment because of a depressed parent. Gene-environment correlation can also arise because an individual’s environment

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depends on his own genotype, for example, in creating adverse life events. In other words, the exposure to a certain environment is under genetic control (Eaves, 1987; Kendler & Eaves, 1986). Gene-environment interaction reflects genetic control of sensitivity to the environment, that is, the effect of an environmental risk factor depends on the genetic make-up of an individual (Eaves, 1987; Kendler & Eaves, 1986). In the next section, methods used to investigate gene-environment correlation and interaction are described beginning with gene-environment correlation. The classic twin design can be used to estimate to what extent the variation in exposure to a specific environment, for example, marital status or the experience of life events, is under genetic control. In other words, the twin design can be used to calculate the heritability of the “environmental” trait. Using the classical twin design, Johnson, McGue, Krueger, and Bouchard (2004) and Middeldorp, Cath, Vink, and Boomsma (2005) found that propensity to marry is heritable and McGue and Lykken (1992) found that divorce risk was, to a substantial degree, genetically mediated. To examine whether the association between a specific environment and a trait is due to gene-environment correlation, the bivariate twin design can be applied (Purcell, 2002). This design investigates whether the genes influencing a behavioral trait also affect the chance of being exposed to a certain environment. Another approach to investigate this issue is the co-twin control design (Cederlof, Friberg, & Lundman, 1977; Kendler et al., 1993). In this design, the relative risk to have a disorder in the presence of a putative risk factor is calculated in a group of monozygotic (MZ) twins discordant for exposure to the risk factor, a group of dizygotic (DZ) twins discordant for exposure to the risk factor, and in a population consisting of unrelated subjects. If the relation between the risk factor and the disorder is causal and gene-environment correlation is absent, the relative risks will be the same in the three groups. If, on the other hand, the correlation between the risk factor and the disorder is due to genes that lead both to a higher risk for the disorder and to a higher risk of exposure to the risk factor, the relative risk will be higher in the total population than in the discordant dizygotic twins, whose relative risk will in turn be higher than the relative risk in the discordant monozygotic twins. Moreover, when gene-environment correlation entirely explains the relation between the risk factor and the disorder, the relative risk in MZ twins will be unity. This is because the unexposed member of MZ twins has the same genetic vulnerability to get the disorder as the twin who is exposed to the risk factor. Since DZ twins share on average half of their genes, the unexposed twin will share some of the genetic

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Genetic Epidemiology of Anxiety and Depression

vulnerability to the disorder with the twin exposed to the risk factor. Unrelated subjects will show the highest relative risk. Kendler et al. (1993) investigated whether the relation between smoking and depression was causal or due to shared genes influencing vulnerability for both traits. Although the relative risk for ever smoking given a lifetime history of depression was 1.48 in the entire sample, it was 1.18 and 0.98, respectively, in DZ and MZ twin pairs discordant for a history of depression. The relative risk for a history of depression given ever smoking was 1.60 in the entire sample, while in DZ and MZ twins discordant for smoking, it was 1.29 and 0.96, respectively. These results suggest that the association between smoking and depression in women is not a causal one but arises largely from familial factors, which are probably genetic, that predispose to both smoking and depression. An approach to investigate gene-environment interaction is to estimate the relative influences of genotype (heritability) and environment on a trait conditional on environmental exposure (Boomsma & Martin, 2002; Eaves, 1987; Heath, Eaves, & Martin, 1998; Heath, Jardine, & Martin, 1989; Kendler & Eaves, 1986). When there is no G  E interaction, the influence of genetic and environmental factors should not differ between subjects with different degrees of exposure. If genetic effects are modified by environmental exposure, such that heritabilities differ significantly between exposure-positive and exposurenegative groups, then this constitutes evidence for gene  environment interaction. Thus, this type of interaction is detected by testing whether the amount of variance explained by genetic factors differs between exposurepositive and exposure-negative groups. Purcell (2002) developed a model to investigate geneenvironment interaction more extensively, for example, when an environmental risk factor is measured on a continuous instead of a dichotomous scale. The genetic effects are partitioned into a mean part, which is independent of the environmental moderator, and a part that is a linear function of the environmental moderator. The model also allows for a test of gene-environment interaction in the presence of gene-environment correlation.

GENETIC EPIDEMIOLOGY OF ANXIETY AND DEPRESSION Univariate Analyses A large number of twin studies have investigated the influence of genetic and environmental factors on depression and anxiety. The review of these studies will be limited to

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population-based twin studies on depression and anxiety disorders as classified by the DSM (American Psychiatric Association, 1980, 1987, 1994), starting with the results of single trait analyses. A meta-analysis showed that major depression is around 37% heritable with the remaining part of the variance explained by individual specific environmental factors (Sullivan, Neale, & Kendler, 2000). Environmental factors shared by family members did not seem to be of major importance. The genetic epidemiology of depression was also investigated in 42,161 twins including 15,493 complete pairs from the national Swedish Twin Registry (Kendler, Gatz, Gardner, & Pedersen, 2006). Due to the large sample, this study detected common environmental influences or differences between men and women in the etiology of depression. The heritability was estimated at 29% in men and 42% in women, which was significantly different. Common environment did not explain any of the familial clustering. The genetic correlation between men and women was 0.69 indicating the existence of sex-specific genetic factors in addition to a set of shared genes. The amount of measurement error is reflected in the estimate of the influence of the unique environment. One study that assessed lifetime diagnosis of major depression at two occasions was able to parse out the effect of measurement error due to unreliability, resulting in a heritability estimate of 66% (Foley, Neale, & Kendler, 1998). This suggests that genetic factors might be more important for major depression than generally assumed. Anxiety disorders have been less extensively investigated. Meta-analyses showed that genetic factors explain 43% of the variance in panic disorder and 32% in generalized anxiety disorder (GAD; Hettema, Neale, & Kendler, 2001). For phobias, the heritability estimates varied somewhat around 30% with a maximum of 48% for agoraphobia (Kendler, Jacobson, Myers, & Prescott, 2002). The findings regarding the influence of common environment were inconsistent for social phobia, animal phobia, and GAD in women (Hettema, Prescott, & Kendler, 2001; Kendler et al., 2002; Kendler, Karkowski, & Prescott, 1999b; Kendler, Neale, Kessler, Heath, & Eaves, 1992). Partly depending on the definition of the disorder, a significant influence of the common environment was found. The authors suggested that these findings were due to stochastic factors and that it is most probable that the effect of the common environment is negligible (Hettema, Prescott, et al., 2001; Kendler et al., 2002). Regarding sex differences in the genetic architecture for anxiety disorders, no major differences were found for GAD (Hettema, Prescott, et al., 2001; Middeldorp, Birley, et al., 2005). For social phobia, one study found quantitative sex differences but another did not (Kendler et al., 2002;

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Middeldorp, Birley, et al., 2005). No sex differences were found for panic syndromes (Kendler, Gardner, & Prescott, 2001), while for agoraphobia, results indicated that the genes conveying the risk are probably not entirely the same (Kendler et al., 2002). The latter finding appeared to be supported by another study that investigated panic disorder and/or agoraphobia together (Middeldorp, Birley, et al., 2005). Qualitative sex differences were also found for situational and blood/injury phobia (Kendler et al., 2002). One study on phobias took measurement error due to unreliable assessment into account (Kendler et al., 1999b). That resulted in heritability estimates around 50% indicating that for the anxiety disorders, as well as for depression, the heritability might be higher than assumed. Multivariate and Longitudinal Analyses Multivariate analyses of depression and anxiety disorders address the etiology of the comorbidity between these disorders. The frequent comorbidity within anxiety disorders and between anxiety disorders and depression is an important issue: Does the comorbidity arise because of shared genetic risk factors or are there other explanations? The results of the Epidemiologic Catchment Area (ECA) Study and the National Comorbidity Survey (NCS) have shown that the occurrence of one anxiety disorder increases the risk of having an additional anxiety disorder (odds ratio on average 6.7; Kessler, 1995). The same holds for the combination of affective disorders (including dysthymia and mania) and anxiety disorders (odds ratio 7.0; Kessler, 1995). These increased odds ratios indicate that comorbidity between anxiety and depression is not only due to chance. Moreover, since the ECA and NCS studies are population based, sampling bias is highly unlikely to explain comorbidity rates. The NCS replication study showed similar results (Kessler, Chiu, Demler, Merikangas, & Walters, 2005). The issue of comorbidity gives rise to questions at a nosological level (Neale & Kendler, 1995). Do anxiety disorders and depression reflect an arbitrary division of a single syndrome? Are the different anxiety disorders and depression distinct entities, possibly influenced by common genetic and environmental etiological factors? Are the comorbid conditions independent of the separate anxiety disorders and depression? A review of twin and family studies investigating comorbidity within anxiety disorders and between anxiety disorders and depression concluded that they are distinct disorders with comorbidity probably partly explained by shared genetic factors (Middeldorp, Cath, van Dyck, & Boomsma, 2005). Possibly, this shared genetic vulnerability is expressed in the personality trait neuroticism (Middeldorp, Cath, et al.,

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2005). Most studies in this review performed bivariate analyses. Three twin studies carried out more extensive analyses (Hettema, Neale, Myers, Prescott, & Kendler, 2006; Hettema, Prescott, Myers, Neale, & Kendler, 2005; Kendler, Prescott, Myers, & Neale, 2003). Earlier factor analyses of common mental disorders showed that the latent structure of these disorders is best described by a three-factor model (Krueger, 1999; Vollebergh et al., 2001). One factor represents externalizing problems. The other two factors, which are subfactors of a higher-order factor representing internalizing problems, reflect anxious misery and fear. Kendler, Prescott, et al. (2003) aimed to extend these findings into a genetic epidemiological model. They showed that there are two genetic risk factors. One predisposes for internalizing disorders and the other for externalizing disorders. In addition, within the internalizing disorders, two genetics factors are seen that predispose to disorders dominated by anxious misery and fear. Hettema et al. (2005, 2006) focused in more detail on the internalizing disorders. In their first study, including GAD, panic disorder, agoraphobia, social phobia, animal phobia, and situational phobia, they confirmed that two genetic factors influence these disorders (Hettema et al., 2005). In their second study, they focused on the association between neuroticism on the one hand and depression and anxiety disorders on the other. They showed that the genetic correlation between neuroticism and these disorders are high with estimates varying between 0.58 and 0.82. They also identified a second neuroticismindependent genetic factor significantly increasing the risk for major depression, generalized anxiety, and panic disorder in addition to disorder-specific genetic factors for the phobias. Comparing their results with the results of the previous studies performed in the same sample, it was hypothesized that a model with a third genetic factor influencing the phobias would provide a better fit. However, that model could not be tested due to computational problems (Hettema et al., 2006). In all three studies, the influence of individual-specific environmental factors was largely disorder specific. The heritability estimates for depression and anxiety disorders were similar to the estimates from the univariate analyses, varying around 20% and 30%. For major depression and generalized anxiety disorder, these studies did not find an effect of the common environment. However, for panic disorder and social phobia, the common environment might explain 10% of the variance. Estimates vary somewhat for animal and situational phobia, but, in general, seem to be negligible (Hettema et al., 2005, 2006; Kendler, Prescott, et al., 2003). Longitudinal studies on anxiety and depression are scarce. There has been one longitudinal twin study in adults with

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Genetic Epidemiology of Anxiety and Depression

a follow-up over 10 years (Gillespie et al., 2004). Symptoms of anxiety and depression measured with self-report questionnaires were assessed in Australian twins aged 20 to 96 years at three points over a period of 16 years. For male anxiety and depression, there was no genetic innovation after age 20, thus the same genes remained to explain variation in anxiety and depression at ages 30, 40, 50, and 60. Most of the lifetime genetic variation in female anxiety and depression could also be explained by a stable set of genetic factors; however, there were also smaller age-dependent genetic innovations at age 30 for anxiety and at ages 40 and 70 for depression. In children, a longitudinal study on anxious depression was carried out in the population-based Netherlands Twin Register (Boomsma, van Beijsterveldt, Bartels, & Hudziak, 2008). Maternal and paternal ratings for anxious depression (A/D) were available for twins at ages 3, 5, 7, 10, and 12 with over 9,025 twin pairs at age 3 and over 2,300 pairs at age 12 being assessed. The influence of genetic factors declined with increasing age. The heritability was around 60% at age 3 and declined to 40% at age 12. The decrease in heritability when children grew older was accompanied by an increase in the influence of the common environment shared by twins (8% at age 3 and 23% at age 12). These results argue for shared environmental factors playing an important role in protecting children from or putting them at risk for the expression of A/D. The contribution of nonshared environmental factors ranged between 26% and 36%. This last result indicates that nonshared environment, or environmental influences that contribute to differences between siblings, plays a substantial role across development when considering the expression of A/D. However, when comparing this estimate for e2 with the estimates from studies in adults, it is clear that the importance of unique environmental factors increases across the life span. The results showed that the stability of A/D was relatively low between age 3 and later ages (correlations around 0.30), but became higher after age 7 (up to 0.67 between ages 10 and 12). The genetic correlations between A/D assessed at age 3 and other ages were modest, suggesting a small overlap of genes that influence A/D in preschool children and in middle childhood (genetic correlations between 0.24 and 0.35 for A/D at age 3 with other ages). These results raise the possibility of different genetic influences of genes across development, either by variable expression patterns, variable response to environmental mediators and modifiers, or simply, evidence of developmental genetic processes. After the age of 7, the genetic correlations were larger (0.63 to 0.70), indicating that the extent to which the same genes operate across ages 7 to 12 was increased. Across ages, the same common environmental factors were suggested because a single C factor could explain

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the covariance pattern across age. Family variables, such as parental conflict, negative familial environments, and separation are likely candidates for these shared environmental influences. Future genetic research should include such environmental variables (e.g., parental divorce) to specify the role of these environmental factors. Nonshared environmental factors operate mainly in a time-specific manner. A part of the shared environment reflects parental bias. By using the same rater at two or more points, the prediction of A/D could reflect some shared rater bias. If this is the case, the observed stability is not only a reflection of stability of children’s problem behavior but also a reflection of the stability of the mother ’s perception. However, by applying a longitudinal model to both father and mother ratings, it is possible to disentangle the effects due to real environment and the effect due to rater bias (Hewitt, Silberg, Neale, Eaves, & Erickson, 1992). The results indicated that there is still evidence for shared environmental influences on the stability of A/D when data from the father and mother are analyzed simultaneously. However, results indicate also that the rater-specific shared environment contributes to stability of A/D. This could point to possible rater bias that is persistent and affects the stability of A/D. Conclusions Depression and anxiety in adults are moderately heritable with genetic influences estimated mostly around 30% and 40%. These might be underestimates because studies taking measurement error into account have found that 50% to 60% of the variance could be explained by genetic factors. A common environment does not seem to play a major role in most internalizing disorders, but might be of importance in panic disorder and social phobia, although accounting for only 10% of the variance. Quantitative and qualitative sex differences in etiological factors appear to be modest. The comorbidity between major depression and anxiety disorders and within anxiety disorders is largely explained by common genetic factors. It seems that one genetic factor, expressed in the personality trait neuroticism, explains the comorbidity between internalizing disorders with two additional genetic factors influencing the risk for anxious misery and fear. Individual specific environmental factors are largely disorder specific. Although the genetic determinants of anxiety and depression appear relatively stable across the adult life span for men and women, there is some evidence to support additional mid-life and late-age gene action in women for depression. In children, the influence of genetic factors decreases with age, while the influence of the common environment becomes more important between age 7 and 12. Correlations between measures of A/D are low

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at younger ages, but become higher after age 7. Most of the stability across age is due to genetic stability and it appeared that other genes become important when children become older. In contrast, there is one common environmental factor influencing anxious depression in children through age 7 and 12. Unique environmental influences are largely age specific. The conclusions regarding the influences of genes and environment across life span are based on one study in adults and one study in children, so confirmation by other studies is required. Gene-Environment Correlation and Interaction An important indication that gene-environment correlation exists is given by a review demonstrating that several environmental factors, such as life events, parenting style, peer deviance, and social support, are modestly influenced by genetic factors (Kendler & Baker, 2006). Weighted heritabilities ranged from 7% to 39% with most estimates falling between 15% and 35%. Studies with multiple measurements of life events and social support found that temporal stability in the environment is influenced to a much greater extent by genetic factors than occasion specific events. Gene-environment correlation has been shown to explain the association between life events and depression in some twin and family studies (Kendler, Karkowski, & Prescott, 1999a; Kendler & Karkowski-Shuman, 1997; McGuffin, Katz, & Bebbington, 1988), but not in others (Farmer et al., 2000; Romanov, Varjonen, Kaprio, & Koskenvuo, 2003). One of the studies that found support for gene-environment correlation suggested that the correlation could be explained by genes that influence personality traits associated with depression (Kendler et al., 1999a). This hypothesis was later confirmed for neuroticism, extraversion, and openness for experience (Kendler, Gardner, & Prescott, 2003; Saudino, Pedersen, Lichtenstein, McClearn, & Plomin, 1997). One study investigated the relation between the exposure to life events and anxious depression measured with a self-report questionnaire in a longitudinal and a genetic design (Middeldorp, Cath, Beem, Willemsen, & Boomsma, 2008). The results suggested that the relation between life events and anxious depressive symptoms is due to a causal reciprocal relation. Gene-environment correlation did not seem to play a role. The personality traits neuroticism and extraversion were also included in the analyses. The latter was not related to life events at all. In contrast, neuroticism scores were increased, but to a lesser extent than depression scores, after life events. Moreover, higher neuroticism scores increased the chance of the exposure to a life event later in life. Again, gene-environment correlation did not seem to be important.

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The results so far have been inconclusive. Some findings are in favor of gene-environment correlation between life events, depression, or neuroticism, but others are not. Investigating gene-environment correlation is important to get more insight into etiological mechanisms. Exclusion of gene-environment correlation is an essential step before investigating gene-environment interaction (Moffitt, Caspi, & Rutter, 2005). Studies on gene-environment interaction for anxiety and depression with unmeasured genes have been limited to life events and marital status (Heath et al., 1998; Kendler et al., 1995; Silberg, Rutter, Neale, & Eaves, 2001). The risk for depression after a life event is higher if there are indications of a genetic vulnerability for depression, that is, a family history of depression and vice versa the genetic variance increases in the presence of life events (Kendler et al., 1995; Silberg et al., 2001). Regarding marital status, it appears that being married protects against the expression of genetic risk for depression (Heath et al., 1998).

GENE FINDING METHODS Linkage Studies Obtaining evidence that a trait is heritable opens up a whole new avenue of research: Where are the genes localized that influence the phenotype and can we identify them? The first question can be addressed in linkage studies, the second question in genetic association studies. Although there is a wide range of dedicated software packages to carry out genetic linkage and association studies (Abecasis, Cardon, & Cookson, 2000; Abecasis, Cherny, Cookson, & Cardon, 2002; Abecasis, Cookson, & Cardon, 2000; Almasy & Blangero, 1998; Gudbjartsson, Thorvaldsson, Kong, Gunnarsson, & Ingolfsdottir, 2005; Kruglyak, Daly, Reeve-Daly, & Lander, 1996; Purcell et al., 2007), we introduce this type of analyses within the context of genetic structural equation modeling (GCSM). GCSM can relatively easy incorporate measured genotypic information into the analysis. Genotypic information derives from polymorphic marker data that are assessed in DNA samples of subjects for whom phenotypic information is also available. If DNA markers are roughly evenly spaced along the genome, if their location is known, and if they are highly polymorphic (e.g., microsatellite markers that have multiple alleles), then they can be used in linkage studies. These studies make use of the fact that when enough DNA markers are measured, a stretch of markers will be close to the gene that influences the trait of interest. The location of a gene that influences a complex, often quantitative trait, is called a quantitative trait locus (QTL).

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Gene Finding Methods

A QTL represents a stretch of a chromosome, which includes a segregating gene, or multiple genes, that contributes to individual differences in the phenotype of interest. The segregating gene has a relatively large contribution to the phenotypic variance compared to the contributions of each polygene making up a genetic latent variable. However, compared to the total effects of the polygenetic and environmental effects, the effect of the QTL may be quite small. For instance, the QTL may account for a mere 5% or 10% of the phenotypic variance. The QTL can be treated in the same way as a polygenetic or an environmental factor, that is, as a latent variable and the relationship between the QTL and the phenotypic individual differences is modeled as a linear regression. If a set of markers is close to the QTL, then resemblance on the markers reflects resemblance for the QTL. The effect of the QTL is modeled on the covariance structure: If siblings who share more markers identical by descent (IBD) across a stretch of chromosome are more alike (their correlation for the trait is higher) than siblings who do not share any markers (Sham, 1997; Vink & Boomsma, 2002), this is evidence for linkage. Figure 60.4 shows a path model for DZ twins or siblings that incorporates the effect of a QTL on a measured phenotype. The correlation between QTL factors of DZ twins or siblings is obtained from measured genotypic (marker) data. IBD status for the marker data determines this correlation. IBD status in sibling pairs can be 0, 1, or 2, depending on whether the two siblings inherit the same marker allele from each parent (in which case, IBD  2), whether they receive one allele IBD from one, but not from the other parent (in which case, IBD  1), or whether they receive a different allele from each parent (in which case, IBD  0). Parents always share one allele

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with their offspring, therefore their IBD probabilities of sharing 0, 1, or 2 alleles will be IBD0  0.0, IBD1  1.0, and IBD2  0.0. For two siblings, the IBD probabilities depend on their genotypes on the marker position and surrounding markers. When their parents are genotyped, the IBD status of siblings can be derived from the transmission of alleles from parents to offspring. When parents are not genotyped, the allele frequencies of markers are used in the estimation of IBD. If parents have four distinct alleles, for example, the father “A” and “B” and mother “C” and “D,” the IBD status of their offspring can easily be determined (see Figure 60.5). If sibling 1 has AC and sibling 2 also has AC, the IBD probabilities will be IBD0  0.0, IBD1  0.0, and IBD2  1.0. If sibling 2 has AD instead, IBD0  0.0, IBD1  1.0, IBD2  0.0, and so on. However, if one of the parents is homozygous (e.g., AA), or if a genotype is missing, the IBD probabilities are calculated by maximum likelihood from all possible combinations of genotypes and their probabilities based on the allele frequencies of the marker, for example, IB0  0.0, IBD1  0.752, and IBD2  0.248. (For details on these procedures, see Haseman & Elston, 1972; Kruglyak & Lander, 1995; or Abecasis et al., 2002.) From the IBD probabilities, the correlation for the QTL marker () is calculated as 0  IBD0  0.5  IBD1  1  IBD2 for each individual pair in each family (Sham, 1997). Within the context of genetic structural equation modeling

x A

B

C

D

AQE Model for Twin Data  DZ/sib0.5 E

Q

A

A

Q

E A

e

q

PS1

a

a

q

e

PS2

Figure 60.4 Linkage model for DZ twin or sibling data: The phenotype assessed in sibling 1 and sibling 2 (PS1 and PS2) is influenced by additive genetic factors (A), environment (E), and a quantitative trait locus (QTL). Note: The correlation π in two siblings for the QTL depends on the measured DNA marker data and is defined based on their identity by descent (IBD) status. If path coefficient q is significant, this is evidence for linkage, that is, the trait locus is close to the markers that defined IBD.

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C 1/4

A

D 1/4

B

C 1/4

B

D 1/4

Figure 60.5 (A) Graph showing the possible allele combination for children from a mother carrying alleles A and B and a father with alleles C and D. (B) The table shows the 16 possible combinations of genotypes and the number of alleles identical by descent (IBD) for each combination for two siblings with a mother carrying alleles A and B and a father carrying alleles C and D. Note: The chance for each combination (AC, AD, BC, and BD) in an offspring is ¼. The probability that two siblings share two parental alleles (IBD2) is 4/16  ¼. The probability that they do not share any parental alleles is also 4/16  ¼, but the probability that they share one parental allele is 8/16  ½.

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(see also Figure 60.4), the test for linkage involves constraining the factor loading (q) of the phenotype on the QTL factor at zero and testing if this constraint leads to a significant decrease in goodness of fit. This test is based on a likelihood ratio test and is distributed as 2. In classical linkage analyses of Mendelian traits (traits that are influenced by a single locus), the commonly used test statistic is the LOD score. In parametric linkage analysis, it is standard practice to summarize the results of a linkage analysis in the form of an LOD score (Morton, 1955). LOD score stands for the logarithm of the odds that the locus is linked to the trait and indicates the strength of the linkage. Evidence for linkage is present when the maximum LOD score exceeds a predefined threshold, which depends on the size of the genome and the number of markers (Lander & Kruglyak, 1995). A commonly used threshold is an LOD score of 3. This critical value can be interpreted as stating that the evidence in favor of linkage is 1,000 times more likely than the null hypothesis of no linkage. If a LOD score of 2 was observed, then the null hypothesis of no linkage is 100 times more likely than the alternative. There is a simple correspondence between 2 and LOD scores: LOD  2/2ln10 (Sham, 1997). Association Studies In contrast to linkage studies that model the covariance structure in relatives, genetic association analysis models the effect of a genetic polymorphism on the trait levels. This can be done in cases and controls (e.g., dichotomous traits), in groups of unrelated individuals (for the analysis of quantitative traits), or within families. Association studies in unrelated subjects are similar in design to classic casecontrol studies in epidemiology. DNA collected from all participants and frequencies of the various allelic variants are compared in subjects with particular phenotypes (e.g., presence or absence of disease) to detect an association between a particular allele and the occurrence of the phenotype. The association test can be carried out for presence or absence of a particular allele or a particular genotype. For quantitative traits, the trait values are compared across the various allelic or genotypic variants of the DNA marker. The advantage of association over linkage analysis is that association studies can detect the region of a QTL that has only very small effects on the trait (Risch & Merikangas, 1996). This increase in statistical power comes from the fact that the test is carried out on first-order statistics (means or prevalences) whereas linkage tests are carried out on second-order statistics (covariances). Provided that either the selection of cases does not introduce population stratification or that the analyses properly control for such stratification, association studies provide

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a good complement to the linkage strategy. However, a potential problem of association studies is the danger that a spurious association is found between the trait of interest and any locus that differs in allele frequency between subpopulations. This situation is illustrated by the chopstick gene story described by Hamer and Sirota (2000). They describe a hypothetical study in which DNA markers were assessed in American and foreign students who often used chopsticks and students who did not. One of the DNA markers showed a correlation to chopstick use. Of course, this gene had nothing to do with chopstick use, but just happened to have different allele frequencies in Asians and Caucasians, who differ in chopstick use for purely cultural rather than biological reasons. Witte, Gauderman, and Thomas (1999) have evaluated the asymptotic bias in relative risk estimates resulting from using population controls when there is confounding due to population stratification. The direction of the bias is what one would expect from the usual principles of confounding in epidemiology: If the allele frequencies and baseline risks are both higher in a population, the bias is positive; if different, the bias is negative. Case-control studies of genetic associations thus can lead to false-positive as well as to false-negative results. To prevent significant findings due to population stratification, within-family association designs have been developed because family members are usually well matched on a number of traits that could give rise to stratification effects (Spielman, McGinnis, & Ewens, 1993). Most available family-based tests for association were initially developed for binary traits, such as the Transmission Disequilibrium Test (TDT) and the Haplotype Relative Risk test (HRR). Those tests are based on a design in which DNA is collected in affected individuals and their biological parents. Affected individuals must have received one or two susceptibility alleles from their parents. These alleles transmitted from parents to the affected individual can be viewed as a group of “case” alleles. The nontransmitted alleles from the parents can be considered as “control” alleles. In other words, those tests only need affected individuals and their parents; no other control group is required. In a different approach, the effects of genotypes on phenotypic means are partitioned into between-family and within-family components, by comparing the association of alleles and trait values across siblings from different families to the association of alleles and trait values across siblings within the same family. Sibling pairs are by definition ethnically and racially homogeneous and any difference in trait scores between siblings of different genotypes at a candidate marker, therefore, reflects true genetic association. By partitioning the mean effect of a locus into a between and a within-sibship component, spurious associations due to population stratification and admixture are controlled (Abecasis, Cookson, et al.,

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2000; Fulker, Cherny, Sham, & Hewitt, 1999; Posthuma, de Geus, Boomsma, & Neale, 2004). One problem with the candidate gene approach for most complex traits is the potentially huge number of genes that can serve as candidates. Several strategies are possible to select an optimal set of candidate genes. First, genes that are part of physiological systems known to influence the trait can be tested as candidates. Second, genes or chromosomal regions that are known to influence the trait in animals can be tested as candidate genes (or regions) in humans. Third, genes lying under a linkage signal can be the focus of research.

GENOME-WIDE ASSOCIATION STUDIES Linkage is usually genome-wide, although until recently association studies were limited to candidate genes or candidate regions. This has changed with the possibility to assess very large numbers of genetic markers within an individual. So called Genome-Wide Association studies (GWA) assess 300,000 to 600,000 markers along the genome and test if an association between a disorder, or a quantitative trait level, and a specific allele can be detected in groups of unrelated cases (e.g., patients) and controls (e.g., healthy subjects) or within families. Association can be found either with functional genetic variants that have biological consequences, or with other variants that are in linkage disequilibrium with these variants. Linkage disequilibrium occurs when a marker allele (i.e., a single nucleotide polymorphism [SNP]) and the QTL are so close on a chromosome that they co-segregate in the population over many generations of meiotic recombination.

Gene Finding Studies in Anxiety and Depression Linkage Studies Seven genome-wide linkage analyses have been performed, aiming to locate genes for MDD on the genome (Camp et al., 2005; Holmans et al., 2004, 2007; McGuffin et al., 2005; Nurnberger et al., 2001; Zubenko et al., 2003). Other studies have focused on quantitative traits associated with a diagnosis of MDD, such as neuroticism (Cloninger et al., 1998; Fullerton et al., 2003; Kuo et al., 2007; Nash et al., 2004; Neale et al., 2005; Wray et al., 2008). Table 60.1 summarizes the most promising results of these studies, excluding the study of Holmans et al. (2004) because this is based on the same sample as used by Holmans et al. (2007). Several regions have shown a linkage signal with a LOD-score 3 in at least one study. The following five regions have reached a LOD-score 3 in one study and a

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LOD-score 1.5 in a second study: chromosome 1 between 126 and 137 cM (Fullerton et al., 2003; Neale et al., 2005), chromosome 8 between 8 and 38 cM (Cloninger et al., 1998; Fullerton et al., 2003), chromosome 11 between 2 and 35 cM (Camp et al., 2005; Zubenko et al., 2003), chromosome 11 between 85 and 99 cM (Fullerton et al., 2003; Zubenko et al., 2003), and chromosome 12 between 105 and 124 cM (Fullerton et al., 2003; McGuffin et al., 2005). The most promising results of genome-wide linkage studies on anxiety phenotypes are summarized in Table 60.2 (Crowe et al., 2001; Fyer et al., 2006; Gelernter et al., 2001, 2003; Gelernter, Page, Stein, & Woods, 2004; Hamilton et al., 2003; Kaabi et al., 2006; Knowles et al., 1998; Middeldorp et al., 2008; Smoller et al., 2001; Thorgeirsson et al., 2003; Weissman et al., 2000). Three regions (chromosome 4, 9, and 13) showing significant linkage have not been replicated yet (Kaabi et al., 2006; Thorgeirsson et al., 2003; Weissman et al., 2000). Three other regions (chromosome 1, 7, and 14) have shown evidence for linkage in two studies (Crowe et al., 2001; Gelernter et al., 2001; Kaabi et al., 2006; Knowles et al., 1998; Middeldorp et al., 2008; Smoller et al., 2001). Gelernter et al. (2001, 2003, 2004) found a suggestive linkage signal on chromosome 14 for simple phobia, social phobia, and panic disorder. These three studies were performed on the same sample, thus the studies are not considered to be replications. Only the region on chromosome 7 has been found in linkage studies on neuroticism and anxiety. No further overlap between Table 60.1 and 60.2 is seen. Association Studies Despite the large number of candidate gene studies, efforts to identify QTLs for depression and anxiety through this approach have met with limited success. For an overview of the results, we refer to Levinson (2006) and Stoppel, Albrecht, Pape, and Stork (2006). In this chapter, we limit the discussion of association studies to the widely investigated association between the promoter-based length polymorphism of the serotonin transporter gene (5-HTTLPR) and anxiety and depression. The 5-HTTLPR polymorphism is located in the promoter of the gene and is defined by a length variation of a repetitive sequence with the short and the long fragment consisting of 484 and 528 base pairs, respectively. These variants are often denoted as “s” and “l.” Genes involved in the serotonin system are considered likely candidates, since medication such as selective serotonin reuptake inhibitors (SSRIs),—Prozac, for example—have been proven to be effective in the treatment of patients with anxiety disorders or depression. 5-HTTLPR seemed an excellent candidate because in vitro analyses showed that the basal activity of

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1192

Genetics and Psychopathology table 60.1 Most promising linkage results in the order of the chromosomes for neuroticism, harm avoidance, MDD, or the subtypes recurrent MDD (R-MDD) and recurrent early-onset MDD (RE-MDD) N SubjectsA/Families

Phenotype

1.7

426/90

MDD-RE

1.6

711/283

Neuroticism

Location (Chromosome, cM)/ References

LOD

Chromosome 1, 42 and 90 cM (Camp et al., 2005; Nash et al., 2004).

b

Chromosome 1, 126–137cM (Fullerton et al., 2003; Neale et al., 2005)

4.0

1122/561

Neuroticism

2.5b

293/129

Neuroticism

Chromosome 2, 237–248 cM (Nurnberger Jr. et al., 2001; Zubenko et al., 2003)

2.2

224 possible pairs

2.5b

?/81

MDD comorbid with alcoholism MDD

Chromosome 4, 176cM (Fullerton et al., 2003)

b

3.8

1122/561

Neuroticism

Chromosome 7, 42 cM (Fullerton et al., 2003)

3.9b

1122/561

Neuroticism

Chromosome 8, 8–38 cM (Cloninger et al., 1998; Fullerton et al., 2003)

3.2

987/105

Harm avoidance

2.9b

1122/561

Neuroticism

Chromosome 10, 5–9 cM (Camp et al., 2005; Wray et al., 2008)

1.6

426/90

RE-MDD

2.0

2030/564

Neuroticism

Chromosome 10, 76 cM (Zubenko et al., 2003)

3.0

?/81

MDD

Chromosome 11, 2–35 cM (Camp et al., 2005; Zubenko et al., 2003)

1.6

426 / 90

RE-MDD and anxiety

4.2

?/81

R-MDD

Chromosome 11, 85–99 cM (Fullerton et al., 2003; Zubenko et al., 2003)

3.7b

1122/561

Neuroticism

2.5

?/81

RE-MDD

4.7b

1122/561

Neuroticism

1.6

994/497

R-MDD

3.8b

1122/561

Neuroticism

Chromosome 12, 105–124 cM (Fullerton et al., 2003; McGuffin et al., 2005) Chromosome 13, 64cM (Fullerton et al., 2003) Chromosome 18, 73cM (Camp et al., 2005)

3.8

96/21

RE-MDD and anxiety

Chromosome 18, 109–117cM (Cloninger et al., 1998; Wray et al., 2008)

1.6

987/105

Harm avoidance

1.9

8552/2509

Neuroticism

Note: Regions with a LOD 3 or with a LOD 1.5 found at least twice are shown. Sex-specific effects are not included. a

For the studies of MDD, the number of affected individuals is given.

b

This is the -logP, not the LOD score.

the long variant was about threefold higher than that of the short variant, indicating that the s-l polymorphism is functional (Heils et al., 1996). In 1996, Lesch et al. (1996) reported an association between the promoter-based length polymorphism of the serotonin transporter gene (5-HTTLPR) and the anxietyrelated personality traits neuroticism and harm avoidance. The association of the short variant with higher neuroticism and harm avoidance scores was not only found in two independent groups of subjects, but also within families. The family population included 459 siblings from 210 families, of which 78 sibling pairs from 61 independent families had discordant 5-HTTLPR genotypes (one or two copies

c60.indd Sec4:1192

of the short form versus homozygous for the long form). The difference in personality scores between siblings with the long form and siblings with the short form of the 5-HTTLPR genotype was statistically significant. This within-family association effect indicated that the significant associations found in the samples of unrelated individuals were not due to population stratification and could be a genuine effect. Remarkably, subjects with the short form scored higher than subjects with the long form. This is in contrast to what would be expected considering the effect of the SSRIs on anxiety and depression, which is thought to be due to an increased serotonin concentration in the synapse. The

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Genome-Wide Association Studies 1193 table 60.2

Promising linkage results for anxiety phenotypes

Location/Reference

LOD

N Subjects/Families

Phenotype

Sample Ascertainment

Chromosome 1, 218–234 cM (Gelernter et al., 2001; Smoller et al., 2001)

2.04

153/20

Panic disorder

Probands with panic disorder

2.05a

99/1

Aniety proneness

Probands with panic disorder

153/20

Broad anxiety phenotype

Probands with panic disorder

Chromosome 4, 157 cM (Kaabi et al., 2006)

4.5b

Chromosome 7, 47–57 cM (Crowe et al., 2001; Knowles et al., 1998)

1.71

–/23

Panic disorder

Probands with panic disorder

2.23

253/23

Panic disorder

Probands with panic disorder

Chromosome 9, 105 cM (Thorgeirsson et al., 2003)

4.18

/25

Anxiety/panic disorder

Probands with panic attacks, GAD, or phobias

4.2

/34

Panic disorder combined with bladder/kidney problems

Probands with panic disorder

Chromosome 13, 96 cM (Weissman et al., 2000) Chromosome 14, 36–45 cM (Gelernter et al., 2001; Gelernter et al., 2003; Gelernter et al., 2004) Chromosome 14, 105 cM (Kaabi et al., 2006; Middeldorp et al., 2008)

3.7

129/14

Simple phobia

Probands with panic disorder

2.93c

163/17

Social phobia

Probands with panic disorder

2.38a

153/20

Panic disorder

Probands with panic disorder

1.7b

153/20

Broad anxiety phenotype

Probands with panic disorder

3.4

1602/1566

Broad anxiety phenotype

Population based twin-family sample

Note: Regions with a LOD 3 or with a LOD 1.5 found at least twice are shown. a

This is the NPL score, not the LOD score.

b c

Lod score based on the nominal P-value reported by the authors.

This is the Zlr score, not the LOD score.

short form of 5-HTTLPR is associated with less activity of the transporter and therefore with a higher serotonin concentration in the synapse. As a consequence, it would be expected that these subjects score lower on anxietyrelated personality traits instead of higher. The authors could not explain the contradictory effect they found. The numerous studies that have investigated the association since then showed conflicting results. Even metaanalyses on the association between 5-HTTLPR and personality traits (Munafo, Clark, & Flint, 2005a; Munafo et al., 2003; Schinka, Busch, & Robichaux-Keene, 2004; Sen, Burmeister, & Ghosh, 2004) or affective disorders (Lasky-Su, Faraone, Glatt, & Tsuang, 2005; Lotrich & Pollock, 2004) reached conflicting conclusions. This might be due to methodological differences between the metaanalyses (Munafo, Clark, & Flint, 2005b; Schinka, 2005; Sen, Burmeister, & Ghosh, 2005). Munafo et al. (2005b), therefore, stated that “Very large, well designed primary studies remain the most reliable way of obtaining reproducible results.” (p. 896). Two such studies have been performed since then. Willis-Owen et al. (2005) carried out an association study in three independent samples including, respectively, 564, 1,001, and 5,000 subjects. Subjects were selected from two general population samples based on their extreme high or

c60.indd Sec4:1193

low scores on neuroticism. The studies retained virtually 100% power to detect a genetic effect accounting for just 0.5% of phenotypic variance at an alpha level of .05. No significant association was found between 5-HTTLPR and neuroticism (measured with the Eysenck Personality Questionnaire; Eysenck & Eysenck, 1975) or major depression (as defined by the DSM-IV, American Psychiatric Association, 1994). Middeldorp et al. (2007) performed a family-based association study in a sample consisting of twins, their siblings, and parents from the Netherlands Twin Register (559 parents and 1,245 offspring). Subjects had participated between one and five times in survey studies measuring neuroticism, anxiety, and depression. Within-family and total association was tested for each time point and for the average scores across time points. Only 3 of the 36 tests showed a significant effect of 5-HTTLPR (p  .05). These effects were in opposite directions, that is, both negative and positive regression coefficients were found for the s allele. Offspring of these families were also approached to participate in a psychiatric interview diagnosing DSM-IV major depression. No additive effect of the s allele was found for DSM-IV depression. Three additional association analyses were carried out selecting (1) subjects aged over 30 years whose personality scores are considered to be most stable, (2) subjects scoring in the middle

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1194

Genetics and Psychopathology

range at each occasion because it was suggested that the effect of 5-HTTLPR is the largest at that part of the distribution (Sirota, Greenberg, Murphy, & Hamer, 1999), and (3) families with sibling pairs scoring concordant high or low since it is conceivable that the genetic load is highest in these families. These analyses converged with the other analyses in not showing an association with 5-HTTLPR. Notwithstanding the well-designed study of Lesch et al. (1996), there does not seem to be a straightforward association between 5-HTTLPR and neuroticism, anxiety, and depression. Caspi et al. (2003) suggested that 5-HTTLPR may not be directly associated with depression, but could moderate the serotonergic response to stress. They showed in a sample of 847 subjects that individuals with one or two copies of the short allele of 5-HTTLPR exhibited more depressive symptoms, diagnosable depression, and suicidality than individuals homozygous for the long allele in relation to stressful life events experienced in the 5 years before assessment. Uher and McGuffin (2008) reviewed the studies attempting to replicate the gene-environment interaction. They conclude that genetic moderation by 5-HTTLPR of vulnerability to adverse environment appears plausible. Findings are most consistent in young adult samples. Contradictory findings have been reported in adolescent boys and elderly people. This is in agreement with the results in a Dutch sample with a mean age of 39.2 years in which no interaction was found between 5-HTTLPR and the sensitivity to the exposure to life events regarding anxious depression scores measured with the Young Adult Self Report (Achenbach, 1990; Verhulst, Ende, & Koot, 1997; Table 60.3). In a regression analysis, including sex as a covariate, only the main effect of the number of life events was significant (p  0.001). The main effect of 5-HTTLPR and the interaction did not reach significance with p values of 0.75 and 0.18, respectively. On the whole, the candidate gene approach with the choice of genes based on the monoamine hypothesis for the etiology of depression has not been very successful.

table 60.3 Log transformed anxious depression scores (SD) per 5-HTTLPR genotype (ss, sl, and ll) N

ss

sl

ll

0 life events

722

18.5 (11.2)

20.1 (10.6)

19.1 (10.4)

1 life event

295

21.6 (10.6)

21.4 (9.6)

20.1 (10.6)

2 or more life events

137

23.9 (9.1)

23.2 (10.7)

21.1 (10.3)

Note: Results for individuals who were (1) not exposed to a negative life event, such as death of a significant other, serious illness, or divorce in the previous year; (2) exposed to one life event; or (3) exposed to two or more life events. There is a significant main effect of the number of life events. The main effect of the 5-HTTLRP or the 5-HTTLPR life-events interaction effect did not reach significance.

c60.indd Sec4:1194

Possibly, trying to find genes underlying the linkage peaks might be a more fruitful approach. Two studies successfully followed up on their linkage results and demonstrated a significant association of the apoptosis protease activating factor 1 (apaf-1) gene and the regulator of G-proteinsignaling 2 (RGS-2) gene with depression and anxiety respectively (Harlan et al., 2006; Leygraf et al., 2006). These findings need replication, but suggest that the genes influencing the vulnerability for anxiety and depression are involved in other biological pathways than previously thought. Genome-Wide Association Studies We are awaiting the results of the first genome-wide association study on major depression. From the Netherlands Twin Register (NTR) and the Netherlands Study of Depression and Anxiety (NESDA), 1,862 participants with a diagnosis of depression and 1,857 controls at low liability for depression have been selected for genome-wide genotyping (Boomsma et al., 2008) by the U.S. Foundation for the National Institutes of Health Genetic Association Information Network (FNIH/GAIN; www.fnih.org/GAIN2/ home_new.shtml). Currently, two genome-wide association studies for bipolar disorder and one for neuroticism (Baum et al., 2007; Shifman et al., 2008; Welcome Trust Case Control Consortium, 2007) have been carried out. Two of these studies (Baum et al., 2007; Shifman et al., 2008) used DNA pooling instead of genotyping 500K SNPs in each individual. This approach is more cost effective, but reduces power. The results confirm the idea that complex diseases are influenced by multiple genes of small effect. Odds ratios for significant associations varied between 1.2 and 1.5. In the two studies on bipolar disorder there is one overlapping finding. Both studies found an association with an SNP in the DFNB31 gene on chromosome 9. The genome-wide association study for neuroticism was followed by a replication study in which the SNPs showing the most significant results were tested in independent samples. Ultimately, one SNP within the phosphodiesterase 4D, cAMP-specific (PDE4D) gene showed the most promising result (Shifman et al., 2008).

SUMMARY There is clear familial clustering for anxiety and depression and the main, or even sole, reason is the genetic relatedness of biological family members. However, no chromosomal region or gene has been unequivocally identified as yet to be involved in anxiety and depression. In the near future, the results of the first genome-wide association study for

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References 1195

depression will be published, no doubt to be followed by several other studies. The exploratory approach seems warranted as other biological pathways than those previously expected might be involved in the etiology of anxiety and depression. The findings from the two genome-wide association studies on bipolar disorder are encouraging, as are the results of the genome-wide association study on neuroticism. To distinguish between false- and true-positives, the chromosomal regions identified in linkage studies might be helpful. This chapter provided the introductory information on commonly used methods in genetic epidemiology and gene hunting studies, some issues have been underexposed. One issue involves the definition of the phenotype in psychiatric genetics. One frequently mentioned hypothesis to explain the divergence in results from gene-finding studies is the definition of the phenotypes according to the DSM (American Psychiatric Association, 1980, 1987, 1994). It is possible that DSM categories cannot double as phenotypes when trying to discover robust genetic markers (Charney et al., 2002). The effect of a gene can, for instance, be missed when this gene leads to a different pattern of symptoms than the disorders as defined by the DSM-IV (for an illustration of this problem, see Hudziak, 2002). A multivariate analysis of the entire range of symptoms instead of using a single end-diagnosis is a way to try to find genes related to psychiatric symptoms (Hottenga & Boomsma, 2008). As an alternative, Gottesman and Gould (2003) suggested focusing on endophenotypes defined as traits along the pathway between genotype and disease. Although this seems a useful approach, it has so far not yielded more conclusive results than the genetic research of the psychiatric disorders themselves. Another approach could be to refine the phenotypes in order to diminish heterogeneity. A family study on depression identified four factors: (1) mood symptoms and psychomotor retardation; (2) anxiety; (3) psychomotor agitation, guilt, and suicidality; and (4) appetite gain and hypersomnia (Korszun et al., 2004). The first three factors showed significant sibling correlations and might be interesting phenotypes for future gene-finding studies. Other areas of research go beyond the investigation of genetic polymorphisms. A new development is the use of expression arrays or so called “gene chips.” Thousands of individual gene sequences can be bound to tiny chips (glass plates). When a sample of RNA is applied, those genes actively express in the sample, bind to their embedded ligand, and the resulting interaction is visualized. This method has also been suggested to investigate depression from an epigenetic perspective (Mill & Petronis, 2007). Epigenetic factors are inherited and acquire modifications of DNA and histones that regulate various genomic

c60.indd Sec5:1195

functions occurring without a change in nuclear DNA sequence. These could provide a direct mechanistic route via which the environment can interact with the genome.

REFERENCES Abecasis, G. R., Cardon, L. R., & Cookson, W. O. (2000). A general test of association for quantitative traits in nuclear families. American Journal of Human Genetics, 66, 279–292. Abecasis, G. R., Cherny, S. S., Cookson, W. O., & Cardon, L. R. (2002). Merlin: Rapid analysis of dense genetic maps using sparse gene flow trees. Nature Genetics, 30, 97–101. Abecasis, G. R., Cookson, W. O., & Cardon, L. R. (2000). Pedigree tests of transmission disequilibrium. European Journal of Human Genetics, 8, 545–551. Achenbach, T. M. (1990). The Young Adult Self Report. Burlington: University of Vermont, Department of Psychiatry. Almasy, L., & Blangero, J. (1998). Multipoint quantitative-trait linkage analysis in general pedigrees. American Journal of Human Genetics, 62, 1198–1211. American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). APA, Washington, DC. American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., rev.). Washington, DC: Author. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. Baum, A. E., Akula, N., Cabanero, M., Cardona, I., Corona, W., Klemens, B., et al. (2007). A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder. Molecular Psychiatry. Boomsma, D., Busjahn, A., & Peltonen, L. (2002). Classical twin studies and beyond. Nature Reviews.Genetics, 3, 872–882. Boomsma, D., & Dolan, C. V. (2000). Multivariate QTL analysis using structural equation modelling: A look at power under simple conditions. In H. S. T. D. Spector & A. M. MacGregor (Eds.), Advances in twin and sib-pair analysis (pp. 203–218). London: Greenwich Medical Media Ltd. Boomsma, D. I., & Martin, N. G. (2002). Gene-environment interactions. In H. D’Haenen, J. A. Den Boer, & P. Willner (Eds.), Biological Psychiatry (pp. 181–187). New York: Wiley, Ltd. Boomsma, D. I., & Molenaar, P. C. (1986). Using LISREL to analyze genetic and environmental covariance structure. Behavior Genetics, 16, 237–250. Boomsma, D. I., Willemsen, G., Sullivan, P. F., de Geus, E. J., Heutink, P., Meijer, P., Sondervan, D., et al. (2008). Genome-wide association of major depression: Description of samples for the GAIN major depressive disorder study: NTR and NESDA Biobank Projects. European Journal of Human Genetics, 16, 335–342. Boomsma, D. I., van Beijsterveldt, C. E. M., Bartels, M., & Hudziak, J. J. (2008). Genetic and environmental influence on anxious/depression: A longitudinal study in 3 to 12 year old children. In J. J. Hudziak (Ed.), Genetic and environmental influences on developmental psychopathology and wellness (pp. 161–189). Washington, DC: American Psychiatric Association. Camp, N. J., Lowry, M. R., Richards, R. L., Plenk, A. M., Carter, C., Hensel, C. H., et al. (2005). Genome-wide linkage analyses of extended Utah pedigrees identifies loci that influence recurrent, early-onset major depression and anxiety disorders. American Journal of Medical Genetics, Part B, Neuropsychiatric Genetics, 135, 85–93.

8/19/09 4:17:05 PM

1196

Genetics and Psychopathology

Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., Harrington, H., et al. (2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 301, 386–389. Cederlof, R., Friberg, L., & Lundman, T. (1977). The interactions of smoking, environment and heredity and their implications for disease etiology. A report of epidemiological studies on the Swedish twin registries. Acta Medica Scandinavica.Supplementum, 612, 1–128. Charney, D. S., Barlow, D. H., Botteron, K., Cohen, J. D., Goldman, D., Gur, R. E., et al. (2002). Neuroscience research agenda to guide development of a pathophysiologically based classification system. In D. J. Kupfer, M. B. First, & D. A. Regier (Eds.), A research agenda for DSM-V (pp. 31–83). Washington, DC: American Psychiatric Association. Cloninger, C. R., Van Eerdewegh, P., Goate, A., Edenberg, H. J., Blangero, J., Hesselbrock, V., et al. (1998). Anxiety proneness linked to epistatic loci in genome scan of human personality traits. American Journal of Medical Genetics, 81, 313–317. Crowe, R. R., Goedken, R., Samuelson, S., Wilson, R., Nelson, J., & Noyes, R., Jr. (2001). Genomewide survey of panic disorder. American Journal of Medical Genetics, 105, 105–109. Derks, E. M., Dolan, C. V., & Boomsma, D. I. (2004). Effects of censoring on parameter estimates and power in genetic modeling. Twin Research, 7, 659–669. Eaves, L. J. (1987). Including the environment in models for genetic segregation. Journal of Psychiatric Research, 21, 639–647. Eysenck, H. J. & Eysenck, S. B. G. (1975). Manual of the Eysenck Personality Questionnaire. San Diego, CA: Educational and Industrial Testing Service. Falconer, D. S., & Mackay, T. F. C. (1996). Quantative genetics. Essex: Longman Group Ltd. Farmer, A., Harris, T., Redman, K., Sadler, S., Mahmood, A., & McGuffin, P. (2000). Cardiff depression study. A sib-pair study of life events and familiality in major depression. British Journal of Psychiatry, 176, 150–155.

of anxiety and depression change with age? A longitudinal study of Australian twins. Twin Research, 7, 39–53. Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry: Etymology and strategic intentions. American Journal of Psychiatry, 160, 636–645. Gudbjartsson, D. F., Thorvaldsson, T., Kong, A., Gunnarsson, G., & Ingolfsdottir, A. (2005). Allegro version 2. Nature Genetics, 37, 1015–1016. Hall, J. G. (2003). Twinning. Lancet, 362, 735–743. Hamer, D., & Sirota, L. (2000). Beware the chopsticks gene. Molecular Psychiatry, 5, 11–13. Hamilton, S. P., Fyer, A. J., Durner, M., Heiman, G. A., Baisre, L., Hodge, S. E., et al. (2003). Further genetic evidence for a panic disorder syndrome mapping to chromosome 13q. Proceedings of the National Academy of Sciences of the United States of America, 100, 2550–2555. Harlan, J., Chen, Y., Gubbins, E., Mueller, R., Roch, J. M., Walter, K., et al. (2006). Variants in Apaf-1 segregating with major depression promote apoptosome function. Molecular Psychiatry, 11, 76–85. Haseman, J. K., & Elston, R. C. (1972). The investigation of linkage between a quantitative trait and a marker locus. Behavior Genetics, 2, 3–19. Heath, A. C., Eaves, L. J., & Martin, N. G. (1998). Interaction of marital status and genetic risk for symptoms of depression. Twin Research, 1, 119–122. Heath, A. C., Jardine, R., & Martin, N. G. (1989). Interactive effects of genotype and social environment on alcohol consumption in female twins. Journal of Studies on Alcohol, 50, 38–48. Heils, A., Teufel, A., Petri, S., Stober, G., Riederer, P., Bengel, D., et al. (1996). Allelic variation of human serotonin transporter gene expression. Journal of Neurochemistry, 66, 2621–2624. Heston, L. L. (1966). Psychiatric disorders in foster home reared children of schizophrenic mothers. British Journal of Psychiatry, 112, 819–825.

Foley, D. L., Neale, M. C., & Kendler, K. S. (1998). Reliability of a lifetime history of major depression: Implications for heritability and co-morbidity. Psychological Medicine, 28, 857–870.

Hettema, J. M., Neale, M. C., & Kendler, K. S. (2001). A review and metaanalysis of the genetic epidemiology of anxiety disorders. American Journal of Psychiatry, 158, 1568–1578.

Fulker, D. W., Cherny, S. S., Sham, P. C., & Hewitt, J. K. (1999). Combined linkage and association sib-pair analysis for quantitative traits. American Journal of Human Genetics, 64, 259–267.

Hettema, J. M., Neale, M. C., Myers, J. M., Prescott, C. A., & Kendler, K. S. (2006). A population-based twin study of the relationship between neuroticism and internalizing disorders. American Journal of Psychiatry, 163, 857–864.

Fullerton, J., Cubin, M., Tiwari, H., Wang, C., Bomhra, A., Davidson, S., et al. (2003). Linkage analysis of extremely discordant and concordant sibling pairs identifies quantitative-trait loci that influence variation in the human personality trait neuroticism. American Journal of Human Genetics, 72, 879–890. Fyer, A. J., Hamilton, S. P., Durner, M., Haghighi, F., Heiman, G. A., Costa, R., et al. (2006). A third-pass genome scan in panic disorder: Evidence for multiple susceptibility loci. Biological Psychiatry, 60, 388–401. Gelernter, J., Bonvicini, K., Page, G., Woods, S. W., Goddard, A. W., Kruger, S., et al. (2001). Linkage genome scan for loci predisposing to panic disorder or agoraphobia. American Journal of Medical Genetics, 105, 548–557. Gelernter, J., Page, G. P., Bonvicini, K., Woods, S. W., Pauls, D. L., & Kruger, S. (2003). A chromosome 14 risk locus for simple phobia: Results from a genomewide linkage scan. Molecular Psychiatry, 8, 71–82. Gelernter, J., Page, G. P., Stein, M. B., & Woods, S. W. (2004). Genomewide linkage scan for loci predisposing to social phobia: Evidence for a chromosome 16 risk locus. American Journal of Psychiatry, 161, 59–66. Gillespie, N. A., Kirk, K. M., Evans, D. M., Heath, A. C., Hickie, I. B., & Martin, N. G. (2004). Do the genetic or environmental determinants

c60.indd Sec5:1196

Hettema, J. M., Prescott, C. A., & Kendler, K. S. (2001). A populationbased twin study of generalized anxiety disorder in men and women. Journal of Nervous and Mental Diseases, 189, 413–420. Hettema, J. M., Prescott, C. A., Myers, J. M., Neale, M. C., & Kendler, K. S. (2005). The structure of genetic and environmental risk factors for anxiety disorders in men and women. Archives of General Psychiatry, 62, 182–189. Hewitt, J. K., Silberg, J. L., Neale, M. C., Eaves, L. J., & Erickson, M. (1992). The analysis of parental ratings of children’s behavior using LISREL. Behavior Genetics, 22, 293–317. Holmans, P., Weissman, M. M., Zubenko, G. S., Scheftner, W. A., Crowe, R. R., DePaulo, J. R., Jr., et al. (2007). Genetics of recurrent early-onset major depression (GenRED): final genome scan report. American Journal of Psychiatry, 164, 248–258. Holmans, P., Zubenko, G. S., Crowe, R. R., DePaulo, J. R., Jr., Scheftner, W. A., Weissman, M. M., et al. (2004). Genomewide significant linkage to recurrent, early-onset major depressive disorder on chromosome 15q. American Journal of Human Genetics, 74, 1154–1167. Hottenga, J. J., & Boomsma, D. I. (2008). QTL detection in multivariate data from sibling pairs. In B. M. Neale, M. A. Ferreira, S. E. Medland, & D. Posthuma (Eds.), Statistical genetics: gene mapping through linkage and association (pp. 239–264). Oxford: Taylor & Francis.

8/19/09 4:17:06 PM

References 1197 Hudziak, J. J. (2002). Importance of phenotype definition in genetic studies of child psychopathology. In J. E. Helzer & J. J. Hudziak (Eds.), Defining psychopathology in the 21st century: DSM-V and beyond (pp. 211–230). Washington, DC: American Psychiatric Publishing, Inc. Johnson, W., McGue, M., Krueger, R. F., & Bouchard, T. J., Jr. (2004). Marriage and personality: A genetic analysis. Journal of Personality and Social Psychology, 86, 285–294. Jöreskog, K. G., & Sörbom, D. (1989). Lisrel 7: A guide to the program and applications (2nd). Chicago: Scientific Software International. Kaabi, B., Gelernter, J., Woods, S. W., Goddard, A., Page, G. P., & Elston, R. C. (2006). Genome scan for loci predisposing to anxiety disorders using a novel multivariate approach: Strong evidence for a chromosome 4 risk locus. American Journal of Human Genetics, 78, 543–553. Kendler, K. S., & Baker, J. H. (2006). Genetic influences on measures of the environment: A systematic review. Psychological Medicine, 1–12. Kendler, K. S., & Eaves, L. (2005). Psychiatric genetics. Arlington, VA: American Psychiatric Publishing. Kendler, K. S., & Eaves, L. J. (1986). Models for the joint effect of genotype and environment on liability to psychiatric illness. American Journal of Psychiatry, 143, 279–289. Kendler, K. S., Gardner, C. O., & Prescott, C. A. (2001). Panic syndromes in a population-based sample of male and female twins. Psychological Medicine, 31, 989–1000. Kendler, K. S., Gardner, C. O., & Prescott, C. A. (2003). Personality and the experience of environmental adversity. Psychological Medicine, 33, 1193–1202. Kendler, K. S., Gatz, M., Gardner, C. O., & Pedersen, N. L. (2006). A Swedish national twin study of lifetime major depression. American Journal of Psychiatry, 163, 109–114. Kendler, K. S., Jacobson, K. C., Myers, J., & Prescott, C. A. (2002). Sex differences in genetic and environmental risk factors for irrational fears and phobias. Psychological Medicine, 32, 209–217. Kendler, K. S., Karkowski, L. M., & Prescott, C. A. (1999a). Causal relationship between stressful life events and the onset of major depression. American Journal of Psychiatry, 156, 837–841. Kendler, K. S., Karkowski, L. M., & Prescott, C. A. (1999b). Fears and phobias: reliability and heritability. Psychological Medicine, 29, 539–553. Kendler, K. S., & Karkowski-Shuman, L. (1997). Stressful life events and genetic liability to major depression: Genetic control of exposure to the environment? Psychological Medicine, 27, 539–547. Kendler, K. S., Kessler, R. C., Walters, E. E., MacLean, C., Neale, M. C., Heath, A. C., et al. (1995). Stressful life events, genetic liability, and onset of an episode of major depression in women. American Journal of Psychiatry, 152, 833–842. Kendler, K. S., Neale, M. C., Kessler, R. C., Heath, A. C., & Eaves, L. J. (1992). Generalized anxiety disorder in women. A population-based twin study. Archives of General Psychiatry, 49, 267–272. Kendler, K. S., Neale, M. C., MacLean, C. J., Heath, A. C., Eaves, L. J., & Kessler, R. C. (1993). Smoking and major depression. A causal analysis. Archives of General Psychiatry, 50, 36–43. Kendler, K. S., Prescott, C. A., Myers, J., & Neale, M. C. (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of General Psychiatry, 60, 929–937. Kessler, R. C. (1995). Epidemiology of psychiatric comorbidity. In M. T. Tsuang, M. Tohen, & G. E. Zahner (Eds.), Textbook in psychiatric epidemiology (pp. 179–198). New York: John Wiley & Sons, Inc. Kessler, R. C., Chiu, W. T., Demler, O., Merikangas, K. R., & Walters, E. E. (2005). Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 617–627. Knowles, J. A., Fyer, A. J., Vieland, V. J., Weissman, M. M., Hodge, S. E., Heiman, G. A., et al. (1998). Results of a genome-wide genetic

c60.indd Sec5:1197

screen for panic disorder. American Journal of Medical Genetics, 81, 139–147. Korszun, A., Moskvina, V., Brewster, S., Craddock, N., Ferrero, F., Gill, M., et al. (2004). Familiality of symptom dimensions in depression. Archives of General Psychiatry, 61, 468–474. Krueger, R. F. (1999). The structure of common mental disorders. Archives of General Psychiatry, 56, 921–926. Kruglyak, L., Daly, M. J., Reeve-Daly, M. P., & Lander, E. S. (1996). Parametric and nonparametric linkage analysis: A unified multipoint approach. American Journal of Human Genetics, 58, 1347–1363. Kruglyak, L., & Lander, E. S. (1995). Complete multipoint sib-pair analysis of qualitative and quantitative traits. American Journal of Human Genetics, 57, 439–454. Kuo, P. H., Neale, M. C., Riley, B. P., Patterson, D. G., Walsh, D., Prescott, C. A., et al. (2007). A genome-wide linkage analysis for the personality trait neuroticism in the Irish affected sib-pair study of alcohol dependence. American Journal of Medical Genetics, Part B, Neuropsychiatric Genetics, 144, 463–468. Lander, E., & Kruglyak, L. (1995). Genetic dissection of complex traits: Guidelines for interpreting and reporting linkage results. Nature Genetics, 11, 241–247. Lasky-Su, J. A., Faraone, S. V., Glatt, S. J., & Tsuang, M. T. (2005). Metaanalysis of the association between two polymorphisms in the serotonin transporter gene and affective disorders. American Journal of Medical Genetics, Part B, Neuropsychiatric Genetics, 133, 110–115. Lesch, K. P., Bengel, D., Heils, A., Sabol, S. Z., Greenberg, B. D., Petri, S., et al. (1996). Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science, 274, 1527–1531. Levinson, D. F. (2006). The genetics of depression: A review. Biological Psychiatry, 60, 84–92. Leygraf, A., Hohoff, C., Freitag, C., Willis-Owen, S. A., Krakowitzky, P., Fritze, J., et al. (2006). Rgs 2 gene polymorphisms as modulators of anxiety in humans? Journal of Neural Transmission, 113, 1921–1925. Lotrich, F. E., & Pollock, B. G. (2004). Meta-analysis of serotonin transporter polymorphisms and affective disorders. Psychiatric Genetics, 14, 121–129. Lynch, M., & Walsh, B. (1998). Genetics and analysis of quantitative traits. Sunderland, MA: Sinauer Associates. Martin, N., Boomsma, D., & Machin, G. (1997). A twin-pronged attack on complex traits. Nature Genetics, 17, 387–392. Martin, N. G., & Eaves, L. J. (1977). The genetical analysis of covariance structure. Heredity, 38, 79–95. McGue, M., & Lykken, D. T. (1992). Genetic influence on risk of divorce. Psychological Science, 3, 368–373. McGuffin, P., Katz, R., & Bebbington, P. (1988). The Camberwell Collaborative Depression Study. III. Depression and adversity in the relatives of depressed probands. British Journal of Psychiatry, 152, 775–782. McGuffin, P., Knight, J., Breen, G., Brewster, S., Boyd, P. R., Craddock, N., et al. (2005). Whole genome linkage scan of recurrent depressive disorder from the depression network study. Human Molecular Genetics, 14, 3337–3345. Middeldorp, C. M., Birley, A. J., Cath, D. C., Gillespie, N. A., Willemsen, G., Statham, D. J., et al. (2005). Familial clustering of major depression and anxiety disorders in Australian and Dutch twins and siblings. Twin Research and Human Genetics, 8, 609–615. Middeldorp, C. M., Cath, D. C., Beem, A. L., Willemsen, G., & Boomsma, D. I. (2008). The association of life events with anxious depression, neuroticism and extraversion. Psychological Medicine, 38, 1557–1565.

8/19/09 4:17:06 PM

1198

Genetics and Psychopathology

Middeldorp, C. M., Cath, D. C., van Dyck, R., & Boomsma, D. I. (2005). The co-morbidity of anxiety and depression in the perspective of genetic epidemiology. A review of twin and family studies. Psychological Medicine, 35, 611–624. Middeldorp, C. M., Cath, D. C., Vink, J. M., & Boomsma, D. I. (2005). Twin and genetic effects on life events. Twin Research and Human Genetics, 8, 224–231. Middeldorp, C. M., de Geus, E. J., Beem, A. L., Lakenberg, N., Hottenga, J. J., Slagboom, P. E., et al. (2007). Family based association analyses between the serotonin transporter gene polymorphism (5-HTTLPR) and neuroticism, anxiety and depression. Behavior Genetics, 37, 294–301. Middeldorp, C. M., Hottenga, J. J., Slagboom, P. E., Sullivan, P. F., de Geus, E. J., Posthuma, D., et al. (2008). Linkage on chromosome 14 in a genomewide linkage study of a broad anxiety phenotype. Molecular Psychiatry, 13, 84–89. Mill, J., & Petronis, A. (2007). Molecular studies of major depressive disorder: The epigenetic perspective. Molecular Psychiatry, 12, 799–814. Moffitt, T. E., Caspi, A., & Rutter, M. (2005). Strategy for investigating interactions between measured genes and measured environments. Archives of General Psychiatry, 62, 473–481.

Purcell, S. (2002). Variance components models for gene-environment interaction in twin analysis. Twin Research, 5, 554–571. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., et al. (2007). PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. American Journal of Human Genetics, 81, 559–575. Risch, N., & Merikangas, K. (1996). The future of genetic studies of complex human diseases. Science, 273, 1516–1517. Romanov, K., Varjonen, J., Kaprio, J., & Koskenvuo, M. (2003). Life events and depressiveness: The effect of adjustment for psychosocial factors, somatic health and genetic liability. Acta Psychiatrica Scandinavica, 107, 25–33. Rutter, M., & Plomin, R. (1997). Opportunities for psychiatry from genetic findings. British Journal of Psychiatry, 171, 209–219. Saudino, K. J., Pedersen, N. L., Lichtenstein, P., McClearn, G. E., & Plomin, R. (1997). Can personality explain genetic influences on life events? Journal of Personality and Social Psychology, 72, 196–206. Schinka, J. A. (2005). Measurement scale does moderate the association between the serotonin transporter gene and trait anxiety: Comments on Munafo and colleagues. Molecular Psychiatry, 10, 892–893.

Morton, N. E. (1955). Sequential tests for the detection of linkage. American Journal of Human Genetics, 7, 277–318.

Schinka, J. A., Busch, R. M., & Robichaux-Keene, N. (2004). A meta-analysis of the association between the serotonin transporter gene polymorphism (5-HTTLPR) and trait anxiety. Molecular Psychiatry, 9, 197–202.

Munafo, M. R., Clark, T., & Flint, J. (2005a). Does measurement instrument moderate the association between the serotonin transporter gene and anxiety-related personality traits? A meta-analysis. Molecular Psychiatry, 10, 415–419.

Sen, S., Burmeister, M., & Ghosh, D. (2004). Meta-analysis of the association between a serotonin transporter promoter polymorphism (5-HTTLPR) and anxiety-related personality traits. American Journal of Medical Genetics, Part B, Neuropsychiatric Genetics, 127, 85–89.

Munafo, M. R., Clark, T., & Flint, J. (2005b). Promise and pitfalls in the meta-analysis of genetic association studies: A response to Sen and Schinka. Molecular Psychiatry, 10, 895–897.

Sen, S., Burmeister, M., & Ghosh, D. (2005). 5-HTTLPR and anxietyrelated personality traits meta-analysis revisited: Response to Munafo and colleagues. Molecular Psychiatry, 10, 893–895.

Munafo, M. R., Clark, T. G., Moore, L. R., Payne, E., Walton, R., & Flint, J. (2003). Genetic polymorphisms and personality in healthy adults: A systematic review and meta-analysis. Molecular Psychiatry, 8, 471–484.

Sham, P. C. (1997). Statistics in human genetics. London: Arnold.

Nash, M. W., Huezo-Diaz, P., Williamson, R. J., Sterne, A., Purcell, S., Hoda, F., et al. (2004). Genome-wide linkage analysis of a composite index of neuroticism and mood-related scales in extreme selected sibships. Human Molecular Genetics, 13, 2173–2182. Neale, B. M., Sullivan, P. F., & Kendler, K. S. (2005). A genome scan of neuroticism in nicotine dependent smokers. American Journal of Medical Genetics, Part B, Neuropsychiatric Genetics, 132, 65–69. Neale, M. C. (2000). QTL mapping with sib-pairs: The flexibility of Mx. In H. S. T. D. Spector & A. M. MacGregor (Eds.), Advances in twin and sibpair analysis (pp. 203–218). London: Greenwich Medical Media Ltd. Neale, M. C., Boker, S. M., Xie, G., & Maes, H. H. (2006). Mx: Statistical modeling. Richmond (VA): Department of Psychiatry, Medical College of Virginia. Neale, M. C., & Cardon, L. R. (1992). Methodology for genetic studies of twins and families. Boston: Kluwer Academic. Neale, M. C., & Kendler, K. S. (1995). Models of comorbidity for multifactorial disorders. American Journal of Human Genetics, 57, 935–953. Nurnberger, J. I., Jr., Foroud, T., Flury, L., Su, J., Meyer, E. T., Hu, K., et al. (2001). Evidence for a locus on chromosome 1 that influences vulnerability to alcoholism and affective disorder. American Journal of Psychiatry, 158, 718–724. Plomin, R., DeFries, J. C., McClearn, G. E., & McGuffin, P. (2008). Behavioral Genetics, 5th ed. New York: Worth Publishers and Freeman and Company. Posthuma, D., Beem, A. L., de Geus, E. J., van Baal, G. C., von Hjelmborg, J. B., Iachine, I., et al. (2003). Theory and practice in quantitative genetics. Twin Research, 6, 361–376. Posthuma, D., de Geus, E. J., Boomsma, D. I., & Neale, M. C. (2004). Combined linkage and association tests in mx. Behavior Genetics, 34, 179–196.

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Shifman, S., Bhomra, A., Smiley, S., Wray, N. R., James, M. R., Martin, N. G., et al. (2008). A whole genome association study of neuroticism using DNA pooling. Molecular Psychiatry, 13, 302–312. Silberg, J., Rutter, M., Neale, M., & Eaves, L. (2001). Genetic moderation of environmental risk for depression and anxiety in adolescent girls. British Journal of Psychiatry, 179, 116–121. Sirota, L. A., Greenberg, B. D., Murphy, D. L., & Hamer, D. H. (1999). Non-linear association between the serotonin transporter promoter polymorphism and neuroticism: A caution against using extreme samples to identify quantitative trait loci. Psychiatric Genetics, 9, 35–38. Smoller, J. W., Acierno, J. S., Jr., Rosenbaum, J. F., Biederman, J., Pollack, M. H., Meminger, S., et al. (2001). Targeted genome screen of panic disorder and anxiety disorder proneness using homology to murine QTL regions. American Journal of Medical Genetics, 105, 195–206. Spielman, R. S., McGinnis, R. E., & Ewens, W. J. (1993). Transmission test for linkage disequilibrium: The insulin gene region and insulindependent diabetes mellitus (IDDM). American Journal of Human Genetics, 52, 506–516. Stoppel, C., Albrecht, A., Pape, H. C., & Stork, O. (2006). Genes and neurons: Molecular insights to fear and anxiety. Genes Brain Behavior, 5 (Suppl. 2), 34–47. Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000). Genetic epidemiology of major depression: Review and meta-analysis. American Journal of Psychiatry, 157, 1552–1562. Thorgeirsson, T. E., Oskarsson, H., Desnica, N., Kostic, J. P., Stefansson, J. G., Kolbeinsson, H., et al. (2003). Anxiety with panic disorder linked to chromosome 9q in Iceland. American Journal of Human Genetics, 72, 1221–1230. Uher, R., & McGuffin, P. (2008). The moderation by the serotonin transporter gene of environmental adversity in the aetiology of mental illness: Review and methodological analysis. Molecular Psychiatry, 13, 131–146. Verhulst, F. C., Ende, J. v., & Koot, H. M. (1997). Handleiding voor de Youth Self Report. Rotterdam: Afdeling Kinder- en Jeugdpsychiatrie,

8/19/09 4:17:07 PM

References 1199 Sophia Kinderziekenhuis/Academisch Ziekenhuis Rotterdam/Erasmus Universiteit Rotterdam. Vink, J. M., & Boomsma, D. I. (2002). Gene finding strategies. Biological Psychology, 61, 53–71. Vollebergh, W. A., Iedema, J., Bijl, R. V., De Graaf, R., Smit, F., & Ormel, J. (2001). The structure and stability of common mental disorders: The NEMESIS study. Archives of General Psychiatry, 58, 597–603. Weissman, M. M., Fyer, A. J., Haghighi, F., Heiman, G., Deng, Z., Hen, R., et al. (2000). Potential panic disorder syndrome: Clinical and genetic linkage evidence. American Journal of Medical Genetics, 96, 24–35. Welcome Trust Case Control Consortium. (2007). Genome-wide association study of 14, 000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661–678. Willis-Owen, S. A., Turri, M. G., Munafo, M. R., Surtees, P. G., Wainwright, N. W., Brixey, R. D., et al. (2005). The serotonin transporter length

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polymorphism, neuroticism, and depression: A comprehensive assessment of association. Biological Psychiatry, 58, 451–456. Witte, J. S., Gauderman, W. J., & Thomas, D. C. (1999). Asymptotic bias and efficiency in case-control studies of candidate genes and geneenvironment interactions: Basic family designs. American Journal of Epidemiology, 149, 693–705. Wray, N. R., Middeldorp, C. M., Birley, A. J., Gordon, S. D., Sullivan, P. F., Visscher, P. M., et al. (2008). Genome wide linkage analysis of multiple measures of neuroticism of two large cohorts form Australia and the Netherlands. Archives of General Psychiatry, 65, 649–658. Zubenko, G. S., Maher, B., Hughes, H. B., III, Zubenko, W. N., Stiffler, J. S., Kaplan, B. B., et al. (2003). Genome-wide linkage survey for genetic loci that influence the development of depressive disorders in families with recurrent, early-onset, major depression. American Journal of Medical Genetics, Part B, Neuropsychiatric Genetics, 123, 1–18.

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Chapter 61

Successful Aging DENISE C. PARK AND JOSHUA GOH

disease and other dementing illnesses, the study of normal, healthy cognitive aging has become of increasing importance. To evaluate neurocognitive pathology occurs, it is critically important to understand what constitutes normal, healthy cognitive function at every stage of life. Moreover, there is increasing evidence that the trajectory of cognitive aging can be delayed, and that interventions can sustain neurocognitive health. The present chapter will focus on neurocognitive theories of normal aging that are rapidly changing as a result of the truly stunning findings that have been revealed by neuroimaging technologies.

Until the advent of neuroimaging techniques, studies of cognitive aging generally showed a gradual but progressive decline across the life span, with a few areas of protected cognition. The behavioral literature demonstrated that older adults have more trouble learning new information, exhibit less efficient reasoning skills, are slower to respond on all types of cognitive tasks, and are more susceptible to disruption from interfering information than younger adults. In addition to decreased efficiency of these processes, which is characteristic of normal aging, the risk of pathological cognitive aging increases with each year of life past middle age. Of individuals aged 85 and older, an important study conducted by Unverzagt et al. (2001) indicated that only 45% of these adults were cognitively normal. A study reported by the Alzheimer ’s Association (2007) indicated that 42% of adults 85 and older had Alzheimer ’s Disease (and this statistic excludes other neurological disorders and sources of dementia). Neuroimaging techniques have breathed new life into the study of cognitive aging, providing us with clear and optimistic evidence that demonstrates that the brain ages in a dynamic manner, with reorganization and remodeling of neural circuitry occurring in response to some of the challenges and atrophy faced by the aging brain. In this chapter, we review basic behavioral mechanisms believed to account for cognitive aging as well as basic changes that occur in brain structure with age. Then we consider theories of neural aging, resulting from behavioral, functional, and structural research, paying particular attention to the notion of the brain as a dynamic, reorganizing structure. We consider, as well, evidence suggesting that there is plasticity in the aging brain which can be exploited by a range of interventions, addressing evidence for the “use it or lose it” hypothesis, that is, the common belief that the maintenance of cognitive vitality in late adulthood can be sustained if individuals remain physically and intellectually active. In the face of the rapidly aging population that is occurring globally, combined with the high incidence of Alzheimer’s

BEHAVIORAL THEORIES OF COGNITIVE AGING Before discussing causes of cognitive aging, it is worthwhile to review findings. Figure 61.1 summarizes findings typical of the cognitive aging literature, presenting data collected by Park et al. (2002) from a large sample of older adults (350 subjects) and includes three measures of speed of processing (measured by the speed at which subjects could make simple perceptual comparisons between two items), multiple measures of visuo-spatial and verbal working memory (measured by traditional computational and visuo-spatial span measures) and long-term memory (measured by free and cued recall of both visuo-spatial and verbal stimuli), as well as measures of world knowledge (measured by different tests of vocabulary knowledge). All the measures except world knowledge show reliable differences across the life span, beginning in the twenties, and that decline does not differ as a function of process (e.g., speed, working memory, long-term memory) or format (visuospatial or verbal). Although the Park et al. data are crosssectional, Figure 61.2 represents a blend of cross-sectional and longitudinal data from cohorts of older adults tested in the Victoria Longitudinal Study (Hultsch, Hertzog, Dixon, & Small, 1998). The findings from this study largely replicate those from the cross-sectional data depicted in

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Figure 61.1, and extend the findings to other domains as well. Figure 61.2A depicts reliable increases in speed of processing (the increases represent slowing) from age 60 to 84 whereas Figure 61.2B demonstrates reliable decreases in working memory in this sample. Figure 61.2C demonstrates age invariance for measures of vocabulary, but agerelated decreases when tasks that required working memory (reading comprehension) or speeded processing (verbal fluency) are used. Finally, Figure 61.2D demonstrates agerelated decreases in fact and word recall, but shows age invariance when subjects recall information from a coherent story or when measures of implicit memory are used. Overall, the findings represented in Figures 61.1 and 61.2 are broadly typical of hundreds of studies that exist on cognitive aging—with age, people become slower and show decreases in working memory capacity, but generally do well on tests that measure world knowledge or implicit memory.

THEORIES OF COGNITIVE AGING There are a range of theories that postulate the cause of age-related declines in cognitive function. Salthouse (1991, 1996) has presented the influential speed of processing theory and argued that age-related declines in the speed at which information is processed account for age differences on essentially all cognitive tasks. Baltes and Lindenberger (1997) have suggested that crude measures of sensory function (visual and auditory acuity) are even more fundamental than speed of processing in explaining age differences and provide a crude overall measure of declining neuronal

integrity, or “dedifferentiation” of function in the older adult. The view that all types of cognitive decline with age are caused by a single mechanism has been labeled the “common cause” hypothesis (Lindenberger & Baltes, 1994). Structural equation models of the data depicted in Figure 61.1 did implicate speed of processing as the most fundamental measure mediating age-related variance in the long-term memory measures (Park et al., 2002), but working memory also played a significant and intermediate role in explaining age-related variance in long-term memory. Park et al. (1996) demonstrated that age-related declines in speed of processing was the cause of declines in relatively low-effort, easy memory tasks, such as memory for spatial location of words. However, when the memory task became more demanding, as in a free or cued recall task, then speed and working memory jointly explained the agerelated variance in recall that could not be explained by speed of processing declines alone. In addition to speed and working memory as important mechanisms whose deterioration may account for cognitive aging, there is also evidence that with age there are declines in inhibitory processes (the ability to ignore irrelevant information or delete irrelevant information from working memory) and the ability to switch among tasks (see Zacks, Hasher, & Li, 2000, for reviews). It is important to recognize that speed, working memory, inhibition, and task-switching are executive functions that are used in service of many cognitive tasks including reasoning, strategic encoding, and retrieval of information in long-term memory, and many everyday or work-related tasks that require learning or responding to novel information. Our own view is that decline in all of these mechanisms are

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fundamental to cognitive aging, and that different individuals may decline at different rates on these mechanisms. Indeed, models of cognitive aging (Park et al., 1996, 2002) suggest a clear separation between speed and working memory, and also demonstrate that different tasks require different contributions from these mechanisms for successful performance. As we shall see, the complexity of the brain and neuroimaging findings have further constrained these theories, suggesting that it is unlikely that any single mechanism is responsible for declines in cognition associated with aging. Both Figure 61.1 and Figure 61.2C also demonstrates that knowledge remains relatively intact and may even show growth with age, suggesting that the robust representation

Figure 61.2 Longitudinal data from the Victoria Longitudinal Study showing cognitive performance declines with aging. Note: Semantic and comprehension speed slows down with increasing age in A: (higher Z-score indicates longer response time), working memory tests also show decline with age in B:. In C: vocabulary performance is relatively preserved, however, verbal fluency (a speeded-test) and reading comprehension (involving working memory) declines. In D: text recall (involving retrieval from coherent context) and stem completion (involving priming) are preserved, whereas fact and word recall decline. From Memory Change in the Aged, by D. F. Hultsch, C. Hertzog, R. A. Dixon, and B. J. Small, 1998, New York: Cambridge University Press. Copyright 1998 by the Cambridge University Press. Reprinted with permission.

of knowledge in the aging cognitive system may be an important mechanism for off-setting deficits on tasks in the real world. As evidence for this, Hedden, Lautenschlager, and Park (2005) have demonstrated that a high level of verbal fluency in older people can play a compensatory role for decreased speed of processing in the free recall of words. Overall, the picture presented in Figures 61.1 and 61.2 (mirrored by many other large studies of cognitive aging across the world) is one of declines in basic mechanisms, the “hardware of the mind,” but with knowledge, the “software,” remaining intact with age. Figure 61.1 also illustrates one poorly understood and little-recognized phenomenon of some importance: Both cross-sectional and longitudinal data averaged across groups

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suggest that decline (or at least measurement of it) results in relatively continuous decreases across the life span, beginning in young adulthood, although it is well recognized that within these groups, individuals may vary considerably in the trajectory of decline (Hultsch et al., 1998; MacDonald Hultsch, Strauss, & Dixon, 2003; Wilson et al., 2002). Because age-related decline has generally been viewed as continuous across the life span in behavioral measures, relatively little attention has been paid to the study of middle age, particularly in cross-sectional studies, because the data points associated with middle age could generally be interpolated without actual data collection. As a rule, including middleaged adults in the behavioral study of cognitive aging provided few new insights into the process of aging, although inclusion of middle-aged adults allowed age to be treated as a continuous variable in modeling the impact of aging on cognitive function. As we shall see, neuroimaging data does not always show a continuous and gradual decline in activation with age, but rather, functional measures show dramatic changes in patterns of neural tissue engaged to performance a task as a function of age. Such findings suggest that inclusion of middle-aged adults may provide important insights to understanding neurocognitive aging, and more information about middle-aged adults is important to develop a clear understanding of the aging mind.

AGING AND BRAIN STRUCTURE Before turning our attention to functional imaging data, which allows us to see the neural circuitry engaged to perform specific cognitive tasks, we examine what happens to neural structures with age. Structural imaging of the brain in life-span samples indicates that the brain shows volumetric changes with age, but that these changes are not equivalent across brain structures. Figure 61.3 shows both cross-sectional and longitudinal change over 5 years in brain volume (Raz et al., 2005). As shown in Figure 61.3, the greatest shrinkage across the life span occurs in the caudate, cerebellum, hippocampus, and prefrontal areas. There is minimal shrinkage in the entorhinal cortex and the visual cortex volume remains stable across the life span (Raz, 2000; Raz, Rodrigue, Head, Kennedy, & Acker, 2004). Resnick, Pham, Kraut, Zonderman, and Davatzikos (2003) reported evidence for decline in gray and white matter over a period as short as 2 years in very healthy older adults over age 59, with the frontal and parietal cortex showing greater decreases than temporal and occipital. Other studies have focused on the characteristics of white matter—the bundles of neuronal axons underlying cortical structures. Davatzikos and Resnick (2002) reported age-related signal changes in white matter and suggested

that this reflected white matter degeneration. White matter in the corpus callosum was also studied (Head et al., 2004) using diffusion tensor imaging, which measures anisotropy and mean diffusivity of water molecules to infer white matter integrity. Results indicated greater deterioration in healthy older adults relative to young adults in anterior callosum, while individuals with mild dementia also showed greater deterioration in the posterior callosum (see Figure 61.4). The authors suggest that normal aging is characterized by decline in anterior structures and pathological aging by declines in posterior lobar regions. Reviews of the research on white matter deterioration suggest that the decline in white matter, much like cognitive behavioral declines, is generally linear from age 20, occurs at the same rate in men and women, and occurs disproportionately in frontal areas (Moseley, 2002; Sullivan & Pfefferbaum, 2006). Moreover, white matter deterioration is related to cognitive performance (Sullivan & Pfefferbaum, 2006). Another important structural measure that appears particularly promising for the study of cognitive function is measurement of thinning in the cerebral cortex (Salat et al., 2004). Salat et al. reported that thinning of the cerebral cortex becomes apparent by middle age, with atrophy most pronounced in the frontal cortex near the primary motor area and in the calcarine cortex near the primary visual area. The measurements were highly reliable and showed sufficient variability to be useful in individual differences analyses. Moreover, this is the first technique that we are aware of that reports structural differences in the occipital cortex. This is important because functional studies (as discussed in the later sections) frequently report a shift from high levels of activation in sensory regions in young adults to high levels of activation in frontal regions in older adults on many cognitive tasks (Cabeza et al., 2004; Park et al., 2004), and it has always been somewhat surprising, given this change, that volumetric measures show invariant volume in the occipital cortex as a function of age.

NEURALLY-BASED THEORIES OF COGNITIVE AGING Neuroimaging data have delineated the complexity and multifactorial nature of cognitive aging. At the same time, neuroimaging data have also constrained behavioral theories of cognitive aging. As our knowledge rapidly increases about the aging brain, it is now required that any behavioral theory of cognitive aging be neurally plausible. Up to this point in this discussion, the behavioral and neural data have generally been congruent with one another. As behavioral measures of cognitive aging declined (e.g., speed,

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Figure 61.3 Cross-sectional age differences and longitudinal change in brain volumes across various brain regions. Note: Each pair of connected dots represents an individual subject’s first and second measurement. The caudate, hippocampal, cerebellar, and frontal regions all show both cross-sectional age differences and longitudinal shrinkage. Volume reduction in the hippocampus has a nonlinear trend;

working memory, long-term memory), so did measures of neural structure (e.g., volumetric, white matter, cortical thickness measures). The picture becomes considerably more complex, however, when functional imaging data are integrated into an overall view of mechanisms of cognitive aging. Functional data provide evidence for age-related shifts in the neural structures engaged during cognitive task performance, and provide evidence for a dynamic brain that remodels in the face of the neural insults associated with aging. In this next section, we discuss theoretical views of cognitive aging that have developed primarily

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the decline accelerates with age. Entorhinal volume reduction is minimal and occipital regions are relatively preserved with age. From “Regional Brain Changes in Aging Healthy Adults: General Trends, Individual Differences, and Modifiers,” by N. Raz et al., 2005, Cerebral Cortex, 15, pp. 1676–1689. Copyright 2005 by the Oxford University Press. Adapted with permission.

from neural data, and relate the theories, as well, to behavioral theories of cognitive aging. Dopamine Receptor Depletion Hypothesis One emerging theory of cognitive aging that is receiving increasing support is the view that much of the behavioral change evident in cognitive aging is due to depletion of dopamine receptors. There is evidence that dopamine D2 receptors decline at the rate of about 10% per decade (Wong, Young, Wilson, Meltzer, & Gjedde, 1997), beginning in the

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White Matter Changes with Age and Alzheimer’s YNG OLD DAT Receptor Density (Bmax/Kd)

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Figure 61.4

Diffusion tensor imaging data.

Note: The top row shows decrease in anisotropy (A), which is a measure of white matter connectivity, from young adults (left) to older adults (middle) and Alzheimer’s patients (right). The bottom row shows increase in diffusivity from young adults to older adults and Alzheimer’s patients, indicating reduction in white matter fiber density. From “Differential Vulnerability of Anterior White Matter in Nondemented Aging with Minimal Acceleration in Dementia of the Alzheimer Type: Evidence from Diffusion Tensor Imaging,” by D. Head et al., 2004, Cerebral Cortex, 14, pp. 410–423. Copyright 2004 by the Oxford University Press. Adapted with permission.

20s in both striate and extrastriatal areas. The extrastriatal declines are in areas particularly susceptible to agerelated atrophy, such as the frontal cortex and hippocampus (Kaasinen et al., 2000; Li, Lindenberger, & Sikström, 2001). Dopamine receptors play a critical role in activation of cortical representations, utilization of environmental cues, and in regulation of attention—all domains that are susceptible to age-related decline. Li et al. (2001) argue that it is the loss of dopamine receptors that is responsible for many aspects of cognitive aging and the authors present striking similarities between declines in speed, working memory, and dopamine receptors (see Figure 61.5). They suggest that additional activation that occurs in functional imaging studies on cognitive tasks in older adults, primarily in the frontal cortex, is a compensatory response to less distinctive cortical representations resulting from deficient neuromodulation due to the decreased numbers of dopamine receptors. In agreement with Li et al. (2001), Wang et al. (1998) and Yang et al. (2003) used radioligands to measure dopaminergic receptors and found strong relationships among age, the number of receptors, and cognition. Backman et al. (2000) reported that literally all age-related variance on perceptual speed and episodic memory tasks was attenuated when dopamine receptor binding was statistically controlled. All of these data suggest that dopaminergic receptors play an important

Note: From “Age-Related Dopamine D2/D3 Receptor Loss in Extrastriatal Regions of the Human Brain,” by V. Kaasinen et al., 2000, Neurobiology of Aging, 21, p. 686. Copyright 2000 by Elsevier Press. Reprinted with permission.

role in at least some aspects of cognitive aging. Whether the receptors are the major, or even sole, factor accounting for normal cognitive aging awaits further research. It should be noted that research in this area is hampered primarily by the high cost of radioligands that bind to dopaminergic receptors that require that PET rather than MRI be used to study this issue. Another problem is that PET requires exposure of subjects to radiation and is even more costly than MRI. Frontal Compensation Hypotheses Functional imaging allows us to examine neural activity in specific brain structures while a cognitive task is being performed and has provided truly surprising findings about neural activity in the aging brain. Given the declines in cognitive performance, neural structures, and dopamine receptors portrayed in Figures 61.1 through 61.5, we might intuitively expect that these declines would be accompanied by decreased activation in the aging brain. The most common finding from the cognitive neuroscience literature on aging, however, is that older adults show activation across more neural structures than young adults, although not necessarily more activation in these structures. One of the most robust findings in the emerging cognitive neuroscience of aging literature is that when performing memory or encoding tasks, activations that are highly lateralized in one hemisphere of the dorsolateral prefrontal cortex in young adults will tend to show activation in both hemispheres for older adults (reviewed in Buckner, 2004; Cabeza, 2002; Hedden & Gabrieli, 2004; Park & Gutchess, 2005; ReuterLorenz, 2002). As shown in Figure 61.6, this finding has been reported for both working memory (Reuter-Lorenz et al., 2000) and long-term memory (Cabeza, 2002; Cabeza

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Neurally-Based Theories of Cognitive Aging

et al., 1997), and has been replicated reliably in many other laboratories beyond these initial reports (Backman, 1997; Grady, Bernstein, Beig, & Siegenthaler, 2002; Madden et al., 1999; Morcom, Good, Frackowiak, & Rugg, 2003; Rosen et al., 2002). Both Reuter-Lorenz et al. (2000) and Cabeza (2002) argue that this bilateral distribution of frontal activation in old adults represents a compensatory activation that occurs in the aging brain to accommodate the decreased volume of neural tissue and declining efficiency of neural circuitry. It makes sense that the frontal cortex might somehow engage in compensatory activations for the neural degradation in volume, white matter, and dopamine receptors that occurs with age because the frontal cortex is the largest and most flexible component of the brain. It is involved in reasoning, strategies, control, and semantic processing, and plays a particularly important role in encoding and retrieval processes in memory. Cabeza (2002) presents the hemispheric asymmetry reduction in older adults (HAROLD) model, which suggests that older adults specifically recruit the contralateral hemisphere to assist in task performance that is primarily unilateral in young adults. Reuter-Lorenz & Mikels’ (2006) compensation-related utilization of neural circuits (CRUNCH) model has some similarities to the HAROLD Model. CRUNCH also suggests that at lower levels of cognitive challenge, older adults will recruit more resources, but as challenge level increases, the challenge will exceed the demands of the older adult. Under high challenge, young and old may not differ, or young will show more engagement than old because the old will have already engaged maximal resources in easier conditions.

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The CRUNCH model explains varying patterns of age differences in neural recruitment as a function of cognitive challenge and integrates considerable data. One approach to assessing whether the additional contralateral frontal recruitment is compensatory for some aspect of neural degeneration with age is to examine differences in hemispheric bilaterality between groups of older adults who are either high or low performers on cognitive tasks. The presumption would be that if the additional frontal activation is compensatory, the high performers would show greater evidence of bilaterality. In examining six studies on this issue, two suggest that bilateral activation patterns are associated with good performance (Cabeza, Anderson, Locantore, & McIntosh, 2002; Rosen et al., 2002), and the other four reach the opposite conclusion (Daselaar, Veltman, Rombouts, Raaijmakers, & Jonker, 2003; Logan, Sanders, Snyder, Morris, & Buckner, 2002; Lustig et al., 2003; Stebbins et al., 2002). It is important to note that Lustig et al. contrasted old normal adults with adults who were in early stages of Alzheimer ’s disease and focused on parietal activations, so this study is not directly relevant to the role of prefrontal activations on cognitive function. The other studies had relatively small sample sizes, and all attempted to study neural differences based on behavioral difference, rather than sorting subjects by neural function and studying the resultant differences in behavior. Generally, little is known about patterns of activation associated with neural health, or whether specific activation patterns from individual subjects provide increased predictability of cognitive function beyond structural measures.

Bilateral Frontal Engagement in Order Adults Young

Young - Verbal WM

Older Adults - Verbal WM

Figure 61.6

Frontal bilaterality is increased with age.

Note: The left panel shows left lateralized frontal engagement in young adults during a verbal working memory (WM) task, whereas in older adults, an additional right frontal engagement is observed. From “Age Differences in Behavior and Pet Activation Reveal Differences in Interference Resolution in Verbal Working Memory,” by J., Jonides, C., Marshuetz, E., Smith, P., Reuter-Lorenz, and R., Koeppe, 2000, Journal of Cognitive Neuroscience, 12, pp. 188–196. Copyright 2000 from MIT

More frontal bilateral activity in older adults during a verbal working memory task (left panel) and in older adults with higher performance in a long-term memory task (right panel).

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Press. Reprinted with permission. The right panel shows right lateralized engagement in young adults and low-performing older adults during a long-term memory task, but bilateral frontal engagement in high-performing older adults. From “Aging Gracefully: Compensatory Brain Activity in High-Performing Older Adults,” by R. Cabeza, N. D. Anderson, J. K. Locantore, and A. R. McIntosh, 2002, NeuroImage, 17, pp. 1394–1402. Copyright 2002 from Elsevier Press. Reprinted with permission.

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Another approach to understanding whether bilaterality is compensatory in older adults is to examine neural patterns of activations uniquely associated with items that are remembered compared to items that are forgotten by older adults. There are three studies in the literature that have used such an approach by employing a subsequent memory paradigm for encoding in young and old adults. In a subsequent memory paradigm (Wagner et al., 1998), the activations associated with encoding items are recorded using an eventrelated fMRI design. Then, based on out-of-the-scanner recognition performance, one can determine which encoded items were actually remembered and look at the neural activations specifically associated with those items. Morcom et al. (2003) studied incidental, deep encoding of words and showed more bilateral anterior prefrontal activation for remembered items in old compared to young adults. The fact that bilaterality was uniquely associated with remembered items strongly suggests a compensatory role for bilateral activation. Gutchess et al. (2005) examined memory for complex scenes in an incidental deep-processing encoding task. Because both young and old showed bilateral activations in prefrontal areas for the pictorial task, it was not possible to find evidence for more bilaterality in old. Nevertheless, as shown in Figure 61.7, Gutchess et al. found more recruitment of the left frontal cortex in old adults compared to young for the remembered items when they subtracted activations associated with forgotten items from remembered. Moreover, Gutchess et al. also reported strong negative correlations for old adults but not young between the inferior frontal cortex and parahippocampal engagement. In old adults, the less the parahippocampus was engaged, the greater the engagement of the frontal cortex for remembered items, suggesting that greater frontal activation Regions Revealing Differences with Age Parahippocampal Region Young  Old

Middle Frontal Region Old  Young T Value 5 4 3 2 1 0

Figure 61.7 Frontal-medial-temporal age differences during an incidental encoding task involving complex scenes. Note: Young adults engaged parahippocampal regions more than older adults, whereas older adults engaged middle frontal regions more than younger adults. From “Aging and the Neural Correlates of Successful Picture Encoding: Frontal Activations Compensate for Decreased MedialTemporal Activity,” by A. H. Gutchess et al., 2005, Journal of Cognitive Neuroscience, 17, pp. 84–96. Copyright 2005 from MIT Press. Reprinted with permission.

was compensatory for deficient medial-temporal activations normally associated with memory. A third approach to assessing the role of bilaterality as a compensatory mechanism is to use transcranial magnetic stimulation (TMS). In a TMS procedure, stimulation is applied to a specific area of cortex—and the stimulation interferes with the function of the brain site being stimulated. Rossi et al. (2004) applied stimulation to the left or right dorsolateral prefrontal cortex while younger and older subjects performed a spatial recognition task. In younger subjects, stimulation of the right hemisphere produced more interference in the retrieval task than left hemisphere stimulation, reflecting right lateralized processing in younger adults. In older adults, this asymmetry disappeared, suggesting that both hemispheres were facilitative for performing the task and confirming the compensatory role of bilateral activation with age. To summarize, the research on the role of the widely observed pattern of additional frontal activation in older adults increasingly points to the probability that the additional activation is functional and enhances performance in older adults. Medial-Temporal Function, Cognition, and Aging Although most initial imaging work focused on the frontal cortex, there is a growing emphasis on the role of the hippocampus and other medial-temporal structures in the aging and memory literature. The hippocampus is intimately tied to episodic memory function (Brewer, Zhao, Desmond, Glover, & Gabrieli, 1998; Cohen & Eichenbaum, 1993; Gabrieli, Brewer, Desmond, & Glover, 1997; Squire et al., 1992) and also plays an important role in binding elements in complex scenes (Cohen et al., 1999). There is a literature specifically devoted to structural differences in the hippocampus and other medial-temporal structures as they relate to both normal and pathological aging. The entorhinal cortex and hippocampus are the initial brain areas attacked by Alzheimer’s disease in its early stages, accounting for the memory dysfunction that is often an early symptom of the disorder. In a recent study, Rodrigue and Raz (2004) measured prefrontal, entorhinal, and hippocampal volume in 48 adults at baseline and at a 5-year follow-up period; they also measured episodic memory function. When age was covaried with structural volume, the only measure that predicted memory function was entorhinal cortical volume, suggesting that even mild shrinkage of the entorhinal cortex may be an important factor in memory function. Similarly, Rosen et al. (2003) also found a strong relationship between memory function and the entorhinal cortex volume in a small sample (n  14) of older adults. Van Petten (2004) reviewed the literature relating hippocampal volume to memory ability in

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Neurally-Based Theories of Cognitive Aging

a meta-analysis and concluded that evidence for a strong relationship between hippocampus size and memory function is weak. In a study that transitions from structural imaging to functional imaging, Small, Tsai, DeLaPaz, Mayeux, and Stern (2002) developed an fMRI technique that permits segmentation of hippocampal structures into four areas: the entorhinal cortex, CA1, the subiculum, and the dentate gyrus. They reported evidence that decline in volume in the subiculum and the dentate gyrus was associated with normal aging, but that decline in the entorhinal cortex was associated with pathological decline. Although the literature relating the size of medial-temporal structures to memory have concluded that relationships are generally weak (Van Petten, 2004), the frequency with which the entorhinal cortex appears as an important structure in understanding memory function suggests that particular attention should be paid to this structure in understanding the relationship between the aging brain and neural function. One question that naturally emerges from the compensation hypothesis is “What is it exactly that the frontal cortex is compensating for?” One possibility is under-recruitment of other key structures outside of the frontal cortex that results in compensatory frontal activations for deficient activation in these structures. Park and Gutchess (2005) provide a detailed review of the relationship between hippocampal and frontal function in the long-term memory literature. They conclude that there is strong evidence for an increased frontal/decreased hippocampal relationship across 24 functional imaging studies, a relationship that strongly suggests that increased frontal activations may be compensatory for decreased hippocampal activations. They also address the issue of hippocampal engagement in picture memory, suggesting that older adults consistently show less engagement of medial temporal areas than young adults when encoding pictures. They note that increased frontal activity in older relative to younger adults has been most likely to occur when the pictures presented are complex meaningful scenes, consistent with Cohen et al.’s (1999) view that the hippocampus plays an important role in binding scene elements, as confirmed in the imaging literature by Goh et al. (2004). In fact, Chee et al. (2006) reported deficient binding regions in the right and left parahippocampus in older adults while viewing complex meaningful scenes. Another study by Persson et al. (2006) demonstrated that age-related cognitive decline in memory was related to hippocampal volume as well as decreased white matter integrity in the interior corpus callosum, and that all of these measures were related to increased activation in the right prefrontal cortex in older subjects. This pattern of findings again confirms that additional frontal activation is compensatory and also that it is associated with decreased hippocampal volume.

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Frontal versus Hippocampal Aging: Normal versus Pathological? Thus far, the neurally based theories on aging have focused on the compensatory function of additional frontal engagement in response to reduction in medial-temporal activity with aging. An alternative to this view is proposed by Head et al. (2004). These authors noted that Alzheimer ’s disease patients are characterized by declining hippocampal volume whereas normal aging is characterized by white matter and frontal deterioration. They suggest that aging has two components: a normal component that is characterized by frontal decline and difficulties in executive function, and a pathological component characterized by declining volume in the medial-temporal areas, particularly the hippocampus. This view suggests frontal and hippocampal aging occur independently and is different from the notion that there is direct linkage between frontal activation and hippocampal deterioration as suggested by Park and Gutchess (2005), and confirmed by Persson et al. (2006). There is considerable support for both views. Recent work by Andrews-Hanna et al. (2007) again showed evidence for age-related disruption of default network activity associated with memory decline and white matter decline, and that the relationship was strongly independent of the amount of amyloid deposition, suggesting a normal/ pathological component dissociation. Only larger studies that take integrative multimodal approaches are likely to yield more definitive evidence for these possibilities. With this in mind, while the frontal and hippocampal regions are the subject of many aging studies, it is also important to consider other brain regions that might provide a clue as to whether the changes observed, both structural and functional, are compensatory or in fact the underlying cause for the compensatory response in complementary brain regions. One such candidate for exploration are the ventral visual areas, which we discuss the following section.

Ventral Visual Function, Memory, and Aging There is only a modest amount of functional imaging data that specifically examines age differences in activation patterns in ventral visual cortex. Before discussing the findings, it is informative to review some very elegant behavioral work conducted by Lindenberger and Baltes (1994) and Baltes and Lindenberger (1997) that foreshadows the importance of the sensory cortex in understanding age differences in neural function. Briefly, these investigators studied a life span sample that included a large number of very old adults—up to the age of 105. They reported that measures of vision and audition explained essentially all of the age-related variance on measures of memory and

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reasoning, and suggested that with age, a dedifferentiation (loss of specificity) in different sensory and cognitive functions occurs, so that a loss in one domain is highly explanatory and interrelated to dysfunction in other domains. Put concisely, this work suggests that with respect to sensory cognitive function, “when it goes, it all goes together,” whereas younger adults maintain some specificity and independence of function across different sensory/cognitive domains. Some of the most specialized neural areas appear to occur in the ventral visual cortex, at least in young adults. There is evidence that in young adults, the fusiform gyrus is specialized to recognize faces (Kanwisher, McDermott, & Chun, 1997); the parahippocampus is specialized for places (Epstein & Kanwisher, 1998); the lateral occipital cortex is specialized for objects (Malach et al., 1995); and the left fusiform gyrus is specialized for letters and words (Polk et al., 2002; Puce, Allison, Asgari, Gore, & McCarthy, 1996). Park et al. (2004) examined neural specificity for faces, places, and words in young and old adults while they passively viewed pictures. They reported that voxels that were highly specialized for faces in young adults showed markedly less activation to places, chairs, and words. In contrast, old adults’ “face voxels” showed substantial activation to other categories, reflecting dedifferentiation. This effect was replicated in the other categories. Young adults’ “place” voxels showed specialization, but old adults also activated to faces and words, and the same was true for specialization of voxels activated to pseudowords. The results were replicated using a broad range of dependent measures, attesting to the reliability and generality of the effect. Chee et al. (2006) demonstrated decreased specificity in the object area (lateral occipital cortex) and binding area (hippocampus), confirming again the decreased specificity in this general region (Figure 61.8). Park et al. (2004) concluded that aging is accompanied by decreased selectivity in ventral visual cortex, or a dedifferentiation of neural specificity, in accord with the theorizing of Baltes and Lindenberger (1997). They consider that older adults may show increased frontal function and perceptual slowing in the behavioral domain as a result of decreased neural specialization in the ventral visual areas. The sample that Park et al. tested was quite small (13 young and 12 old), so there were insufficient subjects to correlate magnitude of neural differentiation with behavior. In accord with the Park et al. (2004) findings, a concurrent paper by Cabeza et al. (2004) demonstrated decreased occipital function with age and increased frontal function across measures of attention, working memory, and long-term memory. They suggest that the findings are “consistent with the common factor view that age-related cognitive deficits are in great part due to a decline in sensory processing, and that some

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While background processing regions are present in young and older adults, objectprocessing regions are present in young adults but greatly reduced or almost not present in older adults.

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Figure 61.8 (Figure C.58 in color section) Object and background-scene processing areas identified in young and older adults during passive viewing of complex pictures. Note: Background processing regions are present in the parahippocampal area in both young and older adults, but object regions in the lateral occipital area are greatly reduced in older adults. From “Age-Related Changes in Object Processing and Contextual Binding Revealed Using FMR Adaptation,” by M. Chee et al., 2006, Journal of cognitive Neuroscience, 18, p. 501. Copyright 2006 from MIT Press. Reprinted with permission.

forms of compensatory prefrontal recruitment are common across tasks.” Cabeza and colleagues labeled the pattern of increased frontal and decreased ventral visual/occipital findings posterior anterior shift in aging (PASA; Davis, Dennis, Daselaar, Fleck, & Cabeza, 2008). The finding of age-related cortical thinning in sensory areas by Salat et al. (2004) also adds to the plausibility of the hypothesis that sensory deficits drive much of the cognitive decline and increased frontal recruitment evidenced in cognitive aging research. The Default Network A relatively recent development in neuroimaging that is important to aging research is the identification of a set of brain regions termed the default network that is observed to be more active during periods of rest compared to when the brain is actively engaged in a task (Beer, 2007; Buckner et al., 2005; Greicius, Krasnow, Reiss, & Menon, 2003; Raichle et al., 2001; Shulman, 1997). This network involves deactivation of multiple regions when a demanding cognitive task is presented. The regions involved include the medial-frontal, inferior-parietal, posterior-cingulate, and medial-temporal structures, and deactivation of these structures during a cognitive task relative to baseline has been observed to occur in many studies across a variety of tasks. Mason et al. (2007) suggested that the default network is engaged during periods of daydreaming, or

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Age and Culture Differences in Neurocognitive Function

mind wandering and it becomes deactivated during a cognitive task because individuals must suppress this mindwandering to perform the task. Mason et al. found that the frequency with which individuals reported being engaged in mind wandering was correlated with activity in the default network. This has an important relation to aging because a study by Giambra (1989) found that older adults were more likely to engage in such mind-wandering compared to younger adults, using various measures of frequency of mind wandering. As the Giambra study would predict, older adults do not deactivate these default areas as much as young adults when presented with a cognitive task. This suggests that older adults may have more trouble staying on task because the mind-wandering network fails to deactivate when a demanding task is presented. Lustig et al. (2003) reported less deactivation in the medial frontal and posterior cingulate default network regions in older adults compared to younger adults in a cognitive task. Furthermore, Alzheimer ’s patients showed even less default-related deactivation in these regions compared to normal older adults, a finding that was replicated by Greicius, Srivastava, Reiss, and Menon (2004). These findings suggest that older adults may find it more difficult to disengage from their default mode of processing in order to fully engage in the active task at hand. This may account in part for poorer behavioral performance in older adults across a number of tasks, just as the default network is consistently identified across a number of different tasks. The findings on the default network reviewed here represent initial work relating default network activity to aging cognitive processes. Thus, there are many caveats to consider such as the specific nature of mind wandering and the validity of its measurements, the cognitive significance of the default network and its function, heterogeneity within the default network, and the methodological issues related to baseline comparisons in neuroimaging data. In summary, aging is associated with reduced deactivation in default network activity during active tasks. Aging is also associated with higher probability of engaging in mind wandering, which is shown to correlate with default network activity. Taken together, these suggest that at least part of the cognitive behavioral decline, as well as functional changes in brain activity observed with aging, may be related to differences in default network function in older adults. Aging, Individual Differences and Neurocognitive Function Age is only one of many individual differences that are important in determining cognition. A large behavioral

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literature examines the role of individual differences in cognitive function across the life span with evidence that (a) speed and working memory explain long-term memory function (Park et al., 2002); (b) education and social class in older adults are important in understanding the response to environmental support (Cherry & Park, 1993; Craik, Byrd, & Swanson, 1987); and (c) old individuals’ variability in performance on free-recall tasks over days is an important predictor of later cognitive decline (D. F. Hultsch, MacDonald, Hunter, Levy-Bencheton, & Strauss, 2000; Li et al., 2001). A relatively unexplored area in the imaging literature that is of considerable importance to understanding neurocognitive aging is the role of individual differences in patterns of neural function in predicting cognitive performance. Equally important is the relationship between activations in different brain areas (e.g., differentiation and laterality indices) in predicting hippocampal and frontal activation on encoding tasks when the encoding data is collected on tasks independent of the differentiation and laterality indices. One of the few examples of a neuroimaging study in which a true individual differences approach has occurred, with predictors measured independently from tasks, and in which brain activation has been used to predict behavioral accuracy, is a study by Gray, Chabris, and Braver (2003). They measured general intelligence along with neural activations associated with performance on a demanding working memory task. They determined that general intelligence predicted performance on working memory accuracy. Hypothesizing that neural activation in two regions of interest (lateral prefrontal and parietal) was the mediator between general intelligence and accuracy on the working memory task, they were able to demonstrate that the activation level in these two brain sites mediated the variance associated with the relationship between intelligence and accuracy. A similar approach that integrates aging, structural, and functional variables could be very useful. Using individual differences, measures of structure along with functional indices of differentiation and laterality to predict behavioral performance could be very informative.

AGE AND CULTURE DIFFERENCES IN NEUROCOGNITIVE FUNCTION Every individual is enmeshed in a culture, and the influences of culture on behavior are often transparent to both the individual and the society in which he or she is enmeshed. Thus, the notion that cultural influences affect cognitive and neural function may not be as obvious as findings that other life experiences affect neurocognitive function. There is a wealth of evidence that culture influences cognitive

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Successful Aging

behavioral function (Nisbett, 2003; Nisbett, Peng, Choi, & Norenzayan, 2001). Nisbett (2003) proposes that beginning in ancient times, Western thought (characterized by the Greeks) and Eastern thought (characterized by Chinese) had fundamentally different philosophical views of the world that have persisted into the present and subtly shape perception, memory, and higher-order cognition, as well as social relationships (Nisbett & Masuda, 2003). Western thought is grounded in an analytic focus on objects and categories, with rules that define objects’ properties and function. In contrast, Eastern thought is grounded in a holistic focus where objects are part of a larger whole, and central and contextual elements of information are given equal focus. Nisbett et al. (2001) note that the social systems of the two cultures reflect these biases because Western cultures tend to focus on the individual with an independent self that is largely unconnected to others, whereas East Asian cultures (e.g., Chinese, Japanese, Singaporean, Korean, Thai) are based on complex interdependent social relationships with the self being defined by relationships to, and function of, a social group (Markus & Kitayama, 1991). The East Asian tendency to focus on others in the group results in a tendency to monitor context and relationships, and to treat complex systems in a relatively unitary, holistic fashion. Unlike East Asians, Westerners tend to process information in an object-based, analytic fashion due to their individualistic bias and relatively unconnected self. These tendencies result in biases to prioritize different types of information for processing, with East Asians attending relatively more to contextual, relational information than Westerners, and Westerners focusing relatively more attention on objects and their properties. Given the striking differences Park et al. (2004) found in specialization of ventral visual cortex with age, and given that culture effects in the behavioral domain are primarily perceptual (e.g., the greater attention to object stimuli in Westerners and to contextual information in East Asians), it makes sense to focus efforts to understand neurocultural differences in the ventral visual area. In an initial functional imaging study of culture, Gutchess, Welsh, Boduroglu, and Park (2006) presented young adults who were either East Asian or Western with a series of photographs (displayed in Figure 22.3) that were of three types: (1) a relatively simple target object, such as an elephant or an airplane; (2) a complex scene with no discernible central object, such as a picture of a jungle or lake; or a (3) target object against a meaningful background scene (e.g., an elephant in a jungle). Through a series of contrasts, they were able to isolate areas uniquely associated with object processing and areas uniquely associated with contextual or background processing in the ventral visual areas (Goh et al., 2004). Of central importance was the finding of evidence

for heightened activation in Americans of the middle temporal gyrus, an area used for object processing, as the culture hypothesis would predict. They saw less evidence for cultural differences in context processing, although they did observe a slight bias for more activation of context areas in East Asians, as cultural theorists would predict. In a second study, Goh et al. (2007) compared the magnitude of adaptation in different areas in the ventral visual cortex adapted when the object and background in complex scenes were repeated. Adaptation occurs when a neural signal declines if a stimulus is repeated. There were three major findings from this study depicted in Figure 61.9. First, there were no differences in patterns of neural activation in young Asians and Americans. Second, older adults in both cultures showed evidence for a diminished object-processing area in the lateral occipital cortex. Third, this object area was significantly more diminished in the older East Asians compared to the older Americans. The older East Asians showed almost no adaptation in the object area whatsoever. Perhaps because the adaptation paradigm is comparatively subtle, cultural differences were not observed in younger adults, unlike in Gutchess et al. (2006), who reported greater neural engagement for object processing regions in young Westerners compared with young East Asians. In Goh et al. (2007), cultural differences were apparent only in older participants who have had more exposure to their respective cultural environments than younger participants. Overall, this pattern of findings provides initial neuroimaging evidence for cultural biases in perceptual processing of objects and further suggests that the long-term exposure to a particular cultural environment that occurs with aging might have a role in shaping brain function. It is also important to note that while the evidence suggests a culture-related bias in neurocognitive processing in individuals, human beings are extremely flexible in approaching various types of situations. Indeed, even while we are steeped in our own cultural tendencies, we have the capacity to engage modes of thinking related to other cultures as well (Gardner, Gabriel, & Lee, 1999; Miyamoto, Nisbett, & Masuda, 2006). Both Gardner et al. (1999) and Miyamoto et al. (2006) have demonstrated that subjects can be primed to respond in a culturally nonpreferred manner. In a brain imaging study, Hedden, Ketay, Aron, Markus, and Gabrieli (2008) showed that such engagement of culturally nonpreferred processes requires greater attentional effort compared to when subjects were using their own cultural biases to approach a problem. Because these studies were conducted on young adults, it would be interesting to investigate this flexibility in older adults to engage different strategies to approach various situations as well.

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Neural Plasticity in Older Adults

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Figure 61.9 Object-and background-scene-processing regions in younger and older Westerners and East Asians. Note: Background-processing activity in the parahippocampal region (top row) was relatively preserved across younger and older Westerners and East Asians. However, object-processing activity in the lateral occipital region (bottom row) was reduced in older adults compared to younger

One important question is whether cultural biases shape brain organization in a fundamental way or merely reflect activation patterns that can be modified by switching to other modes of thought. The data thus far argue more for the latter option. However, experience has been shown to alter brain structures in fundamental ways (see the section that follows). The cross-cultural study of neural aging provides a potential avenue to assess the possibility that culture alters structure and/or function permanently because effects of culture should be magnified with age due to the sustained exposure older adults have to culture. In addition, and at least as importantly, the cross-cultural study of neural aging allows investigators to determine what the biologically invariant aspects of neural aging are and what aspects of aging are determined by environmental and social factors. It is with this in mind that we next consider issues pertaining to neural plasticity and aging.

NEURAL PLASTICITY IN OLDER ADULTS There is evidence from the animal literature suggesting that mental stimulation and enriched environments enhance

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adults, with an even greater reduction for older East Asians compared to older Westerners. From “Age and Culture Modulate Object Processing and Object-Scene Binding in the Ventral Visual Area,” by J. O. Goh et al., 2007, Cognitive, Affective and Behavioral Neuroscience, 7, pp. 44–52. Copyright 2007 by Psychonomic Society Publications. Adapted with permission.

cognition. Kempermann, Kuhn, and Gage (1998) demonstrated that old rats maintained in complex visual and play environments showed birth of new neurons in the hippocampus in old age unlike control subjects. Kobayashi, Ohashi, and Ando (2002) reported improved learning ability in rats exposed to an enriched environment, even in old age, and concluded, “These results show that aged animals still have appreciable plasticity in cognitive function and suggest that environmental stimulation could benefit aging humans as well.” Kempermann, Gast, and Gage (2002) reported a similar effect and demonstrated that short-term exposure of rats to an enriched environment, even in old age, led to a fivefold increase in hippocampal neurogenesis (Figure 61.10), as well as a decrease in age-related atrophy in the dentate gyrus. The authors make a direct link of their findings to human behavior and ask, “Could this plastic response be relevant for explaining the beneficial effects of leading an ‘active life’ on brain function?” Is the “use it or lose it” hypothesis correct with respect to cognitive aging? There is evidence from many correlational and longitudinal studies that individuals who lead more engaged lives have more resilient cognitive systems in late adulthood.

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Successful Aging Neurogenesis in Senescent Rats 120 110 100

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Hippocampal neurogenesis in aging rats.

Note: Rats raised in an enriched environment showed increased neurogenesis of neuronal cells compared to control rats (black bars). From “Neuroplasticity in Old Age: Sustained Fivefold Induction of Hippocampal Neurogenesis by Long-Term Environmental Enrichment,” by G. Kempermann, D. Gast, and F. H. Gage, 2002, Annals of Neurology, 52, pp. 135–143. Copyright 2002 by John Wiley & Sons. Reprinted with permission.

For example, Schooler, Mulatu, and Oates (1999) reported that engagement in “substantively complex” work across the life span, predicted better intellectual functioning in old age than engagement in less challenging work, even after education and other related factors were controlled. Other evidence in support of the productive engagement hypothesis comes from the Maastricht Longitudinal Study. At the beginning of the study, none of the participants enrolled (aged 50 to 80) showed cognitive impairment, but 3 years later, 4% of those with low cognitively demanding jobs showed some cognitive impairment, whereas only 1.5% of those with mentally demanding jobs were impaired (Bosma et al., 2003). There are also data suggesting that individuals who self-report engagement in more intense cognitive activity across their life span show higher levels of cognitive function, as measured by text recall, perceptual speed, and the Mini-Mental Status Exam (Wilson et al., 1999). Finally, a number of studies have reported that highly educated people who tend to be involved in more cognitively stimulating activities are more cognitively resilient in the early stages of Alzheimer ’s disease (Bennett et al., 2003; Wilson & Bennett, 2003; Wilson, Gilley, Bennett, Beckett, & Evans, 2000), even after controlling for other

related variables. In sum, there is experimental evidence that animals raised in engaging environments show enhanced neural structure and function in old age, while correlational studies of humans indicate that productive engagement across the life span is related to higher cognitive function and resilience in the face of mild levels of brain disease. There is also good evidence that experience changes the brains of older adults. Nyberg et al. (2003) demonstrated increased occipitoparietal activation in older adults after they had been trained in a method of loci mnemonic technique. Colcombe et al. (2004) demonstrated that several months of aerobic training in older adults increased functional activations in both the prefrontal and parietal cortices while performing a flanker task. There is rapidly growing evidence that sustained aerobic exercise provides neuroprotection for older adults, although the mechanism of improvement is still somewhat unclear. Finally, perhaps the most direct evidence that aging brains change in response to experience comes from aged stroke patients. Despite devastating brain injuries, with enormous amounts of training and practice, even very old adults show dramatic improvement from strokes, due to the residual plasticity and malleability of their brains (Hallett, 2001). Summary Our understanding of normal cognitive aging has been radically changed by the introduction of neuroimaging tools. We now know that the aging brain is more dynamic and active than was previously thought and that despite the gradual decline of functions observed in behavior, the aging brain “does not go gently into the night.” Rather, the aging brain remodels and reorganizes, activating new and increased neural circuitry in response to the declining structure and function confronting it. The past decade has resulted in tremendous increases in understanding the aging mind through the integration of structural and functional imaging techniques with behavioral data. Future direction will involve integrating even more modalities and including more studies that integrate genomics. Additionally, integration of animal and human work may provide greater understanding of mechanisms and facilitation than is currently possible. The increasing prevalence of Alzheimer ’s disease and other age-associated neurological disorders has become a public health crisis and a threat to the wellbeing, not only of older citizens, but to their families as well. Understanding normal aging and conditions that lead to a slowed rate of cognitive aging will enhance economic productivity and quality of life for citizens of all ages. The significant gains made in the past 10 years lead to optimism that we may understand the mechanisms underlying rates of cognitive aging sufficiently well to slow them in the near future.

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References 1217

REFERENCES Alzheimer ’s Association (2007). Alzheimer ’s disease facts and figures 2007. Retrieved December 23, 2008, from http://www.alz.org/national/ documents/ Report_2007FactsAndFigures.pdf Andrews-Hanna, J. R., Snyder, A. Z., Vincent, J. L., Lustig, C., Head, D., & Raichle, M. E. (2007). Disruption of large-scale brain systems in advanced aging. Neuron, 56, 924–935. Backman, L. (1997). Brain activation in young and older adults during implicit and explicit retrieval. Journal of Cognitive Neuroscience, 9, 378–391. Backman, L., Ginovart, N., Dixon, R. A., Wahlin, T. B., Wahlin, A., Halldin, C., et al. (2000). Age-related cognitive deficits mediated by changes in the striatal dopamine system. The American Journal of Psychiatry, 157, 635–637. Baltes, P., & Lindenberger, U. (1997). Emergence of a powerful connection between sensory and cognitive functions across the adult life span: A new window to the study of cognitive aging? Psychology and Aging, 12, 12–21. Beer, J. S. (2007). The default self: Feeling good or being right? Trends in Cognitive Sciences, 11, 187–189. Bennett, D. A., Wilson, R. S., Schneider, J. A., Evans, D. A., Mendes de Leon, C. F., Arnold, S. E., et al. (2003). Education modifies the relation of AD pathology to level of cognitive function in older persons. Neurology, 60, 1909–1915. Bosma, H., van Boxtel, M. P., Ponds, R. W., Houx, P. J., Burdorf, A., & Jolles, J. (2003). Mental work demands protect against cognitive impairment: MAAS prospective cohort study. Experimental Aging Research, 29, 33–45. Brewer, J. B., Zhao, Z., Desmond, J. E., Glover, G. H., & Gabrieli, J. D. (1998). Making memories: Brain activity that predicts how well visual experience will be remembered. Science, 281, 1185–1187. Buckner, R. L. (2004). Memory and executive function in aging and AD: Multiple factors that cause decline and reserve factors that compensate. Neuron, 44, 195–208. Buckner, R. L., Snyder, A. Z., Shannon, B. J., LaRossa, G., Sachs, R., Fotenos, A. F., et al. (2005). Molecular, structural, and functional characterization of alzheimer ’s disease: Evidence for a relationship between default activity, amyloid, and memory. Journal of Neuroscience: Official Journal of the Society for Neuroscience, 25, 7709–7717. Cabeza, R. (2002). Hemispheric asymmetry reduction in older adults: The HAROLD model. Psychology and Aging, 17, 85–100.

Cohen, N., Ryan, J., Hunt, C., Romine, L., Wszalek, T., & Nash, C. (1999). The hippocampal system and declarative (relational) memory: Evidence from functional neuroimaging studies. Hippocampus, 9, 83–98. Colcombe, S. J., Kramer, A. F., Erickson, K. I., Scalf, P., McAuley, E., Cohen, N. J., et al. (2004). Cardiovascular fitness, cortical plasticity, and aging. Proceedings of the National Academy of Sciences, USA, 101, 3316–3321. Craik, F. I., Byrd, M., & Swanson, J. M. (1987). Patterns of memory loss in three elderly samples. Psychology and Aging, 2, 79–86. Daselaar, S., Veltman, D., Rombouts, S., Raaijmakers, J., & Jonker, C. (2003). Neuroanatomical correlates of episodic encoding and retrieval in young and elderly subjects. Brain, 126, 43. Davatzikos, C., & Resnick, S. M. (2002). Degenerative age changes in white matter connectivity visualized in vivo using magnetic resonance imaging. Cerebral Cortex, 12, 767–771. Davis, S. W., Dennis, N. A., Daselaar, S. M., Fleck, M. S., & Cabeza, R. (2008). Que PASA? the posterior anterior shift in aging. Cerebral Cortex, 18, 1201–1209. Epstein, R., & Kanwisher, N. (1998). A cortical representation of the local visual environment. Nature, 392, 598–601. Gabrieli, J. D., Brewer, J. B., Desmond, J. E., & Glover, G. H. (1997). Separate neural bases of two fundamental memory processes in the human medial temporal lobe. Science, 276, 264–266. Gardner, W. L., Gabriel, S., & Lee, A. Y. (1999). “I” value freedom, but “we” value relationships: Self-construal priming mirrors cultural differences in judgment. Psychological Science, 10, 321–326. Giambra, L. M. (1989). Task-unrelated-thought frequency as a function of age: A laboratory study. Psychology and Aging, 4, 136–143. Goh, J. O., Chee, M. W., Tan, J. C., Venkatraman, V., Hebrank, A., Leshikar, E. D., et al. (2007). Age and culture modulate object processing and object-scene binding in the ventral visual area. Cognitive, Affective and Behavioral Neuroscience, 7, 44–52. Goh, J. O., Siong, S. C., Park, D., Gutchess, A., Hebrank, A., & Chee, M. W. (2004). Cortical areas involved in object, background, and object-background processing revealed with functional magnetic resonance adaptation. Journal of Neuroscience: Official Journal of the Society for Neuroscience, 24, 10223–10228. Grady, C. L., Bernstein, L. J., Beig, S., & Siegenthaler, A. L. (2002). The effects of encoding task on age-related differences in the functional neuroanatomy of face memory. Psychology and Aging, 17, 7–23. Gray, J. R., Chabris, C. F., & Braver, T. S. (2003). Neural mechanisms of general fluid intelligence. Nature Neuroscience, 6, 316–322.

Cabeza, R., Anderson, N. D., Locantore, J. K., & McIntosh, A. R. (2002). Aging gracefully: Compensatory brain activity in high-performing older adults. NeuroImage, 17, 1394–1402.

Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences, USA, 100, 253–258.

Cabeza, R., Daselaar, S. M., Dolcos, F., Prince, S. E., Budde, M., & Nyberg, L. (2004). Task-independent and task-specific age effects on brain activity during working memory, visual attention and episodic retrieval. Cerebral Cortex, 14, 364–375.

Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004). Defaultmode network activity distinguishes alzheimer ’s disease from healthy aging: Evidence from functional MRI. Proceedings of the National Academy of Sciences, USA, 101, 4637–4642.

Cabeza, R., Grady, C. L., Nyberg, L., McIntosh, A. R., Tulving, E., Kapur, S., et al. (1997). Age-related differences in neural activity during memory encoding and retrieval: A positron emission tomography study. Journal of Neuroscience: Official Journal of the Society for Neuroscience, 17, 391–400.

Gutchess, A. H., Welsh, R. C., Hedden, T., Bangert, A., Minear, M., Liu, L. L., et al. (2005). Aging and the neural correlates of successful picture encoding: Frontal activations compensate for decreased medialtemporal activity. Journal of Cognitive Neuroscience, 17, 84–96.

Chee, M. W. L., Goh, J. O. S., Venkatraman, V., Chow Tan, J., Gutchess, A., Sutton, B., et al. (2006). Age-related changes in object processing and contextual binding revealed using fMR adaptation. Journal of Cognitive Neuroscience, 18, 495–507. Cherry, K. E., & Park, D. C. (1993). Individual difference and contextual variables influence spatial memory in younger and older adults. Psychology and Aging, 8, 517–526. Cohen, N. J., & Eichenbaum, H. (1993). Memory, amnesia, and the hippocampal system Bradford Book.

Gutchess, A., Welsh, R., Boduroglu, A., & Park, D. C. (2006). Cultural differences in neural function associated with object processing. Cognitive, Affective, and Behavioral Neuroscience, 6, 102–109. Hallett, M. (2001). Brain plasticity and recovery from hemiplegia. Journal of Medical Speech-Language Pathology, 9, 107–115. Head, D., Buckner, R. L., Shimony, J. S., Williams, L. E., Akbudak, E., Conturo, T. E., et al. (2004). Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the alzheimer type: Evidence from diffusion tensor imaging. Cerebral Cortex, 14, 410–423.

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Successful Aging

Hedden, T., Ketay, S., Aron, A., Markus, H., & Gabrieli, J. D. E. (2008). Cultural influences on neural substrates of attentional control. Psychological Science, 19, 12–17.

Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C. N. (2007). Wandering minds: The default network and stimulus-independent thought. Science, 315, 393.

Hedden, T., & Gabrieli, J. D. E. (2004). Insights into the ageing mind: A view from cognitive neuroscience. Nature Reviews Neuroscience, 5, 87–96.

Miyamoto, Y., Nisbett, R. E., & Masuda, T. (2006). Culture and the Physical Environment: Holistic versus Analytic Perceptual Affordances. Psychological Science, 14, 113–119.

Hedden, T., Lautenschlager, G., & Park, D. C. (2005). Contributions of processing ability and knowledge to verbal memory tasks across the adult life-span. The Quarterly Journal of Experimental Psychology: A, Human Experimental Psychology, 58, 169–190.

Morcom, A. M., Good, C. D., Frackowiak, R. S. J., & Rugg, M. D. (2003). Age effects on the neural correlates of successful memory encoding. Brain, 126, 213–229.

Hultsch, D. F., Hertzog, C., Dixon, R. A., & Small, B. J. (1998). Memory change in the aged. New York: Cambridge University Press. Hultsch, D. F., MacDonald, S. W., Hunter, M. A., Levy-Bencheton, J., & Strauss, E. (2000). Intraindividual variability in cognitive performance in older adults: Comparison of adults with mild dementia, adults with arthritis, and healthy adults. Neuropsychology, 14, 588–598. Jonides, J., Marshuetz, C., Smith, E., Reuter-Lorenz, P., Koeppe, R. (2000). Age differences in behavior and PET activation reveal differences in interference resolution in verbal working memory. Journal of Cognitive Neuroscience, 12, 188–196. Kaasinen, V., Vilkman, H., Hietala, J., Nagren, K., Helenius, H., Olsson, H., et al. (2000). Age-related dopamine D2/D3 receptor loss in extrastriatal regions of the human brain. Neurobiology of Aging, 21, 683–688. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 4302–4311. Kempermann, G., Gast, D., & Gage, F. H. (2002). Neuroplasticity in old age: Sustained fivefold induction of hippocampal neurogenesis by longterm environmental enrichment. Annals of Neurology, 52, 135–143. Kempermann, G., Kuhn, H. G., & Gage, F. H. (1998). Experience-induced neurogenesis in the senescent dentate gyrus. Journal of Neuroscience: Official Journal of the Society for Neuroscience, 18, 3206–3212. Kobayashi, S., Ohashi, Y., & Ando, S. (2002). Effects of enriched environments with different durations and starting times on learning capacity during aging in rats assessed by a refined procedure of the hebb-williams maze task. Journal of Neuroscience Research, 70, 340–346. Li, S. C., Lindenberger, U., & Sikström, S. (2001). Aging cognition: From neuromodulation to representation. Trends in Cognitive Sciences, 5, 479–486. Lindenberger, U., & Baltes, P. B. (1994). Sensory functioning and intelligence in old age: A strong connection. Psychology and Aging, 9, 339–355. Logan, J. M., Sanders, A. L., Snyder, A. Z., Morris, J. C., & Buckner, R. L. (2002). Under-recruitment and nonselective recruitment dissociable neural mechanisms associated with aging. Neuron, 33, 827–840. Lustig, C., Snyder, A. Z., Bhakta, M., O’Brien, K. C., McAvoy, M., Raichle, M. E., et al. (2003). Functional deactivations: Change with age and dementia of the alzheimer type. Proceedings of the National Academy of Sciences, USA, 100, 14504–14509. MacDonald, S. W., Hultsch, D. F., Strauss, E., & Dixon, R. A. (2003). Agerelated slowing of digit symbol substitution revisited: What do longitudinal age changes reflect? The Journals of Gerontology, Series B, Psychological Sciences and Social Sciences, 58, P187–P194. Madden, D. J., Turkington, T. G., Provenzale, J. M., Denny, L. L., Hawk, T. C., Gottlob, L. R., et al. (1999). Adult age differences in the functional neuroanatomy of verbal recognition memory. Human Brain Mapping, 7, 115–135. Malach, R., Reppas, J. B., Benson, R. R., Kwong, K. K., Jiang, H., Kennedy, W. A., et al. (1995). Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex. Proceedings of the National Academy of Sciences, USA, 92, 8135–8139. Markus, H. R., & Kitayama, S. (1991). Culture and the self: Implications for cognition, emotion, and motivation. Psychological Review, 98, 224–253.

Moseley, M. (2002). Diffusion tensor imaging and aging: A review. NMR in Biomedicine, 15(7–8), 553–560. Nisbett, R. E. (2003). The geography of thought: How asians and westerners think differently: And whyFree Press. Nisbett, R. E., & Masuda, T. (2003). Culture and point of view. Proceedings of the National Academy of Sciences, USA, 100, 11163–11170. Nisbett, R. E., Peng, K., Choi, I., & Norenzayan, A. (2001). Culture and systems of thought: Holistic versus analytic cognition. Psychological Review, 108, 291–310. Nyberg, L., Sandblom, J., Jones, S., Neely, A. S., Petersson, K. M., Ingvar, M., et al. (2003). Neural correlates of training-related memory improvement in adulthood and aging. Proceedings of the National Academy of Sciences, USA, 100, 13728–13733. Park, D. C., & Gutchess, A. H. (2005). Long-term memory and aging: A cognitive neuroscience perspective. In R. Cabeza, L. Nyberg, & D. C. Park (Eds.), Cognitive neuroscience of aging: Linking cognitive and cerebral aging (pp. 218–245). New York, USA: Oxford University Press. Park, D. C., Polk, T. A., Park, R., Minear, M., Savage, A., & Smith, M. R. (2004). Aging reduces neural specialization in ventral visual cortex. Proceedings of the National Academy of Sciences, USA, 101, 13091–13095. Park, D. C., Smith, A. D., Lautenschlager, G., Earles, J. L., Frieske, D., Zwahr, M., et al. (1996). Mediators of long-term memory performance across the life span. Psychology and Aging, 11, 621–637. Park, D. C., Lautenschlager, G., Hedden, T., Davidson, N. S., Smith, A. D., & Smith, P. K. (2002). Models of visuospatial and verbal memory across the adult life span. Psychology and Aging, 17, 299–320. Persson, J., Nyberg, L., Lind, J., Larsson, A., Nilsson, L., Ingvar, M., et al. (2006). Structure–function correlates of cognitive decline in aging. Cerebral Cortex, 16, 907–915. Polk, T. A., Stallcup, M., Aguirre, G. K., Alsop, D. C., D’Esposito, M., Detre, J. A., et al. (2002). Neural specialization for letter recognition. Journal of Cognitive Neuroscience, 14, 145–159. Puce, A., Allison, T., Asgari, M., Gore, J. C., & McCarthy, G. (1996). Differential sensitivity of human visual cortex to faces, letterstrings, and textures: A functional magnetic resonance imaging study. Journal of Neuroscience: Official Journal of the Society for Neuroscience, 16, 5205–5215. Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). Inaugural article: A default mode of brain function. Proceedings of the National Academy of Sciences, USA, 98, 676. Raz, N. (2000). Aging of the brain and its impact on cognitive performance: Integration of structural and functional findings. In F. I. M. Craik & T. A. Salthouse (Eds.), Handbook of aging and cognition (pp. 1–90). Mahwah, NJ: Erlbaum. Raz, N., Lindenberger, U., Rodrigue, K. M., Kennedy, K. M., Head, D., Williamson, A., et al. (2005). Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cerebral Cortex, 15, 1676–1689. Raz, N., Rodrigue, K. M., Head, D., Kennedy, K. M., & Acker, J. D. (2004). Differential aging of the medial temporal lobe: A study of a five-year change. Neurology, 62, 433–438. Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003). Longitudinal magnetic resonance imaging studies of older

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References 1219 adults: A shrinking brain. Journal of Neuroscience: Official Journal of the Society for Neuroscience, 23, 3295–3301.

humans: A functional anatomical study of memory. Proceedings of the National Academy of sciences, USA, 89, 1837–1841.

Reuter-Lorenz, P. A. (2002). New visions of the aging mind and brain. Trends in Cognitive Sciences, 6, 394–400.

Stebbins, G. T., Carrillo, M. C., Dorfman, J., Dirksen, C., Desmond, J. E., Turner, D. A., et al. (2002). Aging effects on memory encoding in the frontal lobes. Psychology and Aging, 17, 44–55.

Reuter-Lorenz, P. A., Jonides, J., Smith, E. E., Hartley, A., Miller, A., Marshuetz, C., et al. (2000). Age differences in the frontal lateralization of verbal and spatial working memory revealed by PET. Journal of Cognitive Neuroscience, 12, 174–187. Reuter-Lorenz, P. A., & Mikels, J. (2006). The aging brain: Implications of enduring plasticity for behavioral and cultural change. In P. B. Baltes, P. Reuter-Lorenz, & F. Rosler (Eds.), Lifespan development and the brain: The perspective of biocultural co-constructivism. United Kingdom: Cambridge University Press. Rodrigue, K. M., & Raz, N. (2004). Shrinkage of the entorhinal cortex over five years predicts memory performance in healthy adults. Journal of Neuroscience: Official Journal of the Society for Neuroscience, 24, 956–963. Rosen, A. C., Prull, M. W., O’Hara, R., Race, E. A., Desmond, J. E., Glover, G. H., et al. (2002). Variable effects of aging on frontal lobe contributions to memory. NeuroReport, 13, 2425–2428. Rosen, A. C., Prull, M. W., Gabrieli, J. D., Stoub, T., O’Hara, R., Friedman, L., et al. (2003). Differential associations between entorhinal and hippocampal volumes and memory performance in older adults. Behavioral Neuroscience, 117, 1150–1160. Rossi, S., Miniussi, C., Pasqualetti, P., Babiloni, C., Rossini, P. M., & Cappa, S. F. (2004). Age-related functional changes of prefrontal cortex in long-term memory: A repetitive transcranial magnetic stimulation study. Journal of Neuroscience: Official Journal of the Society for Neuroscience, 24, 7939–7944. Salat, D. H., Buckner, R. L., Snyder, A. Z., Greve, D. N., Desikan, R. S., Busa, E., et al. (2004). Thinning of the cerebral cortex in aging. Cerebral Cortex, 14, 721–730. Salthouse, T. A. (1991). Theoretical perspectives on cognitive aging Lawrence Erlbaum. Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403–428. Schooler, C., Mulatu, M. S., & Oates, G. (1999). The continuing effects of substantively complex work on the intellectual functioning of older workers. Psychology and Aging, 14, 483–506. Shulman, G. L. (1997). Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. Journal of Cognitive Neuroscience, 9, 648–663. Small, S. A., Tsai, W. Y., DeLaPaz, R., Mayeux, R., & Stern, Y. (2002). Imaging hippocampal function across the human life span: Is memory decline normal or not? Annals of Neurology, 51, 290–295. Squire, L. R., Ojemann, J. G., Miezin, F. M., Petersen, S. E., Videen, T. O., & Raichle, M. E. (1992). Activation of the hippocampus in normal

Sullivan, E. V., & Pfefferbaum, A. (2006). Diffusion tensor imaging and aging. Neuroscience and Biobehavioral Reviews, 30, 749–761. Unverzagt, F. W., Gao, S., Baiyewu, O., Ogunniyi, A. O., Gureje, O., Perkins, A., et al. (2001). Prevalence of cognitive impairment: Data from the Indianapolis Study of Health and Aging. Neurology, 57, 1655–1662. Van Petten, C. (2004). Relationship between hippocampal volume and memory ability in healthy individuals across the lifespan: Review and meta-analysis. Neuropsychologia, 42, 1394–1413. Wagner, A. D., Schacter, D. L., Rotte, M., Koutstaal, W., Maril, A., Dale, A. M., et al. (1998). Building memories: Remembering and forgetting of verbal experiences as predicted by brain activity. Science, 281, 1188–1191. Wang, Y., Chan, G. L., Holden, J. E., Dobko, T., Mak, E., Schulzer, M., et al. (1998). Age-dependent decline of dopamine D1 receptors in human brain: A PET study. Synapse, 30(1), 56–61. Wilson, R. S., & Bennett, D. A. (2003). Cognitive activity and risk of alzheimer ’s disease. Current Directions in Psychological Science, 12, 87–91. Wilson, R. S., Beckett, L. A., Barnes, L. L., Schneider, J. A., Bach, J., Evans, D. A., et al. (2002). Individual differences in rates of change in cognitive abilities of older persons. Psychology and Aging, 17(2), 179–193. Wilson, R. S., Bennett, D. A., Beckett, L. A., Morris, M. C., Gilley, D. W., Bienias, J. L., et al. (1999). Cognitive activity in older persons from a geographically defined population. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 54(3), P155–P160. Wilson, R. S., Gilley, D. W., Bennett, D. A., Beckett, L. A., & Evans, D. A. (2000). Person-specific paths of cognitive decline in alzheimer ’s disease and their relation to age. Psychology and Aging, 15, 18–28. Wong, D. F., Young, D., Wilson, P. D., Meltzer, C. C., & Gjedde, A. (1997). Quantification of neuroreceptors in the living human brain: III. D2-like dopamine receptors: Theory, validation, and changes during normal aging. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism, 17, 316–330. Yang, Y. K., Chiu, N. T., Chen, C. C., Chen, M., Yeh, T. L., & Lee, I. H. (2003). Correlation between fine motor activity and striatal dopamine D2 receptor density in patients with schizophrenia and healthy controls. Psychiatry Research, 123, 191–197. Zacks, R. T., Hasher, L., & Li, K. Z. H. (2000). Human memory. In F. I. M. Craik & T. A. Salthouse (Eds.), The handbook of aging and cognition (pp. 293–357). Mahwah, NJ: Erlbaum.

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Chapter 62

Stress and Coping BRUCE S. McEWEN

The brain is the organ that decides what is stressful and determines the behavioral and physiological responses, whether health promoting or health damaging. The brain is a biological organ that changes under acute and chronic stress and directs many systems of the body—metabolic, cardiovascular, immune—that are involved in the shortand long-term consequences of being stressed out. What does chronic stress do to the body and brain? This chapter emphasizes how the stress hormones can play both protective and damaging roles in brain and body over the life course, depending on how tightly their production is regulated. The chapter also discusses approaches for dealing with stress in a complex world at a personal as well as organizational and societal level.

INTRODUCTION “Stress” is a commonly used word that generally refers to experiences that cause feelings of anxiety and frustration because they push us beyond our ability to successfully cope. “There is so much to do and so little time!” is a common expression. Besides time pressures and daily hassles at work and home, there are stressors related to economic insecurity, poor health, and interpersonal conflict. More rarely, there are life-threatening situations—accidents, natural disasters, violence—and these evoke the classical “fight-or-flight” response. In contrast to daily hassles, these stressors are acute and yet they also often lead to post-traumatic reactions and chronic stress in the aftermath of the tragic event. The most common stressors are, therefore, ones that operate chronically, often at a low level, and cause us to behave in certain ways. Being stressed out may cause individuals to be anxious or depressed, to lose sleep at night, to eat comfort foods and take in more calories than our bodies need, and to smoke or drink alcohol excessively. Being stressed out may also cause us to neglect seeing friends, or to take time off or engage in regular physical activity as we, for example, sit at a computer and try to get out from under the burden of too much to do. Often we are tempted to take medications—anxiolytics, sleeppromoting agents—to help us cope, and with time, our bodies may increase in weight and develop other symptoms of being chronically stressed out.

CHARACTERISTICS OF HOMEOSTATIC SYSTEMS First, it is important to understand the fundamental concept of homeostasis as the basis for the rest of this chapter. Homeostasis refers to the ability of an organism to maintain the internal environment of the body within limits that allow it to survive. Homeostasis also refers to selfregulating processes that return critical systems of the body to a set point within a narrow range of operation, consistent with survival of the organism. Homeostasis is highly developed in warm-blooded animals living on land because they must maintain body temperature, fluid balance, blood pH, and oxygen tension within rather narrow limits, while obtaining nutrition to provide the energy to maintain homeostasis. This is because maintaining homeostasis requires the expenditure of energy. Energy is used for locomotion, as the animal seeks and consumes food and water for maintaining body temperature via the controlled release of calories from metabolism of food or fat stores, and for sustaining cell membrane function as it resorbs electrolytes in the kidney and intestine and maintains neutral blood pH.

Many of the long-term consequences of being stressed out have their origins in the genetic constitution and early experiences of each individual. Alleles of certain genes are increasingly recognized as contributing to vulnerability or resilience, and early life experiences have powerful effects on the ability to cope with stressors during the life course. Moreover, the social environment determines the context in which individuals cope with their own experiences, as exemplified by the well-recognized gradients of mortality and morbidity as a function of socioeconomic status (SES). 1220

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Historical Note on the Concept of Stress

Homeostasis also refers to the body’s defensive mechanisms. These include protective reflexes against inhaling matter into the lungs, the vomiting reflex to expel toxic materials from the esophagus or stomach, the eyeblink reflex, and the withdrawal response to hot or otherwise painful skin sensations. There is also the defense against pathogens through innate and acquired immunity. According to Walter B. Cannon (Cannon, 1932): Bodily homeostasis . . . results in liberating those functions of the nervous systems that adapt the organism to new situations, from the necessity of paying routing attention to the management of the details of bare existence. Without homeostatic devices, we should be in constant danger of disaster, unless we were always on the alert to correct voluntarily what normally is corrected automatically. With homeostatic devices, however, that keep essential body processes steady, we as individuals are free from such slavery—free to enter into agreeable relations with our fellows, free to enjoy beautiful things, to explore and understand the wonders of the world about us, to develop new ideas and interests, and to work and play, untrammeled by the anxieties concerning our bodily affairs. (p. 323)

HISTORICAL NOTE ON THE CONCEPT OF STRESS It is important to provide some historical perspective about the concept of stress. The late Hans Selye (1907–1983) is credited with introducing the concept of stress into popular as well as medical discussions (Selye, 1936, 1973). Before the enormous advances in biomedical research of the past 5 decades that have added more detailed knowledge of the so-called stress hormones and their actions throughout the body, Selye used the emergency reaction of the sympathetic nervous system and adrenocortical system for his classic theory of stress (Selye, 1936). This has been captured in the classic “fight-or-flight response” of a gazelle chased by a lion. Selye postulated the general adaptation syndrome, a stereotyped physiological response that takes the form of a series of three stages in the reaction to a stressor: (1) The alarm reaction in which the adrenal medulla releases epinephrine and the adrenal cortex produces glucocorticoids. The alarm reaction promotes a process of adaptation and restores homeostasis. (2) Restoration of homeostasis leads to the stage of resistance, in which defense and adaptation are sustained and optimal. (3) If the stress response persists, the stage of exhaustion follows, the adaptive responding ceases, and the consequence may be illness and death. How has this changed in light of new information? First, Selye’s general adaptation syndrome is no longer

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interpreted to mean that there is a stereotyped response of stress mediators to all types of stress. Rather, there are different patterns of response of the HPA axis and the noradrenergic and adrenergic nerves that are related to the type of stressor (Chrousos, 1998; Goldstein, 1995). Another important qualification to the classic stress theory, as captured in the fight-or-flight response, is that this response most accurately characterizes the response of male animals under threat. Dr. Shelley Taylor (Taylor et al., 1998) has characterized the female response to stress as “tend-and-befriend,” not fight-or-flight. Thus, although females flee from extreme danger, gender differences need to be factored into the understanding of allostasis and allostatic load. These differences include not only the different perceptions and behavioral responses to stressors, as implied in the terminology tend-and-befriend versus fight-or-flight, but also physiological differences in the regulation of mediators of allostasis. Estrogens appear to attenuate the HPA response to stress and preserve HPA regulation of cortisol release, in that postmenopausal women exhibit larger, age-related increases in cortisol secretion, higher 24-hour cortisol excretion, and a greater response to CRH stimulation than men of the same age (Van Cauter, Leproult, & Kupfer, 1996). Moreover, in response to the

BOX 62.1 REINTERPRETATION OF SELYE’S 3 STAGES OF THE STRESS RESPONSE In the new terminology of allostasis, Selye’s alarm response is reinterpreted as the process leading to adaptation, or allostasis, in which glucocorticoids and epinephrine, as well as other mediators, promote adaptation to the stressor. Selye’s stage of resistance reflects the protective effects of the adaptation to the stressor. But if the alarm response is sustained and the glucocorticoids and adrenal medulla are repeatedly elevated over many days, an allostatic state may ensue leading to allostatic load that replaces Selye’s phase of exhaustion, with the important distinction that this represents the almost inevitable wear and tear produced by repeated exposure to mediators of allostasis: too much of a good thing! Thus, Selye’s diseases of adaptation are the result of the allostatic state leading to allostatic load and resulting in the exacerbation of pathophysiological change. As noted, examples of allostatic load include the acceleration of atherosclerosis, abdominal obesity, as well as loss of minerals from bone, and immunosuppression, as well as atrophy and damage to the brain, especially the hippocampus (McEwen, 1998; Sapolsky, 1996).

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stress of a driving simulation challenge, postmenopausal women exhibited a greater HPA response than men (Seeman, Singer, & Charpentier, 1995). Furthermore, it has been shown that short-term estrogen replacement in postmenopausal women attenuates the glucocorticoid response to a psychological stress paradigm (Komesaroff, Elser, & Suhir, 1999) and physical stress (Lindheim et al., 1992). Finally, it is Selye’s “stage of exhaustion” that needs to be reinterpreted in light of newer knowledge that the stress mediators can have both protective and damaging effects depending on the time course of their secretion. Thus, rather than exhaustion of defense mechanisms, it is the stress mediators that can turn on the body and cause problems. This leads to a discussion of the concepts of allostasis and allostatic load. See Box 62.1. Allostasis, Allostatic Load, and Allostatic Overload Central to this discussion is a concept of how the body systems that mediate the effects of stress and promote adaptation can also contribute to pathophysiology (McEwen, 1998). Allostasis is a concept introduced by Sterling and Eyer (Sterling & Eyer, 1988) to refer to blood pressure and heart rate responses to changes in posture as well as physical activity and emotional arousal. Allostasis means “achieving stability through change” and refers to the mediators that are actively produced to achieve a new operating state and adaptation. This is all well and good as long as the system returns to a baseline after the challenge, but it is not good if it remains elevated over longer time periods (e.g., atherosclerosis and hypertension). Anticipation plays a major role, at least in the human organism, and worries about what may or may not transpire can alter brain function and physiology (Sapolsky, 2004; Schulkin, McEwen, & Gold, 1994). McEwen and Stellar (1993) generalized the allostasis concept to include other mediators of adaptation to changes imposed on or made by the animal, and they introduced the notion of allostatic load referring to the wear and tear that results from prolonged operation of physiological systems in an elevated state, as well as a dysregulated state, in which the normal balance between mediators is distorted. Koob and LeMoal (2001) introduced the term allostatic state to refer to a state of elevated activity; a prolonged allostatic state is what leads to wear and tear on physiological systems. The concepts of allostasis and allostatic load/overload are intended to complement and clarify ambiguities in the usage of homeostasis and stress for all situations that involve adaptation to a changing environment. In particular, they make clear that the mediators of adaptation are also involved in pathophysiology. Furthermore, they

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help to distinguish between the parameters that must be maintained within narrow limits to support life (e.g., pH, oxygen tension, body temperature) and those systems and mediators that maintain homeostasis. McEwen and Wingfield (2003) suggested using “load” and “overload” to refer to different degrees of cumulative change. Allostatic load refers to reversible changes such as bears putting on fat for winter; whereas allostatic overload is intended for cumulative changes that contribute to pathophysiology such as obesity, atherosclerosis, elevated blood sugar levels, and a chronic inflammatory state. Many people prefer the stress/homeostasis terminology, and, indeed, the popular use of stress is pervasive, even if there is ambiguity (see Box 62.1 for reinterpretation of Selye’s 3 stages of the stress response). Therefore, whatever the terminology used, whether stress/homeostasis or allostasis/allostatic load, or a combination of both, the following five aspects are important for our understanding of the relationships between adaptation and pathophysiology that result from experiences or behaviors often related to being stressed out: Key aspects of allostasis and allostatic overload 1. Same mediators—adaptation and damage 2. How experiences and behaviors interact and affect health 3. Utility of allostatic load battery in epidemiology, health psychology 4. Central role of the brain 5. Gene-environment interactions Allostasis Involves Multiple, Interacting Mediators That Operate Nonlinearly The mediators of allostasis and allostatic load/overload operate as a nonlinear network, with reciprocal regulation by each mediator of other mediators. It is the dysregulation of these networks of mediators that can lead to pathophysiology (see Figure 62.1). Some of these interactions are well known: Parasympathetic activity slows the heart, whereas sympathetic activity increases heart rate. Yet other interactions are less known (e.g., sympathetic activity increases proinflammatory cytokine production; Bierhaus et al., 2003), whereas parasympathetic activity is anti-inflammatory (Borovikova et al., 2000). The renin-angiotensin system also plays a role in promoting proinflammatory responses, as well as elevated blood pressure (Saavedra, Benicky, & Zhou, 2006). One of the consequences of increased inflammatory tone is the activation of glucocorticoid secretion, which can be regarded as activating a feedback loop that reduces inflammation in many cases (Saavedra et al., 2006).

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Historical Note on the Concept of Stress

CNS Function e.g. Cognition Depression Aging Diabetes Alzheimer’s

Metabolism e.g. Diabetes Obesity Cortisol

DHEA

Inflammatory Cytokines

Sympathetic

Parasympathetic Cardiovascular Function e.g. Endothelial Cell Damage Atherosclerosis Figure 62.1 Nonlinear network of mediators of allostasis involved in the stress response. Note: Arrows indicate that each system regulates the others in a reciprocal manner, creating a nonlinear network. Moreover, there are multiple pathways for regulation; inflammatory cytokine production is negatively regulated via anti-inflammatory cytokines as well as via parasympathetic

BOX 62.2 STRESS SYSTEMS Many systems in the body are involved in the response to stressors. The autonomic nervous system consists of both parasympathetic and sympathetic components. The sympathetic component involves the adrenal medulla, which releases adrenalin, and the sympathetic nerves that innervate blood vessels and many other organs. The parasympathetic nervous system is a vast, distributed neural network, with both sensory and motor components, that has many functions including the slowing of the heart and the reduction of inflammation. The hypothalamo-pituitary-adrenal axis involves the production of cortisol in response to chemical signals from the brain, namely corticotrophin-releasing factor and vasopressin, that activate the pituitary gland to release ACTH, which, in turn, causes cortisol to be produced by the adrenal cortex. Besides these systems, the proand anti-inflammatory cytokines produced by immune cells and also by other cells (e.g., nerve cells, microglial cells in brain) are also activated under stressful conditions. Furthermore, other hormones, such as prolactin from the pituitary gland, insulin from the pancreas and other metabolic hormones are regulated by the body’s response to stressors. The complexity of these interactions is indicated in Figure 62.1.

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Anti-Inflammatory Cytokines

Oxidative Stress Immune Function e.g. Immune Enhancement Immune Suppression and glucocorticoid pathways, whereas sympathetic activity increases inflammatory cytokine production. Parasympathetic activity, in turn, contains sympathetic activity. From “Protective and Damaging Effects of Stress Mediators: Central Role of the Brain,” by B. S. McEwen, 2006a, Dialysis in Clinical Neurosciences: Stress, 8, pp. 286. Reprinted with permission.

Indeed, glucocorticoids are also anti-inflammatory but there are conditions in which glucocorticoids can have proinflammatory effects that are dose dependent (Sorrells & Sapolsky, 2007). A more accurate description of glucocorticoids is that they are modulators of innate and acquired immune responses (Munck, Guyre, & Holbrook, 1984; Sapolsky, Romero, & Munck, 2000). Yet glucocorticoid actions can also be modified and glucocorticoid resistance is a condition that arises in pro-inflammatory states as well as in major depressive illness (Avitsur, Stark, & Sheridan, 2001; Desouza et al., 2005; Raison, Capuron, & Miller, 2006). In conditions such as septic shock, when proinflammatory processes threaten to kill the organism, glucocorticoids may or may not be effective depending on the state of glucocorticoid resistance (Schelling et al., 1999), and parasympathetic activation (Borovikova et al., 2000) or even angiotensin receptor blockade (Saavedra et al., 2006) may be useful. The Same Mediators Are Involved in Promoting Adaptation and Promoting Pathophysiology Acute stress can have beneficial effects, as in the “emergency life history” stage in which the elevation of cortisol in birds during a Spring snowstorm or flood causes reproductive function to be suspended, at least temporarily, and also increases food-seeking and locomotor activity to find

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a safe place (McEwen & Wingfield, 2003). More generally, acute elevations of glucocorticoids enhance immune system activity (Dhabhar & McEwen, 1997), promote certain types of memory connected to emotional arousal (Roozendaal, Okuda, Van der Zee, & McGaugh, 2006), mediate energy replenishment and enhanced locomotor activity (McEwen, Sakai, & Spencer, 1993), and contribute to more efficient cardiovascular function (Ramey & Goldstein, 1957). However, chronic cortisol elevation or exogenous treatment suppresses immune function (Munck et al., 1984), impairs certain types of memory (Lupien, 2002), promotes bone mineral loss and muscle wasting, and contributes to the metabolic syndrome (Brindley & Rolland, 1989; Chrousos, 2000). Chronic elevation of sympathetic activity is associated with hypertension, diabetes and obesity, and cardiovascular disease (Ramey & Goldstein, 1957). Chronic elevation of proinflammatory tone is associated with many disorders such as diabetes, cardiovascular disease, arthritis, and musculoskeletal disorders from repetitive motion (Barbe & Barr, 2006; Black & Garbutt, 2002; Bierhaus et al., 2001; Brami-Cherrier Lavaur, Pages, Arthur, & Cabache, 2007; Turek et al., 2005) as well as neural conditions such as depression (Raison et al., 2006) and Alzheimer ’s disease (McGeer & McGeer, 2001). The transition between acute and chronic stress can lead to a reversal of direction of the effects. A single, acute restraint stress potentiates delayed-type hypersensitivity by promoting immune cell trafficking to the ear, when the antigen is applied to the ear of a rat or mouse that has formed an acquired immune response to a systemically applied antigen (Dhabhar & McEwen, 1997). However, chronic stress for 21-days has the opposite effect; it markedly suppresses immune cell trafficking to the ear (Dhabhar & McEwen, 1997). Both catecholamines and glucocorticoids are involved in the mechanism of trafficking along with locally produced cytokines such as interferon gamma (Dhabhar, 2002; Dhabhar, Satoskar, Bluethmann, David, & McEwen, 2000). There are also changes over a time course of repeated stress in the metabonomic profile of small molecules associated with lipid and energy metabolism that indicate a change in how the body handles stress with repetition (Teague et al., 2007). The chronic stress— restraint—is of the same type that causes remodeling over 21d of neuronal connections in the hippocampus, prefrontal cortex, and amygdala, whereas acute restraint stress does not produce these effects. This is discussed later in this chapter (see Boxes 62.3 and 62.4). A major challenge is to understand how the adaptive actions associated with acute stress are permuted into dysregulation and malfunction associated with some forms of chronic stress. There are many examples of the

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BOX 62.3 THE HIPPOCAMPUS One of the ways that stress hormones modulate function within the brain is by changing the structure of neurons. The hippocampus is one of the most sensitive and malleable regions of the brain and is also important in cognitive function (see Figure 62.2). Within the hippocampus, the input from the entorhinal cortex to the dentate gyrus is ramified by the connections between the dentate gyrus and the CA3 pyramidal neurons. The dentate gyrus-CA3 system is believed to play a role in the memory of sequences of events, although long-term storage of memory occurs in other brain regions (Lisman & Otmakhova, 2001). But, because the DG-CA3 system is so delicately balanced between its normal function and vulnerability to damage, there is also adaptive structural plasticity, in that new neurons continue to be produced in the dentate gyrus throughout adult life, and CA3 pyramidal cells undergo a reversible remodeling of their dendrites in conditions such as hibernation and chronic stress (Magarinos, McEwen, Saboureau, & Pevet, 2006; McEwen, 1999; Popov & Bocharova, 1992; Popov, Bocharova, & Bragin, 1992). The role of this plasticity may be to protect against permanent damage, as well as to provide for renewal of memory storage processes (Leuner, Gould, & Shors, 2006). Regarding the replacement of neurons, the subgranular layer of the dentate gyrus contains cells that have some properties of astrocytes (e.g., expression of glial fibrillary acidic protein) and which give rise to granule neurons (Kempermann & Gage, 1999; Seri, GarciaVerdugo, McEwen, & Alvarez-Buylla, 2001). These newly born cells appear as clusters in the inner part of the granule cell layer, where a majority of them will go on to differentiate into granule neurons and establish connections with hippocampal circuitry. In the adult rat, 9,000 new neurons are estimated to be born per day and survive with a half-life of 28 days (Cameron & McKay, 2001). There are many hormonal, neurochemical, and behavioral modulators of neurogenesis and cell survival in the dentate gyrus, including estradiol, insulin-like growth factors-1 (IGF-1), antidepressants, voluntary exercise, and hippocampal-dependent learning (Aberg, Aberg, Hedbacker, Oscarsson, & Eriksson, 2000; Czeh et al., 2001; Trejo, Carro, & Torres-Aleman, 2001). With respect to stress, certain types of acute stress and many chronic stressors suppress neurogenesis or cell survival in the dentate gyrus, and the mediators of these inhibitory effects include excitatory amino acids acting via NMDA receptors and endogenous opioids (Gould, McEwen, Tanapat, Galea, & Fuchs, 1997).

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Historical Note on the Concept of Stress

BOX 62.3 (Continued ) Another form of structural plasticity is the remodeling of dendrites in the hippocampus. Chronic restraint stress causes retraction and simplification of dendrites in the CA3 region of the hippocampus (McEwen, 1999; Sousa, Lukoyanov, Madeira, Almeida, & PaulaBarbosa, 2000). Such dendritic reorganization is found in both dominant and subordinate rats undergoing adaptation of psychosocial stress in the visible burrow system, and it is independent of adrenal size (McKittrick et al., 2000). What this result emphasizes is that it is not adrenal size or presumed amount of physiological stress per se that determines dendritic remodeling, but a complex set of other factors that modulate neuronal structure. Indeed, in species of mammals that hibernate, dendritic remodeling is a reversible process and occurs within hours of the onset of hibernation in European hamsters and ground squirrels, and it is also reversible within hours of wakening of the animals from torpor (Arendt et al., 2003; Magarinos et al., 2006; Popov & Bocharova, 1992; Popov et al., 1992). This implies that reorganization of the cytoskeleton is taking place rapidly and reversibly and that changes in dendrite length and branching are not damage but a form of adaptive structural plasticity. Regarding the mechanism of structural remodeling, adrenal steroids are important mediators of remodeling of hippocampal neurons during repeated stress, and exogenous adrenal steroids can also cause remodeling in the absence of an external stressor. The role of adrenal steroids involve many interactions with neurochemical systems in the hippocampus, including serotonin, gamma amino butyric acid, and excitatory amino acids (McEwen, 1999; McEwen & Chattarji, 2004). Moreover, extracellular molecules such as polysialated neural cell adhesion molecule and tissue plasminogen activator (tPA) play a regulatory role (Pawlak, Magarinos, Melchor, McEwen, & Strickland, 2003; Pawlak, et al., 2005; Sandi, 2004), and neurotrophic factors such as brain derived neurotrophic factor (BDNF) are also implicated in maintaining dendritic shape (Govindarajan et al., 2006). Besides endogenous factors, other hormones affect hippocampal structure and function. The hippocampus has receptors for metabolic and growth-related hormones such as insulin, IGF-1, ghrelin, and leptin, along with transport mechanisms for getting them from the blood into the brain (McEwen, 2007).

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1225

BOX 62.4 PREFRONTAL CORTEX AND AMYGDALA Repeated stress also causes structural remodeling in other brain regions such as the prefrontal cortex and amygdala (Figure 62.2). Repeated stress causes dendritic shortening in medial prefrontal cortex (Brown, Henning, & Wellman, 2005; Cook & Wellman, 2004; Kreibich & Blendy, 2004; Liston et al., 2006; Radley, Rocher, Janssen, et al., 2005; Radley, Rocher, Miller, et al., 2005; Radley, et al., 2003, 2004; Sousa et al., 2000; Vyas, Mitra, Rao, & Chattarji, 2002; Wellman, 2001) but produces dendritic growth in neurons in amygdala (Vyas et al., 2002), as well as in orbitofrontal cortex (Liston et al., 2006). Along with many other brain regions, the amygdala and prefrontal cortex also contain adrenal steroid receptors; however, the role of adrenal steroids, excitatory amino acids, and other mediators has not yet been studied in these brain regions. Nevertheless, in the amygdala, there is some evidence regarding mechanism, in that tissue plasminogen activator (tPA) is required for acute stress not only to activate indexes of structural plasticity but also to enhance anxiety (Melchor, Pawlak, & Strickland, 2003). These effects occur in the medial and central amygdala and not in the basolateral amygdala, and the release of CRH acting via CRH1 receptors appears to be responsible (Matys et al., 2004). Acute stress induces spine synapses in the CA1 region of the hippocampus (Shors, Chua, & Falduto, 2001), and both acute and chronic stress will increase spine synapse formation in the amygdala (Mitra, Kadhav, McEwen, Vyas, & Chattarji, 2005; Vyas et al., 2002), but chronic stress decreases it in the hippocampus (Pawlak et al., 2005). Moreover, chronic stress for 21 days or longer impairs hippocampal-dependent cognitive function (McEwen, 1999) and enhances amygdala-dependent unlearned fear and fear conditioning (Conrad, Magarinos, LeDoux, & McEwen, 1999), which are consistent with the opposite effects of stress on hippocampal and amygdala structure. Chronic stress also increases aggression between animals living in the same cage, and this is likely to reflect another aspect of hyperactivity of the amygdala (Wood, Young, Reagan, & McEwen, 2003). Behavioral correlates of remodeling in the prefrontal cortex include impairment in attention set shifting, possibly reflecting structural remodeling in the medial prefrontal cortex (Liston et al., 2006).

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contribute to being stressed out may not be easily identified through marked elevations of classical stress hormones such as cortisol or adrenalin, except perhaps through continuous monitoring or repeated sampling. Nevertheless, the net result is a dysregulation of networks of allostasis that often cannot be measured until it lasts for days, weeks, months, or years, and produces more easily measurable consequences such as persistent hypertension, abdominal obesity, chronically elevated blood glucose, and atherosclerotic plaques. Some of the physiological consequences of sleep deprivation are summarized in the following list: Figure 62.2 Brain regions that are involved in perception and response to stress and that show structural remodeling as a result of stress, as described in the text.

negative consequences of chronic stress, such as the shortening of telomeres associated with being a caregiver (Epel et al., 2004) or the impairment of immune function and wound healing associated with marital conflict (KiecoltGlaser et al., 2005), or intensely studying for an exam (Marucha, Kiecolt-Glaser, & Favagehi, 1998). There are also investigations of the effects of job strain and demand versus sense of control on cardiovascular health (Reed, Lacroix, Karasek, Miller, & MacLean, 1989; Pickering et al., 1996; Schnall et al., 1990; Theorell et al., 1998). These effects are not necessarily a matter of duration of stress but rather factors such as sense of control (Theorell, Westerlund, Alfredsson, & Oxenstierna, 2005); feelings of time pressure (Williams et al., 1997); sense of reward and accomplishment or lack thereof, as in burnout (David et al., 2005); and sense of optimism or pessimism (Folkman, 1997; Steptoe, Wardle, & Marmot, 2005). Self-esteem and locus of control play an important role and are linked to different patterns of response of mediators of allostasis and also neurological features such as the size of the hippocampus (Pruessner et al., 2005; Pruessner, Hellhammer, & Kirschbaum, 1999).

How Experiences and Behaviors Interact and Affect Health A common experience is being stressed out, and this often involves the subjective feeling of being under time pressure or feeling a lack of control as well as anxiety. Being stressed out leads to coping behaviors such as eating excessively, smoking, drinking alcohol, and neglecting regular moderate exercise and social interactions. Sleep quality and quantity may also suffer and produce negative effects (Friedman et al., 2005; McEwen, 2006b; Spiegel, Leproult, & Van Cauter, 1999; see following list). The individual experiences that

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Increased blood pressure; decreased parasympathetic tone Elevated evening cortisol, glucose, insulin Elevated inflammatory cytokines Increased appetite and caloric load, which can increase physiological measures: blood pressure, cortisol, and inflammatory cytokines Abdominal fat deposition Depressed mood Impaired cognitive function Note: Adapted from "Sleep Deprivation as a Neurobiologic and Physiologic Stressor: Allostasis and Allostatic Load" B. S. McEwen, 2006b, Metabolism, 55, pp. S20–S23.

Physiological dysregulation can be detected and involves alterations in many of the mediators of allostasis discussed earlier; this can be illustrated for the consequences of poor sleep. Sleep deprivation is a common problem in modern life, and chronic stress and resulting changes in coping behaviors, together with the lack of adequate sleep, contribute to allostatic overload. These factors can be assembled into an overview of what happens physiologically from being stressed out. Chronic life stressors (e.g., interpersonal conflicts, caregiving, pressure at work, crowded, noisy living and working conditions) create a sense of chaos and conflict and a lack of control. The individual may already have a preexisting history of negative or positive experiences that predispose him/her to certain types of reaction to external events. The result of chronic stress will often be chronic anxiety and depressed mood with poor quality sleep. Anxiety, mood changes, and inadequate sleep lead to self-medication through eating comfort foods, drinking alcohol, smoking, or in some cases, anorexia. These alterations in mood and anxiety, along with the distress associated with ongoing events, may cause the person to become socially isolated and to neglect regular physical activity. Together with anxiety, depressed mood, and poor sleep, all the listed behaviors contribute to dysregulated physiological responses and contribute to an ongoing allostatic

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From Animal Models to the Human Brain 1227

overload involving elevated cortisol, insulin, and inflammatory cytokines at night, along with increased heart rate and blood pressure and reduced parasympathetic tone. If this abnormal dysregulated state persists for months and years, there are likely to be adverse health outcomes such as hypertension, coronary disease, stroke, obesity, diabetes, arthritis, major depression, GI disorders, chronic pain, and chronic fatigue. In contrast to the situation for human beings in modern society, in nature, eating a lot of food and putting on body mass in the autumn is an adaptive behavior in animals that hibernate and burn off the fat and excess calories during the winter; however, it is only when animals are captives in zoos that symptoms reminiscent of allostatic overload in human beings begin to appear (McEwen & Wingfield, 2003). Central Role of the Brain in Stress, Allostasis, and Allostatic Load/Overload As depicted in Figure 62.3, the brain is the central organ of stress, allostasis, and allostatic load because it controls behavioral and physiological responses to any change in the external world and also is part of an internal state of the individual that anticipates and worries about events and situations that may or may not take place. The brain determines if an external event is threatening and activates the fight-or-flight stress response. As discussed later in this chapter and noted in Figure 62.3, individual differences due to genetic factors, early life experiences, and experiences in adult life determine the state of the brain and body for the response to stressors and other events. Major life events, trauma and abuse, and daily hassles of work, family, and community constitute

Environmental Stressors

the major categories of experiences that activate allostasis and contribute to allostatic load and overload. What happens to the brain under acute and chronic stress? Studies in animal models have revealed that the brain responds to stressors and is capable of structural remodeling of neurons in a largely reversible way. In rats and mice, repeated restraint stress, as well as certain chronic psychosocial stressors, cause neurons in hippocampus to undergo remodeling—shortening of dendrites and reduction in synapse density—a process that is mediated by excitatory amino acids in combination with glucocorticoids and other mediators (e.g., local growth factors, extracellular proteases, insulin, and glucose). There is modest impairment of hippocampal dependent learning. The remodeling and cognitive impairment are reversible in 7–10 days with the termination of the daily stress. A similar type of chronic stress induced remodeling occurs in the medial prefrontal cortex, whereas the basolateral amygdala and orbitofrontal cortex show expansion of dendrites and increased spine synapse density as a result of the same stress regimen. Chronic stress causes increases in anxiety and aggression and decreases in cognitive flexibility that are likely to be the result of these neuroanatomical changes.

FROM ANIMAL MODELS TO THE HUMAN BRAIN Much of the impetus for studying the effects of stress on the structure of the human brain has come from the animal studies summarized thus far, and there is emerging information on the effects of both acute and chronic

Major Life Events

(Work, Home, Neighborhood)

Trauma, Abuse

Behavioral Responses

Individual Differences

(Fight or Flight) (Personal BehaviorᎏDiet, Smoking, Drinking, Exercise)

(Genes, Development, Experience)

Physiologic Responses Allostasis

Adaptation Allostatic Load

Figure 62.3 Central role of the brain in allostasis and the behavioral and physiological response to stressors. Note: From “Protective and Damaging Effects of Stress Mediators,” by B. S. McEwen, 1998, New England Journal of Medicine, 338, p. 172. Reprinted with permission.

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stress on human brain structure and function that is consistent with the animal model findings. Functional imaging of individuals undergoing acute stressors, such as counting backward, show that there are changes in neural activity that last for many minutes after the end of the stressor and may be caused by mediators such as cortisol (Wang et al., 2005). Regarding chronic stress, a 20-year assessment of chronic perceived life stress in women has revealed a dose-response relationship, with hippocampal volume being smaller in those individuals with the highest average stress level over the 2 decades, although there were no initial volumetric measures for comparison since structural MRI did not exist at that time (Gianaros et al., 2007). Moreover, the study of depressive illness and anxiety disorders has also provided some insights of what happens when the brain and body cannot successfully cope. Life events are known to precipitate depressive illness in individuals with certain genetic predispositions (Kendler, 1998; Caspi et al., 2003; Kessler, 1997). Moreover, brain regions such as the hippocampus, amygdala, and prefrontal cortex show altered patterns of activity in PET and fMRI and also demonstrate changes in volume with recurrent depression: that is, decreased volume of hippocampus and prefrontal cortex and amygdala (Drevets et al., 1997; Sheline, Gado, & Kraemer, 2003; Sheline, Sanghavi, Mintun, & Gado, 1999). Interestingly, amygdala volume has been reported to increase in the first episode of depression, whereas hippocampal volume is not decreased until the depression has existed for a number of years (Frodl et al., 2003; MacQueen et al., 2003). Major depressive illness is also associated with increased risk for cardiovascular disease, and abdominal obesity, as well as a comorbidity with Type 2 diabetes (Evans et al., 2005). It has been known for some time that stress hormones, such as cortisol, are involved in psychopathology, reflecting emotional arousal and psychic disorganization rather than the specific disorder per se (Sachar et al., 1973). Indeed, we know that adrenocortical hormones enter the brain and produce a wide range of effects on it. One of the consequences, in Cushing’s disease, is depressive symptoms that can be relieved by surgical correction of the hypercortisolemia (Murphy, 1991; Starkman & Schteingart, 1981). Both major depression and Cushing’s disease are associated with chronic elevation of cortisol that results in gradual loss of minerals from bone and abdominal obesity (Steingart et al., 2000). In major depressive illness, as well as in Cushing’s disease, the duration of the illness and not the age of the subjects predicts a progressive reduction in volume of the hippocampus, determined by structural magnetic resonance imaging (Sheline et al., 1999; Starkman, Gebarski, Berent, & Schteingart, 1992). Moreover, there

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are other anxiety-related disorders, such as post-traumatic stress disorder (PTSD; Bremner, 2002; Pitman, 2001) and borderline personality disorder (Driessen et al., 2000), in which atrophy of the hippocampus has been reported, suggesting that this is a common process reflecting chronic imbalance in the activity of adaptive systems, such as the HPA axis, but also including endogenous neurotransmitters, such as glutamate. Another important factor in hippocampal volume and function is glucose regulation. Poor glucose regulation is associated with smaller hippocampal volume and poorer memory function in individuals with Type 2 diabetes, and hippocampal volume decrease is linearly related to elevated glycosylated hemoglobin levels in blood. Moreover, prior to outright Type 2 diabetes, there is also hippocampal volume reduction related to poor glucose control in individuals in their 60s and 70s who have “mild cognitive impairment” (MCI; Convit, Wolf, Tarshish, & de Leon, 2003). Both MCI and Type 2, as well as Type 1, diabetes are recognized as risk factors for dementia (de Leon et al., 2001; Haan, 2006; Ott et al., 1996). Linking Psychology of Coping with Neurobiology and Physiology of Adaptation Recent studies provide linkages between the biology of allostasis and adaptation and the psychology of coping. A risk factor for poor coping ability is low self-esteem and there are now neurobiological and physiological links to the conditions that create allostatic overload. Poor selfesteem and low locus of control has been shown to cause recurrent increases in cortisol levels during a repetition of a public speaking challenge in which those individuals with good self-esteem can habituate, that is, attenuate their cortisol response after the first speech (Kirschbaum et al., 1995). Furthermore, poor self-esteem and low internal locus of control have been related to 12% to 13% smaller volume of the hippocampus, as well as higher cortisol levels during a mental arithmetic stressor (Pruessner et al., 1999, 2005). A smaller hippocampus may be a causal factor in elevated cortisol, given its role in shutting off the HPA axis in the aftermath of a stressor (Herman & Cullinan, 1997; Jacobson & Sapolsky, 1991). An important question is when and how these changes in the brain come about— whether they have a genetic component or are the result of early life experiences. Another future challenge is whether improving self-esteem would enlarge the hippocampus! Having a positive outlook on life and good self-esteem also appear to have long-lasting health consequences (Pressman & Cohen, 2005). Positive affect, assessed by aggregating momentary experiences throughout a working or leisure day, was found to be associated with lower

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From Animal Models to the Human Brain 1229

cortisol production and higher heart rate variability (showing higher parasympathetic activity), as well as a lower fibrinogen response to a mental stress test (Steptoe et al., 2005). Related to both positive affect and self-esteem is the role of friends and social interactions in maintaining a healthy outlook on life. Loneliness, often found in people with low self-esteem, has been associated with larger cortisol responses to wakening in the morning and higher fibrinogen and natural killer cell responses to a mental stress test, as well as sleep problems (Steptoe, Owen, Kunz-Ebrecht, & Brydon, 2004). Having three or more regular social contacts, as opposed to zero to two such contacts, is associated with lower allostatic load scores (Seeman, Singer, Ryff, Dienberg, & Levy-Storms, 2002). Social ties and social support play a role in the beneficial effects of physical activity on physiological and cognitive health, as demonstrated in numerous studies with regard to reducing the incidence of cardiovascular disease (Bernadet, 1995; Colcombe et al., 2004) and dementia (Larson et al., 2006; Rovio et al., 2005). Regular physical activity is also an effective treatment for depression. In the Diabetes Prevention Trial, 30 minutes of walking per day sustained over a 6-year period, along with lifestyle and behavioral interventions, reduced the incidence of Type 2 diabetes by almost 60% (http://diabetes.niddk.nih .gov/dm/pubs/preventionprogram). A similar intervention program, but lasting only 6 months, improved executive function and brain activation patterns in cerebral cortical areas linked to attention and executive function (Kramer et al., 1999, 2003). To get people to maintain a long-term program of daily physical activity, it is necessary to establish and maintain social ties and social support systems; hence, the effects of physical activity per se are difficult to distinguish from the social support (Wing, 2003). In one of the studies noted here, however, social support was similar in two groups of participants, in which one group did aerobic exercise and the other a toning program; only the aerobic exercise was found to benefit executive function (Kramer et al., 1999). Gene-Environment Interactions and Allostasis and Allostatic Overload Individuals differ in how they respond to stressors and this is based on at least four factors: First, experiences in adult life; second, experiences early in life; third, genetic constitution; fourth, how the experiential factors are manifested in epigenetic effects. As for experiences in adult life, positive or negative experiences in school, at work, or in romantic and family interpersonal relationships can bias an individual toward either a positive or negative response in a new situation;

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someone who has been treated badly in a job by an abusive supervisor or has been fired will approach a new job situation quite differently from someone who has had positive experiences in employment. Early life experiences carry an even greater weight in terms of how an individual reacts to new situations. Animal models have been useful in providing insights into behavioral and physiological mechanisms by revealing effects of prenatal stress, postnatal abuse by mothers, and food insecurity by mothers on anxiety and attachment of offspring (see McEwen, 2007, for summary). Early life maternal care in rodents is a powerful determinant of lifelong emotional reactivity and stress hormone reactivity and increases in both are associated with earlier cognitive decline and a shorter life span (Cavigelli & McClintock, 2003; Francis, Diorio, Liu, & Meaney, 1999). Strong maternal behavior, involving licking and grooming of the offspring, produces a neophilic animal that is more exploratory of novel environments, less emotionally reactive, and produces a lower and more contained glucocorticoid stress response in novel situations. Poor maternal care leads to a neophobic phenotype with increased emotional and HPA reactivity and less exploration of a novel situation (Meaney et al., 1994). Effects of early maternal care are transmitted across generations by the subsequent behavior of the female offspring as they become mothers, and methylation of DNA on key genes appears to play a role in this epigenetic transmission (Francis et al., 1999; Weaver et al., 2004). More generally, epigenetics, meaning “above the genome” was originally defined to mean the gene-environment interactions that bring about the phenotype of an individual. Now, epigenetics means something more specific in molecular terms: the methylation of cytosine bases in DNA along with modifications of histones that modify unfolding of chromatin to expose DNA sequences that can be read and transcribed (Jirtle & Skinner, 2007; see Box 62.5). Translation to Human Physiology and Behavior Animal models help us understand how early life experiences affect human physiology and behavior. Early life physical and sexual abuse carry with it a lifelong burden of behavioral and pathophysiological problems (Felitti et al., 1998; Heim & Nemeroff, 2001). Moreover, cold and uncaring families produce long-lasting emotional problems in children (Repetti, Taylor, & Seeman, 2002). Some of these effects are seen on brain structure and function and in the risk for later depression and post-traumatic stress disorder (Kaufman & Charney, 1999; Kaufman, Plotsky, Nemeroff, & Charney, 2000; Vermetten, Schmahl, Lindner, Loewenstein, & Bremner, 2006). Prenatal stress is believed to be a factor in causing preterm birth, as well

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BOX 62.5 EPIGENETICS AND HISTONE MODIFICATIONS The pioneering work of Allfrey, Mirsky, and colleagues in the 1960s and 1970s demonstrated the relationship of transcriptional activation of chromatin and the modification of histones by acetylation and phosphorylation (Frenster, Allfrey, & Mirsky, 1963), and presented a conceptual framework for understanding the role of histone modifications in the unfolding of DNA-protein complexes to allow transcription during states of gene activation (Allfrey, 1977). Recent work has revealed that there is a complex language of epigenetic modifications that regulate transcription, both up and down (Berger, 2007; Strahl & Allis, 2000). Epigenetic modifications are of several types. There are genes with metastable epialleles, in which modifications that affect gene expression are reversible. The murine agouti gene is linked to altered coat color, diabetes, obesity, and tumorigenesis, and genetically identical mice can be induced to show these traits by dietary manipulations (Jirtle & Skinner, 2007). There are also imprinted genes that transfer the epigenetic state through the germline (e.g., the murine IGF2 gene, which is passed on in a modified state in the paternal genome, and the IGF2R gene that is transferred in a modified state in the maternal genome). In humans, the severe developmental disorders, Prader-Willi and Angelman syndromes, are examples of epigenetic modifications that are transmitted in the germ line (Jirtle & Skinner, 2007). as full-term birth with low birth weight (Barker, 1997; Wadhwa, Sandman, & Garite, 2001). Low birth weight is a risk factor for cardiovascular disease and high body mass (Barker, 1997; Power, Li, Manor, & Davey Smith, 2003). Childhood experiences in emotionally cold families increase the likelihood of poor mental and physical health later in life (Repetti et al., 2002), and abuse in childhood is a well-known risk factor for depression, post-traumatic stress disorder, idiopathic chronic pain disorders, substance abuse, and antisocial behavior, as well as obesity, diabetes, and cardiovascular disease (Anda et al., 2006; Felitti et al., 1998; Heim & Nemeroff, 2001). Chaos in the home environment is a key determinant of poor self-regulatory behaviors, a sense of helplessness, and psychological distress (Evans, Gonnella, Marcynyszyn, Gentile, & Salpekar, 2004), as well as increased body mass and elevated blood pressure (Evans, 2003). One of the lasting consequences of low socioeconomic status in childhood is an elevation in body mass as well as poor dental health (Poulton et al., 2002). Social isolation in childhood

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increases the risk of cardiovascular disease later in life (Caspi, Harrington, Moffitt, Milne, & Poulton, 2006) and childhood abuse is linked to an increased pro-inflammatory tone, as measured by elevated C-reactive protein levels (McEwen & Chattarji, 2007) that were made decades later in life. In addition to the effects of experiences, genetic differences also play an important role as part of the naturenurture interaction. This is a vast and growing topic, and only some examples will be noted here. The short form of the serotonin transporter is associated with conditions such as alcoholism (Barr et al., 2004; Herman et al., 2005), and individuals who have this allele are more vulnerable to respond to stressful experiences by developing depressive illness (Caspi et al., 2003). In childhood, individuals with an allele of the monoamine oxidase A gene are more vulnerable to abuse in childhood and more likely themselves to become abusers and to show antisocial behaviors compared with individuals who have another commonly occurring allele (Caspi et al., 2002). Yet another example is the consequence of having the Val66Met allele of the BDNF gene on hippocampal volume, memory, and mood disorders (Chen et al., 2006; Hariri et al., 2003; Jiang et al., 2005; Pezawas et al., 2004; Szeszko et al., 2005). A mouse model of this genotype has revealed reduced dendritic branching in the hippocampus, impaired contextual fear conditioning, and increased anxiety that is less sensitive to antidepressant treatment (Chen et al., 2006). Finally, certain alleles of the glucocorticoid receptor gene that are found in the normal population confer a higher sensitivity to glucocorticoids for both negative feedback and insulin responsiveness (Huizenga et al., 1998) or glucocorticoid resistance (van Rossum et al., 2006), and there is evidence of increased likelihood of depression in several alleles and increased response to antidepressants in one of them.

SUMMARY Given the pervasive nature of allostatic load resulting from chronic stress and altered health-related behaviors, it is important to consider briefly the most effective ways of intervening to prevent or treat these conditions. For the purposes of this overview, there are three overlapping categories to consider: (1) individual behavioral change; (2) policies of government and the private sector that provide services, create incentives, and offer opportunities for individuals to develop healthier lifestyles and behaviors; and (3) medications to treat disorders or slow down or prevent disease processes.

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References 1231

From the standpoint of the individual, it almost goes without saying that a major goal should be to try to improve sleep quality and quantity, have good social support and a positive outlook on life, maintain a healthy diet, avoid smoking, and engage in regular moderate physical activity. Concerning physical activity, it is not necessary to become an extreme athlete, and seemingly any amount of moderate physical activity helps (Bernadet, 1995; Rovio et al., 2005). From the standpoint of policy of government and the private sector, the goal should be to create incentives at home and in work situations and build community services and opportunities that encourage the development of beneficial individual lifestyle practices. Providing access to affordable, healthy food, creating recreational opportunities, providing safe neighborhoods, and enhancing social cohesion are all likely to be beneficial (Sampson, Raudenbush, & Earls, 1997). As easy as it is to say these things, finding the political will and creating effective policies is a daunting task. By the same token, for the individual, changing behavior and solving problems that cause stress at work and at home is often difficult and may require professional help on the personal level, or even a change of job or profession. Yet, these are important goals because the prevention of later disease is important for full enjoyment of life and also to reduce the financial burden on the individual and on society. Nevertheless, many people often lack the proactive, long-term view of themselves or feel that they must maintain a stressful lifestyle, and if they deal with these issues at all, they want to treat their problems with a pill. There are many useful pharmaceutical agents: Sleep-inducing agents, anxiolytics, beta-blockers, and antidepressants are all drugs that counteract some of the problems associated with being stressed out. Likewise, drugs that reduce oxidative stress or inflammation, or block cholesterol synthesis or absorption, or treat insulin resistance or chronic pain can help deal with the metabolic and neurological consequences of being stressed out and slow down the progression of allostatic load and associated pathophysiology. All such medications are valuable to some degree, and yet each one has side effects and limitations that are based in part on the fact that all the systems that are dysregulated in allostatic overload are also systems that interact with each other and perform normal functions when properly regulated. Because of the nonlinearity of the systems of allostasis, the consequences of any drug treatment may be either to inhibit the beneficial effects of the systems in question or to perturb other systems that interact with it in a direction that promotes an unwanted side effect. So the best solution may be to avoid relying solely on such medications and to search for ways of changing lifestyle in a positive direction that reduces the need of such agents.

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REFERENCES Aberg, M. A., Aberg, N. D., Hedbacker, H., Oscarsson, J., & Eriksson, P. S. (2000). Peripheral infusion of IGF-1 selectively induces neurogenesis in the adult rat hippocampus. Journal of Neuroscience, 20, 2896–2903. Allfrey, V. G. (1977). Post-synthetic modifications of histone structure. A mechanism for the control of chromosome structure by the modulation of DNA-histone interactions. In Chromatin and Chromosome Structure (pp. 167–199). New York: Academic Press. Anda, R. F., Felitti, V. J., Bremner, J. D., Walker, J. D., Whitfield, C., Perry, B. D., et al. (2006). The enduring effects of abuse and related adverse experiences in childhood. European Archives of Psychiatry and Clinical Neuroscience, 256, 174–186. Arendt, T., Stieler, J., Strijkstra, A. M., Hut, R. A., Rudiger, J., Van der Zee, E. A., et al. (2003). Reversible paired helical filament-like phosphorylation of tau is an adaptive process associated with neuronal plasticity in hibernating animals. Journal of Neuroscience, 23, 6972–6981. Avitsur, R., Stark, J. L., & Sheridan, J. F. (2001). Social stress induces glucocorticoid resistance in subordinate animals. Hormones and Behavior, 39, 247–257. Barbe, M. F., & Barr, A. E. (2006). Inflammation and the pathophysiology of work-related musculoskeletal disorders. Brain, Behavior, and Immunity, 20, 423–429. Barker, D. J. P. (1997). The fetal origins of coronary heart disease. Acta Paediatrica: Supplement, 422, 78–82. Barr, C. S., Newman, T. K., Lindell, S., Shannon, C., Champoux, M., Lesch, K. P., et al. (2004). Interaction between serotonin transporter gene variation and rearing condition in alcohol preference and consumption in female primates. Archives of General Psychiatry, 61, 1146–1152. Berger, S. L. (2007). The complex language of chromatin regulation during transcription. Nature, 447, 347–412. Bernadet, P. (1995). Benefits of physical activity in the prevention of cardiovascular disease. Journal of Cardiovascular Pharmacology, 25 (Suppl. 1), S3–S8. Bierhaus, A., Schiekofer, S., Schwaninger, M., Andrassy, M., Humpert, P. M., Chen, J., et al., (2001). Diabetes-associated sustained activation of the transcription factor nuclear factor-kB. Diabetes, 50, 2792–2808. Bierhaus, A., Wolf, J., Andrassy, M., Rohleder, N., Humpert, P. M., Petrov, D., et al., (2003). A mechanism converting psychosocial stress into mononuclear cell activation. Proceedings of the National Academy of Sciences, USA, 100, 1920–1925. Black, P. H., & Garbutt, L. D. (2002). Stress, inflammation and cardiovascular disease. Journal of Psychosomatic Research, 52, 1–23. Borovikova, L. V., Ivanova, S., Zhang, M., Yang, H., Botchkina, G. I., Watkins, L. R., et al. (2000). Vagus nerve stimulation attenuates the systemic inflammatory response to endotoxin. Nature, 405, 458–462. Brami-Cherrier, K., Lavaur, J., Pages, C., Arthur, J. S. C., & Caboche, J. (2007). Glutamate induces histone H3 phosphorylation but not acetylation in striatal neurons: Role of mitogen- and stress-activated kinase-1. Journal of Neurochemistry, 101, 697–708. Bremner, J. D. (2002). Neuroimaging studies in post-traumatic stress disorder. Current Psychiatry Reports, 4, 254–263. Brindley, D. N., & Rolland, Y. (1989). Possible connections between stress, diabetes, obesity, hypertension and altered lipoprotein metabolism that may result in atherosclerosis. Clinical Science, 77, 453–461. Brown, S. M., Henning, S., & Wellman, C. L. (2005). Mild, short-term stress alters dendritic morphology in rat medial prefrontal cortex. Cerebral Cortex, 30, 1–9. Cameron, H. A., & McKay, R. D. G. (2001). Adult neurogenesis produces a large pool of new granule cells in the dentate gyrus. Journal of Comparative Neurology, 435, 406–417.

8/19/09 5:13:41 PM

1232

Stress and Coping

Cannon, W. B. The wisdom of the body. (1932). New York: Norton and Co. Caspi, A., Harrington, H. L., Moffitt, T. E., Milne, B. J., & Poulton, R. (2006). Socially isolated children 20 years later. Risk of cardiovascular disease. Archives of Pediatrics and Adolescent Medicine, 160, 805–811. Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., Craig, I. W., et al. (2002). Role of genotype in the cycle of violence in maltreated children. Science, 297, 851–854.

Dhabhar, F. S., Satoskar, A. R., Bluethmann, H., David, J. R., & McEwen, B. S. (2000). Stress-induced enhancement of skin immune function: A role for g interferon. Proceedings of the National Academy of Sciences, USA, 97, 2846–2851. Drevets, W. C., Price, J. L., Simpson Jr, J. R., Todd, R. D., Reich, T., Vannier, M., et al. (1997). Subgenual prefrontal cortex abnormalities in mood disorders. Nature, 386, 824–827.

Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., Harrington, H., et al. (2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 301, 386–389.

Driessen, M., Hermann, J., Stahl, K., Zwaan, M., Meier, S., Hill, A., et al. (2000). Magnetic resonance imaging volumes of the hippocampus and the amygdala in women with borderline personality disorder and early traumatization. Archives of General Psychiatry, 57, 1115–1122.

Cavigelli, S. A., & McClintock, M. K. (2003). Fear of novelty in infant rats predicts adult corticosterone dynamics and an early death. Proceedings of the National Academy of Sciences, USA, 100, 16131–16136.

Epel, E. S., Blackburn, E. H., Lin, J., Dhabhar, F. S., Adler, N. E., Morrow, J. D., et al. (2004). Accelerated telomere shortening in response to life stress. Proceedings of the National Academy of Sciences, USA, 101, 17312–17315.

Chen, Z.-Y., Jing, D., Bath, K. G., Ieraci, A., Khan, T., Siao, C.-J., et al. (2006). Genetic variant BDNF (Val66Met) polymorphism alters anxietyrelated behavior. Science, 314, 140–143. Chrousos, G. P. (1998). Stressors, stress, and neuroendocrine integration of the adaptive response. Annals, N. Y. Acad. Sci., 851, 311–335. Chrousos, G. P. (2000). The role of stress and the hypothalamic-pituitaryadrenal axis in the pathogenesis of the metabolic syndrome: Neuroendocrine and target tissue-related causes. International Journal of Obesity, 24, S50–S55. Colcombe, S. J., Kramer, A. F., Erickson, K. I., Scalf, P., McAuley, E., Cohen, N. J., et al. (2004). Cardiovascular fitness, cortical plasticity, and aging. Proceedings of the National Academy of Sciences, USA, 101, 3316–3321. Conrad, C. D., Magarinos, A. M., LeDoux, J. E., & McEwen, B. S. (1999). Repeated restraint stress facilitates fear conditioning independently of causing hippocampal CA3 dendritic atrophy. Behavioral Neuroscience, 113, 902–913. Convit, A., Wolf, O. T., Tarshish, C., & de Leon, M. J. (2003). Reduced glucose tolerance is associated with poor memory performance and hippocampal atrophy among normal elderly. Proceedings of the National Academy of Sciences, USA, 100, 2019–2022. Cook, S. C., & Wellman, C. L. (2004). Chronic stress alters dendritic morphology in rat medial prefrontal cortex. Journal of Neurobiology, 60, 236–248. Czeh, B., Michaelis, T., Watanabe, T., Frahm, J., de Biurrun, G., van Kampen, M., et al. (2001). Stress-induced changes in cerebral metabolites, hippocampal volume and cell proliferation are prevented by antidepressant treatment with tianeptine. Proceedings of the National Academy of Sciences, USA, 98, 12796–12801.

Evans, D. L., Charney, D. S., Lewis, L., Golden, R. N., Gorman, J. M., Krishnan, K. R. R., et al. (2005). Mood disorders in the medically ill: Scientific review and recommendation. Biological Psychiatry, 58, 175–189. Evans, G. W. (2003). A multimethodological analysis of cumulative risk and allostatic load among rural children. Developmental Psychology, 39, 924–933. Evans, G. W., Gonnella, C., Marcynyszyn, L. A., Gentile, L., & Salpekar, N. (2004). The role of chaos in poverty and children’s socioemotional adjustment. Psychol Science, 16, 560–565. Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., et al. (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The adverse childhood experiences (ACE) study. American Journal of Preventive Medicine, 14, 245–258. Folkman, S. (1997). Positive psychological states and coping with severe stress. Social Science and Medicine, 45, 1207–1221. Francis, D., Diorio, J., Liu, D., & Meaney, M. J. (1999). Nongenomic transmission across generations of maternal behavior and stress responses in the rat. Science, 286, 1155–1158. Frenster, J. H., Allfrey, V. G., & Mirsky, A. E. (1963). Repressed and active chromatin isolated from interphase lymphocytes. Proceedings of the National Academy of Sciences, USA, 50, 1026–1032. Friedman, E. M., Hayney, M. S., Love, G. D., Urry, H. L., Rosenkranz, M. A., Davidson, R. J., et al. (2005). Social relationships, sleep quality, and interleukin-6 in aging women. Proceedings of the National Academy of Sciences, USA, 102, 18757–18762.

David, S., Stegenga, S. L., Hu, P., Xiong, G., Kerr, E., Becker, K. B., et al. (2005). Expression of serum- and glucocorticoid-inducible kinase is regulated in an experience-dependent manner and can cause dendrite growth. Journal of Neuroscience, 25, 7048–7053.

Frodl, T., Meisenzahl, E. M., Zetzsche, T., Born, C., Jager, M., Groll, C., et al. (2003). Larger amygdala volumes in first depressive episode as compared to recurrent major depression and healthy control subjects. Biological Psychiatry, 53, 338–344.

de Leon, M. J., Convit, A., Wolf, O. T., Tarshish, C. Y., DeSanti, S., Rusinek, H., et al. (2001). Prediction of cognitive decline in normal elderly subjects with 2-[18F]fluoro-2-deoxy-D-glucose/positronemission tomography (FDG/PET). Proceedings of the National Academy of Sciences, USA, 98, 10966–10971.

Gianaros, P. J., Jennings, J. R., Sheu, L. K., Greer, P. J., Kuller, L. H., & Matthews, K. A. (2007). Prospective reports of chronic life stress predict decreased grey matter volume in the hippocampus. NeuroImage, 35, 795–803.

Desouza, L. A., Ladiwala, U., Daniel, S. M., Agashe, S., Vaidya, R. A., & Vaidya, V. A. (2005). Thyroid hormone regulates hippocampal neurogenesis in the adult rat brain. Molecular and Cellular Neurosciences, 29, 414–426. Dhabhar, F. S. (2002). Stress-induced augmentation of immune function— The role of stress hormones, leukocyte trafficking, and cytokines. Brain, Behavior, and Immunity, 16, 785–798. Dhabhar, F. S., & McEwen, B. S. (1997). Acute stress enhances while chronic stress suppresses cell-mediated immunity in vivo: A potential role for leukocyte trafficking. Brain, Behavior, and Immunity, 11, 286–306.

c62.indd Sec3:1232

Goldstein, D. S. (1995). Stress, catecholamines, and cardiovascular disease. New York: Oxford University Press. Gould, E., McEwen, B. S., Tanapat, P., Galea, L. A. M., & Fuchs, E. (1997). Neurogenesis in the dentate gyrus of the adult tree shrew is regulated by psychosocial stress and NMDA receptor activation. Journal of Neuroscience, 17, 2492–2498. Govindarajan, A., Rao, B. S. S., Nair, D., Trinh, M., Mawjee, N., Tonegawa, S., et al. (2006). Transgenic brain-derived neurotrophic factor expression causes both anxiogenic and antidepressant effects. Proceedings of the National Academy of Sciences, USA, 103, 13208–13213.

8/19/09 5:13:42 PM

References 1233 Haan, M. N. (2006). Therapy insight: Type 2 diabetes mellitus and the risk of late-onset Alzheimer ’s disease. Nature Clinical Practice: Neurology, 2, 159–166.

Kreibich, A. S., & Blendy, J. A. (2004). CAMP response elementbinding protein is required for stress but not cocaine-induced reinstatement. Journal of Neuroscience, 24, 6686–6692.

Hariri, A. R., Goldberg, T. E., Mattay, V. S., Kolachana, B. S., Callicott, J. H., Egan, M. F., et al. (2003). Brain-derived neurotrophic factor val66 met polymorphism affects human memory-related hippocampal activity and predicts memory performance. Journal of Neuroscience, 23, 6690–6694.

Larson, E. B., Wang, L., Bowen, J. D., McCormick, W. C., Teri, L., Crane, P., et al. (2006). Exercise is associated with reduced risk for incident dementia among persons 65 years of age or older. Annals of Internal Medicine, 144, 73–81.

Heim, C., & Nemeroff, C. B. (2001). The role of childhood trauma in the neurobiology of mood and anxiety disorders: Preclinical and clinical studies. Biological Psychiatry, 49, 1023–1039. Herman, A. I., Kaiss, K. M., Ma, R., Philbeck, J. W., Hasan, A., Dasti, H., et al. (2005). Serotonin transporter promoter polymorphism and monoamine oxidase type A VNTR allelic variants together influence alcohol binge drinking risk in young women. American Journal of Medical Genetics: Part B, Neuropsychiatric Genetics, 133B, 74–78. Herman, J. P., & Cullinan, W. E. (1997). Neurocircuitry of stress: Central control of the hypothalamo-pituitary-adrenocortical axis. Trends in Neuroscience, 20, 78–84. Huizenga, N. A. T. M., Koper, J. W., De Lange, P., Pols, H. A. P., Stolk, R. P., Burger, H., et al. (1998). A polymorphism in the glucocorticoid receptor gene may be associated with an increased sensitivity to glucocorticoids in vivo. J. Clin. Endocrin. & Metab., 83, 144–151. Jacobson, L., & Sapolsky, R. (1991). The role of the hippocampus in feedback regulation of the hypothalamic-pituitary-adrenocortical axis. Endocrine Reviews, 12, 118–134. Jiang, X., Xu, K., Hoberman, J., Tian, F., Marko, A. J., Waheed, J. F., et al. (2005). BDNF variation and mood disorders: A novel functional promoter polymorphism and Val66Met are associated with anxiety but have opposing effects. Neuropsychopharmacology, 30, 1353–1361. Jirtle, R. L., & Skinner, M. K. (2007). Environmental epigenomics and disease susceptibility. Nature Reviews: Genetics, 8, 253–262. Kaufman, J., & Charney, D. S. (1999). Neurobiological correlates of child abuse. Biological Psychiatry, 45, 1235–1236. Kaufman, J., Plotsky, P. M., Nemeroff, C. B., & Charney, D. S. (2000). Effects of early adverse experiences on brain structure and function: Clinical implications. Biological Psychiatry, 48, 778–790. Kempermann, G., & Gage, F. H. (1999). New nerve cells for the adult brain. Scientific American, 280, 48–53. Kendler, K. S. (1998). Major depression and the environment: A psychiatric genetic perspective. Pharmacopsychiat. 31, 5–9. Kessler, R. C. (1997). The effects of stressful life events on depression. Annual Review of Psychology, 48, 191–214. Kiecolt-Glaser, J. K., Loving, T. J., Stowell, J. R., Malarkey, W. B., Lemeshow, S., Dickinson, S. L., et al. (2005). Hostile marital interactions, proinflammatory cytokine production, and wound healing. Archives of General Psychiatry, 62, 1–8. Kirschbaum, C., Prussner, J. C., Stone, A. A., Federenko, I., Gaab, J., Lintz, D., et al. (1995). Persistent high cortisol responses to repeated psychological stress in a subpopulation of healthy men. Psychosomatic Medicine, 57, 468–474. Komesaroff, P. A., Esler, M. D., & Sudhir, K. (1999). Estrogen supplementation attenuates glucocorticoid and catecholamine responses to mental stress in perimenopausal women. Journal of Clinical Endocrinology and Metabolism, 84, 606–610. Koob, G. F., & LeMoal, M. (2001). Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology, 24, 97–129. Kramer, A. F., Colcombe, S. J., McAuley, E., Eriksen, K. I., Scalf, P., Jerome, G. J., et al. (2003). Enhancing brain and cognitive function of older adults through fitness training. Journal of Molecular Neuroscience, 20, 213–221. Kramer, A. F., Hahn, S., Cohen, N. J., Banich, M. T., McAuley, E., Harrison, C. R., et al. (1999). Ageing, fitness and neurocognitive function. Nature, 400, 418–419.

c62.indd Sec3:1233

Leuner, B., Gould, E., & Shors, T. J. (2006). Is there a link between adult neurogenesis and learning? Hippocampus, 26, 216–224. Lindheim, S. R., Legro, R. S., Bernstein, L., Stanczyk, F. Z., Vijod, M. A., Presser, S. C., et al. (1992). Behavioral stress responses in premenopausal and postmenopausal women and the effects of estrogen. American Journal of Obstetrics and Gynecology, 167, 1831–1836. Lisman, J. E., & Otmakhova, N. A. (2001). Storage, recall, and novelty detection of sequences by the hippocampus: Elaborating on the SOCRATIC model to account for normal and aberrant effects of dopamine. Hippocampus, 11, 551–568. Liston, C., Miller, M. M., Goldwater, D. S., Radley, J. J., Rocher, A. B., Hof, P. R., et al. (2006). Stress-induced alterations in prefrontal cortical dendritic morphology predict selective impairments in perceptual attentional set-shifting. Journal of Neuroscience, 26, 7870–7874. Lupien, S. J. (2002). The neuroendocrinology of cognitive disorders. (Vol. 15, pp. 273–282). Wiley, Ltd. MacQueen, G. M., Campbell, S., McEwen, B. S., Macdonald, K., Amano, S., Joffe, R. T., et al. (2003). Course of illness, hippocampal function, and hippocampal volume in major depression. Proceedings of the National Academy of Science, USA 100, 1387–1392. Magarinos, A. M., McEwen, B. S., Saboureau, M., & Pevet, P. (2006). Rapid and reversible changes in intrahippocampal connectivity during the course of hibernation in European hamsters. Proceedings of the National Academy of Science, USA 103, 18775–18780. Marucha, P. T., Kiecolt-Glaser, J. K., & Favagehi, M. (1998). Mucosal wound healing is impaired by examination stress. Psychosomatic Medicine, 60, 362–365. Matys, T., Pawlak, R., Matys, E., Pavlides, C., McEwen, B. S., & Strickland S. (2004). Tissue plasminogen activator promotes the effects of corticotropin releasing factor on the amygdala and anxietylike behavior. Proceedings of the National Academy of Science, USA, 101, 16345–16350. McEwen, B. S. (1998). Protective and damaging effects of stress mediators. New England Journal of Medicine, 338, 171–179. McEwen, B. S. (1999). Stress and hippocampal plasticity. Annual Review of Neuroscience, 22, 105–122. McEwen, B. S. (2006a). Protective and damaging effects of stress mediators: central role of the brain. Dial. in Clin. Neurosci: Stress, 8, 283–297. McEwen, B. S. (2006b). Sleep deprivation as a neurobiologic and physiologic stressor: allostasis and allostatic load. Metabolism, 55, S20–S23. McEwen, B. S. (2007). The physiology and neurobiology of stress and adaptation: Central role of the brain. Physiological Reviews. McEwen, B. S., & Chattarji, S. (2004). Molecular mechanisms of neuroplasticity and pharmacological implications: The example of tianeptine. European Neuropsychopharmacology, 14, S497–S502. McEwen, B. S., & Chattarji, S. (2007). Neuroendocrinology of Stress. Handbook of Neurochemistry and Molecular Neurobiology (3rd ed., pp. 572–593). New York: Springer-Verlag. McEwen, B. S., Sakai, R. R., & Spencer, R. L. (1993). Adrenal steroid effects on the brain: Versatile hormones with good and bad effects, J. Schulkin. Hormonally-induced changes in mind and brain (pp. 157– 189). San Diego, CA: Academic Press. McEwen, B. S., & Stellar, E. (1993). Stress and the individual. Mechanisms leading to disease. Arch Intern Med, 153, 2093–2101.

8/19/09 5:13:42 PM

1234

Stress and Coping

McEwen, B. S., & Wingfield, J. C. (2003). The concept of allostasis in biology and biomedicine. Hormones and Behavior, 43, 2–15. McGeer, P. L., & McGeer, E. G. (2001). Inflammation, autotoxicity and Alzheimer disease. Neurobiology of Aging, 22, 799–809. McKittrick, C. R., Magarinos, A. M., Blanchard, D. C., Blanchard, R. J., McEwen, B. S., & Sakai, R. R. (2000). Chronic social stress reduces dendritic arbors in CA3 of hippocampus and decreases binding to serotonin transporter sites. Synapse, 36, 85–94. Meaney, M. J., Tannenbaum, B., Francis, D., Bhatnagar, S., Shanks, N., Viau, V., et al. (1994). Early environmental programming hypothalamicpituitary-adrenal responses to stress. Seminars in Neurosciences, 6, 247–259. Melchor, J. P., Pawlak, R., & Strickland, S. (2003). The tissue plasminogen activator: Plasminogen proteolytic cascade accelerates amyloid-b (Ab) degradation and inhibits Ab-induced neurodegeneration. Journal of Neuroscience, 23, 8867–8871. Mitra, R., Jadhav, S., McEwen, B. S., Vyas, A., & Chattarji, S. (2005). Stress duration modulates the spatiotemporal patterns of spine formation in the basolateral amygdala. Proceedings of the National Academy of Science, USA 102, 9371–9376. Munck, A., Guyre, P. M., & Holbrook, N. (1984). Physiological functions of glucocorticoids in stress and their relation to pharmacological actions. Endocrine Reviews, 5, 25–44.

Pruessner, J. C., Baldwin, M. W., Dedovic, K., Renwick, R. M. N. K., Lord, C., Meaney, M., et al. (2005). Self-esteem, locus of control, hippocampal volume, and cortisol regulation in young and old adulthood. NeuroImage, 28, 815–826. Pruessner, J. C., Hellhammer, D. H., & Kirschbaum, C. (1999). Low self-esteem, induced failure and the adrenocortical stress response. Personality and Individual Differences, 27, 477–489. Radley, J. J., Rocher, A. B., Janssen, W. G. M., Hof, P. R., McEwen, B. S., & Morrison, J. H. (2005a). Reversibility of apical dendritic retraction in the rat medial prefrontal cortex following repeated stress. Experimental Neurology, 196, 199–203. Radley, J. J., Rocher, A. B., Miller, M., Janssen, W. G. M., Liston, C., Hof, P. R., et al. (2005b). Repeated stress induces dendritic spine loss in the rat medial prefrontal cortex. Cerebral Cortex. Radley, J. J., Sisti, H. M., Hao, J., Rocher, A. B., McCall, T., Hof, P. R., et al. (2004). Chronic behavioral stress induces apical dendritic reorganization in pyramidal neurons of the medial prefrontal cortex. Neuroscience, 125, 1–6. Raison, C. L., Capuron, L., & Miller, A. H. (2006). Cytokines sing the blues: Inflammation and the pathogenesis of depression. Trends in Immunology, 27, 24–31. Ramey, E., & Goldstein, M. (1957). The adrenal cortex and sympathetic nervous system. Physiological Review, 37, 155–195.

Murphy, B. E. P. (1991). Treatment of major depression with steroid suppressive drugs. Journal of Steroid Biochemistry and Molecular Biology, 39, 239–244

Reed, D. M., Lacroix, A. Z., Karasek, R. A., Miller, D., & MacLean, C. A. (1989). Occupational strain and the incidence of coronary heart disease. American Journal of Epidemiology, 129, 495–502.

Ott, A., Stolk, R. P., Hofman, A., van Harskamp, F., Grobbee, D. E., & Breteler, M. M. B. (1996). Association of diabetes mellitus and dementia: The Rotterdam study. Diabetologia, 39, 1392–1397.

Repetti, R. L., Taylor, S. E., & Seeman, T. E. (2002). Risky families: Family social environments and the mental and physical health of offspring. Physiological Bulletin, 128, 330–366.

Pawlak, R., Magarinos, A. M., Melchor, J., McEwen, B., & Strickland, S. (2003). Tissue plasminogen activator in the amygdala is critical for stress-induced anxiety-like behavior. Nature Neuroscience, 6, 168–174.

Roozendaal, B., Okuda, S., Van der Zee, E. A., & McGaugh, J. L. (2006). Glucocorticoid enhancement of memory requires arousal-induced noradrenergic activation in the basolateral amygdala. Proceedings of the National Academy of Sciences, USA 103, 6741–6746.

Pawlak, R., Rao, B. S. S., Melchor, J. P., Chattarji, S., McEwen, B., & Strickland, S. (2005). Tissue plasminogen activator and plasminogen mediate stress-induced decline of neuronal and cognitive functions in the mouse hippocampus. Proceedings of the National Academy of Science, USA 102, 18201–18206. Pezawas, L., Verchinski, B. A., Mattay, V. S., Callicott, J. H., Kolachana, B. S., Straub, R. E., et al. (2004). The brain-derived neurotrophic factor val66met polymorphism and variation in human cortical morphology. Journal of Neuroscience, 24, 10099–10102. Pickering, T. G., Devereux, R. B., James, G. D., Gerin, W., Landsbergis, P., Schnall, P. L., et al. (1996). Environmental influences on blood pressure and the role of job strain. Journal of Hypertension, 14, S179–S185. Pitman, R. K. (2001). Hippocampal diminution in PTSD: More (or less?) than meets the eye. Hippocampus, 11, 73–74. Popov, V. I., & Bocharova, L. S. (1992). Hibernation-induced structural changes in synaptic contacts between mossy fibres and hippocampal pyramidal neurons. Neuroscience, 48, 53–62. Popov, V. I., Bocharova, L. S., & Bragin, A. G. (1992). Repeated changes of dendritic morphology in the hippocampus of ground squirrels in the course of hibernation. Neuroscience, 48, 45–51. Poulton, R., Caspi, A., Milne, B. J., Thomson, W. M., Taylor, A., Sears, M. R., et al. (2002). Association between children’s experience of socioeconomic disadvantage and adult health: A life-course study. Lancet, 360, 1640–1645. Power, C., Li, L., Manor, O., & Davey Smith, G. (2003). Combination of low birth weight and high adult body mass index: At what age is it established and what are its determinants? Journal of Epidemiology and Community Health, 57, 969–973. Pressman, S. D., & Cohen, S. (2005). Does positive affect influence health? Psychological Bulletin, 131, 925–971.

c62.indd Sec3:1234

Rovio, S., Kareholt, I., Helkala, E.-L., Viitanen, M., Winblad, B., Tuomilehto, J., et al. (2005). Leisure-time physical activity at midlife and the risk of dementia and Alzheimer ’s disease. Lancet. Saavedra, J. M., Benicky, J., & Zhou, J. (2006). Angiotensin II: Multitasking in the brain. Journal of Hypertension, 24(Suppl. 1), S131–S137. Sachar, E. J., Hellman, L., Roffwarg, H. P., Halpern, F. S., Fukushima, D. K., & Gallagher, T. F. (1973). Disrupted 24-hour patterns of cortisol secretion in psychotic depression. Archives of General Psychiatry, 28, 19–24. Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective effects. Science, 277, 918–924. Sandi, C. (2004). Stress, cognitive impairment and cell adhesion molecules. Nature Reviews: Neuroscience, 5, 917–930. Sapolsky, R. M. (1996). Why stress is bad for your brain. Science, 273, 749–750. Sapolsky, R. M. (2004). Why Zebras Don’t Get Ulcers. New York: Henry Holt. Sapolsky, R. M., Romero, L. M., & Munck, A. U. (2000). How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocrine Reviews, 21, 55–89. Schelling, G., Stoll, C., Kapfhammer, H.-P., Rothenhausler, H.-B., Krauseneck, T., Durst, K., et al. (1999). The effect of stress doses of hydrocortisone during septic shock or post-traumatic stress disorder and health-related quality of life in survivors. Critical Care Medicine, 27, 2678–2682. Schnall, P. L., Pieper, C., Schwartz, J. E., Karasek, R. A., Schlussel, Y., Devereux, R. B., et al. (1990). The relationship between “job strain,” workplace diastolic blood pressure, and left ventricular mass index. Journal of American Medical Association, 263, 1929–1935.

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References 1235 Schulkin, J., McEwen, B. S., & Gold, P. W. (1994). Allostasis, amygdala, and anticipatory angst. Neuroscience and Biobehavioral Reviews, 18, 385–396. Seeman, T. E., Singer, B., & Charpentier, P. (1995). Gender differences. In Patterns of HPA axis response to challenge: MacArthur studies of successful aging. Psychoneuroendocrinology, 20, 711–725. Seeman, T. E., Singer, B. H., Ryff, C. D., Dienberg, G., & Levy-Storms, L. (2002). Social relationships, gender, and allostatic load across two age cohorts. Psychosomatic Medicine, 64, 395–406. Selye, H. (1936). A syndrome produced by diverse nocuous agents. Nature, 138, 32. Selye, H. (1973). The evolution of the stress concept. American Scientist, 61, 692–699. Seri, B., Garcia-Verdugo, J. M., McEwen, B. S., & Alvarez-Buylla, A. (2001). Astrocytes give rise to new neurons in the adult mammalian hippocampus. Journal of Neuroscience, 21, 7153–7160. Sheline, Y. I., Gado, M. H., & Kraemer, H. C. (2003). Untreated depression and hippocampal volume loss. American Journal of Psychiatry, 160, 1516–1518. Sheline, Y. I., Sanghavi, M., Mintun, M. A., & Gado, M. H. (1999). Depression duration but not age predicts hippocampal volume loss in medically healthy women with recurrent major depression. Journal of Neuroscience, 19, 5034–5043. Shors, T. J., Chua, C., & Falduto, J. (2001). Sex differences and opposite effects of stress on dendritic spine density in the male versus female hippocampus. Journal of Neuroscience, 21, 6292–6297. Sorrells, S. F., & Sapolsky, R. M. (2007). An inflammatory review of glucocorticoid actions in the CNS. Brain, Behavior, and Immunity, 21, 259–272. Sousa, N., Lukoyanov, N. V., Madeira, M. D., Almeida, O. F. X., & Paula-Barbosa, M. M. (2000). Reorganization of the morphology of hippocampal neurites and synapses after stress-induced damage correlates with behavioral improvement. Neuroscience, 97, 253–266. Spiegel, K., Leproult, R., & Van Cauter, E. (1999). Impact of sleep debt on metabolic and endocrine function. Lancet, 354, 1435–1439. Starkman, M. N., Gebarski, S. S., Berent, S., & Schteingart, D. E. (1992). Hippocampal formation volume, memory dysfunction, and cortisol levels in patients with Cushing’s syndrome. Biological Psychiatry, 32, 756–765. Starkman, M. N., & Schteingart, D. E. (1981). Neuropsychiatric manifestations of patients with Cushing’s syndrome. Arch Interm Medicine, 141, 215–219. Steingart, R. A., Silverman, W. F., Barron, S., Slotkin, T. A., Awad, Y., & Yanai, J. (2000). Neural grafting reverses prenatal drug-induced alterations in hippocampal, P. K. C., & related behavioral deficits. Developmental Brain Research, 125, 9–19. Steptoe, A., Owen, N., Kunz-Ebrecht, S. R., & Brydon, L. (2004). Loneliness and neuroendocrine, cardiovascular, and inflammatory stress responses in middle-aged men and women. Psychoneuroendocrinology, 29, 593–611.

Taylor, S. E., Klein, L. C., Lewis, B. P., Gruenewald, T. L., Gurung, R. A. R., & Updegraff, J. A. (1998). Female responses to stress: Tend-andbefriend, not fight-or-flight. Personal Communication. Teague, C. R., Dhabhar, F. S., Barton, R. H., Beckwith-Hall, B., Powell, J., Cobain, M., et al. (2007). Metabonomic studies on the physiological effects of acute and chronic psychological stress in Sprague-Dawley rats. Journal of Proteome Research, 6, 2080–2093. Theorell, T., Tsutsumi, A., Hallquist, J., Reuterwall, C., Hogstedt, C., Fredlund, P., et al. (1998). Decision latitude, job strain, and myocardial infarction: A study of working men in Stockholm. American Journal of Public Health, 88, 382–388. Theorell, T., Westerlund, H., Alfredsson, L., & Oxenstierna, G. (2005). Coping with critical life events and lack of control: The exertion of control. Psychoneuroendocrinology, 30, 1027–1032. Trejo, J. L., Carro, E., & Torres-Aleman, I. (2001). Circulating insulin-like growth factor I mediates exercise-induced increases in the number of new neurons in the adult hippocampus. Journal of Neuroscience, 21, 1628–1634. Turek, F. W., Joshu, C., Kohsaka, A., Lin, E., Ivanova, G., McDearmon, E., et al. (2005). Obesity and metabolic syndrome in circadian clock mutant mice. Science, 308, 1043–1045. Van Cauter, E., Leproult, R., & Kupfer, D. J. (1996). Effects of gender and age on the levels and circadian rhythmicity of plasma cortisol. Journal of Clinical Endocrinology and Metabolism, 81, 2468–2473. van Rossum, E. F. C., Binder, E. B., Majer, M., Koper, J. W., Ising, M., Modell, S., et al. (2006). Polymorphisms of the glucocorticoid receptor gene and major depression. Biological Psychiatry, 59, 681–688. Vermetten, E., Schmahl, C., Lindner, S., Loewenstein, R. J., & Bremner, J. D. (2006). Hippocampal and amygdalar volumes in dissociative identity disorder. American Journal of Psychiatry, 163, 630–636. Vyas, A., Mitra, R., Rao, B. S. S., & Chattarji, S. (2002). Chronic stress induces contrasting patterns of dendritic remodeling in hippocampal and amygdaloid neurons. Journal of Neuroscience, 22, 6810–6818. Wadhwa, P. D., Sandman, C. A., & Garite, T. J. (2001). The neurobiology of stress in human pregnancy: Implications for prematurity and development of the fetal central nervous system. Prog in Brain Res 133 131–142 Wang, J., Rao, H., Wetmore, G. S., Furlan, P. M., Korczykowski, M., Dinges, D. F., et al. (2005). Perfusion functional MRI reveals cerebral blood flow pattern under psychological stress. Proceedings of the National Academy of Sciences, USA 102, 17804–17809. Weaver, I. C. G., Cervoni, N., Champagne, F. A., D’Alessio, A. C., Sharma, S., Seckl, J. R., et al. (2004). Epigenetic programming by maternal behavior. Nature Neuroscience, 7, 847–854. Wellman, C. L. (2001). Dendritic reorganization in pyramidal neurons in medial prefrontal cortex after chronic corticosterone administration. Journal of Neurobiology, 49, 245–253.

Steptoe, A., Wardle, J., & Marmot, M. (2005). Positive affect and healthrelated neuroendocrine, cardiovascular, and inflammatory processes. Proceedings of the National Academy of Sciences of the United States of America, 102, 6508–6512.

Williams, R. B., Barefoot, J. C., Blumenthal, J. A., Helms, M. J., Luecken, L., Pieper, C. F., et al. (1997). Psychosocial correlates of job strain in a sample of working women. Archives of General Psychiatry, 54, 543.

Sterling, P., & Eyer, J. (1988). Allostasis: A new paradigm to explain arousal pathology, S. Fisher, & J. Reason. Handbook of life stress, cognition and health (pp. 629–649). New York: Wiley.

Wing, R. R. (2003). Behavioral interventions for obesity: Recognizing our progress and future challenges. Obesity Research, 11, S3–S6.

Strahl, B. D., & Allis, C. D. (2000). The language of covalent histone modifications. Nature, 403, 41–45.

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Szeszko, P. R., Lipsky, R., Mentschel, C., Robinson, D., Gunduz-Bruce, H., Sevy, S., et al. (2005). Brain-derived neurotrophic factor val66met polymorphism and volume of the hippocampal formation. Molecular Psychiatry, 10, 631–636.

Wood, G. E., Young, L. T., Reagan, L. P., & McEwen, B. S. (2003). Acute and chronic restraint stress alter the incidence of social conflict in male rats. Hormones and Behavior, 43, 205–213.

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Chapter 63

Placebo Effects LAUREN Y. ATLAS, TOR D. WAGER, KATHARINE P. DAHL, AND EDWARD E. SMITH

A placebo is a treatment that is expected to have no inherent pharmacological or physical benefit—for instance, a starch capsule given for anxiety or pain, or sham surgery in which the critical surgical procedure is not performed. Placebos are often used for comparison in clinical studies, as a baseline against which to evaluate the efficacy of investigational clinical treatments. However, placebo treatments often elicit observable improvements in signs or symptoms on their own—these are placebo effects. For this reason, placebos have been used as healing agents for a variety of ailments; they have had a place in the healer ’s repertoire for thousands of years, and they are still used as a viable treatment option by physicians in industrialized countries with surprising frequency.

to expect worsening of symptoms; changes in the negative direction are observed as part of the nocebo effect. There is some evidence that the two involve separate mechanisms, although the placebo response has been much more thoroughly studied. A literature on experimental manipulations of placebo treatments has produced substantial evidence that placebo effects result, in many cases, from active brain responses to context, rather than statistical artifacts and reporting biases. Neuroimaging and related techniques have allowed us to begin to understand the brain mechanisms by which placebos exert their effects.

Psychologists and neuroscientists today are most interested in the placebo response, the brain and body response to the psychosocial (and perhaps neurobiological) context surrounding treatment. The study of the placebo response reveals active processes that provide a powerful window into brainbody interactions and the brain substrates of human behavior.

PLACEBO TREATMENTS IN EXPERIMENTAL RESEARCH VERSUS CLINICAL STUDIES The potential significance of the placebo response has led to the standard use of placebo groups in clinical trials examining the efficacy of medicine or other specific treatments on clinical conditions. Patients are assigned to receive either active treatment or placebo, and comparisons between groups are performed to test whether the active treatment elicits greater improvement than placebo. Two critical assumptions underlie the rationale behind the placebo-controlled clinical trial. First, it is assumed that psychological and nonspecific effects, such as natural course of disease, effects of being in a healing environment, and patient expectation and motivation to heal, have equal effects on outcomes in active treatment and placebo groups. Second, it is assumed that nonspecific effects and treatment effects combine additively, so that subtracting outcomes for the placebo group from the treatment group will reveal the specific effects of the drug or procedure. Although these assumptions may not always hold, the placebo-controlled randomized clinical trial is perhaps the best tool for medical practitioners and pharmaceutical companies to determine treatment efficacy.

Studies of drug treatments for various disorders have investigated the effects of exogenous regulation of neural and psychological end-points, such as reported emotion, behavioral responses, and disease-specific brain activity. The brain, however, comprises interlocking feedback mechanisms that provide powerful endogenous control of neural and psychological processes. These endogenous processes regulate perceptual, affective, and cognitive processes based on the evaluation of situational context. Contextual information leading to placebo responses arises from either conscious expectancies about anticipated effects of treatment, or from prior learning in the form of conditioning with active treatments. In some cases, these two sources of placebo responses can be complementary, while in other cases, they may be mutually exclusive in their influence on observed placebo effects. The context surrounding placebo administration may lead individuals to expect improvement, and positive outcomes would compose the placebo effect. Alternatively, contextual information can lead individuals 1236

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Psychologists and neuroscientists are interested, however, in whether and how the psychological components of treatment—expectancies, appraisals, learning, context effects, and the relationship between patient and practitioner—can directly affect the bodily state: What are the effects of the treatment context, and how do they affect physiology? These questions can be answered by studying the placebo response. In the clinical context, this can be achieved through a three-arm version of the clinical trial; in addition to the active treatment and placebo groups, a subset of patients is assigned to a “natural history” comparison group that receives no treatment.1 Comparisons between this no-treatment group and the placebo group allow researchers to avoid many potential statistical artifacts, some of which are described later in this chapter, to assess the placebo response—the active effects of psychological state. While contrasts between the placebo and natural history arms of clinical trials allow researchers to examine the breadth of placebo effects, laboratory research on placebo allows researchers to examine the placebo response. Experimental investigations assess the psychological components of the placebo response, and the mechanisms by which these factors modulate physiological endpoints. Through mechanistic approaches to the study of the placebo response, researchers may gain insights into fundamental processes underlying mind-body interactions. These processes link placebo to many other psychological domains, as the central mechanisms supporting placebo responses involve many key concepts in psychology, including cognitive processes, such as appraisals, expectancies, learning, context effects, and valuation. Interpersonal processes also play a critical role in the placebo response; the patient-practitioner relationship may cultivate feelings of trust and “being cared for,” which may not only influence patient expectancies, but may also directly contribute to the development of the placebo response (Barrett et al., 2006; Hall, Dugan, Zheng, & Mishra, 2001). Studying the placebo response offers the opportunity to increase our understanding of how these social and cognitive processes may interact with the endogenous regulatory mechanisms to control the body’s physiological state. Laboratory placebo research generally examines conditions whose onset can be controlled by experimenters, since there is no prior disease state on which to measure effects in healthy volunteers. In laboratory experiments on placebo 1

Denying patients treatment for conditions when accepted treatments do indeed exist may be viewed as unethical, so this no-treatment control group can be achieved by assigning participants with non-life-threatening conditions to waiting lists, so that they ultimately do receive treatment.

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effects on pain, anxiety, or Parkinson’s disease, experimenters can compare performance (pain response, affective ratings, motor performance) in a control condition to a condition in which placebo treatment was administered in the form of a sham medication or procedure. Improvements (decreased pain, decreased negative emotion, increased motor performance) with the placebo treatment indicate positive effects of placebo. For this reason and others, pain is a particularly well-studied domain in placebo research, as the intensity of noxious stimulation can be experimentally controlled. Placebo effects in pain are typically measured as decreases in pain ratings (or, alternatively, pain-induced physiological activity) under placebo relative to a nonplacebo control condition.

ALL PLACEBO EFFECTS ARE NOT EQUAL: ACTIVE PLACEBO RESPONSES VERSUS STATISTICAL ARTIFACTS Many factors may influence the reporting process to resemble placebo effects on subjective outcome measures without a concomitant active placebo response. Active placebo responses are those processes that interact with and affect the normal processing underlying a disease or condition. True placebo effects—those that have direct impact on the course of disease—necessarily involve active placebo responses, and placebo researchers must differentiate between active placebo responses and other psychological factors that influence subjective outcome measures (Wager & Nitschke, 2005). This is not to say that subjective outcomes without a physiological basis are not desirable in and of themselves; patient quality of life is of the utmost importance in the clinic, and any treatment that eliminates suffering arguably offers great benefit to the patient, no matter whether it affects disease physiology. Nonetheless, in the interest of using the placebo response as a window into mind-body interactions, we are most interested in the breadth and extent of active placebo responses. Statistical Artifacts It is essential to account for the natural course of a disease in clinical studies of placebo, as numerous factors may lead to observations of clinical improvement, yet have nothing to do with actual placebo administration (and are thus not part of an active placebo response). These factors include natural symptom fluctuation, regression to the mean, spontaneous remission, and participant sampling bias; Figure 63.1 illustrates the contribution of such factors to apparent disease progression. All these factors can

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Natural History

Observable

Latent

A

B

Placebo Treatment C

Social/Study Context D

E

Organic Disease Process

Organic Disease Process

Decision-Making

Decision-Making

Sample Characteristics

Sample Characteristics

Baseline Assessment

Outcome Assessment

Figure 63.1 Factors that contribute to observed placebo effects in clinical trials. Note: Improvement from baseline to outcome assessment is the measure of recovery or healing. These measures are observable outcomes based on some observable factors, such as the sample characteristics, and unobservable factors that include organic disease progression and decision-making processes about how to report signs and symptoms. Improvemen t in the average of the study sample may be influenced by factors that include effects of natural history, study context, and psychobiological responses

be adequately accounted for in clinical trials by comparing the placebo group to a natural history control group, assuming effective randomization and other standard statistical assumptions. Natural History: Spontaneous Remission and Natural Symptom Fluctuation Without treatment, outcomes (e.g., signs and symptoms) in all diseases follow a time course, referred to as a natural history. In many conditions, remission is part of the natural course of the illness, and with enough time, healing is likely to occur on its own. Patients are likely to eventually recover from many psychiatric illnesses, sleep disorders, and other conditions that may otherwise cause patients to seek treatment. In some other conditions, patients tend to get progressively worse. Even for conditions in which spontaneous remission is rare—such as chronic pain, Parkinson’s disease, and irritable bowel syndrome—signs and symptoms fluctuate over time. The constant variation in symptomatology within an individual could easily lead to apparent improvement with a treatment if patients are enrolled when their symptoms are particularly intense or the study terminates during a relatively symptom-free period.

to placebo. A: Effects of time on organic disease: Natural history. B: Effects of time or perceived disease severity over time on sample characteristics: Sampling bias. C: Effects of placebo treatment on organic disease and decision-making processes: Active psychobiological placebo responses. D: Effects of being in the study on decision making: Hawthorne effects and demand characteristics. E: Effects of social factors (being cared for, implicit social contracts) on continued participation: Sampling bias. Experimental studies of placebo treatment compared with no-treatment controls can isolate active psychobiological placebo responses (C).

closer to the mean with each successive measurement. Thus, a subgroup of patients will appear to improve over time in virtually any study, even if there is no actual improvement. What is decreasing in these improved patients is not the underlying symptom, but the value of the measurement error. If patients tend to enroll in a study when their symptoms are relatively severe, the entire group may appear to improve, whether treated with a drug, placebo, or nothing at all. In many cases, patients are likely to seek treatment at extreme points in the course of illness, leading to a high likelihood that symptoms will have diminished by the time a second measurement is made, simply due to the natural course of disease. This phenomenon was demonstrated in a study that compared chronic pain patients who had sought treatment to a matched group who had not sought treatment (Whitney & Von Korff, 1992). The former group reported more pain at initial assessment than the latter group; both groups’ reported pain levels approached the mean at a 1-year follow-up, with the group that sought treatment demonstrating steeper reductions in reported pain, demonstrating that self-selection can influence regression to the mean in a way that would affect observed treatment results.

Regression to the Mean

Participant Sampling Bias

It is well known that if repeated measurements are made of a variable (e.g., severity of a symptom) that is measured with error or fluctuates around a mean, extreme values tend to be

Another important potential source of artifact in clinical trials comes about because participants who experience beneficial effects over the course of the study are more

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All Placebo Effects Are Not Equal: Active Placebo Responses versus Statistical Artifacts

likely to adhere to treatment regimens and remain enrolled in the study than participants who experience no change. Attrition rates will be higher among those receiving no benefit from treatment, and an observed improvement with treatment will actually reflect changes in the sample over time (Turk & Rudy, 1990). Comparisons between placebo and nontreatment control groups allow researchers to control for these various artifacts and assess the efficacy of placebo administration on a given condition. To our knowledge, the efficacy of placebo treatment has been examined with comparisons between placebo and nontreatment controls in Major Depressive Disorder, heart disease/hypertension, chronic pain, nausea, erectile dysfunction, and obesity. Placebo Effects on Decision Making Experimental research allows researchers to differentiate between active placebo responses and placebo effects on decision making that involve no changes in the underlying physiology of the disease or condition. A recent meta-analysis of clinical trials that compared placebo and no-treatment control groups found no significant benefit of placebo administration across clinical conditions, and only found significant placebo improvement in the context of placebos for pain (Hrobjartsson & Gotzsche, 2001, 2004). The authors argued that the observed placebo effects may be an artifact of reporting bias, since pain was always measured by self-report. Placebo administration may have caused changes in participant decision making, but may have had no effect on disease processes. This study has been criticized on at least two counts. First, its conclusions are based on clinical trials that were not intended to examine placebo effects, and so strong placebo expectancies may not have been formed in many cases (Wickramasekera, 2001). Second, their main analyses averaged across many different disease processes—obesity, hypertension, pain, and marital dysfunction trials were all considered together—and there may have been too few studies within particular disorders to achieve adequate power (Kirsch & Scoboria, 2001). Those criticisms notwithstanding, the issue of whether changes in reported experience are purely subjective is an important one. Placebo effects may reflect subjective responses in two senses. First, they may be caused by brain processes that modulate subjective experiences of emotion, pain, and suffering. Such subjective effects are clinically relevant: A treatment that affects pain and quality of life is important whether it affects an organic disease process or simply the patient’s ability to cope with it. Alternatively, subjective responses may be caused by biases in the cognitive decision-making processes involved in making reports

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to an experimenter or physician. In this case, the placebo does not change the organic disease and subjective suffering, but may affect decisions about how to describe the painful experiences. In many disorders, subjective assessment is a critical component; pain is a subjective phenomenon, and there is no better way to measure pain than to ask the patient. Nonsubjective measures that are responsive to pain, such as pupillometry and skin conductance, are indirectly related to pain experience, and can be affected by other factors without a change in perceived pain. Thus, while these measures are of interest partly because they are not subject to cognitive reporting biases, they cannot entirely replace self-report as an index of the pain experience. The problem with relying solely on self-report based measures when studying the placebo response is that they can be influenced by factors that have little to do with the disease process being studied; placebo effects may be observed with self-report measures, yet the course of disease may remain unaffected. We review sources of reporting bias and then present evidence from carefully controlled experimental placebo research demonstrating that, despite these sources of potential error, active placebo responses do indeed exist in many clinical and psychological domains. Hawthorne Effects Participants often change their behavior as a result of being observed in the study environment. This phenomenon is referred to as the Hawthorne effect, after a series of landmark studies at the Hawthorne Works of the Western Electric Company (Roethlisberger & Dickson, 1939). These studies were designed to assess how several variables—break length, work-week duration, and company subsidation of meals and beverages—affected productivity. Irrespective of these manipulations, productivity increased relative to preexperiment levels. Researchers concluded that this increased productivity resulted from the attention and special privileges that study participants received. Hawthorne effects are particularly relevant in the consideration of natural history control groups in clinical trials; to avoid Hawthorne effects, control participants should receive the same amount of attention as placebo and treatment groups. Demand Characteristics Demand characteristics refer to changes in participants’ behavior due to expectations about how they are expected to behave or what they are expected to report. In response to hypotheses about study aims, patients may exhibit social compliance effects—patients may say what they feel should be said (Kelman, 1958). The question, “How

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much did your pain decrease?” implies that participants should have felt less pain, and they may feel pressure to report decreases despite no actual perceived changes. In the case of self-presentation biases, individuals often say what makes them look better in the eyes of others (Arkin, Gabrenya, Appelman, & Cochran, 1979); this is especially relevant if the experimenter is seen as an authority figure or relevant social figure. Finally, self-consistency biases may cause individuals to respond in ways that are consistent with past behavior or with views of the self (Wells & Sweeney, 1986). Early research on placebo effects took advantage of then-current stage models of perception and decision making, and tested for effects of placebo treatments on measures derived from signal detection theory (SDT; Swets, Tanner, & Birdsall, 1961). The SDT characterization relied on the notion that sensory processes register a mixture of a true signal (such as a change in the strength of noxious input) and noise. The output of sensory processes is passed to a decision maker, which chooses a response (“signal present” or “signal absent”) based on the perceived sensation. Thus, the likelihood of a particular decision depends not only on the perceived signal strength, but also on the relative costs of false positive decisions and missed true signals. Studies of placebo effects asked participants to provide ratings of pain with and without placebo treatment. In a classic study, the SDT measure of discriminability assessed whether participants showed a reduced tendency to rate slightly more intense stimuli with higher pain ratings (to discriminate temperatures) with placebo (Clark, 1969). The SDT measure of response bias assessed whether they rated a given stimulus as less painful with the placebo. The study found that placebo affected response bias but not discriminability, whereas an opiate drug affected both. Though this was an important finding, the issue is complex because pain is not a two-stage sensory-decision process. The ability to discriminate stimulus intensities and the experience of pain are not the same thing; a complex network of brain circuits creates the pain experience from a combination of sensory input and internal processes, and different sensory receptors at the peripheral level may even carry different information about sensory and nociceptive (pain-related) aspects of the stimulus (Price & Dubner, 1977; Price, Greenspan, & Dubner, 2003). Thus, what is at stake in placebo research is not stimulus discriminability, but the intensity of the feeling of pain, which is likely to be captured by the response bias SDT measure. The placebo effects on response bias observed by Clark et al. could be caused by either changes in decision-making processes—the standard interpretation in SDT—or by widespread decreases in pain processing in the brain or spinal cord, with very different implications in each alternative.

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The direct measurement of brain responses to noxious stimulation can help disentangle these alternatives. Placebo shifts in reporting bias would presumably affect the pain reporting process without affecting pain processing—under placebo, neural activity would increase in decision-making circuitry (dorsolateral prefrontal cortex [DLPFC] and orbitofrontal cortex [OFC], primarily) facilitating changes in evaluative report criteria, but pain-processing activity would remain unaffected. Alternatively, finding decreases in nociceptive processing in brain regions related to pain would suggest that the second alternative is more likely to be true. We review evidence on brain placebo effects later. In considering the various artifacts previously described, it becomes clear that experimental research manipulating placebo treatments plays a key role in testing for active placebo responses. Sound experimental designs can eliminate issues of sampling bias and regression to the mean that can plague clinical trials. In addition, sensitivity to detect active placebo responses can be enhanced by considering and minimizing cognitive biases in self-report, either by using implicit behavioral measures or physiological outcome measures that are relatively nonsusceptible to reporting biases. Active Placebo Responses Physiological outcome measures, including neuroimaging and electrophysiological measures of central and peripheral nervous system activity as well as peripheral outcome measures such as hormone secretion, provide powerful evidence for the existence of placebo responses beyond reporting biases. In the remainder of this chapter, we review evidence for the existence of active placebo responses in a variety of domains. We specifically examine placebo analgesia as a model system, and consider candidate central and proximal mechanisms of the placebo response. Central Nervous System Processes in Placebo Neuroimaging and electrophysiological methodologies provide evidence of active placebo responses in domains in which physical outcome measures may not exist or may map indirectly to stimulus processing. These methodologies— functional magnetic resonance imaging (fMRI), positron emission tomography (PET), electroencephalography (EEG), magnetoencephalography (MEG), and single-unit recording—reveal not only changes in brain-related outcomes (decreases in pain-related neural activity under placebo analgesia), but also underlying mechanisms subserving the placebo response, allowing for insights into how these mind-body interactions unfold. Placebo responses have now been systematically studied using neuroimaging and electrophysiology techniques across several conditions and diseases, including

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Placebo Analgesia: Pain as a Model System 1241

Parkinson’s disease (Benedetti et al., 2004; de la FuenteFernandez et al., 2002), Major Depressive Disorder (Mayberg et al., 2002), irritable bowel syndrome (Lieberman et al., 2004; Vase, Robinson, Verne, & Price, 2005), anxiety (Petrovic et al., 2005), drug reinforcement (Volkow et al., 2003), and pain (Bingel, Lorenz, Schoell, Weiller, & Buchel, 2006; Kong et al., 2006; Lieberman et al., 2004; Price, Craggs, Verne, Perlstein, & Robinson, 2007; Wager, Matre, & Casey, 2006; Wager, Rilling, et al., 2004; Wager, Scott, & Zubieta, 2007; Watson, El-Deredy, Vogt, & Jones, 2007; Zubieta, Yau, Scott, & Stohler, 2006). Results from these studies can be combined with knowledge of the neural bases of basic cognitive processes that may be involved in the placebo response to gain synergistic insight into the mechanisms supporting placebo responses. A central thesis of this chapter is that common effects of placebo treatment on the brain suggest the involvement of common central brain mechanisms across disorders. Other brain processes and outcomes, however, appear to be disease-specific, and we examine evidence for these domain-specific proximal mechanisms as well. In the following sections, we use pain as a model system to discuss the brain mechanisms supporting active placebo responses and use observed commonalities to compare placebo responses in pain with those in other domains.

PLACEBO ANALGESIA: PAIN AS A MODEL SYSTEM In the laboratory context, the majority of studies of placebo have been conducted in the realm of pain. Pain is a unique domain with sensory, affective, and evaluative components, and one that has great significance for an organism’s well-being. Pain is an interoceptive modality, yet can be quantitatively manipulated in the laboratory. Furthermore, the network of regions involved in the pain experience, known as the “pain matrix,” has been well characterized in human and animal studies (for a detailed review, see Chapter 33, this volume). Finally, pain is known to have a strong expectancy component, and may arguably be considered more open to influence of the central nervous system than many disease processes. Placebo analgesia occurs when (a) an individual is experiencing pain, either due to natural ongoing sources or controlled noxious stimulation (heat, cold, pressure, shock, ischemia, or other painful stimulation); (b) the individual receives placebo treatment, in the form of a cream, inert medication, or other sham procedure, often with accompanying instructions that treatment will relieve pain; (c) pain with placebo is compared with a nonplacebo control condition, and pain reports decrease under placebo.

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The first powerful support for the existence of an active placebo response came in the late 1970s, when Levine and colleagues showed that placebo analgesia was reversed with administration of naloxone, a μ-opioid receptor antagonist (Levine, Gordon, Jones, & Fields, 1978). This suggested that endogenous opioids were involved in the placebo response. The known effects of opiates on pain in both humans and animals led to the conclusion that placebo painkillers may be engaging the brain’s natural paincontrol mechanisms. Since this initial insight, researchers have explored placebo responses in pain using a variety of methodological approaches. Contemporary neuroimaging and electrophysiological techniques offer powerful tools for investigating the brain processes affected by placebo treatments and the brain mechanisms responsible for the placebo response. Researchers now can examine placeboinduced changes in brain regions known to be involved in pain processing. This provides support for the existence of active psychobiological mechanisms underlying placebo analgesia, and offers insights into the mechanisms by which the placebo response may modulate physiological endpoints. Pain-Related Processes Affected by Placebo Treatments Earlier, we described potential sources of reporting bias and noted that these concerns are particularly valid in consideration of placebo effects on pain, since pain is a subjective phenomenon. We and others have used neuroimaging techniques to provide nonsubjective evidence for placebo effects on pain, and to begin an investigation of their underlying mechanisms. This approach allows us to examine differences in physiological correlates of pain processing under placebo. In an initial study, we induced expectations of analgesia in participants using an inert cream that participants were told would have an analgesic effect (Wager, Rilling, et al., 2004). A series of thermal stimuli were delivered, and participants rated the intensity of their pain experience several seconds after the termination of each stimulus. Identical stimulation sequences were delivered on placebo- and control-treated skin regions for each participant (with locations and testing order counterbalanced). Compared with the control condition, the placebo treatment decreased the reported painfulness of both shock and heat stimulation, which replicated the placebo effect on reported pain shown in many experimental studies (Benedetti et al., 1998; Montgomery & Kirsch, 1997; Price et al., 1999; Voudouris, Peck, & Coleman, 1985). Concurrent fMRI showed decreased responsiveness to noxious stimulation in the placebo condition in rostral anterior cingulate cortex

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Placebo Effects

(rACC), anterior insula (aINS), and thalamus, regions of the pain matrix thought to be critical for the affective experience of pain. Furthermore, the magnitude of these decreases correlated with placebo effects in reported pain. These data are consistent with the idea that placebo treatment directly affects the pain experience, and suggest that the affective component of pain might be particularly important. Subsequent studies have shown differences in the brain’s response to noxious stimulation under placebo (Kong et al., 2006; Price et al., 2007)—though only Price et al. reported decreases in pain-processing regions— supporting the notion that placebo effects on nociceptive processing are indeed active processes, and that placebo effects on reported pain reflect real changes in pain processing, rather than simple reporting biases. This approach also allows us to examine the temporal patterns of neural activity in response to pain. If the placebo effect is due entirely to reporting bias, then activity during pain under placebo should be the same as activity during pain in control conditions; differences might be largest later on, during the pain-reporting process. Examining the time course of placebo-related effects during thermal pain suggested that the decreased activity under placebo occurred both early and late in the pain period. Placebo decreases in rACC pain activity, which were correlated with reported placebo effects, were found in the early heat period. However, the largest main effects of placebo (control placebo) appeared in the contralateral insula and thalamus only in the late phase of stimulation. One explanation is that placebo effects may require a period of pain to develop or be strongest when pain is intense; alternatively, placebo responses may be most evident during residual pain after noxious stimulation has ended. A third interpretation is that the placebo reductions during late stimulation could reflect altered evaluation of pain rather than alterations in early sensory/perceptual nociceptive processes. To test for placebo effects on early sensory/perceptual processing, we conducted a study using laser pain-evoked event-related potentials (Wager et al., 2006) that allowed us to examine activity at a higher temporal resolution than fMRI or PET. Laser-evoked potentials (LEPs) are a reliable marker of pain processing (Bromm & Treede, 1984) and arise from nociceptive processes that occur before most decision processes begin. Thus, the cognitive biases that some have argued may influence reported placebo effects are unlikely to affect LEPs. We focused on the N2/P2 complex (200 to 300 ms; Lorenz & Garcia-Larrea, 2003), which arises from the activation of A∂ fibers and is sometimes followed by a later component thought to arise from C-fiber activation (Bromm & Treede, 1984). The P2 increases as a function of laser intensity and reported pain (Iannetti et al., 2004), and its likely source is the ACC

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(Garcia-Larrea, Frot, & Valeriani, 2003; Lenz et al., 1998), a region important for both attention and pain that has been shown to be modulated by placebo in pain and emotion. P2 amplitude was indeed reduced under placebo, supporting placebo effects on early nociceptive processing. Consistent with these results, expectations of analgesia have been shown to directly modulate spinal nociceptive reflexes (Goffaux, Redmond, Rainville, & Marchand, 2007), providing direct evidence for placebo effects on even the earliest levels of nociceptive processing. Mechanisms of Placebo Analgesia The studies reviewed in the previous section demonstrate the existence of psychobiological placebo effects on pain processing in the central nervous system. We now turn to consideration of the mechanisms by which these effects take place. Placebo treatments may affect several aspects of the continuum from sensation to experience to reporting that comprises pain processing: sensory transmission and processing, appraisal and the generation of subjective pain, and the pain reporting process (see Figure 63.2). The issue of which aspects are affected has been at the heart of the debate over whether placebo treatments activate physiological pain-control systems and have clinically meaningful effects. We examine the levels of the nervous system at which the placebo response can be mediated, the role of specific neurotransmitters, and central mechanisms that give rise to placebo responses in pain and other conditions; this evidence is summarized in Table 63.1. We adopt the perspective that these mechanisms may not be mutually exclusive. Finally, we examine current knowledge about central nervous system placebo responses in other domains, and placebo effects on physiological outcome measures. Sensory Transmission and Processing: Spinal Inhibition An important mechanism by which placebo analgesia could take place was initially put forth in Melzack and Wall’s gate control theory (Melzack & Wall, 1965). This theory posits that central control mechanisms interact with afferent information to prevent nociceptive signals from reaching the central nervous system, leading to decreases in cortical nociceptive processing. It is difficult to convincingly demonstrate inhibition of signals in the human spinal cord; the reductions in P2 amplitude previously discussed are expected if nociceptive afferents are inhibited, but they could also be caused by interactions within the brain. One approach is to test for placebo-based modulation of nociceptive effects that have been shown to be spinally mediated in animal literature. Two such effects are secondary

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Placebo Analgesia: Pain as a Model System 1243 Noxious Stimulus

⫻

Sensation

A Gate Control Affect Expectancy

B

Subjective Experience Appraisal

C Reporting Bias/ Decision-Making Pain Report

Figure 63.2 Mechanisms of placebo analgesia. Note: There are several routes by which placebo manipulations may lead to decreases in reported pain (refer also to Table 63.1). Dashed lines in both figures represent normal stages and pathways of pain processing. Pain begins when sensory signals from the spinal cord reach the brain via the thalamus and are sent to the primary (SI) and secondary somatosensory cortex (S2). From there, signals are sent to the anterior insula (AINS) and anterior cingulate (ACC), which are involved (along with regions in the limbic system) in the subjective experience and emotional quality of pain. A: According to the gate control theory (Melzack & Wall, 1965), inhibition of spinal nociceptive input is possible through endogenous opioid release by the periaqueductal gray (PAG), which receives direct projections from dorsolateral prefrontal cortex (DLPFC), as well as orbitofrontal cortex (OFC), ACC, and amygdala. B: A second alternative is that placebo responses are a result of changes in affective appraisals and the generation of subjective pain. Appraisals are generated through interactions among the OFC, AINS, ACC, and other regions, and may be maintained in the DLPFC. C: Changes in decision making without a concomitant effect on pain perception are likely to involve increases in DLPFC activity and decreases in reported pain without modulating pain processing.

hyperalgesia—the tendency for skin around the site of painful stimulation to become sensitized—and the suppression of spinal nociceptive reflexes by painful stimulation of another body part. Matre, Casey, and Knardahl (2006) examined placebo effects on secondary hyperalgesia in humans by heating the skin at 46°C for five minutes. Expectation of pain relief reduced the size of the secondary hyperalgesic area, compared with a control condition in which pain relief was not expected. Sensitization of the skin area surrounding the simulation site is thought to result from sensitization in the spinal dorsal horn. These results therefore implicate a spinal mechanism in the placebo

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effect. Reported pain was still the primary outcome measure in this study, leaving open the possibility that central processes as well as spinal ones may play a role in secondary hyperalgesia in humans. Goffaux et al. (Goffaux et al., 2007) took a complementary approach by measuring spinal reflexes. They examined a leg muscle contraction reflex called R3 that is triggered by stimulation of the sural nerve, which runs along the outside of the ankle. The reflex is mediated by spinal circuits that operate at very short latency, with measurable onset in EMG at ~50 ms and a peak at ~90 ms (Dowman, 2001). Both this reflex and EEG measures of early negative (N100/150) and positive evoked cerebral potentials are dampened by painful stimulation of another limb, for example, by immersing the arm in cold water. The interaction across body parts is thought to be mediated by central pain-control circuits in the brain stem, and is termed the diffuse noxious inhibitory control (DNIC) effect (Le Bars, Dickenson, & Besson, 1979). The DNIC effect can be produced in anesthetized animals, so it is thought to be a reflexive anti-nociceptive response to noxious stimulation that involves inhibition at the spinal level. Goffaux et al. manipulated expectations about the effects of cold-water immersion: One group was told that it would reduce pain, and another that it would increase pain. Interestingly, they found that expectancy modulated the strength of the DNIC effect. Expectations for pain relief decreased the amplitude of the R3 reflex and P260 evoked potentials relative to expectations for increased pain during the cold-water immersion, but did not affect very early cortical potentials. These findings suggest that expectancy can modulate activity at the level of the spinal cord, but that some components of cortical processing are more affected than others. If the expectancy manipulation inhibited nociceptive transmission, the R3 reflex and all cortical evoked potentials ought to have been affected. Whereas expectancy effects in early small components may be difficult to detect, it is notable that, similar to the study of Wager et al. (2006), the P260 potential showed the largest effect. This potential is thought to be localized to the anterior cingulate cortex and may overlap with the P3a or P3b potential (Dowman, 2001; Garcia-Larrea, Peyron, Laurent, & Mauguiere, 1997), and it appears to be sensitive to cognitive expectations even under conditions when pain reports are not (Dowman, 2001). These effects may reflect attentional orienting or evaluative aspects of nociceptive processing. The idea that expectancy effects influence attention-related processes does not preclude spinal inhibition, as suggested by effects on the R3 reflex. Indeed, attention has been shown to influence activity in the spinal dorsal horn in direct recordings in monkeys (Bushnell, Duncan, Dubner, & He, 1984).

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Placebo Effects TABLE 63.1

Mechanisms of placebo analgesia. Expected Central Nervous System Effects

A) Gate control: Spinal inhibition of sensory transmission and processing

Widespread decreases in pain-processing regions Placebo-induced opioid release and PAG activation

Secondary hyperalgesia (Matre, Casey, & Knardahl, 2006) DNIC effects (Goffaux et al., 2007) PAG anticipatory increases (Wager, Rilling et al., 2004) PAG opioid release (Wager et al., 2007)

B) Subjective experience: Changes in appraisal and generation of subjective pain

Decreases in select pain-processing regions Increases in modulatory and affective regions

Reductions in pain-processing regions: insula, thalamus, ACC (Wager, Rilling et al., 2004; Price et al., 2007) OFC and rACC anticipatory increases (Wager, Rilling et al., 2004) Dopamine-opioid correlations (Scott et al., 2008) Reward-placebo correlations (Scott et al., 2007) Opioid binding and connectivity (Wager et al., 2007; Zubieta et al., 2005)

C) Reporting bias: Changes in decision making only

Changes in decision-making circuits during/after pain (pain processing decreases not expected)

Indirect evidence: Placebo modulated activity in pain matrix regions primarily during late pain period (Wager, Rilling et al., 2004) Indirect evidence: Insula is involved in decision making (Grinband, Hirsch, & Ferrara, 2006)

There is substantial evidence for centrally activated descending control systems in animals. In many (but not all) cases, these effects are mediated by endogenous opioids in the periacqueductal gray (PAG) and their projections to brain stem structures such as the rostral ventromedial medulla (RVM). The RVM, among other structures, contains neurons that exert powerful excitatory and inhibitory control (so-called On and Off cells) on spinal neurons (Fields, 2004). Thus, evidence implicating the PAG and opioids in placebo analgesia would provide further support for the spinal inhibition model. The role of endogenous opioids in placebo analgesia is supported by studies using naloxone, an opioid antagonist that reverses placebo effects on reported pain in studies of expectancy-based placebo analgesia (Benedetti, Arduino, & Amanzio, 1999; Grevert, Albert, & Goldstein, 1983; Levine & Gordon, 1984; Levine, Gordon, & Fields, 1978; Petrovic, Kalso, Petersson, & Ingvar, 2002; Pollo, Vighetti, Rainero, & Benedetti, 2003). These results suggest that endogenous opioids indeed play a critical role in placebo analgesia. Neuroimaging methodologies allow researchers to elaborate on the knowledge available from naloxone studies to better understand the role of endogenous opioids in the placebo response. In our early study of placebo analgesia using fMRI (Wager, Rilling et al., 2004), we observed increases in an area of the midbrain surrounding the PAG during anticipation of pain under placebo. This could be consistent with the gate control theory, in that placebo expectancy would increase opioid release by the PAG, and descending opioids would inhibit subsequent

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Supporting Evidence

pain at the level of the spinal cord’s dorsal horn. Molecular imaging with PET provides an opportunity to understand where in the brain placebo changes opioid release. Radioactive tracers selective for μ-opioid receptors (MORs) allow researchers to infer endogenous opioid activity, as tracer binding is inversely related to endogenous MOR opioid binding. Placebo did increase opioid binding in the PAG during noxious stimulation (Wager et al., 2007). Another important observation was that placebo administration resulted in decreased PAG opioid binding during pain anticipation, relative to the control condition, suggesting that the placebo response may reduce the threat normally associated with noxious stimulation. Importantly, PAG was not the only region to show increases in opioid binding with placebo; placebo administration resulted in increased opioid binding during pain in many other cortical regions, particularly in frontal and limbic regions (Wager et al., 2007; Zubieta et al., 2005). Furthermore, connectivity analyses revealed that placebo increased functional integration between PAG and these regions, among other functional networks (Wager et al., 2007). These results suggest that while PAG opioid release plays an important role in placebo analgesia, it may not only induce descending inhibition of nociception, as the gate control theory would suggest, but might also facilitate changes in brain networks that lead to reduced aversion to a given noxious stimulus, by virtue of PAG correlations with opioid release in central appraisal and valuation networks (including the OFC, NAC, amygdala, insula, medial thalamus, and rACC). We consider the role of central processing in the following section.

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Placebo Analgesia: Pain as a Model System 1245

Placebo analgesia is not always opioid-mediated, since naloxone administration does not always reverse placebo effects on reported pain. In one study, hidden naloxone administration failed to reverse placebo effects on postsurgical pain (Gracely, Dubner, Wolskee, & Deeter, 1983). Placebo significantly decreased pain regardless of naloxone administration; naloxone increased pain across placebo and control conditions; and there was no interaction between the two. Similarly, a study of placebo analgesia in patients with irritable bowel syndrome (Vase et al., 2005) found large placebo effects that were not reversible by naloxone. There are several possible explanations, including that naloxone has different effects at different doses (Levine, Gordon, & Fields, 1979) and may affect placebo analgesia without overall effects on pain only at some doses, and that irritable bowel syndrome patients may be opioid-insensitive and have developed nonopioid painrelief mechanisms through conditioning with medication. An intriguing possibility is that placebo effects may be opioid-mediated when central expectancy is involved. An experiment by Amanzio and Benedetti (1999) suggested placebo analgesia created by verbal instructions (and thus mediated by conscious expectancies of pain relief) was reversible by naloxone. Conditioning responses to ketorolac, a nonopioid analgesic, by repeatedly injecting the drug produced placebo analgesia as well. After repeating the injection experience-drug effect pairing, injecting saline alone reduced pain. However, this type of placebo effect was completely naloxone-insensitive. Thus, placebo analgesia may involve both opioid and nonopioid mechanisms, and this may be determined by whether there is an expectancy component to the placebo response, as well as the specific neurochemical pathways involved in the learning process. Placebo Effects on Central Pain Processing Though there is mounting evidence for the involvement of descending pain-control systems and endogenous opioids in placebo analgesia, there is also substantial evidence that spinal inhibition cannot provide a complete account. Wager et al. (2007) observed that placebo effects in reported pain were relatively large and persisted throughout the experimental session, whereas placebo effects in P2 amplitudes were smaller and decreased over time. We conducted a formal comparison of the actual P2 reduction versus the expected reduction if input-reduction were a complete account. The analysis revealed that reported pain effects were too large relative to P2 effects to be caused solely by input blockade, suggesting that there may be multiple sources of placebo effects on reported pain. Part of the effect may be due to spinal inhibition, and another part to changes in central pain processing and/or reporting bias.

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In a classic study, Benedetti and colleagues showed that opioid-mediated placebo effects are involved with sitespecific expectancies for analgesia (Benedetti, Arduino, et al., 1999). They induced specific expectations of analgesia on participants’ hands or feet, and showed that naloxone reverses these specific analgesic effects. These results suggest that there is still a critical interaction with expectancy and the significance of pain that plays an important role in the ultimate pain experience. Opioid release may interact with frontal regions associated with the significance of the pain experience, rather than solely being responsible for a widespread descending inhibition. Thus, the evidence on opioid involvement leaves ample room for central brain effects that do not involve the spinal cord. Whereas endogenous opioids are powerfully implicated in spinal inhibition, they are also known for several effects mediated largely or entirely within the brain, including their soporific and euphoric effects and their addictive potential. Opioids in DLPFC, nucleus accumbens (NAC), and insula are correlated with reported emotion during pain processing (Zubieta et al., 2006), and placebo treatments have been shown to reduce MOR binding (and thus likely increase opioid release) in brain structures implicated in the determination of affective value, including rACC, OFC, VMPFC, aINS, and NAC (Scott et al., 2007; Wager et al., 2007; Zubieta et al., 2005). Appraisal and Subjective Pain—Changes in Pain Significance There are several possibilities for cognitive mechanisms that would lead to changes in pain significance with placebo administration. These processes require appraisals of the significance or meaning of treatment (Moerman & Jonas, 2002), which may lead to expectancies about positive treatment outcomes, decreases in attention to pain, and changes in pain-related affect (reduced anxiety/threat; increased appetitive processing). Geers, Helfer, Weiland, and Kosbab (2006) found that reported symptoms induced by placebo treatment were greater when attention was focused on the body. Placebo-induced anxiety reduction could also lead to reduction in pain processing (Turner, Deyo, Loeser, Von Korff, & Fordyce, 1994); this is supported by research demonstrating that decreases in reported anxiety correlate with decreases in pain under placebo and lidocaine (Vase et al., 2005). Changes in affect are supported by fMRI and opioid binding studies, in which placebo effects are localized to brain systems critical for affective appraisal, evaluation of the significance of stimuli for the self, and motivation; these results and their implications are discussed in more detail later. Figure 63.3 illustrates these processes and their potential respective contributions as mediators of the relationship between noxious stimulation and reported pain under placebo.

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Placebo Effects Threat/Safety Appraisal Placebo Treatment

Noxious Stimulation

Nociceptive Processes

Appetitive Motivation

Reported Pain

Attention Brain Measures of Pain Processing

Note: The effect of placebo treatment on reported pain and brain measures of pain processing may be mediated by key psychological processes, including affective appraisals and executive attention. Placebo

analgesia may come about through affect modulation, by reducing threat or anxiety appraisals, or by increasing appetitive motivation. Placebo analgesia may also result from decreased attention to noxious stimulation.

While more research needs to be done to directly assess the differential contributions of these brain mechanisms, such processes are likely to be similar to those involved in executive functions—basic cognitive processes coordinating the maintenance and manipulation of information. Information about context is known to require dorsolateral prefrontal cortex (DLPFC), which interacts with working memory—a system that maintains information in an active state in the brain—to maintain expectancies induced by placebo manipulations. Placebo instructions are likely to exert their effects by changing the cognitive context in which pain stimulation is perceived, and these altered appraisals of the situation give rise to changes in expectations about pain, harm, and pain relief. Thus, maintenance of placebo context is likely to be evident through increases in DLPFC activity, which ought to inhibit pain-processing regions of cortex; indeed, DLPFC increases during pain anticipation correlated with subsequent decreases during thermal stimulation in the thalamus, insula, and rACC, and also correlated with PAG activity during anticipation (Wager, Rilling, et al., 2004). It is important to acknowledge, however, that such correlations do not conclusively determine whether the observed modulation of pain-processing regions was mediated by changes in attention, affect, or anxiety: All these require maintenance of the placebo context by DLPFC and would be expected to ultimately lead to decreases such as those observed. An approach that would allow researchers to differentiate between the contributions of these central processes would be to directly manipulate these variables to identify brain processes that mediate the relationship between DLPFC increases and subsequent pain matrix activity. Effective placebo administration induces expectations for reduced symptomatology or diminished pain, which affects how the brain processes the condition or stimulus. The processes most likely to be altered are those that assign

value and meaning (for the self, or survival) to the stimulus. Orbitofrontal cortices and rACC have been shown to be highly involved in the process of valuation. Placebo administration has been shown to result in increased opioid binding in rACC and OFC during pain in placebo relative to control (Wager et al., 2007). Increases in rACC during pain anticipation under placebo correlated with placebo effects on reported pain, and placebo-induced decreases in rACC during pain correlated with anticipatory increases in DLPFC and PAG (Wager, Rilling, et al., 2004), supporting the possible connection between executive function, descending modulation, and placebo-induced changes in pain affect. Finally, Bingel and colleagues demonstrated that rACC activity covaried with PAG and amygdala activity during placebo, but not control, conditions (Bingel et al., 2006). An emerging model is that the largest effects of placebo are found in brain regions at the interface between nociceptive afferents and cognitive contextual processes. These regions, which include midlateral OFC, rACC, medial thalamus, and anterior insula, are part of a broader network of structures thought to be a neuroanatomical substrate for the computation of abstract reward/punishment value—or, in other terms, appraisal of the significance of a stimulus or context for the well-being and survival of the organism. This extended appraisal network includes medial prefrontal cortex (MPFC), OFC, extended amygdala, nucleus accumbens, ventral striatum, medial thalamus, and the medial temporal lobes. All these regions have shown fMRI and opioid-binding changes during placebo analgesia. More research must be done to establish how this network functions in the appraisal process and what particular roles its individual regions play. At a broad level, however, these regions are connected both anatomically—via monosynaptic and largely bidirectional projections (Price, 2000)—and functionally, as evidenced by coactivation in studies of placebo and emotional responses (Kober et al., 2008).

Figure 63.3 Psychological mediators of the placebo response.

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Placebo Analgesia: Pain as a Model System 1247

Expanding on the fact that placebo treatments may modulate valuation processes, some researchers have suggested that placebo analgesia may be thought of as a special case of reward processing; pain relief may be considered to be a positive outcome (Fields, 2004; Irizarry & Licinio, 2005). Scott and colleagues (Scott et al., 2007) used PET molecular imaging to examine the role of dopamine in placebo analgesia, using [11C]raclopride to label dopamine binding during placebo. The authors also examined correlations between the dopamine binding results and fMRI activation during a separate session. During the fMRI session, participants performed a monetary incentive delay (MID) task (Knutson, Fong, Adams, Varner, & Hommer, 2001), and analyses focused on activity during anticipated monetary reward in the nucleus accumbens (NAC), a region rich in dopaminergic neurons. Dopamine binding levels correlated with the anticipated effectiveness of the placebo, and the magnitude of the dopamine response to pain anticipation correlated with reported placebo analgesia during pain. Furthermore, high placebo responders were found to recruit nucleus accumbens to a greater extent during reward anticipation in the MID task, and nucleus accumbens activity during reward anticipation correlated with dopamine activity during placebo analgesia. The role of dopamine in placebo analgesia is further supported by work showing that dopamine D2 receptor agonists produce analgesia (Lin, Wu, Chandra, & Tsay, 1981; Magnusson & Fisher, 2000; Morgan & Franklin, 1991). Scott et al. (2008) used PET to image both opioid and dopamine receptor binding in the same individuals. Placebo induced both opioid and dopamine release (reduced binding) in the NAC, among other brain regions. Strikingly, endogenous dopamine increases in NAC were correlated with both opioid increases in NAC and reported placebo analgesia. Finally, pain processing and placebo treatments have been shown to involve components of the ventral striatum, a region rich in dopaminergic neurons that has been shown to be critical in reward processing and learning. Pain tolerance is correlated with drops in dopamine D2 receptor binding in the putamen (Hagelberg et al., 2002), and placebo analgesia induces increased MOR binding in the nucleus accumbens (Wager et al., 2007; Zubieta et al., 2005). Mechanisms of the Nocebo Response As the nocebo response involves inducing expectations for worse symptomatology or pain, its mechanisms are considerably less understood than the placebo response due to ethical constraints. However, researchers have begun to examine the nocebo response by investigating neurochemical activity in paradigms that induce expectations for increased pain, and current knowledge suggests that placebo

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and nocebo effects may involve similar brain mechanisms; they may involve opposite manipulations of the affective appraisal systems that evaluate the survival value of potential actions and outcomes. Scott et al. (2008) have reported that participants experiencing placebo and nocebo responses to a verbal suggestion of analgesia are at opposite ends (high and low, respectively) of a continuum of placebo-induced opioid and dopamine activity in the NAC, a key component of the brain’s motivational circuitry. There is also evidence that nocebo manipulations can affect HPA axis activity, and that they share some pharmacological similarity with placebo responses. Benedetti, Amanzio, and Maggi (1995) found that placebo responses were potentiated by proglumide, a cholecystokinin (CCK) antagonist. This was significant because CCK, in turn, blocks opioids; thus, the results suggested that proglumide disinhibited an endogenous opioid response to placebo. More recently, nocebo effects were shown to be reversed by proglumide, providing evidence for opposing effects of placebo and nocebo on the same neurochemical system. Benedetti and colleagues administered saline to postoperative patients with the instruction that it would increase pain for a short time (Benedetti, Amanzio, Casadio, Oliaro, & Maggi, 1997), which successfully induced increases in reported pain. These nocebo effects were reversed with administration of proglumide. These results were replicated in a later study, in which proglumide was found to reverse the nocebo effect in healthy subjects during ischemic arm pain (Benedetti, Amanzio, Vighetti, & Asteggiano, 2006). Interestingly, nocebo manipulations in this latter study also induced increases in cortisol and adrenocorticotropic hormone, indexes of the hypothalamic-pituitary-adrenal (HPA) axis. The antianxiety drug diazepam reversed both HPA effects and hyperalgesia, suggesting that increased anxiety (in a general sense of the word) underlies the nocebo response (Benedetti, Lanotte, Lopiano, & Colloca, 2007). Proglumide, by contrast, reversed only the hyperalgesia, suggesting that it works on neural circuits more specific to pain processing and evaluation, and further that it is the cortisol response to threat that was affected by the nocebo manipulation rather than the cortisol response to pain. This is because one treatment (proglumide) affected pain without affecting the cortisol nocebo response; thus, the cortisol nocebo response is unlikely to be caused by the pain itself. Interestingly, placebo treatment in this study did not reliably reduce HPA axis responses, suggesting that placebo and nocebo may be dissociable. It is unknown whether the difference between placebo and nocebo responses resulted from floor effects in the cortisol response to threat; if the subjects were not substantially threatened by the pain, there would be little threat-related cortisol response to be reduced by placebo treatment.

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Placebo Effects

CENTRAL VERSUS PROXIMAL MECHANISMS: PLACEBO RESPONSES ACROSS DOMAINS Having reviewed current knowledge of mechanisms supporting placebo analgesia in depth, we now turn to placebo responses across domains. We expect that many elements of the placebo response will be domain-specific; however, neuroimaging studies of the placebo response in different modalities and conditions reveal certain commonalities that may reflect general modulatory and appraisal processes. We refer to processes involved across domains as central mechanisms, and domain-specific central nervous system processes as proximal mechanisms.

Central Mechanisms Many neuroimaging studies of placebo reveal placeboinduced activation of rACC (Casey et al., 2000; Kong et al., 2006; Lieberman et al., 2004; Petrovic et al., 2005; Petrovic & Ingvar, 2002; Price et al., 2007; Wager, Rilling, et al., 2004; Wager et al., 2007) and lateral OFC (Lieberman et al., 2004; Petrovic et al., 2005; Wager, Rilling, et al., 2004; Wager et al., 2007), regions highly involved in affective appraisal and cognitive control; results from these studies are presented in Figure 63.4A and 63.4B. Thus, processes subserved by these regions are likely to serve as central mechanisms, supporting the etiology and maintenance of placebo responses across domains.

(A) Placebo-Induced Decreases in Functional Activity

Placebo-Induced Increases in Functional Activity

Placebo-Induced Increases in Opioid Release

Opiate-Induced Increases in Functional Activity

C

Wager, 2004; fMRI Placebo for Pain (Increase During Anticipation)

Kong, 2006; fMRI Placebo for Pain

Zubieta, 2005; Placebo for Pain

Petrovic, 2002; PET Opiate Treatment

Adapted from Lieberman, 2004; fMRI Placebo for Pain

Adapted from Petrovic, 2005; fMRI Placebo for Negative Emotion

Wager, 2007; Placebo for Pain

Casey, 2000; PET Opiate Treatment

Adapted from Price, 2007; fMRI Placebo for Pain

Petrovic, 2002; PET Placebo for Pain

Figure 63.4 (Figure C.59A in color section) Consistency of placebo results across studies. Note: Neuroimaging studies of the placebo response and related processes reveal certain commonalities that provide synergistic insight into the mechanisms underlying the placebo response across domains.

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A: Many studies reveal placebo-induced modulation of dorsal rostral anterior cingulate (rACC) a region involved in regulating affect and appraisal. B: Numerous studies also reveal placebo increases in lateral orbitofrontal cortex (OFC), a region highly involved in cognitive control and evaluative processing.

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Central versus Proximal Mechanisms: Placebo Responses across Domains 1249 (B) Placebo-Induced Increases in Functional Activity

Wager, 2004; fMRI Placebo for Pain

Placebo-Induced Increases in Opioid Release

Adapted from Lieberman, 2004; fMRI Placebo for Pain

Wager, 2007; Opioid Release Placebo for Pain

Adapted from Petrovic, 2005; fMRI Placebo for Negative Emotion

Figure 63.4B (Figure C.59B in color section)

Placebo treatment was used to induce expectations of anxiety relief during the viewing of emotional images (Petrovic et al., 2005). Regions that exhibited increased activity under placebo during the viewing of unpleasant emotional pictures (rACC, lateral OFC) were the same that were shown to exhibit increased activity in anticipation of noxious stimulation under placebo analgesia (Wager, Rilling, et al., 2004). The specific instantiations of the placebo response, however, are modality-specific, creating differing downstream placebo effects; placebo during unpleasant picture viewing elicited decreased amygdala and extrastriate activity, while placebo during painful stimulation elicited decreased activity in rACC, aINS, and thalamus, areas responsible for pain processing. Thus, a common modulatory network may be active in maintaining positive expectancies and contextual knowledge, and may serve to downregulate whichever network of regions is responsible for producing the modality-specific appraisal of one’s current state. Lateral OFC and rACC are thus likely to influence placebo by regulating affect and appraisal. These regions are known to be an important part of a cognitive control network responsible for maintaining goals, rules, and expectations in both cognitive and affective domains. Figure 63.5 presents results from a meta-analysis of cognitive control studies, demonstrating an overlap in lateral OFC between

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Cognitive Context Goals, Rules

Executive Working memory Task/Attention Switching Response Inhibition Wager & Smith, 2003; Wager, Jonides, & Reading, 2004; Nee, Wager, & Jonides, 2007

Affective Context: Positive Expectation

Placebo Emotion Regulation Opioid Pharm.

R Benedetti et al., 2005

Figure 63.5 (Figure C.60 in color section) Prefrontal regulation of pain and affect. Note: Meta-analyses of neuroimaging studies(Benedetti, Mayberg, Wager, Stohler, & Zubieta, 2005; Nee, Wager, & Jonides, 2007; Wager, Jonides, & Reading, 2004; Wager & Smith, 2003) that examined regulation of cognitive and affective context: Each point shows results from a study. Overlap between cognitive and affective regulation can be seen in dorsolateral prefrontal cortex (DLPFC) and lateral orbitofrontal cortex (OFC). These regions likely support central modulatory mechanisms of placebo, providing placebo-induced regulation of domain-specific processes.

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studies that examined cognitive context (e.g., attention switching), and studies that examined affective context (e.g., placebo). The prominence of placebo responses in central systems for affective appraisal establishes a link between placebo analgesia and manipulations of threat and safety appraisals in other paradigms, including threats to social status and physical safety. In addition to the important role of rACC and OFC across domains, appetitive motivational shifts (involving dopamine) in the ventral striatum/NAC have also been proposed as a common mechanism for placebo effects across disorders (de la Fuente-Fernandez, Schulzer, & Stoessl, 2004), and recent studies have found that placebo analgesia is predicted by fMRI responses to anticipated monetary reward (Scott et al., 2007) and dopamine activity (Scott et al., 2008) in the nucleus accumbens. Overall, these results are promising, and more study is needed to establish the role of central appraisal systems in placebo responses across different conditions. Proximal Mechanisms Proximal mechanisms are the domain-specific central nervous system pathways subserving the placebo response. We briefly review current knowledge of these pathways in the domains of Parkinson’s disease (PD), Major Depressive Disorder (MDD), and anxiety. Parkinson’s Disease While the role of dopamine (DA) in placebo analgesia points to an appetitive motivational/appraisal account of placebo that may be relevant across domains, DA arguably plays a much more critical role in the placebo response in PD. PD is a debilitating movement disorder known to result from the degeneration of dopamine-producing neurons in the nigrostriatal pathway. Researchers have learned about the mechanisms of the PD-specific placebo response by examining placebo effects on motor performance, dopamine release, and single neuron activity in the substantia nigra. PET studies of dopamine D2 receptor activity have provided evidence that placebo treatments lead to dopamine release in the striatum (de la Fuente-Fernandez et al., 2001). Complementary evidence has been obtained from neurosurgical studies, in which researchers have examined placebo effects on activity in the subthalamic nucleus (STN), a stimulation site used in the treatment of Parkinson’s, and have examined interactions between placebo expectancies and STN stimulation. Placebo administration directly affected STN activity in placebo responders (those who demonstrated placebo effects on muscle rigidity, a clinical sign of the disease), evidenced by decreased bursting and neuronal frequency discharge (Benedetti et al., 2004).

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Researchers have also demonstrated differing effects of ventral STN stimulation on autonomic activity between hidden and open stimulation (Lanotte et al., 2005), and expectations for poor versus enhanced motor performance have been shown to modulate the effects of STN stimulation (Pollo et al., 2002). Finally, the placebo response in PD is thought to arise primarily through expectancies, as verbal instructions to induce expectations of improved motor performance were found to reverse the effects of conditioning trials in which STN stimulation was turned off, resulting in decreased motor performance (Benedetti et al., 2003). Major Depressive Disorder In MDD, placebo effects on the brain were examined by using PET imaging to measure baseline metabolic activity before, during, and after treatment with either placebo or fluoxetine, a common selective-serotonin reuptake inhibitor prescribed as an antidepressant (Mayberg et al., 2002). Many changes that were observed as part of successful treatment with the active drug were also observed in placebo responders, including metabolic decreases in subgenual ACC. This region has been shown to be consistently affected in depression and is a target of deep-brain stimulation in patients who do not otherwise respond to treatment. Other common sites of activity over the course of treatment with either fluoxetine or placebo included metabolic increases in prefrontal, parietal, and posterior cingulate cortex, and decreases in parahippocampus and thalamus. Importantly, these common results differ from patterns of brain activation over the course of other types of treatment, such as cognitive behavioral therapy and interpersonal psychotherapy (Brody et al., 2001; Goldapple et al., 2004), which tend to lead to metabolic decreases in prefrontal activity, rather than increases. These results suggest that both active drug and placebo treatments work in part by changing central systems involved in affective valuation and motivation. It is important to point out that this study was longitudinal, and due to ethical constraints about denying treatment to patients when successful treatments are known to exist, no natural history control group was included in these analyses. It is therefore possible that observed results in both conditions may include factors attributable to the natural course of MDD. Much more work remains to be done to unpack the brain mechanisms involved in both verum and placebo treatment for depression. Anxiety While studies of the placebo response in PD and MDD have generally examined effects in clinical populations, Petrovic and colleagues used emotional images and active anxiolytics to examine placebo effects on anxiety

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Etiology of the Placebo Response: Expectancy versus Conditioning 1251

and emotion processing in healthy participants (Petrovic et al., 2005). On their first day in the laboratory, participants viewed and rated neutral and unpleasant images without treatment, then were given benzodiazepine, which decreased ratings of unpleasantness, followed by a benzodiazepine antagonist that reversed the effects of the anxiolytic. On the following visit, participants were scanned using fMRI while they were told they would undergo the same procedure. During this session, saline was administered in place of both the benzodiazepine and its antagonist, resulting in placebo and control conditions, respectively. As mentioned, this study revealed decreases in regions specific to emotion processing, and increases in regions that overlapped with modulatory regions that had been identified in studies of placebo analgesia (Petrovic et al., 2002; Wager, Rilling, et al., 2004). More specifically, placebo induced decreases in extrastriate activity and amygdala that correlated with reported placebo effects, and increases in OFC, rACC, and ventrolateral PFC activity (only rACC and vlPFC correlated with subjective placebo effects). Finally, treatment expectations on day 1 correlated with the extent of decreases in extrastriate cortex, increases in rACC, and placebo-induced activity in ventral striatum.

ETIOLOGY OF THE PLACEBO RESPONSE: EXPECTANCY VERSUS CONDITIONING Given evidence that the placebo response does indeed involve active psychobiological mechanisms in multiple domains, the question arises of exactly how the response comes about. For about half a century, placebo researchers have focused on two possible sources: classical conditioning or conscious expectancies. Briefly, conditioning-based placebo responses result from the association between active treatment outcomes and the context or procedures surrounding treatment, regardless of the organism’s awareness of the contingencies between stimuli. Expectancybased placebo responses result from appraisals of anticipated treatment outcomes that inherently depend on the organism’s beliefs about treatment. Thus, an important distinction is that only expectancy-based placebo effects can be altered by verbal instructions to participants. We assess how each factor may contribute to the development of the placebo response, and examine research directly comparing the two processes. We suggest that some placebo responses may be mediated entirely by expectancies; others may be primarily due to conditioning; and in other cases the two may not be mutually exclusive, as conditioning can serve to induce conscious expectations about placebo treatment outcomes.

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Conditioning-Based Placebo Responses Many psychologists became interested in placebo research in the 1950s, with the publication of Beecher’s The Powerful Placebo (Beecher, 1955). This coincided with psychology’s shift toward behaviorist views of psychological phenomena. Consistent with the dominant trends, the placebo response was explained in terms of classical conditioning. In the original Pavlovian stimulus-substitution model of classical conditioning, organisms learn to pair a neutral stimulus (a stimulus that elicits no response on its own) with an unconditioned stimulus (UCS) that normally elicits an unconditioned response (UCR). With repeated pairings, the neutral stimulus comes to elicit the same response as the UCS; the neutral stimulus has become a conditioned stimulus (CS), and the evoked behavior is referred to as a conditioned response (CR). Conditioning can occur in aversive contexts (in fear conditioning, a light may be paired with a shock, to elicit freezing in response to the light) or appetitive contexts (as in Pavlov’s classic experiments, food can be paired with a tone and animals eventually salivate in response to the tone). A simple classical conditioning account of placebo would propose that a pharmacological agent serves as a UCS that elicits healing effects (UCR; Montgomery & Kirsch, 1997; Wickramasekera, 1980); when the agent is delivered in pill form, the pill becomes the CS, and later administration of the pill without the pharmacological agent will elicit the active effects of the drug as the CR. Proponents of the classical conditioning view of placebo have even suggested that over a lifetime of pairings, neutral stimuli related to the medical context—doctors’ offices, the procedures surrounding medicine administration, medical devices, and doctors themselves—become associated with the results of treatment, and that placebo effects are the result of conditioning to these contextual stimuli. Several findings offer support for a classical conditioning account of the placebo response. Benedetti and colleagues showed that placebo effects following active administration of an opiate analgesic included respiratory depression, a side effect of the active medication, although participants reported no awareness of this associated side effect (Benedetti, Amanzio, Baldi, Casadio, & Maggi, 1999). In another study, participants were pretreated with pharmacological agents that elicited increases or decreases in cortisol and growth hormone release (Benedetti et al., 2003). Placebos were subsequently administered alongside verbal suggestions that treatment would produce the opposite effect, but the directionality of the physical outcomes in these two domains was not reversed. These results suggest that some placebo responses involve learning that is not modifiable by conscious expectancies.

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Primary mechanistic evidence that conditioning can serve to recruit endogenous disease-related processes comes from human and animal studies of conditioned immunosuppression and conditioned induction of antiallergic effects. Immunosuppression refers to the reduction of immune system efficacy; this may be deliberately induced in medical procedures such as organ transplants to prevent the immune system from rejecting the foreign organ. Rats that receive cyclophosphamide, an immunosuppressive agent, alongside saccharin during conditioning exhibit decreased antibodies when saccharin solution is later presented on its own, relative to rats that were not conditioned and conditioned rats that were not exposed to saccharin at test (Ader & Cohen, 1975). A later study used ß-adrenoceptor antagonists and 6-OHDA, a chemical that depletes noradrenaline, in a similar case of conditioned immunosuppression using the immunosuppressive drug cyclosporin A (CsA) and found that immunosuppressive effects in the spleen were mediated by the sympathetic nervous system (Exton et al., 2002). Finally, humans who received CsA paired with a unique beverage over repeated sessions demonstrated immunosuppression when they were exposed to the CS a week later, as indexed by lymphocyte proliferation, mRNA expression, and cytokine production and release (Goebel et al., 2002). A similar approach was taken to induce conditioning of antiallergic effects in humans (Goebel, Meykadeh, Kou, Schedlowski, & Hengge, 2008). A unique beverage was repeatedly paired with antihistamine in patients with allergic rhinitis. Participants who received the beverage alongside a placebo pill at test exhibited levels of basophil, a white blood cell that releases histamine, that were comparable to those who received the active medication, while participants who received water alongside placebo demonstrated no basophil inhibition. These data suggest that conditioning can indeed recruit powerful endogenous mechanisms related to immune functioning, and offer preliminary windows into central nervous system pathways that may be involved in the etiology of a conditioning-based placebo response. However, while this offers some insight into the role of conditioning in endogenous processes, it is unknown how mechanisms supporting conditioned immunosuppression may generalize to other domains, such as pain and Parkinson’s disease. Furthermore, participant expectancies were not assessed in these studies, and it is possible that conscious expectancies about the novel beverage and its effects on immune function may have played an important role in the observed effects. Little is known about brain mechanisms that would specifically support a classical conditioning account of placebo, although there have been decades of research on conditioning in animal models, and aversive conditioning

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mechanisms have been studied in humans using fMRI (Phelps, Delgado, Nearing, & LeDoux, 2004; Phelps et al., 2001). Different conditioning mechanisms are responsible for effects in different systems, so despite the wealth of knowledge about the specific neural circuitry involved in the realm of aversive conditioning, it is difficult to generalize across domains or infer mechanisms in domains that have not been directly examined. In eyelid conditioning, an auditory stimulus (CS) is paired with an air puff (US) to the eye, and this gradually leads to the conditioned response of an eyeblink (CR) when the tone is presented. Although these two stimuli originate in different modalities and travel through separate pathways (CS via mossy fibers, US via climbing fibers), both pathways include synapses onto the purkinje cells in the cerebellar cortex (Kim & Thompson, 1997). In fear conditioning, an auditory stimulus (CS) is paired with a shock (US), creating a startle response (CR). Again, these stimuli are processed separately (CS travels from auditory thalamus, US from brain stem), but both share common synapses in the lateral amygdala (Rogan & LeDoux, 1995; Rogan, Staubli, & LeDoux, 1997). These examples suggest that for these specific types of conditioned learning to occur, US and CS pathways must share common nuclei; furthermore, conditioning recruits unique pathways depending on stimulus and response modalities. Thus, conditioning-based placebo mechanisms are likely to involve similar neural mechanisms in principle, but our knowledge of mechanisms specific to aversive conditioning are unlikely to generalize to positive conditioning-based placebo responses. Though little was known about the brain mechanisms of conditioned immunosuppression for many years, recent studies have begun to investigate them. An important study by Pacheco-Lopez et al. (Pacheco-Lopez et al., 2005) used a rat model to assess the effects of lesions of the insula, amygdala, and ventromedial hypothalamus on conditioned immunosuppression and taste aversion. Insula lesions disrupted both aversion to the taste paired with the immunosuppressive drug (the CS) and several peripheral markers of immunosuppression, both before the conditioning procedure and afterward, during evocation by presenting the CS. Thus, the insula is implicated in both acquisition and retrieval of the memory that leads to immunosuppression. Amygdala lesions disrupted immunosuppression only before conditioning, implicating it in the learning process but not the expression of the learned response. Conversely, hypothalamic lesions disrupted expression but not acquisition of the immunosuppressive response. The neural mechanisms for conditioned placebo responses in pain and other domains remain unknown, and more research is needed to disentangle pathways that subserve conditioned CS-US or CS-UR learning and

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Etiology of the Placebo Response: Expectancy versus Conditioning 1253

conscious expectancies. In one fMRI study, researchers compared brain responses to painful stimulation under opioid-based analgesia with responses to placebo analgesia (Petrovic et al., 2002). Opioid administration always preceded the placebo analgesia condition, which may have induced a conditioning-based placebo response. Brain responses to each were compared with a pain control condition, and both were associated with increased activity in rACC, and increased rACC-brain stem connectivity. While this and other brain-based studies are promising, they have not directly compared conditioning processes with nonconditioning expectancy manipulations (verbal instructions only), and the nature of the conditioning-specific placebo response remains yet to be elucidated. Expectancy-Based Placebo Responses A central question in cognitive neuroscience over the past 50 years concerns the processes affected by expectancies, which shape perception across virtually every sensory and affective domain. Expectancies involve appraisals of an event’s significance in the context of its anticipated outcome; appraisal systems can affect brain stem and hypothalamus nuclei as part of coordinated behavioral and physiological responses that promote homeostasis. In the expectancy view of placebo, beliefs and expectations associated with treatment administration are responsible for recruitment of endogenous mechanisms to produce the requisite changes associated with the placebo response. Expectancy-based placebo effects are mediated by beliefs about upcoming experience, and do not necessitate prior exposure to an active treatment for the effect to occur. An important distinction between expectancies and conditioning-based learning is that expectancies are generally conscious at the time when decisions are made (StewartWilliams & Podd, 2004); if they are not conscious, they can be made conscious with directed attention (Kirsch, 2004; Kirsch & Lynn, 1999). Many conditioning theories posit that conditioning will occur regardless of the organism’s awareness of the contingencies between stimuli. Thus, only expectancy effects depend on an individual’s state of mind, which suggests that expectancy-based placebo effects can be altered by verbal instructions to participants, whereas conditioning-based placebo effects cannot. In the following section, we review several studies that have compared the respective contributions of expectancies and conditioning-based learning to the placebo response. In most cases, these studies suggest that expectancies provide a stronger account for observed placebo effects on reported pain. As expectancies modulate perception across many domains, we have much to draw from in positing potential brain mechanisms underlying expectancy-based placebo

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effects. In many studies, expectancy mechanisms are probed by paradigms that employ novel stimuli that are predictive of different levels of stimulation. This allows researchers to examine the development of expectancies over time, as participants learn to predict stimulation based on cues. Conditioning explanations cannot account for behavior when participants have no prior experience with the predictive stimuli, or in paradigms that include contingency reversals. These paradigms allow researchers to investigate how these expectancies affect processing; and neuroimaging and electrophysiology methodologies can reveal brain mechanisms responsible for maintaining expectancies and supporting the relationship between expectancies and perceived experience. Expectancy manipulations have been shown to modulate stimulus processing in neuroimaging and electrophysiology studies of pain (Keltner et al., 2006; Koyama, McHaffie, Laurienti, & Coghill, 2005; Lorenz et al., 2005), emotion (Bermpohl et al., 2006), taste (Nitschke et al., 2006; Sarinopoulos, Dixon, Short, Davidson, & Nitschke, 2006), and reward (Hampton, Adolphs, Tyszka, & O’Doherty, 2007; Spicer et al., 2007). As described earlier, researchers can define reasonable hypotheses about brain mechanisms supporting an expectancy-based placebo response by drawing on knowledge from brain mechanisms of cognitive control. These can be tested by contrasting anticipatory activity in a placebo condition with anticipatory activity during a control condition, so that the researcher can identify processes related to pain expectancy that are shaped by placebo treatment. This approach was used in an fMRI study of placebo analgesia (Wager, Rilling, et al., 2004) that revealed increases in DLPFC, OFC, and rACC activity during anticipation of pain with placebo. These anticipatory increases correlated with placebo effects on reported pain, and anticipatory increases in DLPFC and OFC correlated with subsequent placebo-induced reductions in pain matrix activity during thermal stimulation. Importantly, anticipatory increases in DLPFC correlated with activity in an area of the midbrain surrounding the PAG (see Figure 63.6), offering support for the interaction between expectancies and opioid release. Other studies have replicated and extended this result, showing that placebo treatments for negative emotion activate the same brain regions (Petrovic et al., 2005), and that endogenous opioids—neurochemicals linked to relaxation, euphoria, and pain relief—are released in these regions following placebo treatment (Wager et al., 2007; Zubieta et al., 2005). Reconciling Expectancy and Conditioning Accounts of Placebo It is difficult to resolve the relative contributions of expectancy and conditioning to placebo effects, because the two

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Anticipation

4 Study 1 Study 2

Midbrain

Midbrain (C-P)

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r  0.51 r  0.60

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Figure 63.6 Placebo-induced activation of the cognitive/evaluative network. Note: Contrasts between placebo and control conditions in an fMRI study of placebo analgesia (Wager, Rilling, et al., 2004) revealed increases in brain regions known to be involved in executive function and affective appraisal, including dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). Increases were observed both in anticipation of

are not always mutually exclusive; in some cases, conditioning procedures are likely to shape both learning and expectations. There are two ways to distinguish between learning and expectancy mechanisms: One relies on behavioral observations, and the other on measurement of the brain. Earlier, we suggested that conditioning results in learning that persists over time, despite expectancies; when a CS is presented without the UCS, extinction of the CR is relatively slow. Thus, effects that can be reversed in a single trial or affected by verbal instructions are not likely to be the result of conditioning, but rather expectancies. A second way to discriminate between conditioning and expectancy is by measuring brain activity. The patterns of activity increases and opioid release with placebo in OFC and rACC, and increases in DLPFC, suggest that general mechanisms of appraisal and expectancy are at work. Such effects have been found in pain and, though less well studied, depression. A difficulty, however, is that there is no way to ensure by looking at the brain that these responses are not the result of some conditioned association being activated. Another difficulty is that it is currently difficult or impossible to measure learned associations directly in the human brain; whereas synapse strength, gene expression, and other molecular markers of learning can be investigated in animal models, the techniques for probing them are invasive and cannot be used in humans—and, in addition, it is

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0 2 Right DLPFC

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pain (top left) and during thermal stimulation (top right). Placebo-induced increases were also observed in an area of the midbrain surrounding the periaqueductal gray (PAG; bottom), and anticipatory activity in this region correlated with anticipatory increases in right posterior DLPFC (bottom right), offering support for the influence of expectancies on placeboinduced pain modulation.

still unknown where in the human brain cellular learning underlying placebo effects may be taking place. Several experiments have attempted to directly compare expectancy-based and conditioning-based placebo effects in studies of placebo analgesia (Benedetti et al., 2003; de Jong, van Baast, Arntz, & Merckelbach, 1996; Montgomery & Kirsch, 1997; Voudouris et al., 1985; Voudouris, Peck, & Coleman, 1989, 1990), using variations of the same basic procedure. Verbal instructions to participants suggest that the placebo treatment is an effective analgesic drug. Some participants receive these instructions alone. Other participants are additionally exposed to an active treatment or procedure, which in most cases has involved the surreptitious reduction of painful stimulus intensity during treatment under the placebo condition. This serves as an unconditioned response (UR) or UCS with which the CS—cues associated with stimulation during placebo conditions—are associated. In some cases, a third group receives the conditioning procedure, but they are not verbally instructed that the placebo is an effective drug. The key comparisons are whether conditioning without verbal instructions reduces pain, and whether conditioning plus instructions is more effective than conditioning alone. Voudouris and colleagues (1985, 1989, 1990) used this approach and reported that conditioning provided the stronger explanation for observed placebo effects,

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Summary

since conditioning plus instruction was much more effective than instruction alone. Conclusions drawn from these experiments were contested, as some argued that such studies do not compare expectancy and conditioning, but instead compare expectancies mediated by physical processes (conscious expectancies that come about as a result of the conditioning procedure) to expectancies mediated by verbal information alone (Stewart-Williams & Podd, 2004). Conscious expectancies may mediate learning in both conditions, and any observed differences may be due to the fact that physiological experiences induce stronger expectancies than verbal suggestion. Results supporting an expectancy account of placebo analgesia were reported by de Jong and colleagues (1996), who used essentially the same experimental design, but added a group of participants who were exposed to conditioning trials with the critical addition that they were informed that noxious stimulation was being lowered during application of the cream. If the conditioning process were entirely responsible for observed placebo effects (if all that matters is the pairing of UCS and CS), then participant knowledge about the procedures would not counteract the efficacy of the conditioning manipulation on observed placebo effects; however, pain reports under placebo in this group did not differ from reported pain in the control condition. Furthermore, de Jong’s group added measures for participants’ conscious expectations of pain relief, which Voudouris and colleagues had not included in their original study, and found that these ratings predicted the magnitude of placebo effects. Later, Montgomery and Kirsch (1997) took a similar approach and demonstrated that although the magnitude of placebo effects seemed to offer support for a conditioning explanation of placebo, results were entirely mediated by participants’ reported expectations of pain relief in the conditioning group. These studies both suggest that expectancy theory provided a better account for observed placebo effects than a conditioning explanation. In a more expansive examination of the contributions of expectancy and conditioning to placebo effects, Benedetti and colleagues (2003) analyzed the contributions of the two potential sources to placebo effects in pain, Parkinson’s disease (PD), and hormone secretion. In each modality, preconditioning with an active treatment (Keterolac for analgesia, turning off subthalamic nucleus stimulation for decreased motor performance in Parkinson’s disease, and Sumatriptan for growth hormone increase and cortisol decrease) was followed by verbal instructions to induce opposing expectations in the respective groups (hyperalgesia, movement velocity increase in PD, and suggestions of GH decrease and cortisol increase). Consistent with de Jong and Montgomery and Kirsch’s earlier findings, conscious expectations reversed the effects of conditioning in

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pain, as well as PD. However, expectancies did not reverse the effects of preconditioning in hormonal secretion. The authors take this to suggest that placebo responses close to behavior (pain reports and motor symptoms) are mediated by expectancies, but that conditioning of some hormonal and peripheral responses can occur outside the regulation of conscious expectancies. While the series of studies reviewed here suggest that classical conditioning and expectancies are competing explanations that must be pitted against one another to determine the true source of the placebo response, some have argued that it may not be necessary to view the two potential mechanisms as competing explanations. We framed our introduction to Pavlovian conditioning in a manner consistent with the early stimulus-substitution models of the phenomenon, which focused on contiguity, the notion that the paired presentation of stimuli is responsible for the acquisition of conditioned responses. However, computational accounts have suggested that conditioning can be explained as a process by which an organism learns the relationships between events, and that organisms learn to pair stimuli with subsequent outcomes, rather than responses (Rescorla, 1988a, 1988b). A response is generated only insofar as the stimulus provides useful information about an upcoming event, and behaviors that are performed during conditioning are performed in anticipation of the expected outcome. Some suggest that this anticipatory behavior is closely linked to expectancies. In cases in which the conditioning process allows for conscious understanding of the relationships involved, expectancies may be likely to mediate the process of learning stimulus-outcome relationships over the course of conditioning, providing a way to reconcile the two accounts.

SUMMARY The placebo response is not only clinically relevant, but also serves as a valuable window into the powerful interaction between basic psychological processes, such as expectancies and affective appraisals, and the bodily state. Although many factors can potentially lead to observed placebo effects without affecting underlying physiology, careful experimental manipulations and supporting neuroimaging investigations provide evidence of active psychobiological placebo responses. Nearly all the studies reviewed identify a subset of participants who do not respond to the placebo manipulation (report no difference between placebo and control conditions). Early placebo researchers believed there was a true population of placebo responders, and behavioral studies sought to identify trait-level personality factors that would differentiate the

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placebo responder from the nonresponder. These attempts proved futile, and researchers adopted the view that anyone could demonstrate placebo reactivity under the correct circumstances (Liberman, 1964). Neuroimaging analyses, such as those reviewed earlier, generally account for responder differences through correlations between brain activity and extent of reported placebo effects, or statistical comparisons between placebo responders and nonresponders. We have reviewed specific evidence of placebo effects on pain, Parkinson’s disease, Major Depressive Disorder, and anxiety, and suggest that placebo responses in these domains may share common central mechanisms, including affective appraisal, cognitive control, and factors related to the etiology of the placebo response. Much more experimental research is needed to elaborate on our knowledge of factors contributing to individual differences in the development of the placebo response, proximal placebo mechanisms in domains other than those reviewed, and to build a more comprehensive account of central and proximal mechanisms of the placebo response.

REFERENCES Ader, R., & Cohen, N. (1975). Behaviorally conditioned immunosuppression. Psychosomatic Medicine, 37, 333–340. Amanzio, M., & Benedetti, F. (1999). Neuropharmacological dissection of placebo analgesia: Expectation-activated opioid systems versus conditioning-activated specific subsystems. Journal of Neuroscience, 19, 484–494. Arkin, R. M., Gabrenya, W. K., Jr., Appelman, A. S., & Cochran, S. T. (1979). Self-presentation, self-monitoring, and the self-serving bias in causal attribution. Personality and Social Psychology Bulletin, 5, 73–76. Barrett, B., Muller, D., Rakel, D., Rabago, D., Marchand, L., & Scheder, J. C. (2006). Placebo, meaning, and health. Perspectives in Biological Medicine, 49, 178–198. Beecher, H. K. (1955). The powerful placebo. Journal of the American Medical Association, 159, 1602–1606. Benedetti, F., Amanzio, M., Baldi, S., Casadio, C., Cavallo, A., Mancuso, M., et al. (1998). The specific effects of prior opioid exposure on placebo analgesia and placebo respiratory depression. Pain, 75(2/3), 313–319. Benedetti, F., Amanzio, M., Baldi, S., Casadio, C., & Maggi, G. (1999). Inducing placebo respiratory depressant responses in humans via opioid receptors. European Journal of Neuroscience, 11, 625–631. Benedetti, F., Amanzio, M., Casadio, C., Oliaro, A., & Maggi, G. (1997). Blockade of nocebo hyperalgesia by the cholecystokinin antagonist proglumide. Pain, 71, 135–140. Benedetti, F., Amanzio, M., & Maggi, G. (1995). Potentiation of placebo analgesia by proglumide. Lancet, 346, 1231. Benedetti, F., Amanzio, M., Vighetti, S., & Asteggiano, G. (2006). The biochemical and neuroendocrine bases of the hyperalgesic nocebo effect. Journal of Neuroscience, 26, 12014–12022. Benedetti, F., Arduino, C., & Amanzio, M. (1999). Somatotopic activation of opioid systems by target-directed expectations of analgesia. Journal of Neuroscience, 19, 3639–3648. Benedetti, F., Colloca, L., Torre, E., Lanotte, M., Melcarne, A., Pesare, M., et al. (2004). Placebo-responsive Parkinson patients show decreased

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activity in single neurons of subthalamic nucleus. Nature Neuroscience, 7, 587–588. Benedetti, F., Lanotte, M., Lopiano, L., & Colloca, L. (2007). When words are painful: Unraveling the mechanisms of the nocebo effect. Neuroscience, 147, 260–271. Benedetti, F., Mayberg, H. S., Wager, T. D., Stohler, C. S., & Zubieta, J. K. (2005). Neurobiological mechanisms of the placebo effect. Journal of Neuroscience, 25, 10390–10402. Benedetti, F., Pollo, A., Lopiano, L., Lanotte, M., Vighetti, S., & Rainero, I. (2003). Conscious expectation and unconscious conditioning in analgesic, motor, and hormonal placebo/nocebo responses. Journal of Neuroscience, 23, 4315–4323. Bermpohl, F., Pascual-Leone, A., Amedi, A., Merabet, L. B., Fregni, F., Gaab, N., et al. (2006). Attentional modulation of emotional stimulus processing: An fMRI study using emotional expectancy. Human Brain Mapping, 27, 662–677. Bingel, U., Lorenz, J., Schoell, E., Weiller, C., & Buchel, C. (2006). Mechanisms of placebo analgesia: RACC recruitment of a subcortical antinociceptive network. Pain, 120(1–2), 8–15. Brody, A. L., Saxena, S., Stoessel, P., Gillies, L. A., Fairbanks, L. A., Alborzian, S., et al. (2001). Regional brain metabolic changes in patients with major depression treated with either paroxetine or interpersonal therapy: Preliminary findings. Archives of General Psychiatry, 58, 631–640. Bromm, B., & Treede, R. D. (1984). Nerve fibre discharges, cerebral potentials and sensations induced by CO2 laser stimulation. Human Neurobiology, 3, 33–40. Bushnell, M. C., Duncan, G. H., Dubner, R., & He, L. F. (1984). Activity of trigeminothalamic neurons in medullary dorsal horn of awake monkeys trained in a thermal discrimination task. Journal of Neurophysiology, 52, 170–187. Casey, K. L., Svensson, P., Morrow, T. J., Raz, J., Jone, C., & Minoshima, S. (2000). Selective opiate modulation of nociceptive processing in the human brain. Journal of Neurophysiology, 84, 525–533. Clark, W. C. (1969). Sensory-decision theory analysis of the placebo effect on the criterion for pain and thermal sensitivity. Journal of Abnormal Psychology, 74, 363–371. de Jong, P. J., van Baast, R., Arntz, A., & Merckelbach, H. (1996). The placebo effect in pain reduction: The influence of conditioning experiences and response expectancies. International Journal of Behavioral Medicine, 3, 14–29. de la Fuente-Fernandez, R., Phillips, A. G., Zamburlini, M., Sossi, V., Calne, D. B., Ruth, T. J., et al. (2002). Dopamine release in human ventral striatum and expectation of reward. Behavioural Brain Research, 136, 359–363. de la Fuente-Fernandez, R., Ruth, T. J., Sossi, V., Schulzer, M., Calne, D. B. Stoessl, A. J. (2001, August 10). Expectation and dopamine release: Mechanism of the placebo effect in Parkinson’s disease. Science, 293, 1164–1166. de la Fuente-Fernandez, R., Schulzer, M., & Stoessl, A. J. (2004). Placebo mechanisms and reward circuitry: Clues from Parkinson’s disease. Biological Psychiatry, 56, 67–71. Dowman, R. (2001). Attentional set effects on spinal and supraspinal responses to pain. Psychophysiology, 38, 451–464. Exton, M. S., Gierse, C., Meier, B., Mosen, M., Xie, Y., Frede, S., et al. (2002). Behaviorally conditioned immunosuppression in the rat is regulated via noradrenaline and beta-adrenoceptors. Journal of Neuroimmunology, 131(1/2), 21–30. Fields, H. (2004). State-dependent opioid control of pain. Nature Reviews Neuroscience, 5, 565–575. Garcia-Larrea, L., Frot, M., & Valeriani, M. (2003). Brain generators of laser-evoked potentials: From dipoles to functional significance. Clinical Neurophysiology, 33, 279–292.

8/19/09 5:25:25 PM

References 1257 Garcia-Larrea, L., Peyron, R., Laurent, B., & Mauguiere, F. (1997). Association and dissociation between laser-evoked potentials and pain perception. NeuroReport, 8, 3785–3789. Geers, A. L., Helfer, S. G., Weiland, P. E., & Kosbab, K. (2006). Expectations and placebo response: A laboratory investigation into the role of somatic focus. Journal of Behavioral Medicine, 29, 171–178. Goebel, M. U., Meykadeh, N., Kou, W., Schedlowski, M., & Hengge, U. R. (2008). Behavioral conditioning of antihistamine effects in patients with allergic rhinitis. Psychotherapy and Psychosomatics, 77(4), 227–234. Goebel, M. U., Trebst, A. E., Steiner, J., Xie, Y. F., Exton, M. S., Frede, S., et al. (2002). Behavioral conditioning of immunosuppression is possible in humans. Federation of American Societies for Experimental Biology Journal, 16, 1869–1873.

Kirsch, I., & Scoboria, A. (2001). Apples, oranges, and placebos: Heterogeneity in a meta-analysis of placebo effects. Advances in Mind-Body Medicine, 17, 307–309, 312–308. Knutson, B., Fong, G. W., Adams, C. M., Varner, J. L., & Hommer, D. (2001). Dissociation of reward anticipation and outcome with event-related fMRI. NeuroReport, 12, 3683–3687. Kober, H., Barrett, L. F., Joseph, J., Bliss-Moreau, E., Lindquist, K., Duncan, S., et al. (2008). Networks of emotion: Functional parcelation and pathway analysis from meta-analytic data. Submitted for publication.

Goffaux, P., Redmond, W. J., Rainville, P., & Marchand, S. (2007). Descending analgesia: When the spine echoes what the brain expects. Pain, 130(1–2), 137–143.

Kong, J., Gollub, R. L., Rosman, I. S., Webb, J. M., Vangel, M. G., Kirsch, I., et al. (2006). Brain activity associated with expectancy-enhanced placebo analgesia as measured by functional magnetic resonance imaging. Journal of Neuroscience, 26, 381–388.

Goldapple, K., Segal, Z., Garson, C., Lau, M., Bieling, P., Kennedy, S., et al. (2004). Modulation of cortical-limbic pathways in major depression: Treatment-specific effects of cognitive behavior therapy. Archives of General Psychiatry, 61, 34–41.

Koyama, T., McHaffie, J. G., Laurienti, P. J., & Coghill, R. C. (2005). The subjective experience of pain: Where expectations become reality. Proceedings of the National Academy of Sciences, USA, 102, 12950–12955.

Gracely, R. H., Dubner, R., Wolskee, P. J., & Deeter, W. R. (1983, November 17). Placebo and naloxone can alter post-surgical pain by separate mechanisms. Nature, 306, 264–265.

Lanotte, M., Lopiano, L., Torre, E., Bergamasco, B., Colloca, L., & Benedetti, F. (2005). Expectation enhances autonomic responses to stimulation of the human subthalamic limbic region. Brain, Behavior, and Immunity, 19, 500–509.

Grevert, P., Albert, L. H., & Goldstein, A. (1983). Partial antagonism of placebo analgesia by naloxone. Pain, 16, 129–143. Grinband, J., Hirsch, J., & Ferrara, V. P. (2006). A neural representation of categorization uncertainty in the human brain. Neuron, 49, 757–763. Hagelberg, N., Martikainen, I. K., Mansikka, H., Hinkka, S., Nagren, K., Hietala, J., et al. (2002). Dopamine D2 receptor binding in the human brain is associated with the response to painful stimulation and pain modulatory capacity. Pain, 99(1/2), 273–279. Hall, M. A., Dugan, E., Zheng, B., & Mishra, A. K. (2001). Trust in physicians and medical institutions: What is it, can it be measured, and does it matter? Milbank Quarterly, 79, 613–639. Hampton, A. N., Adolphs, R., Tyszka, M. J., & O’Doherty, J. P. (2007). Contributions of the amygdala to reward expectancy and choice signals in human prefrontal cortex. Neuron, 55, 545–555. Hrobjartsson, A., & Gotzsche, P. C. (2001). Is the placebo powerless? An analysis of clinical trials comparing placebo with no treatment. New England Journal of Medicine, 344, 1594–1602. Hrobjartsson, A., & Gotzsche, P. C. (2004). Is the placebo powerless? Update of a systematic review with 52 new randomized trials comparing placebo with no treatment. Journal of Internal Medicine, 256, 91–100. Iannetti, G. D., Leandri, M., Truini, A., Zambreanu, L., Cruccu, G., & Tracey, I. (2004). Adelta nociceptor response to laser stimuli: Selective effect of stimulus duration on skin temperature, brain potentials and pain perception. Clinical Neurophysiology, 115, 2629–2637. Irizarry, K. J., & Licinio, J. (2005, March 4). An explanation for the placebo effect? Science, 307, 1411–1412.

c63.indd Sec6:1257

Kirsch, I., & Lynn, S. J. (1999). Automaticity in clinical psychology. American Psychologist, 54, 504–515.

Le Bars, D., Dickenson, A. H., & Besson, J. M. (1979). Diffuse noxious inhibitory controls (DNIC): Pt. I. Effects on dorsal horn convergent neurones in the rat. Pain, 6, 283–304. Lenz, F. A., Rios, M., Zirh, A., Chau, D., Krauss, G., & Lesser, R. P. (1998). Painful stimuli evoke potentials recorded over the human anterior cingulate gyrus. Journal of Neurophysiology, 79, 2231–2234. Levine, J. D., & Gordon, N. C. (1984, December 20). Influence of the method of drug administration on analgesic response. Nature, 312, 755–756. Levine, J. D., Gordon, N. C., & Fields, H. L. (1978). The mechanism of placebo analgesia. Lancet, 2, 654–657. Levine, J. D., Gordon, N. C., & Fields, H. L. (1979, April 19). Naloxone dose dependently produces analgesia and hyperalgesia in postoperative pain. Nature, 278, 740–741. Levine, J. D., Gordon, N. C., Jones, R. T., & Fields, H. L. (1978, April 27). The narcotic antagonist naloxone enhances clinical pain. Nature, 272, 826–827. Liberman, R. (1964). An experimental study of the placebo response under three different situations of pain. Journal of Psychiatric Research, 2, 233–246. Lieberman, M. D., Jarcho, J. M., Berman, S., Naliboff, B. D., Suyenobu, B. Y., Mandelkern, M., et al. (2004). The neural correlates of placebo effects: A disruption account. NeuroImage, 22, 447–455. Lin, M. T., Wu, J. J., Chandra, A., & Tsay, B. L. (1981). Activation of striatal dopamine receptors induces pain inhibition in rats. Journal of Neural Transmission, 51(3/4), 213–222.

Kelman, H. C. (1958). Compliance, identification, and internalization: Three processes of attitude change. Journal of Conflict Resolution, 2, 51–60.

Lorenz, J., & Garcia-Larrea, L. (2003). Contribution of attentional and cognitive factors to laser evoked brain potentials. Clinical Neurophysiology, 33, 293–301.

Keltner, J. R., Furst, A., Fan, C., Redfern, R., Inglis, B., & Fields, H. L. (2006). Isolating the modulatory effect of expectation on pain transmission: A functional magnetic resonance imaging study. Journal of Neuroscience, 26, 4437–4443.

Lorenz, J., Hauck, M., Paur, R. C., Nakamura, Y., Zimmermann, R., Bromm, B., et al. (2005). Cortical correlates of false expectations during pain intensity judgments: A possible manifestation of placebo/ nocebo cognitions. Brain, Behavior, and Immunity, 19, 283–295.

Kim, J. J., & Thompson, R. F. (1997). Cerebellar circuits and synaptic mechanisms involved in classical eyeblink conditioning. Trends in Neurosciences, 20, 177–181.

Magnusson, J. E., & Fisher, K. (2000). The involvement of dopamine in nociception: The role of D(1) and D(2) receptors in the dorsolateral striatum. Brain Research, 855, 260–266.

Kirsch, I. (2004). Conditioning, expectancy, and the placebo effect: Comment on Stewart-Williams and Podd. Psychological Bulletin, 130, 341–343; discussion 344–345.

Matre, D., Casey, K. L., & Knardahl, S. (2006). Placebo-induced changes in spinal cord pain processing. Journal of Neuroscience, 26, 559–563.

8/19/09 5:25:26 PM

1258

Placebo Effects

Mayberg, H. S., Silva, J. A., Brannan, S. K., Tekell, J. L., Mahurin, R. K., McGinnis, S., et al. (2002). The functional neuroanatomy of the placebo effect. American Journal of Psychiatry, 159, 728–737. Melzack, R., & Wall, P. D. (1965, November 19). Pain mechanisms: A new theory. Science, 150, 971–979. Moerman, D. E., & Jonas, W. B. (2002). Deconstructing the placebo effect and finding the meaning response. Annals of Internal Medicine, 136, 471–476. Montgomery, G. H., & Kirsch, I. (1997). Classical conditioning and the placebo effect. Pain, 72(1–2), 107–113. Morgan, M. J., & Franklin, K. B. (1991). Dopamine receptor subtypes and formalin test analgesia. Pharmacology Biochemistry and Behavior, 40, 317–322. Nee, D. E., Wager, T. D., & Jonides, J. (2007). Interference resolution: Insights from a meta-analysis of neuroimaging tasks. Cognitive Affective and Behavioral Neuroscience, 7, 1–17. Nitschke, J. B., Dixon, G. E., Sarinopoulos, I., Short, S. J., Cohen, J. D., Smith, E. E., et al. (2006). Altering expectancy dampens neural response to aversive taste in primary taste cortex. Nature Neuroscience, 9, 435–442. Pacheco-Lopez, G., Niemi, M. B., Kou, W., Harting, M., Fandrey, J., & Schedlowski, M. (2005). Neural substrates for behaviorally conditioned immunosuppression in the rat. Journal of Neuroscience, 25, 2330–2337. Petrovic, P., Dietrich, T., Fransson, P., Andersson, J., Carlsson, K., & Ingvar, M. (2005). Placebo in emotional processing: Induced expectations of anxiety relief activate a generalized modulatory network. Neuron, 46, 957–969. Petrovic, P., & Ingvar, M. (2002). Imaging cognitive modulation of pain processing. Pain, 95(1/2), 1–5. Petrovic, P., Kalso, E., Petersson, K. M., & Ingvar, M. (2002, March 1). Placebo and opioid analgesia: Imaging a shared neuronal network. Science, 295, 1737–1740. Phelps, E. A., Delgado, M. R., Nearing, K. I., & LeDoux, J. E. (2004). Extinction learning in humans: Role of the amygdala and vmPFC. Neuron, 43, 897–905. Phelps, E. A., O’Connor, K. J., Gatenby, J. C., Gore, J. C., Grillon, C., & Davis, M. (2001). Activation of the left amygdala to a cognitive representation of fear. Nature Neuroscience, 4, 437–441. Pollo, A., Torre, E., Lopiano, L., Rizzone, M., Lanotte, M., Cavanna, A., et al. (2002, August 7). Expectation modulates the response to subthalamic nucleus stimulation in Parkinsonian patients. NeuroReport, 13, 1383–1386. Pollo, A., Vighetti, S., Rainero, I., & Benedetti, F. (2003). Placebo analgesia and the heart. Pain, 102(1–2), 125–133. Price, D. D. (2000, June 9). Psychological and neural mechanisms of the affective dimension of pain. Science, 288, 1769–1772. Price, D. D., Craggs, J., Verne, G. N., Perlstein, W. M., & Robinson, M. E. (2007). Placebo analgesia is accompanied by large reductions in pain-related brain activity in irritable bowel syndrome patients. Pain, 127(1–2), 63–72.

Rescorla, R. A. (1988b). Pavlovian conditioning: It’s not what you think it is. American Psychologist, 43, 151–160. Roethlisberger, F. J., & Dickson, W. J. (1939). Management and the worker. Cambridge, MA: Harvard University Press. Rogan, M. T., & LeDoux, J. E. (1995). LTP is accompanied by commensurate enhancement of auditory-evoked responses in a fear conditioning circuit. Neuron, 15, 127–136. Rogan, M. T., Staubli, U. V., & LeDoux, J. E. (1997, December 11). Fear conditioning induces associative long-term potentiation in the amygdala. Nature, 390, 604–607. Sarinopoulos, I., Dixon, G. E., Short, S. J., Davidson, R. J., & Nitschke, J. B. (2006). Brain mechanisms of expectation associated with insula and amygdala response to aversive taste: Implications for placebo. Brain, Behavior, and Immunity, 20, 120–132. Scott, D. J., Stohler, C. S., Egnatuk, C. M., Wang, H., Koeppe, R. A., & Zubieta, J. K. (2007). Individual differences in reward responding explain placebo-induced expectations and effects. Neuron, 55, 325–336. Scott, D. J., Stohler, C. S., Egnatuk, C. M., Wang, H., Koeppe, R. A., & Zubieta, J. K. (2008). Placebo and nocebo effects are defined by opposite opioid and dopaminergic responses. Archives of General Psychiatry, 65. Spicer, J., Galvan, A., Hare, T. A., Voss, H., Glover, G., & Casey, B. (2007). Sensitivity of the nucleus accumbens to violations in expectation of reward. Neuroimage, 34, 455–461. Stewart-Williams, S., & Podd, J. (2004). The placebo effect: Dissolving the expectancy versus conditioning debate. Psychological Bulletin, 130, 324–340. Swets, J., Tanner, W. P., Jr., & Birdsall, T. G. (1961). Decision processes in perception. Psychological Review, 68, 301–340. Turk, D. C., & Rudy, T. E. (1990). Neglected factors in chronic pain treatment outcome studies: Referral patterns, failure to enter treatment, and attrition. Pain, 43, 7–25. Turner, J. A., Deyo, R. A., Loeser, J. D., Von Korff, M., & Fordyce, W. E. (1994). The importance of placebo effects in pain treatment and research. Journal of the American Medical Association, 271, 1609–1614. Vase, L., Robinson, M. E., Verne, G. N., & Price, D. D. (2005). Increased placebo analgesia over time in irritable bowel syndrome (IBS) patients is associated with desire and expectation but not endogenous opioid mechanisms. Pain, 115, 338–347. Volkow, N. D., Wang, G. J., Ma, Y., Fowler, J. S., Zhu, W., Maynard, L., et al. (2003). Expectation enhances the regional brain metabolic and the reinforcing effects of stimulants in cocaine abusers. Journal of Neuroscience, 23, 11461–11468. Voudouris, N. J., Peck, C. L., & Coleman, G. (1985). Conditioned placebo responses. Journal of Personality and Social Psychology, 48, 47–53. Voudouris, N. J., Peck, C. L., & Coleman, G. (1989). Conditioned response models of placebo phenomena: Further support. Pain, 38, 109–116. Voudouris, N. J., Peck, C. L., & Coleman, G. (1990). The role of conditioning and verbal expectancy in the placebo response. Pain, 43, 121–128.

Price, D. D., & Dubner, R. (1977). Neurons that subserve the sensorydiscriminative aspects of pain. Pain, 3, 307–338.

Wager, T. D., Jonides, J., & Reading, S. (2004). Neuroimaging studies of shifting attention: A meta-analysis. NeuroImage, 22, 1679–1693.

Price, D. D., Greenspan, J. D., & Dubner, R. (2003). Neurons involved in the exteroceptive function of pain. Pain, 106, 215–219.

Wager, T. D., Matre, D., & Casey, K. L. (2006). Placebo effects in laserevoked pain potentials. Brain, Behavior, and Immunity, 20, 219–230.

Price, D. D., Milling, L. S., Kirsch, I., Duff, A., Montgomery, G. H., & Nicholls, S. S. (1999). An analysis of factors that contribute to the magnitude of placebo analgesia in an experimental paradigm. Pain, 83, 147–156.

Wager, T. D., & Nitschke, J. B. (2005). Placebo effects in the brain: Linking mental and physiological processes. Brain, Behavior, and Immunity, 19, 281–282.

Rescorla, R. A. (1988a). Behavioral studies of Pavlovian conditioning. Annual Review of Neuroscience, 11, 329–352.

c63.indd Sec6:1258

Wager, T. D., Rilling, J. K., Smith, E. E., Sokolik, A., Casey, K. L., Davidson, R. J., et al. (2004, February 20). Placebo-induced changes in fMRI in the anticipation and experience of pain. Science, 303, 1162–1167.

8/19/09 5:25:26 PM

References 1259 Wager, T. D., Scott, D. J., & Zubieta, J. K. (2007). Placebo effects on human μ-opioid activity during pain. Proceedings of the National Academy of Sciences, USA, 104, 11056–11061.

Wickramasekera, I. (1980). A conditioned response model of the placebo effect predictions from the model. Biofeedback and Self Regulation, 5, 5–18.

Wager, T. D., & Smith, E. E. (2003). Neuroimaging studies of working memory: A meta-analysis. Cognitive Affective and Behavioral Neuroscience, 3, 255–274.

Wickramasekera, I. (2001). The placebo efficacy study: Problems with the definition of the placebo and the mechanisms of placebo efficacy. Advances in Mind-Body Medicine, 17, 309–312; discussion 312–308.

Watson, A., El-Deredy, W., Vogt, B. A., & Jones, A. K. (2007). Placebo analgesia is not due to compliance or habituation: EEG and behavioural evidence. NeuroReport, 18, 771–775. Wells, L. E., & Sweeney, P. D. (1986). A test of three models of bias in selfassessment. Social Psychology Quarterly, 49, 1–10. Whitney, C. W., & Von Korff, M. (1992). Regression to the mean in treated versus untreated chronic pain. Pain, 50, 281–285.

c63.indd Sec6:1259

Zubieta, J. K., Bueller, J. A., Jackson, L. R., Scott, D. J., Xu, Y., Koeppe, R. A., et al. (2005). Placebo effects mediated by endogenous opioid activity on μ-opioid receptors. Journal of Neuroscience, 25, 7754–7762. Zubieta, J. K., Yau, W. Y., Scott, D. J., & Stohler, C. S. (2006). Belief or need? Accounting for individual variations in the neurochemistry of the placebo effect. Brain, Behavior, and Immunity, 20, 15–26.

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Chapter 64

Psychological Influences on Neuroendocrine and Immune Outcomes LISA M. CHRISTIAN, NATHAN T. DEICHERT, JEAN-PHILIPPE GOUIN, JENNIFER E. GRAHAM, AND JANICE K. KIECOLT-GLASER

Over the past 25 years, substantial evidence has established that psychological factors affect clinically relevant immune and neuroendocrine outcomes. In particular, psychosocial stress reliably causes immunological changes that are not only measurable, but also meaningful in terms of health. Moreover, alterations in neuroendocrine function are primary mediators of immune changes seen in response to stress. This chapter focuses on work linking psychosocial factors with immune function among humans in three outcome areas. We first review substantial evidence linking the psychological states of stress and depression to inflammation, a key outcome because of its clinical relevance to serious health conditions. Next, we summarize research linking stress and wound healing, a clinically vital process in which inflammation plays an important role. Finally, we review effects of stress on susceptibility to infectious illness including studies of vaccination, exposure to infectious agents, and immune control of latent viruses. This chapter focuses primarily on the immune effects of stress, although other specific psychosocial factors (including depression, hostility, and anxiety) are also discussed. Although we emphasize human studies, we also describe key animal studies, primarily those elucidating physiological mechanisms underlying links between psychosocial factors and immune outcomes. Throughout, the role of neuroendocrine mediators is highlighted. We also describe the positive effects of social support and promising interventions that target the effects of stress.

OVERVIEW OF THE IMMUNE SYSTEM The protective physical barrier formed by the skin provides the body’s first line of defense against foreign invaders. The second line of defense is the innate immune system, which responds very rapidly (within minutes to hours) but in a nonspecific manner when exogenous antigens such as bacteria and viruses are detected. The key elements of the innate immune system are neutrophils, macrophages, natural killer (NK) cells, and complement proteins. When the innate immune system cannot effectively eliminate or control the antigen in question, the adaptive immune system provides the third line of defense. Although the adaptive immune system may take several days to mount an optimal response, its action is highly targeted. The main cell type of the adaptive immune system is the lymphocyte, which includes T-cells and B-cells. Importantly, after the adaptive immune system is exposed to a particular antigen, certain T-cells and B-cells retain memory of that antigen, which allows a stronger and more rapid response on subsequent exposure; the ability of the adaptive immune system to form memory in this way provides the basis for vaccination (see Figure 64.1). Cytokines are soluble proteins that are involved in communication between immune cells. Cytokines also have more far-reaching effects (e.g., effects of cytokines on the brain are key to behavioral changes related to illness). Cytokines are produced by cells of both the innate and adaptive immune systems as well as several other nonlymphoid cells in the body, such as adipocytes (fat cells). Among their multiple functions, cytokines play a key role in inflammatory immune responses, which involve the recruitment of key proteins and immune cells to an affected area. Inflammation is a critical response to infection or injury; however, chronic or excessive inflammation is linked to negative health outcomes. Thus, an adequate,

Funding: Work on this chapter was supported by training grant T32AI55411 (L.M.C., N.T.D.), a Doctoral Research Training Award from the Fonds de la recherche en santé du Québec (J-P.G.), and grants AT002971, AG025732, AG029562, CA126857, CA131029, M01-RR-0034, and CA16058 from the National Institutes of Health. 1260

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Stress, Depression, and Inflammation 1261 First Line of Defense Skin Key Features: Body’s largest organ Prevents bacteria, viruses, and other exogenous antigens from entering Second Line of Defense Innate Immune System Key Features: Rapid, but nonspecific response Major components: Neutrophils, macrophages, natural killer cells, complement proteins Third Line of Defense Adaptive Immune System Key Features: Slower, highly targeted response Formation of immunological memory Major components: T-cells and B-cells

Figure 64.1 Divisions of the immune system.

but not exaggerated inflammatory response to immune challenge is optimal. For more detailed coverage of the elements of the immune system, see Chapter 7, Volume 1.

EFFECTS OF HEALTH BEHAVIORS ON IMMUNE OUTCOMES Although this chapter focuses on neuroendocrine pathways linking psychological stress and immune outcomes, behavioral pathways are another important area of investigation. In particular, heightened distress is associated with less adaptive health behavior, including more smoking and alcohol use, poorer diet, and less sleep (Steptoe, Wardle, Pollard, Canaan, & Davies, 1996; Vitaliano, Scanlan, Zhang, Savage, & Hirsch, 2002). In turn, health behaviors affect neuroendocrine function, immune function, and related health outcomes, including wound healing and response to infectious agents (Figure 64.2). For each of the outcomes discussed in this chapter, effects of stress remain after accounting for effects of health behaviors, indicating that more direct physiological pathways exist between psychological factors and immune outcomes. However, because health behaviors may partly explain or exacerbate the effects of stress, assessing and appropriately controlling for health behaviors is an important component of research aimed at identifying and separating physiological versus behavioral pathways linking stress and immune function. Moreover, because health behaviors are modifiable, they represent a key target for interventions. One pathway by which behavioral interventions can benefit immune function is by improving physiological responses to stress.

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STRESS, DEPRESSION, AND INFLAMMATION Inflammation is an essential immune response to infection or injury. Among multiple other functions, inflammation promotes destruction (phagocytosis) and clearance of pathogens and initiates wound healing. As described, the production of cytokines, which are soluble proteins involved in communication between immune and other cells, is an important component of the inflammatory response. Cytokines can be classified as pro- or anti-inflammatory, although some cytokines demonstrate both proand anti-inflammatory characteristics. As the name implies, pro-inflammatory cytokines—including interleukin (IL)-6, IL-1, and tumor necrosis factor (TNF)-␣—promote inflammation. Anti-inflammatory cytokines such as interleukin-10 (IL-10) act as important regulators of the immune response, in part by inhibiting the production of pro-inflammatory cytokines (Opal & DePalo, 2000; Parham, 2005). A local inflammatory response involves increased vascular permeability and the recruitment of key proteins and immune cells to the affected area. It can be characterized by redness, swelling, pain, and fever (Rabin, 1999). In the case of infection or injury, inflammation is beneficial, as it aids recovery. In fact, pro-inflammatory cytokines are administered therapeutically to treat hepatitis and some cancers (Capuron & Miller, 2004; Dantzer & Kelley, 2007). However, exaggerated or chronic inflammation is detrimental to health. Chronic inflammation has been implicated in serious medical conditions including cardiovascular disease, arthritis, diabetes, inflammatory bowel disease, periodontal disease, certain cancers, and age-related functional decline (Black & Garbutt, 2002; Bruunsgaard, Pedersen, & Pedersen, 2001; Ershler & Keller, 2000; Hamerman, Berman, Albers, Brown, & Silver, 1999; Ishihara & Hirano, 2002). An insufficient anti-inflammatory response can contribute to excessive inflammation. Relatedly, the administration of anti-inflammatory cytokines has been implicated as a useful therapeutic strategy for diseases marked by inflammation, particularly rheumatoid arthritis (Opal & DePalo, 2000). Excessive anti-inflammatory control can overly inhibit inflammation, resulting in increased risk for infection and illness (Opal & DePalo, 2000). Thus, an appropriate balance of inflammatory and anti-inflammatory function is necessary for optimal health. Conceptualizing Stress and Depression Although conceptually distinct, stress and depression are similar in that they involve negative mood, activation of the hypothalamic-pituitary-adrenal (HPA) axis, and associated negative health outcomes (Anisman & Merali, 2003;

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Health Behaviors Environmental Factors

(A) Objective Stressors

Psychological Factors

(D)

Perceived Stress Depression Anxiety

Coping Resources

Age Race Sex Socioeconomic Status

Social Support Spirituality Problem-Solving Ability Emotion-Focused Coping Tangible Resources

Figure 64.2 Pathways by which psychological factors affect health outcomes. Note: An important pathway by which psychological factors affect health is health behaviors (A). Thus, appropriate control for health behaviors is important to research aiming to delineate physiological pathways linking psychological factors to health. Effects of psychological factors on health outcomes remain after accounting for effects

Connor & Leonard, 1998). Stress can be defined and measured in many ways. Objective definitions generally focus on characteristics of the stressor experienced. Completing a 10-minute speech task can be defined as a mild acute stressor. In contrast, subjective measures of stress reflect an individual’s perceptions of stress in their lives as well as their perceived ability to cope with that stress. In this way, subjective measures can capture important individual differences in how people react to the same stressor. Common depressive symptoms include negative mood, loss of interest or pleasure, difficulty concentrating, changes in appetite, sleep disturbance, and thoughts of death. Importantly, psychological stress is a frequent precursor of clinical depression (Kendler, Karkowski, & Prescott, 1999). Moreover, as will be reviewed briefly, stress-associated overactivation of the sympathetic nervous system and HPA axis may play a causal role in the development of depression (also see Chapters 6 and 7, Volume 1; and Chapters 55 and 62, this volume). Stress and Inflammation Using various stressors, both animal and human models have demonstrated effects of stress on inflammation. In terms of animal studies, acute stress in the form of exposure to a novel environment or foot/tailshock induces increases in plasma IL-6 levels in rats (LeMay, Vander, &

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Health Outcomes Inflammation Wound Healing Response to Infectious Illness

(C)

(B) Demographic Characteristics

Diet Exercise Sleep Substance Use Medication Use

Physiological Factors Nervous System Endocrine System

of health behaviors, indicating that more direct physiological pathways between psychological factors and health outcomes exist (B). Importantly, health behaviors are modifiable and can moderate physiological responses to stress (C). Thus, health behaviors represent a key target for intervention. Another important target of interventions is coping resources, which moderate effects of environmental stressors on psychological responses (D).

Kluger, 1990; Zhou, Kusnecov, Shurin, DePaoli, & Rabin, 1993). For example, rats exposed to footshock exhibited heightened plasma IL-6. Moreover, IL-6 rose as an increasing number of footshocks were administered (Zhou et al., 1993). Notably, this physiological response to stress can be conditioned; after repeated shocking, exposure to stimuli (e.g., auditory tones) that were present when shocks were administered also elicited increases in IL-6 (Johnson et al., 2002; Zhou et al., 1993). Pro-inflammatory cytokines also rise in response to acute stressors such as public speaking and mental arithmetic (Brydon, Edwards, Mohamed-Ali, & Steptoe, 2004; Steptoe, Willemsen, Owen, Flower, & Mohamed-Ali, 2001). Circulating IL-6 and IL-1 receptor antagonist (IL-1ra) increased two hours after completion of Stroop and mirror-tracing tasks, while control participants did not change (Steptoe et al., 2001). This time lag between acute stressors and cytokine responses in humans has been reported in other studies. Some null findings (e.g., Heesen et al., 2002; Lutgendorf, Logan, Costanzo, & Lubaroff, 2004) may be explained by the fact that samples were taken at time points that were too close to the stressor. It is also notable that the magnitude of inflammation seen in response to objective stressors is not necessarily predicted by perceived stress (e.g., Brydon et al., 2004). This is consistent with evidence that subjective evaluations are often poor predictors of cardiovascular reactivity to acute stressors (e.g., Christian & Stoney, 2006).

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Importantly, repeated exposure to a stressor may not lead to habituation of inflammatory responses. A sample of 21 healthy middle-aged men completed the Trier Social Stress Test (a combined speech and mental arithmetic task; see Kirschbaum, Pirke, & Hellhammer, 1993) three times with 1-week intervals between sessions. As expected, the stressor resulted in increases in plasma IL-6. Notably, although participants demonstrated habituation of cortisol and systolic blood pressure reactivity to the task between weeks 1 and 3, they demonstrated similar stress-induced elevations in IL-6 across visits (von Kanel, Kudielka, Preckel, Hanebuth, & Fischer, 2005). If such lack of habituation also occurs in naturalistic settings, inflammatory responses to relatively minor but recurrent stress in daily life may contribute to morbidity and mortality. Given the effects demonstrated in response to acute stress, it would be expected that chronic stress could have an even greater impact on inflammation. Caregiving provides an excellent model for assessing the effects of chronic stress on health; individuals who provide care for loved ones with chronic medical conditions, such as a spouse with dementia, commonly experience ongoing stress, significant life change, and social isolation. Relatedly, caregivers experience heightened risk of negative mental and physical health outcomes, including depressive symptoms, infectious illness, and poorer response to vaccination (Kiecolt-Glaser, Dura, Speicher, Trask, & Glaser, 1991; Pinquart & Sorensen, 2004; Vitaliano, Zhang, & Scanlan, 2003). Notably, Schulz and Beach (1999) found that strained caregivers experienced 63% greater risk of mortality over a 4-year time frame compared with noncaregiving control participants. Inflammation from chronic stress may contribute to morbidity and mortality among caregivers. Older women caregivers had higher levels of IL-6 compared with older women undergoing moderate stress (housing relocation) and low stress (Lutgendorf et al., 1999). Additional research with caregivers has demonstrated that caregiving exacerbates typical age-related increases in IL-6; caregivers experienced fourfold greater increases in IL-6 over a 6-year follow-up period compared with controls (KiecoltGlaser et al., 2003). These data suggest that the experience of chronic stress can accelerate the aging process. Notably, although caregivers reported greater perceived stress, depressive symptoms, and loneliness than controls, the effects of caregiver status on inflammation were not accounted for by these factors. Thus, effects of chronic stress on inflammation were not simply a reflection of greater stress, depression, or loneliness. Depression and Inflammation Relationships between depression and inflammation are seen across the life span. Among young adults who were

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30 years old on average, those experiencing major depression had significantly higher circulating levels of inflammatory markers than controls with no psychiatric history (Maes et al., 1995). These markers included IL-6 and the soluble receptor of IL-6 (IL-6sR), which can widen the action of IL-6 (Jones, Horiuchi, Topley, Yamamoto, & Fuller, 2001). Similarly, among middle-aged adults, those with major depression had elevated serum levels of IL-6, IL-6sr, as well as the receptor antagonist for IL-1 (IL-1ra); IL-1ra is often elevated in individuals with diseases marked by inflammation (Maes et al., 1997). These effects are also seen in older adults. In a sample of adults over 60 years of age compared with individuals with no prior history of psychiatric disorder, those who met criteria for clinical depression had 171% higher serum levels of IL-1␤ (Thomas et al., 2005). Moreover, among the depressed individuals, depression severity was positively correlated with IL-1␤ levels. Along with other proinflammatory cytokines, IL-1␤ is implicated in sickness behavior. Similarly, in a study of 1,686 participants over 70 years of age, those who exceeded a clinical cutoff on the Center for Epidemiologic Studies Depression scale (CES-D) had higher levels of IL-6 compared with individuals reporting fewer depressive symptoms, a relationship that held after controlling for age, race, and gender (Dentino et al., 1999). In addition, in a sample of 3,024 adults ages 70 to 79 years, those who exceeded a clinical cutoff on the CES-D had higher levels of IL-6 as well as TNF-␣ and C-reactive protein (CRP; Penninx et al., 2003). CRP is an inflammatory marker that is an emerging risk factor for cardiovascular disease (Hackam & Anand, 2003). These studies demonstrate associations between depressive symptoms and inflammatory markers across the life span. In the preceding studies that examined depressive symptoms and inflammatory markers, perceived stress was not measured or statistically controlled. This may be an important consideration, however, because perceived stress tends to covary with depressive symptoms. McDade, Hawkley, and Cacioppo (2006) found that perceived stress was a more robust predictor of CRP than depressive symptoms in a population-based study of middle-aged and older adults. Moreover, the association between depressive symptoms and CRP was attenuated after controlling for perceived stress. Future research should aim to clarify the predictive value of perceived stress versus depressive symptoms in the context of clinical depression as well as milder depressive symptomatology. Relatedly, depressive symptomatology may be an important moderator of physiological responses to objective stressors (e.g., Miller, Freedland, & Carney, 2005). Thus, the specific and interactive effects of objective stressors, perceived stress, and depressive symptomatology warrant further investigation.

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Physiological Mechanisms Linking Stress, Depression, and Inflammation The experience of life stress is a common precursor of depression and inflammatory responses to stressors may play a causal role in this relationship. Stress-induced activation of the sympathetic-adrenal-medullary (SAM) and HPA axes provokes the release of stress hormones (e.g., epinephrine and norephinephrine) that stimulate the release of inflammatory markers, including cytokines, that affect the CNS. It is well documented that cytokines elicit sickness behaviors (e.g., lethargy and withdrawal) that parallel symptoms of depression (Dantzer & Kelley, 2007). Cytokines can affect the brain by entering from the periphery or via neural pathways that induce cytokine production within the brain. Although transfer of cytokines from peripheral circulation to the brain is largely prevented by the blood-brain barrier, cytokines can enter the brain via weaker areas of the blood-brain barrier as well as through active cytokine transporters (Raison, Capuron, & Miller, 2006). In addition, a key proposed route by which peripheral inflammation can affect the brain is via stimulation of peripheral afferent vagal nerves that innervate organs of the abdominal cavity (Konsman, Parnet, & Dantzer, 2002). Notably, cytokine receptors, including those for IL-1, IL-6, and TNF, are located throughout the brain. In particular, IL-1 has significant effects on the hypothalamus and hippocampus, which are key regulators of sickness behaviors (Bailey, Engler, Hunzeker, & Sheridan, 2003; Konsman et al., 2002). In addition to direct action via cytokine receptors in the brain, cytokines also affect mood and behavior by altering function of neurotransmitters including dopamine, norepinephrine, and serotonin, which are known to affect depressive symptomatology. In particular, a clear causal pathway linking inflammation to decreased serotonin (5-HT) availability has been described. Specifically, heightened levels of proinflammatory cytokines reduce the availability of trytophan (TRP), the precursor of 5-HT synthesis (Schiepers, Wichers, & Maes, 2005). For healthy individuals, mechanisms exist that help to self-limit stress responses. Normally, cortisol, a key hormone for the regulation of inflammation and stress responses, is released by the HPA axis during stress responses and then signals back to the HPA axis, eliciting termination of HPA stress responses (Figure 64.3). In addition, cortisol has robust anti-inflammatory effects on cytokine-producing cells. However, extended exposure to elevated levels of glucocorticoids (GC), such as that seen in conditions of repeated or chronic stress, may produce GC insensitivity at the level of both cytokine-producing cells and the HPA axis (Sapolsky & McEwen, 1985; Spencer, Miller, Stein, & McEwen, 1991). GC insensitivity is marked by

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Stress

Depression

Hypothalamus CRH

Brain C

Anterior Pituitary A

Cytokines ACTH

Adrenal Gland B

Cytokine Producing Cells

Cortisol

Figure 64.3 Physiological pathways linking stress, depression, and inflammation. Note: Stress-induced activation of the HPA axis results in the release of cortisol which signals back to the hypothalamus and pituitary (A), eliciting termination of HPA stress responses. Cortisol also has powerful antiinflammatory effects on cytokine-producing cells (B). However, extended exposure to elevated cortisol can produce glucocorticoid insensitivity in the HPA axis and cytokine-producing cells, provoking greater production of proinflammatory cytokines. Cytokines can affect the brain via transfer across the blood-brain barrier from peripheral circulation as well as via stimulation of afferent vagal nerves. In addition, inflammation can affect mood by reducing the availability of tryptophan, a precursor of serotonin (5-HT) (C). ACTH ⫽ Adrenocorticotropic hormone. Solid lines indicate a stimulatory association. CRH ⫽ Corticotropin-releasing hormone. Dashed lines ⫽ An inhibitory relationship.

a diminished ability of the HPA axis and cytokine producing cells to respond to cortisol, resulting in more sustained HPA axis responses and greater production of inflammatory markers. Thus, the development of GC resistance and resulting elevations in inflammatory markers has been proposed as an important pathway by which stress can contribute to depressive symptomatology (Raison & Miller, 2003). Clinical depression is frequently characterized by GC resistance, as evidenced by a reduced capacity to suppress HPA axis secretion of cortisol after administration of dexamethasone, a synthetic glucocorticoid (Modell, Yassouridis, Huber, & Holsboer, 1997). In humans, the best evidence that inflammation can play a causal role in the development of depression comes from studies in which cytokines are administered therapeutically. In particular, the proinflammatory cytokine interferonalpha is used with some cancers as well as some infectious illnesses (e.g., hepatitis). This treatment produces significant depressive symptomatology in a high percentage of

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individuals (Capuron & Miller, 2004; Dantzer & Kelley, 2007). Interferon treatment can also increase circulating IL-6 and TNF-␣, alter in HPA axis function, and dysregulate serotonin metabolism (Capuron & Miller, 2004; Dantzer & Kelley, 2007). Thus, studies of interferon treatment support the proposition that inflammation can play a causal role in the development of depression. For additional coverage of physiological pathways underlying the link between stress and depression, please refer to Chapters 6 and 7, Volume 1; and Chapters 55 and 62, this volume.

Interventions Targeting Stress, Depression, and Inflammation Certain interventions may help to break the negative cycle of stress, depression, and inflammation. For one, interventions targeting social support may be helpful. Among female cancer patients, greater social support has predicted lower levels of inflammatory markers in circulating blood and ascitic fluid (Costanzo et al., 2005; Lutgendorf, Anderson, Sorosky, Buller, & Lubaroff, 2000; Lutgendorf et al., 2002). Relatedly, religious participation, a key source of social support for many people, has predicted lower levels of IL-6 among community-based samples (Koenig et al., 1997; Lutgendorf, Russell, Ullrich, Harris, & Wallace, 2004). Interventions aimed at improving the availability and utilization of social support warrant investigation, particularly for individuals experiencing both significant stress and lack of support. Other interventions targeting stress include yoga, tai chi, and meditation. Participation in both tai chi and yoga is associated with improved mood (Waelde, Thompson, & GallagherThompson, 2004; Woolery, Myers, Sternlieb, & Zeltzer, 2004); however, limited research has attempted to link such activities with changes in inflammatory activity. One study examined effects of mindfulness-based stress reduction, which included elements of meditation and gentle yoga among breast cancer patients. Results demonstrated improvements in mood, reductions in perceived stress, and beneficial immunological changes including decreased production of interferon (IFN)—␥ and increased production of IL-4, an anti-inflammatory cytokine, by stimulated T-cells (Carlson, Speca, Patel, & Goodey, 2003). Notably, regular physical activity is associated with reductions in circulating inflammatory markers (e.g., Ford, 2002). Therefore, further investigation of activities such as tai chi and yoga that involve elements of both meditation and physical activity holds promise. In terms of clinical depression, both antidepressant medication and cognitive-behavioral therapy have been associated with reductions in inflammatory markers (Basterzi et al., 2005; Doering, Cross, Vredevoe, Martinez-Maza, & Cowan, 2007; Sharpe et al., 2001; Tuglu, Kara, Caliyurt,

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Vardar, & Abay, 2003). Such anti-inflammatory effects may contribute to the efficacy of these treatments. Indeed, the depressive symptoms induced by interferon treatment can largely be prevented or reversed by treatment with antidepressant medications (Hauser et al., 2002; Musselman et al., 2001). Accumulating research also speaks to the importance of omega-3 (n-3) polyunsaturated fatty acids (PUFAs) for both mental health and inflammatory processes. Specifically, low plasma levels of n-3 PUFA as well as high omega-6 (n-6) to n-3 ratios have been associated with the presence and severity of depressive symptoms in several studies (e.g., Frasure-Smith, Lesperance, & Julien, 2004; Hibbeln, 1998; Kiecolt-Glaser et al., 2007; Maes & Smith, 1998). PUFAs inhibit the release of pro-inflammatory cytokines including IL-6, IL-1␤, and TNF-␣ (Logan, 2003). Consistent with this evidence, higher circulating levels of n-3 PUFAs are related to lower levels of circulating pro-inflammatory cytokines (Ferrucci et al., 2006; Kiecolt-Glaser et al., 2007). Moreover, a number of studies have demonstrated that n-3 PUFA supplementation decreases depressive symptoms (for review, see Parker et al., 2006), supporting the argument that interventions targeting inflammation are a promising direction for depression treatment.

STRESS AND WOUND HEALING Psychological stress and psychosocial factors have also been linked with wound healing (Christian, Graham, Padgett, Glaser, & Kiecolt-Glaser, 2007), a clinically critical outcome. The skin is the body’s largest organ and primary immune defense, preventing bacteria, viruses, and other exogenous antigens from entering (Elias, 2005) and limiting the movement of water in and out of the body (Marks, 2004). As such, the skin’s ability to heal wounds effectively is essential to good health. The effects of stress on healing have important implications in the context of surgery and naturally occurring wounds, particularly among at-risk and chronically ill populations.

The Wound-Healing Process When tissue damage occurs in healthy individuals, healing progresses sequentially through three overlapping phases: inflammation, proliferation, and remodeling (see Baum & Arpey, 2005, figure 2; Singer & Clark, 1999). Success in later phases is highly dependent on preceding phases. The inflammatory phase, which typically lasts 5 to 7 days, is marked by vasoconstriction, blood coagulation, platelet activation, and the release of substances that attract cells to clean the area, that is, remove bacteria (Singer & Clark, 1999;

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Van De Kerkhof, Van Bergen, Spruijt, & Kuiper, 1994). The proliferative phase is characterized by recruitment and replication of cells necessary for tissue regeneration and capillary regrowth. The final stage, which may continue for weeks or months, involves contraction and tissue remodeling (see Figure 64.4). The sequence and mechanisms described here apply best to acute wounds: The molecular mechanisms by which stress affects chronic wounds, such as diabetic foot ulcers, are less well understood and are complicated by other factors (Vileikyte, 2007). A key pathway by which stress affects healing is via inflammatory processes at the site of the wound. Although prolonged or exaggerated inflammation is detrimental to health, inflammatory cytokines play a critical role in the healing cascade and a robust localized inflammatory response is ideal. Inflammatory cytokines help prevent infection, prepare injured tissue for repair, enhance recruitment and activation of additional phagocytic cells, and regulate the ability of cells to remodel damaged tissue (Lowry, 1993). Stress-induced elevations in glucocorticoids can transiently suppress pro-inflammatory cytokine production in humans (DeRijk et al., 1997). Moreover, mice treated with glucocorticoids showed impairment in the induction

Injury or Wound

Effects of Stress on Wound Healing The first human study to demonstrate the effects of stress on healing examined women experiencing the chronic stress of caregiving for a loved one with dementia. In this study, caregivers took 24% longer to heal a small standardized punch biopsy wound than did well-matched controls (Kiecolt-Glaser, Marucha, Malarkey, Mercado, & Glaser, 1995). Healing rate was determined using photographs to compare wound size to a standard dot. The same study revealed that circulating peripheral blood leukocytes (PBLs) from caregivers expressed less IL-1␤ in messenger RNA (mRNA) in response to lipopolysaccharide (LPS) stimulation than did cells from controls (Kiecolt-Glaser et al., 1995). As described earlier, a strong IL-1␤ response is desirable in the context of healing.

Time Frame

Inflammatory Phase Clot Formation Neutrophil Accumulation to control bacterial contamination Macrophage Activation to limit further tissue damage, remove injured tissue, and start tissue repair

IL-1␤ M IP -1 ␣

Chemokines Proinflammatory Cytokines Growth Factors

of IL-1 and TNF, as well as deficient wound repair (Hübner et al., 1996). Although other mechanisms are implicated in the link between stress and healing, the interactive roles of pro-inflammatory cytokines and glucocorticoid hormones are the best delineated to date. Further evidence of their role is demonstrated in the studies described in the following subsection.

␣

F-

TN

Proliferative Phase Re-Epithelialization to cover the injured tissue Granulation Tissue Formation which includes: Fibroplasia: fibroblast proliferation and new tissue deposition Angiogenesis: new blood vessel formation

GM IL-8 -C SF

Minutes

IL-1␣ CCL2

Hours

PD

GF

Days bFGF

Weeks

VEGF

Remodeling Phase

-1

KGF

CP

IL-6

M

IL-10 CXCL1

F-

␤

Progressive restoration of structure and function Collagen Maturation Reduction in Cellularity Scar Formation

Months

TG

Years

Figure 64.4 Stages of wound healing. Note: In healthy individuals, healing progresses sequentially through three overlapping phases: (1) inflammatory phase, (2) proliferative phase, and (3) remodeling phase. Stress can affect progression through these stages via multiple immune and neuroendocrine pathways. This chapter focuses on the interactive role of glucocorticoids and cytokines (e.g., IL-8, IL-1␣, IL-1␤, IL-6, and TNF-␣). However, additional cytokines, chemokines, and growth factors are important to healing. These include CXC-chemokine ligand 1 (CXCL1), CC-chemokine ligand 2 (CCL2), granulocyte

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macrophage colony-stimulating factor (GM-CSF), monocyte chemotactic protein-1 (MCP-1), macrophage inflammatory protien-1 alpha (MIP-1␣), vascular endothelial growth factor (VEGF), transforming growth factor-␤, (TGF-␤), keratinocyte growth factor (KGF), platelet-derived growth factor (PDGF), and basic fibroblast growth factor (bFGF). For a broader review of physiological mechanisms relevant to wound healing, see Werner and Grose (2003). From “Stress and Wound Healing,” by L. M. Christian, J. E. Graham, D. A. Padgett, R. Glaser, and J. K. Kiecolt-Glaser, 2007, Neuroimmunomodulation, 13, p. 338. Reprinted with permission.

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Subsequent research demonstrated that milder stress also impairs healing. In a sample of 11 dental students, mucosal punch biopsy wounds placed in the hard palate healed an average of 40% more slowly during an examination period than during a vacation period, which was rated as less stressful by participants (Marucha, KiecoltGlaser, & Favagehi, 1998). This effect was remarkably reliable: Every student in the study healed more slowly during exams than during vacation. Moreover, in concordance with studies on caregiving stress, production of IL1␤ mRNA by LPS-stimulated peripheral blood leukocytes (PBLs) was reduced in every student during the examination period compared with the vacation period. Further research has examined cytokine production at the local wound site. In a study of 36 women, blister wounds were created using a suction blister device that produced 8 sterile 8 mm blister wounds (Glaser, Kiecolt-Glaser, et al., 1999). A plastic template with 8 wells was placed over the blister wounds and each well was filled with the woman’s serum and a salt solution, allowing cells to migrate into the blister chambers. Women reporting greater stress had significantly lower levels of two key cytokines (IL-1␣ and IL-8) at the wound site (Glaser, Kiecolt-Glaser, et al., 1999). By demonstrating an association between stress and local cytokine production, this study significantly extended previous data linking stress and mechanisms associated with healing. Dramatically, even a single 30-minute marital conflict discussion in a laboratory setting can slow wound healing: Married couples healed standardized blister wounds more slowly after a conflictive interaction than after a supportive interaction (Kiecolt-Glaser et al., 2005). Decreased production of three key cytokines—IL-6, IL-1␤, and TNF-␣—was observed at the wound site following conflict compared with a supportive interaction. Furthermore, couples who demonstrated consistently high levels of hostile behavior during both conflictive and supportive interactions healed wounds at 60% of the rate of low-hostile couples. Other research has examined relationships between wound healing and subjective stress. Among healthy males, greater perceived stress predicted slower healing of a punch biopsy wound from 7 to 21 days postwounding (Ebrecht et al., 2004). Healing was also significantly related to cortisol levels: Greater morning increases in cortisol predicted slower healing. Healing in this study was assessed with ultrasound biomicroscopy, a relatively new imaging technique that uses high-resolution ultrasound scanning to measure wound depth as well as circumference. This approach provides an assessment of healing in deep tissue layers and enables measurement of wound circumference that is not impeded by scab formation (Dyson et al., 2003). Stress also has measurable effects on healing outside a controlled laboratory setting. Depression and anxiety were

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associated with healing among elderly men and women with chronic leg ulcers: Those reporting greater than average symptoms of depression or anxiety were four times more likely to be categorized as slow healers compared with those reporting less distress (Cole-King & Harding, 2001). Similarly, in a sample of adults undergoing hernia surgery, self-reported worry about surgery predicted slower healing time, even after controlling for age, gender, and type of anesthetic (Broadbent, Petrie, Alley, & Booth, 2003). Moreover, greater preoperative stress predicted lower levels of IL-1 in the wound fluid, while greater worry about surgery was related to lower matrix metalloproteinase-9 (MMP-9) in the wound fluid (Broadbent et al., 2003). MMP-9, which is regulated by cytokines IL-1 and IL-6, facilitates cellular migration within the wound area, and thus aids in tissue remodeling (Pajulo et al., 1999). Animal models support and extend findings related to causal mechanisms linking stress and healing. Mice exposed to periods of restraint stress for 3 days before and 5 days following wounding healed punch biopsy wounds 27% more slowly than did nonstressed controls (Padgett, Marucha, & Sheridan, 1998). Restraint-stressed mice also had reduced cellularity in the margins of their punch biopsy wounds, particularly early in the healing process and significantly higher levels of serum corticosterone compared with unstressed controls. Notably, when the stressed animals were treated with the glucocorticoid receptor antagonist RU40555, their healing rates were equivalent to nonstressed animals (Padgett et al., 1998). Thus, results from animal models confirm that the suppressive effects of glucocorticoids on inflammatory activity play an important role in stress-induced delays in healing. Thus, the effects of stress on glucocorticoid functioning and subsequent effects on decreases in localized inflammatory responses to injury represent a primary pathway by which stress impairs wound healing (Figure 64.5). Glucocorticoids have multiple effects that can disrupt the inflammatory stage of healing, particularly early on. These effects include (a) suppression of immune cell differentiation and proliferation, (b) reduced expression of cell adhesion molecules that play an important role in trafficking cells to the site of the wound, (c) decreases in nuclear factor kappa B (NF␬-B) activity, which results in decreased pro-inflammatory gene expression (for review, see Glaser & Kiecolt-Glaser, 2005; Godbout & Glaser, 2006; Padgett & Glaser, 2003). In addition, disruption of inflammatory processes can prevent the proper cleaning and clearance of bacteria from a wound site, resulting in greater risk for infection, which is also associated with delayed healing. Dysregulation of the inflammatory stage of healing is important because the stages of healing are overlapping and interdependent. Therefore, delay in the early inflammatory

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STRESS

HPA Axis Activation

Glucocorticoid Release

Disruption of Inflammatory Stage of Wound Healing • Suppression of immune cell proliferation and differentiation • Reduced expression of cell adhesion molecules • Decreases in NF␬-B activity, reducing proinflammatory gene expression at site of wound • Increased likelihood of infection at wound site

Disruption of Later Stages of Wound Healing

not result in changes in resting cortisol levels or perceived stress over time. Further research is needed to examine mechanistic pathways by which exercise may benefit healing. Animal work suggests that social contact may mitigate effects of stress on healing. Among hamsters subjected to restraint stress, those who were individually housed (isolated) showed significant impairment of cutaneous wound healing, whereas those who were pair-housed did not (Detillion, Craft, Glasper, Prendergast, & DeVries, 2004). The adverse effect on healing observed in isolated hamsters was driven by stress-induced increases in serum cortisol; socially housed hamsters had significantly lower serum cortisol concentrations than their isolated counterparts. The protective effects of social housing in this study appeared to be at least partly mediated by oxytocin, a hormone that is released during social contact and that may facilitate social bonding. The administration of an oxytocin antagonist to socially housed animals delayed healing and treatment of isolated animals with oxytocin, attenuated their stress-induced cortisol increases, and speeded healing (Detillion et al., 2004). Thus, these data support the notion that cortisol plays a key role in the stress-healing link.

STRESS AND INFECTIOUS AGENTS Delayed Healing

Figure 64.5 healing.

Physiological pathways linking stress to wound

Note: Stress delays wound healing by affecting early inflammatory processes via glucocorticoids. Delays in the inflammatory stage of healing causes dysregulation of later stages, resulting in delayed healing.

stage disrupts progression of subsequent stages of healing, ultimately resulting in increased time to healing. This, in turn, can have important clinical implications for recovery from surgery and naturally occurring wounds. Interventions for Enhancing Wound Healing Exercise and social contact are two interventions that have been used to target stress or the effects of stress with the goal of improving healing. First, recent data demonstrate that regular physical activity can speed healing. Older adults who completed a 4-week exercise intervention healed standard punch biopsy wounds 25% more quickly than did their less active counterparts (Emery, Kiecolt-Glaser, Glaser, Malarkey, & Frid, 2005). These effects were notable because of the low self-reported stress across the sample; effects may be even greater among individuals reporting more distress. In this study, exercise did

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Turning to other models used to study effects of stress on immune-relevant outcomes, stress has been linked to impaired response to infectious illness in three related areas: vaccination, experimental exposure to infectious illness, and latent viruses. Each of these models has its own strengths, and each provides clinically relevant information about immune function in a unique manner (see Figure 64.6). These models are useful, in part, because there is unexplained variability in immune responses to each type of challenge. Individuals demonstrate varying degrees of susceptibility to infection on exposure to the same infectious agent. In addition, among those who do become infected, there is a significant range of severity and duration of illness experienced. Stress contributes to such variability in response to infectious agents. Vaccination Measuring Immune Responses to Vaccination Studies of immune responses to vaccination provide clinically relevant information in at least two ways. First, an adequate immune response to vaccination is required for the vaccine to provide protection against the antigen in question. Second, immune responses to vaccination serve as a proxy measure of how well an individual’s immune system

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Vaccination Outcomes

• Cellular and humoral immune responses

Strengths

• Highly ethical model for studying immune response in humans

Clinical relevance

• An adequate immune response is required for a vaccine to confer immunity • Responses to vaccination provide a proxy for responses to naturally occurring antigens Infectious Illness

Outcomes

• Infection and clinical symptoms, cellular and humoral immune responses

Strengths

• Laboratory exposure provides excellent control of possible confounding variables

Clinical relevance

• Evidence of effects of stress on both susceptibility to and severity of infectious illness Latent Viruses

Outcomes

• Antibody titers, cellular immune responses

Strengths

• Study of common latent viruses does not require exposure to an infectious agent

Clinical relevance

• Poor control of latent viruses indicates impaired cellular immune function • Latent virus reactivation contributes to morbidity and mortality among immunocompromised

Figure 64.6 Models for studying immune responses to infectious agents.

would respond if he or she were exposed to the actual infectious agent (Kiecolt-Glaser, Glaser, Gravenstein, Malarkey, & Sheridan, 1996). Consistent with this notion, poorer immune responses to vaccination are predictive of greater likelihood of experiencing clinical illness (Plotkin, 2001). A notable strength of this methodology is that vaccination is beneficial to people, with some vaccines being highly recommended for at-risk populations (e.g., influenza vaccination among the elderly). Therefore, vaccination provides a highly ethical methodology for studying clinically relevant immune outcomes in humans. As described, the immune system can be broadly divided into two arms: the innate immune system (the rapid nonspecific defense against an antigen), and the adaptive immune system, which mounts a slower, antigen-specific response. Studies of vaccine response examine the ability of the adaptive immunity to form and maintain immunological memory after exposure to an antigen. Effective responses to vaccination involve activation of both the humoral and cellular arms of the adaptive immune system. The humoral immune response is governed by Bcells and marked by antibody production. Antigen-specific antibodies, produced by B-lymphocytes, can opsonize the antigen (tag it for destruction by other immune cells), and neutralize it by preventing it from further interacting with the host’s cells. Therefore, it is beneficial for an individual to demonstrate a robust antibody response to vaccination that is maintained well over time. Parameters for a sufficient antibody response, referred to as seroconversion, depend on the vaccine in question. A fourfold increase in antibody titers is considered to be the standard for a sufficient response to influenza vaccine.

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The cellular immune response is governed by T-cells. Cellular immune responses to vaccination are commonly quantified in terms of production of certain cytokines (e.g., IL-2, IFN-␥), that promote cell-mediated immune responses. In the context of vaccination, higher IL-2 and IFN-␥ cytokine production to in vitro virus exposure is desirable because these cytokines activate virus-specific cytotoxic T-cells as well as natural killer cells. Effects of Psychological Factors on Vaccine Response As described above, the chronic stress of caring for a relative with dementia promotes systemic inflammation and slows wound healing. In addition, exposure to this chronic stressor impairs immune responses to vaccination. Three studies to date have demonstrated that caregivers are less likely to seroconvert following influenza vaccination compared with well-matched control subjects (Glaser, KiecoltGlaser, Malarkey, & Sheridan, 1998; Kiecolt-Glaser et al., 1996; Vedhara et al., 1999). For example, in a study of 32 caregivers and 32 demographically matched controls, caregivers were significantly less likely to achieve a fourfold increase in influenza-specific antibody levels 1 month after vaccination; this effect was more pronounced among older subjects (Kiecolt-Glaser et al., 1996). In addition, caregivers’ peripheral blood lymphocytes produced less IL-2 in response to in vitro influenza stimulation, evidencing a poorer cellular response to vaccination. In a similar study, caregivers showed impaired maintenance of the response to pneumoccocal pneumonia vaccination. Although caregiver and control groups demonstrated equivalent responses to the vaccine initially (at 2 weeks

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and 1 month), current caregivers had lower levels of antibody at 3 months and 6 months postvaccination compared with both former caregivers and controls (Glaser, Sheridan, Malarkey, MacCallum, & Kiecolt-Glaser, 2000). Notably, the immune response to influenza is largely mediated by T-lymphocytes while the immune response to pneummococcal pneumonia is not dependent on T-lymphocytes. Therefore, studies of caregivers indicate that chronic stress affects responses to both classes of vaccine. Greater self-reported stress also predicts impaired vaccine responses among younger populations. For example, in a study of 31 college students who received influenza vaccination, those who reported less perceived stress and fewer stressful life events in the period following vaccination demonstrated significantly better maintenance of antibody levels compared with students reporting greater stress (Burns, Carroll, Drayson, Whitham, & Ring, 2003). Similarly, among 260 healthy college students, a greater number of negative life events in the previous year predicted lower hepatitis B antibody levels only among those who had been vaccinated more than 1 year prior; number of negative life events was not associated with antibody level among those who were vaccinated within the past year (Burns, Carroll, Ring, Harrison, & Drayson, 2002). These data indicate that stress affected long-term maintenance of immunological memory for the antigen. Additional research has examined the effects of stress among college students on both T-cell dependent (influenza) and T-cell independent (meningococcal C) vaccinations. Those who reported a greater number of stressful life events in the year prior to vaccination had lower antibody responses to one strain of the influenza vaccine at both 5 weeks and 5 months. Moreover, although the final antibody response levels were similar at 5 months, students who reported greater stress mounted a slower antibody response to the meningococcal C vaccination (Phillips, Burns, Carroll, Ring, & Drayson, 2005). It is important to consider perceived stress in the context of objectively stressful experiences. A sample of 48 medical students underwent a series of 3 hepatitis B inoculations scheduled to coincide with three major examination periods. Those who seroconverted after the first vaccination reported significantly less stress and anxiety across the three exam periods. Moreover, following the third exam period, students who reported lower anxiety and stress across exam periods demonstrated higher antibody responses to the vaccine and a stronger T-cell response to a hepatitis B challenge in vitro (Glaser et al., 1992). A study addressed the question of which time points are most critical in terms of stress affecting immune responses to vaccination. A sample of 83 healthy young adults received influenza vaccination, and their subjective stress levels were

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measured for 2 days prior to vaccination, the day of vaccination, and the 10 days following vaccination. Although stress prior to or on the day of vaccination did not predict antibody responses, greater stress levels in the 10 days following vaccination were associated with poorer antibody response (Miller et al., 2004). The studies reviewed thus far focus on chronic stress, perceived stress, and brief naturalistic stressors (e.g., exam stress). Some evidence suggests that acute stress (e.g., 2-hour restraint stress in mice; mental arithmetic or exercise stress in people) can enhance immune responses to vaccination (Edwards et al., 2006; Silberman, Wald, & Genaro, 2003). Additional research is needed to describe in better detail the processes involved in brief acute stress relative to longer-lasting stress experiences. As reviewed, evidence for effects of stress on vaccine response is seen across a variety of vaccines and types of stressors. Variability between specific outcomes across studies may be explained in part by the different vaccines and measurement time frames used. Moreover, prior vaccination or naturalistic exposure to an antigen will affect responses to vaccination. This can cause a range restriction problem that impedes the ability to detect effects of psychosocial factors (Vedhara et al., 1999). These methodological factors should be carefully considered in research using vaccination. Studies to date demonstrate that stressors ranging from relatively brief (e.g., academic exams) to chronic (e.g., caregiving) can significantly affect the rapidity and magnitude of antibody response as well as long-term maintenance of immunological memory conferred by vaccination. Such stress is also predictive of decreased cellular immune responses to vaccination. Although beyond the scope of this chapter, animal models demonstrate multiple effects of glucocorticoid hormones on cell-trafficking, and production of pro-inflammatory cytokines and chemokines that contribute to these effects (for review, see Glaser & Kiecolt-Glaser, 2005; Godbout & Glaser, 2006; Padgett & Glaser, 2003). Notably, effects of stress on responses to vaccination are seen in both older and younger adults, although effects tend to be stronger among older adults because of age-related decreases in immune responses to vaccination. In addition, deficits in immune response to vaccination have particular importance for older adults who are more vulnerable than younger adults to serious complications and death in the face of illness such as influenza (Yoshikawa, 1983). Infectious Illness Measuring Infectious Illness Consistent with findings that stress affects immune responses to vaccination, stress also affects susceptibility, severity, and duration of infectious illnesses including influenza

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Stress and Infectious Agents

and rhinovirus (the common cold). In studies of infectious illness, the rates of both respiratory infection and clinical illness are of interest. Respiratory infection is defined by the presence of the virus in circulation or significant increases in virus-specific antibody titers following experimental exposure to the infectious agent. In contrast, clinical illness is typically defined by physician-judged severity of illness symptoms. Assessing both respiratory infection and clinical illness is important because among those exposed to a virus, only a portion will become infected. In turn, among those infected, only a portion will develop clinical symptoms. Effects of Psychosocial Factors on Infectious Illness Naturalistic studies have reported associations between stress and frequency of infectious illness. In a sample of 117 adults, the experience of stressful life events in the previous 12 months and during a 15-week observation period was assessed. During the observation period, 29 participants experienced at least one clinically verified upper respiratory illness. Risk of illness was greater among those who reported a greater number of stressful life events (Turner-Cobb & Steptoe, 1996). Although naturalistic studies provide good evidence of effects of stress on susceptibility to infectious illness, such methodology allows for limited control of important confounding variables including rates of exposure to infectious agents. The strongest evidence of effects of stress on infectious illness comes from studies in which participants have been purposefully exposed to infectious agents that can cause upper respiratory infections (URIs) and then tracked over time in a well-controlled environment. In a key study using experimental exposure methodology, Cohen and colleagues demonstrated that self-reported stress predicted susceptibility to respiratory viruses in a dose-response manner. Specifically, 394 healthy subjects were exposed to 1 of 5 respiratory viruses, while 26 control subjects were given saline nasal drops. Participants were then quarantined and their respiratory symptoms as well as their virus-specific antibody titer levels were assessed (Cohen, Tyrrell, & Smith, 1991). Individuals reporting greater stress (as determined by a composite measure of major life events, perceptions of stress, and negative affect) showed greater likelihood of developing respiratory infections as well as clinically defined colds. This effect was found across each of the five types of virus, and the effect remained after controlling for potential confounding factors including age, sex, education, season, and personality factors. Later research from the same laboratory focused on better delineating the importance of different types of stressful life events. In this study, 276 participants completed in-depth interviews assessing occurrence, severity,

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and emotional significance of life stressors in the past year (Cohen et al., 1998). Participants were exposed to one of two rhinoviruses and kept in quarantine for the following 5 days during which their experience of infectious illness was assessed. Results indicated that acute stressors did not predict increased risk of infection or clinical colds. However, the experience of chronic stress lasting 1 month or longer was associated with significantly increased risk of developing a cold. These effects were not accounted for by differences in social network characteristics, personality, or health behaviors. The same study explored potential endocrine mediators linking stress and virus susceptibility. In this sample, elevations in epinephrine and norepinephrine were associated with increased risk of developing colds. However, unexpectedly, the experience of chronic stress was not associated with higher levels of these stress hormones. This could be due in part to the stress and novelty of the experimental situation, which may have caused acute stress in all participants and masked effects of chronic stressors on these endocrine markers (Cohen et al., 1998). Other research indicates that variability in physiological responses to stress affects stress-induced susceptibility to infection. In a sample of 115 healthy individuals, those who showed larger cortisol responses to laboratory stressors experienced greater risk of developing clinically verified colds under conditions of higher stress (Cohen et al., 2002). Stress level was unrelated to URI risk among those showing smaller cortisol responses to acute stress. Thus, individuals who experience greater physiological reactivity to stress may be more vulnerable to infectious illness in conditions of stress. Subsequent research, also from the same laboratory, explored the role of inflammation in explaining the link between stress and symptom severity. In a study of 55 subjects who were exposed to an influenza virus, those reporting higher stress experienced greater symptoms of illness and greater mucous weight, as well as greater inflammatory responses to the infection, as indicated by higher IL-6 levels in nasal secretions (Cohen, Doyle, & Skoner, 1999). These data suggest that stress may contribute to an exaggerated local inflammatory response to infection, contributing to illness severity. However, in the context of infectious illness, effects of stress may differ for the production of other cytokines and at other sites (e.g., lungs). In mice exposed to influenza virus, restraint stress reduced the production of the pro-inflammatory cytokine IL-1␣, but did not affect the production of IL-6 in the lungs (Konstantinos & Sheridan, 2001). In another animal study, restraint stress resulted in reduced production of IL-6 from splenocytes, but enhanced production of IL-6 from regional lymph nodes (Dobbs, Feng, Beck, & Sheridan, 1996). Thus, the effects

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of stress on inflammatory mediators during viral infection are complex and may best be understood as dysregulated, rather than enhanced or suppressed. Moreover, the clinical relevance of noted alterations may differ for susceptibility versus severity of illness. In sum, several well-controlled laboratory studies have demonstrated a relationship between stress and susceptibility, severity, and duration of infectious illness (see Figure 64.7). Naturalistic studies report parallel findings. Human models implicate glucocorticoids and local cytokine production in the link between stress and illness susceptibility and severity. Animal models support and extend these findings, providing evidence for effects of glucocorticoids, dysregulated cytokine production, alterations in cell trafficking, and dysregulated antibody responses (Bonneau, Padgett, & Sheridan, 2001; Konstantinos & Sheridan, 2001). A continued focus on physiological pathways will help to better delineate the role of specific immune alterations in affecting the susceptibility, duration, and severity of infectious illness. Latent Viruses Measuring Immune Control of Latent Viruses Normally, viruses are eliminated from the host when infection is resolved. However, some viruses are maintained in the body in a latent state in asymptomatic individuals after primary infection. Such viruses include those from the herpesviruses family, such as herpes simplex virus (HSV) I and II, varicella-zoster virus (VZV), Epstein-Barr virus (EBV), and cytomegalovirus (CMV). After primary infection,

latent viruses are maintained within certain cells (e.g., Blymphocytes for EBV). Although the immune system is typically quite effective in controlling latent viruses, reactivation of the opportunistic virus can occur when cellular immunity wanes. During reactivation, the virus produces greater quantities of viral proteins. This elicits cellular and humoral immune responses and results in the production of virus-specific antibody. Thus, higher levels of antigenspecific antibody can be used as an indicator of impaired cell-mediated control of a latent virus. One strength of examining immune control of latent viruses is that some forms are ubiquitous and can therefore be studied in a variety of populations. More than 95% of the adult population is infected with EBV (Wolf & Morag, 1998). Therefore, the study of such latent viruses does not require experimental exposure to an antigen, a clear benefit for human studies. Ineffective control of latent viruses can have important clinical implications among immunosuppressed individuals. Reactivation of latent viruses predicts increased mortality and morbidity among organ transplant recipients (Gray et al., 1995) and individuals infected with HIV (Cruess et al., 2000). Although reactivation of EBV typically causes no symptoms in healthy individuals (Hess, 2004), reactivation of HSV I or II can cause cold sores (Bystricka & Russ, 2005), and VZV reactivation can cause shingles (Quinlivan & Breuer, 2006). Even in the absence of clinical disease, reactivation of a latent virus provides a sensitive marker of impairment in cell-mediated immunity. Thus, studies of viral latency provide clinically relevant information even among asymptomatic individuals.

STRESS

Health and Social Behaviors

Exposure to Infectious Agents

Neuroendocrine Effects

Immune Effects

Impaired Response to Infectious Agents ↑ susceptibility, duration, severity of infectious illness ↓ humoral and cellular responses to vaccination ↓ cellular control of latent viruses

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Figure 64.7 Behavioral and neuroendocrine pathways linking stress and response to infectious agents. Note: By affecting health behaviors, social behaviors, and neuroendocrine function, stress can impair both humoral and cellular immune responses to infectious agents. This has important clinical implications for immune responses to exposure to infectious illnesses, vaccination, and control of latent viruses. (Although social support can buffer negative effects of stress, contact with a diverse social network can result in greater exposure to infectious agents.)

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Stress and Infectious Agents

Effects of Psychosocial Factors on Immune Control of Latent Viruses Given the effects of chronic stress on other aspects of immune function, it is not surprising that chronic stress impairs immune control of latent viruses. Individuals who are caregivers for a family member with dementia exhibited higher EBV (Kiecolt-Glaser et al., 1991) and HSV-1 (Glaser & Kiecolt-Glaser, 1997) antibody levels compared with well-matched controls. In addition, individuals experiencing the enduring stress of living near the Three Mile Island damaged nuclear plant showed higher HSV-1 antibody levels, compared with matched community controls living 80 miles away from the nuclear plant (McKinnon, Weisse, Reynolds, Bowles, & Baum, 1989). Stress from disrupted significant relationships also affects immune function (Graham, Christian, & KiecoltGlaser, 2006a), including immune control of latent viruses. Women who had been divorced or separated for 1 year or less showed higher levels of EBV antibody compared with demographically matched women who were currently married (Kiecolt-Glaser et al., 1987). Similarly, men who were unsatisfied in their marriage had higher levels of EBV antibody than their happily married counterparts (KiecoltGlaser et al., 1988). More transient stressors also affect viral latency. In prospective studies of examination stress, medical students exhibited higher EBV, HSV-1, and CMV antibody titers on the day of an academic examination, compared with several weeks before or after the exam (Glaser, Friedman, et al., 1999; Glaser, Kiecolt-Glaser, Speicher, & Holliday, 1985; Sarid, Anson, Yaari, & Margalith, 2001). Other research has shown that the intense stress of space flight (Mehta, Stowe, Feiveson, Tyring, & Pierson, 2000, 2004; Payne, Mehta, Tyring, Stowe, & Pierson, 1999; Pierson, Stowe, Phillips, Lugg, & Mehta, 2005) and Antarctic expeditions (Mehta, Pierson, Cooley, Dubow, & Lugg, 2000) resulted in higher EBV- and CMV-specific antibody titers and decreased EBV-specific T-cell responses. This is not surprising, given that effects of stress on viral latency are seen in response to much less significant stressors (e.g., examinations). In addition to effects of objective stressors, other psychological factors have also been associated with poorer control of latent virus. Higher levels of EBV-specific antibodies have been found in students who reported a greater tendency to repress their emotions (Esterling, Antoni, Kumar, & Schneiderman, 1990), higher levels of anxiety (Esterling, Antoni, Kumar, & Schneiderman, 1993), and greater loneliness (Glaser et al., 1985). Similarly, patients with syndromal or subsyndromal symptoms of depression have shown higher levels of HSV-1 antibody and poorer VZV-specific T-cell immunity than those without depressive

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symptoms (Delisi et al., 1986; Irwin et al., 1998; Robertson et al., 1993) Conversely, older women reporting higher vigor during housing relocation had lower EBV-antibody titers compared with women who reported lower levels of vigor (Lutgendorf et al., 2001). Thus, in addition to objective stressors, certain psychological characteristics (e.g., mood, ways of coping) may be associated with impaired control of latent viruses. Other research has addressed physiological mechanisms by which stress may impair control of latent viruses. In studies of examination stress, students exhibited poorer cytotoxic and proliferative T-cell responses to in vitro EBV exposure on examination days compared with nonexamination days (Glaser et al., 1987, 1993). In addition, exam stress predicted suppression of leukocyte migration inhibition factor (MIF), a condition associated with HSV-2 lesions (Sheridan, Donnenberg, Aurelian, & Elpern, 1982). Glucocorticoids may also play a role in stress-induced reactivation of latent viruses; indeed, glucocorticoids can induce latent EBV and CMV replication in vitro (Tanaka et al., 1984). However, some human studies have failed to find an association between basal cortisol levels and control of latent viruses (Cruess et al., 2000; Glaser, Pearl, Kiecolt-Glaser, & Malarkey, 1994). Cacioppo et al. (2002) observed that increased tonic plasma concentration of synthetic glucocorticoid hormone did not lead to enhanced in vitro EBV replication. However, when the synthetic glucocorticoid hormone was administered in a pulsative manner with varying concentration, mimicking a dysregulation of its diurnal variation, increased EBV replication occurred. Thus, assessments of patterns of cortisol release may be key to understanding the role of glucocorticoids in stressinduced virus reactivation. Interventions to Improve Response to Infectious Agents A variety of factors have been demonstrated to reduce stress and benefit immune response to infectious agents. As emphasized throughout this chapter, social support can serve as an important stress buffer. College students who reported greater social support showed stronger antibody responses to influenza vaccination (Phillips et al., 2005) as well as better cellular and humoral immune responses to hepatitis B vaccination (Glaser et al., 1992). Similarly, when exposed to infectious agents in a controlled laboratory environment, participants reporting more social ties were less likely to develop colds than those reporting fewer social ties (Cohen, Doyle, Skoner, Rabin, & Gwaltney, 1997). Although social support may buffer the effects of stress, having a diverse social network may also result in greater exposure to a broader diversity of viruses. In fact,

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in a naturalistic study, greater social network diversity was associated with fewer URIs only under conditions of low stress (Hamrick, Cohen, & Rodriguez, 2002). A key beneficial component of social support may be that it provides an outlet for emotional disclosure. Indeed, interventions designed to encourage disclosure benefit immune function. In a sample of 40 students, those who completed a writing task involving emotional disclosure prior to receiving hepatitis B vaccination had higher antibody titers at 6 months postvaccination than did control participants (Petrie, Booth, Pennebaker, Davison, & Thomas, 1995). Similarly, college students who were randomly assigned to write or talk about a traumatic event had lower EBV antibody titers at the end of the 4-week intervention while students in a control condition showed no such reduction (Esterling, Antoni, Fletcher, Margulies, & Schneiderman, 1994). Other studies have examined stress management groups that involved elements of social support and emotional disclosure, as well as a focus on coping or problem solving. In a study of caregivers, those who participated in a stress management group for an hour per week for 8 weeks and subsequently received influenza vaccination were more likely to achieve a fourfold increase in antibody titer than caregivers who did not receive the intervention (Vedhara et al., 2003). A number of studies have examined effects of cognitive-behavioral stress management interventions among HIV-infected and at-risk gay men. Such interventions have resulted in decreases in HSV-2 and EBV antibody titers, indicating improved control of latent viruses (Carrico et al., 2005; Esterling et al., 1992; Lutgendorf et al., 1997). Stress reduction through relaxation and meditation has also been examined. In a study of 45 older adults, those randomized to relaxation training (3 practices/week for 1 month) exhibited decreased HSV-1 antibody titers and less distress at the end of the intervention, whereas no such changes were seen among control participants (Kiecolt-Glaser et al., 1985). Reductions seen in HSV-1 were maintained at 1-month follow-up. A more recent study demonstrated that meditation is a promising intervention strategy. In a study of 48 healthy adults, those who completed an 8-week meditation intervention prior to receiving influenza vaccination exhibited better antibody responses to vaccination compared with a waitinglist control group. The meditation group also demonstrated decreased trait anxiety and increased left-sided brain activation, presumably reflecting more positive affect (Davidson et al., 2003). In addition, a recent study examined effects of tai chi, which involves physical activity as well as meditation (Irwin, Olmstead, & Oxman, 2007). A group of 112 older adults completed either a 16-week tai chi intervention or a

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health education group. At the end of the intervention, participants in both groups received a varicella-zoster virus (VZV) vaccine. VZV antibody levels were measured at the conclusion of the intervention, and following vaccination. Remarkably, tai chi alone resulted in increases in VZVspecific immunity equivalent to that conferred by vaccination. Moreover, tai chi in combination with vaccination had an additive effect. Thus, participants in the tai chi intervention group exhibited greater VZV-specific cell-meditated immunity than the health education group both at the end of the 16-week intervention and 9 weeks after the vaccination. Participants in the tai chi intervention group also showed significant improvements in mental health as measured by the SF-36, a quality-of-life index.

SUMMARY Stressors ranging in magnitude and duration affect clinically meaningful health outcomes including inflammation, wound healing, and responses to viruses. Effects of stress on neuroendocrine parameters via the SAM and HPA axes play a primary mechanistic role in the link between stress and immune variables. In addition, mediators that are beyond the scope of this chapter are also implicated in this complex system (e.g., opioids, growth hormones, neuropeptides). Future studies should aim to more clearly delineate the multiple and complex neuroendocrine pathways linking stress to immune outcomes. We have focused on physiological mediators in the link between psychological factors and immune outcomes. However, behavior change resulting from stress also plays an important role. The appropriate measurement and control of health behaviors will continue to be important for studies seeking to elucidate physiological pathways linking psychological factors with neuroendocrine and immune function. This is especially true for research with patients experiencing chronic illness as they may show considerable variability in terms of health behaviors including medication adherence, sleep, and diet. Research to date indicates that age and stress interact to produce more significant immunological and neuroendocrine changes among older adults. An important area of future research is the examination of how early developmental experiences may set the stage for vulnerability in later life; psychosocial stressors during fetal development and early life can have lasting effects on physiology (Graham, Christian, & Kiecolt-Glaser, 2006b). A continued emphasis on understanding the interactive effects of stress and age throughout the life span will contribute to our understanding of the clinical significance of stressrelated immune dysregulation.

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References 1275

There have been successful attempts to intervene in stress processes to benefit immune-related health outcomes. Such interventions include stress management, meditation, yoga, tai chi, exercise, dietary changes, psychotherapy, and antidepressant medications. In addition, social support can provide an important buffer from the effects of stress on health. In future research, an increased emphasis on the identification of physiological mechanisms underlying successful interventions would be of great value. Over the past 25 years, research examining effects of stress on immune outcomes has grown dramatically. A continued emphasis on appropriately controlling for behavioral variables, delineating physiological mechanisms, and demonstrating the clinical significance of noted physiological alterations will contribute to future advances.

REFERENCES Anisman, H., & Merali, Z. (2003). Cytokines, stress, and depressive illness: Brain-immune interaction. Annals of Medicine, 35, 2–11. Bailey, M., Engler, H., Hunzeker, J., & Sheridan, J. F. (2003). The hypothalamic-pituitary-adrenal axis and viral infection. Viral Immunology, 16, 141–157. Basterzi, A. D., Aydemir, C., Kisa, C., Aksaray, S., Tuzer, V., Yazici, K., et al. (2005). Il-6 levels decrease with ssri treatment in patients with major depression. Human Psychopharmacology, 20, 473–476. Baum, C. L., & Arpey, C. J. (2005). Normal cutaneous wound healing: Clinical correlation with cellular and molecular events. Dermatologic Surgery, 31, 674–686. Black, P. H., & Garbutt, L. D. (2002). Stress, inflammation and cardiovascular disease. Journal of Psychosomatic Research, 52, 1–23. Bonneau, R. H., Padgett, D. A., & Sheridan, J. F. (2001). Psychoneuroimmune interactions in infectious disease: Studies in animals. In R. Ader, D. L. Felten, & N. Cohen (Eds.), Psychoneuroimmunology (3rd ed., Vol. 2, pp. 483–498). San Diego, CA: Academic Press.

Carlson, L. E., Speca, M., Patel, K. D., & Goodey, E. (2003). Mindfulnessbased stress reduction in relation to quality of life, mood, symptoms of stress, and immune parameters in breast and prostate cancer outpatients. Psychosomatic Medicine, 65, 571–581. Carrico, A. W., Antoni, M. H., Pereira, D. B., Fletcher, M. A., Klimas, N., Lechner, S. C., et al. (2005). Cognitive behavioral stress management effects on mood, social support, and a marker of antiviral immunity are maintained up to 1 year in HIV-infected gay men. International Journal of Behavioral Medicine, 12, 218–226. Christian, L. M., Graham, J. E., Padgett, D. A., Glaser, R., & Kiecolt-Glaser, J. K. (2007). Stress and wound healing. Neuroimmunomodulation, 13, 337–346. Christian, L. M., & Stoney, C. M. (2006). Social support versus social evaluation: Unique effects on vascular and myocardial response patterns. Psychosomatic Medicine, 68, 914–921. Cohen, S., Doyle, W. J., & Skoner, D. P. (1999). Psychological stress, cytokine production, and severity of upper respiratory illness. Psychosomatic Medicine, 61, 175–180. Cohen, S., Doyle, W. J., Skoner, D. P., Rabin, B. S., & Gwaltney, J. M. (1997). Social ties and susceptibility to the common cold. Journal of the American Medical Association, 277, 1940–1944. Cohen, S., Frank, E., Doyle, W. J., Skoner, D. P., Rabin, B. S., & Gwaltney, J. M. (1998). Types of stressors that increase susceptibility to the common cold in healthy adults. Health Psychology, 17, 214–223. Cohen, S., Harmrick, N., Rodriguez, M. S., Fedman, P. J., Rabin, B. S., & Manuck, S. B. (2002). Reactivity and vulnerability to stress-associated risk for upper respiratory illness. Psychosomatic Medicine, 64, 302–312. Cohen, S., Tyrrell, D. A., & Smith, A. P. (1991). Psychological stress and susceptibility to the common cold. New England Journal of Medicine, 325, 606–612. Cole-King, A., & Harding, K. G. (2001). Psychological factors and delayed healing in chronic wounds. Psychosomatic Medicine, 63, 216–220. Connor, T. J., & Leonard, B. E. (1998). Depression, stress and immunological activation: The role of cytokines in depressive disorders. Life Sciences, 62, 583–606. Costanzo, E. S., Lutgendorf, S. K., Sood, A. K., Anderson, B., Sorosky, J., & Lubaroff, D. M. (2005). Psychosocial factors and interleukin-6 among women with advanced ovarian cancer. Cancer, 104, 305–313.

Broadbent, E., Petrie, K. J., Alley, P. G., & Booth, R. J. (2003). Psychological stress impairs early wound repair following surgery. Psychosomatic Medicine, 65, 865–869.

Cruess, S., Antoni, M., Cruess, D., Fletcher, M. A., Ironson, G., Kumar, M., et al. (2000). Reductions in herpes simplex virus type 2 antibody titers after cognitive behavioral stress management and relationships with neuroendocrine function, relaxation skills, and social support in HIV-positive men. Psychosomatic Medicine, 62, 828–837.

Bruunsgaard, H., Pedersen, M., & Pedersen, B. K. (2001). Aging and proinflammatory cytokines. Current Opinion in Hematology, 8, 131–136.

Dantzer, R., & Kelley, K. W. (2007). Twenty years of research on cytokineinduced sickness behavior. Brain, Behavior, and Immunity, 21, 153–160.

Brydon, L., Edwards, S., Mohamed-Ali, V., & Steptoe, A. (2004). Socioeconomic status and stress-induced increases in interleukin-6. Brain, Behavior, and Immunity, 18, 281–290.

Davidson, R. J., Kabat-Zinn, J., Schumacher, J., Rosenkranz, M., Muller, D., Santorelli, S. F., et al. (2003). Alterations in brain and immune function produced by mindfulness meditation. Psychosomatic Medicine, 65, 564–570.

Burns, V. E., Carroll, D., Drayson, M., Whitham, M., & Ring, C. (2003). Life events, perceived stress and antibody response to influenza vaccination in young, healthy adults. Journal of Psychosomatic Research, 55, 569–572. Burns, V. E., Carroll, D., Ring, C., Harrison, L. K., & Drayson, M. (2002). Stress, coping, and hepatitis b antibody status. Psychosomatic Medicine, 64, 287–293.

c64.indd Sec4:1275

Capuron, L., & Miller, A. H. (2004). Cytokines and psychopathology: Lessons from interferon-␣. Biological Psychiatry, 56, 819–824.

Delisi, L. E., Smith, S. B., Hamovit, J. R., Maxwell, M. E., Glodwin, L. R., Dingman, C. W., et al. (1986). Herpes simplex virus, cytomegalovirus and Epstein-Barr virus antibody titres in sera from schizophrenic patients. Psychological Medicine, 16, 757–763.

Bystricka, M., & Russ, G. (2005). Immunity in latent herpes simplex virus infection. Acta Virologica, 49, 59–67.

Dentino, A. N., Pieper, C. F., Rao, M. K., Currie, M. S., Harris, T., Blazer, D. G., et al. (1999). Association of interleukin-6 and other biologic variables with depression in older people living in the community. Journal of the American Geriatrics Society, 47, 6–11.

Cacioppo, J., Kiecolt-Glaser, J. K., Malarkey, W. B., Laskowski, B. F., Rozlog, L. A., Poehlmann, K. M., et al. (2002). Autonomic and glucocorticoid associations with steady-state expression of latent EpsteinBarr virus. Hormones and Behavior, 42, 32–41.

DeRijk, R., Michelson, D., Karp, B., Petrides, J., Galliven, E., Deuster, P., et al. (1997). Exercise and circadian rhythm-induced variations in plasma cortisol differentially regulate interleukin-1␤ (il-1␤), il-6, and tumor necrosis factor-␣ (tnf-␣) production in humans: High sensitivity

8/19/09 5:16:08 PM

1276

Psychological Influences on Neuroendocrine and Immune Disorders

of tnf-␣ and resistance of il-6. Journal of Clinical Endocrinology and Metabolism, 82, 2182–2192.

Glaser, R., & Kiecolt-Glaser, J. K. (2005). Stress-induced immune dysfunction: Implications for health. Nature Reviews Immunology, 5, 243–251.

Detillion, C. E., Craft, T. K. S., Glasper, E. R., Prendergast, B. J., & DeVries, A. C. (2004). Social facilitation of wound healing. Psychoneuroendocrinology, 29, 1004–1011.

Glaser, R., Kiecolt-Glaser, J. K., Bonneau, R. H., Malarkey, W., Kennedy, S., & Hughes, J. (1992). Stress-induced modulation of the immune response to recombinant hepatitis b vaccine. Psychosomatic Medicine, 54, 22–29.

Dobbs, C. M., Feng, N., Beck, F. M., & Sheridan, J. F. (1996). Neuroendocrine regulation of cytokine production during experimental influenza viral infection: Effects of restraint stress-induced elevation in endogenous corticosterone. Journal of Immunology, 157, 1870–1877.

Glaser, R., Kiecolt-Glaser, J. K., Malarkey, W. B., & Sheridan, J. F. (1998). The influence of psychological stress on the immune response to vaccines. Annals of the New York Academy of Sciences, 840, 656–663.

Doering, L. V., Cross, R., Vredevoe, D., Martinez-Maza, O., & Cowan, M. J. (2007). Infection, depression, and immunity in women after coronary artery bypass: A pilot study of cognitive behavioral therapy. Alternative Therapies in Health and Medicine, 13, 18–21.

Glaser, R., Kiecolt-Glaser, J. K., Marucha, P. T., MacCallum, R. C., Laskowski, B. F., & Malarkey, W. B. (1999). Stress-related changes in proinflammatory cytokine production in wounds. Archives of General Psychiatry, 56, 450–456.

Dyson, M., Moodley, S., Verjee, L., Verling, W., Wienman, J., & Wilson, P. (2003). Wound healing assessment using 20 mhz ultrasound and photography. Skin Research and Technology, 9, 116–121.

Glaser, R., Kiecolt-Glaser, J. K., Speicher, C. E., & Holliday, J. E. (1985). Stress, loneliness, and changes in herpesvirus latency. Journal of Behavioral Medicine, 8, 249–260.

Ebrecht, M., Hextall, J., Kirtley, L.-G., Taylor, A., Dyson, M., & Weinman, J. (2004). Perceived stress and cortisol levels predict speed of wound healing in healthy male adults. Psychoneuroendocrinolgy, 29, 798–809.

Glaser, R., Pearl, D. K., Kiecolt-Glaser, J. K., & Malarkey, W. B. (1994). Plasma cortisol levels and reactivation of latent Epstein-Barr virus in response to examination stress. Psychoneuroendocrinology, 19, 765–772.

Edwards, K. M., Burns, V. E., Reynolds, T., Carroll, D., Drayson, M., & Ring, C. (2006). Acute stress exposure prior to influenza vaccination enhances antibody response in women. Brain, Behavior, and Immunity, 20, 159–168. Elias, P. M. (2005). Stratum corneum defensive functions: An integrated view. Journal of Investigative Dermatology, 125, 183–200. Emery, C. F., Kiecolt-Glaser, J. K., Glaser, R., Malarkey, W. B., & Frid, D. J. (2005). Exercise accelerates wound healing among healthy older adults: A preliminary investigation. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 60, 1432–1436. Ershler, W., & Keller, E. (2000). Age-associated increased interleukin6 gene expression, late-life diseases, and frailty. Annual Review of Medicine, 51, 245–270. Esterling, B. A., Antoni, M. H., Fletcher, M. A., Margulies, S., & Schneiderman, N. (1994). Emotional disclosure through writing or speaking modulates latent Epstein-Barr virus antibody titers. Journal of Consulting and Clinical Psychology, 62, 130–140. Esterling, B. A., Antoni, M. H., Kumar, M., & Schneiderman, N. (1990). Emotional repression, stress disclosure responses, and Epstein-Barr viral capsid antigen titers. Psychosomatic Medicine, 52, 397–410. Esterling, B. A., Antoni, M. H., Kumar, M., & Schneiderman, N. (1993). Defensiveness, trait anxiety, and Epstein-Barr viral capsid antigen antibody titers in healthy college students. Health Psychology, 12, 132–139. Esterling, B. A., Antoni, M. H., Schneiderman, N., Carver, C. S., LaPerriere, A., Ironson, G., et al. (1992). Psychosocial modulation of antibody to Epstein-Barr viral capsid antigen and human herpesvirus type-6 in HIV-1-infected and at-risk gay men. Psychosomatic Medicine, 54, 354–371. Ferrucci, L., Cherubini, A., Bandinelli, S., Bartali, B., Corsi, A., Lauretani, F., et al. (2006). Relationship of plasma polyunsaturated fatty acids to circulating inflammatory markers. Journal of Clinical Endocrinology and Metabolism, 91, 439–446.

Glaser, R., Pearson, G. R., Bonneau, R. H., Esterling, B. A., Atkinson, C., & Kiecolt-Glaser, J. K. (1993). Stress and the memory t-cell response to Epstein-Barr virus in healthy medical students. Health Psychology, 12, 435–442. Glaser, R., Rice, J., Sheridan, J., Fertel, R., Stout, J., Speicher, C. E., et al. (1987). Stress-related immune suppression: Health implications. Brain, Behavior, and Immunity, 1, 7–20. Glaser, R., Sheridan, J. F., Malarkey, W. B., MacCallum, R. C., & KiecoltGlaser, J. K. (2000). Chronic stress modulates the immune response to a pneumococcal pneumonia vaccine. Psychosomatic Medicine, 62, 804–807. Godbout, J. P., & Glaser, R. (2006). Stress-induced immune dysregulation: Implications for wound healing, infectious disease, and cancer. Journal of Neuroimmune Pharmacology, 1, 421–427. Graham, J. E., Christian, L. M., & Kiecolt-Glaser, J. K. (2006a). Close relationships and immunity. In R. Ader (Ed.), Psychoneuroimmunology (4th ed., Vol. 2, pp. 781–798). Burlington, MA: Elsevier Academic Press. Graham, J. E., Christian, L. M., & Kiecolt-Glaser, J. K. (2006b). Stress, age, and immune function: Toward a lifespan approach. Journal of Behavioral Medicine, 29. Gray, J., Wreghitt, T. G., Pavel, P., Smyth, R. L., Parameshwar, J., Stewart, S., et al. (1995). Epstein-Barr virus infection in heart and heart-lung transplant recipients: Incidence and clinical impact. Journal of Heart and Lung Transplantation, 14, 640–646. Hackam, D. G., & Anand, S. S. (2003). Emerging risk factors for atherosclerotic vascular disease: A critical review of the evidence. Journal of the American Medical Association, 290, 932–940.

Ford, E. S. (2002). Does exercise reduce inflammation? Physical activity and c-reactive protein among US adults. Epidemiology, 13, 561–568.

Hamerman, D., Berman, J. W., Albers, G. W., Brown, D. L., & Silver, D. (1999). Emerging evidence for inflammation in conditions frequently affecting older adults: Report of a symposium. Journal of the American Geriatrics Society, 47, 1016–1025.

Frasure-Smith, N., Lesperance, F., & Julien, P. (2004). Major depression is associated with lower omega-3 fatty acid levels in patients with recent acute coronary syndromes. Biological Psychiatry, 55, 891–896.

Hamrick, N., Cohen, S., & Rodriguez, M. S. (2002). Being popular can be healthy or unhealthy: Stress, social network diversity, and incidence of upper respiratory infection. Health Psychology, 21, 294–298.

Glaser, R., Friedman, S. B., Smyth, J., Ader, R., Bijur, P., Brunell, P., et al. (1999). The differential impact of training stress and final examination stress on herpesvirus latency at the United States military academy at West Point. Brain, Behavior, and Immunity, 13, 240–251.

Hauser, P., Khosla, J., Aurora, H., Laurin, J., Kling, M. A., Hill, J., et al. (2002). A prospective study of the incidence and open-label treatment of interferon-induced major depressive disorder in patients with hepatitis C. Molecular Psychiatry, 7, 942–947.

Glaser, R., & Kiecolt-Glaser, J. K. (1997). Chronic stress modulates the virus-specific immune response to latent herpes simplex virus type 1. Annals of Behavioral Medicine, 19, 78–82.

Heesen, C., Schulz, H., Schmidt, M., Gold, S., Tessmer, W., & Schulz, K. H. (2002). Endocrine and cytokine responses to acute psychological stress in multiple sclerosis. Brain, Behavior, and Immunity, 16, 282–287.

c64.indd Sec4:1276

8/19/09 5:16:09 PM

References 1277 Hess, R. D. (2004). Routine Epstein-Barr virus diagnostics from the laboratory perspective: Still challenging after 35 years. Journal of Clinical Microbiology, 42, 3381–3387. Hibbeln, J. R. (1998). Fish consumption and major depression. Lancet, 351, 1213. Hübner, G., Brauchle, M., Smola, H., Madlener, M., Fassler, R., & Werner, S. (1996). Differential regulation of pro-inflammatory cytokines during wound healing in normal and glucocorticoid-treated mice. Cytokine, 8, 548–556. Irwin, M., Costlow, C., Williams, H., Artin, K. H., Chan, C. Y., Stinson, D. L., et al. (1998). Cellular immunity to varicella-zoster virus in patients with major depression. Journal of Infectious Diseases, 178, S104–S108. Irwin, M., Olmstead, R., & Oxman, M. N. (2007). Augmenting immune responses to varicella zoster virus in older adults: A randomized, controlled trial of tai chi. Journal of the American Geriatrics Society, 55, 511–517. Ishihara, K., & Hirano, T. (2002). Il-6 in autoimmune disease and chronic inflammatory proliferative disease. Cytokine and Growth Factor Reviews, 13, 357–368. Johnson, J. D., O’Connor, K. A., Deak, T., Stark, M., Watkins, L. R., & Maier, S. F. (2002). Prior stressor exposure sensitizes lps-induced cytokine production. Brain, Behavior, and Immunity, 16, 461–476. Jones, S. A., Horiuchi, S., Topley, N., Yamamoto, N., & Fuller, G. M. (2001). The soluble interleukin 6 receptor: Mechanisms of production and implications in disease. FASEB Journal, 15, 43–58. Kendler, K. S., Karkowski, L. M., & Prescott, C. A. (1999). Causal relationship between stressful life events and the onset of major depression. American Journal of Psychiatry, 156, 837–841. Kiecolt-Glaser, J. K., Belury, M. A., Porter, K., Beversdorf, D., Lemeshow, S., & Glaser, R. (2007). Depressive symptoms, omega-6: Omega-3 fatty acids, and inflammation in older adults. Psychosomatic Medicine, 69, 217–224.

Konsman, J. P., Parnet, P., & Dantzer, R. (2002). Cytokine-induced sickness behavior: Mechanisms and implications. Trends in Neurosciences, 25, 154–159. Konstantinos, A. P., & Sheridan, J. F. (2001). Stress and influenza viral infection: Modulation of proinflammatory cytokine responses in the lung. Respiration Physiology, 128, 71–77. LeMay, L. G., Vander, A. J., & Kluger, M. J. (1990). The effects of psychological stress on plasma interleukin-6 activity in rats. Physiology and Behavior, 47, 957. Logan, A. C. (2003). Neurobehavioral aspects of omega-3 fatty acids: Possible mechanisms and therapeutic value in major depression. Alternative Medicine Review: Journal of Clinical Therapeutic, 8, 410–425. Lowry, S. F. (1993). Cytokine mediators of immunity and inflammation. Archives of Surgery, 28, 1235–1241. Lutgendorf, S. K., Anderson, B., Sorosky, J. I., Buller, R. E., & Lubaroff, D. M. (2000). Interleukin-6 and use of social support in gynecologic cancer patients. International Journal of Behavioral Medicine, 7, 127–142. Lutgendorf, S. K., Antoni, M. H., Ironson, G., Klimas, N., McCabe, P., Cleven, K., et al. (1997). Cognitive-behavioral stress management decreases dysphoric mood and herpes simplex virus-type 2 antibody titers in symptomatic HIV-seropositive gay men. Journal of Consulting and Clinical Psychology, 65, 31–43. Lutgendorf, S. K., Garand, L., Buckwalter, K. C., Reimer, T. T., Hong, S., & Lubaroff, D. M. (1999). Life stress, mood disturbance, and elevated interleukin-6 in healthy older women. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 54, M434–M439. Lutgendorf, S. K., Johnsen, E. L., Cooper, B., Anderson, B., Sorosky, J. I., Buller, R. E., et al. (2002). Vascular endothelial growth factor and social support in patients with ovarian carcinoma. Cancer, 95, 808–815.

Kiecolt-Glaser, J. K., Dura, J. R., Speicher, C. E., Trask, O. J., & Glaser, R. (1991). Spousal caregivers of dementia victims: Longitudinal changes in immunity and health. Psychosomatic Medicine, 53, 345–362.

Lutgendorf, S. K., Logan, H., Costanzo, E., & Lubaroff, D. (2004). Effects of acute stress, relaxation, and a neurogenic inflammatory stimulus on interleukin-6 in humans. Brain, Behavior, and Immunity, 18, 55–64.

Kiecolt-Glaser, J. K., Fisher, L. D., Ogrocki, P., Stout, J. C., Speicher, C. E., & Glaser, R. (1987). Marital quality, marital disruption, and immune function. Psychosomatic Medicine, 49, 31–34.

Lutgendorf, S. K., Reimer, T. T., Harvey, J. H., Marks, G., Hong, S. Y., Hillis, S. L., et al. (2001). Effects of housing relocation on immunocompetence and psychosocial functioning in older adults. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 56, M97–M105.

Kiecolt-Glaser, J. K., Glaser, R., Gravenstein, S., Malarkey, W. B., & Sheridan, J. (1996). Chronic stress alters the immune response to influenza virus vaccine in older adults. Proceedings of the National Academy of Sciences, USA, 93, 3043–3047. Kiecolt-Glaser, J. K., Glaser, R., Williger, D., Stout, J., Messick, G., Sheppard, S., et al. (1985). Psychosocial enhancement of immunocompetence in a geriatric population. Health Psychology, 4, 25–41. Kiecolt-Glaser, J. K., Kennedy, S., Malkoff, S., Fisher, L., Speicher, C. E., & Glaser, R. (1988). Marital discord and immunity in males. Psychosomatic Medicine, 50, 213–229. Kiecolt-Glaser, J. K., Loving, T. J., Stowell, J. R., Malarkey, W. B., Lemeshow, S., Dickinson, S. L., et al. (2005). Hostile marital interactions, proinflammatory cytokine production, and wound healing. Archives of General Psychiatry, 62, 1377–1384. Kiecolt-Glaser, J. K., Marucha, P. T., Malarkey, W. B., Mercado, A. M., & Glaser, R. (1995). Slowing of wound healing by psychological stress. Lancet, 346, 1194–1196. Kiecolt-Glaser, J. K., Preacher, K. J., MacCallum, R. C., Atkinson, C., Malarkey, W. B., & Glaser, R. (2003). Chronic stress and age-related increases in the proinflammatory cytokine il-6. Proceedings of the National Academy of Sciences, USA, 100, 9090–9095. Kirschbaum, C., Pirke, K. M., & Hellhammer, D. H. (1993). The “trier social stress test”: A tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology, 28, 76–81.

c64.indd Sec4:1277

Koenig, H. G., Cohen, H. J., George, L. K., Hays, J. C., Larson, D. B., & Blazer, D. G. (1997). Attendance at religious services, interleukin-6, and other biological parameters of immune function in older adults. International Journal of Psychiatry in Medicine, 27, 233–250.

Lutgendorf, S. K., Russell, D., Ullrich, P., Harris, T. B., & Wallace, R. (2004). Religious participation, interleukin-6, and mortality in older adults. Health Psychology, 23, 465–475. Maes, M., Bosmans, E., De Jongh, R., Kenis, G., Vandoolaeghe, E., & Neels, H. (1997). Increased serum il-6 and il-1 receptor antagonist concentrations in major depression and treatment resistant depression. Cytokine, 9, 853–858. Maes, M., Meltzer, H. Y., Bosmans, E., Bergmans, R., Vandoolaeghe, E., Ranjan, R., et al. (1995). Increased plasma concentrations of interleukin-6, soluble interleukin-6, soluble interleukin-2 and transferrin receptor in major depression. Journal of Affective Disorders, 34, 301–309. Maes, M., & Smith, R. S. (1998). Fatty acids, cytokines, and major depression. Biological Psychiatry, 43, 313–314. Marks, R. (2004). The stratum corneum barrier: The final frontier. Journal of Nutrition, 134, S2071–S2021. Marucha, P. T., Kiecolt-Glaser, J. K., & Favagehi, M. (1998). Mucosal wound healing is impaired by examination stress. Psychosomatic Medicine, 60, 362–365. McDade, T. W., Hawkley, L. C., & Cacioppo, J. T. (2006). Psychosocial and behavioral predictors of inflammation in middle-aged and older adults: The Chicago health, aging, and social relations study. Psychosomatic Medicine, 68, 376–381.

8/19/09 5:16:09 PM

1278

Psychological Influences on Neuroendocrine and Immune Disorders

McKinnon, W., Weisse, C. S., Reynolds, C. P., Bowles, C. A., & Baum, A. (1989). Chronic stress, leukocyte subpopulations, and humoral response to latent viruses. Health Psychology, 8, 389–402.

Pinquart, M., & Sorensen, S. (2004). Associations of caregiver stressors and uplifts with subjective well-being and depressive mood: A metaanalytic comparison. Aging and Mental Health, 8, 438–449.

Mehta, S. K., Pierson, D. L., Cooley, H., Dubow, R., & Lugg, D. (2000). Epstein-Barr virus reactivation associated with diminished cell-mediated immunity in Antarctic expeditioners. Journal of Medical Virology, 61, 235–240.

Plotkin, S. A. (2001). Immunologic correlates of protection induced by vaccination. Pediatric Infectious Disease, 20, 73–75.

Mehta, S. K., Stowe, R. P., Feiveson, A. H., Tyring, S. K., & Pierson, D. L. (2000). Reactivation and shedding of cytomegalovirus in astronauts during spaceflight. Journal of Infectious Diseases, 182, 1761–1764.

Rabin, B. S. (1999). Stress, immune function, and health: The connection. New York: Wiley-Liss.

Mehta, S. K., Stowe, R. P., Feiveson, A. H., Tyring, S. K., & Pierson, D. L. (2004). Stress-induced subclinical reactivation of varicella zoster virus in astronauts. Journal of Medical Virology, 72, 174–179. Miller, G. E., Cohen, S., Pressman, S., Barkin, A., Rabin, B. S., & Treanor, J. J. (2004). Psychological stress and antibody response to influenza vaccination: When is the critical period for stress, and how does it get inside the body? Psychosomatic Medicine, 66, 215–223. Miller, G. E., Freedland, K. E., & Carney, R. M. (2005). Depressive symptoms and the regulation of proinflammatory cytokine expression in patients with coronary heart disease. Journal of Psychosomatic Research, 59, 231–236. Modell, S., Yassouridis, A., Huber, J., & Holsboer, F. (1997). Corticosteroid receptor function is decreased in depressed patients. Neuroendocrinology, 65, 216–222. Musselman, D. L., Lawson, D. H., Gumnick, J. F., Manatunga, A. K., Penna, S., Goodkin, R. S., et al. (2001). Paroxetine for the prevention of depression induced by high-dose interferon alfa. New England Journal of Medicine, 344, 961–966. Opal, S. M., & DePalo, V. A. (2000). Anti-inflammatory cytokines. Chest, 117, 1162–1172. Padgett, D. A., & Glaser, R. (2003). How stress influences the immune response. Trends in Immunology, 24, 444–448. Padgett, D. A., Marucha, P. T., & Sheridan, J. F. (1998). Restraint stress slows cutaneous wound healing in mice. Brain, Behavior, and Immunity, 12, 64–73. Pajulo, O. T., Pulkki, K. J., Alanen, M. S., Reunanen, M. S., Lertola, K. K., Mattila-Vuori, A. I., et al. (1999). Correlation between interleukin6 and matrix metalloproteinase-9 in early wound healing in children. Wound Repair and Regeneration, 7, 453–457. Parham, P. (2005). The immune system (2nd ed.). New York: Garland Science. Parker, G., Gibson, N. A., Brotchie, H., Heruc, G., Rees, A. M., & HadziPavlovic, D. (2006). Omega-3 fatty acids and mood disorders. American Journal of Psychiatry, 163, 969–978. Payne, D. A., Mehta, S. K., Tyring, S. K., Stowe, R. P., & Pierson, D. L. (1999). Incidence of Epstein-Barr virus in astronaut saliva during spaceflight. Aviation Space and Environmental Medicine, 70, 1211–1213. Penninx, B. W. J. H., Kritchevsky, S. B., Yaffe, K., Newman, A. B., Simonsick, E. M., Rubin, S., et al. (2003). Inflammatory markers and depressed mood in older persons: Results from the health, aging, and body composition study. Biological Psychiatry, 54, 566–572. Petrie, K. J., Booth, R. J., Pennebaker, J. W., Davison, K. P., & Thomas, M. G. (1995). Disclosure of trauma and immune response to a hepatitis b vaccination program. Journal of Consulting and Clinical Psychology, 63, 787–792.

Quinlivan, M., & Breuer, J. (2006). Molecular studies of varicella zoster virus. Reviews in Medical Virology, 16, 225–250.

Raison, C. L., Capuron, L., & Miller, A. H. (2006). Cytokines sing the blues: Inflammation and the pathogenesis of depression. Trends in Immunology, 27, 24–31. Raison, C. L., & Miller, A. H. (2003). When not enough is too much: The role of insufficient glucocorticoid signaling in the pathophysiology of stress-related disorders. American Journal of Psychiatry, 160, 1554–1565. Robertson, K. R., Wilkins, J. W., Handy, J., Van Der Horst, C., Robertson, W. T., Fryer, J. G., et al. (1993). Psychoimmunology and aids: Psychological distress and herpes simplex virus in human immunodeficiency virus infected individuals. Psychology and Health, 8, 317–327. Sapolsky, R. M., & McEwen, B. S. (1985). Down-regulation of neural corticosterone receptors by corticosterone and dexamethasone. Brain Research, 339, 161–165. Sarid, O., Anson, O., Yaari, A., & Margalith, M. (2001). Epstein-Barr virus specific salivary antibodies as related to stress caused by examinations. Journal of Medical Virology, 64, 149–156. Schiepers, O. J. G., Wichers, M. C., & Maes, M. (2005). Cytokines and major depression. Progress in Neuro-psychopharmacology and Biological Psychiatry, 29, 201–217. Schulz, R., & Beach, S. R. (1999). Caregiving as a risk factor for mortality: The caregiver health effects study. Journal of the American Medical Association, 282, 2215–2219. Sharpe, L., Sensky, T., Timberlake, N., Ryan, B., Brewin, C. R., & Allard, S. (2001). A blind, randomized, controlled trial of cognitive-behavioural intervention for patients with recent onset rheumatoid arthritis: Preventing psychological and physical morbidity. Pain, 89, 275–283. Sheridan, J. F., Donnenberg, A. D., Aurelian, L., & Elpern, D. J. (1982). Immunity to herpes simplex virus type 2: Pt. IV. Impaired lymphokine production during recrudescence correlates with an imbalance in t lymphocyte subsets. Journal of Immunology, 129, 326–331. Silberman, D. M., Wald, M. R., & Genaro, A. M. (2003). Acute and chronic stress exert opposing effects on antibody responses associated with changes in stress hormone regulation of t-lymphocyte reactivity. Journal of Neuroimmunology, 144, 53–60. Singer, A. J., & Clark, R. A. (1999). Cutaneous wound healing. New England Journal of Medicine, 341, 738–746. Spencer, R. L., Miller, A. H., Stein, M., & McEwen, B. S. (1991). Corticosterone regulation of type I and type II adrenal steroid receptors in brain, pituitary, and immune tissue. Brain Research, 549, 236–246. Steptoe, A., Wardle, J., Pollard, T. M., Canaan, L., & Davies, G. J. (1996). Stress, social support and health-related behavior: A study of smoking, alcohol consumption and physical exercise. Journal of Psychosomatic Research, 41, 171–180. Steptoe, A., Willemsen, G., Owen, N., Flower, L., & Mohamed-Ali, V. (2001). Acute mental stress elicits delayed increases in circulating inflammatory cytokine levels. Clinical Science, 101, 185–192.

Phillips, A. C., Burns, V. E., Carroll, D., Ring, C., & Drayson, M. (2005). The association between life events, social support, and antibody status following thymus-dependent and thymus-independent vaccinations in healthy young adults. Brain, Behavior, and Immunity, 19, 325–333.

Tanaka, J., Ogura, T., Kamiya, S., Sato, H., Yoshie, T., Ogura, H., et al. (1984). Enhanced replication of human cytomegalovirus in human fibroblasts treated with dexamethasone. Journal of General Virology, 65, 1759–1767.

Pierson, D. L., Stowe, R. P., Phillips, T. M., Lugg, D. J., & Mehta, S. K. (2005). Epstein-Barr virus shedding by astronauts during space flight. Brain, Behavior, and Immunity, 19, 235–242.

Thomas, A. J., Davis, S., Morris, C., Jackson, E., Harrison, R., & O’Brien, J. T. (2005). Increase in interleukin-1beta in late-life depression. American Journal of Psychiatry, 162, 175–177.

c64.indd Sec4:1278

8/19/09 5:16:10 PM

References 1279 Tuglu, C., Kara, S. H., Caliyurt, O., Vardar, E., & Abay, E. (2003). Increased serum tumor necrosis factor-alpha levels and treatment response in major depressive disorder. Psychopharmacology, 170, 429–433. Turner-Cobb, J. M., & Steptoe, A. (1996). Psychosocial stress and susceptibility to upper respiratory tract illness in and adult population sample. Psychosomatic Medicine, 58, 404–412. Van De Kerkhof, P. C. M., Van Bergen, B., Spruijt, K., & Kuiper, J. P. (1994). Age-related changes in wound healing. Clinical and Experimental Dermatology, 19, 369–374. Vedhara, K., Bennett, P. D., Clark, S., Lightman, S. L., Shaw, S., Perks, P., et al. (2003). Enhancement of antibody responses to influenza vaccination in the elderly following a cognitive-behavioural stress management intervention. Psychother Psychosom, 72, 245–252. Vedhara, K., Cox, N. K. M., Wilcock, G. K., Perks, P., Hunt, M., Anderson, S., et al. (1999). Chronic stress in elderly carers of dementia patients and antibody response to influenza vaccination. Lancet, 353, 627–631. Vileikyte, L. (2007). Stress and wound healing. Clinics in Dermatology, 25, 49–55. Vitaliano, P. P., Scanlan, J. M., Zhang, J., Savage, M. V., & Hirsch, I. B. (2002). A path model of chronic stress, the metabolic syndrome, and coronary heart disease. Psychosomatic Medicine, 64, 418–435.

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Vitaliano, P. P., Zhang, J., & Scanlan, J. M. (2003). Is caregiving hazardous to one’s physical health? A meta-analysis. Psychological Bulletin, 129, 946–972. von Kanel, R., Kudielka, B. M., Preckel, D., Hanebuth, D., & Fischer, J. E. (2005). Delayed response and lack of habituation in plasma interleukin-6 to acute mental stress in men. Brain, Behavior, and Immunity, 20, 40–48. Waelde, L. C., Thompson, L., & Gallagher-Thompson, D. (2004). A pilot study of a yoga and meditation intervention for dementia caregiver stress. Journal of Clinical Psychology, 60, 677–687. Werner, S., Grose, R. (2003). Regulation of wound healing by growth factors and cytokines. Physiological Reviews, 83, 835–870. Wolf, H. J., & Morag, A. J. (1998). Epstein-Barr virus vaccines. In P. G. Medveczky, M. Bendinelli, & H. Friedman (Eds.), Herpesviruses and immunity (pp. 231–246). New York: Plenum Press. Woolery, A., Myers, H., Sternlieb, B., & Zeltzer, L. (2004). A yoga intervention for young adults with elevated symptoms of depression. Alternative Therapies in Health and Medicine, 10, 60–63. Yoshikawa, T. T. (1983). Geriatric infectious diseases: An emerging problem. Journal of the American Geriatrics Society, 31, 34–39. Zhou, D., Kusnecov, A. W., Shurin, M. R., DePaoli, M., & Rabin, B. S. (1993). Exposure to physical and psychological stressors elevates plasma interleukin 6: Relationship to the activation of hypothalamicpituitary-adrenal axis. Endocrinology, 133, 2523–2530.

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Chapter 65

Telomeres, Telomerase, Stress, and Aging JUE LIN, ELISSA S. EPEL, AND ELIZABETH H. BLACKBURN

the fruit fly. The mutants recovered often had chromosome fragments that rejoined. However, it appeared that the rejoining did not occur between two natural free chromosomal ends or between one broken end and one natural free end. Barbara McClintock had independently made comparable observations while studying chromosomes in maize. Muller called the natural free ends of chromosomes “telomeres.” Telomeres thus behave differently from a broken chromosome end in that they are refractory to the molecular machinery that joins the broken ends.

Why should a behavioral scientist study telomeres? Maintenance of telomeres, the natural ends of linear chromosomes, is a fundamental biological mechanism of all eukaryotic cells, from protozoa to humans. Gradual shortening of telomeres after each cell division eventually can lead to loss of cellular division capacity and cell death, and contribute to genomic instability, a characteristic of cancer. Telomeres and telomerase, the enzyme that adds nucleotides to telomere ends, have been linked to human aging and aging-related diseases (Aubert & Lansdorp, 2008). Further, lifestyle and psychological state are increasingly being associated with telomere length and telomerase activity changes. Thus, telomere length and telomerase activity emerge as new biomarkers for cellular aging and may serve as surrogate markers for factors that contribute to aging and aging-related diseases. Therefore, scientists interested in understanding early onset of aging-related diseases, as well as longevity, may want to include this measure of cell aging. This chapter gives the behavioral scientist a general understanding of the telomere/telomerase maintenance system, from its molecular basis, to clinical observations, to measurement. It addresses such questions as: Why do telomeres shorten? What are the consequences of telomere shortening? How are telomeres and telomerase related to cancer and diseases of aging? And, lastly and most relevant to behavioral scientists, what environmental (nongenetic) factors modulate telomere length? For detailed discussion of each topic, readers are encouraged to read the literature cited in this chapter.

The first telomeric DNA sequence was determined from the abundant minichromosomes of the ciliated protozoan Tetrahymena thermophila. This telomeric DNA consists of approximately 50 tandem repeats of the sequence TTGGGG, with each 3’-OH end of the duplex chromosomal DNA molecule being the G-rich strand (Blackburn et al., 1983; Blackburn & Gall, 1978). Since then, telomeric sequences from many organisms have been determined, including those of many species of yeast, plants, ciliates, birds, and mammals. Human telomeres contain 5 to 10 kilobases of TTAGGG repeats (Moyzis et al., 1989), while the lab strain of mouse Mus musculus has telomeres over 40 to 80 kb long of the same sequence as human (Blasco et al., 1997). Interestingly, the fruit fly Drosophila melanogaster that Muller used to discover the unusual properties of telomeres lacks the canonical repetitive telomeric sequences characteristic of most eukaryotes. Instead, Drosophila telomeres contain arrays of retrotransposon elements (evolutionally related to retroviruses such as HIV viruses, Pardue & DeBaryshe, 2003). Nevertheless, telomeres in Drosophila are still protected from being recognized as broken ends. Telomeres are organized into a high-order DNAprotein complex by the binding of multiple telomeric proteins. Evidence for a higher order structure called a T-loop has been found, in which the 3’ overhang of the telomeric end is tucked into the double-stranded portion of the telomere sequence to form a loop structure (Figure 65.1). Overall, this higher order structure protects telomeres from being recognized as broken ends. Furthermore, the concerted

TELOMERE MAINTENANCE AND THE AGING OF CELLS AND ORGANISMS Telomeres Defined First named by Hermann Muller in 1938, telomeres are the natural ends of eukaryotic chromosomes. Muller used X-rays to break chromosomes, thus generating mutants of 1280

Handbook of Neuroscience for the Behavioral Science, edited by Gary G. Berntson and John T. Cacioppo. Copyright # 2009 John Wiley & Sons, Inc. c65.indd 1280

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Telomere Maintenance and the Aging of Cells and Organisms 1281 (A)

3‘

5‘ 3‘ (B)

5‘

Protein complex at telomeres

5‘ 3‘

Figure 65.1 Telomeres: A DNA-protein complex at the end of chromosomes, showing the “T-loop” structure. Note: A: shows the 3’ end of the single stranded region tucked into the double-stranded region to form the “T-loop.” B: shows that the T-loop is bound by a multiprotein complex. From “Structure and Variability of Human Chromosome Ends,” by de Lange et al., 1990, Molecular and Cellular Biology, 10, pp. 518–527. Adapted with permission.

3‘ 5‘ gap

gap

actions of telomeric proteins and other factors determine the length of telomeres under different conditions.

3‘

Telomere Shortening Leads to Cellular Senescence Hayflick and colleagues first described the limited proliferation capacity of normal human fibroblasts when cultured in vitro (Hayflick & Moorhead, 1961). The term cellular senescence refers to this state of irreversible cell cycle arrest. Since it is predicted that gradual shortening of telomeres will lead to eventual cell cycle arrest due to the end replication problem,

c65.indd Sec1:1281

3‘ 5‘

5‘

RNA primer

Why Do Telomeres Shorten? The DNA End Replication Problem and Telomerase As conventional DNA polymerases need a primer from which nucleotide extension occurs, the removal of the RNA primer at the lagging DNA synthesis strand will result in a 5’-terminal gap after DNA replication (Figure 65.2). Due to this end-replication problem (Watson, 1972), telomeric sequences are lost in each cell division. For immortal singlecell organisms and germ cells, an active cellular mechanism is needed to prevent the loss of telomeres. Since the presentation of the replication problem in the 1970s and early 1980s, many models have been put forward to explain this fundamental phenomenon (for a historical view of this, see Blackburn, 1984). This end-replication problem is solved for eukaryotic chromosomes by the cellular enzyme telomerase, a specialized ribonucleoprotein reverse transcriptase that uses its integral RNA molecule as the template to synthesize telomeric sequences (Greider & Blackburn, 1985, 1987; Figure 65.3).



Newly synthesized DNA Figure 65.2 The DNA end replication problem.

GT TAG 5' 3'

GTTAGGGTTAGGGTTAGG CAAUCCCAAUC CAATCCC Chromosome terminus

3’

5’

TERT: protein TER: RNA

Figure 65.3 Telomerase: the enzyme that adds nucleotides to and protects telomeric ends. Telomerase activity was originally discovered in Tetrahymena, using extracts from freshly mated cells (Greider & Blackburn, 1985). This developmental stage was carefully chosen because soon after mating, in the somatic nucleus the chromosomes are fragmented, and hundreds of new telomeres are generated de novo, which requires especially high telomerase activity formation. Telomerase activity was subsequently identified in a variety of organisms, including yeast (Cohn & Blackburn, 1995) and human (Morin, 1989). The gene for the RNA component of human telomerase (TER or TERC) was cloned in 1995 (Feng et al., 1995), while the gene for the core protein component (TERT) was cloned in 1997 (Counter, Meyerson, Eaton, & Weinberg, 1997; Lingner et al., 1997; Nakamura et al., 1997). We now know that only the RNA and this protein component are required for minimal enzymatic activity by the telomerase ribonucleoprotein (RNP) enzyme. However, in vivo addition of telomeric DNA onto chromosomal ends requires the collaboration and coordination of dozens of proteins (reviewed in Cong, Wright, & Shay, 2002; Smogorzewska & de Lange, 2004).

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1282 Telomeres, Telomerase, Stress, and Aging

the finite doubling capacity of mammalian cells described by Hayflick is proposed to be caused by attrition of telomeric sequence down to a critically short length. Soon after the identification of human telomere sequences, Harley and colleagues reported that telomeres progressively shorten during in vitro culturing of human primary fibroblasts (Harley, Futcher, & Greider, 1990). Furthermore, when fibroblasts directly taken from donors were examined for their telomere length, a loose negative correlation between the age of the donor and their telomere length was reported (Harley et al., 1990). Since then, the correlation between telomere shortening and cellular senescence has been supported by a large body of literature. Although cellular senescence can be induced by critically shortened telomeres, other cellular signals can induce cellular senescence as well. Current models postulate that two major pathways can lead to cellular senescence (Campisi, 2005). The replicative senescence pathway caused by erosion of telomeres and their dysfunction is dependent on the tumor suppressor p53 (Campisi, 2005). Other cellular stresses, including oxidative stress and overexpression of oncogenes, can lead to stress-induced premature senescence, which is dependent on the p16/pRB pathway (Campisi, 2005). PRB and p16 are tumor suppressor proteins; on activation by cellular stress signals, they cause a chain of reactions that leads to cell-cycle arrest, therefore inhibiting cell proliferation (reviewed in Kim & Sharpless, 2006). Senescent cells, whether caused by telomere dysfunction or cellular stress, display distinct characteristics. A hallmark of senescent cells is the irreversibility of cell cycle arrest. Senescent cells do not respond to growth stimuli, although they remain metabolically active for long periods. Morphologically, senescent cells are larger than their young counterparts and appear to be flat. They stain positive for the enzyme ß-galactosidase (Senescence-Associated ß-gal, SA-ß-gal) and express p53/p21 (telomere dependent senescence only) and p16 (stress-induced premature senescence). In telomere dependent senescence, DNA damage foci—cytologically visible clusters of proteins involved in DNA damage responses, including histone -H2AX and 53BP1—colocalize with telomeres (d’Adda di Fagagna et al., 2003; Herbig, Ferreira, Condel, Carey, & Sedivy, 2004; Takai, Smogorzewska, & de Lange, 2003). An interesting feature of senescence is that cells are now more resistant to apoptosis; they do not respond to signals that cause apoptosis in normal cells. This may have physiological significance for the mechanism of immunosenescence, the aging of the immune system (discussed later). The gene expression profile of senescent cells is distinctively different from that of young cells. Two cell-cycle inhibitors, p21 and p16, are predominantly expressed in senescent cells. p21 expression is upregulated by the tumor suppressor p53, and p16 is induced

c65.indd Sec1:1282

by pRB. In addition, senescent fibroblasts express proteins that remodel the extracellular matrix, including metallomatrix proteases and pro-inflammatory cytokines. These proteins are thought to contribute to carcinogenesis by creating a microenvironment advantageous for cancer cell growth (Campisi & d’Adda di Fagagna, 2007). It is worth pointing out that for rodents, telomeredependent replication senescence is not the primary pathway for cellular senescence of in vitro cell cultures; rodent telomeres are long and telomerase expression is generally higher than in the comparable normal human cell types (Chadeneau, Siegel, Harley, Muller, & Bacchetti, 1995; Prowse & Greider, 1995). The observed limited proliferation for mouse cells cultured in vitro is mainly due to insult from high oxidative stress under in vitro culture conditions, which triggers a p53 dependent DNA damage response. Despite this difference, as discussed later in this chapter, telomerase knockout mice, with telomere length similar to that of humans, have proved to be useful in vivo models to examine the potential roles of telomeres and telomerase in human diseases including cancer and other aging-related diseases. Although the cellular senescence observed for in vitro cultured cells has been proposed to reflect in vivo organismal aging, there has been much controversy about what cellular senescence really means. The relevance of cellular senescence to aging became apparent when markers of cellular senescence were observed in vivo. Telomeres shorten during aging in many tissues that can be renewed throughout life, including peripheral blood, liver, kidney, spleen dermal fibroblasts, and keratinocytes, but not in postmitotic cells (e.g., neurons and cardiomyocytes; Djojosubroto, Choi, Lee, & Rudolph, 2003, and reference therein). This is consistent with the idea that in older organisms, the cells in the self-renewing tissues have gone through more divisions than younger people. Staining of the senescence associated enzyme ß-galactosidase (SA-ß-gal) has been reported in senescent fibroblast and keratinocytes in aging human skin (Dimri et al., 1995) as well as in damaged tissues in various diseases (reviewed in Erusalimsky & Kurz, 2005). Furthermore, expression of the senescence marker protein p16 was also reported (Krishnamurthy et al., 2004). Localization of DNA damage proteins at telomeres was also observed in skin cells of aging baboons and was taken as evidence of replicative senescence in vivo during aging (Herbig et al., 2006). In addition, the link between telomere dysfunction and pathological conditions has now been well established (to be discussed later). Regulation of Telomerase Human telomerase activity is highly regulated, both during development and tissue-specifically. Telomerase activity is

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Telomere Maintenance and the Aging of Cells and Organisms 1283

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Single cell organisms germ line Som

Telomere Length

expressed in embryonic stem cells and germ lines, but is decreased later in development (Forsyth, Wright, & Shay, 2002; Wright, Piatyszek, Rainey, Byrd, & Shay, 1996). In adults, low levels of telomerase activity were found in stem cells and progenitor cells including hematopoietic stem cells and neuronal, skin, intestinal crypt, mammary epithelial, pancreas, adrenal cortex, kidney, and mesenchymal stem cells (reviewed in E. Hiyama & Hiyama, 2007). Also, very low levels of telomerase activity have been detected in proliferating smooth muscle cells (Cao et al., 2002; Haendeler et al., 2004) and fibroblasts (Masutomi et al., 2003). However, telomerase activity in the adult progenitor cells is not high enough to prevent telomere attrition, as progenitor cells were shown to lose telomeric DNA length as the organism ages. This suggests that modulating telomerase activity in these cells may alter the rates of telomere shortening, thus affecting their proliferation capacity. The regulation of telomerase activity in lymphocytes has been extensively studied. Telomerase activity is high in early stages of T and B cell development, but decreased at later stages. Only very low activity was detected in mature resting circulating T and B cells from peripheral blood mononuclear cells (K. Hiyama et al., 1995; Weng, Levine, June, & Hodes, 1996). However, telomerase activity is upregulated in T and B cells on stimulation by mitogens or antigens (Broccoli, Young, & de Lange, 1995; K. Hiyama et al., 1995); and this upregulation is required for clonal expansion of T and B cells during an immune response. Upregulated telomerase activity in lymphocytes is still not enough to compensate for telomere loss during proliferation, as telomere shortening was observed during in vitro culturing of activated T and B cells and these cells have a finite life span (Perillo, Walford, Newman, & Effros, 1989; Son, Murray, Yanovski, Hodes, & Weng, 2000). In vivo, memory T cells were found to have shorter telomeres than naive cells, indicating that their telomeres were shortened during clonal expansion. Since a proper adaptive immune response requires extensive and rapid clonal expansion of T and B cells, limited proliferation capacity may lead to compromised immune functions over the long term. A host of environmental factors regulate telomerase activity (Figure 65.4). Reactive oxidative species (ROS) are reported to decrease telomerase activity in both cancer cells and human umbilical vein endothelial cells (Haendeler, Hoffmann, Brandes, Zeiher, & Dimmeler, 2003; Haendeler, Hoffmann, Rahman, Zeiher, & Dimmeler, 2003; Haendeler et al., 2004). Estrogen upregulates telomerase activity, while progesterone activates telomerase activity transiently, but inhibits activity in longer term experiments. Androgen activates telomerase activity in prostate cancer cells while inhibiting it in normal cells (reviewed in Bayne & Liu, 2005). Many growth hormones

atic cell s (a Pre dult m stem agi atur e ag ngcell rela ing s) ted dis eas es

Cancer

Genetics Environmental factors lifestyle

Aging-related Diseases Age

Figure 65.4 Telomere length can be modulated by various factors. Note: From “The Common Biology of Cancer and Aging,” by T. Finkel, M. Serrano, and M. A. Blasco, 2007, Nature, 448, pp. 767–774. Adapted with permission.

also play important roles in telomerase activity regulation. Insulin, IGF-1, VEGF and EGF upregulate telomerase activity, while TGF-ß inhibits it (Maida et al., 2002; Torella et al., 2004; Wetterau, Francis, Ma, & Cohen, 2003; Zaccagnini et al., 2005). Many cytokines, including IL-2, IL-6, IL-15, TNF- and IFN-, ß, and  were also reported to regulate telomerase activity (Akiyama et al., 2004; Das, Banik, & Ray, 2007; Kawauchi, Ihjima, & Yamada, 2005; Li, Zhi, Wareski, & Weng, 2005; Xu et al., 2000; Yamagiwa, Meng, & Patel, 2006). Regulation of telomerase activity is often executed at the level of transcriptional regulation of TERT. Several transcriptional factors have been reported to activate or repress telomerase activity through the hTERT promoter (Flores, Benetti, & Blasco, 2006). Modulations of alternative splicing, posttranslational modification, and subcellular localization and epigenetic modifications have also been reported (Anderson, Hoare, Ashcroft, Bilsland, & Keith, 2006; Flores et al., 2006; Jalink et al., 2007; Liu, Hodes, & Weng, 2001; Saeboe-Larssen, Fossberg, & Gaudernack, 2006). Telomere Length-Independent Role(s) of Telomerase There is now evidence suggesting that telomerase may have roles independent of its telomere lengthening function. Constitutive overexpression of hTERT in cancer cells that otherwise maintain their telomere length by telomeraseindependent pathways (ALT cells) facilitates malignant transformation, suggesting that this cancer-promoting effect does not rely on extension of telomere length (Stewart et al., 2002). Furthermore, in normal fibroblast cells, further suppression of the already low amount of hTERT in these cells by RNA interference using a shRNA (small hairpin RNA)

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1284 Telomeres, Telomerase, Stress, and Aging

impairs the DNA damage response (Masutomi et al., 2003). Telomerase RNA knockdown by shRNAs or ribozymes in malignant cancer cells—rapidly causes a change in gene expression profile (Li et al., 2004; Li & Blackburn, 2005). In postmitotic cells, telomerase may protect against neurotoxicity: PC 12 cells overexpressing hTERT become more resistant to amyloid ß-peptide induced apoptosis (Zhu, Fu, & Mattson, 2000) and to DNA damaging drugs (Lu, Fu, & Mattson, 2001). Along the same lines, TERT mRNA is induced in cortical neurons after ischemic injury in mice, and transgenic mice overexpressing TERT are more resistant to neurotoxicity caused by NMDA (Kang et al., 2004). Although these experiments do not directly address whether it was telomere lengthening or telomerase activity that contributed to these effects, given the short period in which the effects are seen in cultured cells, they suggest that telomerase, as opposed to telomere length change, is the cause mechanistically. Most convincingly, transgenic mice that overexpress TERT in their skin epithelium have grossly increased proliferation of their hair follicle stem cells. Since this effect is also seen in a telomerase RNA knockout background, this demonstrated that the enzymatic activity (telomere extension function) is not required (Sarin et al., 2005). The mechanisms of telomere length-independent telomerase function(s) remain unknown. A possible pathway is through the maintenance of the very tip of telomeres, that is, the capping function of telomerase. The physical presence of a telomerase complex that includes its RNA component may serve this purpose. An alternative, but not mutually exclusive mechanism is suggested by microarray gene-profiling data, where overexpression of hTERT is shown to upregulate growth-controlling genes (Smith, Coller, & Roberts, 2003). Consistent with this, a recent publication showed that TERT facilitates activation of progenitor cells in the skin and hair follicle by triggering a change in gene expression that significantly overlaps the program controlling natural hair follicle cycling (Choi et al., 2008).

TELOMERE MAINTENANCE AND HUMAN DISEASES Telomeres and Aging-Related Diseases The possibility that cellular senescence is associated with organismal aging is consistent with observations that in vivo, senescent cells accumulate with aging (Dimri et al., 1995). Whether replicative senescence caused by telomere erosion is relevant to organismal aging is under debate (Patil, Mian, & Campisi, 2005). However, several lines of evidence

c65.indd Sec1:1284

strongly suggest a link between telomere dysfunction and aging and aging-related diseases. First, numerous clinical studies link short telomere length in white blood cells (specifically, peripheral blood mononuclear cells, PBMCs) to aging-related disease or preclinical conditions of diseases. A short list of them includes increased mortality from cardiovascular disease and infectious disease (Cawthon, Smith, O’Brien, Sivatchenko, & Kerber, 2003), coronary atherosclerosis (Samani, Boultby, Butler, Thompson, & Goodall, 2001), premature myocardial infarction (Brouilette, Singh, Thompson, Goodall, & Samani, 2003), vascular dementia (von Zglinicki, Pilger, & Sitte, 2000), hypertension with carotid atherosclerosis (Benetos et al., 2004), agerelated calcific aortic stenosis (Kurz et al., 2004), increased pulse pressure (Jeanclos et al., 2000), obesity and smoking (Valdes et al., 2005), Alzheimer ’s disease (Panossian et al., 2003; Zhang et al., 2003), and insulin-resistance, a preclinical condition for diabetes (Brouilette et al., 2007; Collerton et al., 2007). The main findings of these and several other clinical studies that examined the relationship between telomere length and aging-related diseases are summarized in Table 65.1 and Appendix 65.1 on page 1295. Second, evidence for in vivo cellular senescence was found in affected tissues of cardiovascular patients. Telomere shortening is accelerated in the atherosclerosisprone areas compared with control areas (Chang & Harley, 1995; Okuda et al., 2000) and telomeres are shorter in diseased coronary arteries than nondiseased age-matched specimens (Ogami et al., 2004). Similarly, putative endothelial cell senescence was observed in human atherosclerosis (Minamino et al., 2002) and vascular smooth muscle cells (VSMCs) with cells in the diseased area containing shorter telomeres than in nondiseased areas in the same patient (Matthews et al., 2006). Third, in vitro studies with cultured cells have recapitulated some aspects of cellular senescence in vivo regarding cardiovascular diseases. Briefly, stimuli and conditions that affect endothelial and vascular smooth muscle (VSMC) cell function in vitro appear to correlate with telomerase and telomere length maintenance: Oxidative stress reduced telomerase activity and accelerated telomere shortening in EC and VSMC, whereas hypoxia and antioxidants induced telomerase activity and promoted proliferation. Furthermore, overexpression of telomerase in endothelial cells and VSMC extended proliferation life span and improves functional properties (Edo & Andres, 2005). Fourth, the most compelling evidence for the role of telomeres and telomerase in aging and aging-related diseases came from studies of the genetic disease dyskeratosis congenita (DC) and other related diseases, as discussed in a later section.

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Telomere Maintenance and Human Diseases 1285 TABLE 65.1

Studies linking telomere maintenance to diseases.

Study

Main Findings

Authors

Telomere shortening and prostate cancer

Telomere attrition in the high-grade prostatic, intraepithelial neoplastia and surrounding stroma is predicative of prostate cancer.

Joshua et al. (2007)

Telomere shortening and breast cancer

Short telomeres in PBMCs are associated with breast cancer risk.

Shen et al. (2007)

Telomere shortening and bladder cancer

Short telomeres appear to be associated with increased risks for human bladder, head and neck, lung, and renal cell cancers.

Wu et al. (2003)

Telomere shortening and bladder cancer

Short telomeres in buccal cells associated with bladder cancer risk.

Broberg, Bjork, Paulsson, Hoglund, & Albin (2005)

Telomere dysfunction and renal cancer

Telomere length in lymphocytes is associated with renal cancer.

Shao et al. (2007)

598 participants of the Leiden 85-plus study (average age  89.8)

Telomere length in leucocytes is not associated with morbidity or mortality in the oldest old.

Martin-Ruiz, Gussekloo, van Heemst, von Zglinicki, & Westendorp (2005)

812 participants (652 twins) 73–101 years of age

In twin pairs, the twin with shorter telomere died first. No association between telomere length and survival in this study.

Bischoff et al. (2006)

Lothian Birth Cohort

Telomere length is not associated with age-related physical and cognitive decline or mortality.

Harris et al. (2006)

Scottish Mental Survey (n  190, born 1921)

Short telomere length is associated with heart disease in old people.

Starr et al. (2007)

183 healthy controls and 620 chronic heart failure patients

Telomere length in PBMCs is shorter in chronic heart failure patients and related to severity of the disease.

van der Harst et al. (2007)

West Scotland Coronary Prevention Study (n  484)

Short telomere length is associated the risk of coronary heart disease. Statin treatment attenuates the association.

Brouilette et al. (2007)

Newcastle 85 study

Telomere length is associated with left ventricular function in the oldest old.

Collerton et al. (2007)

Chennai Urban Rural Epidemiology Study (India)

Short telomere length is associated with impaired glucose and diabetic macroangiopathy.

Adaikalakoteswari, Balasubramanyam, Ravikumar, Deepa, & Mohan (2007)

Cardiovascular Health Study

Short telomere length associated with diabetes, diastolic blood pressure, carotid intima-media thickness, and IL-6.

Fitzpatrick et al. (2007)

2509 Caucasian of Askelpios study (n  35–55 free of overt CVD)

No association between telomere length and cholesterol and blood pressure. Short telomeres are associated with levels of inflammation and oxidative stress markers. Shorter telomere length is associated with unhealthy lifestyle in men.

Bekaert et al. (2007)

Women ages 18–79 (N  2150)

Telomere length in leukocytes positively correlates with bone mineral density. Shorter telomere length is correlated with osteoporosis.

Valdes et al. (2007)

1086 from TwinsUK Adult Twin Registry

Short telomeres in leucocytes is associated with radiographic hand osteoarthritis.

Zhai et al. (2006)

Caucasian men ages 40–89 from the Framingham Heart Study (N  327)

Shorter telomere length associated with hypertension, increased insulin resistance, and oxidative stress.

Demissie et al. (2006)

Women ages 18–79 (N  1517)

Insulin resistance, leptin, and C-reactive protein levels are inversely related to leucocyte telomere length in premenopausal women, but not in postmenopausal women.

Aviv et al. (2006)

Type II diabetes (N  21) and control (N  29)

Mean monocyte telomere length in the diabetic group is lower than in control, without significant differences in lymphocyte telomere length.

Sampson, Winterbone, Hughes, Dozio, & Hughes (2006)

Young adults of the Bogalusa Heart Study

Telomere attrition is correlated with insulin resistance and changes in the body mass index.

Gardner et al. (2005)

Women ages 18–76 (N  1122)

Shorter telomere length is associated with obesity and cigarette smoking.

Valdes et al. (2005) (continues)

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1286 Telomeres, Telomerase, Stress, and Aging TABLE 65.1

(Contineud )

Study

Main Findings

Authors

Hypertensive men (N  163)

Telomeres are shorter in hypertensive men with carotid artery plaques than hypertensive men without carotid artery plaques

Benetos et al. (2004)

N  143

Short telomeres associated with high rates of mortality from cardiovascular disease and infection.

Cawthon, Smith, O’Brien, Sivatchenko, & Kerber (2003)

Premature myocardial infarction (N  203) and control (N  180)

Telomeres are shorter in leucocytes in premature myocardial infarction than controls.

Brouilette, Singh, Thompson, Goodall, & Samani (2003)

10 patients and 20 controls

Telomere length in white blood cells shorter in patients with severe coronary artery disease.

Samani, Boultby, Butler, Thompson, & Goodall (2001)

49 twin pairs from the Danish Twin Register

Short telomere length in leucocytes correlates with high pulse pressure.

Jeanclos et al. (2000)

Note: From de Lange, T. (2005) Shelterin. The protein complex that shapes and safeguards human telomeres. Genes & Development, 19, 2100–2110

The finite life span of somatic cells in multicellular organisms has been suggested to be an antitumor mechanism to prevent accumulation of mutations that leads to cancer. However, the secretion of cancer-promoting factors by senescent human cells, as described earlier, argues against this notion. Therefore, it is uncertain whether replicative senescence, caused by critically short telomeres, is a tumor suppression mechanism in humans. Telomerase activity is essential for tumor growth since in over 90% of human cancers, telomerase becomes upregulated during tumorigenesis (Kim et al., 1994), while the rest adopt recombination pathways to maintain their telomere length (ALT for alternative lengthening of telomeres; Bryan, Englezou, DallaPozza, Dunham, & Reddel, 1997). Yet cancer cells often have shorter telomeres than adjacent normal cells (de Lange et al., 1990; Joshua et al., 2007). A commonly invoked model for tumorigenesis, although unproven in humans, is that in normal cells, short telomeres induce replicative senescence, a p53-dependent DNA damage response that results in cell cycle arrest, to serve as antitumor mechanism. Rare events of inactivation of p53 allow cells to bypass this short telomere-induced arrest, known as M1, and continue to grow. Further shortening of telomeres leads to a second cell cycle arrest, M2, also known as crisis. At M2, most cells die from apoptosis, but a few escape by reactivation of telomerase, thereby become cancer cells (Figure 65.5). Recent clinical studies have also linked short telomeres in PBMCs or, in some cases, buccal cells to greater risk factors for various cancers, including bladder, head and neck, lung, breast and renal cancer (Broberg, Bjork, Paulsson, Hoglund, & Albin, 2005; Shao et al., 2007; Shen et al., 2007; Wu et al., 2003). It is not clear whether the short telomeres in PBMCs and buccal cells reflect genetic predisposition to cancer and/or environmental factors thought to contribute to high cancer risks (e.g., oxidative stress and chronic inflammation).

c65.indd Sec2:1286

Telomere Length

Telomerase and Cancer

Lack of telomerase activity

p53 dependent

Senescence

p53/Rb inactivation

Crisis Telomerase reactivated

p53 independent Cancer Death

Population Doublings

Figure 65.5 One model for telomere maintenance and tumorigenesis.

Telomerase Knockout and TERT Overexpression Mice as Models for Roles of Telomere and Telomerase in Aging-Related Diseases Studies using telomerase knockout mice have provided essential evidence for the role of telomere and telomerase in health. The first telomerase knockout mouse was created by deleting the RNA component of telomerase mTER (Blasco et al., 1997). As this lab strain of Mus musculus normally has over 50 kb-long telomeres (Prowse & Greider, 1995), it was not surprising that the first three generations of TERC-/- did not show any early cytogenetic, morphologic, or physiological phenotypes, despite the expected telomere shortening due to the lack of telomerase activity. However, a notable phenotype is that G1 knockout mice have shorter life span despite their still-long telomeres, indicating that telomerase may have telomere-length independent roles in longevity. Successive breeding of the mTERC-/- mice resulted in phenotypes characterized by deficiencies in tissue renewal. At the sixth generation (G6), TERC-/- mice are

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sterile, with males exhibiting testicular atrophy and declined spermatogenesis, while the females have a decreased number of oocytes. In the hematopoietic system, reduced progenitor cell numbers, spleen atrophy, and decreased proliferation of T and B cells on induction by mitogens were reported (reviewed in Blasco, 2005). Thus the phenotypes exhibited in late generation mTERC-/- mouse support the role of telomerase in maintaining telomere length required for cell proliferation for tissue renewal. The phenotypes of mTERC-/- mouse with regard to cancer seem to be complicated. Chromosomal end-to-end fusions of critically short telomeres, which lead to massive genome instability, were observed in late generation mTERC-/- mice. In addition, gross chromosomal rearrangements, such as nonreciprocal translocations, a common feature of human cancer, were also found. This is consistent with the proposal that for human cancers, critically short telomeres lead to chromosome fusion and breakage—fusionbridge cycles. Consistent with the role of short telomeres in promoting cancer, these mice have higher incidences of tumors. However, the growth of tumors derived from mTERC-/- mice is decreased compared with wild type, in agreement with the notion that upregulation of telomerase is required for cancer cell growth. Thus telomerase appears to have antagonistic effects on tumorigenesis. Mice overexpressing the telomerase core protein gene, mTERT, had increased death risk during the first half of their life due to increased tumorigenesis, but had extended life span in the second half of life. These long-lived mice had decreased degenerative lesions in testis, uterus and ovary, and kidney (Gonzalez-Suarez, Flores, & Blasco, 2002; Gonzalez-Suarez, Geserick, Flores, & Blasco, 2005; Gonzalez-Suarez et al., 2001). Notably, kidney dysfunction is a common cause of death in elderly humans. These findings in telomerase knockout and TERT overexpression mice strengthen the idea that a delicate balance is important to ensure just the right amount of telomerase activity. When the activity is too low, it is not sufficient to allow proliferation of renewable tissues or full protection from cancer, thus contributing to premature aging and aging-related diseases. However, at least in mice, too much telomerase activity predisposes the organism to higher cancer incidence.

Telomerase and Human Genetic Diseases The strongest evidence suggesting a direct role of telomerase and telomere maintenance in human aging and agingrelated diseases came from studies of the rare multisystem disorder dyskeratosis congenita (Vulliamy et al., 2006). Classically, dyskeratosis congenita (DC) is characterized by a triad of mucoctaneous symptoms: abnormal skin

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pigmentation, nail dystrophy, and mucosal leukoplakia. However, a host of other symptoms, including hair graying and loss, pulmonary disease, and predisposition to cancer, were also reported. Patients die of eventual failure of the hematopoietic system (bone marrow failure). Three genetic forms of DC were reported: most DC patients have the X-linked recessive form, caused by mutations in the gene for dyskerin, a protein required for pseudo-uridylation of ribosomal RNA, which is also a component of the mammalian cellular telomerase RNP complex. Of interest here, a more rare genetic type of DC is autosomal-dominant and is caused by mutations in the RNA or protein component of telomerase (TERC or TERT). The apparent autosomaldominant inheritance mode is due to haploinsufficiency for telomerase activity. Most recently, the genetic cause for one subtype of the autosomal recessive DC is reported to be mutations in another telomerase-associated protein, NOP 10 (Walne et al., 2007). It has now become clear that the primary molecular basis for the defects in all forms of DC studied so far lies in a deficiency for telomerase activity, which leads to shorter telomeres, especially in the affected tissues. Over a dozen hTER deletion or point mutations have now been reported in DC patients. Using a reconstituted cell-free system that expresses the DC-forms of hTER, or by introducing the DC mutant copy of hTER into a cell line that does not have endogenous hTER, several labs have demonstrated that mutant hTER found in DC patients leads to reduced telomerase activity (Comolli, Smirnov, Xu, Blackburn, & James, 2002; Fu & Collins, 2003; Ly et al., 2005; Marrone, Stevens, Vulliamy, Dokal, & Mason, 2004). Similarly, mutations in the protein subunit of telomerase were also reported in DC patients (Marrone et al., 2007). Retroviral expression of hTER and/or hTERT extended telomere length and rescued DC cells from premature senescence (Westin et al., 2007). Interestingly, even in the X-linked form of DC caused by dyskerin mutations, telomerase RNA level appears to be the limiting factor for telomere length maintenance (Wong & Collins, 2006), as reintroduction of the wild type hTER and hTERT into cells from DC patients restores the cells to normal rRNA processing and proliferation. DC patients have shorter telomere length than unaffected family members and patients with the severest phenotypes have shorter telomeres than patients with milder symptoms (Vulliamy et al., 2006). Furthermore, autosomal-dominant DC families show an earlier age of onset and more severe disease phenotypes in succeeding generations (Armanios et al., 2005), a phenomenon called disease anticipation. This is consistent with the progressive shortening of telomeres over generations. Analysis of the immune system of a large family with autosomal dominant DC caused by a TERC mutation

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1288 Telomeres, Telomerase, Stress, and Aging

revealed immune abnormalities including severe B lymphopenia and decreased immunoglobulin M (IgM) levels, and T cells that overexpressed senescent cell surface markers. In vitro culturing of the cells from these DC patients showed their lymphocytes had reduced proliferative capacity and increased basal apoptotic rate (Knudson, Kulkarni, Ballas, Bessler, & Goldman, 2005). Diseases caused by deficient telomerase activity are not limited to family histories of hereditary disorders. Sporadic cases of bone marrow failure syndromes including aplastic anemia (Ly et al., 2005; Xin et al., 2007), melodysplastic syndrome (Field et al., 2006; Ortmann et al., 2006; Yamaguchi, 2006, 2007), and essential thrombocytemia (Ly et al., 2005) were also found to have mutations in hTERC or hTERT. The spectrum of diseases caused by telomerase mutations has now been broadened to include idiopathic pulmonary fibrosis (Armanios et al., 2007). What Does the Length of Telomeres in Humans Really Reflect? The length of telomeres is determined by several factors: telomeres that were inherited (genetic), level of telomerase, environmental factors that influence the rate of attrition and telomerase activity, and number of cell divisions (history of division). In the following subsections, we discuss common genetic variations, replicative history, and biochemical environment factors that contribute to person-to-person variation in telomere length and rate of telomere shortening. Genetic Transmission A study in 115 twin pairs, 2 to 63 years of age, indicated a 78% heritability for mean telomere length in this age cohort (Slagboom, Droog, & Boomsma, 1994). In 2,050 unselected women aged 18 to 80 years, comprising 1,025 complete dizygotic twin pairs, telomere length was reported to have 36% to 90% heritability (Andrew et al., 2006). In another study of 383 adults including 258 twin pairs, the heritability of telomere length was reported to be 81.9% ± 11.8% (Vasa-Nicotera et al., 2005). While the preceding studies suggested autosomal genes contribute to telomere length heritability, some reports also suggest X-linked modes of inheritance (between fathers and daughters, between mothers and sons and daughters; and among siblings, but not between father and son). A paternal inheritance (father-son and father-daughter) of telomere length was found in Old Order Amish people (Njajou et al., 2007). However, a study examining telomere length in an elderly population of 686 males including monozygotic (MZ) and dizygotic (DZ) twins reported no evidence of heritable effects, but rather that telomere length was largely associated with shared environmental factors (Huda et al., 2007). While it is still not known why there are discrepancies

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among findings from different groups, it is likely that it is due to different populations and different ages when the telomeres were measured. It seems plausible that the older the general population, the more environmental impacts might override genetic influences. Further, while there is strong genetic transmission of telomere length, it is not known whether maternal telomere length (e.g., in immune cells) is transmitted to offspring through nongenetic means. In other words, can short telomeres due to environmental exposures in mothers be transmitted through nongenetic or epigenetic means (Epel, in press). Replication History Examination of telomere length in various human tissues has suggested that the rate of telomere length attrition roughly reflects the rate of cell turnover in the tissue. A rapid telomere shortening in peripheral blood cells was seen in the first year of life (Frenck, Blackburn, & Shannon, 1998; Rufer et al., 1999), although it is not clear that this reflects higher cell turnover rates. The rate of telomere attrition in adults is estimated to be 31 to 62 bp/year (Takubo et al., 2002). Telomeres are shorter in patients with diseases characterized by high cell turnover rates compared with their age-matched peers, including chronic viral infection: HIV, CMV, lupus, and rheumatoid arthritis (Nakajima et al., 2006; Steer et al., 2007). In brain tissues, no evidence for telomere shortening was seen in a cross-sectional study comparing adults of different ages (Allsopp et al., 1995). However, human adrenocortical cells, which divide continuously throughout life, showed a strong agerelated decline in telomere length (Yang, Suwa, Wright, Shay, & Hornsby, 2001). The average rate of telomere attrition in PBMCs in women is lower than in men, when measured in cross-sectional studies. It is not clear why, but it has been suggested that estrogen, which is known to upregulate telomerase, may play a role in slowing telomere attrition. Lifestyle Factors Lifestyle factors may affect telomere length, such as factors that promote obesity, which is linked to shorter telomeres (Valdes et al., 2005). Cigarette smoking is linked to both shorter telomere length (Valdes et al., 2005) and lower telomerase (Epel et al., 2006). Exercise has been associated with longer leukocyte telomere length (Cherkas et al., 2008). No studies have examined other health behaviors such as alcohol use, although given their relations with insulin and obesity, it is likely that nutrition, dysregulated eating patterns, and overeating would all affect cell aging. Dietary restraint, which is linked to stress, cortisol, and dysregulated eating patterns, is linked to shorter telomere length (Kiefer, Lin, Blackburn, & Epel, 2008). It appears

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Measurement of Cell Aging and Potential Confounding Factors 1289

that nutrition in early life may affect telomeres, at least in certain tissues such as kidney (Jennings, Ozanne, & Hales, 2000). Protein restriction while in utero was related to shorter telomeres, whereas protein restriction during early life was related to longer kidney telomeres (Jennings et al., 2000). Lastly, psychological stress is related to shorter PBMC telomeres. Our group has found this both in young maternal caregivers (Epel et al., 2004) and in elderly dementia caregivers (unpublished data). Others have now replicated this finding in dementia caregivers (Damjanovic et al., 2007), in major depression (Simon et al., 2006), and in stressed mice (Kotrschal, Ilmonen, & Penn, 2007). The mediating pathways are unknown, but likely involve many of the pathways described earlier; both behavioral (such as poor nutrition and fitness, and insufficient sleep) and biochemical pathways are affected by stress. Telomere length is not equivalent to a biological measure of stress, which becomes obvious when we take into account the myriad other factors that modulate telomere length. Telomere length is, however, reflective of stress, as well as of a multitude of other biological, and environmental factors, extending from early in life to throughout the life span. Possible Mechanisms Relating Telomere Maintenance to Aging-Related Diseases With the growing body of literature demonstrating the link between telomere dysfunction and aging-related diseases, we might start to ask how mechanistically telomere dysfunction is related to, and contributes to, aging-related diseases. As discussed, telomere shortening is the end result of a multitude of pathways, making attribution of cause and effect difficult. In a notable exception, our group has found evidence that chronicity of psychological stress is quantitatively associated with the degree of telomere shortness, thus implicating chronic psychological stress in a causative role in telomere shortening. Beyond that, the causality of the relationships between telomere shortness and disease and disease risk factors is likely to be challenging to unravel. In human studies, telomere shortness in leukocytes has been studied extensively. As well as having the practical advantage of being readily obtained, these cells may be closely linked to disease processes. Here we consider three possible, nonmutually exclusive, mechanisms that could link leukocyte aging to risk for aging-related diseases—in particular, cardiovascular diseases: Mechanism 1: cellular aging in leukocytes simply reflects the same process that is ongoing in cells of other tissues, but does not directly contribute to the aging or disease development of those tissues. Just like cells of the immune system, accumulation of senescent cells

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limits the proliferation capacity of other tissues that require self-renewal and repair, including tissue stem and progenitor cells. It has been shown in recent years that even in organs that are believed to be postmitotic, progenitor cells are involved in repair after damage or even normal functions. Cardiac stem cells are involved in repair from ischemic injury (Anversa, Kajstura, Leri, & Bolli, 2006; Leri, Kajstura, & Anversa, 2005), and neuronal progenitor cells in the hippocampus may be involved in memory (Kempermann & Gage, 2000). Mechanism 2: The same detrimental factors (e.g., stress hormones, oxidative stress, pro-inflammatory cytokines and other risk factors) that cause telomere dysfunction in leukocytes cause damage to cells of other organs through different mechanisms. Therefore, leukocyte telomere dysfunction is not the contributing factor, but rather, it serves as a cellular readout for these damaging factors (telomere dysfunction may be a surrogate marker of other damaging factors that cause aging-related disease). Telomere length thus reflects the cumulative assault the cells receive over the cause of life; particular candidates of interest are oxidative stress and pro-inflammatory cytokines, as they are known to be associated with CVD. Results from clinical studies are now showing a consensus that PBMC telomere length is linked to the family of biochemical factors reflecting metabolic stress, such as oxidative stress, pro-inflammatory cytokines, insulin and leptin (Aviv et al., 2006; Bekaert et al., 2007; Demissie et al., 2006; Fitzpatrick et al., 2007). Telomere length in PBMCs may therefore be predictive of morbidity and mortality, in that it reflects cumulative effects, as opposed to the current status. Mechanism 3: Senescent cells of the immune system secrete proteins detrimental to the surrounding cells. Immunosenescent CD8CD28 cells are known to produce high levels of proinflammatory cytokines IL-6 and TNF-. Since local inflammation plays a pivotal role in some aging-related disease including atherosclerosis (Tedgui & Mallat, 2006), the senescent cells of the immune system may contribute to cardiovascular disease through this pathway. In addition, senescent fibroblasts were shown to secrete growth factors that promoted cancer cell growth, providing another potential contributor to the increased risk of cancer with age.

MEASUREMENT OF CELL AGING AND POTENTIAL CONFOUNDING FACTORS Accurate measurement of telomere length and telomerase activity is crucial, but prone to pitfalls with current

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1290 Telomeres, Telomerase, Stress, and Aging

techniques. So for the immediate future, behavioral researchers will need to rely on expertise specifically in these measurements, as well as expert study design to obtain interpretable results. Telomere Length Measurement As with any assay, a new lab should test their measure and its reliability against a gold standard lab. Small differences in blood or other tissue collection, reagent solution freshness, DNA quality, and storage conditions have all been found to affect telomere length measurement accuracy. Currently, Southern blot analysis, quantitative-PCR based (Cawthon, 2002) and q-FISH (quantitative fluorescence in situ hybridization (Poon and Lansdorp, 2001) methods are used to measure telomere length. A detailed review of these methods can be found in a recent review (Canela, Klatt, & Blasco, 2007). Telomerase Detection The most commonly used method to detect telomerase activity is Telomere Repeat Amplification Protocol (TRAP), developed by Kim and colleagues (Kim et al., 1994; Kim & Wu, 1997). A commercial kit, called TRAPeze, based on the design of the TRAP method, is available from Chemicon, Inc. The TRAP method has been adapted to run on quantitative PCR platforms. Nonetheless, the sensitivity of the method does not reach that of the gel-TRAP method and this lack of sensitivity has to date precluded its use in quantitative measures of telomerase activity in clinical samples of normal human cells such as PBMCs. In the future, further development of this method may render it (or an adaptation) usable for such clinical samples. A detailed review of methods to detect telomerase activity can be found (e.g., Fajkus, 2006). A second important consideration is that interpretations of telomere length will be affected by subject selection and sample collection. Because measuring cell aging in vivo, in humans, is a new field, we know relatively little about effects of hormones, medications, certain diseases, current infections, and lifestyle factors. Therefore, it is essential either to carefully rule out or to measure these confounding factors. Large studies that have not carefully quantified such health history factors that may leave imprints in telomere length or affect telomerase activity regulation may only pick up the largest effects. For example, the effects of smoking, a large source of oxidative stress, and medications like statins, which are known to alter telomerase, would likely override effects of psychosocial factors, which are typically of smaller magnitude.

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SUMMARY AND CONCLUSIONS The telomere/telomerase maintenance system will be an important focus of behavioral neuroscience research in the coming years. Telomere length and telomerase together appear to be cellular indicators of the potential for viability and self-renewal of cells. We have reviewed the multitude of factors that influence telomere length throughout the life span, including genetics and early nutrition, as well as adulthood obesity, chronic life stress, and biochemical factors. With an in-depth understanding of telomere length and of telomere maintenance by telomerase; their fundamental biological, environmental, and behavioral modifiers; and accurate measurement, behavioral scientists have a valuable role to play in shedding further light on the interwoven environmental, psychological and behavioral modifiers of cell aging.

REFERENCES Adaikalakoteswari, A., Balasubramanyam, M., Ravikumar, R., Deepa, R., & Mohan, V. (2007). Association of telomere shortening with impaired glucose tolerance and diabetic macroangiopathy. Atherosclerosis, 195, 83–89. Akiyama, M., Yamada, O., Hideshima, T., Yanagisawa, T., Yokoi, K., Fujisawa, K., et al. (2004). TNFalpha induces rapid activation and nuclear translocation of telomerase in human lymphocytes. Biochemical and Biophysical Research Communications, 316, 528–532. Allsopp, R. C., Chang, E., Kashefi-Aazam, M., Rogaev, E. I., Piatyszek, M. A., Shay, J. W., et al. (1995). Telomere shortening is associated with cell division in vitro and in vivo. Experimental Cell Research, 220, 194–200. Anderson, C. J., Hoare, S. F., Ashcroft, M., Bilsland, A. E., & Keith, W. N. (2006). Hypoxic regulation of telomerase gene expression by transcriptional and post-transcriptional mechanisms. Oncogene, 25, 61–69. Andrew, T., Aviv, A., Falchi, M., Surdulescu, G. L., Gardner, J. P., Lu, X., et al. (2006). Mapping genetic loci that determine leukocyte telomere length in a large sample of unselected female sibling pairs. American Journal of Human Genetics, 78, 480–486. Anversa, P., Kajstura, J., Leri, A., & Bolli, R. (2006). Life and death of cardiac stem cells: A paradigm shift in cardiac biology. Circulation, 113, 1451–1463. Armanios, M., Chen, J. L., Chang, Y. P., Brodsky, R. A., Hawkins, A., Griffin, C. A., et al. (2005). Haploinsufficiency of telomerase reverse transcriptase leads to anticipation in autosomal dominant dyskeratosis congenita. Proceedings of the National Academy of Sciences, USA, 102, 15960–15964. Armanios, M., Chen, J. J., Cogan, J. D., Alder, J. K., Ingersoll, R. G., Markin, C., et al. (2007). Telomerase mutations in families with idiopathic pulmonary fibrosis. New England Journal of Medicine, 356, 1317–1326. Aubert, G., & Lansdorp, P. M. (2008). Telomeres and aging. Physiology Review, 88, 557–579. Aviv, A., Valdes, A., Gardner, J. P., Swaminathan, R., Kimura, M., & Spector, T. D. (2006). Menopause modifies the association of leukocyte telomere length with insulin resistance and inflammation. Journal of Clinical Endocrinology and Metabolism, 91, 635–640.

8/19/09 5:17:50 PM

References 1291 Bayne, S., & Liu, J. P. (2005). Hormones and growth factors regulate telomerase activity in ageing and cancer. Molecular and Cellular Endocrinology, 240, 11–22.

Chadeneau, C., Siegel, P., Harley, C. B., Muller, W. J., & Bacchetti, S. (1995). Telomerase activity in normal and malignant murine tissues. Oncogene, 11, 893–898.

Bekaert, S., De Meyer, T., Rietzschel, E. R., De Buyzere, M. L., De Bacquer, D., Langlois, M., et al. (2007). Telomere length and cardiovascular risk factors in a middle-aged population free of overt cardiovascular disease. Aging Cell, 6, 639–647.

Chang, E., & Harley, C. B. (1995). Telomere length and replicative aging in human vascular tissues. Proceedings of the National Academy of Sciences, USA, 92, 11190–11194.

Benetos, A., Gardner, J. P., Zureik, M., Labat, C., Xiaobin, L., Adamopoulos, C., et al. (2004). Short telomeres are associated with increased carotid atherosclerosis in hypertensive subjects. Hypertension, 43, 182–185.

Cherkas, L. F., Hunkin, J. L., Kato, B. S., Richards, B., Gardner, J. P., Surdulescu, G. L., et al. (2008). The association between physical activity in leisure time and leukocyte telomere length. Archives of Internal Medicine, 168, 154–158.

Bischoff, C., Petersen, H. C., Graakjaer, J., Andersen-Ranberg, K., Vaupel, J. W., Bohr, V. A., et al. (2006). No association between telomere length and survival among the elderly and oldest old. Epidemiology, 17, 190–194.

Choi, J., Southworth, L. K., Sarin, K. Y., Venteicher, A. S., Ma, W., Chang, W., et al. (2008). TERT promotes epithelial proliferation through transcriptional control of a Myc- and Wnt-related developmental program. PLoS Genetics, 4, e10.

Blackburn, E. H. (1984). The molecular structure of centromeres and telomeres. Annual Review of Biochemistry, 53, 163–194.

Cohn, M., & Blackburn, E. H. (1995, July 21). Telomerase in yeast. Science, 269, 396–400.

Blackburn, E. H., Budarf, M. L., Challoner, P. B., Cherry, J. M., Howard, E. A., Katzen, A. L., et al. (1983). DNA termini in ciliate macronuclei. Cold Spring Harbor Symposia on Quantitative Biology, 47(Pt. 2), 1195–1207.

Collerton, J., Martin-Ruiz, C., Kenny, A., Barrass, K., von Zglinicki, T., Kirkwood, T., et al. (2007). Telomere length is associated with left ventricular function in the oldest old: The Newcastle 85 study. European Heart Journal, 28, 172–176.

Blackburn, E. H., & Gall, J. G. (1978). A tandemly repeated sequence at the termini of the extrachromosomal ribosomal RNA genes in Tetrahymena. Journal of Molecular Biology, 120, 33–53.

Comolli, L. R., Smirnov, I., Xu, L., Blackburn, E. H., & James, T. L. (2002). A molecular switch underlies a human telomerase disease. Proceedings of the National Academy of Sciences, USA, 99, 16998–17003.

Blasco, M. A. (2005). Mice with bad ends: Mouse models for the study of telomeres and telomerase in cancer and aging. European Molecular Biology Organization Journal, 24, 1095–1103.

Cong, Y. S., Wright, W. E., & Shay, J. W. (2002). Human telomerase and its regulation. Microbiology and Molecular Biology Reviews, 66, 407–425.

Blasco, M. A., Lee, H. W., Hande, M. P., Samper, E., Lansdorp, P. M., DePinho, R. A., et al. (1997). Telomere shortening and tumor formation by mouse cells lacking telomerase RNA. Cell, 91, 25–34.

Counter, C. M., Meyerson, M., Eaton, E. N., & Weinberg, R. A. (1997). The catalytic subunit of yeast telomerase. Proceedings of the National Academy of Sciences, USA, 94, 9202–9207.

Broberg, K., Bjork, J., Paulsson, K., Hoglund, M., & Albin, M. (2005). Constitutional short telomeres are strong genetic susceptibility markers for bladder cancer. Carcinogenesis, 26, 1263–1271.

d’Adda di Fagagna, F., Reaper, P. M., Clay-Farrace, L., Fiegler, H., Carr, P., Von Zglinicki, T., et al. (2003, November 13). A DNA damage checkpoint response in telomere-initiated senescence. Nature, 426, 194–198.

Broccoli, D., Young, J. W., & de Lange, T. (1995). Telomerase activity in normal and malignant hematopoietic cells. Proceedings of the National Academy of Sciences, USA, 92, 9082–9086. Brouilette, S., Moore, J. S., McMahon, A. D., Thompson, J. R., Ford, I., Shepherd, J., et al. (2007). Telomere length, risk of coronary heart disease, and statin treatment in the West of Scotland Primary Prevention Study: A nested case-control study. Lancet, 369, 107–114. Brouilette, S., Singh, R. K., Thompson, J. R., Goodall, A. H., & Samani, N. J. (2003). White cell telomere length and risk of premature myocardial infarction. Arteriosclerosis, Thrombosis, and Vascular Biology, 23, 842–846. Bryan, T. M., Englezou, A., Dalla-Pozza, L., Dunham, M. A., & Reddel, R. R. (1997). Evidence for an alternative mechanism for maintaining telomere length in human tumors and tumor-derived cell lines. Nature Medicine, 3, 1271–1274. Campisi, J. (2005). Senescent cells, tumor suppression, and organismal aging: Good citizens, bad neighbors. Cell, 120, 513–522. Campisi, J., & d’Adda di Fagagna, F. (2007). Cellular senescence: When bad things happen to good cells. Nature Reviews: Molecular Cell Biology, 8, 729–740. Canela, A., Klatt, P., & Blasco, M. A. (2007). Telomere length analysis. Methods in Molecular Biology, 371, 45–72. Cao, Y., Li, H., Mu, F. T., Ebisui, O., Funder, J. W., & Liu, J. P. (2002). Telomerase activation causes vascular smooth muscle cell proliferation in genetic hypertension. Federation of American Societies for Experimental Biology Journal, 16, 96–98. Cawthon, R. M. (2002). Telomere measurement by quantitative PCR. Nucleic Acids Research, 30, e47. Cawthon, R. M., Smith, K. R., O’Brien, E., Sivatchenko, A., & Kerber, R. A. (2003). Association between telomere length in blood and mortality in people aged 60 years or older. Lancet, 361, 393–395.

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Damjanovic, A. K., Yang, Y., Glaser, R., Kiecolt-Glaser, J. K., Nguyen, H., Laskowski, B., et al. (2007). Accelerated telomere erosion is associated with a declining immune function of caregivers of Alzheimer ’s disease patients. Journal of Immunology, 179, 4249–4254. Das, A., Banik, N. L., & Ray, S. K. (2007). Differentiation decreased telomerase activity in rat glioblastoma c6 cells and increased sensitivity to IFN-gamma and taxol for apoptosis. Neurochemical Research, 32, 2167–2183. de Lange, T., Shiue, L., Myers, R. M., Cox, D. R., Naylor, S. L., Killery, A. M., et al. (1990). Structure and variability of human chromosome ends. Molecular and Cellular Biology, 10, 518–527. Demissie, S., Levy, D., Benjamin, E. J., Cupples, L. A., Gardner, J. P., Herbert, A., et al. (2006). Insulin resistance, oxidative stress, hypertension, and leukocyte telomere length in men from the Framingham Heart Study. Aging Cell, 5, 325–330. Dimri, G. P., Lee, X., Basile, G., Acosta, M., Scott, G., Roskelley, C., et al. (1995). A biomarker that identifies senescent human cells in culture and in aging skin in vivo. Proceedings of the National Academy of Sciences, USA, 92, 9363–9367. Djojosubroto, M. W., Choi, Y. S., Lee, H. W., & Rudolph, K. L. (2003). Telomeres and telomerase in aging, regeneration and cancer. Molecules and Cells, 15, 164–175. Edo, M. D., & Andres, V. (2005). Aging, telomeres, and atherosclerosis. Cardiovascular Research, 66, 213–221. Epel, E. S. (in press). Telomeres in a lifespan perspective: A new psychobiomarker? Current Directions in Psychological Science. Epel, E. S., Blackburn, E. H., Lin, J., Dhabhar, F. S., Adler, N. E., Morrow, J. D., et al. (2004). Accelerated telomere shortening in response to life stress. Proceedings of the National Academy of Sciences, USA, 101, 17312–17315.

8/19/09 5:17:51 PM

1292 Telomeres, Telomerase, Stress, and Aging Epel, E. S., Lin, J., Wilhelm, F. H., Wolkowitz, O. M., Cawthon, R., Adler, N. E., et al. (2006). Cell aging in relation to stress arousal and cardiovascular disease risk factors. Psychoneuroendocrinology, 31, 277–287. Erusalimsky, J. D., & Kurz, D. J. (2005). Cellular senescence in vivo: Its relevance in ageing and cardiovascular disease. Experimental Gerontology, 40, 634–642. Fajkus, J. (2006). Detection of telomerase activity by the TRAP assay and its variants and alternatives. Clinica Chimica Acta, 371, 25–31. Feng, J., Funk, W. D., Wang, S. S., Weinrich, S. L., Avilion, A. A., Chiu, C. P., et al. (1995, September 1). The RNA component of human telomerase. Science, 269, 1236–1241. Field, J. J., Mason, P. J., An, P., Kasai, Y., McLellan, M., Jaeger, S., et al. (2006). Low frequency of telomerase RNA mutations among children with aplastic anemia or myelodysplastic syndrome. American Journal of Pediatric Hematology/Oncology, 28, 450–453. Finkel, T., Serrano, M., & Blasco, M. A. (2007, August 16). The common biology of cancer and aging. Nature, 448, 767–774. Fitzpatrick, A. L., Kronmal, R. A., Gardner, J. P., Psaty, B. M., Jenny, N. S., Tracy, R. P., et al. (2007). Leukocyte telomere length and cardiovascular disease in the cardiovascular health study. American Journal of Epidemiology, 165, 14–21. Flores, I., Benetti, R., & Blasco, M. A. (2006). Telomerase regulation and stem cell behaviour. Current Opinion in Cell Biology, 18, 254–260. Forsyth, N. R., Wright, W. E., & Shay, J. W. (2002). Telomerase and differentiation in multicellular organisms: Turn it off, turn it on, and turn it off again. Differentiation, 69, 188–197.

by protein-protein interaction and phosphorylation. Federation of European Biochemical Societies Letters, 536, 180–186. Harley, C. B., Futcher, A. B., & Greider, C. W. (1990, May 31). Telomeres shorten during ageing of human fibroblasts. Nature, 345, 458–460. Harris, S. E., Deary, I. J., MacIntyre, A., Lamb, K. J., Radhakrishnan, K., Starr, J. M., et al. (2006). The association between telomere length, physical health, cognitive ageing, and mortality in non-demented older people. Neuroscience Letters, 406, 260–264. Hayflick, L., & Moorhead, P. S. (1961). The serial cultivation of human diploid cell strains. Experimental Cell Research, 25, 585–621. Herbig, U., Ferreira, M., Condel, L., Carey, D., & Sedivy, J. M. (2006, March 3). Cellular senescence in aging primates. Science, 311, 1257. Hiyama, E., & Hiyama, K. (2007). Telomere and telomerase in stem cells. British Journal of Cancer, 96, 1020–1024. Hiyama, K., Hirai, Y., Kyoizumi, S., Akiyama, M., Hiyama, E., Piatyszek, M. A., et al. (1995). Activation of telomerase in human lymphocytes and hematopoietic progenitor cells. Journal of Immunology, 155, 3711–3715. Huda, N., Tanaka, H., Herbert, B. S., Reed, T., & Gilley, D. (2007). Shared environmental factors associated with telomere length maintenance in elderly male twins. Aging Cell, 6, 709–713. Jalink, M., Ge, Z., Liu, C., Bjorkholm, M., Gruber, A., & Xu, D. (2007). Human normal T lymphocytes and lymphoid cell lines do express alternative splicing variants of human telomerase reverse transcriptase (hTERT) mRNA. Biochemical and Biophysical Research Communications, 353, 999–1003.

Frenck, R. W., Jr., Blackburn, E. H., & Shannon, K. M. (1998). The rate of telomere sequence loss in human leukocytes varies with age. Proceedings of the National Academy of Sciences, USA, 95, 5607–5610.

Jeanclos, E., Schork, N. J., Kyvik, K. O., Kimura, M., Skurnick, J. H., & Aviv, A. (2000). Telomere length inversely correlates with pulse pressure and is highly familial. Hypertension, 36, 195–200.

Fu, D., & Collins, K. (2003). Distinct biogenesis pathways for human telomerase RNA and H/ACA small nucleolar RNAs. Molecules and Cells, 11, 1361–1372.

Jennings, B. J., Ozanne, S. E., & Hales, C. N. (2000). Nutrition, oxidative damage, telomere shortening, and cellular senescence: Individual or connected agents of aging? Molecular Genetics and Metabolism, 71, 32–42.

Gardner, J. P., Li, S., Srinivasan, S. R., Chen, W., Kimura, M., Lu, X., et al. (2005). Rise in insulin resistance is associated with escalated telomere attrition. Circulation, 111, 2171–2177. Gonzalez-Suarez, E., Flores, J. M., & Blasco, M. A. (2002). Cooperation between p53 mutation and high telomerase transgenic expression in spontaneous cancer development. Molecular and Cellular Biology, 22, 7291–7301. Gonzalez-Suarez, E., Geserick, C., Flores, J. M., & Blasco, M. A. (2005). Antagonistic effects of telomerase on cancer and aging in K5-mTert transgenic mice. Oncogene, 24, 2256–2270.

Joshua, A. M., Vukovic, B., Braude, I., Hussein, S., Zielenska, M., Srigley, J., et al. (2007). Telomere attrition in isolated high-grade prostatic intraepithelial neoplasia and surrounding stroma is predictive of prostate cancer. Neoplasia, 9, 81–89. Kang, H. J., Choi, Y. S., Hong, S. B., Kim, K. W., Woo, R. S., Won, S. J., et al. (2004). Ectopic expression of the catalytic subunit of telomerase protects against brain injury resulting from ischemia and NMDAinduced neurotoxicity. Journal of Neuroscience, 24, 1280–1287.

Gonzalez-Suarez, E., Samper, E., Ramirez, A., Flores, J. M., MartinCaballero, J., Jorcano, J. L., et al. (2001). Increased epidermal tumors and increased skin wound healing in transgenic mice overexpressing the catalytic subunit of telomerase, mTERT, in basal keratinocytes. European Molecular Biology Organization Journal, 20, 2619–2630.

Kawauchi, K., Ihjima, K., & Yamada, O. (2005). IL-2 increases human telomerase reverse transcriptase activity transcriptionally and posttranslationally through phosphatidylinositol 3’-kinase/Akt, heat shock protein 90, and mammalian target of rapamycin in transformed NK cells. Journal of Immunology, 174, 5261–5269.

Greider, C. W., & Blackburn, E. H. (1985). Identification of a specific telomere terminal transferase activity in Tetrahymena extracts. Cell, 43, 405–413.

Kempermann, G., & Gage, F. H. (2000). Neurogenesis in the adult hippocampus. Novartis Foundation Symposium, 231, 220–235; discussion 235–241, 302–226.

Greider, C. W., & Blackburn, E. H. (1987). The telomere terminal transferase of Tetrahymena is a ribonucleoprotein enzyme with two kinds of primer specificity. Cell, 51, 887–898. Haendeler, J., Hoffmann, J., Brandes, R. P., Zeiher, A. M., & Dimmeler, S. (2003). Hydrogen peroxide triggers nuclear export of telomerase reverse transcriptase via Src kinase family-dependent phosphorylation of tyrosine 707. Molecular and Cellular Biology, 23, 4598–4610. Haendeler, J., Hoffmann, J., Diehl, J. F., Vasa, M., Spyridopoulos, I., Zeiher, A. M., et al. (2004). Antioxidants inhibit nuclear export of telomerase reverse transcriptase and delay replicative senescence of endothelial cells. Circulation Research, 94, 768–775. Haendeler, J., Hoffmann, J., Rahman, S., Zeiher, A. M., & Dimmeler, S. (2003). Regulation of telomerase activity and anti-apoptotic function

c65.indd Sec4:1292

Kiefer, A., Lin, J., Blackburn, E., & Epel, E. (2008). Dietary restraint and telomere length in pre- and postmenonpausal women. Psychosom Med., 70, 845–849. Kim, N. W., Piatyszek, M. A., Prowse, K. R., Harley, C. B., West, M. D., Ho, P. L., et al. (1994, December 23). Specific association of human telomerase activity with immortal cells and cancer. Science, 266, 2011–2015. Kim, N. W., & Wu, F. (1997). Advances in quantification and characterization of telomerase activity by the telomeric repeat amplification protocol (TRAP). Nucleic Acids Research, 25, 2595–2597. Kim, W. Y., & Sharpless, N. E. (2006). The regulation of INK4/ARF in cancer and aging. Cell, 127, 265–275.

8/19/09 5:17:51 PM

References 1293 Knudson, M., Kulkarni, S., Ballas, Z. K., Bessler, M., & Goldman, F. (2005). Association of immune abnormalities with telomere shortening in autosomal-dominant dyskeratosis congenita. Blood, 105, 682–688.

Matthews, C., Gorenne, I., Scott, S., Figg, N., Kirkpatrick, P., Ritchie, A., et al. (2006). Vascular smooth muscle cells undergo telomere-based senescence in human atherosclerosis: Effects of telomerase and oxidative stress. Circulation Research, 99, 156–164.

Kotrschal, A., Ilmonen, P., & Penn, D. J. (2007). Stress impacts telomere dynamics. Biology Letters, 3, 128–130.

Minamino, T., Miyauchi, H., Yoshida, T., Ishida, Y., Yoshida, H., & Komuro, I. (2002). Endothelial cell senescence in human atherosclerosis: Role of telomere in endothelial dysfunction. Circulation, 105, 1541–1544.

Krishnamurthy, J., Torrice, C., Ramsey, M. R., Kovalev, G. I., Al-Regaiey, K., Su, L., et al. (2004). Ink4a/Arf expression is a biomarker of aging. Journal of Clinical Investigation, 114, 1299–1307. Kurz, D. J., Decary, S., Hong, Y., Trivier, E., Akhmedov, A., & Erusalimsky, J. D. (2004). Chronic oxidative stress compromises telomere integrity and accelerates the onset of senescence in human endothelial cells. Journal of Cell Science, 117, 2417–2426. Leri, A., Kajstura, J., & Anversa, P. (2005). Cardiac stem cells and mechanisms of myocardial regeneration. Physiological Reviews, 85, 1373–1416. Li, S., & Blackburn, E. H. (2005). Cellular and gene expression responses involved in the rapid growth inhibition of human cancer cells by RNA interference-mediated depletion of telomerase RNA. Journal of Biological Chemistry, 280, 23709–23717. Li, S., Rosenberg, J. E., Donjacour, A. A., Botchkina, I. L., Hom, Y. K., Cunha, G. R., et al. (2004). Rapid inhibition of cancer cell growth induced by lentiviral delivery and expression of mutant-template telomerase RNA and anti-telomerase short-interfering RNA. Cancer Research, 64, 4833–4840. Li, Y., Zhi, W., Wareski, P., & Weng, N. P. (2005). IL-15 activates telomerase and minimizes telomere loss and may preserve the replicative life span of memory CD81 T cells in vitro. Journal of Immunology, 174, 4019–4024.

Moyzis, R. K., Torney, D. C., Meyne, J., Buckingham, J. M., Wu, J. R., Burks, C., et al. (1989). The distribution of interspersed repetitive DNA sequences in the human genome. Genomics, 4, 273–289. Muller, H. J. (1938). The remaking of chromosomes. Collecting Net, 8, 182–195. Nakajima, T., Moriguchi, M., Katagishi, T., Sekoguchi, S., Nishikawa, T., Takashima, H., et al. (2006). Premature telomere shortening and impaired regenerative response in hepatocytes of individuals with NAFLD. Liver International, 26, 23–31. Nakamura, T., Morin, G. B., Chapman, K. B., Weinrich, S. L., Andrews, W. H., Lingner, J., et al. (1997, August 15). Telomerase catalytic subunit homologs from fission yeast and human. Science, 277, 955–959. Njajou, O. T., Cawthon, R. M., Damcott, C. M., Wu, S. H., Ott, S., Garant, M. J., et al. (2007). Telomere length is paternally inherited and is associated with parental lifespan. Proceedings of the National Academy of Sciences, USA, 104, 12135–12139. Ogami, M., Ikura, Y., Ohsawa, M., Matsuo, T., Kayo, S., Yoshimi, N., et al. (2004). Telomere shortening in human coronary artery diseases. Arteriosclerosis, Thrombosis, and Vascular Biology, 24, 546–550.

Lingner, J., Hughes, T. R., Shevchenko, A., Mann, M., Lundblad, V., & Cech, T. R. (1997, April 25). Reverse transcriptase motifs in the catalytic subunit of telomerase. Science, 276, 561–567.

Okuda, K., Khan, M. Y., Skurnick, J., Kimura, M., Aviv, H., & Aviv, A. (2000). Telomere attrition of the human abdominal aorta: Relationships with age and atherosclerosis. Atherosclerosis, 152, 391–398.

Liu, K., Hodes, R. J., & Weng, N. (2001). Cutting edge: Telomerase activation in human T lymphocytes does not require increase in telomerase reverse transcriptase (hTERT) protein but is associated with hTERT phosphorylation and nuclear translocation. Journal of Immunology, 166, 4826–4830.

Ortmann, C. A., Niemeyer, C. M., Wawer, A., Ebell, W., Baumann, I., & Kratz, C. P. (2006). TERC mutations in children with refractory cytopenia. Haematologica, 91, 707–708.

Lu, C., Fu, W., & Mattson, M. P. (2001). Telomerase protects developing neurons against DNA damage-induced cell death. Brain Research: Developmental Brain Research, 131, 167–171. Ly, H., Calado, R. T., Allard, P., Baerlocher, G. M., Lansdorp, P. M., Young, N. S., et al. (2005). Functional characterization of telomerase RNA variants found in patients with hematologic disorders. Blood, 105, 2332–2339. Maida, Y., Kyo, S., Kanaya, T., Wang, Z., Yatabe, N., Tanaka, M., et al. (2002). Direct activation of telomerase by EGF through Ets-mediated transactivation of TERT via MAP kinase signaling pathway. Oncogene, 21, 4071–4079. Marrone, A., Stevens, D., Vulliamy, T., Dokal, I., & Mason, P. J. (2004). Heterozygous telomerase RNA mutations found in dyskeratosis congenita and aplastic anemia reduce telomerase activity via haploinsufficiency. Blood, 104, 3936–3942. Marrone, A., Walne, A., Tamary, H., Masunari, Y., Kirwan, M., Beswick, R., et al. (2007). Telomerase reverse transcriptase homozygous mutations in autosomal recessive dyskeratosis congenita and HoyeraalHreidarsson syndrome. Blood, 110, 4198–4205. Martin-Ruiz, C. M., Gussekloo, J., van Heemst, D., von Zglinicki, T., & Westendorp, R. G. (2005). Telomere length in white blood cells is not associated with morbidity or mortality in the oldest old: A populationbased study. Aging Cell, 4, 287–290. Masutomi, K., Yu, E. Y., Khurts, S., Ben-Porath, I., Currier, J. L., Metz, G. B., et al. (2003). Telomerase maintains telomere structure in normal human cells. Cell, 114, 241–253.

c65.indd Sec4:1293

Morin, G. B. (1989). The human telomere terminal transferase enzyme is a ribonucleoprotein that synthesizes TTAGGG repeats. Cell, 59, 521–529.

Panossian, L. A., Porter, V. R., Valenzuela, H. F., Zhu, X., Reback, E., Masterman, D., et al. (2003). Telomere shortening in T cells correlates with Alzheimer ’s disease status. Neurobiology of Aging, 24, 77–84. Pardue, M. L., & DeBaryshe, P. G. (2003). Retrotransposons provide an evolutionarily robust non-telomerase mechanism to maintain telomeres. Annual Review of Genetics, 37, 485–511. Patil, C. K., Mian, I. S., & Campisi, J. (2005). The thorny path linking cellular senescence to organismal aging. Mechanisms of Ageing and Development, 126, 1040–1045. Perillo, N. L., Walford, R. L., Newman, M. A., & Effros, R. B. (1989). Human T lymphocytes possess a limited in vitro life span. Experimental Gerontology, 24, 177–187. Poon, S. S., & Lansdorp, P. M. (2001). Quantitative fluorescence in situ hybridization (Q-FISH). Current Protocols in Cell Biology, Chapter 18: Unit 18.4. Prowse, K. R., & Greider, C. W. (1995). Developmental and tissue-specific regulation of mouse telomerase and telomere length. Proceedings of the National Academy of Sciences, USA, 92, 4818–4822. Rufer, N., Brummendorf, T. H., Kolvraa, S., Bischoff, C., Christensen, K., Wadsworth, L., et al. (1999). Telomere fluorescence measurements in granulocytes and T lymphocyte subsets point to a high turnover of hematopoietic stem cells and memory T cells in early childhood. Journal of Experimental Medicine, 190, 157–167. Saeboe-Larssen, S., Fossberg, E., & Gaudernack, G. (2006). Characterization of novel alternative splicing sites in human telomerase reverse transcriptase (hTERT): Analysis of expression and mutual correlation in mRNA isoforms from normal and tumour tissues. BMC Molecular Biology, 7, 26.

8/19/09 5:17:51 PM

1294 Telomeres, Telomerase, Stress, and Aging Samani, N. J., Boultby, R., Butler, R., Thompson, J. R., & Goodall, A. H. (2001). Telomere shortening in atherosclerosis. Lancet, 358, 472–473. Sampson, M. J., Winterbone, M. S., Hughes, J. C., Dozio, N., & Hughes, D. A. (2006). Monocyte telomere shortening and oxidative DNA damage in type 2 diabetes. Diabetes Care, 29, 283–289. Sarin, K. Y., Cheung, P., Gilison, D., Lee, E., Tennen, R. I., Wang, E., et al. (2005, August 18). Conditional telomerase induction causes proliferation of hair follicle stem cells. Nature, 436, 1048–1052. Shao, L., Wood, C. G., Zhang, D., Tannir, N. M., Matin, S., Dinney, C. P., et al. (2007). Telomere dysfunction in peripheral lymphocytes as a potential predisposition factor for renal cancer. Journal of Urology, 178, 1492–1496. Shen, J., Terry, M. B., Gurvich, I., Liao, Y., Senie, R. T., & Santella, R. M. (2007). Short telomere length and breast cancer risk: A study in sister sets. Cancer Research, 67, 5538–5544. Simon, N. M., Smoller, J. W., McNamara, K. L., Maser, R. S., Zalta, A. K., Pollack, M. H., et al. (2006). Telomere shortening and mood disorders: Preliminary support for a chronic stress model of accelerated aging. Biological Psychiatry, 60, 432–435. Slagboom, P. E., Droog, S., & Boomsma, D. I. (1994). Genetic determination of telomere size in humans: A twin study of three age groups. American Journal of Human Genetics, 55, 876–882. Smith, L. L., Coller, H. A., & Roberts, J. M. (2003). Telomerase modulates expression of growth-controlling genes and enhances cell proliferation. Nature Cell Biology, 5, 474–479. Smogorzewska, A., & de Lange, T. (2004). Regulation of telomerase by telomeric proteins. Annual Review of Biochemistry, 73, 177–208. Son, N. H., Murray, S., Yanovski, J., Hodes, R. J., & Weng, N. (2000). Lineage-specific telomere shortening and unaltered capacity for telomerase expression in human T and B lymphocytes with age. Journal of Immunology, 165, 1191–1196. Starr, J. M., McGurn, B., Harris, S. E., Whalley, L. J., Deary, I. J., & Shiels, P. G. (2007). Association between telomere length and heart disease in a narrow age cohort of older people. Experimental Gerontology, 42, 571–573.

van der Harst, P., van der Steege, G., de Boer, R. A., Voors, A. A., Hall, A. S., Mulder, M. J., et al. (2007). Telomere length of circulating leukocytes is decreased in patients with chronic heart failure. Journal of the American College of Cardiology, 49, 1459–1464. Vasa-Nicotera, M., Brouilette, S., Mangino, M., Thompson, J. R., Braund, P., Clemitson, J. R., et al. (2005). Mapping of a major locus that determines telomere length in humans. American Journal of Human Genetics, 76, 147–151. von Zglinicki, T., Pilger, R., & Sitte, N. (2000). Accumulation of singlestrand breaks is the major cause of telomere shortening in human fibroblasts. Free Radical Biology and Medicine, 28, 64–74. Vulliamy, T. J., Marrone, A., Knight, S. W., Walne, A., Mason, P. J., & Dokal, I. (2006). Mutations in dyskeratosis congenita: Their impact on telomere length and the diversity of clinical presentation. Blood, 107, 2680–2685. Walne, A. J., Vulliamy, T., Marrone, A., Beswick, R., Kirwan, M., Masunari, Y., et al. (2007). Genetic heterogeneity in autosomal recessive dyskeratosis congenita with one subtype due to mutations in the telomerase-associated protein NOP10. Human Molecular Genetics, 16, 1619–1629. Watson, J. D. (1972). Origin of concatemeric T7 DNA. New Biology, 239, 197–201. Weng, N. P., Levine, B. L., June, C. H., & Hodes, R. J. (1996). Regulated expression of telomerase activity in human T lymphocyte development and activation. Journal of Experimental Medicine, 183, 2471–2479. Westin, E. R., Chavez, E., Lee, K. M., Gourronc, F. A., Riley, S., Lansdorp, P. M., et al. (2007). Telomere restoration and extension of proliferative lifespan in dyskeratosis congenita fibroblasts. Aging Cell, 6, 383–394. Wetterau, L. A., Francis, M. J., Ma, L., & Cohen, P. (2003). Insulin-like growth factor I stimulates telomerase activity in prostate cancer cells. Journal of Clinical Endocrinology and Metabolism, 88, 3354–3359. Wong, J. M., & Collins, K. (2006). Telomerase RNA level limits telomere maintenance in X-linked dyskeratosis congenita. Genes and Development, 20, 2848–2858. Wright, W. E., Piatyszek, M. A., Rainey, W. E., Byrd, W., & Shay, J. W. (1996). Telomerase activity in human germline and embryonic tissues and cells. Developmental Genetics, 18, 173–179.

Steer, S. E., Williams, F. M., Kato, B., Gardner, J. P., Norman, P. J., Hall, M. A., et al. (2007). Reduced telomere length in rheumatoid arthritis is independent of disease activity and duration. Annals of the Rheumatic Diseases, 66, 476–480.

Wu, X., Amos, C. I., Zhu, Y., Zhao, H., Grossman, B. H., Shay, J. W., et al. (2003). Telomere dysfunction: A potential cancer predisposition factor. Journal of the National Cancer Institute, 95, 1211–1218.

Stewart, S. A., Hahn, W. C., O’Connor, B. F., Banner, E. N., Lundberg, A. S., Modha, P., et al. (2002). Telomerase contributes to tumorigenesis by a telomere length-independent mechanism. Proceedings of the National Academy of Sciences, USA, 99, 12606–12611.

Xin, Z. T., Beauchamp, A. D., Calado, R. T., Bradford, J. W., Regal, J. A., Shenoy, A., et al. (2007). Functional characterization of natural telomerase mutations found in patients with hematologic disorders. Blood, 109, 524–532.

Takai, H., Smogorzewska, A., & de Lange, T. (2003). DNA damage foci at dysfunctional telomeres. Current Biology, 13, 1549–1556.

Xu, D., Erickson, S., Szeps, M., Gruber, A., Sangfelt, O., Einhorn, S., et al. (2000). Interferon alpha down-regulates telomerase reverse transcriptase and telomerase activity in human malignant and nonmalignant hematopoietic cells. Blood, 96, 4313–4318.

Takubo, K., Izumiyama-Shimomura, N., Honma, N., Sawabe, M., Arai, T., Kato, M., et al. (2002). Telomere lengths are characteristic in each human individual. Experimental Gerontology, 37, 523–531. Tedgui, A., & Mallat, Z. (2006). Cytokines in atherosclerosis: Pathogenic and regulatory pathways. Physiological Reviews, 86, 515–581. Torella, D., Rota, M., Nurzynska, D., Musso, E., Monsen, A., Shiraishi, I., et al. (2004). Cardiac stem cell and myocyte aging, heart failure, and insulin-like growth factor-1 overexpression. Circulation Research, 94, 514–524. Valdes, A. M., Andrew, T., Gardner, J. P., Kimura, M., Oelsner, E., Cherkas, L. F., et al. (2005). Obesity, cigarette smoking, and telomere length in women. Lancet, 366, 662–664. Valdes, A. M., Richards, J. B., Gardner, J. P., Swaminathan, R., Kimura, M., Xiaobin, L., et al. (2007). Telomere length in leukocytes correlates with bone mineral density and is shorter in women with osteoporosis. Osteoporosis International, 18, 1203–1210.

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Yamagiwa, Y., Meng, F., & Patel, T. (2006). Interleukin-6 decreases senescence and increases telomerase activity in malignant human cholangiocytes. Life Sciences, 78, 2494–2502. Yamaguchi, H. (2006). Bone marrow failure due to telomerase complex gene mutations. Rinsho Ketsueki, 47, 1431–1437. Yamaguchi, H. (2007). Mutations of telomerase complex genes linked to bone marrow failures. Journal of Nippon Medical School, 74, 202–209. Yang, L., Suwa, T., Wright, W. E., Shay, J. W., & Hornsby, P. J. (2001). Telomere shortening and decline in replicative potential as a function of donor age in human adrenocortical cells. Mechanisms of Ageing and Development, 122, 1685–1694. Zaccagnini, G., Gaetano, C., Della Pietra, L., Nanni, S., Grasselli, A., Mangoni, A., et al. (2005). Telomerase mediates vascular endothelial

8/19/09 5:17:52 PM

References 1295 growth factor-dependent responsiveness in a rat model of hind limb ischemia. Journal of Biological Chemistry, 280, 14790–14798. Zhai, G., Aviv, A., Hunter, D. J., Hart, D. J., Gardner, J. P., Kimura, M., et al. (2006). Reduction of leucocyte telomere length in radiographic hand osteoarthritis: A population-based study. Annals of the Rheumatic Diseases, 65, 1444–1448.

APPENDIX 65.1

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Zhang, J., Kong, Q., Zhang, Z., Ge, P., Ba, D., & He, W. (2003). Telomere dysfunction of lymphocytes in patients with Alzheimer disease. Cognitive and Behavioral Neurology, 16, 170–176. Zhu, H., Fu, W., & Mattson, M. P. (2000). The catalytic subunit of telomerase protects neurons against amyloid beta-peptide-induced apoptosis. Journal of Neurochemistry, 75, 117–124.

Updates to Table 65.1 Studies linking telomere maintenance to diseases.

Study

Main Findings

Reference

1067 breast cancer cases and 1110 controls in the Long Island Breast Cancer Study project

In premenopausal women short telomere associated with risk for breast cancer.

Shen, J., Gammon, M. D., Terry, M. B., Wang, Q., Bradshaw, P., Teitelbaum, S. L., Neugut, A. I., & Santella, R. M. (2009). Telomere length, oxidative damage, antioxidants and breast cancer risk. Int J Cancer 124, 1637-1643.

105 patients (61 men and 44 women)

Clear cell renal cell carcinoma patients with long telomeres had a worse prognosis than patients with short telomeres. TL in kidney cortex and tumor tissue did not predict survival.

Svenson, U., Ljungberg, B., & Roos, G. (2009). Telomere length in peripheral blood predicts survival in clear cell renal cell carcinoma. Cancer Res 69, 2896-2901.

265 newly diagnosed breast cancer and 446 female controls

Patients have longer telomeres than controls. Patients with short telomeres have increased survival

Svenson, U., Nordfjall, K., Stegmayr, B., Manjer, J., Nilsson, P., Tavelin, B., Henriksson, R., Lenner, P., & Roos, G. (2008). Breast cancer survival is associated with telomere length in peripheral blood cells. Cancer Res 68, 3618-3623.

388 hypertensive patients and 379 controls in Chinese population

Telomeres are shorter in patients. After 5 year follow up, subjects with short telomeres are at high risk of developing coronary artery diseases

Yang, Z., Huang, X., Jiang, H., Zhang, Y., Liu, H., Qin, C., Eisner, G. M., Jose, P., Rudolph, L., & Ju, Z. (2009). Short telomeres and prognosis of hypertension in a chinese population. Hypertension 53, 639-645.

1203 Framingham Study participants (mean age, 59 years; 51% women).

LTL was shorter in individuals with a higher renin-to-aldosterone ratio, especially in participants with hypertension.

Vasan, R. S., Demissie, S., Kimura, M., Cupples, L. A., Rifai, N., White, C., Wang, T. J., Gardner, J. P., Cao, X., Benjamin, E. J., et al. (2008). Association of leukocyte telomere length with circulating biomarkers of the renin-angiotensin-aldosterone system: the Framingham Heart Study. Circulation 117, 1138-1144.

1062 individuals (496 men, 566 women) aged 33 to 86 years in the Framingham Offspring Study

In obese men, shortened LTL is a powerful marker of increased carotid intimal medial thickness

O’Donnell, C. J., Demissie, S., Kimura, M., Levy, D., Gardner, J. P., White, C., D’Agostino, R. B., Wolf, P. A., Polak, J., Cupples, L. A., & Aviv, A. (2008). Leukocyte telomere length and carotid artery intimal medial thickness: the Framingham Heart Study. Arterioscler Thromb Vasc Biol 28, 1165-1171.

41 subjects with nonaffective psychosis (prior to antipsychotic drugs) and 41 controls

Patients have decreased telomere content (as measured by dotblot) and increased pulse pressure

Fernandez-Egea, E., Bernardo, M., Heaphy, C. M., Griffith, J. K., Parellada, E., Esmatjes, E., Conget, I., Nguyen, L., George, V., Stoppler, H., & Kirkpatrick, B. (2009). Telomere length and pulse pressure in newly diagnosed, antipsychotic-naive patients with nonaffective psychosis. Schizophr Bull 35, 437-442.

62 participants in the Nurses’ Health study

Short telomere associated with preclinical dementia states and decreasing hippocampal volume

Grodstein, F., van Oijen, M., Irizarry, M. C., Rosas, H. D., Hyman, B. T., Growdon, J. H., & De Vivo, I. (2008). Shorter telomeres may mark early risk of dementia: preliminary analysis of 62 participants from the nurses’ health study. PLoS ONE 3, e1590.

National Institute of Environmental Health Science Sister Study, n=647 women

High BMI and hip circumference associated with shorter telomeres.

Kim, S., Parks, C. G., DeRoo, L. A., Chen, H., Taylor, J. A., Cawthon, R. M., & Sandler, D. P. (2009). Obesity and weight gain in adulthood and telomere length. Cancer Epidemiol Biomarkers Prev 18, 816-820.

National Institute of Environmental Health Science Sister Study, n=647 women

High stress and urinary stress catecholamine associated with shorter telomeres. Short telomeres associated with increase age, obesity and current smoking

Parks, C. G., Miller, D. B., McCanlies, E. C., Cawthon, R. M., Andrew, M. E., DeRoo, L. A., & Sandler, D. P. (2009). Telomere length, current perceived stress, and urinary stress hormones in women. Cancer Epidemiol Biomarkers Prev 18, 551-560.

134 healthy seniors over 85 years with no disease and 47 random 40-50 olds

Healthy seniors have reduced telomere length variations compared to the random mid-age group

Halaschek-Wiener, J., Vulto, I., Fornika, D., Collins, J., Connors, J. M., Le, N. D., Lansdorp, P. M., & Brooks-Wilson, A. (2008). Reduced telomere length variation in healthy oldest old. Mech Ageing Dev 129, 638-641.

24 men with biopsy-diagnosed PBMC telomerase activity increased from low-risk prostate cancer who made baseline to 3 month. The increases in comprehensive lifestyle changes. telomerase activity were significantly associated with decreases in low-density lipoprotein (LDL) cholesterol and decreases in psychological distress.

Ornish, D., Lin, J., Daubenmier, J., Weidner, G., Epel, E., Kemp, C., Magbanua, M. J., Marlin, R., Yglecias, L., Carroll, P. R., & Blackburn, E. H. (2008). Increased telomerase activity and comprehensive lifestyle changes: a pilot study. Lancet Oncol 9, 1048-1057.

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Chapter 66

Constraint-Induced Movement Therapy: A Paradigm for Translating Advances in Behavioral Neuroscience into Rehabilitation Treatments EDWARD TAUB AND GITENDRA USWATTE

naturally arises whether there is some relation between the spontaneous recovery or behavioral plasticity that occurs after CNS damage and brain plasticity, and ultimately whether this relationship can be manipulated to improve the potential for recovery of function so that it can help a patient who has suffered neurological injury. Research involving somatosensory deafferentation in monkeys provides a paradigm for answering this question. After the surgical abolition of somatic sensation from a single forelimb in monkeys, the animals never use that extremity in the free situation. However, they can be induced to use the affected limb by one of two general techniques: prolonged restraint of the unaffected forelimb and repetitive training of the affected limb (Taub, 1977a). The application of these procedures produces a substantial rehabilitation of movement or plasticity of behavior where this was not thought to be possible. The same protocol has been successfully translated from the primate laboratory to humans, where it has also been shown to result in a substantial rehabilitation of function in patients with stroke for the upper extremity (Taub, 1980; Taub et al., 1993; Taub, Uswatte, King, et al., 2006; Wolf et al., 2006), lower extremity (Taub, Uswatte, & Pidikiti, 1999), multiple sclerosis (Mark et al., 2008), speech (Pulvermüller et al., 2001), and cerebral palsy (Taub, Griffin, et al., 2006; Taub, Ramey, DeLuca, & Echols, 2004). These techniques, which constitute a new approach to the rehabilitation of movement after neurological damage, have been termed constraint-induced movement therapy (CI therapy). Derivations of this approach have also been used successfully for the treatment of such formerly intractable conditions as focal hand dystonia (Candia et al., 1999) and phantom limb pain (Weiss, Miltner, Adler, Bruckner, & Taub, 1999).

After injury to the central nervous system (CNS), the initial deficit in behavior, perception, or cognitive ability is frequently followed by spontaneous recovery of function. One might characterize this resiliency as a type of behavioral plasticity. In apparent contrast, the traditional view in neuroscience during the first three quarters of the twentieth century was that the mature CNS has little capacity to reorganize and repair itself in response to injury. This view extends well back into the nineteenth century, influenced initially by Broca’s studies of localization of function within the brain (Broca, 1861); it emphasized the constancy of organization of the mature CNS even after substantial injury. Though contrary views were expressed (e.g., Fleurens, 1842; Fritsch & Hitzig, 1870; Lashley, 1938; Munk, 1881), the mature CNS was generally believed (Kaas, 1995) to exhibit little or no plasticity (Hubel & Wiesel, 1970; Ruch, 1960). Hughlings Jackson’s hierarchical view that lower centers of the brain substituted in function for higher damaged centers after CNS insult (Jackson, 1873, 1884) and other related formulations influenced thought concerning recovery of function for most of the twentieth century. However, the phenomenon of spontaneous recovery of function was never fully explained and received little experimental attention, largely because the techniques to explore this process had not yet been developed. Beginning in the 1970s, research from laboratories, including those of Merzenich (Merzenich et al., 1983, 1984), Kaas (Kaas, Merzenich, & Killackey, 1983), and Wall (Dostrovsky, Millar, & Wall, 1976; Wall & Egger, 1971), showed that, contrary to the established belief, the adult mammalian nervous system does have some capacity to reorganize itself functionally after injury. This phenomenon was referred to as cortical reorganization but is now more commonly termed brain plasticity. The question 1296

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Behavioral Plasticity after Somatosensory Deafferentation in Monkeys 1297

In a parallel line of investigation, Pons and coworkers (1991) showed that after somatosensory deafferentation of an upper extremity in monkeys, “massive cortical reorganization” takes place over the entire cortical arm area. This work was significant because the area of cortical reorganization was sufficiently large to promise relevance for recovery of function after CNS damage. This experiment led directly to a series of studies employing magnetoencepholograpy which showed that “massive cortical reorganization” takes place in human beings after amputation (Elbert et al., 1994) and that cortical reorganization has a strong correlation with such perceptual phenomena as phantom limb pain (N. P. Birbaumer et al., 1997; Flor et al., 1995; Taub, Flor, Knecht, & Elbert, 1995), tinnitus (Mühlnickel, Elbert, Taub, & Flor, 1998) and focal hand dystonia (Elbert et al., 1998). Other work in this line of investigation followed the seminal research of Recanzone, Merzenich, Jenkins, and coworkers (Jenkins, Merzenich, Ochs, Allard, & Guic-Robles, 1990; Recanzone, Jenkins, & Merzenich, 1992; Recanzone, Merzenich, & Jenkins, 1992; Recanzone, Merzenich, Jenkins, Grajski, & Dinse, 1992) on use-dependent cortical reorganization in monkeys that showed that increased use of a body part leads to an enlargement of its cortical representation. Elbert, Taub, and coworkers, elaborating on these observations, demonstrated that the same type of cortical reorganization occurs in humans (Elbert, Pantev, Wienbruch, Rockstroh, & Taub, 1995; Elbert et al., 1997, 1998; Sterr, Mueller, Elbert, & Rockstroh, et al., 1998; Sterr, Mueller, Elbert, & Taub, 1998). There are thus two types of brain plasticity that our group and other investigators have studied. One kind follows injuries to the nervous system that result in a reduction in somatosensory input and may be characterized as input-decrease cortical reorganization; the other type results from increased use of a body part or sensory system and may be termed input-increase cortical reorganization (Elbert et al., 1997). Input-decrease cortical reorganization seems to be correlated with the presence of adverse symptoms, while input-increase or use-dependent cortical reorganization generally appears to be related to the development of skills and with results that are advantageous to the individual. This is a general characterization and there are important exceptions. Stroke and the consequent reduced use or nonuse of the upper extremity on the more-affected side of the body has been shown to be associated with a marked reduction of the cortical representation of that extremity (Liepert, Bauder, Miltner, Taub, & Weiller, 2000; Liepert et al., 1998). As noted, CI therapy enhances recovery of function in many patients after stroke. Work from two laboratories has shown that this substantial plasticity of behavior is associated with a massive increase in the cortical representation

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of the more-affected arm following CI therapy (Bauder, Balzer, Miltner, & Taub, 1999; Kopp et al., 1999; Liepert et al., 1998, 2000). One might hypothesize, therefore, that the use-dependent brain plasticity produced by CI therapy counteracts the injury-related contraction of the cortical representation zone of the arm and is importantly involved in the rehabilitative effect produced by this intervention. A significant question that remains now is whether brain plasticity and functional recovery after CNS damage can be further enhanced by pharmacological and additional behavioral means.

BEHAVIORAL PLASTICITY AFTER SOMATOSENSORY DEAFFERENTATION IN MONKEYS When somatic sensation is surgically abolished from a single forelimb by severing all dorsal spinal nerve roots innervating that limb, the animal does not make use of it in the free situation (Knapp, Taub, & Berman, 1958, 1963; Lassek, 1953; Mott & Sherrington, 1885; Twitchell, 1954). This is the case even though the motor outflow over the ventral roots remains intact. However, monkeys can be induced to use the deafferented extremity by restricting movement of the intact limb (Knapp et al., 1963; Taub & Berman, 1963, 1968). The monkey may not have used the affected extremity for several years, but the application of this simple technique results in a striking conversion of the useless forelimb into a limb that is used for a wide variety of purposes, usually within a period of hours (Taub, 1977b, 1980). The movements are not normal; they are clumsy since somatic sensation has been abolished, but they are extensive and effective. This may be characterized as a substantial rehabilitation of movement, though the term is not usually applied to monkeys. If the restraint device is left in place for a period of 1 week or more, this reversal in use of the limb is permanent, persisting for the animal’s entire life. Training procedures are another means of overcoming the inability to use a single deafferented limb in primates (Knapp et al., 1958, 1963; Taub, 1976, 1977b, 1980; Taub, Bacon, & Berman, 1965; Taub & Berman, 1963, 1968; Taub, Ellman, & Berman, 1966; Taub, Goldberg, & Taub, 1975; Taub, Williams, Barro, & Steiner, 1978). Transfer from the experimental to the life situation was never observed when using conditioned response techniques to train limb use. However, when shaping was employed, there was substantial improvement in the motor ability of the deafferented limb in the life situation (Taub, 1976, 1977b; Taub & Berman, 1968; Taub, Goldberg, et al., 1975). Shaping is an operant training method in which a desired

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motor or behavioral objective is approached in small steps, by “successive approximations,” so that the improvement required for successful performance at any one point in the training is small (Morgan, 1974; Panyan, 1980; Risley & Baer, 1973; Skinner, 1938, 1968; Taub et al., 1994). The actions shaped included (a) pointing at visual targets (Taub, Goldberg, et al., 1975) and (b) prehension in juveniles deafferented on day of birth (Taub, Perrella, & Barro, 1973) and prenatally (Taub, Perrella, Miller, & Barro, 1975) who had never exhibited any prehension previously. In both cases, shaping produced an almost complete reversal of the motor disability, which progressed from total absence of the target behavior to very good (although not normal) performance. During the course of this century, several other investigators have found that a behavioral technique could be employed in animals to substantially improve a motor deficit resulting from neurological damage (Chambers, Konorski, Liu, Yu, & Anderson, 1972; Lashley, 1924; Ogden & Franz, 1917; Tower, 1940). However, none of these observations was embedded in a formal theoretical context that permitted prediction nor was the generality of the mechanisms recognized. Consequently, these findings remained a set of disconnected observations that received little attention. A Possible Mechanism: Learned Nonuse Several converging lines of evidence suggest that nonuse of a single deafferented limb is a learning phenomenon involving a conditioned suppression of movement termed learned nonuse. The restraint and training techniques appear to be effective because they overcome learned nonuse. We offer the following explanation for further empirical test and hypothesis formation and recognize that important modulating influences have not been taken into consideration, such as the site of the CNS lesion and interactions with other reinforcement mechanisms. However, if the learned nonuse hypothesis is proven incorrect, this development would not negate the clinical efficacy of CI therapy, which has been demonstrated in multiple experiments that are described in later sections. Substantial neurological injury usually leads to a depression in motor and/or perceptual function that is considerably worse than the level of function that will be attained after spontaneous recovery has taken place. The processes responsible for the initial depression of function and the later gradual recovery that occurs at the level of both the spinal cord and the brain is, at present, incompletely understood. Whatever the mechanism, however, recovery processes come into operation following deafferentation so that after a period of time movements can be once again,

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at least potentially, be expressed. In monkeys the initial period of depressed function lasts from 2 to 6 months following forelimb deafferentation (Taub, 1977b, 1980). Thus, immediately after surgical deafferentation of a limb, monkeys cannot use that extremity; recovery from the initial depression of function requires considerable time. Animals with one deafferented limb are unsuccessful in attempts to use that extremity during this period. Efforts to use the deafferented limb often lead to painful and otherwise aversive consequences, such as incoordination and falling, loss of food objects, and in general, failure of any activity attempted with the deafferented limb. Many learning experiments have demonstrated that punishment has the effect of suppressing the behavior associated with it (Azrin & Holz, 1966; Catania, 1998; Estes, 1944); in addition, individuals learn to avoid performance of the punished behavior. The monkeys, meanwhile, get along quite well in the laboratory environment on three limbs and are therefore positively reinforced for this pattern of behavior which, as a result, is strengthened. Thus, the response tendency to not use the affected limb persists and, consequently, monkeys never learn that the limb has become potentially useful several months after surgery. The mechanism by which learned nonuse develops is depicted schematically in Figure 66.1 and can best be appreciated as sequential processes that proceed from left to right in the diagram. When the movements of the intact limb are restricted several months after unilateral deafferentation, the situation is changed dramatically. Animals either use the deafferented limb, or cannot with any degree of efficiency feed themselves, locomote, or carry out large portions of their daily activities. This new constraint on behavior increases the drive to use the deafferented limb, thereby inducing monkeys to use it and overcoming the learned nonuse. However, current ongoing environmental contingencies, such as the relative inefficiency of the affected upper extremity compared with the unaffected arm, continue to affect the contingencies of reinforcement for use of the affected extremity. If the movement-restriction device is removed a short while after the early display of purposive movement, the newly learned use of the deafferented limb acquires little strength and is quickly overwhelmed by the well-learned tendency to not use the limb. However, ongoing environmental factors, such as the relative inefficiency of the affected upper extremity compared with the unaffected arm, continue to affect the contingencies of reinforcement for use of the affected extremity. If the movement-restriction device is left on for several days or longer, use of the deafferented limb acquires strength and then when the device is removed can compete successfully with the strongly overlearned nonuse of that limb. The

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Behavioral Plasticity after Somatosensory Deafferentation in Monkeys 1299

Injury (e.g., stroke, deafferentation)

Depressed CNS and motor activity

Less movement

Contraction of cortical representation zones

Movement more effortful

Unsuccessful motor attempts

Punishment (pain, failure, incoordination)

Behavior suppression and masked ability

Compensatory behavior patterns

Positive reinforcement

Less effective behavior strengthened

Learned Nonuse— normally permanent, reversal possible

Figure 66.1 Schematic model for the development of learned nonuse.

counterconditioning of learned nonuse is depicted schematically in Figure 66.2. The conditioned response and shaping conditions described in the previous section, just like the restriction of the intact limb, place major constraints on the animals’ behavior. In the conditioned response situation, if the monkeys do not perform the required response with the deafferented limb, they are either punished or do not receive food pellets or liquid when hungry or thirsty, respectively. Similarly, during shaping, reward is contingent on making an improved movement with the deafferented limb. The monkeys cannot get by using just the intact forelimb as they can in the colony environment. These new sets of conditions, just as the movement-restriction device, constrain the animals to use their deafferented limb to avoid punishment or obtain reward and thereby induce the animals to use their deafferented limb and overcome the learned nonuse. As noted, use of the deafferented limb does not transfer from the conditioned response situation to the life situation of the monkeys. This lack of transfer may be due to the restriction of training in the conditioning paradigm to a few specific movements within a narrow context; training of arm use is not generalized to a variety of movements or situations. The shaping situation, however, is more flexible and free-form; it appears to provide a bridge from the training to the life situation. What is learned in the shaping situation transfers to the colony environment and even generalizes to movement categories other than those trained. The movement restriction, conditioned response, and shaping situations share a common feature; each involves

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a constraint-induced (CI) facilitation of impaired movement that has the effect of overcoming the learned nonuse of the deafferented limb. Thus, the induction of movement by CI therapy would appear to be the agent responsible in each case for the rehabilitation of motor ability. The term constraint in the name of the treatment used with humans conveys the meaning that physical restraint of the less affected arm and training or shaping procedures both constitute forms of constraint, the training situation no less so than physical restraint of the less affected arm. In addition, all three of these situations involved massed or repetitive practice in using the deafferented extremity for prolonged periods over a set of consecutive days (for a more detailed description of these experiments, see Taub, 1977b, 1980). Direct Test of the Learned Nonuse Hypothesis An experiment was carried out to test the learned nonuse formulation directly (Taub, 1977b, 1980). Movement of a unilaterally deafferented forelimb was prevented with a restraining device in several animals so that they could not attempt to use that extremity for a period of 3 months following surgery. Restraint was begun while the animals were still under anesthesia. The reasoning was that by preventing animals from trying to use the deafferented limb during the period before spontaneous recovery of function had taken place, they would be unable to learn that the limb could not be used during that interval. Learned nonuse of the affected extremity should therefore not develop. In addition, the intact limb was restrained for the same period so that the animals could not receive reinforcement for use of that extremity alone. In conformity with the prediction,

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Learned Nonuse; masked recovery of limb use

Increased motivation

Use-dependent cortical reorganization

Figure 66.2

Affected limb use

Further practice and reinforcement

Further practice and reinforcement

Learned nonuse reversed; limb used in life situation permanently

Use-dependent cortical reorganization

Schematic model of mechanism for overcoming learned nonuse.

the animals were able to use their deafferented extremity in the free situation after the restraint was removed 3 months after operation, and this was permanent, persisting for the rest of the animals’ lives. Suggestive evidence in support of the operation of the learned nonuse mechanism was also obtained during deafferentation experiments carried out prenatally (Taub et al., 1973; Taub, Perrella, et al., 1975). Life in the physically restricted uterine environment imposes major constraints on the ability to use the forelimbs (while not preventing use of the limbs entirely), thereby functioning like a sling or a protective safety mitt in a CI therapy experiment (to be discussed later). Four animals were studied that had received unilateral forelimb deafferentation during the prenatal period; three when twothirds the way through gestation and one when two-fifths of the way through gestation. These animals exhibited purposive use of the deafferented extremity from the first day of extrauterine life, at which time they all employed the limb for postural support during “sprawling” and for pushing into a sitting position. Subsequently, though the intact limb was never restrained, the ability to use the deafferented limb continued to develop in ontogeny until it was similar to that of animals given unilateral deafferentation when mature. This, then, constitutes a second direct line of evidence supporting the learned nonuse formulation. Bilateral Forelimb Deafferentation One of the most striking features of the primate somatosensory deafferentation literature is the diametrically opposed result produced by deafferentation of just one forelimb versus deafferentation of both forelimbs. Until this point in the discussion, attention has been focused on the fact that following unilateral forelimb deafferentation, monkeys never use the affected extremity in the free situation;

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Positive reinforcement

this demonstration has been replicated (Lassek, 1953; Twitchell, 1954) and was accorded considerable theoretical significance (Sherrington, 1910, 1913, 1931). In contrast, we found that after bilateral forelimb deafferentation, a lesion of twice the extent, monkeys made extensive use of their upper extremities after a period of gradual recovery of function. It would appear that bilateral forelimb deafferentation, by affecting both extremities, has the effect of constraining an animal to use its deafferented forelimbs to carry out daily activities, much like placing a movementrestriction device on the intact forelimb of an unilaterally deafferented animal constrains it to use the deafferented forelimb. As in monkeys who receive unilateral deafferentation and wear the movement-restriction device on their intact limb or receive shaping of the deafferented limb, the bilaterally deafferented monkeys achieve clumsy, but effective control of their forelimb movements. Research on bilateral forelimb deafferentation has theoretical significance because it provides evidence, in addition to the work described in the two previous sections, that somatosensory feedback and spinal reflexes are not necessary in the performance of purposive movement and learning (Taub, 1977b; Taub & Berman, 1968). Applicability of the Model to Humans after Stroke Given the general nature of the learned nonuse mechanism proposed by the formulation, it was reasoned that the constraint-induced techniques developed in the experiments with monkeys might be an appropriate approach to the rehabilitation of motor disability due to nervous system injury in humans. For example, stroke often leaves patients with an apparently permanent loss of function in an upper extremity, although the limb is not paralyzed.

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A Linked, but Independent, Mechanism: Use-Dependent Plastic Brain Reorganization 1301

Additionally, the motor impairment is preponderantly unilateral. These factors are similar to those that pertain after unilateral forelimb deafferentation in monkeys. Therefore, it seemed reasonable to formulate a protocol that simply transferred the techniques used for overcoming learned nonuse of a deafferented limb in monkeys to humans who had experienced a cerebrovascular accident (Taub, 1980). A question might arise whether a mechanism that leads to rehabilitation of movement after somatosensory deafferentation would have relevance to the rehabilitation of movement after stroke since the two injuries involve very different types of damage to the nervous system. Somatosensory deafferentation interrupts afferent neurons at spinal cord level, whereas a stroke damages motor and other neurons at cortical level. However, both deafferentation and stroke provide the necessary circumstances for learned nonuse to develop; both result in an initial period of inability to use an affected extremity followed by a slow spontaneous recovery of function. The initial depression of motor ability creates contingencies of reinforcement (punishment for attempted use of the initially useless or seriously impaired extremity, and reward for use of the intact extremity) that lead to a conditioned nonuse of the affected body part. This nonuse is overlearned so that it persists and suppresses motor behavior even when the potential for more extensive use of a limb returns as a result of spontaneous recovery of function. Thus, the final motor disability is in excess of the eventual, true level of motor impairment. As learned nonuse is a function of the punishments and rewards that result from attempted activity after an injury, it should operate whatever the location and type of nervous system injury. The model does not at present incorporate some modifiers, such as comorbid disorders, self-generated discipline in practicing use of an extremity, or psychosocial support, that could potentially influence the mechanisms underlying the development of learned nonuse and those that overcome it. Moreover, in the case of stroke, the model also does not in any way minimize the possible general correlation between the location of neural damage following stroke and the amount of motor function that is spontaneously recovered on the more-affected side. Such a correlation could be a sufficient explanation for the observed differences in the actual use of a more-affected extremity among many patients. However, the fact that some patients with a given extent and locus of lesion exhibit greater use of a more-affected extremity than other patients having similar lesions suggests that additional factors may be involved. One of these factors might be the operation of a learned nonuse mechanism. The validity of this analysis can be assessed empirically. If two functional deficits that

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are similar in nature but different in anatomic origin can be overcome by the same techniques, we would have suggestive evidence that the same mechanism is involved in their remediation. It would still be possible that the resemblance was just phenotypic and that different mechanisms were involved in the two cases. However, the more parsimonious explanation would be that the same mechanism was involved in each case, especially if there was a strong conceptual basis for this hypothesis.

A LINKED, BUT INDEPENDENT, MECHANISM: USE-DEPENDENT PLASTIC BRAIN REORGANIZATION New findings in neuroscience suggested an additional mechanism responsible for the effectiveness of CI therapy. Cortical reorganization involves changes in the area and location of the cortex devoted to the representation of a particular sensory or motor function. The general principles of cortical reorganization as they can be understood on the basis of current research are as follows: • Deafferentation, as produced by amputation or dorsal rhizotomy, results in the invasion of the cortical representation zone of the affected body part by adjacent cortical representations of intact parts of the body. T. Pons and coworkers found that massive cortical reorganization had occurred in monkeys that had received somatosensory deafferentation of an entire forelimb in Taub’s laboratory some years earlier (Pons et al., 1991). Tactile stimulation of the monkeys’ face gave rise not only to evoked single unit responses in the somatosensory face area, but also to responses in the somatosensory cortical zone formerly representing the now-deafferented arm. The cortical zone representing the deafferented arm had been invaded by the face area. In several more recent studies in humans, we determined that invasion of deafferented representational zones is strongly correlated with amount of symptomatology in pathological conditions such as phantom limb pain (Birbaumer et al., 1997; Flor et al., 1995) and tinnitus (Mühlnickel et al., 1998). Prior to our findings, these conditions were enigmatic entities in that they had no agreed-on etiology. CNS correlates of these conditions had been long sought; however, it was not possible to identify them until our group (Elbert et al., 1994) and a group in San Diego (Yang et al., 1994) showed in 1994 that massive cortical reorganization takes place in humans after CNS injury. • Increased use of a limb leads to an expansion of the cortical representation zone of that body part and

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to a reduction in the size of the receptive fields of the neurons in that representation zone. Following the seminal work of Recanzone, Merzenich, and coworkers on use-dependent cortical reorganization in monkeys (Jenkins et al., 1990; Recanzone, Jenkins, et al., 1992; Recanzone, Merzenich, & Jenkins, 1992; Recanzone, Merzenich, Jenkins, Grajski, et al., 1992), neuroimaging studies by Elbert, Taub, and coworkers among others showed that the same phenomenon occurs in humans (Braun, Schweizer, Elbert, Birbaumer, & Taub, 2000; Elbert et al., 1997). For example, it was found that the cortical somatosensory representation of the digits of the left hand was larger in string players, who use their left hand for the dexterity-demanding task of fingering the strings, than in nonmusician controls (Elbert et al., 1995). • Training of extremity use after a CNS injury affecting the cortical tissue representing that body part results in improved extremity function and reorganization in brain activity. Intensive, extended training (see following bullet on massed practice), in producing a use-dependent cortical reorganization, may constitute a countervailing influence that reverses the adverse consequences of alterations in the functional organization of the brain (injury-related cortical reorganization) that occur consequent to stroke. Nudo and coworkers carried out a groundbreaking intracortical microstimulation study demonstrating that in adult squirrel monkeys that were surgically given an ischemic infarct in the cortical area controlling the movements of a hand, training of the affected limb resulted in improved behavioral function and in cortical reorganization. Specifically, the area surrounding the infarct, which would not normally be involved in control of the hand, came to participate in that function (Nudo, Wise, SiFuentes, & Milliken, 1996). • Massing or intensity of practice: Plastic brain reorganization emerges in response to a heavy training schedule (e.g., several hours a day for several successive days). CI therapy, which involves repetitive practice of tasks for multiple hours per day for 10 or 15 consecutive weekdays, has been found to produce “massive” cortical reorganization. Our data also show that CI therapy is highly effective when administered several hours a day over a limited period, though not necessarily on consecutive days. The optimal amount of massing or intensity of practice for producing maximal plastic brain change or CI therapy treatment effect has yet to be determined (Dettmers et al., 2005; Page, Sisto, Levine, & McGrath, 2004; Sterr et al., 2002; Taub, Lum, Hardin, Mark, & Uswatte, 2005).

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• Use-dependent cortical reorganization requires a high motivational drive. The behavioral relevance of sensory experience has been found to determine whether cortical reorganization will occur (Jenkins et al., 1990). Projections from the basal forebrain signal the importance of sensory stimuli to the individual, enhancing the CNS response to relevant events and diminishing it to others. “Absorption” in focus on a task may be the operative factor in “behavioral relevance” and, thus, may be important in determining the amount of usedependent cortical reorganization that will occur with repetitive stimulation.

Several collaborative neurophysiological studies have now shown that CI therapy produces a large use-dependent cortical reorganization in humans with stroke-related paresis of an upper limb similar to that observed by Nudo et al. (1996) in monkeys. In one study, Liepert and coworkers (1998) used focal transcranial magnetic stimulation to map the area of the motor cortex that controls an important muscle of the hand (abductor pollicis brevis) in six patients with a chronic upper extremity hemiparesis (mean chronicity ⫽ 6 years) before and after CI therapy. We first replicated the clinical result that CI therapy produces a very large increase in patients’ amount of arm use in the home over a 2-week treatment period. Over the same interval, the cortical region from which electromyography responses of the abductor pollicis brevis muscle could be elicited by transcranial magnetic stimulation was greatly increased. In a follow-up study with nine additional subjects (total N ⫽15), we found that both the motor rehabilitation effect and the alteration in brain function persisted for the 6 months tested (Liepert et al., 2000). CI therapy had led to an increase in the excitability and recruitment of a large number of neurons in the innervation of movements of the more-affected limb adjacent to those originally involved in control of the extremity prior to treatment. The effect was sufficiently large that it represented a return to normal size of the motor output area for the abductor pollicis brevis muscle in the infarcted side of the brain, though it was the size of excitable cortical area that had become normal, not its function; the affected hand, though much improved after CI therapy, was not normal in function. In a third study, Kopp et al. (1999) carried out dipole modeling of steady-state movement-related cortical potentials (EEG) of patients before and after CI therapy. We found that 3 months after treatment the motor cortex ipsilateral to the affected arm, which normally controls movements of the contralateral (less-affected) arm, had been recruited to generate movements of the affected arm. This effect was not in evidence immediately after treatment and was

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Application of the Learned Nonuse Model to Humans after Stroke

presumably due to the sustained increase in more-affected arm use in the life situation produced by CI therapy over the 3-month follow-up period. This experimental evidence that CI therapy is associated with substantial changes in brain activity has been confirmed by convergent data from two other neurophysiological studies using two additional techniques in association with the administration of CI therapy. Bauder, Sommer, Taub, and Miltner (1999) showed that there is a large increase in the amplitude of the late components of the Bereitschaftspotential (a movement-related cortical potential) after CI therapy, suggesting that an enhanced neuronal excitability is induced in the damaged hemisphere; this is consistent with the results of Liepert et al. (1998, 2000). We also found that after CI therapy there was a large increase in the activation of the (usually little-activated) healthy, ipsilateral hemisphere with more-affected hand movement. The increased activity of an ipsilateral source or generator after CI therapy confirms the findings of Kopp et al. (1999). In addition, Wittenberg et al. (2003) found in a positron emission tomography study that before CI therapy there was a larger activation in bilateral primary sensorimotor cortices with more-affected side movement compared to healthy control subjects. This excessive activation diminished after CI therapy. The preliminary interpretation of this result is that less effort is required to produce movements after CI therapy than before treatment. Since these initial studies, there have been approximately 20 other studies demonstrating an alteration in brain structure or function associated with a CI therapy-induced improvement in movement after CNS damage. By providing a physiological basis for the treatment effect reported for CI therapy, these results have tended to increase confidence in the clinical results. The findings suggest that CI therapy produces a longterm increase in arm use by two linked but independent mechanisms (Kopp et al., 1999; Liepert et al., 1998; Taub & Uswatte, 2000). As noted, CI therapy changes the contingencies of reinforcement (provides opportunities for reinforcement of use of the more-affected arm and aversive consequences for its nonuse) by three techniques: (1) intensive training procedures, (2) a set of techniques that promote transfer of therapeutic gains made in the laboratory to the life situation, and (3) restraint of the less-affected arm so that the nonuse of the more-affected arm learned in the acute and early subacute periods is counter-conditioned or lifted. The consequent increase in more-affected arm use, involving sustained and repeated practice of functional arm movements in both the laboratory and home environments, induces expansion of the contralateral cortical area controlling movement of the more-affected arm and recruitment of new ipsilateral areas. This use-dependent brain plasticity

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may serve as the neural basis for the long-term increase in use of the affected arm.

APPLICATION OF THE LEARNED NONUSE MODEL TO HUMANS AFTER STROKE Initial Studies on the Application of CI Therapy to the Rehabilitation of Paretic Arm Use The initial studies of the application of CI therapy to humans were carried out by Ince (1969) and Halberstam, Zaretsky, Brucker, and Guttman (1971). Ince transferred the conditioned response techniques used with the deafferented monkeys that he had observed in Taub’s laboratory (e.g., Taub & Berman, 1968) directly to the rehabilitation of movement of the paretic upper extremity of three patients with chronic stroke. He tied the less-affected upper extremity of the patients to the arm of a chair, while asking the patients to flex their more-affected arm to avoid an electric shock. The motor status of two of the patients did not change; the third patient, however improved substantially in the training and life situations (Ince, 1969). Halberstam et al. (1971), from a nearby institution, used a similar treatment protocol with a sample of 20 elderly patients and 20 age-matched controls. The treatment group was asked to either flex their more-affected arm or to make a lateral movement at the elbow to avoid electric shock; the less-affected arm was not tied down. Most of the patients in the treatment group increased the amplitude of their movements in the two conditioned response tasks; some showed very large improvements (Halberstam et al., 1971). There was no report of whether this improvement transferred to the life situation. Steven Wolf and coworkers (Ostendorf & Wolf, 1981; Wolf, Lecraw, Barton, & Jann, 1989) applied the lessaffected limb constraint portion, but not the more-affected limb training component, of the CI therapy protocol published by Taub (1980) to the rehabilitation of movement in persons with a chronic upper extremity hemiparesis. The study included 25 stroke and traumatic brain injury patients who were more than 1 year postinjury and who possessed a minimum of 10 degrees extension at the metacarpophalangeal and interphalangeal joints and 20 degrees extension at the wrist of the more-affected arm. The patients were asked to wear a sling on the less-affected arm all day for 2 weeks, except during a half-hour exercise period and sleeping hours. The patients demonstrated significant but small improvements in speed or force of movement, depending on the task, on 19 out of 21 tasks on the Wolf Motor Function Test (WMFT), a laboratory test involving simple upper extremity movements. There was no report

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of whether the improvements transferred to the life situation. Though the effect size was small (.2), it was reliable. The results appeared promising, especially since training had not been used and there was some question of compliance with the instruction to wear the sling for most of waking hours during the intervention period for some subjects. This type of intervention involving only use of a restraint device is now termed Forced Use therapy and not CI therapy. Demonstration of Efficacy of CI Therapy for Paretic Arm Rehabilitation at the University of Alabama at Birmingham (UAB) Laboratory Taub et al. (1993) applied both the paretic arm training and contralateral arm restraint portions of the CI therapy protocol and also a set of behavioral techniques termed the Transfer Package (Morris, Taub, & Mark, 2006; Taub, Uswatte, King, et al., 2006; Taub, Uswatte, Mark, & Morris, 2006) to the rehabilitation of persons with a chronic upper extremity hemiparesis in a study that employed an attention-placebo control group and emphasized transfer of therapeutic gains in the laboratory to the life situation. Patients with chronic stroke were selected as subjects for this study because according to the research literature (Bard & Hirschberg, 1965; Parker, Wade, & LangtonHewer, 1986; Twitchell, 1951), and clinical experience, motor recovery usually reaches a plateau within 1 year after stroke. Therefore, any marked improvement in the motor function of individuals with chronic stroke would be of increased therapeutic significance. After a long-standing plateau, the probability would be very low that an abrupt, large improvement in motor ability could be due to spontaneous recovery. Four treatment subjects signed a behavioral contract in which they agreed to wear a sling on their less-affected arm for 90% of waking hours for 14 days. On 10 of those days, the treatment subjects received 6 hours of supervised task practice using their more-affected arm (e.g., eating lunch, throwing a ball, playing dominoes, Chinese checkers or card games, writing, pushing a broom, and using the Purdue Pegboard and Minnesota Rate of Manipulation Test) interspersed with one hour of rest. Five control subjects were told they had much greater movement in their more-affected limb than they were exhibiting, were led through a series of passive movement exercises in the treatment center, and were given passive movement exercises to perform at home. All experimental and control subjects were at least 1 year poststroke (M ⫽ 4.4 yr) and had passed the minimum motor criterion employed by Wolf et al. (1989) before intake into the study. This kind of motor deficit could be characterized as mild/moderate

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or Grade 2 in the UAB system of classifying motor deficit at the impairment level based on active range of motion at each of the upper extremity joints. Treatment efficacy was evaluated by the WMFT (Morris, Uswatte, Crago, Cook, & Taub, 2001; Taub et al., 1993; Wolf et al., 1989, 2005), the Arm Motor Ability Test (Kopp et al., 1997; McCulloch et al., 1988), and the Motor Activity Log (Taub et al., 1993; Uswatt, Taub, Morris, Light, & Thompson, 2006; Uswatt, Taub, Morris, Vignolo, & McCulloch, 2005; van der Lee, Beckerman, Knol, de Vet, & Bouter, 2004), a structured scripted interview tracking arm use in a variety of activities of daily living (ADL). The treatment group demonstrated a significant increase in motor ability as measured by both laboratory motor tests (WMFT, Arm Motor Ability Test) over the treatment period, whereas the control subjects showed no change or a decline in arm motor ability. On the Motor Activity Log (MAL), the treatment group showed a large increase in real-world arm use over the 2-week period and no decrease in retention of the treatment gain in real-world use when tested 2 years after treatment. In other experiments, we have found a 20% decrement in retention over a 2-year posttreatment period in patients with a similar, mild/moderate deficit as the patients in this experiment. The control subjects exhibited no change or a decline in real-world arm use over the 2-week treatment period. These results have since been confirmed in an experiment using less-affected arm constraint and shaping (Morgan, 1974; Panyan, 1980; Skinner, 1938, 1968) of the more-affected arm, instead of task practice. This experiment also had a larger sample (N ⫽ 41) and a more credible control procedure than in the first study. The shaping procedure involved requiring that improvements in performance be made in small steps (successive approximations), providing explicit feedback and verbal reinforcement for small improvements in task performance, and selecting tasks that were tailored to address the motor deficits of the individual patient (Taub, Burgio, et al., 1994; Taub, Pidikiti, DeLuca, & Crago, 1996). Modeling and prompting of task performance were also used. The control group was designed to control for the duration and intensity of the therapist–patient interaction and the duration and intensity of the therapeutic activities. The control procedure was a general fitness program in which subjects performed strength, balance, and stamina training exercises, played games that stimulated cognitive activity, and practiced relaxation skills for 10 days. Both experimental and control subjects were at least 1-year poststroke (M ⫽ 4.5 yr) and exceeded the minimum motor criterion used in the first experiment prior to entry into the study. In addition, all subjects exhibited a substantial lack of spontaneous use of their more affected arm in their daily life, as defined by a score of less than 2.5 on the MAL (half as much use of

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Application of the Learned Nonuse Model to Humans after Stroke

the more impaired arm as before the stroke in the life situation). The motor deficit and amount of arm use of subjects in the two groups prior to treatment was not significantly different (Taub, Pidikiti, Uswatte, Shaw, & Yakley, 1998; Taub & Uswatte, 2000; Taub, Uswatte, King, et al., 2006). As in the first experiment, the treatment group demonstrated a significant increase in motor ability on the WMFT and a large increase in real-world arm use over the intervention, whereas the control subjects did not. Control subjects’ answers to an expectancy and self-efficacy questionnaire about their expectations for rehabilitation prior to the control intervention and their reported increase in quality of life after the intervention, as measured by the Medical Outcomes Study 36-Item Short-Form Health Survey (Ware & Sherbourne, 1992), suggested that they found the control intervention to be credible.

CI Therapy: A Family of Treatments Other experiments carried out at UAB have indicated that there is a family of techniques that can overcome learned nonuse (Taub, Crago, & Uswatte, 1998; Taub et al., 1996; Taub & Uswatte, 2000; Uswatt, Taub, Morris, Barman, & Crago, 2006). The other interventions that have been tested are (a) placement of a half-glove on the less-affected arm as a reminder not to use it and shaping of the paretic arm, (b) shaping of the paretic arm only, and (c) intensive physical therapy (aquatic therapy, neurophysiological facilitation, and task practice) of the paretic arm for 5 hours a day for 10 consecutive weekdays (Uswatt, Taub, Morris, Barman, et al., 2006). The half-glove intervention was designed so that CI therapy could be employed with patients who have balance problems and might be at risk for falls when wearing a sling; this intervention expands the population of stroke patients amenable to CI therapy threefold. Currently, a “padded or protective safety mitt” is used instead of the half glove for patients with balance problems. This restraint leaves the less-affected arm free so as not to compromise safety, but prevents use of the hand and fingers in ADL. The shaping-only intervention was tested to evaluate the relative importance of the constraint and task practice components of the intervention. The intensive physical therapy intervention did not involve physical constraint of the less-affected arm; however, subjects were asked not to use their less-affected arm and this regimen was monitored. To our knowledge, such a concentrated application of physical therapy had not been evaluated before this trial. All three of these groups showed very large increases in arm use in the life situation over the treatment period equivalent to that observed for the sling plus task practice and the sling plus shaping groups. Two

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years after treatment, each of the groups retained approximately 80% of their original treatment effect.

Effect Size Several hundred subjects with mild/moderate stroke motor symptoms (Grade 2; approximately 25% to 35% of the stroke population) have been given upper extremity CI therapy to date in this laboratory. The mean effect size (ES) for the WMFT, a laboratory motor function test, in all of these studies was .9; the mean ES for the MAL, which records ADL in the life situation, was 3.3. The much larger ES for the MAL than for the WMFT indicates that CI therapy has its greatest effect on increasing the actual amount of use of a more-affected upper extremity in the real-world setting, though the improvement in quality of movement as indexed by the WMFT is still substantial. In the meta-analysis literature, an ES (d ⬘ ) of .2 is considered small, a .4 to .6 ES is moderate, while ESs of .8 and above are large (Cohen, 1988). Thus, the ES of CI therapy for real-world outcome in patients with chronic stroke from the upper quartile of motor functioning is extremely large.

Replications in Other Laboratories Over 400 patients with stroke in the UAB laboratory have been given one variant or another of CI therapy and all but three of these patients have demonstrated substantial improvement in motor ability (improvement greater than a Minimum Clinically Important Difference; defined in Lum et al., 2004; Uswatte & Taub, 2005; van der Lee et al. 2004). This laboratory’s results have been replicated quantitatively with patients with chronic stroke in published studies from four laboratories; the therapists were trained in this laboratory and monitored by one of us (ET) twice yearly (Dettmers et al., 2005; Kunkel et al., 1999; Miltner, Bauder, Sommer, Dettmers, & Taub, 1999; Sterr et al., 2002). There have been over 200 other papers on adult and pediatric CI therapy published to date. To our knowledge all the studies up to now have reported positive results. Some of the papers report outcomes as large as those obtained in this and related laboratories. Many studies, however, have results that are significant, but one half to one third as large as those obtained here. The likely reasons for this disparity are twofold: (1) There was incomplete or lack of use of the procedures of the transfer package (to be described), which, though reported in the papers from this laboratory, had been largely unremarked and underemphasized. We have replicated the reduced treatment effect obtained by others by duplicating everything that is normally done in treatment here except implementing the

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transfer package. (2) A protocol with attenuated intensity (movement per unit time) was used, such as in van der Lee, Beckerman, Lankhorst, & Bouter (1999).

Massing of Practice as a Therapeutic Factor The question arises as to what is the common factor or factors underlying the therapeutic effect in the different CI therapy interventions tested to date. Although most of the techniques involve restraining movement of the lessaffected arm, the shaping-only and intensive physical therapy interventions do not. There is thus nothing talismanic about use of a sling or other restraining device on the less-affected extremity. Until recently, the common and most important factor appeared to be repeatedly practicing use of the paretic arm. It was thought that any technique that induced a patient to use an affected extremity intensively for an extended duration should be therapeutically efficacious. Butefisch, Hummelsheim, Kenzler, and Mauritz (1995) have also shown that repetitive practice is an important factor in stroke rehabilitation. However, we have found that there is a factor more important still in producing increased spontaneous use of a more affected arm in the free situation—a set of techniques referred to collectively as the transfer package. These techniques are discussed in the following section. However, this finding does not vitiate the importance of massing of practice as an independent therapeutic factor. Studies that make use of restraint of the less-affected arm but do not give concentrated, extended practice in use of the more-affected arm cannot be said to have administered CI therapy (Taub & Uswatte, 2000). Wolf et al.’s (1989) study provided evidence that this type of procedure yields a markedly reduced treatment effect. In a more recent study (van der Lee, Wagenaar, et al., 1999), investigators who trained in the UAB laboratory were counseled before they left that the practice schedule incorporating activities modeled on hobbies they were planning to use would not work well because of its relaxed, attenuated nature. Subjects were treated in groups of four with one or two therapists in attendence, in contrast to the much more intensive one-on-one therapy administered in the UAB laboratory. Not surprisingly, their attenuated intervention did not produce treatment effects as large as those obtained in the UAB laboratory. It should also be noted that in the van der Lee et al. (1999) study an intensive conventional physical therapy intervention, which concentrated the administration of therapy far beyond the schedule with which conventional therapy is usually delivered, was contrasted with the attenuated form of CI therapy they used. This procedure would not provide a meaningful control for evaluating the efficacy of CI therapy even if CI

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therapy was administered correctly. We had already shown with our intensive physical therapy group (Taub, Crago, et al., 1998; Uswatt, Taub, Morris, Barman, et al., 2006) that massing practice of conventional therapy gives results as good as those achieved with restraint of the less-affected arm combined with intensive task practice or shaping. In apparent contradiction to the identification of massed practice as an important therapeutic factor in CI therapy, the motor learning literature indicates that massed practice has only a neutral or negative effect on the learning of continuous tasks and a variable effect on the learning of discrete tasks (Schmidt & Lee, 1999). However, CI therapy employs massed practice to increase the tendency of patients to use their more-impaired limb and overcome learned nonuse. There is little evidential basis for believing that operant learning resulting in the increased frequency of already learned and previously performed movement and the lifting of the conditioned inhibition associated with learned nonuse is governed by the same principles as the acquisition of new types of skilled movement that is the focus of the motor learning approach. Although both processes involve learning, they would appear to be of different types. Therefore, it is no surprise that findings from the motor learning literature concerning massed versus spaced practice do not apply to the effectiveness demonstrated for massed practice in CI therapy.

The Transfer Package In recent work, we unexpectedly found that a set of techniques now termed the transfer package (TP) made a much more important contribution to the treatment effect produced by CI therapy than had been anticipated. The purpose of the TP techniques is to promote transfer of the therapeutic gains from the laboratory to the real-world environment. They are behavioral procedures that are used routinely in behavioral interventions. We had previously viewed the TP as a transparent aspect of CI therapy. It was something we had routinely done, but not directed much attention toward, though several rehabilitation professionals on joining our research team had remarked that the procedures were radically different from those generally carried out in rehabilitation treatment centers. The TP includes the following seven components: (1) daily administration of the MAL, which collects information about use of the more affected arm in 30 important ADL; (2) a patient-kept daily diary that details what a patient did when out of the laboratory overnight and the extent to which there was compliance with an agreed-on amount of use of the more affected arm; (3) problem solving to help patients overcome perceived barriers to real-world use

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Application of the Learned Nonuse Model to Humans after Stroke

of the more affected arm reported during monitoring; (4) behavioral contracts for patients and caregivers specifying agreed-on real-world activities for which the more affected arm would be used exclusively (items 1, 2, and 4 are monitoring and accountability components); (5) home practice of specified exercises; (6) restraint of the less affected arm in a padded mitt; and (7) weekly telephone contacts with patients for the first month after the end of treatment in which the MAL is administered and problem solving is carried out. (For a more detailed description of the elements of the TP, see Morris et al., 2006; Taub, Uswatte, Mark, et al., 2006). In most physical rehabilitation regimens, there is a passive element; the patient is responsible primarily for carrying out the therapist’s instructions only during treatment sessions. A major difference in CI therapy is the involvement of the patient as an active participant in all requirements of the therapy not only during the treatment sessions but also at home during the treatment period and for the first month after laboratory therapy has been completed (and afterward though it is not checked on). The TP makes patients responsible for adhering to the requirements of the therapy, and therefore in effect they become responsible for their own improvement. The study population was chronic stroke patients with mild/moderate upper extremity hemiparesis (Grade 2). Half the patients were given intensive training for 3 hr/day for 10 consecutive weekdays and also received the TP. The remaining subjects were treated in exactly the same way but received no TP. On a laboratory motor function test (WMFT) where patients are asked to use the more affected arm as rapidly and as well as they can, the two groups showed an equivalent significant improvement after treatment. The subjects without the TP performed just as well in the laboratory as the subjects with the TP. The results were very different for spontaneous use of the more affected arm in ADL in the life situation. Though the subjects without the TP showed a significant and clinically meaningful improvement in amount of use of the more affected arm in the life situation, the subjects given the TP had 2.5 to 3 times greater real-world limb use than the subjects without the TP. However, the TP by itself does not have a therapeutic effect. In a placebo-controlled experiment (Taub, Uswatte, King, et al., 2006), use of the TP with a placebo treatment did not result in a significant effect. Confirmation of this result was obtained from a separate group of 10 subjects who were given no TP during training. However, two TP components, the MAL and problem solving about perceived barriers to the use of the arm in ADL in the home, were administered by telephone on a weekly basis for a month after treatment. Participants in this group showed the same modest improvement from pre- to posttreatment in spontaneous real-world use of the

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more affected extremity as the other subjects who did not receive the TP during training, but they showed a substantial increase in real-world use of their more impaired arm after the end of treatment when the four weekly MAL follow-ups were introduced. At the end of this process, the MAL scores, though still much lower than those of the TP subjects, had increased so that they were approximately 75% greater than those of the subjects who did not receive the TP at any time. It was felt that the results of this group were particularly suggestive as to the therapeutic power of the elements of the TP. In addition, it seems likely that the TP can be separated from CI therapy and used with other rehabilitation interventions. The TP makes patients active participants, in fact the critical factors, in their own improvement. What then is the importance of massed, intensive practice? The data indicate that it is critical. It is a precondition for the operation of the TP. Thus, when it was used in conjunction with a placebo intervention, there was no treatment effect. However, once massed extensive practice is employed, the TP becomes the dominant factor. It increases the real-world treatment effect by 2.5 times; and numerous commentators agree that improvement of use of an extremity in the life situation is the primary purpose of rehabilitation. Structural Imaging Studies with CI Therapy and Brain Plasticity We have described studies showing that alterations in afferent input could alter the function and organization of specific brain regions, but until recently there was no evidence that environmental stimuli could measurably alter brain structures in adult humans. It has now been shown that seasoned taxi drivers have significantly expanded hippocampi (Maguire et al., 2000), jugglers acquire significantly increased temporal lobe density (Draganski et al., 2004), and thalamic density significantly declines after limb amputation (Draganski et al., 2006). Moreover, in an animal model of stroke, CI therapy combined with exercise reduced tissue loss associated with stroke (DeBow, Davies, Clarke, & Colbourne, 2003). Accordingly, structural imaging studies became a logical initial step toward understanding whether there are anatomical changes following the administration of CI therapy and whether these are correlated with clinical improvements. Moreover, anatomical studies making use of structural MRI have advantages over fMRI studies, including the fact that there is no need to control specific, restricted movements during scanning. Longitudinal voxel-based morphometry (pre- vs. posttreatment) was performed on subjects enrolled in our study of the contribution made by the TP to CI therapy outcome (Gauthier et al., 2008). It was found that structural

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brain changes paralleled changes in amount of use of the impaired extremity for activities of daily living. Groups receiving the TP showed profuse increases in grey matter tissue in sensorimotor cortices both contralateral and ipsilateral to the affected arm, as well as in bilateral hippocampi. The aforementioned sensorimotor clusters were bilaterally symmetrical and encompassed the hand/ arm regions of primary sensory and motor cortices as well as the anterior supplementary motor area and portions of Brodmann’s area 6 (Figure 66.3). In contrast, the groups that did not receive the TP showed relatively small improvements in real-world arm use and failed to demonstrate grey matter increases. Moreover, the increase in grey matter from pre- to posttreatment differed significantly between groups. It was of importance that increases in grey matter were significantly correlated with increases on the MAL for the sensorimotor clusters on both sides of the brain and the predefined hippocampus region of interest (r’s ⬎ .45). Thus, this change in the brain’s morphology is directly related to administration of the TP which in turn substantially increases the amount of realworld use of the affected arm. The fact that this anatomical change is directly related to the TP both lends increased credibility to the importance of the TP and provides a mechanism for its operation. Increases were also observed in the grey matter of the hippocampus, which may have included the adjacent subventricular zone. The hippocampus is known to be involved in learning and memory and these two processes are associated with the improved limb use that occurs with CI therapy. Evidence also indicates that stem cells are located at this site in the adult mammalian brain (Eriksson (B) CI Therapy Group Comparison Group Contralateral Ipsilateral Contralateral Ipsilateral

(A)

Figure 66.3 change.

Cortical surface-rendered images of grey matter

Note: Grey matter increases displayed on a standard brain for (A) participants who received the CI therapy transfer package and (B) those who did not. Surface rendering was performed with a depth of 20 mm. Regions in which significant changes in grey matter were clustered are indicated by hatch marks.

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et al., 1998; Yamashima et al., 2004) and simulated stroke in animals can increase the quantity of these cells (Yamashima et al., 2004). One might speculate that the increases in grey matter observed in the hippocampal region and sensory and motor areas of the brain are mediated in part by increased production of neuronal or glial stem cells that might participate in the migratory repair of an infarcted area (Kolb et al., 2007). Alternatively, or in addition, grey matter increases may result from rehabilitation-induced increases in dendritic arborization and synaptic density (Briones, Suh, Jozsa, & Woods, 2006), and possibly gliosis or angiogenesis. Notably, the grey matter increases that we observed occurred over the course of just 2 weeks of therapy, emphasizing the rapid time course in which structural neuroplastic changes can take place. Application of CI Therapy to the Lower Extremity The UAB laboratory has applied CI therapy techniques to the rehabilitation of the lower extremity of patients with chronic CVA with substantial success (Taub et al., 1999). The 38 patients treated to date have had a wide range of disability extending from being close to nonambulatory to having moderately impaired coordination. The treatment consisted of massed or repetitive practice of lower extremity tasks (e.g., treadmill walking with and without a partial body weight support harness, over-ground walking, sit-to-stand, lie-to-sit, step climbing, various balance and support exercises) for 6 hours/day with interspersed rest intervals as needed over 3 weeks and 0.5 hours/day devoted to TP procedures. Task performance was shaped as in our upper extremity work. Training was enhanced through the use of force feedback (limb load monitor) and joint angle feedback (electric goniometer) devices. No restraining device was placed on the less-affected leg. The lowerextremity procedure is considered to be a form of CI therapy because of the use of the TP, the strong massed practice element, and because the reinforcement of adaptive patterns of ambulation over maladaptive patterns in our training procedure constitutes a significant form of constraint. Control data were provided by the general fitness control group (procedure described later in this chapter), who received the same battery of lower-extremity tests as the lower-extremity treatment subjects. Among the 38 lower extremity patients, five minimally ambulatory patients who required support from a person to walk improved to the status of fully independent (but impaired) ambulation in two cases and ambulation with minimal assistance in three other cases. An additional minimally ambulatory patient improved, but not a great deal. Each of the 32 subjects with a moderate level of impairment improved substantially on most

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Application of CI Therapy to the Treatment of Other Neurological and Orthopedic Disorders 1309

or all our measures. On a group basis, the lower-extremity patients showed a significantly greater improvement than the fitness control subjects both on laboratory motor tests and real-world measures of lower-extremity use. The treatment gain obtained for the upper extremity and the lower limb interventions is difficult to compare because different instruments are used to measure upperand lower- function. The effect sizes (ESs) for real-world lower extremity use (MAL) have been above 2.0 in all the upper extremity CI therapy studies in which the intervention was properly administered. The effect size (ES) on the measure of real-world extremity use for the lower extremity after treatment is somewhat smaller (ES ⫽ 1.6). However, since 0.8 is considered a large ES, the lower-extremity ES we have obtained is still very large. Approximately 90% of patients with chronic CVA ambulate but may do so with a degraded pattern of coordination. These disordered patterns may be partly due to the persistence of movements learned in the early postinjury period before spontaneous recovery of function would have permitted an improved mode of ambulation. This phenomenon may be viewed as learned misuse rather than learned nonuse. Initially, we thought that it might be more difficult to overcome learned misuse than learned nonuse, if it was possible at all. In the case of learned misuse, bad habits of coordination need to be overcome before more appropriate patterns of coordination can be substituted. In the case of learned nonuse, as with the upper extremity after stroke, there is simply an absence or greatly reduced amount of extremity use in the life situation; surmounting improper coordination as an initial step is not a primary problem. We were surprised that our expectation of a substantially reduced lower-extremity treatment outcome proved to be incorrect. Data obtained concerning long-term retention are of interest. Over a 2-year period of follow-up, there was virtually no loss in retention of the treatment effect even in patients with substantial deficits. This is in contrast to the case for the upper extremity, where there is some loss over 2 years that is greater as the pretreatment amount of deficit increases. The lack of loss of treatment gains over time for the lower-extremity therapy may relate to the fact that if patients are not to be helpless, they must ambulate, and while doing so, they presumably keep practicing the improved patterns of coordination learned during their lower-extremity CI therapy. This is not the case for the upper extremity. If patients become depressed, otherwise ill, or experience a life crisis, they may lose the motivation or attentional focus to expend the extra effort to use the more affected extremity before it becomes habitual. This would have the effect of reducing retention recorded for a group of subjects.

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Consistency of CI Therapy Efficacy with General Clinical Experience In 1979, Andrews and Stewart published an article entitled “Stroke Recovery: He Can But Does He?” in which they reported, “a difference in what the patients could do in the unit and what they did do at home. Each activity of daily living was less well performed in the home situation in 25% to 45% of cases” (p. 43; emphasis added). Most clinicians recognize the veracity of this statement. Indeed, decrement in performance outside the clinic environment is frequently reported as a source of intense frustration. Clinicians often work with patients intensively for one or more sessions, with the result that there is a substantial improvement in some aspect of movement. However, by the time of the next therapy session, there have been varying degrees of regression. In fact, some clinicians report that they sometimes see degradation in motor patterns as soon as the patient crosses the threshold into the corridor just outside the therapy room. Very little explicit attention is paid to this dimension of treatment. A reasonably intensive search of the literature failed to reveal a single reference to this phenomenon. Similarly, very little attention has been paid to the Andrews and Stewart (1979) paper, which has been virtually “lost in the literature.” For many stroke and other types of patients we have worked with, there is undeniably a gap between performance in the clinic on laboratory motor tests when specific activities are requested and the actual amount of lower extremity use in the home. This gap may be viewed as an index of learned nonuse; CI therapy operates in this window. It establishes a bridge between the laboratory or clinic and the life setting so that the therapeutic gains made in the clinic transfer maximally and contribute to the functional independence of the patient in the real world. Thus, many patients, though exhibiting a pronounced deficit in spontaneous real-world more affected limb use, might have a considerable latent capacity for motor improvement that could be brought to expression by CI therapy.

APPLICATION OF CI THERAPY TO THE TREATMENT OF OTHER NEUROLOGICAL AND ORTHOPEDIC DISORDERS The range of disorders for which CI therapy might be an effective treatment encompasses conditions in which motor disability is in apparent excess of the underlying pathology. A possible explanation for the excess motor disability in some of these cases might be that it is being maintained by learned nonuse (Taub, 1980, 1994). The research with deafferented monkeys suggests that learned nonuse is established

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whenever (a) organic damage results in an initial inability to use a body part so that an individual is punished for attempts to use that part of the body and rewarded for use of other parts of the body, and (b) there is recovery from or healing of the organic damage so that the person recovers the ability to use that body part, but the suppression of use conditioned in the acute phase remains in force. Thus, in the most general terms, CI therapy may be viewed as a treatment for excess motor disability. Clinicians know that there is a very large amount of excess motor disability. It costs the health care system billions of dollars each year, and yet there is no treatment. For example, a woman breaks her hip, the bone heals, but the woman never gets out of bed; or if she does, she does not resume normal activity. Why? The usual explanation is muscular deconditioning from a prolonged period in bed or psychological problems, that is, it is all in her head. In any case, nothing that can be treated very easily. The advantage of the learned nonuse or CI therapy approach is that there is something you can try to do to remediate the situation. It does not involve a therapeutic philosophy of despair. In this section, we summarize our initial work on applying CI therapy to rehabilitate arm use in persons with traumatic brain injury, progressive multiple sclerosis, speech in persons with aphasia consequent to stroke, ambulation in persons with spinal cord injury or fractured hip, and in finger coordination in musicians with focal hand dystonia. An additional application is the alleviation of phantom limb pain in persons with an upper extremity amputation. This work, however, does not exhaust the conditions that appear to involve considerable learned nonuse for which there is at present no effective treatment.

Upper Extremity Use in Persons with Traumatic Brain Injury (TBI) TBI patients with predominantly unilateral upper limb motor deficits show gains in motor function after CI therapy are similar to those shown by stroke patients with equivalent initial deficits (Morris, Shaw, et al., 2006; Shaw et al., 2005; Shaw, Morris, Yakley, McKay, & Taub, 2000). Two Iraq War veterans with TBI whom we have treated had better than average success. They approached the treatment exercises with focused attention and military discipline. They were ideal patients.

Upper Extremity Use in Persons with Progressive Multiple Sclerosis (MS) Few therapeutic approaches have been shown to benefit real-world disability in MS. A preliminary trial from our laboratory of CI therapy in progressive MS patients with chronic upper extremity hemiparesis suggests gains that

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are similar to those for stroke patients after CI therapy (Mark et al., 2008). The findings suggest the possibility that other slowly progressive neurological disorders may benefit from CI therapy and involve learned nonuse.

Cerebral Palsy The origin of this work was in some research carried out with infant monkeys given somatosensory deafferentation of one or both forelimbs on their day of birth or prenatally (Taub et al., 1973; Taub, Perrella, et al., 1975). The same techniques that had been used successfully with monkeys with mature nervous systems were also found to be effective with infant monkeys. Consequently, after the initial work with adult stroke survivors (Taub et al., 1993), it was suggested that CI therapy ought to be at least as effective with children who have suffered damage to the CNS because the immature nervous system is so much more plastic than the nervous system of adults (Taub & Crago, 1995). We have carried out two randomized controlled trials of CI therapy with young children, the first with children with asymmetric upper extremity motor deficits of varied etiologies from 8 months to 8 years of age and the second with children with hemiparesis consequent to prenatal, perinatal, or early antenatal stroke from 2 to 6 years old (Taub et al., 2004). The procedures used with children were similar to those used with adults and diverged simply to make the basic techniques age-appropriate. Posttreatment gains in the children were better than those obtained in adults. Marked changes were observed in (a) quality of movement in the laboratory scored by masked observers from videotapes, (b) actual amount of use of the more affected arm in the life situation, (c) active range of motion, and (d) emergence of new classes of behavior never performed before, such as in individual cases, fine thumb-forefinger grasp, supination, and use of the more affected extremity in crawling with palmar placement and rhythmic alteration. CI therapy does not make movement normal in children with cerebral palsy with asymmetric upper extremity motor disorders. However, it can produce a substantial improvement in a large majority of cases.

Ambulation in Persons with Spinal Cord Injury or Fractured Hip Six incomplete spinal cord injury patients have been treated with the same lower extremity protocol used with patients with stroke (King, Willcutt, & Taub, 1999). The patients were ambulatory but with severe initial deficits: They spent most of their time in a wheelchair and reported that they never ambulated over distances greater than 5 feet. All six subjects improved substantially; their results

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Application of CI Therapy to the Treatment of Other Neurological and Orthopedic Disorders 1311

Aphasia arises as a consequence of focal brain damage, often in association with stroke. The demonstration that motor behavior is modifiable in patients with chronic stroke led us to believe that another consequence of stroke, language impairment (which often has an important motor component), might be sufficiently plastic to be rehabilitated by an appropriate modification of the CI therapy techniques used for rehabilitating movement of the extremities. In the first study (Pulvermüller et al., 2001), aphasic subjects with chronic stroke who had previously received extensive conventional speech therapy and had reached an apparent maximum in recovery of language function received CI aphasia therapy (CIAT) or perhaps more appropriately CI language therapy (CILT). They were induced to talk and improve their language skills three hours each weekday over a 2-week period. Groups of three patients and a therapist participated in language game activities in which success was achieved by progressively improving the naming of pictured objects and explicitly requesting that other participants conform to the rules of the game (Pulvermüller, 1990; Pulvermüller & Schonle, 1993). CILT patients in a randomized controlled trial improved significantly both in performance on laboratory tests of language ability and in the amount of talking they did in the life situation (Pulvermüller et al., 2001). The procedures employed in this work that were developed on the basis of the CI therapy model in motor rehabilitation were massing of practice, constraining patients to communicate using speech during the language game, shaping patients’ speech during the game, and emphasis on measuring the patients’ real-world behavior. This study has since been replicated and expanded on in a series of studies by Meinzer, Elbert, and coworkers (Meinzer et al., 2007) and by Maher and coworkers (2006).

who engage in extensive and forceful use of the digits. To date, no treatments have been found to be effective on more than a temporary basis. Using magnetic source imaging, we found that musicians with focal hand dystonia exhibit a use-dependent overlap or smearing of the representational zones of the digits of the dystonic hand in the somatosensory cortex (Elbert et al., 1998; Elbert, Candia, Rockstroh, & Taub, 2000). Another laboratory has obtained similar results (Bara-Jimenez, Catalan, Hallett, & Gerloff, 1998). Digital overuse had previously been found to produce a similar phenomenon in monkeys in the laboratory of M. Merzenich. Since behavioral mechanisms apparently underlie both the cortical disorder and the involuntary incoordination of movement, we hypothesized that a behavioral intervention could reduce or eliminate both of these correlated abnormalities. The procedures employed in our treatment approach to focal hand dystonia (Candia et al., 1999, 2002) were derived in part from CI therapy. Eight professional musicians (six pianists and two guitarists) with long-standing symptoms were studied. Our therapy involved immobilization by splint(s) of one or more of the digits other than the focal dystonic finger. The musicians were required to carry out repetitive exercises with the focal dystonic finger in coordination with one or more of the other digits for 1.5 to 2.5 hours daily (depending on patient fatigue) over a period of 8 consecutive days (14 days in one case) under therapist supervision. The practice was thus massed; practice of this intensity and duration was very taxing and was at the limit of the patients’ capacity. After the end of the primary period of treatment, the patients continued practicing the exercises with the splint for 1 hour every day or every other day at home in combination with progressively longer periods of repertoire practice without the splint. All patients showed significant and substantial improvements without the splint at the end of treatment in the smoothness of finger movement, as determined by a device that measured finger displacement, and self-reported dystonia symptoms. The improvement persisted for the 2 years of follow-up in all the patients but one who did not comply with home practice regimen prescribed. Half of the subjects have returned to the normal or almost normal range of digit function in music performance. The treatment is characterized as a form of CI therapy because it has all its main components: massed practice, the main elements of the TP, frequent feedback during exercises and shaping of improved finger movements, and restraint of a body part.

Digital Coordination in Musicians with Focal Hand Dystonia

Phantom Limb Pain in Persons with an Upper Extremity Amputation

Focal hand dystonia is a condition involving manual incoordination that occurs in individuals, including musicians,

Weiss et al. (1999) studied the experience of phantom limb pain, nonpainful phantom limb sensation, and telescoping

were just as good as those of patients with chronic stroke after lower-extremity CI therapy. In addition, five subjects with residual motor deficit after fractured hip and having no apparent organic basis for their disability were treated with the CI therapy lower extremity intervention. All five subjects improved substantially. This work shows that CI therapy has applicability not only after CNS damage but after substantial injury to any other system in the body, such as the skeletal system, where healing or recovery is sufficiently slow so that learned nonuse can supervene. Speech in Persons with Aphasia

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by questionnaire in a group of persons with upper extremity amputation wearing a functionally effective prosthesis that allowed extensive use of the residual limb, and in a group of patients wearing a cosmetic prosthesis that did little to increase the utilization of the amputation stump. We found that the functionally effective prosthesis group reported a significant and very large decrease in phantom limb pain. After obtaining the prosthesis, 9 of 11 patients reported a disappearance of phantom limb pain by the time of the study. In contrast, the cosmetic prosthesis group displayed a trend toward an increase in phantom limb pain over time. Neither group experienced a decrease in nonpainful phantom limb sensation or telescoping. As noted, work in collaboration with H. Flor and others had shown that the amount of phantom limb pain is strongly correlated with the amount of injury-related, afferent-decrease cortical reorganization (Birbaumer et al., 1997; Flor et al., 1995). It is possible that the increased use of the residual limb induced by wearing a functionally effective prosthesis produced a countervailing usedependent afferent-increase type of cortical reorganization. This would have the effect of reducing the injury-related afferent-decrease cortical reorganization and would thereby reverse the phantom limb pain. These preliminary results require replication and direct experimental test. Phantom limb pain has proved to be refractory to all the many therapeutic approaches tested to date. If the preliminary observations reported in this study are confirmed, the findings would suggest that it might be of value to fit persons with upper extremity amputation with functional prostheses to reduce the occurrence of phantom limb pain.

AN IMPENDING PARADIGM SHIFT The relative dearth of effective interventions in neurorehabilitation may be attributable to the weak contribution from basic sciences such as behavioral psychology and neuroscience. These two disciplines arguably should have been the parent sciences of rehabilitation. Behavioral psychology has contributed much to the treatment of chronic pain (Fordyce, 1976), but has little or no place in the curriculum of physical therapy schools or in developing treatments for movement disorders. Neuroscience holds an important place in the curriculum of physical therapy schools, but its influence has been largely didactic and has had little bearing on clinical practice. Other reasons for this lack of development have been discussed by investigators from within this field (Duncan, 1997; Horak, 1992; Shumway-Cook & Woollacott, 1995). In other health-related fields, basic research has been of inestimable value in enabling the development of new therapeutic

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interventions. CI therapy is a new approach to the rehabilitation of movement that, as noted at the beginning of this chapter, emerged directly from basic research in behavioral science and neuroscience. The success of this clinical technique is probably based importantly on its firm grounding in a body of replicated and generally accepted basic research. However, CI therapy is not alone in this regard. Research involving CI therapy may constitute part of the leading edge of an impending paradigm shift in which other advances in behavioral science and neuroscience are employed for the development of new strategies in the fields of rehabilitation and remediation. An important case in point is the intervention for children with specific language impairments (SLI) and dyslexia developed by Merzenich, Tallal, and coworkers (Merzenich et al., 1996; Tallal et al., 1996). Children with SLI show limitations in a wide range of expressive and/or receptive oral language abilities revealed by poor vocabulary and deficits in syntax production or comprehension. Generally, children with SLI develop difficulties in reading, writing, and spelling (become dyslexic) despite having normal intellectual capacity and educational resources. The remedial treatment for SLI is derived in important part from basic research studies investigating language functions in SLI and dyslexic children. Psychoacoustic studies have revealed that auditory phoneme processing is deficient in many children with SLI and dyslexia (Reed, 1989; Stark & Heinz, 1996; Tallal & Piercy, 1974, 1975; Tallal & Stark, 1981; Tallal, Stark, & Mellits, 1985; for reviews, see Farmer & Klein, 1995; Tallal, Miller, & Fitch, 1993). SLI children have greater difficulty than children with normal language development in integrating brief and rapidly changing sounds, and therefore experience difficulties in discriminating stop consonant-vowel syllables with their short (40 ms) transitional periods. That this may constitute a basic impairment is supported by the fact that deficits in stop consonant perception are, in fact, highly correlated with language comprehension scores of SLI children (Tallal et al., 1985). The impairment can be overcome by synthetically extending the brief transitional periods (Tallal & Piercy, 1975). Using the principles underlying neuroplasticity and cortical reorganization, Merzenich and Tallal designed a computer-based training program (FastForward) and demonstrated that impaired processing of rapidly changing sounds could be greatly improved in 5- to 10-year-old children with SLI (Merzenich et al., 1996; Tallal et al., 1996). Children were trained for about 100 min/day, 5 days a week for 20 training days with audiovisual games. Rapid transitional speech and nonspeech stimuli were initially made more discriminable by extending them in time and amplifying them. As training progressed and the children demonstrated success, the modified acoustic stimuli were presented in

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An Impending Paradigm Shift

progressively less modified form until the stimuli approximated sounds as they occur in natural speech. Thus, as in the case of CI therapy, the training protocol involved a shaping procedure. Other training principles included in the therapy were that it had to be “applied with a heavy schedule” on successive days and “would require intense practice schedules” (massed practice, again as in CI therapy), and “high motivational drive” (Merzenich et al., 1996, p. 89). These elements originated in neuroplasticity studies, many of them carried out in Merzenich’s own laboratory (Jenkins et al., 1990; Recanzone, Jenkins, et al., 1992; Recanzone, Merzenich, & Jenkins, 1992; Recanzone, Merzenich, Jenkins, et al., 1992). Seven percent of preschool children are estimated to suffer from SLI (Tomblin et al., 1997); prevalence estimates for dyslexia vary between 4% to 9% (Shaywitz, Shaywitz, Fletcher, & Escobar, 1990). Therefore, this treatment may be of benefit to large numbers of children (Merzenich, personal communication, November, 1998; Tallal, personal communication, March, 2000). Both CI therapy and the intervention of Merzenich, Tallal, and coworkers for dyslexia depend in part on manipulations that produce a use-dependent alteration in taskrelated portions of the brain through the massed repetition of appropriate experiences. The potential for extending this approach to other conditions for which effective treatments do not at present exist has only begun to be tapped. It could well be a major new wave, just beginning to gather force, that may sweep the field of rehabilitation. Another potentially significant therapeutic development derived from basic research is the development of devices that enable people to control their environment by biofeedback-aided self-regulation of the electrical activity of the brain. Birbaumer and coworkers have recently trained totally paralyzed “locked-in” individuals with amyotrophic lateral sclerosis (ALS) to first construct individual words and then complete messages, letter-by-letter, using a computer-based spelling program; a computer interface permitted the detection of self-regulated amplitude increases in the individual’s slow cortical potentials (SCP) and triggered the execution of a command when the SCP amplitude exceeded a criterion level. The individuals with ALS were trained to regulate their brain activity using EEG biofeedback and shaping techniques (Birbaumer et al., 1999; Kuebler et al., 1999). The first report of this type of approach was from the laboratory of E. Donchin with healthy subjects in which letters were selected on the basis of the appearance of an enhanced P300 wave in the slow cortical potential elicited by the presentation of the desired letter in an alphabet set (Farwell & Donchin, 1988). Wolpaw and colleagues worked with the selfregulation of the sensorimotor rhythm in healthy and partially paralyzed subjects with ALS who still had extensive

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control of their striate musculature (Wolpaw, Flotzinger, Pfurtscheller, & McFarland, 1997; Wolpaw & McFarland, 1994; Wolpaw, McFarland, Neat, & Forneris, 1991). A group at Johns Hopkins University has worked on techniques for enabling the self-regulation of electrical activity recorded from an electrode implanted directly in the brain (Kennedy & Bakay, 1998). These emerging techniques for controlling the environment by the self-regulation of brain activity have implications that are broader than providing a means of communication for individuals with total paralysis, as important as that goal is. Another development stems from animal research in the laboratory of D. Feeney in which it was found that dextroamphetamine improves the recovery of function in rats after motor cortex lesions (Feeney, 1997; Feeney & Baron, 1986; Feeney, Gonzalez, & Law, 1982). Of particular interest is that this pharmacological intervention has an effect on recovery of function primarily when it is used in conjunction with one or another behavioral training paradigm. There are several studies in which an attempt has been made to use d-amphetamine to improve the rehabilitation of limb movement (Crisostomo, Duncan, Propst, Dawson, & Davis, 1988) or language function in aphasic patients (Walker-Batson, Smith, Curtis, Unwin, & Greenlee, 1995; Walker-Batson et al., 1992) after stroke. Other pharmacological approaches include the administration of methylphenidate in animals (Kline, Chen, Tso-Oliveras, & Feeney, 1994) and humans (Grade, Redford, Chrostowski, Toussaint, & Blackwell, 1998); glycine to enhance weight bearing and stepping in spinal cats (de Leon, Tamaki, Hodgson, Roy, & Edgerton, 1999); noradrenergic agonists to help initiate locomotion or increase treadmill speed in animals (Barbeau, Chau, & Rossignol, 1993; Barbeau & Rossignol, 1991) and humans with spinal cord injuries (Norman, Pepin, & Barbeau, 1998; Stewart, Barbeau, & Gauthier, 1991); serotonergic antagonists to increase weight bearing and treadmill speed in persons with SCI (Norman et al., 1998; Wainberg, Barbeau, & Gauthier, 1990); and combinations of drugs to enhance ambulatory activity (e.g., serotonin and N-methyl-D-L-aspartate in neonatal rats, Bertrand & Cazalets, 1998; and clonidine and cyproheptadine in persons with SCI, Fung, Stewart, & Barbeau, 1990). These studies are mainly preliminary; they involve small numbers of patients and do not include the controls that will be appropriate when this field is more mature. However, taken together and in conjunction with the animal research, these early results are suggestive and exciting. CI therapy has potential value in this type of experimental enterprise. It is one of the few rehabilitation treatments for which there is controlled evidence of efficacy (Duncan, 1997; Wolf et al., 2006, 2008), and the animal research shows that an effective behavioral method for improving

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function is of importance as a substrate, so to speak, for the effectiveness of the pharmacological intervention. There has been a long history of research on enabling the regeneration of neural tissue within the mammalian CNS after neurological injury so that new and functional synaptic connections could be formed that would provide a basis for improved function. The work in this area has now entered a very promising phase. Here, again, the improvements in function that have been observed in animals come largely in connection with the concomitant use of behavioral techniques. The recent discovery that undifferentiated stem cells exist in the mature mammalian nervous system that are capable of assuming the role of many different types of CNS cell types, during learning, exercise, and after loss due to injury, opens up new vistas in this area. The investigation of the effect of enriched environments on the central nervous system is another area of research that has a long history with promise of important practical applications for remediation and rehabilitation. Most of the basic animal research has been on the ways in which environmental enrichment can greatly enhance the development of the immature nervous system. The work of C. T. and S. L. Ramey has shown that use of a comprehensively enriched environment starting at an early age (e.g., 1 year) can increase IQ by a mean of 15 points in children from disadvantaged homes compared with control children (Ramey & Ramey, 1998a, 1998b). In the area of rehabilitation, Fischer and Peduzzi (1997) have shown that enriching the environment of rats with novel objects and pathways to explore can substantially improve hind-limb function after spinal cord injury. From this brief summary, it is evident that most of these new or promising treatments in the fields of rehabilitation and remediation either (a) emerge from behavioral research or research in behavior and neuroscience, (b) involve behavioral techniques in conjunction with other types of interventions, or (c) make use of behavioral methods to produce an advantageous effect on the nervous system. These approaches are not entirely new, but their explicit formulation and the effectiveness with which they are currently being applied to the enhancement of impaired human abilities are new. It is this development that we feel justifies the designation of these approaches as an impending paradigm shift in the field of rehabilitation.

Azrin, N. H., & Holz, W. C. (1966). Punishment. In W. K. Honig (Ed.), Operant behavior: Areas of research and application (pp. 380–447). New York: Appleton-Century-Crofts. Bara-Jimenez, W., Catalan, M. J., Hallett, M., & Gerloff, C. (1998). Abnormal somatosensory homunculus in dystonia of the hand. Annals of Neurology, 44, 828–831. Barbeau, H., Chau, C., & Rossignol, S. (1993). Noradrenergic agonists and locomotor training affect locomotor recovery after cord transection in adult cats. Brain Research Bulletin, 30, 387–393. Barbeau, H., & Rossignol, S. (1991). Initiation and modulation of the locomotor pattern in the adult chronic spinal cat by noradrenergic, serotonergic, and dopaminergic drugs. Brain Research, 546, 250–260. Bard, G., & Hirschberg, G. G. (1965). Recovery of voluntary movement in upper extremity following hemiplegia. Archives of Physical Medicine and Rehabilitation, 46, 567–572. Bauder, H., Balzer, C., Miltner, W. H. R., & Taub, E. (1999). Change in movement-related cortical potentials following CI therapy after stroke. Manuscript submitted for publication. Bauder, H., Sommer, M., Taub, E., & Miltner, W. H. R. (1999). Effect of CI therapy on movement-related brain potentials. Psychophysiology, 36(Suppl, 1), S31 (Abstract). Bertrand, S., & Cazalets, J.-R. (1998). GABA-A and GABA-B modulations of synaptic transmission between L1–L2 locomotor network and the motorneurons in the newborn rat isolated spinal cord. Annals of the New York Academy of Sciences, 860, 470–471. Birbaumer, N., Ghanayim, N., Hinterberger, T., Iverson, S., Kotchoubey, B., Kubler, A., et al. (1999, March 25). A spelling device for the paralyzed. Nature, 398, 297–298. Birbaumer, N., Lutzenberger, W., Montoya, P., Larbig, W., Unertl, K., Topfner, S., et al. (1997). Effects of regional anesthesia on phantom limb pain are mirrored in changes in cortical reorganization. Journal of Neuroscience, 17, 5503–5508. Braun, C., Schweizer, R., Elbert, T., Birbaumer, N., & Taub, E. (2000). Differential activation in somatosensory cortex for different discrimination tasks. Journal of Neuroscience, 20, 446–450. Briones, T. L., Suh, E., Jozsa, L., & Woods, J. (2006). Behaviorally induced synaptogenesis and dendritic growth in the hippocampal region following transient global cerebral ischemia are accompanied by improvement in spatial learning. Experimental Neurology, 198, 530–538. Broca, P. (1861). Nouvelle observation d’aphemie produite par une lesion de la motie posterieure des deuxieme et troisieme circonvulutions frontales [New observations on aphasia produced by a lesion of the posterior portion of the second and third frontal gyri]. Bulletin de la Societe Anatomique de Paris, 6, 398–407. Butefisch, C., Hummelsheim, H., Kenzler, P., & Mauritz, K.-H. (1995). Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand. Journal of Neurological Sciences, 130, 59–68. Candia, V., Elbert, T., Altenmüller, E., Rau, H., Schäfer, T., & Taub, E. (1999). Constraint-induced movement therapy for focal hand dystonia in musicians. Lancet, 353, 42. Candia, V., Schafer, T., Taub, E., Rau, H., Altenmuller, E., Rockstroh, B., et al. (2002). Sensory motor retuning: A behavioral treatment for focal hand dystonia of pianists and guitarists. Archives of Physical Medicine and Rehabilitation, 83, 1342–1348. Catania, A. C. (1998). Learning (4th ed.). Upper Saddle River, NJ: Prentice-Hall.

REFERENCES

Chambers, W. W., Konorski, J., Liu, C. N., Yu, J., & Anderson, R. (1972). The effects of cerebellar lesions upon skilled movements and instrumental conditioned reflexes. Acta Neurobiologiae Experimentalis, 32, 721–732.

Andrews, K., & Stewart, J. (1979). Stroke recovery: He can but does he? Rheumatology and Rehabilitation, 18, 43–48.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

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References 1315 Crisostomo, E. A., Duncan, P. W., Propst, M. A., Dawson, D. V., & Davis, J. N. (1988). Evidence that amphetamine with physical therapy promotes recovery of motor function in stroke patients. Annals of Neurology, 23, 94–97. DeBow, S. B., Davies, M. L. A., Clarke, H. L., & Colbourne, F. (2003). Constraint-induced movement therapy and rehabilitation exercises lessen motor deficits and volume of brain injury after striatal hemorrhagic stroke in rats. Stroke, 34, 1021–1026. de Leon, R. D., Tamaki, H., Hodgson, J. A., Roy, R. R., & Edgerton, V. R. (1999). Hindlimb locomotor and postural training modulates glycinergic inhibition in the spinal cord of the adult spinal cat. Journal of Neurophysiology, 82, 359–369. Dettmers, C., Teske, U., Hamzei, F., Uswatte, G., Taub, E., & Weiller, C. (2005). Distributed form of constraint-induced movement therapy improves functional outcome and quality of life after stroke. Archives of Physical Medicine and Rehabilitation, 86, 204–209. Dostrovsky, J. O., Millar, J., & Wall, P. D. (1976). The immediate shift of afferent drive to dorsal column nucleus cells following deafferentation: A comparison of acute and chronic deafferentation in gracile nucleus and spinal cord. Experimental Neurology, 52, 480–495. Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004, January 22). Changes in grey matter induced by training. Nature, 427, 311–312. Draganski, B., Moser, T., Lummel, N., Ganssbauer, S., Bogdahn, U., Haas, F., et al. (2006). Decrease of thalamic gray matter following limb amputation. NeuroImage, 31, 951–957. Duncan, P. W. (1997). Synthesis of intervention trials to improve motor recovery following stroke. Topics in Stroke Rehabilitation, 3, 1–20. Elbert, T., Candia, B., Altenmuller, E., Rau, H., Sterr, A., Rockstroh, B., et al. (1998). Alteration of digital representations in somatosensory cortex in focal hand dystonia. NeuroReport, 9, 3571–3575. Elbert, T., Candia, V., Rockstroh, B., & Taub, E. (2000, March). Maladaptive use-dependent cortical reorganization: Smearing of the somatotopy in individuals with focal hand dystonia reverses after successful treatment. Paper presented at the Third Berlin Workshop on Cortical Plasticity, Berlin, Germany. Elbert, T., Flor, H., Birbaumer, N., Knecht, S., Hampson, S., Larbig, W., et al. (1994). Extensive reorganization of the somatosensory cortex in adult humans after nervous system injury. NeuroReport, 5, 2593–2597. Elbert, T., Pantev, C., Wienbruch, C., Rockstroh, B., & Taub, E. (1995, October 13). Increased cortical representation of the fingers of the left hand in string players. Science, 270, 305–307. Elbert, T., Sterr, A., Flor, H., Rockstroh, B., Knecht, S., Pantev, C., et al. (1997). Input-increase and input-decrease types of cortical reorganization after upper extremity amputation in humans. Experimental Brain Research, 117, 161–164. Eriksson, P. S., Perfilieva, E., Bjork-Eriksson, T., Alborn, A. M., Nordborg, C., & Peterson, D. A. (1998). Neurogenesis in the adult human hippocampus. Natural Medicine, 4, 1313–1317. Estes, W. K. (1944). An experimental study of punishment. Psychological Monographs, 57(Serial No. 263). Farmer, M. E., & Klein, R. M. (1995). The evidence for a temporal processing deficit linked to dyslexia: A review. Psychonomic Bulletin and Review, 2, 460–493. Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70, 510–523. Feeney, D. M. (1997). From laboratory to clinic: Noradrenergic enhancement of physical therapy for stroke or trauma patients. In H. J. Freund, B. A. Sabel, & O. W. Witte (Eds.), Brain plasticity: Advances in neurology (Vol. 73, pp. 383–394). Philadelphia: Lippencott-Raven. Feeney, D. M., & Baron, J. (1986). Diaschisis. Stroke, 17, 817–830.

c66.indd Sec6:1315

Feeney, D. M., Gonzalez, A., & Law, W. A. (1982). Amphetamine, haloperidol and experience interact to affect rate of recovery after motor cortex injury. Science, 217, 855–857. Fischer, F. R., & Peduzzi, J. D. (1997). Functional improvement in rats with chronic spinal cord injuries after exposure to an enriched environment [Abstract]. Society for Neuroscience Abstracts, 23, 2188. Fleurens, P. (1842). Recherches experimentales sur les propietes et les functions du systeme neurveux dans les animaux [Experiments on the properties and functions of the nervous system of animals] (2nd ed.). Paris: Belliere. Flor, H., Elbert, T., Knecht, S., Wienbruch, C., Pantev, C., Birbaumer, N., et al. (1995, June 8). Phantom limb pain as a perceptual correlate of massive reorganization in upper limb amputees. Nature, 375, 482–484. Fordyce, W. E. (1976). Behavioral methods for chronic pain and illness. St. Louis, MO: Mosby. Fritsch, G., & Hitzig, E. (1870). Über die electrische erregbarkeit des grosshirns [On the electrical excitability of the cerebral cortex]. Archiv für Anatomie und Physiologie, 37, 300–332. Fung, J., Stewart, J. E., & Barbeau, H. (1990). The combined effects of clonidine and cyproheptadine with interactice training on the modulation of locomotion in spinal cord injured subjects. Journal of the Neurological Sciences, 100, 85–93. Gauthier, L.V., Taub, E., Perkins, C., Ortmann, M., Mark, V.W., & Uswatte, G. (2008). Remodeling the brain: Plastic structural brain changes produced by different motor therapies after stroke. Stroke, 39, 1520–1525. Grade, C., Redford, B., Chrostowski, J., Toussaint, L., & Blackwell, B. (1998). Methylphenidate in early poststroke recovery: A double-blind, placebo-control study. Archives of Physical Medicine and Rehabilitation, 79, 1047–1050. Halberstam, J. L., Zaretsky, H. H., Brucker, B. S., & Guttman, A. (1971). Avoidance conditioning of motor responses in elderly brain-damaged patients. Archives of Physical Medicine and Rehabilitation, 52, 318–328. Horak, K. (1992). Assumptions underlying motor control for neurologic rehabilitation. In Contemporary management of motor control problems: Proceedings of the II step conference (pp. 11–28). Alexandria, VA: American Physical Therapy Association. Hubel, D. H., & Wiesel, T. N. J. (1970). The period of susceptibility to the physiological effects of unilateral eye closure in kittens. Journal of Physiology, 206, 419–436. Ince, L. P. (1969). Escape and avoidance conditioning of response in the plegic arm of stroke patients: A preliminary study. Psychonomic Science, 16, 49–50. Jackson, J. H. (1873). On the anatomical and physiological localization of movements in the brain. Lancet, 1, 84–85, 162–164, 232–234. Jackson, J. H. (1884). Evolution and dissolution of the nervous system (the Croonian Lectures). British Medical Journal, 1, 591–754. Jenkins, W. M., Merzenich, M. M., Ochs, M. T., Allard, T., & Guic-Robles, E. (1990). Functional reorganization of primary somatosensory cortex in adult owl monkeys after behaviorally controlled tactile stimulation. Journal of Neurophysiology, 63, 82–104. Kaas, J. H. (1995, June 29). Neurobiology: How cortex reorganizes [news; comment]. Nature, 375, 735–736. Kaas, J. H., Merzenich, M. M., & Killackey, H. P. (1983). The reorganization of somatosensory cortex following peripheral nerve damage in adult and developing mammals. Annual Review of Neuroscience, 6, 325–356. Kennedy, P. R., & Bakay, R. A. E. (1998). Restoration of neural output from a paralyzed patient by a direct brain connection. NeuroReport, 9, 1707–1711. King, D. K., Willcutt, C., & Taub, E. (1999). [CI therapy: Application to the lower extremities in spinal cord injury patients]. Unpublished raw data.

8/19/09 5:58:56 PM

1316

Constraint-Induced Movement Therapy

Kline, A. E., Chen, M. J., Tso-Oliveras, D. Y., & Feeney, D. M. (1994). Methylphenidate treatment following ablation induced hemiplegia: Experience during drug action alters effects on recovery of function. Phamacology, Biochemistry and Behavior, 48, 773–779.

Meinzer, M., Elbert, T., Barthel, G., Djundja, D., Taub, E., & Rockstroh, B. (2007). Extending the constraint-induced movement therapy (CIMT) approach to cognitive functions: Constraint-induced aphasia therapy (CIAT) of chronic aphasia. Neurorehabilitation, 22, 311–318.

Knapp, H. D., Taub, E., & Berman, A. J. (1958, October 10). Effects of deafferentation on a conditioned avoidance response. Science, 128, 842–843.

Merzenich, M. M., Jenkins, W. M., Johnston, P., Schreiner, C., Miller, S. L., & Tallal, P. (1996, January 5). Temporal processing deficits of languagelearning impaired children ameliorated by training. Science, 271, 77–80.

Knapp, H. D., Taub, E., & Berman, A. J. (1963). Movements in monkeys with deafferented limbs. Experimental Neurology, 7, 305–315.

Merzenich, M. M., Kaas, J. H., Wall, J., Nelson, R. J., Sur, M., & Felleman, D. (1983). Topographic reorganization of somatosensory cortical areas 3b and 1 in adult monkeys following restricted deafferentation. Neuroscience, 8, 33–55.

Kolb, B., Morshead, C., Gonzalez, C., Kim, M., Gregg, C., Shingo, T., et al. (2007). Growth factor-stimulated generation of new cortical tissue and functional recovery after stroke damage to the motor cortex of rats. Journal of Cerebral Blood Flow and Metabolism, 27, 983–997. Kopp, B., Kunkel, A., Flor, H., Platz, T., Rose, U., Mauritz, K.-H., et al. (1997). The Arm Motor Ability Test (AMAT): Reliability, validity, and sensitivity to change of an instrument for assessing ADL disability. Archives of Physical Medicine and Rehabilitation, 78, 615–620. Kopp, B., Kunkel, A., Mühlnickel, W., Villringer, K., Taub, E., & Flor, H. (1999). Plasticity in the motor system related to therapy-induced improvement of movement after stroke. NeuroReport, 10, 807–810.

Merzenich, M. M., Nelson, R. J., Stryker, M. P., Cynader, M. S., Schoppman, A., & Zook, J. M. (1984). Somatosensory cortical map changes following digit amputation in adult monkeys. Journal of Comparative Neurology, 224, 591–605. Miltner, W. H. R., Bauder, H., Sommer, M., Dettmers, C., & Taub, E. (1999). Effects of constraint-induced movement therapy on chronic stroke patients: A replication. Stroke, 30, 586–592. Morgan, W. G. (1974). The shaping game: A teaching technique. Behavior Therapy, 5, 271–272.

Kuebler, A., Kotchoubey, B., Hinterberger, T., Ghanayim, N., Perelmouter, J., Schauer, M., et al. (1999). The thought translation device: A neurophysiological approach to communication in total motor paralysis. Experimental Brain Research, 124, 223–232.

Morris, D., Shaw, S., Mark, V., Uswatte, G., Barman, J., & Taub, E. (2006). The influence of neuropsychological characteristics on the use of CI therapy with persons with traumatic brain injury. Neurorehabilitation, 21, 131–137.

Kunkel, A., Kopp, B., Muller, G., Villringer, K., Villringer, A., Taub, E., et al. (1999). Constraint-induced movement therapy for motor recovery in stroke patients. Archives of Physical Medicine and Rehabilitation, 80, 624–628.

Morris, D., Taub, E., & Mark, V. (2006). Constraint-induced movement therapy (CI therapy): Characterizing the intervention protocol. Europa Medicophysica, 42, 257–268.

Lashley, K. S. (1924). Studies of cerebral function in learning: Pt. V. The retention of motor areas in primates. Archives of Neurological Psychiatry, 12, 249–276.

Morris, D., Uswatte, G., Crago, J., Cook, E. W., III., & Taub, E. (2001). The reliability of the Wolf Motor Function Test for assessing upper extremity motor function following stroke. Archives of Physical Medicine and Rehabilitation, 82, 750–755.

Lashley, K. S. (1938). Factors limiting recovery after central nervous lesions. Journal of Nervous and Mental Diseases, 88, 733–755. Lassek, A. M. (1953). Inactivation of voluntary motor function following rhizotomy. Journal of Neuropathology and Experimental Neurology, 3, 83–87. Liepert, J., Bauder, H., Miltner, W. H. R., Taub, E., & Weiller, C. (2000). Treatment-induced cortical reorganization after stroke in humans. Stroke, 31, 1210–1216. Liepert, J., Bauder, H., Sommer, M., Miltner, W. H. R., Dettmers, C., Taub, E., et al. (1998). Motor cortex plasticity during Constraintinduced movement therapy in chronic stroke patients. Neuroscience Letters, 250, 5–8. Lum, P. S., Taub, E., Schwandt, D., Postman, M., Hardin, P., & Uswatte, G. (2004). Automated constraint-induced therapy extension (AutoCITE) for movement deficits after stroke. Journal of Rehabilitation Research and Development, 41, 249–258. Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S. J., et al. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences, USA, 97, 4398–4403. Maher, L. M., Kendall, D., Swearengin, J. A., Rodriguez, A., Leon, S. A., Pingel, K., et al. (2006). A pilot study of use-dependent learning in the context of constraint induced language therapy. Journal of the International Neuropsychological Society, 12, 843–852. Mark, V., Taub, E., Bashir, K., Uswatte, G., Delgado, A., Bowman, M. H., et al. (2008). Constraint-induced movement therapy for hemiparetic progressive multiple sclerosis: Preliminary results. Multiple Sclerosis, 14, 992–994. McCulloch, K., Cook, E. W., III., Fleming, W. C., Novack, T. A., Nepomuceno, C. S., & Taub, E. (1988). A reliable test of upper extremity ADL function [Abstract]. Archives of Physical Medicine and Rehabilitation, 69, 755.

c66.indd Sec6:1316

Mott, F. W., & Sherrington, C. S. (1885). Experiments upon the influence of sensory nerves upon movement and nutrition of the limbs. Proceedings of the Royal Society of London, 57, 481–488. Mühlnickel, W., Elbert, T., Taub, E., & Flor, H. (1998). Reorganization of primary auditory cortex in tinnitus. Proceedings of the National Academy of Sciences, USA, 95, 10340–10343. Munk, H. (1881). Über die funktionen der grosshirnrinde, gesammelte mitteilungen aus den jahren 1877–1880 [On the functions of the cerebral cortex, collected writing from the years 1877–1880]. Berlin, Germany: Hirshwald. Norman, K. E., Pepin, A., & Barbeau, H. (1998). Effects of drugs on walking after spinal cord injury. Spinal Cord, 36, 699–715. Nudo, R. J., Wise, B. M., SiFuentes, F., & Milliken, G. W. (1996, June 21). Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science, 272, 1791–1794. Ogden, R., & Franz, S. I. (1917). On cerebral motor control: The recovery from experimentally produced hemiplegia. Psychobiology, 1, 33–47. Ostendorf, C. G., & Wolf, S. L. (1981). Effect of forced use of the upper extremity of a hemiplegic patient on changes in function: A single-case design. Physical Therapy, 61, 1022–1028. Page, S., Sisto, S., Levine, P., & McGrath, E. (2004). Efficacy of modified constraint-induced movement therapy in chronic stroke: A singleblinded randomized controlled trial. Archives of Physical Medicine and Rehabilitation, 85, 14–18. Panyan, M. V. (1980). How to use shaping. Lawrence, KS: H & H Enterprises. Parker, V. M., Wade, D. T., & Langton-Hewer, R. (1986). Loss of arm function after stroke: Measurement, frequency, and recovery. International Journal of Rehabilitation Medicine, 8, 69–73. Pons, T. P., Garraghty, A. K., Ommaya, A. K., Kaas, J. H., Taub, E., & Mishkin, M. (1991, June 28). Massive cortical reorganization after sensory deafferentation in adult macaques. Science, 252, 1857–1860.

8/19/09 5:58:56 PM

References 1317 Pulvermüller, F. (1990). Aphasische kommunikation: Grundfragen ihrer analyse und therapie [Communication in aphasics: Basic questions and therapeutic approaches]. Tuebingen, Germany: Gunter Narr Verlag. Pulvermüller, F., Neininger, B., Elbert, T., Mohr, B., Rockstroh, B., Köbbel, P., et al. (2001). Constraint-induced therapy of chronic aphasia following stroke. Stroke, 32, 1621–1626. Pulvermüller, F., & Schonle, P. (1993). Behavioral and neuronal changes during treatment of mixed transcortical aphasia. Cognition, 48, 139–161. Ramey, C. T., & Ramey, S. L. (1998a). Early intervention and early experience. American Psychologist, 53, 109–120. Ramey, C. T., & Ramey, S. L. (1998b). Prevention of intellectual disabilities: Early interventions to improve cognitive development. Preventive Medicine, 27, 224–232.

Stark, R. E., & Heinz, J. M. (1996). Perception of stop consonants in children with expressive and receptive-expressive language impairments. Journal of Speech and Hearing Research, 39, 676–686. Sterr, A., Elbert, T., Berthold, I., Kölbel, S., Rockstroh, B., & Taub, E. (2002). CI therapy in chronic hemiparesis: The more the better? Archives of Physical Medicine and Rehabilitation, 83, 1374–1377. Sterr, A., Mueller, M., Elbert, T., Rockstroh, B., Pantev, C., & Taub, E. (1998, January 8). Changed perceptions in braille readers. Nature, 391, 134–135. Sterr, A., Mueller, M., Elbert, T., & Taub, E. (1998). Perceptual correlates of use-dependent changes in cortical representation of the fingers in blind braille readers. Journal of Neuroscience, 18, 4417–4423.

Recanzone, G. H., Jenkins, W. M., & Merzenich, M. M. (1992). Progressive improvement in discriminative abilities in adult owl monkeys performing a tactile frequency discrimination task. Journal of Neurophysiology, 67, 1015–1030.

Stewart, J. E., Barbeau, H., & Gauthier, S. (1991). Modulation of locomotor patterns and spasticity with clonidine in spinal cord injured patients. Canadian Journal of Neurological Sciences, 18, 321–332.

Recanzone, G. H., Merzenich, M. M., & Jenkins, W. M. (1992). Frequency discrimination training engaging a restricted skin surface results in an emergence of a cutaneous response zone in cortical area 3a. Journal of Neurophysiology, 67, 1057–1070.

Tallal, P., Miller, S., Bedi, G., Byma, G., Wang, X., Srikantan, S., et al. (1996, January 5). Language comprehension in language-learning impaired children improved with acoustically modified speech. Science, 271, 81–84.

Recanzone, G. H., Merzenich, M. M., Jenkins, W. M., Grajski, A., & Dinse, H. R. (1992). Topographic reorganization of the hand representation in area 3b of owl monkeys trained in a frequency discrimination task. Journal of Neurophysiology, 67, 1031–1056.

Tallal, P., Miller, S., & Fitch, R. H. (1993). Untitled. In P. Tallal, A. M. Galaburda, R. R. Llinas, & C. von Euler (Eds.), Temporal information processing in the nervous system: Special reference to dyslexia and dysphagia (pp. 27–47). New York: New York Academy of Sciences.

Reed, M. A. (1989). Speech perception and the discrimination of brief auditory cues in reading disabled children. Journal of Experimental Child Psychology, 48, 270–292.

Tallal, P., & Piercy, M. (1974). Developmental aphasia: Rate of auditory processing and selective impairment of consonant perception. Neuropsychologia, 12, 83–93.

Risley, T. R., & Baer, D. M. (1973). Operant behavior modification: The deliberate development of behavior. In M. Caldwell & H. N. Riccuiti (Eds.), Review of child development research: Development and social action (Vol. 3, pp. 283–329). Chicago: University of Chicago Press.

Tallal, P., & Piercy, M. (1975). Developmental aphasia: The perception of brief vowels and extended stop consonants. Neuropsychologia, 13, 69–74.

Ruch, T. C. (1960). The cerebral cortex: Its structure and motor functions. In T. C. Ruch & J. F. Fulton (Eds.), Medical physiology and biophysics (18th ed., pp. 249–276). Philadelphia: Saunders. Schmidt, R. A., & Lee, T. D. (1999). Conditions of practice. Motor control and learning: A behavioral emphasis (3rd ed., pp. 285–321). Champaign, IL: Human Kinetics. Shaw, S. E., Morris, D. M., Uswatte, G., McKay, S., Meythaler, J. M., & Taub, E. (2005). Constraint-induced movement therapy for recovery of upper-limb function following traumatic brain injury. Journal of Rehabilitation Research and Development, 42, 769–778. Shaw, S. E., Morris, D. M., Yakley, S. R., McKay, S. B., & Taub, E. (2000, June). Constraint induced movement therapy to improve upper extremity function in subjects following traumatic brain injury: A case study. Paper presented at Physical Therapy 2000: APTA Scientific Meeting and Exposition, Indianapolis, IN. Shaywitz, S. E., Shaywitz, B. A., Fletcher, J. M., & Escobar, M. D. (1990). Prevalence of reading disability in boys and girls: Results of the Connecticut Longitudinal Study. Journal of the American Medical Association, 264, 998–1002. Sherrington, C. S. (1910). Remarks on the reflex mechanism of the step. Brain, 33, 1–25. Sherrington, C. S. (1913). Further observations on the production of reflex stepping by combination of reflex excitation with reflex inhibition. Journal of Physiology, 47, 196–214. Sherrington, C. S. (1931). Quantitative management of contraction in lowest level coordination. Brain, 54, 1–28. Shumway-Cook, A., & Woollacott, M. H. (1995). Motor control: Theory and practical applications. Baltimore: Williams & Wilkins. Skinner, B. F. (1938). The behavior of organisms. New York: AppletonCentury-Crofts.

c66.indd Sec6:1317

Skinner, B. F. (1968). The technology of teaching. New York: AppletonCentury-Crofts.

Tallal, P., & Stark, R. E. (1981). Speech acoustic-cue discrimination abilities of normally developing and language impaired children. Journal of the Acoustical Society of America, 69, 568–574. Tallal, P., Stark, R. E., & Mellits, E. D. (1985). Identification of languageimpaired children on the basis of rapid perception and production skills. Brain and Language, 25, 314–322. Taub, E. (1976). Motor behavior following deafferentation in the developing and motorically mature monkey. In R. Herman, S. Grillner, H. J. Ralston, P. S. G. Stein, & D. Stuart (Eds.), Neural control of locomotion (pp. 675–705). New York: Plenum Press. Taub, E. (1977a). Deafferentation techniques in the investigation of sensorymotor integration. In D. M. Landers & R. W. Christina (Eds.), Motor behavior (Vol. 1, pp. 207–213). Champaign, IL: Human Kinetics. Taub, E. (1977b). Movement in nonhuman primates deprived of somatosensory feedback. In Exercise and sports science reviews (Vol. 4, pp. 335–374). Taub, E. (1980). Somatosensory deafferentation research with monkeys: Implications for rehabilitation medicine. In L. P. Ince (Ed.), Behavioral psychology in rehabilitation medicine: Clinical applications (pp. 371–401). New York: Williams & Wilkins. Taub, E. (1994). Overcoming learned nonuse: A new behavioral medicine approach to physical medicine. In J. G. Carlson, S. R. Seifert, & N. Birbaumer (Eds.), Clinical applied psychophysiology (pp. 185–220). New York: Plenum Press. Taub, E., Bacon, R., & Berman, A. J. (1965). The acquisition of a traceconditioned avoidance response after deafferentation of the responding limb. Journal of Comparative and Physiological Psychology, 58, 275–279. Taub, E., & Berman, A. J. (1963). Avoidance conditioning in the absence of relevant proprioceptive and exteroceptive feedback. Journal of Comparative and Physiological Psychology, 56, 1012–1016.

8/19/09 5:58:57 PM

1318

Constraint-Induced Movement Therapy

Taub, E., & Berman, A. J. (1968). Movement and learning in the absence of sensory feedback. In S. J. Freedman (Ed.), The neuropsychology of spatially oriented behavior (pp. 173–192). Homewood, IL: Dorsey Press.

Taub, E., Uswatte, G., & Pidikiti, R. (1999). Constraint-induced movement therapy: A new family of techniques with broad application to physical rehabilitation: A clinical review. Journal of Rehabilitation Research and Development, 36, 237–251.

Taub, E., Burgio, L., Miller, N. E., Cook, E. W., Groomes, T., DeLuca, S., et al. (1994). An operant approach to overcoming learned nonuse after CNS damage in monkeys and man: The role of shaping. Journal of the Experimental Analysis of Behavior, 61, 281–293.

Taub, E., Williams, E., Barro, G., & Steiner, S. S. (1978). Comparison of the performance of deafferented and intact monkeys on continuous and fixed ratio schedules of reinforcement. Experimental Neurology, 58, 1–13.

Taub, E., & Crago, J. (1995). Behavioral plasticity following central nervous system damage in monkeys and man. In B. Julesz & I. Kovacs (Eds.), Maturational windows and adult cortical plasticity: SFI studies in the sciences of complexity (Vol. 23, pp. 201–215). Redwood City, CA: Addison-Wesley. Taub, E., Crago, J., & Uswatte, G. (1998). Constraint-induced movement therapy: A new approach to treatment in physical rehabilitation. Rehabilitation Psychology, 43, 152–170.

Tomblin, J. B., Records, N. L., Buckwalter, P., Zhang, X., Smith, E., & O’Brien, M. (1997). Prevalence of specific language impairment in kindergarten children. Journal of Speech, Language, and Hearing Research, 40, 1245–1260. Tower, S. S. (1940). Pyramidal lesions in the monkey. Brain, 63, 36–90. Twitchell, T. E. (1951). The restoration of motor function following hemiplegia in man. Brain, 74, 443–480. Twitchell, T. E. (1954). Sensory factors in purposive movement. Journal of Neurophysiology, 17, 239–254.

Taub, E., Ellman, S. J., & Berman, A. J. (1966, February 4). Deafferentation in monkeys: Effect on conditioned grasp response. Science, 151, 593–594.

Uswatte, G., & Taub, E. (2005). Implications of the learned nonuse formulation for measuring rehabilitation outcomes: Lessons from constraintinduced movement therapy. Rehabilitation Psychology, 50, 34–42.

Taub, E., Flor, H., Knecht, S., & Elbert, T. (1995). Correlation between phantom limb pain and cortical reorganization. Journal of NIH Research, 7, 49–50.

Uswatte, G., Taub, E., Morris, D., Barman, J., & Crago, J. (2006). Contribution of the shaping and restraint components of constraint-induced movement therapy to treatment outcome. Neurorehabilitation, 21, 147–156.

Taub, E., Goldberg, I. A., & Taub, P. B. (1975). Deafferentation in monkeys: Pointing at a target without visual feedback. Experimental Neurology, 46, 178–186. Taub, E., Griffin, A., Gammons, K., Nick, J., Uswatte, G., & Law, C. R. (2006, October). CI therapy for young children with congenital hemiparesis. Paper presented at the Society for Neuroscience, Atlanta, GA. Taub, E., Lum, P. S., Hardin, P., Mark, V., & Uswatte, G. (2005). AutoCITE: Automated delivery of CI therapy with reduced effort by therapists. Stroke, 36, 1301–1304. Taub, E., Miller, N. E., Novack, T. A., Cook, E. W., Fleming, W. C., Nepomuceno, C. S., et al. (1993). Technique to improve chronic motor deficit after stroke. Archives of Physical Medicine and Rehabilitation, 74, 347–354. Taub, E., Perrella, P. N., & Barro, G. (1973, September 7). Behavioral development following forelimb deafferentation on day of birth in monkeys with and without blinding. Science, 181, 959–960. Taub, E., Perrella, P. N., Miller, D., & Barro, G. (1975). Diminution of early environmental control through perinatal and prenatal somatosensory deafferentation. Biological Psychiatry, 10, 609–626. Taub, E., Pidikiti, R., DeLuca, S., & Crago, J. (1996). Effects of motor restriction of an unimpaired upper extremity and training on improving functional tasks and altering brain/behaviors. In J. Toole (Ed.), Imaging and neurologic rehabilitation (pp. 133–154). New York: Demos. Taub, E., Pidikiti, R., Uswatte, G., Shaw, S., & Yakley, S. (1998). Constraint-induced (CI) movement therapy: Application to lower functioning stroke patients [Abstract]. Society for Neuroscience Abstracts, 24, 1769. Taub, E., Ramey, S. L., DeLuca, S., & Echols, E. (2004). Efficacy of constraint-induced (CI) movement therapy for children with cerebral palsy with asymmetric motor impairment. Pediatrics, 113, 305–312. Taub, E., & Uswatte, G. (2000). Constraint-induced movement therapy: Rehabilitation based on behavioral neuroscience. In R. G. Frank & T. R. Elliott (Eds.), Handbook of rehabilitation psychology (pp. 475– 496). Washington, DC: American Psychological Association. Taub, E., Uswatte, G., King, D. K., Morris, D., Crago, J., & Chatterjee, A. (2006). A placebo controlled trial of constraint-induced movement therapy for upper extremity after stroke. Stroke, 37, 1045–1049. Taub, E., Uswatte, G., Mark, V., & Morris, D. (2006). The learned nonuse phenomenon: Implications for rehabilitation. Europa Medicophysica, 42, 241–255.

c66.indd Sec6:1318

Uswatte, G., Taub, E., Morris, D., Light, K., & Thompson, P. (2006). The motor activity log-28: A method for assessing daily use of the hemiparetic arm after stroke. Neurology, 67, 1189–1194. Uswatte, G., Taub, E., Morris, D., Vignolo, M., & McCulloch, K. (2005). Reliability and validity of the upper-extremity motor activity log-14 for measuring real-world arm use. Stroke, 36, 2493–2496. van der Lee, J., Beckerman, H., Knol, D., de Vet, H., & Bouter, L. (2004). Clinimetric properties of the Motor Activity Log for the assessment of arm use in hemiparetic patients. Stroke, 35, 1–5. van der Lee, J., Beckerman, H., Lankhorst, G., & Bouter, L. (1999). Constraint-induced movement therapy [Letter to the Editor]. Archives of Physical Medicine and Rehabilitation, 80, 1606. van der Lee, J., Wagenaar, R., Lankhorst, G., Vogelaar, T., Deville, W., & Bouter, L. (1999). Forced use of the upper extremity in chronic stroke patients: Results from a single-blind randomized clinical trial. Stroke, 30, 2369–2375. Wainberg, M., Barbeau, H., & Gauthier, S. (1990). The effects of cyproheptadine on locomotion and spasticity in patients with spinal cord injuries. Journal of Neurology, Neurosurgery, and Psychiatry, 53, 754–763. Walker-Batson, D., Smith, P., Curtis, S., Unwin, H., & Greenlee, R. (1995). Amphetamine paired with physical therapy accelerates motor recovery after stroke: Further evidence. Stroke, 26, 2254–2259. Walker-Batson, D., Unwin, H., Curtis, S., Allan, E., Wood, M., Devous, M., et al. (1992). Use of amphetamine in the treatment of aphasia. Restorative Neurology and Neuroscience, 4, 47–50. Wall, P. D., & Egger, M. D. (1971, August 20). Formation of new connections in adult rat brains following partial deafferentation. Nature, 232, 542–545. Ware, J. E., & Sherbourne, C. D. (1992). The MOS 36-item Short-Form Health Survey (SF-36): Pt. I. Conceptual framework and item selection. Medical Care, 30, 473–483. Weiss, T., Miltner, W. H. R., Adler, T., Bruckner, L., & Taub, E. (1999). Decrease in phantom limb pain associated with prosthesis-induced increased use of an amputation stump in humans. Neuroscience Letters, 272, 131–134. Wittenberg, G. F., Chen, R., Ishii, K., Bushara, K. O., Eckloff, S., Croarkin, E., et al. (2003). Constraint-induced therapy in stroke: Magnetic-stimulation motor maps and cerebral activation. Neurorehabilitation and Neural Repair, 17, 48–57.

8/19/09 5:58:57 PM

References 1319 Wolf, S., Lecraw, D., Barton, L., & Jann, B. (1989). Forced use of hemiplegic upper extremities to reverse the effect of learned nonuse among chronic stroke and head-injured patients. Experimental Neurology, 104, 125–132. Wolf, S., Thompson, P., Morris, D., Rose, D., Winstein, C., Taub, E., et al. (2005). The EXCITE trial: Attributes of the Wolf Motor Function Test in patients with subacute stroke. Neurorehabilitation and Neural Repair, 19, 194–205. Wolf, S., Winstein, C., Miller, J., Taub, E., Uswatte, G., Morris, D., et al. (2006). Effect of constraint-induced movement therapy on upper extremity function 3–9 months after stroke: The EXCITE randomized clinical trial. Journal of the American Medical Association, 296, 2095–2104. Wolf, S., Winstein, C., Miller, J., Thompson, P., Taub, E., Uswatte, G., et al. (2008). Upper limb function in stroke survivors who have received constraint-induced movement therapy: Follow-up of the EXCITE randomized trial. Lancet Neurology, 7, 33–40.

c66.indd Sec6:1319

Wolpaw, J. R., Flotzinger, D., Pfurtscheller, G., & McFarland, D. (1997). Timing of EEG-based cursor control. Journal of Clinical Neurophysiology, 14, 529–538. Wolpaw, J. R., & McFarland, D. J. (1994). Multichannel EEG-based brain-computer communication. Electroencephalography and Clinical Neurophysiology, 78, 314–317. Wolpaw, J. R., McFarland, D. J., Neat, G. W., & Forneris, C. A. (1991). An EEG-based brain-computer interface for cursor control. Electroencephalography and Clinical Neurophysiology, 78, 252–259. Yamashima, T., Tonchev, A. B., Vachkov, I. H., Popivanova, B. K., Seki, T., Sawamoto, K., et al. (2004). Vascular adventitia generates neuronal progenitors in the monkey hippocampus after ischemia. Hippocampus, 14, 861–875. Yang, T. T., Gallen, C., Schwartz, B., Bloom, F. E., Ramachandran, V. S., & Cobb, S. (1994, April 14). Sensory maps in the human brain. Nature, 368, 592–593.

8/19/09 5:58:58 PM

c66.indd Sec6:1320

8/19/09 5:58:58 PM